Update parquet files (step 13 of 249)

spaces/1gistliPinn/ChatGPT4/Crea Il Tuo Personaggio Cartoon Fix.md

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-## crea il tuo personaggio cartoon
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-# Come creare il tuo personaggio cartoon online gratis
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spaces/1gistliPinn/ChatGPT4/Examples/Enjoy Lords of the Realm III Without Verification Download Here.md

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spaces/1phancelerku/anime-remove-background/Dark Riddle Hack How I Beat the Game with Unlimited Resources.md

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-<p>If you are a fan of stealth games, you might have heard of Dark Riddle, a popular mobile game that lets you explore your neighbor's house and discover his secrets. But did you know that you can use a hack tool to unlock all the skins and quests in the game? In this article, we will show you how to do that and more.</p>
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-<p>Dark Riddle is a 3D adventure game developed by Nika Entertainment. It is available for both Android and iOS devices. In this game, you play as a curious character who wants to find out what your neighbor is hiding in his basement. You have to sneak into his house, avoid his traps and cameras, and solve puzzles to progress. You can also interact with various objects and characters in the game, such as a cat, a dog, a crow, a pizza delivery guy, and more.</p>
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-<p>However, using Dark Riddle hack also comes with some risks that you should be aware of. These include:</p>
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-<li>Use your cheats sparingly. Cheats can help you have fun with different modes, such as flying mode, ghost mode, god mode, etc. You can use the hack tool to activate them, but don't abuse them or you will lose the fun and challenge of the game.</li>
-<li>Use your settings moderately. Settings can help you modify the game's environment, such as the time of day, the weather, the sound effects, and more. You can use the hack tool to change them, but don't alter them too much or you will ruin the atmosphere and realism of the game.</li>
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-<p>Dark Riddle is a great game that offers a lot of fun and challenge for stealth game lovers. However, if you want to unlock all the skins and quests in the game without spending any money or completing any tasks, you can use Dark Riddle hack. This hack tool allows you to access a hidden menu that gives you unlimited resources and options to customize your game experience. However, you should also be aware of the risks of using the hack tool and use it with caution and moderation. We hope this article has helped you learn how to use Dark Riddle hack and enjoy the game more.</p>
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spaces/1phancelerku/anime-remove-background/Download 8 Ball Pool for Java and Play with Friends Online.md

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-<h1>Download 8 Ball Pool for Java: A Guide for Pool Lovers</h1>
-<p>If you are a fan of pool games, you might have heard of <strong>8 Ball Pool</strong>, one of the most popular and addictive online pool games in the world. But did you know that you can also play it on your <strong>Java platform</strong>? In this article, we will show you how to download and play 8 Ball Pool on Java, as well as some tips and tricks to improve your game. Let's get started!</p>
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-<p>8 Ball Pool is an online pool game where you can compete with other players from around the world in various game modes, such as <em>PvP</em>, <em>Tournaments</em>, <em>9 Ball</em>, and more. You can also customize your cue and table, buy new items in the <em>Pool Shop</em>, and join clubs to chat with other players. The game is developed by <strong>Miniclip</strong>, a leading online gaming company, and has over <strong>500 million downloads</strong> on Google Play. It is also available on other platforms, such as iOS, Windows, and web browsers.</p>
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-<p>Java platform is a suite of programs that facilitate developing and running programs written in the <strong>Java programming language</strong>. It includes an execution engine (called a <em>virtual machine</em>), a compiler, and a set of libraries. Java platform is independent of any particular operating system, which makes Java programs run identically on all of them. Java is a programming language and computing platform first released by <strong>Sun Microsystems</strong> in 1995. The Java Virtual Machine (JVM) is platform-dependent, meaning that different operating systems require different JVMs.</p>
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-<p>To download and play 8 Ball Pool on Java, you need to follow these steps:</p>
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-<p>Before you download the game, make sure that your device meets the minimum system requirements for running Java programs. You can check them here. Also, make sure that your device supports the <strong>Jar format</strong>, which is the file format for Java applications. You can check this by looking at the file extension of your downloaded files. If they end with <code>.jar. </code>, then they are Jar files and you can run them on your Java platform. If they end with <code>.jad</code>, then they are Jad files and you need to convert them to Jar files first. You can use online tools like this one to do that.</p>
-<h3>Step 2: Download and install Java Runtime Environment (JRE)</h3>
-<p>Java Runtime Environment (JRE) is a software package that provides the libraries and components needed to run Java applications. You can download the latest version of JRE from the official website. Choose the version that matches your operating system and device architecture. After downloading, follow the instructions to install JRE on your device.</p>
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-<p>There are many websites that offer 8 Ball Pool for Java, but not all of them are safe and reliable. Some of them may contain malware or viruses that can harm your device or steal your personal information. To avoid this, you should only download 8 Ball Pool from a trusted source, such as the official Miniclip website. You can also use other reputable sources, such as Mobile9 or Phoneky. After downloading, locate the Jar file on your device and open it to install 8 Ball Pool on your Java platform.</p>
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-<h3>Step 1: Launch the game and sign in with your account</h3>
-<p>After installing 8 Ball Pool on your Java platform, launch the game by clicking on its icon. You will see the main menu with several options, such as <em>Play Online</em>, <em>Play Offline</em>, <em>Pool Shop</em>, <em>Settings</em>, and more. To play online, you need to sign in with your Miniclip account or create one if you don't have one. You can also sign in with your Facebook account if you want to sync your progress and access your friends list. To play offline, you don't need to sign in, but you will have limited features and modes.</p>
-<h3>Step 2: Choose a game mode and a table</h3>
-<p>After signing in, you can choose a game mode from the following options: <em>PvP</em>, where you can play against another player in real time; <em>Tournaments</em>, where you can join a bracket of players and compete for prizes; <em>9 Ball</em>, where you can play a different variation of pool with only nine balls; and <em>Practice</em>, where you can practice your shots without any pressure. You can also choose a table from different themes and styles, such as <em>London</em>, <em>Sydney</em>, <em>Moscow</em>, and more. Each table has a different entry fee and reward, so choose wisely according to your skill level and budget.</p>
-<h3>Step 3: Aim and shoot your cue ball</h3>
-<p>Once you enter a game, you will see the pool table with the balls arranged in a triangle. The game follows the standard rules of 8 ball pool, which means that you have to pot all the balls of your assigned group (solid or striped) before potting the black 8 ball. To aim your cue ball, use the arrow keys or the number keys on your keypad. To adjust the power of your shot, use the * key or the # key on your keypad. To shoot, press the OK button or the 5 key on your keypad. You can also use the spin feature by pressing the left soft key or the right soft key on your keypad. This will allow you to control the direction and speed of your cue ball after hitting another ball.</p>
- <h2>Tips and Tricks for Playing 8 Ball Pool on Java</h2>
-<p>To improve your game and have more fun playing 8 Ball Pool on Java, here are some tips and tricks that you should know:</p>
-<h3>Tip 1: Use the spin feature to control the cue ball</h3>
-<p>The spin feature is one of the most useful tools in 8 Ball Pool, as it can help you avoid scratches, get better positions, and make trick shots. To use it, press the left soft key or the right soft key on your keypad when aiming your cue ball. You will see a circle with four arrows around it, indicating the direction of the spin. You can choose from four types of spin: top spin, back spin, left spin, and right spin. Each type of spin has a different effect on the cue ball's movement and angle. For example, top spin will make the cue ball move forward after hitting another ball, while back spin will make it move backward. Left spin and right spin will make the cue ball curve to the left or right, respectively. You can use these effects to avoid obstacles, get closer to your target ball, or make difficult shots.</p>
-<h3>Tip 2: Practice your shots in offline mode</h3>
-<p>If you want to improve your skills and confidence in 8 Ball Pool, you should practice your shots in offline mode. Offline mode allows you to play against the computer or yourself without any internet connection or entry fee. You can choose from different difficulty levels and table themes, and you can also adjust the rules and settings of the game. Offline mode is a great way to learn the basics of the game, test your strategies, and have fun without any pressure or risk.</p>
-<h3>Tip 3: Challenge your friends and other players online</h3>
-<p>One of the best features of 8 Ball Pool is that you can challenge your friends and other players online. You can invite your friends from Facebook or Miniclip to play with you, or you can join a random match with someone from anywhere in the world. You can also chat with your opponent during the game, send them emojis, and add them as friends. Playing online is not only fun and exciting, but also rewarding and challenging. You can earn coins, items, trophies, and ranking points by winning matches, and you can also join clubs and tournaments to compete with other players and teams.</p>
- <h2>Conclusion</h2>
-<p>8 Ball Pool is a fantastic online pool game that you can play on your Java platform. It has amazing graphics, realistic physics, and addictive gameplay. You can download it from a trusted source, install it on your device, and enjoy playing it anytime and anywhere. You can also use some tips and tricks to improve your game and have more fun. Whether you are a beginner or a pro, 8 Ball Pool has something for everyone. So what are you waiting for? Download 8 Ball Pool for Java today and join the millions of pool lovers around the world!</p>
- <h2>FAQs</h2>
-<h3>Q1: Is 8 Ball Pool free to play on Java?</h3>
-<p>A1: Yes, 8 Ball Pool is free to play on Java. However, some features and items may require in-app purchases or real money.</p>
-<h3>Q2: How can I get more coins and items in 8 Ball Pool?</h3>
-<p>A2: You can get more coins and items in 8 Ball Pool by winning matches, completing missions, spinning the wheel, watching ads, or buying them with real money.</p>
-<h3>Q3: How can I improve my skills and ranking in 8 Ball Pool?</h3>
-<p>A3: You can improve your skills and ranking in 8 Ball Pool by practicing your shots in offline mode, learning from other players online, using the spin feature wisely, and joining clubs and tournaments.</p>
-<h3>Q4: What are the differences between 8 Ball Pool on Java and other platforms?</h3>
-<p>A4: The main differences between 8 Ball Pool on Java and other platforms are the controls and interface. On Java platform, you use the keypad to aim and shoot your cue ball, while on other platforms, you use the touch screen or the mouse. The interface on Java platform is also simpler and less cluttered than on other platforms.</p>
-<h3>Q5: What are the best sources to download 8 Ball Pool for Java?</h3>
-<p>A5: The best sources to download 8 Ball Pool for Java are the official Miniclip website, Mobile9, Phoneky, or any other reputable website that offers safe and reliable downloads.</p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Free Download IGNOU Solved Assignment for M.Com 2023 All Subjects and Languages.md

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spaces/1toTree/lora_test/ppdiffusers/pipelines/pndm/__init__.py

@@ -1,17 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# flake8: noqa
-from .pipeline_pndm import PNDMPipeline

spaces/AIFILMS/generate_human_motion/VQ-Trans/dataset/prepare/download_glove.sh

@@ -1,9 +0,0 @@
-echo -e "Downloading glove (in use by the evaluators)"
-gdown --fuzzy https://drive.google.com/file/d/1bCeS6Sh_mLVTebxIgiUHgdPrroW06mb6/view?usp=sharing
-rm -rf glove
-
-unzip glove.zip
-echo -e "Cleaning\n"
-rm glove.zip
-
-echo -e "Downloading done!"

spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/audio/__init__.py

@@ -1,82 +0,0 @@
-import librosa
-import numpy as np
-import pyloudnorm as pyln
-
-from text_to_speech.utils.audio.vad import trim_long_silences
-
-
-def librosa_pad_lr(x, fsize, fshift, pad_sides=1):
-    '''compute right padding (final frame) or both sides padding (first and final frames)
-    '''
-    assert pad_sides in (1, 2)
-    # return int(fsize // 2)
-    pad = (x.shape[0] // fshift + 1) * fshift - x.shape[0]
-    if pad_sides == 1:
-        return 0, pad
-    else:
-        return pad // 2, pad // 2 + pad % 2
-
-
-def amp_to_db(x):
-    return 20 * np.log10(np.maximum(1e-5, x))
-
-
-def db_to_amp(x):
-    return 10.0 ** (x * 0.05)
-
-
-def normalize(S, min_level_db):
-    return (S - min_level_db) / -min_level_db
-
-
-def denormalize(D, min_level_db):
-    return (D * -min_level_db) + min_level_db
-
-
-def librosa_wav2spec(wav_path,
-                     fft_size=1024,
-                     hop_size=256,
-                     win_length=1024,
-                     window="hann",
-                     num_mels=80,
-                     fmin=80,
-                     fmax=-1,
-                     eps=1e-6,
-                     sample_rate=22050,
-                     loud_norm=False,
-                     trim_long_sil=False):
-    if isinstance(wav_path, str):
-        if trim_long_sil:
-            wav, _, _ = trim_long_silences(wav_path, sample_rate)
-        else:
-            wav, _ = librosa.core.load(wav_path, sr=sample_rate)
-    else:
-        wav = wav_path
-
-    if loud_norm:
-        meter = pyln.Meter(sample_rate)  # create BS.1770 meter
-        loudness = meter.integrated_loudness(wav)
-        wav = pyln.normalize.loudness(wav, loudness, -22.0)
-        if np.abs(wav).max() > 1:
-            wav = wav / np.abs(wav).max()
-
-    # get amplitude spectrogram
-    x_stft = librosa.stft(wav, n_fft=fft_size, hop_length=hop_size,
-                          win_length=win_length, window=window, pad_mode="constant")
-    linear_spc = np.abs(x_stft)  # (n_bins, T)
-
-    # get mel basis
-    fmin = 0 if fmin == -1 else fmin
-    fmax = sample_rate / 2 if fmax == -1 else fmax
-    mel_basis = librosa.filters.mel(sample_rate, fft_size, num_mels, fmin, fmax)
-
-    # calculate mel spec
-    mel = mel_basis @ linear_spc
-    mel = np.log10(np.maximum(eps, mel))  # (n_mel_bins, T)
-    l_pad, r_pad = librosa_pad_lr(wav, fft_size, hop_size, 1)
-    wav = np.pad(wav, (l_pad, r_pad), mode='constant', constant_values=0.0)
-    wav = wav[:mel.shape[1] * hop_size]
-
-    # log linear spec
-    linear_spc = np.log10(np.maximum(eps, linear_spc))
-    return {'wav': wav, 'mel': mel.T, 'linear': linear_spc.T, 'mel_basis': mel_basis}

spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/diffusionmodules/custom_openaimodel.py

@@ -1,368 +0,0 @@
-from abc import abstractmethod
-from functools import partial
-import math
-from typing import Iterable
-
-import numpy as np
-import torch as th
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ldm.modules.diffusionmodules.util import (
-    checkpoint,
-    conv_nd,
-    linear,
-    avg_pool_nd,
-    zero_module,
-    normalization,
-    timestep_embedding,
-)
-from ldm.modules.attention import SpatialTransformer
-from ldm.modules.diffusionmodules.openaimodel import convert_module_to_f16, convert_module_to_f32, AttentionPool2d, \
-    TimestepBlock, TimestepEmbedSequential, Upsample, TransposedUpsample, Downsample, ResBlock, AttentionBlock, count_flops_attn, \
-    QKVAttentionLegacy, QKVAttention
-
-
-class UNetModel(nn.Module):
-    """
-    The full UNet model with attention and timestep embedding.
-    :param in_channels: channels in the input Tensor.
-    :param model_channels: base channel count for the model.
-    :param out_channels: channels in the output Tensor.
-    :param num_res_blocks: number of residual blocks per downsample.
-    :param attention_resolutions: a collection of downsample rates at which
-        attention will take place. May be a set, list, or tuple.
-        For example, if this contains 4, then at 4x downsampling, attention
-        will be used.
-    :param dropout: the dropout probability.
-    :param channel_mult: channel multiplier for each level of the UNet.
-    :param conv_resample: if True, use learned convolutions for upsampling and
-        downsampling.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param num_classes: if specified (as an int), then this model will be
-        class-conditional with `num_classes` classes.
-    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
-    :param num_heads: the number of attention heads in each attention layer.
-    :param num_heads_channels: if specified, ignore num_heads and instead use
-                               a fixed channel width per attention head.
-    :param num_heads_upsample: works with num_heads to set a different number
-                               of heads for upsampling. Deprecated.
-    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
-    :param resblock_updown: use residual blocks for up/downsampling.
-    :param use_new_attention_order: use a different attention pattern for potentially
-                                    increased efficiency.
-    """
-
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        out_channels,
-        num_res_blocks,
-        attention_resolutions,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        num_classes=None,
-        use_checkpoint=False,
-        use_fp16=False,
-        num_heads=-1,
-        num_head_channels=-1,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=False,
-        resblock_updown=False,
-        use_new_attention_order=False,
-        use_spatial_transformer=False,    # custom transformer support
-        transformer_depth=1,              # custom transformer support
-        context_dim=None,                 # custom transformer support
-        n_embed=None,                     # custom support for prediction of discrete ids into codebook of first stage vq model
-        legacy=True,
-        use_context_project=False,        # custom text to audio support
-        use_context_attn=True             # custom text to audio support
-    ):
-        super().__init__()
-        if use_spatial_transformer:
-            assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
-
-        if context_dim is not None and not use_context_project:
-            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
-            from omegaconf.listconfig import ListConfig
-            if type(context_dim) == ListConfig:
-                context_dim = list(context_dim)
-
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        if num_heads == -1:
-            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
-
-        if num_head_channels == -1:
-            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
-
-        self.image_size = image_size
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        self.num_res_blocks = num_res_blocks
-        self.attention_resolutions = attention_resolutions
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.num_classes = num_classes
-        self.use_checkpoint = use_checkpoint
-        self.dtype = th.float16 if use_fp16 else th.float32
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-        self.predict_codebook_ids = n_embed is not None
-
-        time_embed_dim = model_channels * 4
-        self.time_embed = nn.Sequential(
-            linear(model_channels, time_embed_dim),
-            nn.SiLU(),
-            linear(time_embed_dim, time_embed_dim),
-        )
-
-        if self.num_classes is not None:
-            self.label_emb = nn.Embedding(num_classes, time_embed_dim)
-
-        self.input_blocks = nn.ModuleList(
-            [
-                TimestepEmbedSequential(
-                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
-                )
-            ]
-        )
-        self._feature_size = model_channels
-        input_block_chans = [model_channels]
-        ch = model_channels
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for _ in range(num_res_blocks):
-                layers = [
-                    ResBlock(
-                        ch,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=mult * model_channels,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = mult * model_channels
-                if ds in attention_resolutions:
-                    if num_head_channels == -1:
-                        dim_head = ch // num_heads
-                    else:
-                        num_heads = ch // num_head_channels
-                        dim_head = num_head_channels
-                    if legacy:
-                        #num_heads = 1
-                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads,
-                            num_head_channels=dim_head,
-                            use_new_attention_order=use_new_attention_order,
-                        ) if not use_spatial_transformer else SpatialTransformer(
-                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
-                        )
-                    )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                out_ch = ch
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            down=True,
-                        )
-                        if resblock_updown
-                        else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch
-                        )
-                    )
-                )
-                ch = out_ch
-                input_block_chans.append(ch)
-                ds *= 2
-                self._feature_size += ch
-
-        if num_head_channels == -1:
-            dim_head = ch // num_heads
-        else:
-            num_heads = ch // num_head_channels
-            dim_head = num_head_channels
-        if legacy:
-            #num_heads = 1
-            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-        self.middle_block = TimestepEmbedSequential(
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-            AttentionBlock(
-                ch,
-                use_checkpoint=use_checkpoint,
-                num_heads=num_heads,
-                num_head_channels=dim_head,
-                use_new_attention_order=use_new_attention_order,
-            ) if not use_spatial_transformer else SpatialTransformer(
-                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
-                        ),
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-        )
-        self._feature_size += ch
-
-        self.output_blocks = nn.ModuleList([])
-        for level, mult in list(enumerate(channel_mult))[::-1]:
-            for i in range(num_res_blocks + 1):
-                ich = input_block_chans.pop()
-                layers = [
-                    ResBlock(
-                        ch + ich,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=model_channels * mult,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = model_channels * mult
-                if ds in attention_resolutions:
-                    if num_head_channels == -1:
-                        dim_head = ch // num_heads
-                    else:
-                        num_heads = ch // num_head_channels
-                        dim_head = num_head_channels
-                    if legacy:
-                        #num_heads = 1
-                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads_upsample,
-                            num_head_channels=dim_head,
-                            use_new_attention_order=use_new_attention_order,
-                        ) if not use_spatial_transformer else SpatialTransformer(
-                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
-                        )
-                    )
-                if level and i == num_res_blocks:
-                    out_ch = ch
-                    layers.append(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            up=True,
-                        )
-                        if resblock_updown
-                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
-                    )
-                    ds //= 2
-                self.output_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-
-        self.out = nn.Sequential(
-            normalization(ch),
-            nn.SiLU(),
-            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
-        )
-        if self.predict_codebook_ids:
-            self.id_predictor = nn.Sequential(
-            normalization(ch),
-            conv_nd(dims, model_channels, n_embed, 1),
-            #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
-        )
-
-        self.use_context_project = use_context_project
-        if use_context_project:
-            self.context_project = linear(context_dim, time_embed_dim)
-        self.use_context_attn = use_context_attn
-
-
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-        self.output_blocks.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-        self.output_blocks.apply(convert_module_to_f32)
-
-    def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
-        """
-        Apply the model to an input batch.
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :param context: conditioning plugged in via crossattn
-        :param y: an [N] Tensor of labels, if class-conditional.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        assert (y is not None) == (
-            self.num_classes is not None
-        ), "must specify y if and only if the model is class-conditional"
-        hs = []
-        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
-        emb = self.time_embed(t_emb)
-
-        if self.num_classes is not None:
-            assert y.shape == (x.shape[0],)
-            emb = emb + self.label_emb(y)
-
-        # For text-to-audio using global CLIP
-        if self.use_context_project:
-            context = self.context_project(context)
-        emb = emb + context.squeeze(1)
-
-        h = x.type(self.dtype)
-        for module in self.input_blocks:
-            h = module(h, emb, context if self.use_context_attn else None)
-            hs.append(h)
-        h = self.middle_block(h, emb, context if self.use_context_attn else None)
-        for module in self.output_blocks:
-            h = th.cat([h, hs.pop()], dim=1)
-            h = module(h, emb, context if self.use_context_attn else None)
-        h = h.type(x.dtype)
-        if self.predict_codebook_ids:
-            return self.id_predictor(h)
-        else:
-            return self.out(h)

spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/modules/lstm.py

@@ -1,25 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from torch import nn
-
-
-class StreamableLSTM(nn.Module):
-    """LSTM without worrying about the hidden state, nor the layout of the data.
-    Expects input as convolutional layout.
-    """
-    def __init__(self, dimension: int, num_layers: int = 2, skip: bool = True):
-        super().__init__()
-        self.skip = skip
-        self.lstm = nn.LSTM(dimension, dimension, num_layers)
-
-    def forward(self, x):
-        x = x.permute(2, 0, 1)
-        y, _ = self.lstm(x)
-        if self.skip:
-            y = y + x
-        y = y.permute(1, 2, 0)
-        return y

spaces/AgentVerse/agentVerse/agentverse/output_parser/__init__.py

@@ -1,5 +0,0 @@
-from agentverse.registry import Registry
-
-output_parser_registry = Registry(name="OutputParserRegistry")
-
-from .output_parser import *

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/lineprogresscanvas/LineProgressCanvas.d.ts

@@ -1,2 +0,0 @@
-import LineProgressCanvas from "../../../plugins/lineprogresscanvas";
-export default LineProgressCanvas;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/perspective/Factory.js

@@ -1,11 +0,0 @@
-import Perspective from './Perspective.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('perspective', function (gameObject, config) {
-    return new Perspective(gameObject, config);
-});
-
-SetValue(window, 'RexPlugins.UI.Perspective', Perspective);
-
-export default Perspective;

spaces/Alexxggs/ggvpnewen/README.md

@@ -1,12 +0,0 @@
----
-title: Ggvpnewen
-emoji: ⚡
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.9
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AlgoveraAI/ocean-marketplace/README.md

@@ -1,46 +0,0 @@
----
-title: Ocean Marketplace
-emoji: 🧺
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-app_file: app.py
-pinned: false
-license: mit
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`models`: _List[string]_
-HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`datasets`: _List[string]_
-HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.

spaces/Alpaca233/SadTalker/scripts/download_models.sh

@@ -1,32 +0,0 @@
-mkdir ./checkpoints  
-
-# lagency download link
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/auido2exp_00300-model.pth -O ./checkpoints/auido2exp_00300-model.pth
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/auido2pose_00140-model.pth -O ./checkpoints/auido2pose_00140-model.pth
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/epoch_20.pth -O ./checkpoints/epoch_20.pth
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/facevid2vid_00189-model.pth.tar -O ./checkpoints/facevid2vid_00189-model.pth.tar
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/shape_predictor_68_face_landmarks.dat -O ./checkpoints/shape_predictor_68_face_landmarks.dat
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/wav2lip.pth -O ./checkpoints/wav2lip.pth
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/mapping_00229-model.pth.tar -O ./checkpoints/mapping_00229-model.pth.tar
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/mapping_00109-model.pth.tar -O ./checkpoints/mapping_00109-model.pth.tar
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/hub.zip -O ./checkpoints/hub.zip
-# unzip -n ./checkpoints/hub.zip -d ./checkpoints/
-
-
-#### download the new links.
-wget -nc https://github.com/OpenTalker/SadTalker/releases/download/v0.0.2-rc/mapping_00109-model.pth.tar -O  ./checkpoints/mapping_00109-model.pth.tar
-wget -nc https://github.com/OpenTalker/SadTalker/releases/download/v0.0.2-rc/mapping_00229-model.pth.tar -O  ./checkpoints/mapping_00229-model.pth.tar
-wget -nc https://github.com/OpenTalker/SadTalker/releases/download/v0.0.2-rc/SadTalker_V0.0.2_256.safetensors -O  ./checkpoints/SadTalker_V0.0.2_256.safetensors
-wget -nc https://github.com/OpenTalker/SadTalker/releases/download/v0.0.2-rc/SadTalker_V0.0.2_512.safetensors -O  ./checkpoints/SadTalker_V0.0.2_512.safetensors
-
-
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/BFM_Fitting.zip -O ./checkpoints/BFM_Fitting.zip
-# unzip -n ./checkpoints/BFM_Fitting.zip -d ./checkpoints/
-
-### enhancer 
-mkdir -p ./gfpgan/weights
-wget -nc https://github.com/xinntao/facexlib/releases/download/v0.1.0/alignment_WFLW_4HG.pth -O ./gfpgan/weights/alignment_WFLW_4HG.pth 
-wget -nc https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth -O ./gfpgan/weights/detection_Resnet50_Final.pth 
-wget -nc https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -O ./gfpgan/weights/GFPGANv1.4.pth 
-wget -nc https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth -O ./gfpgan/weights/parsing_parsenet.pth 
-

spaces/Amrrs/DragGan-Inversion/stylegan_human/pti/training/projectors/w_projector.py

@@ -1,161 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Project given image to the latent space of pretrained network pickle."""
-
-import copy
-import wandb
-import numpy as np
-import torch
-import torch.nn.functional as F
-from tqdm import tqdm
-from pti.pti_configs import global_config, hyperparameters
-from utils import log_utils
-import dnnlib
-
-
-def project(
-        G,
-        # [C,H,W] and dynamic range [0,255], W & H must match G output resolution
-        target: torch.Tensor,
-        *,
-        num_steps=1000,
-        w_avg_samples=10000,
-        initial_learning_rate=0.01,
-        initial_noise_factor=0.05,
-        lr_rampdown_length=0.25,
-        lr_rampup_length=0.05,
-        noise_ramp_length=0.75,
-        regularize_noise_weight=1e5,
-        verbose=False,
-        device: torch.device,
-        use_wandb=False,
-        initial_w=None,
-        image_log_step=global_config.image_rec_result_log_snapshot,
-        w_name: str
-):
-    print(target.shape, G.img_channels, G.img_resolution, G.img_resolution//2)
-    assert target.shape == (
-        G.img_channels, G.img_resolution, G.img_resolution // 2)
-
-    def logprint(*args):
-        if verbose:
-            print(*args)
-
-    G = copy.deepcopy(G).eval().requires_grad_(
-        False).to(device).float()  # type: ignore
-
-    # Compute w stats.
-    logprint(
-        f'Computing W midpoint and stddev using {w_avg_samples} samples...')
-    z_samples = np.random.RandomState(123).randn(w_avg_samples, G.z_dim)
-    w_samples = G.mapping(torch.from_numpy(
-        z_samples).to(device), None)  # [N, L, C]
-    w_samples = w_samples[:, :1, :].cpu(
-    ).numpy().astype(np.float32)  # [N, 1, C]
-    w_avg = np.mean(w_samples, axis=0, keepdims=True)  # [1, 1, C]
-    w_avg_tensor = torch.from_numpy(w_avg).to(global_config.device)
-    w_std = (np.sum((w_samples - w_avg) ** 2) / w_avg_samples) ** 0.5
-
-    start_w = initial_w if initial_w is not None else w_avg
-
-    # Setup noise inputs.
-    noise_bufs = {name: buf for (
-        name, buf) in G.synthesis.named_buffers() if 'noise_const' in name}
-
-    # Load VGG16 feature detector.
-    url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt'
-    with dnnlib.util.open_url(url) as f:
-        vgg16 = torch.jit.load(f).eval().to(device)
-
-    # Features for target image.
-    target_images = target.unsqueeze(0).to(device).to(torch.float32)
-    if target_images.shape[2] > 256:
-        target_images = F.interpolate(
-            target_images, size=(256, 256), mode='area')
-    target_features = vgg16(
-        target_images, resize_images=False, return_lpips=True)
-
-    w_opt = torch.tensor(start_w, dtype=torch.float32, device=device,
-                         requires_grad=True)  # pylint: disable=not-callable
-    optimizer = torch.optim.Adam([w_opt] + list(noise_bufs.values()), betas=(0.9, 0.999),
-                                 lr=hyperparameters.first_inv_lr)
-
-    # Init noise.
-    for buf in noise_bufs.values():
-        buf[:] = torch.randn_like(buf)
-        buf.requires_grad = True
-
-    for step in range(num_steps):
-
-        # Learning rate schedule.
-        t = step / num_steps
-        w_noise_scale = w_std * initial_noise_factor * \
-            max(0.0, 1.0 - t / noise_ramp_length) ** 2
-        lr_ramp = min(1.0, (1.0 - t) / lr_rampdown_length)
-        lr_ramp = 0.5 - 0.5 * np.cos(lr_ramp * np.pi)
-        lr_ramp = lr_ramp * min(1.0, t / lr_rampup_length)
-        lr = initial_learning_rate * lr_ramp
-        for param_group in optimizer.param_groups:
-            param_group['lr'] = lr
-
-        # Synth images from opt_w.
-        w_noise = torch.randn_like(w_opt) * w_noise_scale
-        ws = (w_opt + w_noise).repeat([1, G.mapping.num_ws, 1])
-        synth_images = G.synthesis(ws, noise_mode='const', force_fp32=True)
-
-        # Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images.
-        synth_images = (synth_images + 1) * (255 / 2)
-        if synth_images.shape[2] > 256:
-            synth_images = F.interpolate(
-                synth_images, size=(256, 256), mode='area')
-
-        # Features for synth images.
-        synth_features = vgg16(
-            synth_images, resize_images=False, return_lpips=True)
-        dist = (target_features - synth_features).square().sum()
-
-        # Noise regularization.
-        reg_loss = 0.0
-        for v in noise_bufs.values():
-            noise = v[None, None, :, :]  # must be [1,1,H,W] for F.avg_pool2d()
-            while True:
-                reg_loss += (noise * torch.roll(noise,
-                             shifts=1, dims=3)).mean() ** 2
-                reg_loss += (noise * torch.roll(noise,
-                             shifts=1, dims=2)).mean() ** 2
-                if noise.shape[2] <= 8:
-                    break
-                noise = F.avg_pool2d(noise, kernel_size=2)
-        loss = dist + reg_loss * regularize_noise_weight
-        if step % 10 == 0:
-            print("project loss", step, loss.data)
-        if step % image_log_step == 0:
-            with torch.no_grad():
-                if use_wandb:
-                    global_config.training_step += 1
-                    wandb.log({f'first projection _{w_name}': loss.detach(
-                    ).cpu()}, step=global_config.training_step)
-                    log_utils.log_image_from_w(w_opt.repeat(
-                        [1, G.mapping.num_ws, 1]), G, w_name)
-
-        # Step
-        optimizer.zero_grad(set_to_none=True)
-        loss.backward()
-        optimizer.step()
-        logprint(
-            f'step {step + 1:>4d}/{num_steps}: dist {dist:<4.2f} loss {float(loss):<5.2f}')
-
-        # Normalize noise.
-        with torch.no_grad():
-            for buf in noise_bufs.values():
-                buf -= buf.mean()
-                buf *= buf.square().mean().rsqrt()
-
-    del G
-    return w_opt.repeat([1, 18, 1])

spaces/Amrrs/DragGan-Inversion/torch_utils/training_stats.py

@@ -1,283 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Facilities for reporting and collecting training statistics across
-multiple processes and devices. The interface is designed to minimize
-synchronization overhead as well as the amount of boilerplate in user
-code."""
-
-import re
-import numpy as np
-import torch
-import dnnlib
-
-from . import misc
-
-# ----------------------------------------------------------------------------
-
-_num_moments = 3             # [num_scalars, sum_of_scalars, sum_of_squares]
-# Data type to use for initial per-tensor reduction.
-_reduce_dtype = torch.float32
-_counter_dtype = torch.float64  # Data type to use for the internal counters.
-_rank = 0             # Rank of the current process.
-# Device to use for multiprocess communication. None = single-process.
-_sync_device = None
-_sync_called = False         # Has _sync() been called yet?
-# Running counters on each device, updated by report(): name => device => torch.Tensor
-_counters = dict()
-# Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor
-_cumulative = dict()
-
-# ----------------------------------------------------------------------------
-
-
-def init_multiprocessing(rank, sync_device):
-    r"""Initializes `torch_utils.training_stats` for collecting statistics
-    across multiple processes.
-
-    This function must be called after
-    `torch.distributed.init_process_group()` and before `Collector.update()`.
-    The call is not necessary if multi-process collection is not needed.
-
-    Args:
-        rank:           Rank of the current process.
-        sync_device:    PyTorch device to use for inter-process
-                        communication, or None to disable multi-process
-                        collection. Typically `torch.device('cuda', rank)`.
-    """
-    global _rank, _sync_device
-    assert not _sync_called
-    _rank = rank
-    _sync_device = sync_device
-
-# ----------------------------------------------------------------------------
-
-
-@misc.profiled_function
-def report(name, value):
-    r"""Broadcasts the given set of scalars to all interested instances of
-    `Collector`, across device and process boundaries.
-
-    This function is expected to be extremely cheap and can be safely
-    called from anywhere in the training loop, loss function, or inside a
-    `torch.nn.Module`.
-
-    Warning: The current implementation expects the set of unique names to
-    be consistent across processes. Please make sure that `report()` is
-    called at least once for each unique name by each process, and in the
-    same order. If a given process has no scalars to broadcast, it can do
-    `report(name, [])` (empty list).
-
-    Args:
-        name:   Arbitrary string specifying the name of the statistic.
-                Averages are accumulated separately for each unique name.
-        value:  Arbitrary set of scalars. Can be a list, tuple,
-                NumPy array, PyTorch tensor, or Python scalar.
-
-    Returns:
-        The same `value` that was passed in.
-    """
-    if name not in _counters:
-        _counters[name] = dict()
-
-    elems = torch.as_tensor(value)
-    if elems.numel() == 0:
-        return value
-
-    elems = elems.detach().flatten().to(_reduce_dtype)
-    moments = torch.stack([
-        torch.ones_like(elems).sum(),
-        elems.sum(),
-        elems.square().sum(),
-    ])
-    assert moments.ndim == 1 and moments.shape[0] == _num_moments
-    moments = moments.to(_counter_dtype)
-
-    device = moments.device
-    if device not in _counters[name]:
-        _counters[name][device] = torch.zeros_like(moments)
-    _counters[name][device].add_(moments)
-    return value
-
-# ----------------------------------------------------------------------------
-
-
-def report0(name, value):
-    r"""Broadcasts the given set of scalars by the first process (`rank = 0`),
-    but ignores any scalars provided by the other processes.
-    See `report()` for further details.
-    """
-    report(name, value if _rank == 0 else [])
-    return value
-
-# ----------------------------------------------------------------------------
-
-
-class Collector:
-    r"""Collects the scalars broadcasted by `report()` and `report0()` and
-    computes their long-term averages (mean and standard deviation) over
-    user-defined periods of time.
-
-    The averages are first collected into internal counters that are not
-    directly visible to the user. They are then copied to the user-visible
-    state as a result of calling `update()` and can then be queried using
-    `mean()`, `std()`, `as_dict()`, etc. Calling `update()` also resets the
-    internal counters for the next round, so that the user-visible state
-    effectively reflects averages collected between the last two calls to
-    `update()`.
-
-    Args:
-        regex:          Regular expression defining which statistics to
-                        collect. The default is to collect everything.
-        keep_previous:  Whether to retain the previous averages if no
-                        scalars were collected on a given round
-                        (default: True).
-    """
-
-    def __init__(self, regex='.*', keep_previous=True):
-        self._regex = re.compile(regex)
-        self._keep_previous = keep_previous
-        self._cumulative = dict()
-        self._moments = dict()
-        self.update()
-        self._moments.clear()
-
-    def names(self):
-        r"""Returns the names of all statistics broadcasted so far that
-        match the regular expression specified at construction time.
-        """
-        return [name for name in _counters if self._regex.fullmatch(name)]
-
-    def update(self):
-        r"""Copies current values of the internal counters to the
-        user-visible state and resets them for the next round.
-
-        If `keep_previous=True` was specified at construction time, the
-        operation is skipped for statistics that have received no scalars
-        since the last update, retaining their previous averages.
-
-        This method performs a number of GPU-to-CPU transfers and one
-        `torch.distributed.all_reduce()`. It is intended to be called
-        periodically in the main training loop, typically once every
-        N training steps.
-        """
-        if not self._keep_previous:
-            self._moments.clear()
-        for name, cumulative in _sync(self.names()):
-            if name not in self._cumulative:
-                self._cumulative[name] = torch.zeros(
-                    [_num_moments], dtype=_counter_dtype)
-            delta = cumulative - self._cumulative[name]
-            self._cumulative[name].copy_(cumulative)
-            if float(delta[0]) != 0:
-                self._moments[name] = delta
-
-    def _get_delta(self, name):
-        r"""Returns the raw moments that were accumulated for the given
-        statistic between the last two calls to `update()`, or zero if
-        no scalars were collected.
-        """
-        assert self._regex.fullmatch(name)
-        if name not in self._moments:
-            self._moments[name] = torch.zeros(
-                [_num_moments], dtype=_counter_dtype)
-        return self._moments[name]
-
-    def num(self, name):
-        r"""Returns the number of scalars that were accumulated for the given
-        statistic between the last two calls to `update()`, or zero if
-        no scalars were collected.
-        """
-        delta = self._get_delta(name)
-        return int(delta[0])
-
-    def mean(self, name):
-        r"""Returns the mean of the scalars that were accumulated for the
-        given statistic between the last two calls to `update()`, or NaN if
-        no scalars were collected.
-        """
-        delta = self._get_delta(name)
-        if int(delta[0]) == 0:
-            return float('nan')
-        return float(delta[1] / delta[0])
-
-    def std(self, name):
-        r"""Returns the standard deviation of the scalars that were
-        accumulated for the given statistic between the last two calls to
-        `update()`, or NaN if no scalars were collected.
-        """
-        delta = self._get_delta(name)
-        if int(delta[0]) == 0 or not np.isfinite(float(delta[1])):
-            return float('nan')
-        if int(delta[0]) == 1:
-            return float(0)
-        mean = float(delta[1] / delta[0])
-        raw_var = float(delta[2] / delta[0])
-        return np.sqrt(max(raw_var - np.square(mean), 0))
-
-    def as_dict(self):
-        r"""Returns the averages accumulated between the last two calls to
-        `update()` as an `dnnlib.EasyDict`. The contents are as follows:
-
-            dnnlib.EasyDict(
-                NAME = dnnlib.EasyDict(num=FLOAT, mean=FLOAT, std=FLOAT),
-                ...
-            )
-        """
-        stats = dnnlib.EasyDict()
-        for name in self.names():
-            stats[name] = dnnlib.EasyDict(num=self.num(
-                name), mean=self.mean(name), std=self.std(name))
-        return stats
-
-    def __getitem__(self, name):
-        r"""Convenience getter.
-        `collector[name]` is a synonym for `collector.mean(name)`.
-        """
-        return self.mean(name)
-
-# ----------------------------------------------------------------------------
-
-
-def _sync(names):
-    r"""Synchronize the global cumulative counters across devices and
-    processes. Called internally by `Collector.update()`.
-    """
-    if len(names) == 0:
-        return []
-    global _sync_called
-    _sync_called = True
-
-    # Collect deltas within current rank.
-    deltas = []
-    device = _sync_device if _sync_device is not None else torch.device('cpu')
-    for name in names:
-        delta = torch.zeros(
-            [_num_moments], dtype=_counter_dtype, device=device)
-        for counter in _counters[name].values():
-            delta.add_(counter.to(device))
-            counter.copy_(torch.zeros_like(counter))
-        deltas.append(delta)
-    deltas = torch.stack(deltas)
-
-    # Sum deltas across ranks.
-    if _sync_device is not None:
-        torch.distributed.all_reduce(deltas)
-
-    # Update cumulative values.
-    deltas = deltas.cpu()
-    for idx, name in enumerate(names):
-        if name not in _cumulative:
-            _cumulative[name] = torch.zeros(
-                [_num_moments], dtype=_counter_dtype)
-        _cumulative[name].add_(deltas[idx])
-
-    # Return name-value pairs.
-    return [(name, _cumulative[name]) for name in names]
-
-# ----------------------------------------------------------------------------

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/colossalai/train_dreambooth_colossalai.py

@@ -1,673 +0,0 @@
-import argparse
-import hashlib
-import math
-import os
-from pathlib import Path
-
-import colossalai
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from colossalai.context.parallel_mode import ParallelMode
-from colossalai.core import global_context as gpc
-from colossalai.logging import disable_existing_loggers, get_dist_logger
-from colossalai.nn.optimizer.gemini_optimizer import GeminiAdamOptimizer
-from colossalai.nn.parallel.utils import get_static_torch_model
-from colossalai.utils import get_current_device
-from colossalai.utils.model.colo_init_context import ColoInitContext
-from huggingface_hub import create_repo, upload_folder
-from PIL import Image
-from torch.utils.data import Dataset
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import AutoTokenizer, PretrainedConfig
-
-from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
-from diffusers.optimization import get_scheduler
-
-
-disable_existing_loggers()
-logger = get_dist_logger()
-
-
-def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str):
-    text_encoder_config = PretrainedConfig.from_pretrained(
-        pretrained_model_name_or_path,
-        subfolder="text_encoder",
-        revision=args.revision,
-    )
-    model_class = text_encoder_config.architectures[0]
-
-    if model_class == "CLIPTextModel":
-        from transformers import CLIPTextModel
-
-        return CLIPTextModel
-    elif model_class == "RobertaSeriesModelWithTransformation":
-        from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation
-
-        return RobertaSeriesModelWithTransformation
-    else:
-        raise ValueError(f"{model_class} is not supported.")
-
-
-def parse_args(input_args=None):
-    parser = argparse.ArgumentParser(description="Simple example of a training script.")
-    parser.add_argument(
-        "--pretrained_model_name_or_path",
-        type=str,
-        default=None,
-        required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--revision",
-        type=str,
-        default=None,
-        required=False,
-        help="Revision of pretrained model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--tokenizer_name",
-        type=str,
-        default=None,
-        help="Pretrained tokenizer name or path if not the same as model_name",
-    )
-    parser.add_argument(
-        "--instance_data_dir",
-        type=str,
-        default=None,
-        required=True,
-        help="A folder containing the training data of instance images.",
-    )
-    parser.add_argument(
-        "--class_data_dir",
-        type=str,
-        default=None,
-        required=False,
-        help="A folder containing the training data of class images.",
-    )
-    parser.add_argument(
-        "--instance_prompt",
-        type=str,
-        default="a photo of sks dog",
-        required=False,
-        help="The prompt with identifier specifying the instance",
-    )
-    parser.add_argument(
-        "--class_prompt",
-        type=str,
-        default=None,
-        help="The prompt to specify images in the same class as provided instance images.",
-    )
-    parser.add_argument(
-        "--with_prior_preservation",
-        default=False,
-        action="store_true",
-        help="Flag to add prior preservation loss.",
-    )
-    parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
-    parser.add_argument(
-        "--num_class_images",
-        type=int,
-        default=100,
-        help=(
-            "Minimal class images for prior preservation loss. If there are not enough images already present in"
-            " class_data_dir, additional images will be sampled with class_prompt."
-        ),
-    )
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        default="text-inversion-model",
-        help="The output directory where the model predictions and checkpoints will be written.",
-    )
-    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
-    parser.add_argument(
-        "--resolution",
-        type=int,
-        default=512,
-        help=(
-            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
-            " resolution"
-        ),
-    )
-    parser.add_argument(
-        "--placement",
-        type=str,
-        default="cpu",
-        help="Placement Policy for Gemini. Valid when using colossalai as dist plan.",
-    )
-    parser.add_argument(
-        "--center_crop",
-        default=False,
-        action="store_true",
-        help=(
-            "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
-            " cropped. The images will be resized to the resolution first before cropping."
-        ),
-    )
-    parser.add_argument(
-        "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
-    )
-    parser.add_argument(
-        "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
-    )
-    parser.add_argument("--num_train_epochs", type=int, default=1)
-    parser.add_argument(
-        "--max_train_steps",
-        type=int,
-        default=None,
-        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
-    )
-    parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.")
-    parser.add_argument(
-        "--gradient_checkpointing",
-        action="store_true",
-        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
-    )
-    parser.add_argument(
-        "--learning_rate",
-        type=float,
-        default=5e-6,
-        help="Initial learning rate (after the potential warmup period) to use.",
-    )
-    parser.add_argument(
-        "--scale_lr",
-        action="store_true",
-        default=False,
-        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
-    )
-    parser.add_argument(
-        "--lr_scheduler",
-        type=str,
-        default="constant",
-        help=(
-            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
-            ' "constant", "constant_with_warmup"]'
-        ),
-    )
-    parser.add_argument(
-        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
-    )
-    parser.add_argument(
-        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
-    )
-
-    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
-    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
-    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
-    parser.add_argument(
-        "--hub_model_id",
-        type=str,
-        default=None,
-        help="The name of the repository to keep in sync with the local `output_dir`.",
-    )
-    parser.add_argument(
-        "--logging_dir",
-        type=str,
-        default="logs",
-        help=(
-            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
-        ),
-    )
-    parser.add_argument(
-        "--mixed_precision",
-        type=str,
-        default=None,
-        choices=["no", "fp16", "bf16"],
-        help=(
-            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
-            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
-            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
-        ),
-    )
-    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-
-    if input_args is not None:
-        args = parser.parse_args(input_args)
-    else:
-        args = parser.parse_args()
-
-    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
-    if env_local_rank != -1 and env_local_rank != args.local_rank:
-        args.local_rank = env_local_rank
-
-    if args.with_prior_preservation:
-        if args.class_data_dir is None:
-            raise ValueError("You must specify a data directory for class images.")
-        if args.class_prompt is None:
-            raise ValueError("You must specify prompt for class images.")
-    else:
-        if args.class_data_dir is not None:
-            logger.warning("You need not use --class_data_dir without --with_prior_preservation.")
-        if args.class_prompt is not None:
-            logger.warning("You need not use --class_prompt without --with_prior_preservation.")
-
-    return args
-
-
-class DreamBoothDataset(Dataset):
-    """
-    A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
-    It pre-processes the images and the tokenizes prompts.
-    """
-
-    def __init__(
-        self,
-        instance_data_root,
-        instance_prompt,
-        tokenizer,
-        class_data_root=None,
-        class_prompt=None,
-        size=512,
-        center_crop=False,
-    ):
-        self.size = size
-        self.center_crop = center_crop
-        self.tokenizer = tokenizer
-
-        self.instance_data_root = Path(instance_data_root)
-        if not self.instance_data_root.exists():
-            raise ValueError("Instance images root doesn't exists.")
-
-        self.instance_images_path = list(Path(instance_data_root).iterdir())
-        self.num_instance_images = len(self.instance_images_path)
-        self.instance_prompt = instance_prompt
-        self._length = self.num_instance_images
-
-        if class_data_root is not None:
-            self.class_data_root = Path(class_data_root)
-            self.class_data_root.mkdir(parents=True, exist_ok=True)
-            self.class_images_path = list(self.class_data_root.iterdir())
-            self.num_class_images = len(self.class_images_path)
-            self._length = max(self.num_class_images, self.num_instance_images)
-            self.class_prompt = class_prompt
-        else:
-            self.class_data_root = None
-
-        self.image_transforms = transforms.Compose(
-            [
-                transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
-                transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
-                transforms.ToTensor(),
-                transforms.Normalize([0.5], [0.5]),
-            ]
-        )
-
-    def __len__(self):
-        return self._length
-
-    def __getitem__(self, index):
-        example = {}
-        instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
-        if not instance_image.mode == "RGB":
-            instance_image = instance_image.convert("RGB")
-        example["instance_images"] = self.image_transforms(instance_image)
-        example["instance_prompt_ids"] = self.tokenizer(
-            self.instance_prompt,
-            padding="do_not_pad",
-            truncation=True,
-            max_length=self.tokenizer.model_max_length,
-        ).input_ids
-
-        if self.class_data_root:
-            class_image = Image.open(self.class_images_path[index % self.num_class_images])
-            if not class_image.mode == "RGB":
-                class_image = class_image.convert("RGB")
-            example["class_images"] = self.image_transforms(class_image)
-            example["class_prompt_ids"] = self.tokenizer(
-                self.class_prompt,
-                padding="do_not_pad",
-                truncation=True,
-                max_length=self.tokenizer.model_max_length,
-            ).input_ids
-
-        return example
-
-
-class PromptDataset(Dataset):
-    "A simple dataset to prepare the prompts to generate class images on multiple GPUs."
-
-    def __init__(self, prompt, num_samples):
-        self.prompt = prompt
-        self.num_samples = num_samples
-
-    def __len__(self):
-        return self.num_samples
-
-    def __getitem__(self, index):
-        example = {}
-        example["prompt"] = self.prompt
-        example["index"] = index
-        return example
-
-
-# Gemini + ZeRO DDP
-def gemini_zero_dpp(model: torch.nn.Module, placememt_policy: str = "auto"):
-    from colossalai.nn.parallel import GeminiDDP
-
-    model = GeminiDDP(
-        model, device=get_current_device(), placement_policy=placememt_policy, pin_memory=True, search_range_mb=64
-    )
-    return model
-
-
-def main(args):
-    if args.seed is None:
-        colossalai.launch_from_torch(config={})
-    else:
-        colossalai.launch_from_torch(config={}, seed=args.seed)
-
-    local_rank = gpc.get_local_rank(ParallelMode.DATA)
-    world_size = gpc.get_world_size(ParallelMode.DATA)
-
-    if args.with_prior_preservation:
-        class_images_dir = Path(args.class_data_dir)
-        if not class_images_dir.exists():
-            class_images_dir.mkdir(parents=True)
-        cur_class_images = len(list(class_images_dir.iterdir()))
-
-        if cur_class_images < args.num_class_images:
-            torch_dtype = torch.float16 if get_current_device() == "cuda" else torch.float32
-            pipeline = DiffusionPipeline.from_pretrained(
-                args.pretrained_model_name_or_path,
-                torch_dtype=torch_dtype,
-                safety_checker=None,
-                revision=args.revision,
-            )
-            pipeline.set_progress_bar_config(disable=True)
-
-            num_new_images = args.num_class_images - cur_class_images
-            logger.info(f"Number of class images to sample: {num_new_images}.")
-
-            sample_dataset = PromptDataset(args.class_prompt, num_new_images)
-            sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
-
-            pipeline.to(get_current_device())
-
-            for example in tqdm(
-                sample_dataloader,
-                desc="Generating class images",
-                disable=not local_rank == 0,
-            ):
-                images = pipeline(example["prompt"]).images
-
-                for i, image in enumerate(images):
-                    hash_image = hashlib.sha1(image.tobytes()).hexdigest()
-                    image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
-                    image.save(image_filename)
-
-            del pipeline
-
-    # Handle the repository creation
-    if local_rank == 0:
-        if args.output_dir is not None:
-            os.makedirs(args.output_dir, exist_ok=True)
-
-        if args.push_to_hub:
-            repo_id = create_repo(
-                repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
-            ).repo_id
-
-    # Load the tokenizer
-    if args.tokenizer_name:
-        logger.info(f"Loading tokenizer from {args.tokenizer_name}", ranks=[0])
-        tokenizer = AutoTokenizer.from_pretrained(
-            args.tokenizer_name,
-            revision=args.revision,
-            use_fast=False,
-        )
-    elif args.pretrained_model_name_or_path:
-        logger.info("Loading tokenizer from pretrained model", ranks=[0])
-        tokenizer = AutoTokenizer.from_pretrained(
-            args.pretrained_model_name_or_path,
-            subfolder="tokenizer",
-            revision=args.revision,
-            use_fast=False,
-        )
-        # import correct text encoder class
-    text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path)
-
-    # Load models and create wrapper for stable diffusion
-
-    logger.info(f"Loading text_encoder from {args.pretrained_model_name_or_path}", ranks=[0])
-
-    text_encoder = text_encoder_cls.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="text_encoder",
-        revision=args.revision,
-    )
-
-    logger.info(f"Loading AutoencoderKL from {args.pretrained_model_name_or_path}", ranks=[0])
-    vae = AutoencoderKL.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="vae",
-        revision=args.revision,
-    )
-
-    logger.info(f"Loading UNet2DConditionModel from {args.pretrained_model_name_or_path}", ranks=[0])
-    with ColoInitContext(device=get_current_device()):
-        unet = UNet2DConditionModel.from_pretrained(
-            args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, low_cpu_mem_usage=False
-        )
-
-    vae.requires_grad_(False)
-    text_encoder.requires_grad_(False)
-
-    if args.gradient_checkpointing:
-        unet.enable_gradient_checkpointing()
-
-    if args.scale_lr:
-        args.learning_rate = args.learning_rate * args.train_batch_size * world_size
-
-    unet = gemini_zero_dpp(unet, args.placement)
-
-    # config optimizer for colossalai zero
-    optimizer = GeminiAdamOptimizer(
-        unet, lr=args.learning_rate, initial_scale=2**5, clipping_norm=args.max_grad_norm
-    )
-
-    # load noise_scheduler
-    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
-
-    # prepare dataset
-    logger.info(f"Prepare dataset from {args.instance_data_dir}", ranks=[0])
-    train_dataset = DreamBoothDataset(
-        instance_data_root=args.instance_data_dir,
-        instance_prompt=args.instance_prompt,
-        class_data_root=args.class_data_dir if args.with_prior_preservation else None,
-        class_prompt=args.class_prompt,
-        tokenizer=tokenizer,
-        size=args.resolution,
-        center_crop=args.center_crop,
-    )
-
-    def collate_fn(examples):
-        input_ids = [example["instance_prompt_ids"] for example in examples]
-        pixel_values = [example["instance_images"] for example in examples]
-
-        # Concat class and instance examples for prior preservation.
-        # We do this to avoid doing two forward passes.
-        if args.with_prior_preservation:
-            input_ids += [example["class_prompt_ids"] for example in examples]
-            pixel_values += [example["class_images"] for example in examples]
-
-        pixel_values = torch.stack(pixel_values)
-        pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
-
-        input_ids = tokenizer.pad(
-            {"input_ids": input_ids},
-            padding="max_length",
-            max_length=tokenizer.model_max_length,
-            return_tensors="pt",
-        ).input_ids
-
-        batch = {
-            "input_ids": input_ids,
-            "pixel_values": pixel_values,
-        }
-        return batch
-
-    train_dataloader = torch.utils.data.DataLoader(
-        train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn, num_workers=1
-    )
-
-    # Scheduler and math around the number of training steps.
-    overrode_max_train_steps = False
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader))
-    if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-        overrode_max_train_steps = True
-
-    lr_scheduler = get_scheduler(
-        args.lr_scheduler,
-        optimizer=optimizer,
-        num_warmup_steps=args.lr_warmup_steps,
-        num_training_steps=args.max_train_steps,
-    )
-    weight_dtype = torch.float32
-    if args.mixed_precision == "fp16":
-        weight_dtype = torch.float16
-    elif args.mixed_precision == "bf16":
-        weight_dtype = torch.bfloat16
-
-    # Move text_encode and vae to gpu.
-    # For mixed precision training we cast the text_encoder and vae weights to half-precision
-    # as these models are only used for inference, keeping weights in full precision is not required.
-    vae.to(get_current_device(), dtype=weight_dtype)
-    text_encoder.to(get_current_device(), dtype=weight_dtype)
-
-    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader))
-    if overrode_max_train_steps:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-    # Afterwards we recalculate our number of training epochs
-    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
-    # Train!
-    total_batch_size = args.train_batch_size * world_size
-
-    logger.info("***** Running training *****", ranks=[0])
-    logger.info(f"  Num examples = {len(train_dataset)}", ranks=[0])
-    logger.info(f"  Num batches each epoch = {len(train_dataloader)}", ranks=[0])
-    logger.info(f"  Num Epochs = {args.num_train_epochs}", ranks=[0])
-    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}", ranks=[0])
-    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}", ranks=[0])
-    logger.info(f"  Total optimization steps = {args.max_train_steps}", ranks=[0])
-
-    # Only show the progress bar once on each machine.
-    progress_bar = tqdm(range(args.max_train_steps), disable=not local_rank == 0)
-    progress_bar.set_description("Steps")
-    global_step = 0
-
-    torch.cuda.synchronize()
-    for epoch in range(args.num_train_epochs):
-        unet.train()
-        for step, batch in enumerate(train_dataloader):
-            torch.cuda.reset_peak_memory_stats()
-            # Move batch to gpu
-            for key, value in batch.items():
-                batch[key] = value.to(get_current_device(), non_blocking=True)
-
-            # Convert images to latent space
-            optimizer.zero_grad()
-
-            latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
-            latents = latents * 0.18215
-
-            # Sample noise that we'll add to the latents
-            noise = torch.randn_like(latents)
-            bsz = latents.shape[0]
-            # Sample a random timestep for each image
-            timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
-            timesteps = timesteps.long()
-
-            # Add noise to the latents according to the noise magnitude at each timestep
-            # (this is the forward diffusion process)
-            noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
-
-            # Get the text embedding for conditioning
-            encoder_hidden_states = text_encoder(batch["input_ids"])[0]
-
-            # Predict the noise residual
-            model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
-
-            # Get the target for loss depending on the prediction type
-            if noise_scheduler.config.prediction_type == "epsilon":
-                target = noise
-            elif noise_scheduler.config.prediction_type == "v_prediction":
-                target = noise_scheduler.get_velocity(latents, noise, timesteps)
-            else:
-                raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-
-            if args.with_prior_preservation:
-                # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
-                model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
-                target, target_prior = torch.chunk(target, 2, dim=0)
-
-                # Compute instance loss
-                loss = F.mse_loss(model_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean()
-
-                # Compute prior loss
-                prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
-
-                # Add the prior loss to the instance loss.
-                loss = loss + args.prior_loss_weight * prior_loss
-            else:
-                loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-
-            optimizer.backward(loss)
-
-            optimizer.step()
-            lr_scheduler.step()
-            logger.info(f"max GPU_mem cost is {torch.cuda.max_memory_allocated()/2**20} MB", ranks=[0])
-            # Checks if the accelerator has performed an optimization step behind the scenes
-            progress_bar.update(1)
-            global_step += 1
-            logs = {
-                "loss": loss.detach().item(),
-                "lr": optimizer.param_groups[0]["lr"],
-            }  # lr_scheduler.get_last_lr()[0]}
-            progress_bar.set_postfix(**logs)
-
-            if global_step % args.save_steps == 0:
-                torch.cuda.synchronize()
-                torch_unet = get_static_torch_model(unet)
-                if local_rank == 0:
-                    pipeline = DiffusionPipeline.from_pretrained(
-                        args.pretrained_model_name_or_path,
-                        unet=torch_unet,
-                        revision=args.revision,
-                    )
-                    save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
-                    pipeline.save_pretrained(save_path)
-                    logger.info(f"Saving model checkpoint to {save_path}", ranks=[0])
-            if global_step >= args.max_train_steps:
-                break
-
-    torch.cuda.synchronize()
-    unet = get_static_torch_model(unet)
-
-    if local_rank == 0:
-        pipeline = DiffusionPipeline.from_pretrained(
-            args.pretrained_model_name_or_path,
-            unet=unet,
-            revision=args.revision,
-        )
-
-        pipeline.save_pretrained(args.output_dir)
-        logger.info(f"Saving model checkpoint to {args.output_dir}", ranks=[0])
-
-        if args.push_to_hub:
-            upload_folder(
-                repo_id=repo_id,
-                folder_path=args.output_dir,
-                commit_message="End of training",
-                ignore_patterns=["step_*", "epoch_*"],
-            )
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    main(args)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_unclip_txt2img_to_image_variation.py

@@ -1,41 +0,0 @@
-import argparse
-
-from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
-
-from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
-
-    parser.add_argument(
-        "--txt2img_unclip",
-        default="kakaobrain/karlo-v1-alpha",
-        type=str,
-        required=False,
-        help="The pretrained txt2img unclip.",
-    )
-
-    args = parser.parse_args()
-
-    txt2img = UnCLIPPipeline.from_pretrained(args.txt2img_unclip)
-
-    feature_extractor = CLIPImageProcessor()
-    image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
-
-    img2img = UnCLIPImageVariationPipeline(
-        decoder=txt2img.decoder,
-        text_encoder=txt2img.text_encoder,
-        tokenizer=txt2img.tokenizer,
-        text_proj=txt2img.text_proj,
-        feature_extractor=feature_extractor,
-        image_encoder=image_encoder,
-        super_res_first=txt2img.super_res_first,
-        super_res_last=txt2img.super_res_last,
-        decoder_scheduler=txt2img.decoder_scheduler,
-        super_res_scheduler=txt2img.super_res_scheduler,
-    )
-
-    img2img.save_pretrained(args.dump_path)

spaces/Andy1621/UniFormerV2_mit_demo/uniformerv2.py

@@ -1,510 +0,0 @@
-#!/usr/bin/env python
-import os
-from collections import OrderedDict
-
-from timm.models.layers import DropPath
-import torch
-from torch import nn
-from torch.nn import MultiheadAttention
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-
-
-MODEL_PATH = './'
-_MODELS = {
-    "ViT-B/16": os.path.join(MODEL_PATH, "vit_b16.pth"),
-    "ViT-L/14": os.path.join(MODEL_PATH, "vit_l14.pth"),
-    "ViT-L/14_336": os.path.join(MODEL_PATH, "vit_l14_336.pth"),
-}
-
-
-class LayerNorm(nn.LayerNorm):
-    """Subclass torch's LayerNorm to handle fp16."""
-
-    def forward(self, x):
-        orig_type = x.dtype
-        ret = super().forward(x.type(torch.float32))
-        return ret.type(orig_type)
-
-
-class QuickGELU(nn.Module):
-    def forward(self, x):
-        return x * torch.sigmoid(1.702 * x)
-
-
-class Local_MHRA(nn.Module):
-    def __init__(self, d_model, dw_reduction=1.5, pos_kernel_size=3):
-        super().__init__() 
-
-        padding = pos_kernel_size // 2
-        re_d_model = int(d_model // dw_reduction)
-        self.pos_embed = nn.Sequential(
-            nn.BatchNorm3d(d_model),
-            nn.Conv3d(d_model, re_d_model, kernel_size=1, stride=1, padding=0),
-            nn.Conv3d(re_d_model, re_d_model, kernel_size=(pos_kernel_size, 1, 1), stride=(1, 1, 1), padding=(padding, 0, 0), groups=re_d_model),
-            nn.Conv3d(re_d_model, d_model, kernel_size=1, stride=1, padding=0),
-        )
-
-        # init zero
-        print('Init zero for Conv in pos_emb')
-        nn.init.constant_(self.pos_embed[3].weight, 0)
-        nn.init.constant_(self.pos_embed[3].bias, 0)
-
-    def forward(self, x):
-        return self.pos_embed(x)
-
-
-class ResidualAttentionBlock(nn.Module):
-    def __init__(
-            self, d_model, n_head, attn_mask=None, drop_path=0.0, 
-            dw_reduction=1.5, no_lmhra=False, double_lmhra=True
-        ):
-        super().__init__() 
-        
-        self.n_head = n_head
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        print(f'Drop path rate: {drop_path}')
-
-        self.no_lmhra = no_lmhra
-        self.double_lmhra = double_lmhra
-        print(f'No L_MHRA: {no_lmhra}')
-        print(f'Double L_MHRA: {double_lmhra}')
-        if not no_lmhra:
-            self.lmhra1 = Local_MHRA(d_model, dw_reduction=dw_reduction)
-            if double_lmhra:
-                self.lmhra2 = Local_MHRA(d_model, dw_reduction=dw_reduction)
-
-        # spatial
-        self.attn = MultiheadAttention(d_model, n_head)
-        self.ln_1 = LayerNorm(d_model)
-        self.mlp = nn.Sequential(OrderedDict([
-            ("c_fc", nn.Linear(d_model, d_model * 4)),
-            ("gelu", QuickGELU()),
-            ("c_proj", nn.Linear(d_model * 4, d_model))
-        ]))
-        self.ln_2 = LayerNorm(d_model)
-        self.attn_mask = attn_mask
-
-    def attention(self, x):
-        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
-        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
-
-    def forward(self, x, T=8, use_checkpoint=False):
-        # x: 1+HW, NT, C
-        if not self.no_lmhra:
-            # Local MHRA
-            tmp_x = x[1:, :, :]
-            L, NT, C = tmp_x.shape
-            N = NT // T
-            H = W = int(L ** 0.5)
-            tmp_x = tmp_x.view(H, W, N, T, C).permute(2, 4, 3, 0, 1).contiguous()
-            tmp_x = tmp_x + self.drop_path(self.lmhra1(tmp_x))
-            tmp_x = tmp_x.view(N, C, T, L).permute(3, 0, 2, 1).contiguous().view(L, NT, C)
-            x = torch.cat([x[:1, :, :], tmp_x], dim=0)
-        # MHSA
-        if use_checkpoint:
-            attn_out = checkpoint.checkpoint(self.attention, self.ln_1(x))
-            x = x + self.drop_path(attn_out)
-        else:
-            x = x + self.drop_path(self.attention(self.ln_1(x)))
-        # Local MHRA
-        if not self.no_lmhra and self.double_lmhra:
-            tmp_x = x[1:, :, :]
-            tmp_x = tmp_x.view(H, W, N, T, C).permute(2, 4, 3, 0, 1).contiguous()
-            tmp_x = tmp_x + self.drop_path(self.lmhra2(tmp_x))
-            tmp_x = tmp_x.view(N, C, T, L).permute(3, 0, 2, 1).contiguous().view(L, NT, C)
-            x = torch.cat([x[:1, :, :], tmp_x], dim=0)
-        # FFN
-        if use_checkpoint:
-            mlp_out = checkpoint.checkpoint(self.mlp, self.ln_2(x))
-            x = x + self.drop_path(mlp_out)
-        else:
-            x = x + self.drop_path(self.mlp(self.ln_2(x)))
-        return x
-
-
-class Extractor(nn.Module):
-    def __init__(
-            self, d_model, n_head, attn_mask=None,
-            mlp_factor=4.0, dropout=0.0, drop_path=0.0,
-        ):
-        super().__init__()
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        print(f'Drop path rate: {drop_path}')
-        self.attn = nn.MultiheadAttention(d_model, n_head)
-        self.ln_1 = nn.LayerNorm(d_model)
-        d_mlp = round(mlp_factor * d_model)
-        self.mlp = nn.Sequential(OrderedDict([
-            ("c_fc", nn.Linear(d_model, d_mlp)),
-            ("gelu", QuickGELU()),
-            ("dropout", nn.Dropout(dropout)),
-            ("c_proj", nn.Linear(d_mlp, d_model))
-        ]))
-        self.ln_2 = nn.LayerNorm(d_model)
-        self.ln_3 = nn.LayerNorm(d_model)
-        self.attn_mask = attn_mask
-
-        # zero init
-        nn.init.xavier_uniform_(self.attn.in_proj_weight)
-        nn.init.constant_(self.attn.out_proj.weight, 0.)
-        nn.init.constant_(self.attn.out_proj.bias, 0.)
-        nn.init.xavier_uniform_(self.mlp[0].weight)
-        nn.init.constant_(self.mlp[-1].weight, 0.)
-        nn.init.constant_(self.mlp[-1].bias, 0.)
-
-    def attention(self, x, y):
-        d_model = self.ln_1.weight.size(0)
-        q = (x @ self.attn.in_proj_weight[:d_model].T) + self.attn.in_proj_bias[:d_model]
-
-        k = (y @ self.attn.in_proj_weight[d_model:-d_model].T) + self.attn.in_proj_bias[d_model:-d_model]
-        v = (y @ self.attn.in_proj_weight[-d_model:].T) + self.attn.in_proj_bias[-d_model:]
-        Tx, Ty, N = q.size(0), k.size(0), q.size(1)
-        q = q.view(Tx, N, self.attn.num_heads, self.attn.head_dim).permute(1, 2, 0, 3)
-        k = k.view(Ty, N, self.attn.num_heads, self.attn.head_dim).permute(1, 2, 0, 3)
-        v = v.view(Ty, N, self.attn.num_heads, self.attn.head_dim).permute(1, 2, 0, 3)
-        aff = (q @ k.transpose(-2, -1) / (self.attn.head_dim ** 0.5))
-
-        aff = aff.softmax(dim=-1)
-        out = aff @ v
-        out = out.permute(2, 0, 1, 3).flatten(2)
-        out = self.attn.out_proj(out)
-        return out
-
-    def forward(self, x, y):
-        x = x + self.drop_path(self.attention(self.ln_1(x), self.ln_3(y)))
-        x = x + self.drop_path(self.mlp(self.ln_2(x)))
-        return x
-
-
-class Transformer(nn.Module):
-    def __init__(
-            self, width, layers, heads, attn_mask=None, backbone_drop_path_rate=0., 
-            use_checkpoint=False, checkpoint_num=[0], t_size=8, dw_reduction=2,
-            no_lmhra=False, double_lmhra=True,
-            return_list=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
-            n_layers=12, n_dim=768, n_head=12, mlp_factor=4.0, drop_path_rate=0.,
-            mlp_dropout=[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], 
-            cls_dropout=0.5, num_classes=400,
-        ):
-        super().__init__()
-        self.T = t_size
-        self.return_list = return_list
-        # backbone
-        b_dpr = [x.item() for x in torch.linspace(0, backbone_drop_path_rate, layers)]
-        self.resblocks = nn.ModuleList([
-            ResidualAttentionBlock(
-                width, heads, attn_mask, 
-                drop_path=b_dpr[i],
-                dw_reduction=dw_reduction,
-                no_lmhra=no_lmhra,
-                double_lmhra=double_lmhra,
-            ) for i in range(layers)
-        ])
-        # checkpoint
-        self.use_checkpoint = use_checkpoint
-        self.checkpoint_num = checkpoint_num
-        self.n_layers = n_layers
-        print(f'Use checkpoint: {self.use_checkpoint}')
-        print(f'Checkpoint number: {self.checkpoint_num}')
-
-        # global block
-        assert n_layers == len(return_list)
-        if n_layers > 0:
-            self.temporal_cls_token = nn.Parameter(torch.zeros(1, 1, n_dim))
-            self.dpe = nn.ModuleList([
-                nn.Conv3d(n_dim, n_dim, kernel_size=3, stride=1, padding=1, bias=True, groups=n_dim)
-                for i in range(n_layers)
-            ])
-            for m in self.dpe:
-                nn.init.constant_(m.bias, 0.)
-            dpr = [x.item() for x in torch.linspace(0, drop_path_rate, n_layers)]
-            self.dec = nn.ModuleList([
-                Extractor(
-                    n_dim, n_head, mlp_factor=mlp_factor, 
-                    dropout=mlp_dropout[i], drop_path=dpr[i],
-                ) for i in range(n_layers)
-            ])
-            self.balance = nn.Parameter(torch.zeros((n_dim)))
-            self.sigmoid = nn.Sigmoid()
-        # projection
-        self.proj = nn.Sequential(
-            nn.LayerNorm(n_dim),
-            nn.Dropout(cls_dropout),
-            nn.Linear(n_dim, num_classes),
-        )
-
-    def forward(self, x):
-        T_down = self.T
-        L, NT, C = x.shape
-        N = NT // T_down
-        H = W = int((L - 1) ** 0.5)
-
-        if self.n_layers > 0:
-            cls_token = self.temporal_cls_token.repeat(1, N, 1)
-
-        j = -1
-        for i, resblock in enumerate(self.resblocks):
-            if self.use_checkpoint and i < self.checkpoint_num[0]:
-                x = resblock(x, self.T, use_checkpoint=True)
-            else:
-                x = resblock(x, T_down)
-            if i in self.return_list:
-                j += 1
-                tmp_x = x.clone()
-                tmp_x = tmp_x.view(L, N, T_down, C)
-                # dpe
-                _, tmp_feats = tmp_x[:1], tmp_x[1:]
-                tmp_feats = tmp_feats.permute(1, 3, 2, 0).reshape(N, C, T_down, H, W)
-                tmp_feats = self.dpe[j](tmp_feats).view(N, C, T_down, L - 1).permute(3, 0, 2, 1).contiguous()
-                tmp_x[1:] = tmp_x[1:] + tmp_feats
-                # global block
-                tmp_x = tmp_x.permute(2, 0, 1, 3).flatten(0, 1)  # T * L, N, C
-                cls_token = self.dec[j](cls_token, tmp_x)
-
-        if self.n_layers > 0:
-            weight = self.sigmoid(self.balance)
-            residual = x.view(L, N, T_down, C)[0].mean(1)  # L, N, T, C
-            return self.proj((1 - weight) * cls_token[0, :, :] + weight * residual)
-        else:
-            residual = x.view(L, N, T_down, C)[0].mean(1)  # L, N, T, C
-            return self.proj(residual)
-
-
-class VisionTransformer(nn.Module):
-    def __init__(
-        self, 
-        # backbone
-        input_resolution, patch_size, width, layers, heads, output_dim, backbone_drop_path_rate=0.,
-        use_checkpoint=False, checkpoint_num=[0], t_size=8, kernel_size=3, dw_reduction=1.5,
-        temporal_downsample=True,
-        no_lmhra=-False, double_lmhra=True,
-        # global block
-        return_list=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
-        n_layers=12, n_dim=768, n_head=12, mlp_factor=4.0, drop_path_rate=0.,
-        mlp_dropout=[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], 
-        cls_dropout=0.5, num_classes=400,
-    ):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.output_dim = output_dim
-        padding = (kernel_size - 1) // 2
-        if temporal_downsample:
-            self.conv1 = nn.Conv3d(3, width, (kernel_size, patch_size, patch_size), (2, patch_size, patch_size), (padding, 0, 0), bias=False)
-            t_size = t_size // 2
-        else:
-            self.conv1 = nn.Conv3d(3, width, (1, patch_size, patch_size), (1, patch_size, patch_size), (0, 0, 0), bias=False)
-
-        scale = width ** -0.5
-        self.class_embedding = nn.Parameter(scale * torch.randn(width))
-        self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
-        self.ln_pre = LayerNorm(width)
-        
-        self.transformer = Transformer(
-            width, layers, heads, dw_reduction=dw_reduction, 
-            backbone_drop_path_rate=backbone_drop_path_rate, 
-            use_checkpoint=use_checkpoint, checkpoint_num=checkpoint_num, t_size=t_size,
-            no_lmhra=no_lmhra, double_lmhra=double_lmhra,
-            return_list=return_list, n_layers=n_layers, n_dim=n_dim, n_head=n_head, 
-            mlp_factor=mlp_factor, drop_path_rate=drop_path_rate, mlp_dropout=mlp_dropout, 
-            cls_dropout=cls_dropout, num_classes=num_classes,
-        )
-
-    def forward(self, x):
-        x = self.conv1(x)  # shape = [*, width, grid, grid]
-        N, C, T, H, W = x.shape
-        x = x.permute(0, 2, 3, 4, 1).reshape(N * T, H * W, C)
-        
-        x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]
-        x = x + self.positional_embedding.to(x.dtype)
-        x = self.ln_pre(x)
-
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        out = self.transformer(x)
-        return out
-
-
-def inflate_weight(weight_2d, time_dim, center=True):
-    print(f'Init center: {center}')
-    if center:
-        weight_3d = torch.zeros(*weight_2d.shape)
-        weight_3d = weight_3d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)
-        middle_idx = time_dim // 2
-        weight_3d[:, :, middle_idx, :, :] = weight_2d
-    else:
-        weight_3d = weight_2d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)
-        weight_3d = weight_3d / time_dim
-    return weight_3d
-
-
-def load_state_dict(model, state_dict):
-    state_dict_3d = model.state_dict()
-    for k in state_dict.keys():
-        if state_dict[k].shape != state_dict_3d[k].shape:
-            if len(state_dict_3d[k].shape) <= 2:
-                print(f'Ignore: {k}')
-                continue
-            print(f'Inflate: {k}, {state_dict[k].shape} => {state_dict_3d[k].shape}')
-            time_dim = state_dict_3d[k].shape[2]
-            state_dict[k] = inflate_weight(state_dict[k], time_dim)
-    model.load_state_dict(state_dict, strict=False)
-
-
-def uniformerv2_b16(
-    pretrained=True, use_checkpoint=False, checkpoint_num=[0],
-    t_size=16, dw_reduction=1.5, backbone_drop_path_rate=0., 
-    temporal_downsample=True,
-    no_lmhra=False, double_lmhra=True,
-    return_list=[8, 9, 10, 11], 
-    n_layers=4, n_dim=768, n_head=12, mlp_factor=4.0, drop_path_rate=0.,
-    mlp_dropout=[0.5, 0.5, 0.5, 0.5], 
-    cls_dropout=0.5, num_classes=400,
-):
-    model = VisionTransformer(
-        input_resolution=224,
-        patch_size=16,
-        width=768,
-        layers=12,
-        heads=12,
-        output_dim=512,
-        use_checkpoint=use_checkpoint,
-        checkpoint_num=checkpoint_num,
-        t_size=t_size,
-        dw_reduction=dw_reduction, 
-        backbone_drop_path_rate=backbone_drop_path_rate, 
-        temporal_downsample=temporal_downsample,
-        no_lmhra=no_lmhra,
-        double_lmhra=double_lmhra,
-        return_list=return_list, 
-        n_layers=n_layers, 
-        n_dim=n_dim, 
-        n_head=n_head, 
-        mlp_factor=mlp_factor, 
-        drop_path_rate=drop_path_rate, 
-        mlp_dropout=mlp_dropout, 
-        cls_dropout=cls_dropout, 
-        num_classes=num_classes,
-    )
-
-    if pretrained:
-        print('load pretrained weights')
-        state_dict = torch.load(_MODELS["ViT-B/16"], map_location='cpu')
-        load_state_dict(model, state_dict)
-    return model.eval()
-
-
-def uniformerv2_l14(
-    pretrained=True, use_checkpoint=False, checkpoint_num=[0],
-    t_size=16, dw_reduction=1.5, backbone_drop_path_rate=0., 
-    temporal_downsample=True,
-    no_lmhra=False, double_lmhra=True,
-    return_list=[20, 21, 22, 23],
-    n_layers=4, n_dim=1024, n_head=16, mlp_factor=4.0, drop_path_rate=0.,
-    mlp_dropout=[0.5, 0.5, 0.5, 0.5], 
-    cls_dropout=0.5, num_classes=400,
-):
-    model = VisionTransformer(
-        input_resolution=224,
-        patch_size=14,
-        width=1024,
-        layers=24,
-        heads=16,
-        output_dim=768,
-        use_checkpoint=use_checkpoint,
-        checkpoint_num=checkpoint_num,
-        t_size=t_size,
-        dw_reduction=dw_reduction, 
-        backbone_drop_path_rate=backbone_drop_path_rate, 
-        temporal_downsample=temporal_downsample,
-        no_lmhra=no_lmhra,
-        double_lmhra=double_lmhra,
-        return_list=return_list, 
-        n_layers=n_layers, 
-        n_dim=n_dim, 
-        n_head=n_head, 
-        mlp_factor=mlp_factor, 
-        drop_path_rate=drop_path_rate, 
-        mlp_dropout=mlp_dropout, 
-        cls_dropout=cls_dropout, 
-        num_classes=num_classes,
-    )
-
-    if pretrained:
-        print('load pretrained weights')
-        state_dict = torch.load(_MODELS["ViT-L/14"], map_location='cpu')
-        load_state_dict(model, state_dict)
-    return model.eval()
-
-
-def uniformerv2_l14_336(
-    pretrained=True, use_checkpoint=False, checkpoint_num=[0],
-    t_size=16, dw_reduction=1.5, backbone_drop_path_rate=0., 
-    no_temporal_downsample=True,
-    no_lmhra=False, double_lmhra=True,
-    return_list=[20, 21, 22, 23],
-    n_layers=4, n_dim=1024, n_head=16, mlp_factor=4.0, drop_path_rate=0.,
-    mlp_dropout=[0.5, 0.5, 0.5, 0.5], 
-    cls_dropout=0.5, num_classes=400,
-):
-    model = VisionTransformer(
-        input_resolution=336,
-        patch_size=14,
-        width=1024,
-        layers=24,
-        heads=16,
-        output_dim=768,
-        use_checkpoint=use_checkpoint,
-        checkpoint_num=checkpoint_num,
-        t_size=t_size,
-        dw_reduction=dw_reduction, 
-        backbone_drop_path_rate=backbone_drop_path_rate, 
-        no_temporal_downsample=no_temporal_downsample,
-        no_lmhra=no_lmhra,
-        double_lmhra=double_lmhra,
-        return_list=return_list, 
-        n_layers=n_layers, 
-        n_dim=n_dim, 
-        n_head=n_head, 
-        mlp_factor=mlp_factor, 
-        drop_path_rate=drop_path_rate, 
-        mlp_dropout=mlp_dropout, 
-        cls_dropout=cls_dropout, 
-        num_classes=num_classes,
-    )
-
-    if pretrained:
-        print('load pretrained weights')
-        state_dict = torch.load(_MODELS["ViT-L/14_336"], map_location='cpu')
-        load_state_dict(model, state_dict)
-    return model.eval()
-
-
-if __name__ == '__main__':
-    import time
-    from fvcore.nn import FlopCountAnalysis
-    from fvcore.nn import flop_count_table
-    import numpy as np
-
-    seed = 4217
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-    num_frames = 16
-
-    model = uniformerv2_l14(
-        pretrained=False, 
-        t_size=num_frames, backbone_drop_path_rate=0., drop_path_rate=0.,
-        dw_reduction=1.5,
-        no_lmhra=False,
-        temporal_downsample=True,
-        return_list=[8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23],
-        mlp_dropout=[0.5]*16,
-        n_layers=16
-    )
-    print(model)
-
-    flops = FlopCountAnalysis(model, torch.rand(1, 3, num_frames, 224, 224))
-    s = time.time()
-    print(flop_count_table(flops, max_depth=1))
-    print(time.time()-s)

spaces/Andy1621/uniformer_image_detection/configs/regnet/README.md

@@ -1,96 +0,0 @@
-# Designing Network Design Spaces
-
-## Introduction
-
-[BACKBONE]
-
-We implement RegNetX and RegNetY models in detection systems and provide their first results on Mask R-CNN, Faster R-CNN and RetinaNet.
-
-The pre-trained modles are converted from [model zoo of pycls](https://github.com/facebookresearch/pycls/blob/master/MODEL_ZOO.md).
-
-```latex
-@article{radosavovic2020designing,
-    title={Designing Network Design Spaces},
-    author={Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Dollár},
-    year={2020},
-    eprint={2003.13678},
-    archivePrefix={arXiv},
-    primaryClass={cs.CV}
-}
-```
-
-## Usage
-
-To use a regnet model, there are two steps to do:
-
-1. Convert the model to ResNet-style supported by MMDetection
-2. Modify backbone and neck in config accordingly
-
-### Convert model
-
-We already prepare models of FLOPs from 400M to 12G in our model zoo.
-
-For more general usage, we also provide script `regnet2mmdet.py` in the tools directory to convert the key of models pretrained by [pycls](https://github.com/facebookresearch/pycls/) to
-ResNet-style checkpoints used in MMDetection.
-
-```bash
-python -u tools/model_converters/regnet2mmdet.py ${PRETRAIN_PATH} ${STORE_PATH}
-```
-
-This script convert model from `PRETRAIN_PATH` and store the converted model in `STORE_PATH`.
-
-### Modify config
-
-The users can modify the config's `depth` of backbone and corresponding keys in `arch` according to the configs in the [pycls model zoo](https://github.com/facebookresearch/pycls/blob/master/MODEL_ZOO.md).
-The parameter `in_channels` in FPN can be found in the Figure 15 & 16 of the paper (`wi` in the legend).
-This directory already provides some configs with their performance, using RegNetX from 800MF to 12GF level.
-For other pre-trained models or self-implemented regnet models, the users are responsible to check these parameters by themselves.
-
-**Note**: Although Fig. 15 & 16 also provide `w0`, `wa`, `wm`, `group_w`, and `bot_mul` for `arch`, they are quantized thus inaccurate, using them sometimes produces different backbone that does not match the key in the pre-trained model.
-
-## Results
-
-### Mask R-CNN
-
-|   Backbone   |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
-| :---------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: |
-|    [R-50-FPN](../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py)| pytorch |   1x    | 4.4      | 12.0           | 38.2   | 34.7    |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205_050542.log.json) |
-|[RegNetX-3.2GF-FPN](./mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py)| pytorch |   1x    |5.0 ||40.3|36.6|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141-2a9d1814.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141.log.json)   |
-|[RegNetX-4.0GF-FPN](./mask_rcnn_regnetx-4GF_fpn_1x_coco.py)| pytorch |   1x    |5.5||41.5|37.4|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco/mask_rcnn_regnetx-4GF_fpn_1x_coco_20200517_180217-32e9c92d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco/mask_rcnn_regnetx-4GF_fpn_1x_coco_20200517_180217.log.json)   |
-|    [R-101-FPN](../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py)| pytorch |   1x    | 6.4      | 10.3           | 40.0   | 36.1    |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204-1efe0ed5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204_144809.log.json) |
-|[RegNetX-6.4GF-FPN](./mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py)| pytorch |   1x    |6.1 ||41.0|37.1|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco/mask_rcnn_regnetx-6.4GF_fpn_1x_coco_20200517_180439-3a7aae83.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco/mask_rcnn_regnetx-6.4GF_fpn_1x_coco_20200517_180439.log.json)   |
-| [X-101-32x4d-FPN](../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py) | pytorch |   1x    | 7.6      | 9.4            | 41.9   | 37.5    |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205-478d0b67.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205_034906.log.json) |
-|[RegNetX-8.0GF-FPN](./mask_rcnn_regnetx-8GF_fpn_1x_coco.py)| pytorch |   1x    |6.4 ||41.7|37.5|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco/mask_rcnn_regnetx-8GF_fpn_1x_coco_20200517_180515-09daa87e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco/mask_rcnn_regnetx-8GF_fpn_1x_coco_20200517_180515.log.json)   |
-|[RegNetX-12GF-FPN](./mask_rcnn_regnetx-12GF_fpn_1x_coco.py)| pytorch |   1x    |7.4 ||42.2|38|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco/mask_rcnn_regnetx-12GF_fpn_1x_coco_20200517_180552-b538bd8b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco/mask_rcnn_regnetx-12GF_fpn_1x_coco_20200517_180552.log.json) |
-|[RegNetX-3.2GF-FPN-DCN-C3-C5](./mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py)| pytorch |   1x    |5.0 ||40.3|36.6|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco_20200520_172726-75f40794.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco_20200520_172726.log.json)   |
-
-### Faster R-CNN
-
-|   Backbone  |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
-| :---------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: |
-|    [R-50-FPN](../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py)| pytorch |   1x    | 4.0      | 18.2           | 37.4   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) |
-|[RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py)| pytorch |   1x    | 4.5||39.9|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco/faster_rcnn_regnetx-3.2GF_fpn_1x_coco_20200517_175927-126fd9bf.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco/faster_rcnn_regnetx-3.2GF_fpn_1x_coco_20200517_175927.log.json)   |
-|[RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py)| pytorch |   2x    | 4.5||41.1|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco/faster_rcnn_regnetx-3.2GF_fpn_2x_coco_20200520_223955-e2081918.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco/faster_rcnn_regnetx-3.2GF_fpn_2x_coco_20200520_223955.log.json)   |
-
-### RetinaNet
-
-|  Backbone   |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
-| :---------: | :-----: | :-----: | :------: | :------------: | :----: | :------: |  :--------: |
-|    [R-50-FPN](../retinanet/retinanet_r50_fpn_1x_coco.py)     | pytorch |   1x    |   3.8    |      16.6      |  36.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130_002941.log.json) |
-|[RegNetX-800MF-FPN](./retinanet_regnetx-800MF_fpn_1x_coco.py)| pytorch |   1x    |2.5||35.6|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-800MF_fpn_1x_coco/retinanet_regnetx-800MF_fpn_1x_coco_20200517_191403-f6f91d10.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-800MF_fpn_1x_coco/retinanet_regnetx-800MF_fpn_1x_coco_20200517_191403.log.json)   |
-|[RegNetX-1.6GF-FPN](./retinanet_regnetx-1.6GF_fpn_1x_coco.py)| pytorch |   1x    |3.3||37.3|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco/retinanet_regnetx-1.6GF_fpn_1x_coco_20200517_191403-37009a9d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco/retinanet_regnetx-1.6GF_fpn_1x_coco_20200517_191403.log.json)   |
-|[RegNetX-3.2GF-FPN](./retinanet_regnetx-3.2GF_fpn_1x_coco.py)| pytorch |   1x    |4.2 ||39.1|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco/retinanet_regnetx-3.2GF_fpn_1x_coco_20200520_163141-cb1509e8.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco/retinanet_regnetx-3.2GF_fpn_1x_coco_20200520_163141.log.json)   |
-
-### Pre-trained models
-
-We also train some models with longer schedules and multi-scale training. The users could finetune them for downstream tasks.
-
-|    Method   |    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
-| :-----: | :-----: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: |  :--------: |
-|Faster RCNN |[RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py)| pytorch |   3x    |5.0 ||42.2|-|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200520_224253-bf85ae3e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200520_224253.log.json)   |
-|Mask RCNN |[RegNetX-3.2GF-FPN](./mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py)| pytorch |   3x    |5.0 ||43.1|38.7|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221-99879813.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221.log.json)   |
-
-### Notice
-
-1. The models are trained using a different weight decay, i.e., `weight_decay=5e-5` according to the setting in ImageNet training. This brings improvement of at least 0.7 AP absolute but does not improve the model using ResNet-50.
-2. RetinaNets using RegNets are trained with learning rate 0.02 with gradient clip. We find that using learning rate 0.02 could improve the results by at least 0.7 AP absolute and gradient clip is necessary to stabilize the training. However, this does not improve the performance of ResNet-50-FPN RetinaNet.

spaces/Andy1621/uniformer_image_segmentation/configs/encnet/encnet_r101-d8_512x512_80k_ade20k.py

@@ -1,2 +0,0 @@
-_base_ = './encnet_r50-d8_512x512_80k_ade20k.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/Andy1621/uniformer_image_segmentation/configs/fcn/README.md

@@ -1,85 +0,0 @@
-# Fully Convolutional Networks for Semantic Segmentation
-
-## Introduction
-
-<!-- [ALGORITHM] -->
-
-```latex
-@article{shelhamer2017fully,
-  title={Fully convolutional networks for semantic segmentation},
-  author={Shelhamer, Evan and Long, Jonathan and Darrell, Trevor},
-  journal={IEEE transactions on pattern analysis and machine intelligence},
-  volume={39},
-  number={4},
-  pages={640--651},
-  year={2017},
-  publisher={IEEE Trans Pattern Anal Mach Intell}
-}
-```
-
-## Results and models
-
-### Cityscapes
-
-| Method | Backbone   | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                     | download                                                                                                                                                                                                                                                                                                                                       |
-| ------ | ---------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN    | R-50-D8    | 512x1024  |   40000 | 5.7      | 4.17           | 72.25 |         73.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608.log.json)         |
-| FCN    | R-101-D8   | 512x1024  |   40000 | 9.2      | 2.66           | 75.45 |         76.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_512x1024_40k_cityscapes.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852-a883d3a1.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852.log.json)     |
-| FCN    | R-50-D8    | 769x769   |   40000 | 6.5      | 1.80           | 71.47 |         72.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_769x769_40k_cityscapes.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104-977b5d02.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104.log.json)             |
-| FCN    | R-101-D8   | 769x769   |   40000 | 10.4     | 1.19           | 73.93 |         75.14 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_769x769_40k_cityscapes.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208-7d4ab69c.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208.log.json)         |
-| FCN    | R-18-D8    | 512x1024  |   80000 | 1.7      | 14.65          | 71.11 |         72.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes_20201225_021327-6c50f8b4.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes-20201225_021327.log.json)         |
-| FCN    | R-50-D8    | 512x1024  |   80000 | -        |                | 73.61 |         74.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_512x1024_80k_cityscapes.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019-03aa804d.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019.log.json)         |
-| FCN    | R-101-D8   | 512x1024  |   80000 | -        | -              | 75.13 |         75.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_512x1024_80k_cityscapes.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038-3fb937eb.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038.log.json)     |
-| FCN    | R-18-D8    | 769x769   |   80000 | 1.9      | 6.40           | 70.80 |         73.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r18-d8_769x769_80k_cityscapes.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes_20201225_021451-9739d1b8.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes-20201225_021451.log.json)             |
-| FCN    | R-50-D8    | 769x769   |   80000 | -        | -              | 72.64 |         73.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_769x769_80k_cityscapes.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749-f5caeabc.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749.log.json)             |
-| FCN    | R-101-D8   | 769x769   |   80000 | -        | -              | 75.52 |         76.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_769x769_80k_cityscapes.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354-45cbac68.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354.log.json)         |
-| FCN    | R-18b-D8   | 512x1024  |   80000 | 1.6      | 16.74          | 70.24 |         72.77 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes_20201225_230143-92c0f445.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes-20201225_230143.log.json)     |
-| FCN    | R-50b-D8   | 512x1024  |   80000 | 5.6      | 4.20           | 75.65 |         77.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50b-d8_512x1024_80k_cityscapes.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes_20201225_094221-82957416.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes-20201225_094221.log.json)     |
-| FCN    | R-101b-D8  | 512x1024  |   80000 | 9.1      | 2.73           | 77.37 |         78.77 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.py)     | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes_20201226_160213-4543858f.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes-20201226_160213.log.json) |
-| FCN    | R-18b-D8   | 769x769   |   80000 | 1.7      | 6.70           | 69.66 |         72.07 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r18b-d8_769x769_80k_cityscapes.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes_20201226_004430-32d504e5.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes-20201226_004430.log.json)         |
-| FCN    | R-50b-D8   | 769x769   |   80000 | 6.3      | 1.82           | 73.83 |         76.60 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes_20201225_094223-94552d38.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes-20201225_094223.log.json)         |
-| FCN    | R-101b-D8  | 769x769   |   80000 | 10.3     | 1.15           | 77.02 |         78.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes_20201226_170012-82be37e2.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes-20201226_170012.log.json)     |
-| FCN-D6 | R-50-D16   | 512x1024  |   40000 | 3.4      | 10.22          | 77.06 |         78.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-20210305_130133.log.json)         |
-| FCN-D6 | R-50-D16   | 512x1024  |   80000 | -        | 10.35          | 77.27 |         78.88 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_80k_cityscapes-20210306_115604.log.json)         |
-| FCN-D6 | R-50-D16   | 769x769   |   40000 | 3.7      | 4.17           | 76.82 |         78.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes-1aab18ed.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes-20210305_185744.log.json)             |
-| FCN-D6 | R-50-D16   | 769x769   |   80000 | -        | 4.15           | 77.04 |         78.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes-109d88eb.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes-20210305_200413.log.json)             |
-| FCN-D6 | R-101-D16  | 512x1024  |   40000 | 4.5      | 8.04           | 77.36 |         79.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes-9cf2b450.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes-20210305_130337.log.json)     |
-| FCN-D6 | R-101-D16  | 512x1024  |   80000 | -        | 8.26           | 78.46 |         80.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-cb336445.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-20210308_102747.log.json)     |
-| FCN-D6 | R-101-D16  | 769x769   |   40000 | 5.0      | 3.12           | 77.28 |         78.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-60b114e9.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-20210308_102453.log.json)         |
-| FCN-D6 | R-101-D16  | 769x769   |   80000 | -        | 3.21           | 78.06 |         79.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-e33adc4f.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-20210306_120016.log.json)         |
-| FCN-D6 | R-50b-D16  | 512x1024  |   80000 | 3.2      | 10.16          | 76.99 |         79.03 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes/fcn_d6_r50b_d16_512x1024_80k_cityscapes-6a0b62e9.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes/fcn_d6_r50b_d16_512x1024_80k_cityscapes-20210311_125550.log.json)     |
-| FCN-D6 | R-50b-D16  | 769x769   |   80000 | 3.6      | 4.17           | 76.86 |         78.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes/fcn_d6_r50b_d16_769x769_80k_cityscapes-d665f231.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes/fcn_d6_r50b_d16_769x769_80k_cityscapes-20210311_131012.log.json)         |
-| FCN-D6 | R-101b-D16 | 512x1024  |   80000 | 4.3      | 8.46           | 77.72 |         79.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes/fcn_d6_r101b_d16_512x1024_80k_cityscapes-3f2eb5b4.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes/fcn_d6_r101b_d16_512x1024_80k_cityscapes-20210311_144305.log.json) |
-| FCN-D6 | R-101b-D16 | 769x769   |   80000 | 4.8      | 3.32           | 77.34 |         78.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes/fcn_d6_r101b_d16_769x769_80k_cityscapes-c4d8bfbc.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes/fcn_d6_r101b_d16_769x769_80k_cityscapes-20210311_154527.log.json)     |
-
-### ADE20K
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                            | download                                                                                                                                                                                                                                                                                                                   |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN    | R-50-D8  | 512x512   |   80000 | 8.5      | 23.49          | 35.94 |         37.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_512x512_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016-f8ac5082.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016.log.json)         |
-| FCN    | R-101-D8 | 512x512   |   80000 | 12       | 14.78          | 39.61 |         40.83 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_512x512_80k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143-bc1809f7.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143.log.json)     |
-| FCN    | R-50-D8  | 512x512   |  160000 | -        | -              | 36.10 |         38.08 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_512x512_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713-4edbc3b4.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713.log.json)     |
-| FCN    | R-101-D8 | 512x512   |  160000 | -        | -              | 39.91 |         41.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816-fd192bd5.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816.log.json) |
-
-### Pascal VOC 2012 + Aug
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                             | download                                                                                                                                                                                                                                                                                                                       |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| FCN    | R-50-D8  | 512x512   |   20000 | 5.7      | 23.28          | 67.08 |         69.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_512x512_20k_voc12aug.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715-52dc5306.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715.log.json)     |
-| FCN    | R-101-D8 | 512x512   |   20000 | 9.2      | 14.81          | 71.16 |         73.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842-0bb4e798.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842.log.json) |
-| FCN    | R-50-D8  | 512x512   |   40000 | -        | -              | 66.97 |         69.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_512x512_40k_voc12aug.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222-5e2dbf40.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222.log.json)     |
-| FCN    | R-101-D8 | 512x512   |   40000 | -        | -              | 69.91 |         72.38 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240-4c8bcefd.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240.log.json) |
-
-### Pascal Context
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                   | download                                                                                                                                                                                                                                                                                                                                               |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| FCN    | R-101-D8 | 480x480   |   40000 | -        | 9.93           | 44.43 |         45.63 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_480x480_40k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context-20210421_154757-b5e97937.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context-20210421_154757.log.json) |
-| FCN    | R-101-D8 | 480x480   |   80000 | -        | -              | 44.13 |         45.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_480x480_80k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context-20210421_163310-4711813f.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context-20210421_163310.log.json) |
-
-### Pascal Context 59
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                   | download                                                                                                                                                                                                                                                                                                                                               |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| FCN    | R-101-D8 | 480x480   |   40000 | -        | -           | 48.42 |         50.4 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_480x480_40k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context_59/fcn_r101-d8_480x480_40k_pascal_context_59_20210415_230724-8cf83682.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context_59/fcn_r101-d8_480x480_40k_pascal_context_59-20210415_230724.log.json) |
-| FCN    | R-101-D8 | 480x480   |   80000 | -        | -              | 49.35 |         51.38 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_480x480_80k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context_59/fcn_r101-d8_480x480_80k_pascal_context_59_20210416_110804-9a6f2c94.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context_59/fcn_r101-d8_480x480_80k_pascal_context_59-20210416_110804.log.json) |

spaces/AnthonyTruchetPoC/persistent-docker/scripts/run-tests.sh

@@ -1,2 +0,0 @@
-#!/usr/bin/env sh
-poetry run pytest

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docs/notes/benchmarks.md

@@ -1,196 +0,0 @@
-
-# Benchmarks
-
-Here we benchmark the training speed of a Mask R-CNN in detectron2,
-with some other popular open source Mask R-CNN implementations.
-
-
-### Settings
-
-* Hardware: 8 NVIDIA V100s with NVLink.
-* Software: Python 3.7, CUDA 10.1, cuDNN 7.6.5, PyTorch 1.5,
-  TensorFlow 1.15.0rc2, Keras 2.2.5, MxNet 1.6.0b20190820.
-* Model: an end-to-end R-50-FPN Mask-RCNN model, using the same hyperparameter as the
-  [Detectron baseline config](https://github.com/facebookresearch/Detectron/blob/master/configs/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_1x.yaml)
-  (it does not have scale augmentation).
-* Metrics: We use the average throughput in iterations 100-500 to skip GPU warmup time.
-  Note that for R-CNN-style models, the throughput of a model typically changes during training, because
-  it depends on the predictions of the model. Therefore this metric is not directly comparable with
-  "train speed" in model zoo, which is the average speed of the entire training run.
-
-
-### Main Results
-
-```eval_rst
-+-------------------------------+--------------------+
-| Implementation                | Throughput (img/s) |
-+===============================+====================+
-| |D2| |PT|                     | 62                 |
-+-------------------------------+--------------------+
-| mmdetection_  |PT|            | 53                 |
-+-------------------------------+--------------------+
-| maskrcnn-benchmark_  |PT|     | 53                 |
-+-------------------------------+--------------------+
-| tensorpack_ |TF|              | 50                 |
-+-------------------------------+--------------------+
-| simpledet_ |mxnet|            | 39                 |
-+-------------------------------+--------------------+
-| Detectron_  |C2|              | 19                 |
-+-------------------------------+--------------------+
-| `matterport/Mask_RCNN`__ |TF| | 14                 |
-+-------------------------------+--------------------+
-
-.. _maskrcnn-benchmark: https://github.com/facebookresearch/maskrcnn-benchmark/
-.. _tensorpack: https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN
-.. _mmdetection: https://github.com/open-mmlab/mmdetection/
-.. _simpledet: https://github.com/TuSimple/simpledet/
-.. _Detectron: https://github.com/facebookresearch/Detectron
-__ https://github.com/matterport/Mask_RCNN/
-
-.. |D2| image:: https://github.com/facebookresearch/detectron2/raw/main/.github/Detectron2-Logo-Horz.svg?sanitize=true
-   :height: 15pt
-   :target: https://github.com/facebookresearch/detectron2/
-.. |PT| image:: https://pytorch.org/assets/images/logo-icon.svg
-   :width: 15pt
-   :height: 15pt
-   :target: https://pytorch.org
-.. |TF| image:: https://static.nvidiagrid.net/ngc/containers/tensorflow.png
-   :width: 15pt
-   :height: 15pt
-   :target: https://tensorflow.org
-.. |mxnet| image:: https://github.com/dmlc/web-data/raw/master/mxnet/image/mxnet_favicon.png
-   :width: 15pt
-   :height: 15pt
-   :target: https://mxnet.apache.org/
-.. |C2| image:: https://caffe2.ai/static/logo.svg
-   :width: 15pt
-   :height: 15pt
-   :target: https://caffe2.ai
-```
-
-
-Details for each implementation:
-
-* __Detectron2__: with release v0.1.2, run:
-  ```
-  python tools/train_net.py  --config-file configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml --num-gpus 8
-  ```
-
-* __mmdetection__: at commit `b0d845f`, run
-  ```
-  ./tools/dist_train.sh configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py 8
-  ```
-
-* __maskrcnn-benchmark__: use commit `0ce8f6f` with `sed -i 's/torch.uint8/torch.bool/g' **/*.py; sed -i 's/AT_CHECK/TORCH_CHECK/g' **/*.cu`
-  to make it compatible with PyTorch 1.5. Then, run training with
-  ```
-  python -m torch.distributed.launch --nproc_per_node=8 tools/train_net.py --config-file configs/e2e_mask_rcnn_R_50_FPN_1x.yaml
-  ```
-  The speed we observed is faster than its model zoo, likely due to different software versions.
-
-* __tensorpack__: at commit `caafda`, `export TF_CUDNN_USE_AUTOTUNE=0`, then run
-  ```
-  mpirun -np 8 ./train.py --config DATA.BASEDIR=/data/coco TRAINER=horovod BACKBONE.STRIDE_1X1=True TRAIN.STEPS_PER_EPOCH=50 --load ImageNet-R50-AlignPadding.npz
-  ```
-
-* __SimpleDet__: at commit `9187a1`, run
-  ```
-  python detection_train.py --config config/mask_r50v1_fpn_1x.py
-  ```
-
-* __Detectron__: run
-  ```
-  python tools/train_net.py --cfg configs/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_1x.yaml
-  ```
-  Note that many of its ops run on CPUs, therefore the performance is limited.
-
-* __matterport/Mask_RCNN__: at commit `3deaec`, apply the following diff, `export TF_CUDNN_USE_AUTOTUNE=0`, then run
-  ```
-  python coco.py train --dataset=/data/coco/ --model=imagenet
-  ```
-  Note that many small details in this implementation might be different
-  from Detectron's standards.
-
-  <details>
-  <summary>
-  (diff to make it use the same hyperparameters - click to expand)
-  </summary>
-
-  ```diff
-  diff --git i/mrcnn/model.py w/mrcnn/model.py
-  index 62cb2b0..61d7779 100644
-  --- i/mrcnn/model.py
-  +++ w/mrcnn/model.py
-  @@ -2367,8 +2367,8 @@ class MaskRCNN():
-        epochs=epochs,
-        steps_per_epoch=self.config.STEPS_PER_EPOCH,
-        callbacks=callbacks,
-  -            validation_data=val_generator,
-  -            validation_steps=self.config.VALIDATION_STEPS,
-  +            #validation_data=val_generator,
-  +            #validation_steps=self.config.VALIDATION_STEPS,
-        max_queue_size=100,
-        workers=workers,
-        use_multiprocessing=True,
-  diff --git i/mrcnn/parallel_model.py w/mrcnn/parallel_model.py
-  index d2bf53b..060172a 100644
-  --- i/mrcnn/parallel_model.py
-  +++ w/mrcnn/parallel_model.py
-  @@ -32,6 +32,7 @@ class ParallelModel(KM.Model):
-      keras_model: The Keras model to parallelize
-      gpu_count: Number of GPUs. Must be > 1
-      """
-  +        super().__init__()
-      self.inner_model = keras_model
-      self.gpu_count = gpu_count
-      merged_outputs = self.make_parallel()
-  diff --git i/samples/coco/coco.py w/samples/coco/coco.py
-  index 5d172b5..239ed75 100644
-  --- i/samples/coco/coco.py
-  +++ w/samples/coco/coco.py
-  @@ -81,7 +81,10 @@ class CocoConfig(Config):
-    IMAGES_PER_GPU = 2
-
-    # Uncomment to train on 8 GPUs (default is 1)
-  -    # GPU_COUNT = 8
-  +    GPU_COUNT = 8
-  +    BACKBONE = "resnet50"
-  +    STEPS_PER_EPOCH = 50
-  +    TRAIN_ROIS_PER_IMAGE = 512
-
-    # Number of classes (including background)
-    NUM_CLASSES = 1 + 80  # COCO has 80 classes
-  @@ -496,29 +499,10 @@ if __name__ == '__main__':
-      # *** This training schedule is an example. Update to your needs ***
-
-      # Training - Stage 1
-  -        print("Training network heads")
-      model.train(dataset_train, dataset_val,
-            learning_rate=config.LEARNING_RATE,
-            epochs=40,
-  -                    layers='heads',
-  -                    augmentation=augmentation)
-  -
-  -        # Training - Stage 2
-  -        # Finetune layers from ResNet stage 4 and up
-  -        print("Fine tune Resnet stage 4 and up")
-  -        model.train(dataset_train, dataset_val,
-  -                    learning_rate=config.LEARNING_RATE,
-  -                    epochs=120,
-  -                    layers='4+',
-  -                    augmentation=augmentation)
-  -
-  -        # Training - Stage 3
-  -        # Fine tune all layers
-  -        print("Fine tune all layers")
-  -        model.train(dataset_train, dataset_val,
-  -                    learning_rate=config.LEARNING_RATE / 10,
-  -                    epochs=160,
-  -                    layers='all',
-  +                    layers='3+',
-            augmentation=augmentation)
-
-    elif args.command == "evaluate":
-  ```
-
-  </details>

spaces/Ayanoaisho/L/README.md

@@ -1,10 +0,0 @@
----
-title: L
-emoji: ⚡
-colorFrom: green
-colorTo: yellow
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AzumaSeren100/XuanShen-Bert-VITS2/text/tone_sandhi.py

@@ -1,351 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from typing import List
-from typing import Tuple
-
-import jieba
-from pypinyin import lazy_pinyin
-from pypinyin import Style
-
-
-class ToneSandhi():
-    def __init__(self):
-        self.must_neural_tone_words = {
-            '麻烦', '麻利', '鸳鸯', '高粱', '骨头', '骆驼', '马虎', '首饰', '馒头', '馄饨', '风筝',
-            '难为', '队伍', '阔气', '闺女', '门道', '锄头', '铺盖', '铃铛', '铁匠', '钥匙', '里脊',
-            '里头', '部分', '那么', '道士', '造化', '迷糊', '连累', '这么', '这个', '运气', '过去',
-            '软和', '转悠', '踏实', '跳蚤', '跟头', '趔趄', '财主', '豆腐', '讲究', '记性', '记号',
-            '认识', '规矩', '见识', '裁缝', '补丁', '衣裳', '衣服', '衙门', '街坊', '行李', '行当',
-            '蛤蟆', '蘑菇', '薄荷', '葫芦', '葡萄', '萝卜', '荸荠', '苗条', '苗头', '苍蝇', '芝麻',
-            '舒服', '舒坦', '舌头', '自在', '膏药', '脾气', '脑袋', '脊梁', '能耐', '胳膊', '胭脂',
-            '胡萝', '胡琴', '胡同', '聪明', '耽误', '耽搁', '耷拉', '耳朵', '老爷', '老实', '老婆',
-            '老头', '老太', '翻腾', '罗嗦', '罐头', '编辑', '结实', '红火', '累赘', '糨糊', '糊涂',
-            '精神', '粮食', '簸箕', '篱笆', '算计', '算盘', '答应', '笤帚', '笑语', '笑话', '窟窿',
-            '窝囊', '窗户', '稳当', '稀罕', '称呼', '秧歌', '秀气', '秀才', '福气', '祖宗', '砚台',
-            '码头', '石榴', '石头', '石匠', '知识', '眼睛', '眯缝', '眨巴', '眉毛', '相声', '盘算',
-            '白净', '痢疾', '痛快', '疟疾', '疙瘩', '疏忽', '畜生', '生意', '甘蔗', '琵琶', '琢磨',
-            '琉璃', '玻璃', '玫瑰', '玄乎', '狐狸', '状元', '特务', '牲口', '牙碜', '牌楼', '爽快',
-            '爱人', '热闹', '烧饼', '烟筒', '烂糊', '点心', '炊帚', '灯笼', '火候', '漂亮', '滑溜',
-            '溜达', '温和', '清楚', '消息', '浪头', '活泼', '比方', '正经', '欺负', '模糊', '槟榔',
-            '棺材', '棒槌', '棉花', '核桃', '栅栏', '柴火', '架势', '枕头', '枇杷', '机灵', '本事',
-            '木头', '木匠', '朋友', '月饼', '月亮', '暖和', '明白', '时候', '新鲜', '故事', '收拾',
-            '收成', '提防', '挖苦', '挑剔', '指甲', '指头', '拾掇', '拳头', '拨弄', '招牌', '招呼',
-            '抬举', '护士', '折腾', '扫帚', '打量', '打算', '打点', '打扮', '打听', '打发', '扎实',
-            '扁担', '戒指', '懒得', '意识', '意思', '情形', '悟性', '怪物', '思量', '怎么', '念头',
-            '念叨', '快活', '忙活', '志气', '心思', '得罪', '张罗', '弟兄', '开通', '应酬', '庄稼',
-            '干事', '帮手', '帐篷', '希罕', '师父', '师傅', '巴结', '巴掌', '差事', '工夫', '岁数',
-            '屁股', '尾巴', '少爷', '小气', '小伙', '将就', '对头', '对付', '寡妇', '家伙', '客气',
-            '实在', '官司', '学问', '学生', '字号', '嫁妆', '媳妇', '媒人', '婆家', '娘家', '委屈',
-            '姑娘', '姐夫', '妯娌', '妥当', '妖精', '奴才', '女婿', '头发', '太阳', '大爷', '大方',
-            '大意', '大夫', '多少', '多么', '外甥', '壮实', '地道', '地方', '在乎', '困难', '嘴巴',
-            '嘱咐', '嘟囔', '嘀咕', '喜欢', '喇嘛', '喇叭', '商量', '唾沫', '哑巴', '哈欠', '哆嗦',
-            '咳嗽', '和尚', '告诉', '告示', '含糊', '吓唬', '后头', '名字', '名堂', '合同', '吆喝',
-            '叫唤', '口袋', '厚道', '厉害', '千斤', '包袱', '包涵', '匀称', '勤快', '动静', '动弹',
-            '功夫', '力气', '前头', '刺猬', '刺激', '别扭', '利落', '利索', '利害', '分析', '出息',
-            '凑合', '凉快', '冷战', '冤枉', '冒失', '养活', '关系', '先生', '兄弟', '便宜', '使唤',
-            '佩服', '作坊', '体面', '位置', '似的', '伙计', '休息', '什么', '人家', '亲戚', '亲家',
-            '交情', '云彩', '事情', '买卖', '主意', '丫头', '丧气', '两口', '东西', '东家', '世故',
-            '不由', '不在', '下水', '下巴', '上头', '上司', '丈夫', '丈人', '一辈', '那个', '菩萨',
-            '父亲', '母亲', '咕噜', '邋遢', '费用', '冤家', '甜头', '介绍', '荒唐', '大人', '泥鳅',
-            '幸福', '熟悉', '计划', '扑腾', '蜡烛', '姥爷', '照顾', '喉咙', '吉他', '弄堂', '蚂蚱',
-            '凤凰', '拖沓', '寒碜', '糟蹋', '倒腾', '报复', '逻辑', '盘缠', '喽啰', '牢骚', '咖喱',
-            '扫把', '惦记'
-        }
-        self.must_not_neural_tone_words = {
-            "男子", "女子", "分子", "原子", "量子", "莲子", "石子", "瓜子", "电子", "人人", "虎虎"
-        }
-        self.punc = "：，；。？！“”‘’':,;.?!"
-
-    # the meaning of jieba pos tag: https://blog.csdn.net/weixin_44174352/article/details/113731041
-    # e.g.
-    # word: "家里"
-    # pos: "s"
-    # finals: ['ia1', 'i3']
-    def _neural_sandhi(self, word: str, pos: str,
-                       finals: List[str]) -> List[str]:
-
-        # reduplication words for n. and v. e.g. 奶奶, 试试, 旺旺
-        for j, item in enumerate(word):
-            if j - 1 >= 0 and item == word[j - 1] and pos[0] in {
-                    "n", "v", "a"
-            } and word not in self.must_not_neural_tone_words:
-                finals[j] = finals[j][:-1] + "5"
-        ge_idx = word.find("个")
-        if len(word) >= 1 and word[-1] in "吧呢啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶":
-            finals[-1] = finals[-1][:-1] + "5"
-        elif len(word) >= 1 and word[-1] in "的地得":
-            finals[-1] = finals[-1][:-1] + "5"
-        # e.g. 走了, 看着, 去过
-        # elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}:
-        #     finals[-1] = finals[-1][:-1] + "5"
-        elif len(word) > 1 and word[-1] in "们子" and pos in {
-                "r", "n"
-        } and word not in self.must_not_neural_tone_words:
-            finals[-1] = finals[-1][:-1] + "5"
-        # e.g. 桌上, 地下, 家里
-        elif len(word) > 1 and word[-1] in "上下里" and pos in {"s", "l", "f"}:
-            finals[-1] = finals[-1][:-1] + "5"
-        # e.g. 上来, 下去
-        elif len(word) > 1 and word[-1] in "来去" and word[-2] in "上下进出回过起开":
-            finals[-1] = finals[-1][:-1] + "5"
-        # 个做量词
-        elif (ge_idx >= 1 and
-              (word[ge_idx - 1].isnumeric() or
-               word[ge_idx - 1] in "几有两半多各整每做是")) or word == '个':
-            finals[ge_idx] = finals[ge_idx][:-1] + "5"
-        else:
-            if word in self.must_neural_tone_words or word[
-                    -2:] in self.must_neural_tone_words:
-                finals[-1] = finals[-1][:-1] + "5"
-
-        word_list = self._split_word(word)
-        finals_list = [finals[:len(word_list[0])], finals[len(word_list[0]):]]
-        for i, word in enumerate(word_list):
-            # conventional neural in Chinese
-            if word in self.must_neural_tone_words or word[
-                    -2:] in self.must_neural_tone_words:
-                finals_list[i][-1] = finals_list[i][-1][:-1] + "5"
-        finals = sum(finals_list, [])
-        return finals
-
-    def _bu_sandhi(self, word: str, finals: List[str]) -> List[str]:
-        # e.g. 看不懂
-        if len(word) == 3 and word[1] == "不":
-            finals[1] = finals[1][:-1] + "5"
-        else:
-            for i, char in enumerate(word):
-                # "不" before tone4 should be bu2, e.g. 不怕
-                if char == "不" and i + 1 < len(word) and finals[i +
-                                                                1][-1] == "4":
-                    finals[i] = finals[i][:-1] + "2"
-        return finals
-
-    def _yi_sandhi(self, word: str, finals: List[str]) -> List[str]:
-        # "一" in number sequences, e.g. 一零零, 二一零
-        if word.find("一") != -1 and all(
-            [item.isnumeric() for item in word if item != "一"]):
-            return finals
-        # "一" between reduplication words shold be yi5, e.g. 看一看
-        elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]:
-            finals[1] = finals[1][:-1] + "5"
-        # when "一" is ordinal word, it should be yi1
-        elif word.startswith("第一"):
-            finals[1] = finals[1][:-1] + "1"
-        else:
-            for i, char in enumerate(word):
-                if char == "一" and i + 1 < len(word):
-                    # "一" before tone4 should be yi2, e.g. 一段
-                    if finals[i + 1][-1] == "4":
-                        finals[i] = finals[i][:-1] + "2"
-                    # "一" before non-tone4 should be yi4, e.g. 一天
-                    else:
-                        # "一" 后面如果是标点，还读一声
-                        if word[i + 1] not in self.punc:
-                            finals[i] = finals[i][:-1] + "4"
-        return finals
-
-    def _split_word(self, word: str) -> List[str]:
-        word_list = jieba.cut_for_search(word)
-        word_list = sorted(word_list, key=lambda i: len(i), reverse=False)
-        first_subword = word_list[0]
-        first_begin_idx = word.find(first_subword)
-        if first_begin_idx == 0:
-            second_subword = word[len(first_subword):]
-            new_word_list = [first_subword, second_subword]
-        else:
-            second_subword = word[:-len(first_subword)]
-            new_word_list = [second_subword, first_subword]
-        return new_word_list
-
-    def _three_sandhi(self, word: str, finals: List[str]) -> List[str]:
-        if len(word) == 2 and self._all_tone_three(finals):
-            finals[0] = finals[0][:-1] + "2"
-        elif len(word) == 3:
-            word_list = self._split_word(word)
-            if self._all_tone_three(finals):
-                #  disyllabic + monosyllabic, e.g. 蒙古/包
-                if len(word_list[0]) == 2:
-                    finals[0] = finals[0][:-1] + "2"
-                    finals[1] = finals[1][:-1] + "2"
-                #  monosyllabic + disyllabic, e.g. 纸/老虎
-                elif len(word_list[0]) == 1:
-                    finals[1] = finals[1][:-1] + "2"
-            else:
-                finals_list = [
-                    finals[:len(word_list[0])], finals[len(word_list[0]):]
-                ]
-                if len(finals_list) == 2:
-                    for i, sub in enumerate(finals_list):
-                        # e.g. 所有/人
-                        if self._all_tone_three(sub) and len(sub) == 2:
-                            finals_list[i][0] = finals_list[i][0][:-1] + "2"
-                        # e.g. 好/喜欢
-                        elif i == 1 and not self._all_tone_three(sub) and finals_list[i][0][-1] == "3" and \
-                                finals_list[0][-1][-1] == "3":
-
-                            finals_list[0][-1] = finals_list[0][-1][:-1] + "2"
-                        finals = sum(finals_list, [])
-        # split idiom into two words who's length is 2
-        elif len(word) == 4:
-            finals_list = [finals[:2], finals[2:]]
-            finals = []
-            for sub in finals_list:
-                if self._all_tone_three(sub):
-                    sub[0] = sub[0][:-1] + "2"
-                finals += sub
-
-        return finals
-
-    def _all_tone_three(self, finals: List[str]) -> bool:
-        return all(x[-1] == "3" for x in finals)
-
-    # merge "不" and the word behind it
-    # if don't merge, "不" sometimes appears alone according to jieba, which may occur sandhi error
-    def _merge_bu(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        new_seg = []
-        last_word = ""
-        for word, pos in seg:
-            if last_word == "不":
-                word = last_word + word
-            if word != "不":
-                new_seg.append((word, pos))
-            last_word = word[:]
-        if last_word == "不":
-            new_seg.append((last_word, 'd'))
-            last_word = ""
-        return new_seg
-
-    # function 1: merge "一" and reduplication words in it's left and right, e.g. "听","一","听" ->"听一听"
-    # function 2: merge single  "一" and the word behind it
-    # if don't merge, "一" sometimes appears alone according to jieba, which may occur sandhi error
-    # e.g.
-    # input seg: [('听', 'v'), ('一', 'm'), ('听', 'v')]
-    # output seg: [['听一听', 'v']]
-    def _merge_yi(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        new_seg = []
-        # function 1
-        for i, (word, pos) in enumerate(seg):
-            if i - 1 >= 0 and word == "一" and i + 1 < len(seg) and seg[i - 1][
-                    0] == seg[i + 1][0] and seg[i - 1][1] == "v":
-                new_seg[i - 1][0] = new_seg[i - 1][0] + "一" + new_seg[i - 1][0]
-            else:
-                if i - 2 >= 0 and seg[i - 1][0] == "一" and seg[i - 2][
-                        0] == word and pos == "v":
-                    continue
-                else:
-                    new_seg.append([word, pos])
-        seg = new_seg
-        new_seg = []
-        # function 2
-        for i, (word, pos) in enumerate(seg):
-            if new_seg and new_seg[-1][0] == "一":
-                new_seg[-1][0] = new_seg[-1][0] + word
-            else:
-                new_seg.append([word, pos])
-        return new_seg
-
-    # the first and the second words are all_tone_three
-    def _merge_continuous_three_tones(
-            self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        new_seg = []
-        sub_finals_list = [
-            lazy_pinyin(
-                word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
-            for (word, pos) in seg
-        ]
-        assert len(sub_finals_list) == len(seg)
-        merge_last = [False] * len(seg)
-        for i, (word, pos) in enumerate(seg):
-            if i - 1 >= 0 and self._all_tone_three(
-                    sub_finals_list[i - 1]) and self._all_tone_three(
-                        sub_finals_list[i]) and not merge_last[i - 1]:
-                # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
-                if not self._is_reduplication(seg[i - 1][0]) and len(
-                        seg[i - 1][0]) + len(seg[i][0]) <= 3:
-                    new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
-                    merge_last[i] = True
-                else:
-                    new_seg.append([word, pos])
-            else:
-                new_seg.append([word, pos])
-
-        return new_seg
-
-    def _is_reduplication(self, word: str) -> bool:
-        return len(word) == 2 and word[0] == word[1]
-
-    # the last char of first word and the first char of second word is tone_three
-    def _merge_continuous_three_tones_2(
-            self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        new_seg = []
-        sub_finals_list = [
-            lazy_pinyin(
-                word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
-            for (word, pos) in seg
-        ]
-        assert len(sub_finals_list) == len(seg)
-        merge_last = [False] * len(seg)
-        for i, (word, pos) in enumerate(seg):
-            if i - 1 >= 0 and sub_finals_list[i - 1][-1][-1] == "3" and sub_finals_list[i][0][-1] == "3" and not \
-                    merge_last[i - 1]:
-                # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
-                if not self._is_reduplication(seg[i - 1][0]) and len(
-                        seg[i - 1][0]) + len(seg[i][0]) <= 3:
-                    new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
-                    merge_last[i] = True
-                else:
-                    new_seg.append([word, pos])
-            else:
-                new_seg.append([word, pos])
-        return new_seg
-
-    def _merge_er(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        new_seg = []
-        for i, (word, pos) in enumerate(seg):
-            if i - 1 >= 0 and word == "儿" and seg[i-1][0] != "#":
-                new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
-            else:
-                new_seg.append([word, pos])
-        return new_seg
-
-    def _merge_reduplication(
-            self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        new_seg = []
-        for i, (word, pos) in enumerate(seg):
-            if new_seg and word == new_seg[-1][0]:
-                new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
-            else:
-                new_seg.append([word, pos])
-        return new_seg
-
-    def pre_merge_for_modify(
-            self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        seg = self._merge_bu(seg)
-        try:
-            seg = self._merge_yi(seg)
-        except:
-            print("_merge_yi failed")
-        seg = self._merge_reduplication(seg)
-        seg = self._merge_continuous_three_tones(seg)
-        seg = self._merge_continuous_three_tones_2(seg)
-        seg = self._merge_er(seg)
-        return seg
-
-    def modified_tone(self, word: str, pos: str,
-                      finals: List[str]) -> List[str]:
-        finals = self._bu_sandhi(word, finals)
-        finals = self._yi_sandhi(word, finals)
-        finals = self._neural_sandhi(word, pos, finals)
-        finals = self._three_sandhi(word, finals)
-        return finals

spaces/Benson/text-generation/Examples/Carx Deriva Carreras Mod Apk Vieja Versin.md

@@ -1,88 +0,0 @@
-<br />
-<h1>CarX Drift Racing Mod APK versión antigua: Una revisión</h1>
-<p>Si usted es un fan de los juegos de carreras de coches, es posible que haya oído hablar de CarX Drift Racing. Es uno de los juegos de deriva más populares y realistas en Android. Puede elegir entre una variedad de coches, personalizarlos y competir en diferentes pistas. También puede competir con otros jugadores en línea y mostrar sus habilidades a la deriva. </p>
-<h2>carx deriva carreras mod apk vieja versión</h2><br /><p><b><b>Download Zip</b> &#8250;&#8250;&#8250;&#8250;&#8250; <a href="https://bltlly.com/2v6Kna">https://bltlly.com/2v6Kna</a></b></p><br /><br />
-<p>Pero ¿y si quieres disfrutar del juego sin limitaciones? ¿Qué pasa si quieres tener dinero y oro ilimitados, desbloquear todos los coches y pistas, y experimentar los mejores gráficos y la física? Bueno, usted puede hacer eso mediante la descarga de la versión apk mod de CarX Drift Racing. Y no solo cualquier apk mod, pero la versión anterior de la misma. </p>
-<p>¿Por qué la versión anterior, se pregunta? Bueno, hay algunas razones por las que podría preferir la versión anterior a la última. En este artículo, vamos a revisar el CarX Drift Racing mod apk versión antigua, y le dirá cómo descargar e instalar en su dispositivo. También vamos a enumerar algunos pros y contras de usar este apk mod, y responder a algunas preguntas frecuentes. </p>
- <h2>¿Qué es CarX Drift Racing? </h2>
-<p>CarX Drift Racing es un juego de carreras desarrollado por CarX Technologies. Fue lanzado en 2014 para dispositivos Android e iOS. El juego se centra en la deriva, que es una técnica de conducción donde el conductor sobreviraje intencionalmente el coche para que se deslice hacia los lados. El juego cuenta con física realista, gráficos impresionantes y controles suaves. Puedes sentir la emoción de la deriva en varios modos, como el modo carrera, el modo en línea, el modo de ataque en el tiempo y el modo de entrenamiento. </p>
-<p></p>
-
- <h2>Características de CarX Drift Racing</h2>
-<p>Algunas de las características principales de CarX Drift Racing son:</p>
-<ul>
-<li>Física realista que simula el comportamiento de los coches reales en diferentes superficies</li>
-<li>Impresionantes gráficos que crean una atmósfera inmersiva</li>
-<li>Controles suaves que te permiten desplazarte fácilmente</li>
-<li>Una variedad de coches para elegir, cada uno con diferentes características y sonidos</li>
-<li>Muchas opciones de personalización para tus coches</li>
-<li>Una variedad de pistas para competir, cada una con diferentes desafíos y entornos</li>
-<li>Un modo de carrera donde se puede progresar a través de los niveles y ganar dinero y oro</li>
-<li>Un modo online donde puedes competir con otros jugadores alrededor del mundo</li>
-<li>Un modo de ataque de tiempo donde puedes establecer tus mejores tiempos de vuelta</li>
-<li>Un modo de entrenamiento donde puedes practicar tus habilidades de deriva</li>
-<li>Una tabla de clasificación donde puedes ver tu ranking entre otros jugadores</li>
-<li>Un modo fantasma donde puedes competir contra tus propios fantasmas o los de otros jugadores</li>
-<li>Un modo de repetición donde puedes ver las carreras de tus otros jugadores</li>
-<li>Un modo de ajuste donde se puede ajustar la configuración de su coche para adaptarse a sus preferencias</li>
-<li>Un garaje donde puedes guardar tus coches y ver sus estadísticas</li>
-</ul>
- <h2>¿Por qué descargar la versión antigua de CarX Drift Racing mod apk? </h2>
-<p>CarX Drift Racing es un gran juego, pero también tiene algunas limitaciones. Por ejemplo, necesitas gastar mucho dinero y oro para desbloquear y mejorar tus coches y pistas. También necesitas ver anuncios para obtener recompensas o bonificaciones. Y es posible que tengas problemas de compatibilidad o errores con la última versión del juego. </p>
-<p>Es por eso que algunos jugadores prefieren descargar la versión anterior de CarX Drift Racing mod apk. Un apk mod es una versión modificada del juego original que tiene algunas características o cambios que no están disponibles en la versión oficial. Por ejemplo, la versión anterior de CarX Drift Racing mod apk ha:</p>
-<ul>
-
-<li>Coches y pistas desbloqueados, para que pueda acceder a todo el contenido sin restricciones</li>
-<li>Física realista y gráficos, para que pueda disfrutar de la mejor experiencia de juego</li>
-</ul>
-<p>Al descargar la versión anterior de CarX Drift Racing mod apk, usted puede tener más diversión y libertad en el juego. También puede evitar algunos de los problemas que podrían ocurrir con la última versión del juego. </p>
- <h2>¿Cómo descargar e instalar CarX Drift Racing mod apk versión antigua? </h2>
-<p>Si desea descargar e instalar la versión antigua de CarX Drift Racing mod apk en su dispositivo, debe seguir estos pasos:</p>
- <h3>Paso 1: Descargar el archivo apk</h3>
-<p>El primer paso es descargar el archivo apk de CarX Drift Racing mod apk versión antigua. Puede encontrarlo en varios sitios web que ofrecen juegos y aplicaciones modificadas. Sin embargo, debe tener cuidado y elegir una fuente confiable y segura. Algunos sitios web pueden tener archivos falsos o maliciosos que pueden dañar su dispositivo o robar sus datos. </p>
-<p>Uno de los sitios web que recomendamos es [APKPure]. Es un sitio web confiable y popular que proporciona archivos apk libres y puros para los usuarios de Android. Puede descargar CarX Drift Racing mod apk versión antigua de este [enlace]. El tamaño del archivo es de unos 300 MB, así que asegúrate de tener suficiente espacio en tu dispositivo. </p>
- <h3>Paso 2: Habilitar fuentes desconocidas</h3>
-<p>El siguiente paso es habilitar fuentes desconocidas en su dispositivo. Esta es una configuración de seguridad que le permite instalar aplicaciones desde fuentes distintas de Google Play Store. Para habilitar fuentes desconocidas, debe ir a la configuración del dispositivo, luego a la seguridad y luego a fuentes desconocidas. Pulse en el interruptor o en la casilla de verificación para activarlo. </p>
- <h3>Paso 3: Instalar el archivo apk</h3>
-<p>El tercer paso es instalar el archivo apk de CarX Drift Racing mod apk versión antigua. Para ello, debe localizar el archivo en el administrador de archivos o la carpeta de descargas de su dispositivo. Toque en el archivo y siga las instrucciones en la pantalla. La instalación puede tardar unos minutos. </p>
-
-<p>El paso final es lanzar el juego y disfrutarlo. Puede encontrar el icono del juego en la pantalla de inicio del dispositivo o en el cajón de la aplicación. Tócalo y empieza a jugar. Verás que tienes dinero y oro ilimitados, coches y pistas desbloqueados, y física y gráficos realistas. </p>
- <h2>Pros y contras de CarX Drift Racing mod apk versión antigua</h2>
-<p>Como con cualquier juego modificado, hay algunos pros y contras de usar CarX Drift Racing mod apk versión antigua. Aquí están algunos de ellos:</p>
- <h3>Pros</h3>
-<ul>
-<li><h4>Dinero y oro ilimitados</h4>
-<p>Esta es una de las principales ventajas de usar CarX Drift Racing mod apk versión antigua. Tendrás dinero y oro ilimitados en el juego, lo que significa que puedes comprar y actualizar lo que quieras. También puedes saltarte anuncios y obtener recompensas o bonos gratis. </p></li>
-<li><h4>Coches y pistas desbloqueados</h4>
-<p>Este es otro beneficio de usar CarX Drift Racing mod apk versión antigua. Usted tendrá acceso a todos los coches y pistas en el juego, sin restricciones. Puedes elegir entre más de 40 coches y más de 30 pistas, cada una con diferentes características y desafíos. </p></li>
-<li><h4>Física y gráficos realistas</h4>
-<p>Este es también un punto a favor de la utilización de CarX Drift Racing mod apk versión antigua. Disfrutará de la mejor experiencia de juego con física y gráficos realistas. Usted sentirá la emoción de la deriva en diferentes superficies, con impresionantes efectos y sonidos. </p></li>
-</ul>
- <h3> Contras</h3>
-<ul>
-<li><h4>No es compatible con algunos dispositivos</h4>
-<p>Este es uno de los principales inconvenientes de usar CarX Drift Racing mod apk versión antigua. Es posible que tenga algunos problemas de compatibilidad con algunos dispositivos, especialmente los más nuevos. Es posible que el juego no funcione sin problemas o que se bloquee en algunos dispositivos, o que no pueda instalarlo en absoluto. </p></li>
-<li><h4>Puede contener errores y problemas técnicos</h4>
-
-<li><h4>No actualizado con nuevas características y contenido</h4>
-<p>Este es también un punto menos de usar CarX Drift Racing mod apk versión antigua. Te perderás las nuevas características y contenido que ofrece la versión oficial del juego. Por ejemplo, no podrás jugar a los nuevos modos, coches, pistas, eventos o desafíos que los desarrolladores añaden al juego regularmente. </p></li>
-</ul>
- <h2>Conclusión</h2>
-<p>CarX Drift Racing es un juego de deriva divertido y realista que puedes jugar en tu dispositivo Android. Sin embargo, si quieres tener más libertad y diversión en el juego, se puede descargar la antigua versión de CarX Drift Racing mod apk. Este apk mod le dará dinero ilimitado y oro, coches desbloqueados y pistas, y la física realista y gráficos. Sin embargo, también debe ser consciente de los contras de usar este apk mod, tales como problemas de compatibilidad, errores y fallas, y la falta de actualizaciones. </p>
-<p>Si está interesado en descargar e instalar CarX Drift Racing mod apk versión antigua, puede seguir los pasos que proporcionamos en este artículo. También recomendamos que utilice una fuente confiable y segura para descargar el archivo apk, como APKPure. Esperamos que este artículo sea útil e informativo para usted. ¡Feliz deriva! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre CarX Drift Racing mod apk versión antigua:</p>
-<ul>
-<li><b>Es CarX Drift Racing mod apk vieja versión segura de usar? </b></li>
-<p>Sí, CarX Drift Racing mod apk versión antigua es seguro de usar, siempre y cuando se descarga desde una fuente confiable y segura. Sin embargo, siempre debe tener cuidado al descargar e instalar cualquier juego o aplicación modificada en su dispositivo, ya que algunos de ellos pueden contener virus o malware que pueden dañar su dispositivo o robar sus datos. </p>
-<li><b>¿Puedo jugar CarX Drift Racing mod apk versión antigua en línea? </b></li>
-
-<li><b>¿Puedo actualizar la versión antigua de CarX Drift Racing mod apk? </b></li>
-<p>No, no se puede actualizar CarX Drift Racing mod apk versión antigua a la última versión del juego. Si intenta actualizarlo, perderá todas las características modificadas y los beneficios que tiene en la versión anterior. Por lo tanto, le aconsejamos que no lo actualice y disfrute de la versión anterior tal como está. </p>
-<li><b>¿Puedo usar CarX Drift Racing mod apk versión antigua con mi cuenta existente? </b></li>
-<p>No, no se puede utilizar CarX Drift Racing mod apk versión antigua con su cuenta existente. Si intentas hacerlo, puedes arriesgarte a perder tu cuenta o a que los desarrolladores del juego te prohíban acceder a ella. Por lo tanto, le recomendamos que cree una nueva cuenta o utilice una cuenta de invitado para jugar con este apk mod. </p>
-<li><b>¿Puedo desinstalar CarX Drift Racing mod apk versión antigua? </b></li>
-<p>Sí, puede desinstalar CarX Drift Racing mod apk versión antigua en cualquier momento que desee. Para ello, es necesario ir a la configuración de su dispositivo, a continuación, aplicaciones, a continuación, CarX Drift Racing. Toque en desinstalar y confirmar su elección. También puede eliminar el archivo apk del almacenamiento de su dispositivo si lo desea. </p>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/dateutil/easter.py

@@ -1,89 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-This module offers a generic Easter computing method for any given year, using
-Western, Orthodox or Julian algorithms.
-"""
-
-import datetime
-
-__all__ = ["easter", "EASTER_JULIAN", "EASTER_ORTHODOX", "EASTER_WESTERN"]
-
-EASTER_JULIAN = 1
-EASTER_ORTHODOX = 2
-EASTER_WESTERN = 3
-
-
-def easter(year, method=EASTER_WESTERN):
-    """
-    This method was ported from the work done by GM Arts,
-    on top of the algorithm by Claus Tondering, which was
-    based in part on the algorithm of Ouding (1940), as
-    quoted in "Explanatory Supplement to the Astronomical
-    Almanac", P.  Kenneth Seidelmann, editor.
-
-    This algorithm implements three different Easter
-    calculation methods:
-
-    1. Original calculation in Julian calendar, valid in
-       dates after 326 AD
-    2. Original method, with date converted to Gregorian
-       calendar, valid in years 1583 to 4099
-    3. Revised method, in Gregorian calendar, valid in
-       years 1583 to 4099 as well
-
-    These methods are represented by the constants:
-
-    * ``EASTER_JULIAN   = 1``
-    * ``EASTER_ORTHODOX = 2``
-    * ``EASTER_WESTERN  = 3``
-
-    The default method is method 3.
-
-    More about the algorithm may be found at:
-
-    `GM Arts: Easter Algorithms <http://www.gmarts.org/index.php?go=415>`_
-
-    and
-
-    `The Calendar FAQ: Easter <https://www.tondering.dk/claus/cal/easter.php>`_
-
-    """
-
-    if not (1 <= method <= 3):
-        raise ValueError("invalid method")
-
-    # g - Golden year - 1
-    # c - Century
-    # h - (23 - Epact) mod 30
-    # i - Number of days from March 21 to Paschal Full Moon
-    # j - Weekday for PFM (0=Sunday, etc)
-    # p - Number of days from March 21 to Sunday on or before PFM
-    #     (-6 to 28 methods 1 & 3, to 56 for method 2)
-    # e - Extra days to add for method 2 (converting Julian
-    #     date to Gregorian date)
-
-    y = year
-    g = y % 19
-    e = 0
-    if method < 3:
-        # Old method
-        i = (19*g + 15) % 30
-        j = (y + y//4 + i) % 7
-        if method == 2:
-            # Extra dates to convert Julian to Gregorian date
-            e = 10
-            if y > 1600:
-                e = e + y//100 - 16 - (y//100 - 16)//4
-    else:
-        # New method
-        c = y//100
-        h = (c - c//4 - (8*c + 13)//25 + 19*g + 15) % 30
-        i = h - (h//28)*(1 - (h//28)*(29//(h + 1))*((21 - g)//11))
-        j = (y + y//4 + i + 2 - c + c//4) % 7
-
-    # p can be from -6 to 56 corresponding to dates 22 March to 23 May
-    # (later dates apply to method 2, although 23 May never actually occurs)
-    p = i - j + e
-    d = 1 + (p + 27 + (p + 6)//40) % 31
-    m = 3 + (p + 26)//30
-    return datetime.date(int(y), int(m), int(d))

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/packaging/specifiers.py

@@ -1,802 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-import abc
-import functools
-import itertools
-import re
-import warnings
-from typing import (
-    Callable,
-    Dict,
-    Iterable,
-    Iterator,
-    List,
-    Optional,
-    Pattern,
-    Set,
-    Tuple,
-    TypeVar,
-    Union,
-)
-
-from .utils import canonicalize_version
-from .version import LegacyVersion, Version, parse
-
-ParsedVersion = Union[Version, LegacyVersion]
-UnparsedVersion = Union[Version, LegacyVersion, str]
-VersionTypeVar = TypeVar("VersionTypeVar", bound=UnparsedVersion)
-CallableOperator = Callable[[ParsedVersion, str], bool]
-
-
-class InvalidSpecifier(ValueError):
-    """
-    An invalid specifier was found, users should refer to PEP 440.
-    """
-
-
-class BaseSpecifier(metaclass=abc.ABCMeta):
-    @abc.abstractmethod
-    def __str__(self) -> str:
-        """
-        Returns the str representation of this Specifier like object. This
-        should be representative of the Specifier itself.
-        """
-
-    @abc.abstractmethod
-    def __hash__(self) -> int:
-        """
-        Returns a hash value for this Specifier like object.
-        """
-
-    @abc.abstractmethod
-    def __eq__(self, other: object) -> bool:
-        """
-        Returns a boolean representing whether or not the two Specifier like
-        objects are equal.
-        """
-
-    @abc.abstractproperty
-    def prereleases(self) -> Optional[bool]:
-        """
-        Returns whether or not pre-releases as a whole are allowed by this
-        specifier.
-        """
-
-    @prereleases.setter
-    def prereleases(self, value: bool) -> None:
-        """
-        Sets whether or not pre-releases as a whole are allowed by this
-        specifier.
-        """
-
-    @abc.abstractmethod
-    def contains(self, item: str, prereleases: Optional[bool] = None) -> bool:
-        """
-        Determines if the given item is contained within this specifier.
-        """
-
-    @abc.abstractmethod
-    def filter(
-        self, iterable: Iterable[VersionTypeVar], prereleases: Optional[bool] = None
-    ) -> Iterable[VersionTypeVar]:
-        """
-        Takes an iterable of items and filters them so that only items which
-        are contained within this specifier are allowed in it.
-        """
-
-
-class _IndividualSpecifier(BaseSpecifier):
-
-    _operators: Dict[str, str] = {}
-    _regex: Pattern[str]
-
-    def __init__(self, spec: str = "", prereleases: Optional[bool] = None) -> None:
-        match = self._regex.search(spec)
-        if not match:
-            raise InvalidSpecifier(f"Invalid specifier: '{spec}'")
-
-        self._spec: Tuple[str, str] = (
-            match.group("operator").strip(),
-            match.group("version").strip(),
-        )
-
-        # Store whether or not this Specifier should accept prereleases
-        self._prereleases = prereleases
-
-    def __repr__(self) -> str:
-        pre = (
-            f", prereleases={self.prereleases!r}"
-            if self._prereleases is not None
-            else ""
-        )
-
-        return f"<{self.__class__.__name__}({str(self)!r}{pre})>"
-
-    def __str__(self) -> str:
-        return "{}{}".format(*self._spec)
-
-    @property
-    def _canonical_spec(self) -> Tuple[str, str]:
-        return self._spec[0], canonicalize_version(self._spec[1])
-
-    def __hash__(self) -> int:
-        return hash(self._canonical_spec)
-
-    def __eq__(self, other: object) -> bool:
-        if isinstance(other, str):
-            try:
-                other = self.__class__(str(other))
-            except InvalidSpecifier:
-                return NotImplemented
-        elif not isinstance(other, self.__class__):
-            return NotImplemented
-
-        return self._canonical_spec == other._canonical_spec
-
-    def _get_operator(self, op: str) -> CallableOperator:
-        operator_callable: CallableOperator = getattr(
-            self, f"_compare_{self._operators[op]}"
-        )
-        return operator_callable
-
-    def _coerce_version(self, version: UnparsedVersion) -> ParsedVersion:
-        if not isinstance(version, (LegacyVersion, Version)):
-            version = parse(version)
-        return version
-
-    @property
-    def operator(self) -> str:
-        return self._spec[0]
-
-    @property
-    def version(self) -> str:
-        return self._spec[1]
-
-    @property
-    def prereleases(self) -> Optional[bool]:
-        return self._prereleases
-
-    @prereleases.setter
-    def prereleases(self, value: bool) -> None:
-        self._prereleases = value
-
-    def __contains__(self, item: str) -> bool:
-        return self.contains(item)
-
-    def contains(
-        self, item: UnparsedVersion, prereleases: Optional[bool] = None
-    ) -> bool:
-
-        # Determine if prereleases are to be allowed or not.
-        if prereleases is None:
-            prereleases = self.prereleases
-
-        # Normalize item to a Version or LegacyVersion, this allows us to have
-        # a shortcut for ``"2.0" in Specifier(">=2")
-        normalized_item = self._coerce_version(item)
-
-        # Determine if we should be supporting prereleases in this specifier
-        # or not, if we do not support prereleases than we can short circuit
-        # logic if this version is a prereleases.
-        if normalized_item.is_prerelease and not prereleases:
-            return False
-
-        # Actually do the comparison to determine if this item is contained
-        # within this Specifier or not.
-        operator_callable: CallableOperator = self._get_operator(self.operator)
-        return operator_callable(normalized_item, self.version)
-
-    def filter(
-        self, iterable: Iterable[VersionTypeVar], prereleases: Optional[bool] = None
-    ) -> Iterable[VersionTypeVar]:
-
-        yielded = False
-        found_prereleases = []
-
-        kw = {"prereleases": prereleases if prereleases is not None else True}
-
-        # Attempt to iterate over all the values in the iterable and if any of
-        # them match, yield them.
-        for version in iterable:
-            parsed_version = self._coerce_version(version)
-
-            if self.contains(parsed_version, **kw):
-                # If our version is a prerelease, and we were not set to allow
-                # prereleases, then we'll store it for later in case nothing
-                # else matches this specifier.
-                if parsed_version.is_prerelease and not (
-                    prereleases or self.prereleases
-                ):
-                    found_prereleases.append(version)
-                # Either this is not a prerelease, or we should have been
-                # accepting prereleases from the beginning.
-                else:
-                    yielded = True
-                    yield version
-
-        # Now that we've iterated over everything, determine if we've yielded
-        # any values, and if we have not and we have any prereleases stored up
-        # then we will go ahead and yield the prereleases.
-        if not yielded and found_prereleases:
-            for version in found_prereleases:
-                yield version
-
-
-class LegacySpecifier(_IndividualSpecifier):
-
-    _regex_str = r"""
-        (?P<operator>(==|!=|<=|>=|<|>))
-        \s*
-        (?P<version>
-            [^,;\s)]* # Since this is a "legacy" specifier, and the version
-                      # string can be just about anything, we match everything
-                      # except for whitespace, a semi-colon for marker support,
-                      # a closing paren since versions can be enclosed in
-                      # them, and a comma since it's a version separator.
-        )
-        """
-
-    _regex = re.compile(r"^\s*" + _regex_str + r"\s*$", re.VERBOSE | re.IGNORECASE)
-
-    _operators = {
-        "==": "equal",
-        "!=": "not_equal",
-        "<=": "less_than_equal",
-        ">=": "greater_than_equal",
-        "<": "less_than",
-        ">": "greater_than",
-    }
-
-    def __init__(self, spec: str = "", prereleases: Optional[bool] = None) -> None:
-        super().__init__(spec, prereleases)
-
-        warnings.warn(
-            "Creating a LegacyVersion has been deprecated and will be "
-            "removed in the next major release",
-            DeprecationWarning,
-        )
-
-    def _coerce_version(self, version: UnparsedVersion) -> LegacyVersion:
-        if not isinstance(version, LegacyVersion):
-            version = LegacyVersion(str(version))
-        return version
-
-    def _compare_equal(self, prospective: LegacyVersion, spec: str) -> bool:
-        return prospective == self._coerce_version(spec)
-
-    def _compare_not_equal(self, prospective: LegacyVersion, spec: str) -> bool:
-        return prospective != self._coerce_version(spec)
-
-    def _compare_less_than_equal(self, prospective: LegacyVersion, spec: str) -> bool:
-        return prospective <= self._coerce_version(spec)
-
-    def _compare_greater_than_equal(
-        self, prospective: LegacyVersion, spec: str
-    ) -> bool:
-        return prospective >= self._coerce_version(spec)
-
-    def _compare_less_than(self, prospective: LegacyVersion, spec: str) -> bool:
-        return prospective < self._coerce_version(spec)
-
-    def _compare_greater_than(self, prospective: LegacyVersion, spec: str) -> bool:
-        return prospective > self._coerce_version(spec)
-
-
-def _require_version_compare(
-    fn: Callable[["Specifier", ParsedVersion, str], bool]
-) -> Callable[["Specifier", ParsedVersion, str], bool]:
-    @functools.wraps(fn)
-    def wrapped(self: "Specifier", prospective: ParsedVersion, spec: str) -> bool:
-        if not isinstance(prospective, Version):
-            return False
-        return fn(self, prospective, spec)
-
-    return wrapped
-
-
-class Specifier(_IndividualSpecifier):
-
-    _regex_str = r"""
-        (?P<operator>(~=|==|!=|<=|>=|<|>|===))
-        (?P<version>
-            (?:
-                # The identity operators allow for an escape hatch that will
-                # do an exact string match of the version you wish to install.
-                # This will not be parsed by PEP 440 and we cannot determine
-                # any semantic meaning from it. This operator is discouraged
-                # but included entirely as an escape hatch.
-                (?<====)  # Only match for the identity operator
-                \s*
-                [^\s]*    # We just match everything, except for whitespace
-                          # since we are only testing for strict identity.
-            )
-            |
-            (?:
-                # The (non)equality operators allow for wild card and local
-                # versions to be specified so we have to define these two
-                # operators separately to enable that.
-                (?<===|!=)            # Only match for equals and not equals
-
-                \s*
-                v?
-                (?:[0-9]+!)?          # epoch
-                [0-9]+(?:\.[0-9]+)*   # release
-                (?:                   # pre release
-                    [-_\.]?
-                    (a|b|c|rc|alpha|beta|pre|preview)
-                    [-_\.]?
-                    [0-9]*
-                )?
-                (?:                   # post release
-                    (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
-                )?
-
-                # You cannot use a wild card and a dev or local version
-                # together so group them with a | and make them optional.
-                (?:
-                    (?:[-_\.]?dev[-_\.]?[0-9]*)?         # dev release
-                    (?:\+[a-z0-9]+(?:[-_\.][a-z0-9]+)*)? # local
-                    |
-                    \.\*  # Wild card syntax of .*
-                )?
-            )
-            |
-            (?:
-                # The compatible operator requires at least two digits in the
-                # release segment.
-                (?<=~=)               # Only match for the compatible operator
-
-                \s*
-                v?
-                (?:[0-9]+!)?          # epoch
-                [0-9]+(?:\.[0-9]+)+   # release  (We have a + instead of a *)
-                (?:                   # pre release
-                    [-_\.]?
-                    (a|b|c|rc|alpha|beta|pre|preview)
-                    [-_\.]?
-                    [0-9]*
-                )?
-                (?:                                   # post release
-                    (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
-                )?
-                (?:[-_\.]?dev[-_\.]?[0-9]*)?          # dev release
-            )
-            |
-            (?:
-                # All other operators only allow a sub set of what the
-                # (non)equality operators do. Specifically they do not allow
-                # local versions to be specified nor do they allow the prefix
-                # matching wild cards.
-                (?<!==|!=|~=)         # We have special cases for these
-                                      # operators so we want to make sure they
-                                      # don't match here.
-
-                \s*
-                v?
-                (?:[0-9]+!)?          # epoch
-                [0-9]+(?:\.[0-9]+)*   # release
-                (?:                   # pre release
-                    [-_\.]?
-                    (a|b|c|rc|alpha|beta|pre|preview)
-                    [-_\.]?
-                    [0-9]*
-                )?
-                (?:                                   # post release
-                    (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
-                )?
-                (?:[-_\.]?dev[-_\.]?[0-9]*)?          # dev release
-            )
-        )
-        """
-
-    _regex = re.compile(r"^\s*" + _regex_str + r"\s*$", re.VERBOSE | re.IGNORECASE)
-
-    _operators = {
-        "~=": "compatible",
-        "==": "equal",
-        "!=": "not_equal",
-        "<=": "less_than_equal",
-        ">=": "greater_than_equal",
-        "<": "less_than",
-        ">": "greater_than",
-        "===": "arbitrary",
-    }
-
-    @_require_version_compare
-    def _compare_compatible(self, prospective: ParsedVersion, spec: str) -> bool:
-
-        # Compatible releases have an equivalent combination of >= and ==. That
-        # is that ~=2.2 is equivalent to >=2.2,==2.*. This allows us to
-        # implement this in terms of the other specifiers instead of
-        # implementing it ourselves. The only thing we need to do is construct
-        # the other specifiers.
-
-        # We want everything but the last item in the version, but we want to
-        # ignore suffix segments.
-        prefix = ".".join(
-            list(itertools.takewhile(_is_not_suffix, _version_split(spec)))[:-1]
-        )
-
-        # Add the prefix notation to the end of our string
-        prefix += ".*"
-
-        return self._get_operator(">=")(prospective, spec) and self._get_operator("==")(
-            prospective, prefix
-        )
-
-    @_require_version_compare
-    def _compare_equal(self, prospective: ParsedVersion, spec: str) -> bool:
-
-        # We need special logic to handle prefix matching
-        if spec.endswith(".*"):
-            # In the case of prefix matching we want to ignore local segment.
-            prospective = Version(prospective.public)
-            # Split the spec out by dots, and pretend that there is an implicit
-            # dot in between a release segment and a pre-release segment.
-            split_spec = _version_split(spec[:-2])  # Remove the trailing .*
-
-            # Split the prospective version out by dots, and pretend that there
-            # is an implicit dot in between a release segment and a pre-release
-            # segment.
-            split_prospective = _version_split(str(prospective))
-
-            # Shorten the prospective version to be the same length as the spec
-            # so that we can determine if the specifier is a prefix of the
-            # prospective version or not.
-            shortened_prospective = split_prospective[: len(split_spec)]
-
-            # Pad out our two sides with zeros so that they both equal the same
-            # length.
-            padded_spec, padded_prospective = _pad_version(
-                split_spec, shortened_prospective
-            )
-
-            return padded_prospective == padded_spec
-        else:
-            # Convert our spec string into a Version
-            spec_version = Version(spec)
-
-            # If the specifier does not have a local segment, then we want to
-            # act as if the prospective version also does not have a local
-            # segment.
-            if not spec_version.local:
-                prospective = Version(prospective.public)
-
-            return prospective == spec_version
-
-    @_require_version_compare
-    def _compare_not_equal(self, prospective: ParsedVersion, spec: str) -> bool:
-        return not self._compare_equal(prospective, spec)
-
-    @_require_version_compare
-    def _compare_less_than_equal(self, prospective: ParsedVersion, spec: str) -> bool:
-
-        # NB: Local version identifiers are NOT permitted in the version
-        # specifier, so local version labels can be universally removed from
-        # the prospective version.
-        return Version(prospective.public) <= Version(spec)
-
-    @_require_version_compare
-    def _compare_greater_than_equal(
-        self, prospective: ParsedVersion, spec: str
-    ) -> bool:
-
-        # NB: Local version identifiers are NOT permitted in the version
-        # specifier, so local version labels can be universally removed from
-        # the prospective version.
-        return Version(prospective.public) >= Version(spec)
-
-    @_require_version_compare
-    def _compare_less_than(self, prospective: ParsedVersion, spec_str: str) -> bool:
-
-        # Convert our spec to a Version instance, since we'll want to work with
-        # it as a version.
-        spec = Version(spec_str)
-
-        # Check to see if the prospective version is less than the spec
-        # version. If it's not we can short circuit and just return False now
-        # instead of doing extra unneeded work.
-        if not prospective < spec:
-            return False
-
-        # This special case is here so that, unless the specifier itself
-        # includes is a pre-release version, that we do not accept pre-release
-        # versions for the version mentioned in the specifier (e.g. <3.1 should
-        # not match 3.1.dev0, but should match 3.0.dev0).
-        if not spec.is_prerelease and prospective.is_prerelease:
-            if Version(prospective.base_version) == Version(spec.base_version):
-                return False
-
-        # If we've gotten to here, it means that prospective version is both
-        # less than the spec version *and* it's not a pre-release of the same
-        # version in the spec.
-        return True
-
-    @_require_version_compare
-    def _compare_greater_than(self, prospective: ParsedVersion, spec_str: str) -> bool:
-
-        # Convert our spec to a Version instance, since we'll want to work with
-        # it as a version.
-        spec = Version(spec_str)
-
-        # Check to see if the prospective version is greater than the spec
-        # version. If it's not we can short circuit and just return False now
-        # instead of doing extra unneeded work.
-        if not prospective > spec:
-            return False
-
-        # This special case is here so that, unless the specifier itself
-        # includes is a post-release version, that we do not accept
-        # post-release versions for the version mentioned in the specifier
-        # (e.g. >3.1 should not match 3.0.post0, but should match 3.2.post0).
-        if not spec.is_postrelease and prospective.is_postrelease:
-            if Version(prospective.base_version) == Version(spec.base_version):
-                return False
-
-        # Ensure that we do not allow a local version of the version mentioned
-        # in the specifier, which is technically greater than, to match.
-        if prospective.local is not None:
-            if Version(prospective.base_version) == Version(spec.base_version):
-                return False
-
-        # If we've gotten to here, it means that prospective version is both
-        # greater than the spec version *and* it's not a pre-release of the
-        # same version in the spec.
-        return True
-
-    def _compare_arbitrary(self, prospective: Version, spec: str) -> bool:
-        return str(prospective).lower() == str(spec).lower()
-
-    @property
-    def prereleases(self) -> bool:
-
-        # If there is an explicit prereleases set for this, then we'll just
-        # blindly use that.
-        if self._prereleases is not None:
-            return self._prereleases
-
-        # Look at all of our specifiers and determine if they are inclusive
-        # operators, and if they are if they are including an explicit
-        # prerelease.
-        operator, version = self._spec
-        if operator in ["==", ">=", "<=", "~=", "==="]:
-            # The == specifier can include a trailing .*, if it does we
-            # want to remove before parsing.
-            if operator == "==" and version.endswith(".*"):
-                version = version[:-2]
-
-            # Parse the version, and if it is a pre-release than this
-            # specifier allows pre-releases.
-            if parse(version).is_prerelease:
-                return True
-
-        return False
-
-    @prereleases.setter
-    def prereleases(self, value: bool) -> None:
-        self._prereleases = value
-
-
-_prefix_regex = re.compile(r"^([0-9]+)((?:a|b|c|rc)[0-9]+)$")
-
-
-def _version_split(version: str) -> List[str]:
-    result: List[str] = []
-    for item in version.split("."):
-        match = _prefix_regex.search(item)
-        if match:
-            result.extend(match.groups())
-        else:
-            result.append(item)
-    return result
-
-
-def _is_not_suffix(segment: str) -> bool:
-    return not any(
-        segment.startswith(prefix) for prefix in ("dev", "a", "b", "rc", "post")
-    )
-
-
-def _pad_version(left: List[str], right: List[str]) -> Tuple[List[str], List[str]]:
-    left_split, right_split = [], []
-
-    # Get the release segment of our versions
-    left_split.append(list(itertools.takewhile(lambda x: x.isdigit(), left)))
-    right_split.append(list(itertools.takewhile(lambda x: x.isdigit(), right)))
-
-    # Get the rest of our versions
-    left_split.append(left[len(left_split[0]) :])
-    right_split.append(right[len(right_split[0]) :])
-
-    # Insert our padding
-    left_split.insert(1, ["0"] * max(0, len(right_split[0]) - len(left_split[0])))
-    right_split.insert(1, ["0"] * max(0, len(left_split[0]) - len(right_split[0])))
-
-    return (list(itertools.chain(*left_split)), list(itertools.chain(*right_split)))
-
-
-class SpecifierSet(BaseSpecifier):
-    def __init__(
-        self, specifiers: str = "", prereleases: Optional[bool] = None
-    ) -> None:
-
-        # Split on , to break each individual specifier into it's own item, and
-        # strip each item to remove leading/trailing whitespace.
-        split_specifiers = [s.strip() for s in specifiers.split(",") if s.strip()]
-
-        # Parsed each individual specifier, attempting first to make it a
-        # Specifier and falling back to a LegacySpecifier.
-        parsed: Set[_IndividualSpecifier] = set()
-        for specifier in split_specifiers:
-            try:
-                parsed.add(Specifier(specifier))
-            except InvalidSpecifier:
-                parsed.add(LegacySpecifier(specifier))
-
-        # Turn our parsed specifiers into a frozen set and save them for later.
-        self._specs = frozenset(parsed)
-
-        # Store our prereleases value so we can use it later to determine if
-        # we accept prereleases or not.
-        self._prereleases = prereleases
-
-    def __repr__(self) -> str:
-        pre = (
-            f", prereleases={self.prereleases!r}"
-            if self._prereleases is not None
-            else ""
-        )
-
-        return f"<SpecifierSet({str(self)!r}{pre})>"
-
-    def __str__(self) -> str:
-        return ",".join(sorted(str(s) for s in self._specs))
-
-    def __hash__(self) -> int:
-        return hash(self._specs)
-
-    def __and__(self, other: Union["SpecifierSet", str]) -> "SpecifierSet":
-        if isinstance(other, str):
-            other = SpecifierSet(other)
-        elif not isinstance(other, SpecifierSet):
-            return NotImplemented
-
-        specifier = SpecifierSet()
-        specifier._specs = frozenset(self._specs | other._specs)
-
-        if self._prereleases is None and other._prereleases is not None:
-            specifier._prereleases = other._prereleases
-        elif self._prereleases is not None and other._prereleases is None:
-            specifier._prereleases = self._prereleases
-        elif self._prereleases == other._prereleases:
-            specifier._prereleases = self._prereleases
-        else:
-            raise ValueError(
-                "Cannot combine SpecifierSets with True and False prerelease "
-                "overrides."
-            )
-
-        return specifier
-
-    def __eq__(self, other: object) -> bool:
-        if isinstance(other, (str, _IndividualSpecifier)):
-            other = SpecifierSet(str(other))
-        elif not isinstance(other, SpecifierSet):
-            return NotImplemented
-
-        return self._specs == other._specs
-
-    def __len__(self) -> int:
-        return len(self._specs)
-
-    def __iter__(self) -> Iterator[_IndividualSpecifier]:
-        return iter(self._specs)
-
-    @property
-    def prereleases(self) -> Optional[bool]:
-
-        # If we have been given an explicit prerelease modifier, then we'll
-        # pass that through here.
-        if self._prereleases is not None:
-            return self._prereleases
-
-        # If we don't have any specifiers, and we don't have a forced value,
-        # then we'll just return None since we don't know if this should have
-        # pre-releases or not.
-        if not self._specs:
-            return None
-
-        # Otherwise we'll see if any of the given specifiers accept
-        # prereleases, if any of them do we'll return True, otherwise False.
-        return any(s.prereleases for s in self._specs)
-
-    @prereleases.setter
-    def prereleases(self, value: bool) -> None:
-        self._prereleases = value
-
-    def __contains__(self, item: UnparsedVersion) -> bool:
-        return self.contains(item)
-
-    def contains(
-        self, item: UnparsedVersion, prereleases: Optional[bool] = None
-    ) -> bool:
-
-        # Ensure that our item is a Version or LegacyVersion instance.
-        if not isinstance(item, (LegacyVersion, Version)):
-            item = parse(item)
-
-        # Determine if we're forcing a prerelease or not, if we're not forcing
-        # one for this particular filter call, then we'll use whatever the
-        # SpecifierSet thinks for whether or not we should support prereleases.
-        if prereleases is None:
-            prereleases = self.prereleases
-
-        # We can determine if we're going to allow pre-releases by looking to
-        # see if any of the underlying items supports them. If none of them do
-        # and this item is a pre-release then we do not allow it and we can
-        # short circuit that here.
-        # Note: This means that 1.0.dev1 would not be contained in something
-        #       like >=1.0.devabc however it would be in >=1.0.debabc,>0.0.dev0
-        if not prereleases and item.is_prerelease:
-            return False
-
-        # We simply dispatch to the underlying specs here to make sure that the
-        # given version is contained within all of them.
-        # Note: This use of all() here means that an empty set of specifiers
-        #       will always return True, this is an explicit design decision.
-        return all(s.contains(item, prereleases=prereleases) for s in self._specs)
-
-    def filter(
-        self, iterable: Iterable[VersionTypeVar], prereleases: Optional[bool] = None
-    ) -> Iterable[VersionTypeVar]:
-
-        # Determine if we're forcing a prerelease or not, if we're not forcing
-        # one for this particular filter call, then we'll use whatever the
-        # SpecifierSet thinks for whether or not we should support prereleases.
-        if prereleases is None:
-            prereleases = self.prereleases
-
-        # If we have any specifiers, then we want to wrap our iterable in the
-        # filter method for each one, this will act as a logical AND amongst
-        # each specifier.
-        if self._specs:
-            for spec in self._specs:
-                iterable = spec.filter(iterable, prereleases=bool(prereleases))
-            return iterable
-        # If we do not have any specifiers, then we need to have a rough filter
-        # which will filter out any pre-releases, unless there are no final
-        # releases, and which will filter out LegacyVersion in general.
-        else:
-            filtered: List[VersionTypeVar] = []
-            found_prereleases: List[VersionTypeVar] = []
-
-            item: UnparsedVersion
-            parsed_version: Union[Version, LegacyVersion]
-
-            for item in iterable:
-                # Ensure that we some kind of Version class for this item.
-                if not isinstance(item, (LegacyVersion, Version)):
-                    parsed_version = parse(item)
-                else:
-                    parsed_version = item
-
-                # Filter out any item which is parsed as a LegacyVersion
-                if isinstance(parsed_version, LegacyVersion):
-                    continue
-
-                # Store any item which is a pre-release for later unless we've
-                # already found a final version or we are accepting prereleases
-                if parsed_version.is_prerelease and not prereleases:
-                    if not filtered:
-                        found_prereleases.append(item)
-                else:
-                    filtered.append(item)
-
-            # If we've found no items except for pre-releases, then we'll go
-            # ahead and use the pre-releases
-            if not filtered and found_prereleases and prereleases is None:
-                return found_prereleases
-
-            return filtered

spaces/BraydenMoore/MARCI-NFL-Betting/Templates/index.html

@@ -1,724 +0,0 @@
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-<html>
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<head>
-    <link rel="shortcut icon" type="image/x-icon" href="https://images.squarespace-cdn.com/content/v1/64790f5777b5d772678cce83/6d71eaee-f825-4324-be9b-2def32469eac/favicon.ico?format=100w">
-    <title>MARCI - NFL Betting</title>
-</head>
-<style>
-    body {
-        max-width: 90vw;
-        margin: auto;
-        background-color: black;
-        font-family: 'Helvetica';
-        justify-content: center;
-        text-align: center;
-        padding-top: 2%;
-        padding-bottom: 5%;
-    }
-    p {
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-        color: #f2f2f2;
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-
-    table {
-        transition: 0.3s ease;
-        margin-top: 20px;
-        width: 80%;
-        border-collapse: collapse;
-        text-align: center;
-    }
-    .table-div {
-        display: flex;
-        justify-content: center;
-    }
-    th, td {
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-        border: 1px solid black;
-        text-align: center;
-        padding: 8px;
-    }
-    th {
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-    }
-    tr {
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-        background-color: rgb(10, 10, 5);
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-    }
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-
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-    .section-container {
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-    .section {
-        padding: 30px;
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-        border-style: solid;
-        border-width: 1px;
-        border-color: rgb(61, 61, 61);
-        width: 48%; 
-    }
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-        height: auto;
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-        z-index: 999;
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-        height: 30px;
-        background-color: green;
-        overflow: hidden;
-        visibility: hidden;
-    }
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-    }
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-        color: rgb(255, 255, 255);
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-    #gradient {
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-        width: 80%;
-        display: inline-block;
-        margin-bottom: 40px;
-    }
-
-    #weekSelector {
-        transition: 0.3s ease;
-        border-radius: 10px;
-        padding: 5px;
-        color: white;
-        background: rgb(30, 30, 30) !important;
-        font-family: Arial, Helvetica, sans-serif;
-    }
-    #weekSelector:hover {
-        opacity: 0.5;
-    }
-
-    @keyframes spin {
-        0% {
-            transform: rotate(0deg);
-        }
-        100% {
-            transform: rotate(360deg);
-        }
-    }
-
-    @media screen and (max-width: 768px) {
-        .table-div {
-            display: block;
-            justify-content: center;
-        }
-        .winner-image {
-            margin: 0;
-        }
-        .emoji {
-            margin: 0;
-        }
-        .table-div{
-            overflow-x: scroll;
-        }
-        .divider {
-            width: 90%;
-        }
-        #modelDetails {
-            width: 90%;
-        }
-        .button {
-            width: 90%;
-        }
-        .section-container {
-            display: inline;
-        }
-        .section {
-            padding: 15px;
-            width: auto;
-        }
-    }
-
-</style>
-
-<div class="scroll-banner">
-    <div class="scroll-text">
-      Predictions will begin at the conclusion of Week 1. Bet at your own risk. Know your limits. And most importantly, have fun!
-    </div>
-</div>
-
-<body>
-    <h1>M A R C I</h1>
-    <div class="info">
-        <span class="label"><i>Moore's Algorithm for Risky Capital Investments</i></span><br><br>
-
-        <span id="gradient">Remember to have fun!</span><br><br>
-
-        <span class="label"><b>Record through {{ latest_game }}</b></span><br>
-        <span class="label">Winners:</span> {{ winners_correct }}-{{winners_incorrect}}{{winners_tie}}<span class="label"> ({{ winners_return }})</span><br>
-        <span class="label">Over/Unders:</span> {{over_unders_correct}}-{{over_unders_incorrect}}{{over_unders_push}}<span class="label"> ({{over_unders_return}})</span><br><br>
-    </div>
-
-    <select id="weekSelector">
-    </select>
-
-    <div class="table-div">
-        <table id="gameTable">
-            <tr>
-                <th>Date</th>
-                <th>Away</th>
-                <th>Home</th>
-                <th>O/U</th>
-                <th>Predicted Winner</th>
-                <th>Predicted O/U</th>
-            </tr>
-        </table>
-    </div>
-    <button id="submitButton">
-        Predict
-    </button>
-
-    <hr class="divider"> 
-
-    <div class="modelDetails">
-        <h2>Model Train/Test Details</h2>
-        <div class="section-container">
-            <div class="section">
-                <h3>Moneyline</h3>
-                <div class="info"></h3><span class="label">Test Accuracy:</span> 71.4%<br></div>
-                <div class="content">
-                    <img src="/Static/xgboost_ML_no_odds_71.4%25_dark.png" alt="Moneyline Model">
-                    <div class="info">
-                        <span class="label">Model:</span> XGBoost<br>
-                        <span class="label">Train/Test Split:</span> 1782/199<br>
-                        <span class="label">Max Depth:</span> 2<br>
-                        <span class="label">Learning Rate:</span> 0.01<br>
-                        <span class="label">Epochs:</span> 500
-                    </div>
-                </div>
-            </div>
-            <div class="section">
-                <h3>Over/Under</h3>
-                <div class="content">
-                    <div class="info"></h3><span class="label">Test Accuracy:</span> 59.8%<br></div>
-                    <img src="/Static/xgboost_OU_no_odds_59.8%25_dark.png" alt="Over/Under Model">
-                    <div class="info">
-                        <span class="label">Model:</span> XGBoost<br>
-                        <span class="label">Train/Test Split:</span> 1782/199<br>
-                        <span class="label">Max Depth:</span> 6<br>
-                        <span class="label">Learning Rate:</span> 0.05<br>
-                        <span class="label">Epochs:</span> 300
-                    </div>
-                </div>
-            </div>
-        </div>
-    </div>
-
-    <div class="modelDetails">
-        <h2>Predictive Accuracy This Year</h2>
-        <div class="section-container">
-            <div class="section">
-                <h3>Moneyline</h3>
-                <div class="info">{{ winners_return }}.</div>
-                <div class="content">
-                    <img src="/Static/Winner_Predictions_dark.png" alt="Moneyline Accuracy">
-                </div>
-                <div class="info"><span class="label">{{ winners_binom }}</span><br></div>
-
-            </div>
-            <div class="section">
-                <h3>Over/Under</h3>
-                <div class="info">{{ over_unders_return }}.</div>
-                <div class="content">
-                    <img src="/Static/Over_Under_Predictions_dark.png" alt="Over/Under Model">
-                </div>
-                <div class="info"><span class="label">{{ over_unders_binom }}</span><br></div>
-            </div>
-        </div>
-    </div>
-
-    <p>🤗<a href="https://huggingface.co/spaces/BraydenMoore/MARCI-NFL-Betting/tree/main">See the Code</a></p>
-
-
-    
-
-    <script>
-    async function fetchGames(selectedWeek) {
-        const response = await fetch(`/get_games?week=${selectedWeek}`);
-        const pulled_games = await response.json();
-        const table = document.getElementById('gameTable');
-        
-        for(let i = table.rows.length - 1; i > 0; i--) {
-            table.deleteRow(i);
-        }
-
-        const columns = ['Date','Away Team', 'Home Team']; 
-        let lines;
-        try {
-            const lines_response = await fetch('/get_lines');
-            if (!lines_response.ok) {
-                throw new Error(`HTTP error! status: ${lines_response.status}`);
-            }
-            lines = await lines_response.json();
-        } 
-        catch (error) {
-            lines = new Array(20).fill(0);
-        }
-
-        pulled_games.forEach((game, index) => {
-            const row = table.insertRow(-1);
-            
-            columns.forEach((column) => {
-                const cell = row.insertCell(-1);
-                if (column === 'Away Team' || column === 'Home Team') {
-                    const img = document.createElement('img');
-                    img.src = `/Static/${game[column]}.webp`;
-                    img.alt = game[column];
-                    img.width = 50; 
-                    cell.appendChild(img);
-                } else {
-                    cell.textContent = game[column];
-                    cell.style.color = "rgb(114, 114, 114)";
-                }
-            });
-            
-            for (let i = 0; i < 3; i++) {
-                const cell = row.insertCell(-1);
-                if (i<1) {
-                    const input = document.createElement('input');
-                    input.type = 'text';
-                    input.value = lines[index];
-                    cell.appendChild(input);
-                }
-            }
-            });
-    }
-
-
-    function submitData() {
-        const predictButton = document.getElementById('submitButton');
-
-        const table = document.getElementById('gameTable');
-        const rows = table.querySelectorAll('tr');
-        const games = [];
-
-        rows.forEach((row, index) => {
-            if (index === 0) return;
-            
-            const winnerCell = row.cells[row.cells.length - 2];
-            const overUnderCell = row.cells[row.cells.length - 1];
-            const spinnerDiv = document.createElement('div');
-            spinnerDiv.className = 'spinner';
-            winnerCell.innerHTML = ''; 
-            overUnderCell.innerHTML = ''; 
-            winnerCell.appendChild(spinnerDiv);
-            overUnderCell.appendChild(spinnerDiv.cloneNode(true));
-
-            const cells = row.querySelectorAll('td');
-            const game = {};
-
-            game.Date = cells[0].textContent;
-            game.AwayTeam = cells[1].querySelector('img').alt;
-            game.HomeTeam = cells[2].querySelector('img').alt;
-            game.OverUnderLine = cells[3].querySelector('input').value;
-            game.rowIndex = index - 1;  
-            games.push(game);
-        });
-
-
-        fetch('/submit_games', {
-            method: 'POST',
-            headers: {
-                'Content-Type': 'application/json',
-            },
-            body: JSON.stringify(games),
-        })
-        .then(response => response.json())
-        .then(data => {
-            if (data.moneylines && data.over_unders) {
-                const table = document.getElementById('gameTable');
-                const rows = table.querySelectorAll('tr');
-
-                data.moneylines.forEach((moneyline, index) => {
-                    const row = rows[parseInt(moneyline.rowIndex) + 1];
-
-                    const winnerCell = row.cells[row.cells.length - 2];
-                    winnerCell.removeChild(winnerCell.querySelector('.spinner'));
-                    winnerCell.innerHTML = ''; 
-
-                    const wrapperDiv = document.createElement('div');
-                    wrapperDiv.className = 'winner-wrapper';
-                    if (moneyline.Probabilities[0] > 0.6){
-                        wrapperDiv.classList.add("highlight");
-                    }
-                    else {
-                        wrapperDiv.style.opacity = "0.5";
-                    }
-
-                    const winnerImg = document.createElement('img');
-                    winnerImg.src = `/Static/${moneyline.Winner}.webp`;
-                    winnerImg.alt = moneyline.Winner;
-                    winnerImg.width = 50;  
-                    winnerImg.className = 'winner-image hidden';
-                    wrapperDiv.appendChild(winnerImg);
-
-                    const winnerEmojiDiv = document.createElement('div');
-                    winnerEmojiDiv.className = 'emoji';
-
-                    wrapperDiv.dataset.proba = Math.floor(moneyline.Probabilities[0] * 100).toFixed(0);
-                    if (moneyline.Winner[0] === moneyline.Result) {
-                        winnerEmojiDiv.textContent = '✅';
-                    } 
-                    else if (moneyline.Result === 'Tie') {
-                        winnerEmojiDiv.textContent = '🔵';
-                    }
-                    else {
-                        winnerEmojiDiv.textContent = '❌';
-                    }
-                    if (moneyline.Result === 'N/A') {
-                        winnerEmojiDiv.textContent = `(${wrapperDiv.dataset.proba}%)`;
-                    }
-                    wrapperDiv.appendChild(winnerEmojiDiv);
-
-                    setTimeout(() => {
-                        winnerImg.classList.remove('hidden');
-                    }, 10);
-
-                    winnerCell.appendChild(wrapperDiv);
-
-                    const overUnderCell = row.cells[row.cells.length - 1];
-                    overUnderCell.removeChild(overUnderCell.querySelector('.spinner'));
-                    overUnderCell.innerHTML = ''; 
-
-                    const overUnderDiv = document.createElement('div');
-                    overUnderDiv.className = 'over-under-wrapper hidden';
-                    if (data.over_unders[index]['Probability'][0] > 0.6){
-                        overUnderDiv.classList.add("highlight");
-                    }
-                    else {
-                        overUnderDiv.style.opacity = "0.5";
-                    }
-
-                    const textDiv = document.createElement('div');
-                    textDiv.className = 'over-under-text';
-                    textDiv.textContent = data.over_unders[index]['Over/Under'];
-                    if (textDiv.textContent === 'Over') {
-                        overUnderDiv.className += ' over';
-                    } else if (textDiv.textContent === 'Under') {
-                        overUnderDiv.className += ' under';
-                    } else {
-                        overUnderDiv.className += ' na';
-                    }
-
-                    overUnderDiv.appendChild(textDiv);
-
-                    const overEmojiDiv = document.createElement('div');
-                    overEmojiDiv.className = 'emoji';
-
-                    overUnderDiv.dataset.proba = Math.floor(data.over_unders[index]['Probability'][0] * 100).toFixed(0);
-                    if (data.over_unders[index]['Over/Under'][0] === data.over_unders[index]['Result']) {  
-                        overEmojiDiv.textContent = '✅';
-                    } 
-                    else if (data.over_unders[index]['Result'] === 'Push') {
-                        overEmojiDiv.textContent = '🔵';
-                    }
-                    else {
-                        overEmojiDiv.textContent = '❌';
-                    }
-                    if (data.over_unders[index]['Result'] === 'N/A') {
-                        overEmojiDiv.textContent = `(${overUnderDiv.dataset.proba}%)`;
-                    }
-                    overUnderDiv.appendChild(overEmojiDiv);
-
-                    setTimeout(() => {
-                        overUnderDiv.classList.remove('hidden');
-                    }, 10);
-
-                    overUnderCell.appendChild(overUnderDiv);
-
-                    showProbabilityOnHover(wrapperDiv);
-                    showProbabilityOnHover(overUnderDiv);
-                    
-                });
-            }
-            });
-
-    }
-
-    //Hover listener
-    function showProbabilityOnHover(div) {
-        let previousValue;
-        let divText = div.children[1];
-        let eventProcessed = false;
-
-        function handleEnter() {
-            if (eventProcessed) return;  // Skip if an event has already been processed
-
-            eventProcessed = true;
-
-            if (divText.textContent !== `(${div.dataset.proba}%)`) {
-                divText.style.opacity = 0;
-
-                setTimeout(() => {
-                    previousValue = divText.textContent;
-                    divText.textContent = `(${div.dataset.proba}%)`;
-                    divText.style.opacity = 1;
-                }, 300);
-
-                setTimeout(() => {
-                    divText.style.opacity = 0;
-                    setTimeout(() => {
-                        divText.textContent = previousValue;
-                        divText.style.opacity = 1;
-                        eventProcessed = false;  // Reset the flag
-                    }, 300);
-                }, 1000);
-            }
-        }
-
-        // For desktop
-        div.addEventListener('mouseenter', handleEnter);
-        // For mobile
-        div.addEventListener('touchstart', handleEnter);
-    }
-
-    // Populate dropdown
-    let selectedWeek;
-    async function populateDropdown() {
-        const weekSelector = document.getElementById('weekSelector');
-        weekSelector.innerHTML = "";
-
-        const response = await fetch('/get_weeks');
-        const data = await response.json();
-        
-        data.forEach((week, index) => {
-            const option = document.createElement('option');
-            option.value = week;
-            option.text = `Week ${week}`;
-            weekSelector.appendChild(option);
-
-            if (index === 0) {
-                selectedWeek = week;
-            }
-        });
-    }
-    
-    
-    // Get new games when new week selected
-    document.getElementById('weekSelector').addEventListener('change', function(event) {
-        selectedWeek = event.target.value;
-        getNew();
-    });
-
-
-    // Initial load
-    function loadThings() { 
-        populateDropdown()
-        .then(() => fetchGames(selectedWeek))
-        .then(() => submitData())
-        .catch(error => console.error(error));
-    }
-
-    // Get new
-    async function getNew() {
-        const table = document.getElementById('gameTable');
-        table.style.opacity = "0.5";
-
-        try {
-            await fetchGames(selectedWeek);
-            await submitData();
-            table.style.opacity = "1";
-        } catch (error) {
-            console.error(error);
-        }
-    }
-
-
-    // Submit on click, enter, and pageload
-    loadThings();
-
-    document.getElementById('submitButton').addEventListener('click', submitData);
-
-    document.addEventListener('keydown', function(event) {
-        if (event.keyCode === 13) {  
-            submitData();
-        }
-    });
-
-
-</script>
-
-</body>
-</html>

spaces/BraydenMoore/a-random-unsecured-camera/Dockerfile

@@ -1,29 +0,0 @@
-# Use the official lightweight Python image.
-FROM python:3.11
-
-# Allow statements and log messages to immediately appear in the logs
-ENV PYTHONUNBUFFERED True
-
-# Copy local code to the container image.
-ENV APP_HOME /app
-WORKDIR $APP_HOME
-COPY . ./
-
-# Install production dependencies.
-RUN pip install --no-cache-dir -r requirements.txt
-
-# Create a non-root user and switch to it
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH
-
-# Set work directory
-WORKDIR $APP_HOME
-
-# Change ownership of app files to the new user
-COPY --chown=user . $HOME/app
-
-# Run the web service on container startup.
-CMD exec gunicorn --bind 0.0.0.0:7860 --workers 4 --threads 16 main:app
-

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/proposal_generator/rrpn.py

@@ -1,74 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import logging
-from typing import Dict
-import torch
-
-from detectron2.layers import ShapeSpec
-
-from ..box_regression import Box2BoxTransformRotated
-from .build import PROPOSAL_GENERATOR_REGISTRY
-from .rpn import RPN
-from .rrpn_outputs import RRPNOutputs, find_top_rrpn_proposals
-
-logger = logging.getLogger(__name__)
-
-
-@PROPOSAL_GENERATOR_REGISTRY.register()
-class RRPN(RPN):
-    """
-    Rotated RPN subnetwork.
-    Please refer to https://arxiv.org/pdf/1703.01086.pdf for the original RRPN paper:
-    Ma, J., Shao, W., Ye, H., Wang, L., Wang, H., Zheng, Y., & Xue, X. (2018).
-    Arbitrary-oriented scene text detection via rotation proposals.
-    IEEE Transactions on Multimedia, 20(11), 3111-3122.
-    """
-
-    def __init__(self, cfg, input_shape: Dict[str, ShapeSpec]):
-        super().__init__(cfg, input_shape)
-        self.box2box_transform = Box2BoxTransformRotated(weights=cfg.MODEL.RPN.BBOX_REG_WEIGHTS)
-
-    def forward(self, images, features, gt_instances=None):
-        # same signature as RPN.forward
-        gt_boxes = [x.gt_boxes for x in gt_instances] if gt_instances is not None else None
-        del gt_instances
-        features = [features[f] for f in self.in_features]
-        pred_objectness_logits, pred_anchor_deltas = self.rpn_head(features)
-        anchors = self.anchor_generator(features)
-
-        outputs = RRPNOutputs(
-            self.box2box_transform,
-            self.anchor_matcher,
-            self.batch_size_per_image,
-            self.positive_fraction,
-            images,
-            pred_objectness_logits,
-            pred_anchor_deltas,
-            anchors,
-            self.boundary_threshold,
-            gt_boxes,
-            self.smooth_l1_beta,
-        )
-
-        if self.training:
-            losses = outputs.losses()
-        else:
-            losses = {}
-
-        with torch.no_grad():
-            # Find the top proposals by applying NMS and removing boxes that
-            # are too small. The proposals are treated as fixed for approximate
-            # joint training with roi heads. This approach ignores the derivative
-            # w.r.t. the proposal boxes’ coordinates that are also network
-            # responses, so is approximate.
-            proposals = find_top_rrpn_proposals(
-                outputs.predict_proposals(),
-                outputs.predict_objectness_logits(),
-                images,
-                self.nms_thresh,
-                self.pre_nms_topk[self.training],
-                self.post_nms_topk[self.training],
-                self.min_box_side_len,
-                self.training,
-            )
-
-        return proposals, losses

spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/extrema.h

@@ -1,568 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-#pragma once
-
-
-#if THRUST_DEVICE_COMPILER == THRUST_DEVICE_COMPILER_NVCC
-#include <thrust/system/cuda/config.h>
-#include <thrust/system/cuda/detail/reduce.h>
-
-#include <thrust/detail/cstdint.h>
-#include <thrust/detail/temporary_array.h>
-#include <thrust/extrema.h>
-#include <thrust/pair.h>
-#include <thrust/distance.h>
-
-namespace thrust
-{
-namespace cuda_cub {
-
-namespace __extrema {
-
-  template <class InputType, class IndexType, class Predicate>
-  struct arg_min_f
-  {
-    Predicate predicate;
-    typedef tuple<InputType, IndexType> pair_type;
-
-    __host__ __device__
-    arg_min_f(Predicate p) : predicate(p) {}
-
-    pair_type __device__
-    operator()(pair_type const &lhs, pair_type const &rhs)
-    {
-      InputType const &rhs_value = get<0>(rhs);
-      InputType const &lhs_value = get<0>(lhs);
-      IndexType const &rhs_key   = get<1>(rhs);
-      IndexType const &lhs_key   = get<1>(lhs);
-
-      // check values first
-      if (predicate(lhs_value, rhs_value))
-        return lhs;
-      else if (predicate(rhs_value, lhs_value))
-        return rhs;
-
-      // values are equivalent, prefer smaller index
-      if (lhs_key < rhs_key)
-        return lhs;
-      else
-        return rhs;
-    }
-  };    // struct arg_min_f
-
-  template <class InputType, class IndexType, class Predicate>
-  struct arg_max_f
-  {
-    Predicate predicate;
-    typedef tuple<InputType, IndexType> pair_type;
-
-    __host__ __device__
-    arg_max_f(Predicate p) : predicate(p) {}
-
-    pair_type __device__
-    operator()(pair_type const &lhs, pair_type const &rhs)
-    {
-      InputType const &rhs_value = get<0>(rhs);
-      InputType const &lhs_value = get<0>(lhs);
-      IndexType const &rhs_key   = get<1>(rhs);
-      IndexType const &lhs_key   = get<1>(lhs);
-
-      // check values first
-      if (predicate(lhs_value, rhs_value))
-        return rhs;
-      else if (predicate(rhs_value, lhs_value))
-        return lhs;
-
-      // values are equivalent, prefer smaller index
-      if (lhs_key < rhs_key)
-        return lhs;
-      else
-        return rhs;
-    }
-  };    // struct arg_max_f
-
-  template<class InputType, class IndexType, class Predicate>
-  struct arg_minmax_f
-  {
-    Predicate predicate;
-
-    typedef tuple<InputType, IndexType> pair_type;
-    typedef tuple<pair_type, pair_type> two_pairs_type;
-
-    typedef arg_min_f<InputType, IndexType, Predicate> arg_min_t;
-    typedef arg_max_f<InputType, IndexType, Predicate> arg_max_t;
-
-    __host__ __device__
-    arg_minmax_f(Predicate p) : predicate(p)
-    {
-    }
-
-    two_pairs_type __device__
-    operator()(two_pairs_type const &lhs, two_pairs_type const &rhs)
-    {
-      pair_type const &rhs_min = get<0>(rhs);
-      pair_type const &lhs_min = get<0>(lhs);
-      pair_type const &rhs_max = get<1>(rhs);
-      pair_type const &lhs_max = get<1>(lhs);
-      return thrust::make_tuple(arg_min_t(predicate)(lhs_min, rhs_min),
-                                arg_max_t(predicate)(lhs_max, rhs_max));
-    }
-
-    struct duplicate_tuple
-    {
-      __device__ two_pairs_type
-      operator()(pair_type const &t)
-      {
-        return thrust::make_tuple(t, t);
-      }
-    };
-  }; // struct arg_minmax_f
-
-  template <class T,
-            class InputIt,
-            class OutputIt,
-            class Size,
-            class ReductionOp>
-  cudaError_t THRUST_RUNTIME_FUNCTION
-  doit_step(void *       d_temp_storage,
-            size_t &     temp_storage_bytes,
-            InputIt      input_it,
-            Size         num_items,
-            ReductionOp  reduction_op,
-            OutputIt     output_it,
-            cudaStream_t stream,
-            bool         debug_sync)
-  {
-    using core::AgentPlan;
-    using core::AgentLauncher;
-    using core::get_agent_plan;
-    using core::cuda_optional;
-
-    typedef typename detail::make_unsigned_special<Size>::type UnsignedSize;
-
-    if (num_items == 0)
-      return cudaErrorNotSupported;
-
-    typedef AgentLauncher<
-        __reduce::ReduceAgent<InputIt, OutputIt, T, Size, ReductionOp> >
-        reduce_agent;
-
-    typename reduce_agent::Plan reduce_plan = reduce_agent::get_plan(stream);
-
-    cudaError_t status = cudaSuccess;
-
-
-    if (num_items <= reduce_plan.items_per_tile)
-    {
-      size_t vshmem_size = core::vshmem_size(reduce_plan.shared_memory_size, 1);
-
-      // small, single tile size
-      if (d_temp_storage == NULL)
-      {
-        temp_storage_bytes = max<size_t>(1, vshmem_size);
-        return status;
-      }
-      char *vshmem_ptr = vshmem_size > 0 ? (char*)d_temp_storage : NULL;
-
-      reduce_agent ra(reduce_plan, num_items, stream, vshmem_ptr, "reduce_agent: single_tile only", debug_sync);
-      ra.launch(input_it, output_it, num_items, reduction_op);
-      CUDA_CUB_RET_IF_FAIL(cudaPeekAtLastError());
-    }
-    else
-    {
-      // regular size
-      cuda_optional<int> sm_count = core::get_sm_count();
-      CUDA_CUB_RET_IF_FAIL(sm_count.status());
-
-      // reduction will not use more cta counts than requested
-      cuda_optional<int> max_blocks_per_sm =
-          reduce_agent::
-              template get_max_blocks_per_sm<InputIt,
-                                             OutputIt,
-                                             Size,
-                                             cub::GridEvenShare<Size>,
-                                             cub::GridQueue<UnsignedSize>,
-                                             ReductionOp>(reduce_plan);
-      CUDA_CUB_RET_IF_FAIL(max_blocks_per_sm.status());
-
-
-
-      int reduce_device_occupancy = (int)max_blocks_per_sm * sm_count;
-
-      int sm_oversubscription = 5;
-      int max_blocks          = reduce_device_occupancy * sm_oversubscription;
-
-      cub::GridEvenShare<Size> even_share;
-      even_share.DispatchInit(num_items, max_blocks,
-                              reduce_plan.items_per_tile);
-
-      // we will launch at most "max_blocks" blocks in a grid
-      // so preallocate virtual shared memory storage for this if required
-      //
-      size_t vshmem_size = core::vshmem_size(reduce_plan.shared_memory_size,
-                                             max_blocks);
-
-      // Temporary storage allocation requirements
-      void * allocations[3] = {NULL, NULL, NULL};
-      size_t allocation_sizes[3] =
-          {
-              max_blocks * sizeof(T),                            // bytes needed for privatized block reductions
-              cub::GridQueue<UnsignedSize>::AllocationSize(),    // bytes needed for grid queue descriptor0
-              vshmem_size                                        // size of virtualized shared memory storage
-          };
-      status = cub::AliasTemporaries(d_temp_storage,
-                                     temp_storage_bytes,
-                                     allocations,
-                                     allocation_sizes);
-      CUDA_CUB_RET_IF_FAIL(status);
-      if (d_temp_storage == NULL)
-      {
-        return status;
-      }
-
-      T *d_block_reductions = (T*) allocations[0];
-      cub::GridQueue<UnsignedSize> queue(allocations[1]);
-      char *vshmem_ptr = vshmem_size > 0 ? (char *)allocations[2] : NULL;
-
-
-      // Get grid size for device_reduce_sweep_kernel
-      int reduce_grid_size = 0;
-      if (reduce_plan.grid_mapping == cub::GRID_MAPPING_RAKE)
-      {
-        // Work is distributed evenly
-        reduce_grid_size = even_share.grid_size;
-      }
-      else if (reduce_plan.grid_mapping == cub::GRID_MAPPING_DYNAMIC)
-      {
-        // Work is distributed dynamically
-        size_t num_tiles = (num_items + reduce_plan.items_per_tile - 1) /
-          reduce_plan.items_per_tile;
-
-        // if not enough to fill the device with threadblocks
-        // then fill the device with threadblocks
-        reduce_grid_size = static_cast<int>(min(num_tiles, static_cast<size_t>(reduce_device_occupancy)));
-
-        typedef AgentLauncher<__reduce::DrainAgent<Size> > drain_agent;
-        AgentPlan drain_plan = drain_agent::get_plan();
-        drain_plan.grid_size = 1;
-        drain_agent da(drain_plan, stream, "__reduce::drain_agent", debug_sync);
-        da.launch(queue, num_items);
-        CUDA_CUB_RET_IF_FAIL(cudaPeekAtLastError());
-      }
-      else
-      {
-        CUDA_CUB_RET_IF_FAIL(cudaErrorNotSupported);
-      }
-
-      reduce_plan.grid_size = reduce_grid_size;
-      reduce_agent ra(reduce_plan, stream, vshmem_ptr, "reduce_agent: regular size reduce", debug_sync);
-      ra.launch(input_it,
-                d_block_reductions,
-                num_items,
-                even_share,
-                queue,
-                reduction_op);
-      CUDA_CUB_RET_IF_FAIL(cudaPeekAtLastError());
-
-
-      typedef AgentLauncher<
-        __reduce::ReduceAgent<T*, OutputIt, T, Size, ReductionOp> >
-        reduce_agent_single;
-
-      reduce_plan.grid_size = 1;
-      reduce_agent_single ra1(reduce_plan, stream, vshmem_ptr, "reduce_agent: single tile reduce", debug_sync);
-
-      ra1.launch(d_block_reductions, output_it, reduce_grid_size, reduction_op);
-      CUDA_CUB_RET_IF_FAIL(cudaPeekAtLastError());
-    }
-
-    return status;
-  }    // func doit_step
-
-  // this is an init-less reduce, needed for min/max-element functionality
-  // this will avoid copying the first value from device->host
-  template <typename Derived,
-            typename InputIt,
-            typename Size,
-            typename BinaryOp,
-            typename T>
-  THRUST_RUNTIME_FUNCTION
-  T extrema(execution_policy<Derived>& policy,
-            InputIt                    first,
-            Size                       num_items,
-            BinaryOp                   binary_op,
-            T*)
-  {
-    size_t       temp_storage_bytes = 0;
-    cudaStream_t stream             = cuda_cub::stream(policy);
-    bool         debug_sync         = THRUST_DEBUG_SYNC_FLAG;
-
-    cudaError_t status;
-    THRUST_INDEX_TYPE_DISPATCH(status, doit_step<T>, num_items,
-        (NULL, temp_storage_bytes, first, num_items_fixed,
-            binary_op, reinterpret_cast<T*>(NULL), stream,
-            debug_sync));
-    cuda_cub::throw_on_error(status, "extrema failed on 1st step");
-
-    size_t allocation_sizes[2] = {sizeof(T*), temp_storage_bytes};
-    void * allocations[2]      = {NULL, NULL};
-
-    size_t storage_size = 0;
-    status = core::alias_storage(NULL,
-                                 storage_size,
-                                 allocations,
-                                 allocation_sizes);
-    cuda_cub::throw_on_error(status, "extrema failed on 1st alias storage");
-
-    // Allocate temporary storage.
-    thrust::detail::temporary_array<thrust::detail::uint8_t, Derived>
-      tmp(policy, storage_size);
-    void *ptr = static_cast<void*>(tmp.data().get());
-
-    status = core::alias_storage(ptr,
-                                 storage_size,
-                                 allocations,
-                                 allocation_sizes);
-    cuda_cub::throw_on_error(status, "extrema failed on 2nd alias storage");
-
-    T* d_result = thrust::detail::aligned_reinterpret_cast<T*>(allocations[0]);
-
-    THRUST_INDEX_TYPE_DISPATCH(status, doit_step<T>, num_items,
-        (allocations[1], temp_storage_bytes, first,
-            num_items_fixed, binary_op, d_result, stream,
-            debug_sync));
-    cuda_cub::throw_on_error(status, "extrema failed on 2nd step");
-
-    status = cuda_cub::synchronize(policy);
-    cuda_cub::throw_on_error(status, "extrema failed to synchronize");
-
-    T result = cuda_cub::get_value(policy, d_result);
-
-    return result;
-  }
-
-  template <template <class, class, class> class ArgFunctor,
-            class Derived,
-            class ItemsIt,
-            class BinaryPred>
-  ItemsIt THRUST_RUNTIME_FUNCTION
-  element(execution_policy<Derived> &policy,
-          ItemsIt                    first,
-          ItemsIt                    last,
-          BinaryPred                 binary_pred)
-  {
-    if (first == last)
-      return last;
-
-    typedef typename iterator_traits<ItemsIt>::value_type      InputType;
-    typedef typename iterator_traits<ItemsIt>::difference_type IndexType;
-
-    IndexType num_items = static_cast<IndexType>(thrust::distance(first, last));
-
-    typedef tuple<ItemsIt, counting_iterator_t<IndexType> > iterator_tuple;
-    typedef zip_iterator<iterator_tuple> zip_iterator;
-
-    iterator_tuple iter_tuple = thrust::make_tuple(first, counting_iterator_t<IndexType>(0));
-
-
-    typedef ArgFunctor<InputType, IndexType, BinaryPred> arg_min_t;
-    typedef tuple<InputType, IndexType> T;
-
-    zip_iterator begin = make_zip_iterator(iter_tuple);
-
-    T result = extrema(policy,
-                       begin,
-                       num_items,
-                       arg_min_t(binary_pred),
-                       (T *)(NULL));
-    return first + thrust::get<1>(result);
-  }
-
-
-}    // namespace __extrema
-
-/// min element
-
-__thrust_exec_check_disable__
-template <class Derived,
-          class ItemsIt,
-          class BinaryPred>
-ItemsIt __host__ __device__
-min_element(execution_policy<Derived> &policy,
-            ItemsIt                    first,
-            ItemsIt                    last,
-            BinaryPred                 binary_pred)
-{
-  ItemsIt ret = first;
-  if (__THRUST_HAS_CUDART__)
-  {
-    ret = __extrema::element<__extrema::arg_min_f>(policy,
-                                                   first,
-                                                   last,
-                                                   binary_pred);
-  }
-  else
-  {
-#if !__THRUST_HAS_CUDART__
-    ret = thrust::min_element(cvt_to_seq(derived_cast(policy)),
-                              first,
-                              last,
-                              binary_pred);
-#endif
-  }
-  return ret;
-}
-
-template <class Derived,
-          class ItemsIt>
-ItemsIt __host__ __device__
-min_element(execution_policy<Derived> &policy,
-            ItemsIt                    first,
-            ItemsIt                    last)
-{
-  typedef typename iterator_value<ItemsIt>::type value_type;
-  return cuda_cub::min_element(policy, first, last, less<value_type>());
-}
-
-/// max element
-
-__thrust_exec_check_disable__
-template <class Derived,
-          class ItemsIt,
-          class BinaryPred>
-ItemsIt __host__ __device__
-max_element(execution_policy<Derived> &policy,
-            ItemsIt                    first,
-            ItemsIt                    last,
-            BinaryPred                 binary_pred)
-{
-  ItemsIt ret = first;
-  if (__THRUST_HAS_CUDART__)
-  {
-    ret = __extrema::element<__extrema::arg_max_f>(policy,
-                                                   first,
-                                                   last,
-                                                   binary_pred);
-  }
-  else
-  {
-#if !__THRUST_HAS_CUDART__
-    ret = thrust::max_element(cvt_to_seq(derived_cast(policy)),
-                              first,
-                              last,
-                              binary_pred);
-#endif
-  }
-  return ret;
-}
-
-template <class Derived,
-          class ItemsIt>
-ItemsIt __host__ __device__
-max_element(execution_policy<Derived> &policy,
-            ItemsIt                    first,
-            ItemsIt                    last)
-{
-  typedef typename iterator_value<ItemsIt>::type value_type;
-  return cuda_cub::max_element(policy, first, last, less<value_type>());
-}
-
-/// minmax element
-
-__thrust_exec_check_disable__
-template <class Derived,
-          class ItemsIt,
-          class BinaryPred>
-pair<ItemsIt, ItemsIt> __host__ __device__
-minmax_element(execution_policy<Derived> &policy,
-               ItemsIt                    first,
-               ItemsIt                    last,
-               BinaryPred                 binary_pred)
-{
-  pair<ItemsIt, ItemsIt> ret = thrust::make_pair(first, first);
-
-  if (__THRUST_HAS_CUDART__)
-  {
-    if (first == last)
-      return thrust::make_pair(last, last);
-
-    typedef typename iterator_traits<ItemsIt>::value_type      InputType;
-    typedef typename iterator_traits<ItemsIt>::difference_type IndexType;
-
-    IndexType num_items = static_cast<IndexType>(thrust::distance(first, last));
-
-
-    typedef tuple<ItemsIt, counting_iterator_t<IndexType> > iterator_tuple;
-    typedef zip_iterator<iterator_tuple> zip_iterator;
-
-    iterator_tuple iter_tuple = thrust::make_tuple(first, counting_iterator_t<IndexType>(0));
-
-
-    typedef __extrema::arg_minmax_f<InputType, IndexType, BinaryPred> arg_minmax_t;
-    typedef typename arg_minmax_t::two_pairs_type  two_pairs_type;
-    typedef typename arg_minmax_t::duplicate_tuple duplicate_t;
-    typedef transform_input_iterator_t<two_pairs_type,
-                                       zip_iterator,
-                                       duplicate_t>
-        transform_t;
-
-    zip_iterator   begin  = make_zip_iterator(iter_tuple);
-    two_pairs_type result = __extrema::extrema(policy,
-                                               transform_t(begin, duplicate_t()),
-                                               num_items,
-                                               arg_minmax_t(binary_pred),
-                                               (two_pairs_type *)(NULL));
-    ret = thrust::make_pair(first + get<1>(get<0>(result)),
-                    first + get<1>(get<1>(result)));
-  }
-  else
-  {
-#if !__THRUST_HAS_CUDART__
-    ret = thrust::minmax_element(cvt_to_seq(derived_cast(policy)),
-                                 first,
-                                 last,
-                                 binary_pred);
-#endif
-  }
-  return ret;
-}
-
-template <class Derived,
-          class ItemsIt>
-pair<ItemsIt, ItemsIt> __host__ __device__
-minmax_element(execution_policy<Derived> &policy,
-               ItemsIt                    first,
-               ItemsIt                    last)
-{
-  typedef typename iterator_value<ItemsIt>::type value_type;
-  return cuda_cub::minmax_element(policy, first, last, less<value_type>());
-}
-
-
-} // namespace cuda_cub
-} // end namespace thrust
-#endif

spaces/CVPR/LIVE/thrust/thrust/system/cuda/error.h

@@ -1,183 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-/*! \file thrust/system/cuda/error.h
- *  \brief CUDA-specific error reporting
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/type_traits.h>
-#include <thrust/system/error_code.h>
-#include <thrust/system/cuda/detail/guarded_driver_types.h>
-
-namespace thrust
-{
-
-namespace system
-{
-
-namespace cuda
-{
-
-// To construct an error_code after a CUDA Runtime error:
-//
-//   error_code(::cudaGetLastError(), cuda_category())
-
-// XXX N3000 prefers enum class errc { ... }
-/*! Namespace for CUDA Runtime errors.
- */
-namespace errc
-{
-
-/*! \p errc_t enumerates the kinds of CUDA Runtime errors.
- */
-enum errc_t
-{
-  // from cuda/include/driver_types.h
-  // mirror their order
-  success                            = cudaSuccess,
-  missing_configuration              = cudaErrorMissingConfiguration,
-  memory_allocation                  = cudaErrorMemoryAllocation,
-  initialization_error               = cudaErrorInitializationError,
-  launch_failure                     = cudaErrorLaunchFailure,
-  prior_launch_failure               = cudaErrorPriorLaunchFailure,
-  launch_timeout                     = cudaErrorLaunchTimeout,
-  launch_out_of_resources            = cudaErrorLaunchOutOfResources,
-  invalid_device_function            = cudaErrorInvalidDeviceFunction,
-  invalid_configuration              = cudaErrorInvalidConfiguration,
-  invalid_device                     = cudaErrorInvalidDevice,
-  invalid_value                      = cudaErrorInvalidValue,
-  invalid_pitch_value                = cudaErrorInvalidPitchValue,
-  invalid_symbol                     = cudaErrorInvalidSymbol,
-  map_buffer_object_failed           = cudaErrorMapBufferObjectFailed,
-  unmap_buffer_object_failed         = cudaErrorUnmapBufferObjectFailed,
-  invalid_host_pointer               = cudaErrorInvalidHostPointer,
-  invalid_device_pointer             = cudaErrorInvalidDevicePointer,
-  invalid_texture                    = cudaErrorInvalidTexture,
-  invalid_texture_binding            = cudaErrorInvalidTextureBinding,
-  invalid_channel_descriptor         = cudaErrorInvalidChannelDescriptor,
-  invalid_memcpy_direction           = cudaErrorInvalidMemcpyDirection,
-  address_of_constant_error          = cudaErrorAddressOfConstant,
-  texture_fetch_failed               = cudaErrorTextureFetchFailed,
-  texture_not_bound                  = cudaErrorTextureNotBound,
-  synchronization_error              = cudaErrorSynchronizationError,
-  invalid_filter_setting             = cudaErrorInvalidFilterSetting,
-  invalid_norm_setting               = cudaErrorInvalidNormSetting,
-  mixed_device_execution             = cudaErrorMixedDeviceExecution,
-  cuda_runtime_unloading             = cudaErrorCudartUnloading,
-  unknown                            = cudaErrorUnknown,
-  not_yet_implemented                = cudaErrorNotYetImplemented,
-  memory_value_too_large             = cudaErrorMemoryValueTooLarge,
-  invalid_resource_handle            = cudaErrorInvalidResourceHandle,
-  not_ready                          = cudaErrorNotReady,
-  insufficient_driver                = cudaErrorInsufficientDriver,
-  set_on_active_process_error        = cudaErrorSetOnActiveProcess,
-  no_device                          = cudaErrorNoDevice,
-  ecc_uncorrectable                  = cudaErrorECCUncorrectable,
-
-#if CUDART_VERSION >= 4020
-  shared_object_symbol_not_found     = cudaErrorSharedObjectSymbolNotFound,
-  shared_object_init_failed          = cudaErrorSharedObjectInitFailed,
-  unsupported_limit                  = cudaErrorUnsupportedLimit,
-  duplicate_variable_name            = cudaErrorDuplicateVariableName,
-  duplicate_texture_name             = cudaErrorDuplicateTextureName,
-  duplicate_surface_name             = cudaErrorDuplicateSurfaceName,
-  devices_unavailable                = cudaErrorDevicesUnavailable,
-  invalid_kernel_image               = cudaErrorInvalidKernelImage,
-  no_kernel_image_for_device         = cudaErrorNoKernelImageForDevice,
-  incompatible_driver_context        = cudaErrorIncompatibleDriverContext,
-  peer_access_already_enabled        = cudaErrorPeerAccessAlreadyEnabled,
-  peer_access_not_enabled            = cudaErrorPeerAccessNotEnabled,
-  device_already_in_use              = cudaErrorDeviceAlreadyInUse,
-  profiler_disabled                  = cudaErrorProfilerDisabled,
-  assert_triggered                   = cudaErrorAssert,
-  too_many_peers                     = cudaErrorTooManyPeers,
-  host_memory_already_registered     = cudaErrorHostMemoryAlreadyRegistered,
-  host_memory_not_registered         = cudaErrorHostMemoryNotRegistered,
-  operating_system_error             = cudaErrorOperatingSystem,
-#endif
-
-#if CUDART_VERSION >= 5000
-  peer_access_unsupported            = cudaErrorPeerAccessUnsupported,
-  launch_max_depth_exceeded          = cudaErrorLaunchMaxDepthExceeded,
-  launch_file_scoped_texture_used    = cudaErrorLaunchFileScopedTex,
-  launch_file_scoped_surface_used    = cudaErrorLaunchFileScopedSurf,
-  sync_depth_exceeded                = cudaErrorSyncDepthExceeded,
-  attempted_operation_not_permitted  = cudaErrorNotPermitted,
-  attempted_operation_not_supported  = cudaErrorNotSupported,
-#endif
-
-  startup_failure                    = cudaErrorStartupFailure
-}; // end errc_t
-
-
-} // end namespace errc
-
-} // end namespace cuda_cub
-
-/*! \return A reference to an object of a type derived from class \p thrust::error_category.
- *  \note The object's \p equivalent virtual functions shall behave as specified
- *        for the class \p thrust::error_category. The object's \p name virtual function shall
- *        return a pointer to the string <tt>"cuda"</tt>. The object's
- *        \p default_error_condition virtual function shall behave as follows:
- *
- *        If the argument <tt>ev</tt> corresponds to a CUDA error value, the function
- *        shall return <tt>error_condition(ev,cuda_category())</tt>.
- *        Otherwise, the function shall return <tt>system_category.default_error_condition(ev)</tt>.
- */
-inline const error_category &cuda_category(void);
-
-
-// XXX N3000 prefers is_error_code_enum<cuda::errc>
-
-/*! Specialization of \p is_error_code_enum for \p cuda::errc::errc_t
- */
-template<> struct is_error_code_enum<cuda::errc::errc_t> : thrust::detail::true_type {};
-
-
-// XXX replace cuda::errc::errc_t with cuda::errc upon c++0x
-/*! \return <tt>error_code(static_cast<int>(e), cuda::error_category())</tt>
- */
-inline error_code make_error_code(cuda::errc::errc_t e);
-
-
-// XXX replace cuda::errc::errc_t with cuda::errc upon c++0x
-/*! \return <tt>error_condition(static_cast<int>(e), cuda::error_category())</tt>.
- */
-inline error_condition make_error_condition(cuda::errc::errc_t e);
-
-} // end system
-
-namespace cuda_cub
-{
-namespace errc = system::cuda::errc;
-} // end cuda_cub
-
-namespace cuda
-{
-// XXX replace with using system::cuda_errc upon c++0x
-namespace errc = system::cuda::errc;
-} // end cuda
-
-using system::cuda_category;
-
-} // end namespace thrust
-
-#include <thrust/system/cuda/detail/error.inl>
-

spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/scan_by_key.h

@@ -1,44 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// the purpose of this header is to #include the scan_by_key.h header
-// of the sequential, host, and device systems. It should be #included in any
-// code which uses adl to dispatch scan_by_key
-
-#include <thrust/system/detail/sequential/scan_by_key.h>
-
-// SCons can't see through the #defines below to figure out what this header
-// includes, so we fake it out by specifying all possible files we might end up
-// including inside an #if 0.
-#if 0
-#include <thrust/system/cpp/detail/scan_by_key.h>
-#include <thrust/system/cuda/detail/scan_by_key.h>
-#include <thrust/system/omp/detail/scan_by_key.h>
-#include <thrust/system/tbb/detail/scan_by_key.h>
-#endif
-
-#define __THRUST_HOST_SYSTEM_SCAN_BY_KEY_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/scan_by_key.h>
-#include __THRUST_HOST_SYSTEM_SCAN_BY_KEY_HEADER
-#undef __THRUST_HOST_SYSTEM_SCAN_BY_KEY_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_SCAN_BY_KEY_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/scan_by_key.h>
-#include __THRUST_DEVICE_SYSTEM_SCAN_BY_KEY_HEADER
-#undef __THRUST_DEVICE_SYSTEM_SCAN_BY_KEY_HEADER
-

spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/tabulate.h

@@ -1,49 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/detail/generic/tag.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-
-
-template<typename DerivedPolicy,
-         typename ForwardIterator,
-         typename UnaryOperation>
-__host__ __device__
-  void tabulate(thrust::execution_policy<DerivedPolicy> &exec,
-                ForwardIterator first,
-                ForwardIterator last,
-                UnaryOperation unary_op);
-
-
-} // end namespace generic
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/detail/generic/tabulate.inl>
-

spaces/CVPR/WALT/mmdet/core/anchor/anchor_generator.py

@@ -1,727 +0,0 @@
-import mmcv
-import numpy as np
-import torch
-from torch.nn.modules.utils import _pair
-
-from .builder import ANCHOR_GENERATORS
-
-
-@ANCHOR_GENERATORS.register_module()
-class AnchorGenerator(object):
-    """Standard anchor generator for 2D anchor-based detectors.
-
-    Args:
-        strides (list[int] | list[tuple[int, int]]): Strides of anchors
-            in multiple feature levels in order (w, h).
-        ratios (list[float]): The list of ratios between the height and width
-            of anchors in a single level.
-        scales (list[int] | None): Anchor scales for anchors in a single level.
-            It cannot be set at the same time if `octave_base_scale` and
-            `scales_per_octave` are set.
-        base_sizes (list[int] | None): The basic sizes
-            of anchors in multiple levels.
-            If None is given, strides will be used as base_sizes.
-            (If strides are non square, the shortest stride is taken.)
-        scale_major (bool): Whether to multiply scales first when generating
-            base anchors. If true, the anchors in the same row will have the
-            same scales. By default it is True in V2.0
-        octave_base_scale (int): The base scale of octave.
-        scales_per_octave (int): Number of scales for each octave.
-            `octave_base_scale` and `scales_per_octave` are usually used in
-            retinanet and the `scales` should be None when they are set.
-        centers (list[tuple[float, float]] | None): The centers of the anchor
-            relative to the feature grid center in multiple feature levels.
-            By default it is set to be None and not used. If a list of tuple of
-            float is given, they will be used to shift the centers of anchors.
-        center_offset (float): The offset of center in proportion to anchors'
-            width and height. By default it is 0 in V2.0.
-
-    Examples:
-        >>> from mmdet.core import AnchorGenerator
-        >>> self = AnchorGenerator([16], [1.], [1.], [9])
-        >>> all_anchors = self.grid_anchors([(2, 2)], device='cpu')
-        >>> print(all_anchors)
-        [tensor([[-4.5000, -4.5000,  4.5000,  4.5000],
-                [11.5000, -4.5000, 20.5000,  4.5000],
-                [-4.5000, 11.5000,  4.5000, 20.5000],
-                [11.5000, 11.5000, 20.5000, 20.5000]])]
-        >>> self = AnchorGenerator([16, 32], [1.], [1.], [9, 18])
-        >>> all_anchors = self.grid_anchors([(2, 2), (1, 1)], device='cpu')
-        >>> print(all_anchors)
-        [tensor([[-4.5000, -4.5000,  4.5000,  4.5000],
-                [11.5000, -4.5000, 20.5000,  4.5000],
-                [-4.5000, 11.5000,  4.5000, 20.5000],
-                [11.5000, 11.5000, 20.5000, 20.5000]]), \
-        tensor([[-9., -9., 9., 9.]])]
-    """
-
-    def __init__(self,
-                 strides,
-                 ratios,
-                 scales=None,
-                 base_sizes=None,
-                 scale_major=True,
-                 octave_base_scale=None,
-                 scales_per_octave=None,
-                 centers=None,
-                 center_offset=0.):
-        # check center and center_offset
-        if center_offset != 0:
-            assert centers is None, 'center cannot be set when center_offset' \
-                f'!=0, {centers} is given.'
-        if not (0 <= center_offset <= 1):
-            raise ValueError('center_offset should be in range [0, 1], '
-                             f'{center_offset} is given.')
-        if centers is not None:
-            assert len(centers) == len(strides), \
-                'The number of strides should be the same as centers, got ' \
-                f'{strides} and {centers}'
-
-        # calculate base sizes of anchors
-        self.strides = [_pair(stride) for stride in strides]
-        self.base_sizes = [min(stride) for stride in self.strides
-                           ] if base_sizes is None else base_sizes
-        assert len(self.base_sizes) == len(self.strides), \
-            'The number of strides should be the same as base sizes, got ' \
-            f'{self.strides} and {self.base_sizes}'
-
-        # calculate scales of anchors
-        assert ((octave_base_scale is not None
-                and scales_per_octave is not None) ^ (scales is not None)), \
-            'scales and octave_base_scale with scales_per_octave cannot' \
-            ' be set at the same time'
-        if scales is not None:
-            self.scales = torch.Tensor(scales)
-        elif octave_base_scale is not None and scales_per_octave is not None:
-            octave_scales = np.array(
-                [2**(i / scales_per_octave) for i in range(scales_per_octave)])
-            scales = octave_scales * octave_base_scale
-            self.scales = torch.Tensor(scales)
-        else:
-            raise ValueError('Either scales or octave_base_scale with '
-                             'scales_per_octave should be set')
-
-        self.octave_base_scale = octave_base_scale
-        self.scales_per_octave = scales_per_octave
-        self.ratios = torch.Tensor(ratios)
-        self.scale_major = scale_major
-        self.centers = centers
-        self.center_offset = center_offset
-        self.base_anchors = self.gen_base_anchors()
-
-    @property
-    def num_base_anchors(self):
-        """list[int]: total number of base anchors in a feature grid"""
-        return [base_anchors.size(0) for base_anchors in self.base_anchors]
-
-    @property
-    def num_levels(self):
-        """int: number of feature levels that the generator will be applied"""
-        return len(self.strides)
-
-    def gen_base_anchors(self):
-        """Generate base anchors.
-
-        Returns:
-            list(torch.Tensor): Base anchors of a feature grid in multiple \
-                feature levels.
-        """
-        multi_level_base_anchors = []
-        for i, base_size in enumerate(self.base_sizes):
-            center = None
-            if self.centers is not None:
-                center = self.centers[i]
-            multi_level_base_anchors.append(
-                self.gen_single_level_base_anchors(
-                    base_size,
-                    scales=self.scales,
-                    ratios=self.ratios,
-                    center=center))
-        return multi_level_base_anchors
-
-    def gen_single_level_base_anchors(self,
-                                      base_size,
-                                      scales,
-                                      ratios,
-                                      center=None):
-        """Generate base anchors of a single level.
-
-        Args:
-            base_size (int | float): Basic size of an anchor.
-            scales (torch.Tensor): Scales of the anchor.
-            ratios (torch.Tensor): The ratio between between the height
-                and width of anchors in a single level.
-            center (tuple[float], optional): The center of the base anchor
-                related to a single feature grid. Defaults to None.
-
-        Returns:
-            torch.Tensor: Anchors in a single-level feature maps.
-        """
-        w = base_size
-        h = base_size
-        if center is None:
-            x_center = self.center_offset * w
-            y_center = self.center_offset * h
-        else:
-            x_center, y_center = center
-
-        h_ratios = torch.sqrt(ratios)
-        w_ratios = 1 / h_ratios
-        if self.scale_major:
-            ws = (w * w_ratios[:, None] * scales[None, :]).view(-1)
-            hs = (h * h_ratios[:, None] * scales[None, :]).view(-1)
-        else:
-            ws = (w * scales[:, None] * w_ratios[None, :]).view(-1)
-            hs = (h * scales[:, None] * h_ratios[None, :]).view(-1)
-
-        # use float anchor and the anchor's center is aligned with the
-        # pixel center
-        base_anchors = [
-            x_center - 0.5 * ws, y_center - 0.5 * hs, x_center + 0.5 * ws,
-            y_center + 0.5 * hs
-        ]
-        base_anchors = torch.stack(base_anchors, dim=-1)
-
-        return base_anchors
-
-    def _meshgrid(self, x, y, row_major=True):
-        """Generate mesh grid of x and y.
-
-        Args:
-            x (torch.Tensor): Grids of x dimension.
-            y (torch.Tensor): Grids of y dimension.
-            row_major (bool, optional): Whether to return y grids first.
-                Defaults to True.
-
-        Returns:
-            tuple[torch.Tensor]: The mesh grids of x and y.
-        """
-        # use shape instead of len to keep tracing while exporting to onnx
-        xx = x.repeat(y.shape[0])
-        yy = y.view(-1, 1).repeat(1, x.shape[0]).view(-1)
-        if row_major:
-            return xx, yy
-        else:
-            return yy, xx
-
-    def grid_anchors(self, featmap_sizes, device='cuda'):
-        """Generate grid anchors in multiple feature levels.
-
-        Args:
-            featmap_sizes (list[tuple]): List of feature map sizes in
-                multiple feature levels.
-            device (str): Device where the anchors will be put on.
-
-        Return:
-            list[torch.Tensor]: Anchors in multiple feature levels. \
-                The sizes of each tensor should be [N, 4], where \
-                N = width * height * num_base_anchors, width and height \
-                are the sizes of the corresponding feature level, \
-                num_base_anchors is the number of anchors for that level.
-        """
-        assert self.num_levels == len(featmap_sizes)
-        multi_level_anchors = []
-        for i in range(self.num_levels):
-            anchors = self.single_level_grid_anchors(
-                self.base_anchors[i].to(device),
-                featmap_sizes[i],
-                self.strides[i],
-                device=device)
-            multi_level_anchors.append(anchors)
-        return multi_level_anchors
-
-    def single_level_grid_anchors(self,
-                                  base_anchors,
-                                  featmap_size,
-                                  stride=(16, 16),
-                                  device='cuda'):
-        """Generate grid anchors of a single level.
-
-        Note:
-            This function is usually called by method ``self.grid_anchors``.
-
-        Args:
-            base_anchors (torch.Tensor): The base anchors of a feature grid.
-            featmap_size (tuple[int]): Size of the feature maps.
-            stride (tuple[int], optional): Stride of the feature map in order
-                (w, h). Defaults to (16, 16).
-            device (str, optional): Device the tensor will be put on.
-                Defaults to 'cuda'.
-
-        Returns:
-            torch.Tensor: Anchors in the overall feature maps.
-        """
-        # keep as Tensor, so that we can covert to ONNX correctly
-        feat_h, feat_w = featmap_size
-        shift_x = torch.arange(0, feat_w, device=device) * stride[0]
-        shift_y = torch.arange(0, feat_h, device=device) * stride[1]
-
-        shift_xx, shift_yy = self._meshgrid(shift_x, shift_y)
-        shifts = torch.stack([shift_xx, shift_yy, shift_xx, shift_yy], dim=-1)
-        shifts = shifts.type_as(base_anchors)
-        # first feat_w elements correspond to the first row of shifts
-        # add A anchors (1, A, 4) to K shifts (K, 1, 4) to get
-        # shifted anchors (K, A, 4), reshape to (K*A, 4)
-
-        all_anchors = base_anchors[None, :, :] + shifts[:, None, :]
-        all_anchors = all_anchors.view(-1, 4)
-        # first A rows correspond to A anchors of (0, 0) in feature map,
-        # then (0, 1), (0, 2), ...
-        return all_anchors
-
-    def valid_flags(self, featmap_sizes, pad_shape, device='cuda'):
-        """Generate valid flags of anchors in multiple feature levels.
-
-        Args:
-            featmap_sizes (list(tuple)): List of feature map sizes in
-                multiple feature levels.
-            pad_shape (tuple): The padded shape of the image.
-            device (str): Device where the anchors will be put on.
-
-        Return:
-            list(torch.Tensor): Valid flags of anchors in multiple levels.
-        """
-        assert self.num_levels == len(featmap_sizes)
-        multi_level_flags = []
-        for i in range(self.num_levels):
-            anchor_stride = self.strides[i]
-            feat_h, feat_w = featmap_sizes[i]
-            h, w = pad_shape[:2]
-            valid_feat_h = min(int(np.ceil(h / anchor_stride[1])), feat_h)
-            valid_feat_w = min(int(np.ceil(w / anchor_stride[0])), feat_w)
-            flags = self.single_level_valid_flags((feat_h, feat_w),
-                                                  (valid_feat_h, valid_feat_w),
-                                                  self.num_base_anchors[i],
-                                                  device=device)
-            multi_level_flags.append(flags)
-        return multi_level_flags
-
-    def single_level_valid_flags(self,
-                                 featmap_size,
-                                 valid_size,
-                                 num_base_anchors,
-                                 device='cuda'):
-        """Generate the valid flags of anchor in a single feature map.
-
-        Args:
-            featmap_size (tuple[int]): The size of feature maps.
-            valid_size (tuple[int]): The valid size of the feature maps.
-            num_base_anchors (int): The number of base anchors.
-            device (str, optional): Device where the flags will be put on.
-                Defaults to 'cuda'.
-
-        Returns:
-            torch.Tensor: The valid flags of each anchor in a single level \
-                feature map.
-        """
-        feat_h, feat_w = featmap_size
-        valid_h, valid_w = valid_size
-        assert valid_h <= feat_h and valid_w <= feat_w
-        valid_x = torch.zeros(feat_w, dtype=torch.bool, device=device)
-        valid_y = torch.zeros(feat_h, dtype=torch.bool, device=device)
-        valid_x[:valid_w] = 1
-        valid_y[:valid_h] = 1
-        valid_xx, valid_yy = self._meshgrid(valid_x, valid_y)
-        valid = valid_xx & valid_yy
-        valid = valid[:, None].expand(valid.size(0),
-                                      num_base_anchors).contiguous().view(-1)
-        return valid
-
-    def __repr__(self):
-        """str: a string that describes the module"""
-        indent_str = '    '
-        repr_str = self.__class__.__name__ + '(\n'
-        repr_str += f'{indent_str}strides={self.strides},\n'
-        repr_str += f'{indent_str}ratios={self.ratios},\n'
-        repr_str += f'{indent_str}scales={self.scales},\n'
-        repr_str += f'{indent_str}base_sizes={self.base_sizes},\n'
-        repr_str += f'{indent_str}scale_major={self.scale_major},\n'
-        repr_str += f'{indent_str}octave_base_scale='
-        repr_str += f'{self.octave_base_scale},\n'
-        repr_str += f'{indent_str}scales_per_octave='
-        repr_str += f'{self.scales_per_octave},\n'
-        repr_str += f'{indent_str}num_levels={self.num_levels}\n'
-        repr_str += f'{indent_str}centers={self.centers},\n'
-        repr_str += f'{indent_str}center_offset={self.center_offset})'
-        return repr_str
-
-
-@ANCHOR_GENERATORS.register_module()
-class SSDAnchorGenerator(AnchorGenerator):
-    """Anchor generator for SSD.
-
-    Args:
-        strides (list[int]  | list[tuple[int, int]]): Strides of anchors
-            in multiple feature levels.
-        ratios (list[float]): The list of ratios between the height and width
-            of anchors in a single level.
-        basesize_ratio_range (tuple(float)): Ratio range of anchors.
-        input_size (int): Size of feature map, 300 for SSD300,
-            512 for SSD512.
-        scale_major (bool): Whether to multiply scales first when generating
-            base anchors. If true, the anchors in the same row will have the
-            same scales. It is always set to be False in SSD.
-    """
-
-    def __init__(self,
-                 strides,
-                 ratios,
-                 basesize_ratio_range,
-                 input_size=300,
-                 scale_major=True):
-        assert len(strides) == len(ratios)
-        assert mmcv.is_tuple_of(basesize_ratio_range, float)
-
-        self.strides = [_pair(stride) for stride in strides]
-        self.input_size = input_size
-        self.centers = [(stride[0] / 2., stride[1] / 2.)
-                        for stride in self.strides]
-        self.basesize_ratio_range = basesize_ratio_range
-
-        # calculate anchor ratios and sizes
-        min_ratio, max_ratio = basesize_ratio_range
-        min_ratio = int(min_ratio * 100)
-        max_ratio = int(max_ratio * 100)
-        step = int(np.floor(max_ratio - min_ratio) / (self.num_levels - 2))
-        min_sizes = []
-        max_sizes = []
-        for ratio in range(int(min_ratio), int(max_ratio) + 1, step):
-            min_sizes.append(int(self.input_size * ratio / 100))
-            max_sizes.append(int(self.input_size * (ratio + step) / 100))
-        if self.input_size == 300:
-            if basesize_ratio_range[0] == 0.15:  # SSD300 COCO
-                min_sizes.insert(0, int(self.input_size * 7 / 100))
-                max_sizes.insert(0, int(self.input_size * 15 / 100))
-            elif basesize_ratio_range[0] == 0.2:  # SSD300 VOC
-                min_sizes.insert(0, int(self.input_size * 10 / 100))
-                max_sizes.insert(0, int(self.input_size * 20 / 100))
-            else:
-                raise ValueError(
-                    'basesize_ratio_range[0] should be either 0.15'
-                    'or 0.2 when input_size is 300, got '
-                    f'{basesize_ratio_range[0]}.')
-        elif self.input_size == 512:
-            if basesize_ratio_range[0] == 0.1:  # SSD512 COCO
-                min_sizes.insert(0, int(self.input_size * 4 / 100))
-                max_sizes.insert(0, int(self.input_size * 10 / 100))
-            elif basesize_ratio_range[0] == 0.15:  # SSD512 VOC
-                min_sizes.insert(0, int(self.input_size * 7 / 100))
-                max_sizes.insert(0, int(self.input_size * 15 / 100))
-            else:
-                raise ValueError('basesize_ratio_range[0] should be either 0.1'
-                                 'or 0.15 when input_size is 512, got'
-                                 f' {basesize_ratio_range[0]}.')
-        else:
-            raise ValueError('Only support 300 or 512 in SSDAnchorGenerator'
-                             f', got {self.input_size}.')
-
-        anchor_ratios = []
-        anchor_scales = []
-        for k in range(len(self.strides)):
-            scales = [1., np.sqrt(max_sizes[k] / min_sizes[k])]
-            anchor_ratio = [1.]
-            for r in ratios[k]:
-                anchor_ratio += [1 / r, r]  # 4 or 6 ratio
-            anchor_ratios.append(torch.Tensor(anchor_ratio))
-            anchor_scales.append(torch.Tensor(scales))
-
-        self.base_sizes = min_sizes
-        self.scales = anchor_scales
-        self.ratios = anchor_ratios
-        self.scale_major = scale_major
-        self.center_offset = 0
-        self.base_anchors = self.gen_base_anchors()
-
-    def gen_base_anchors(self):
-        """Generate base anchors.
-
-        Returns:
-            list(torch.Tensor): Base anchors of a feature grid in multiple \
-                feature levels.
-        """
-        multi_level_base_anchors = []
-        for i, base_size in enumerate(self.base_sizes):
-            base_anchors = self.gen_single_level_base_anchors(
-                base_size,
-                scales=self.scales[i],
-                ratios=self.ratios[i],
-                center=self.centers[i])
-            indices = list(range(len(self.ratios[i])))
-            indices.insert(1, len(indices))
-            base_anchors = torch.index_select(base_anchors, 0,
-                                              torch.LongTensor(indices))
-            multi_level_base_anchors.append(base_anchors)
-        return multi_level_base_anchors
-
-    def __repr__(self):
-        """str: a string that describes the module"""
-        indent_str = '    '
-        repr_str = self.__class__.__name__ + '(\n'
-        repr_str += f'{indent_str}strides={self.strides},\n'
-        repr_str += f'{indent_str}scales={self.scales},\n'
-        repr_str += f'{indent_str}scale_major={self.scale_major},\n'
-        repr_str += f'{indent_str}input_size={self.input_size},\n'
-        repr_str += f'{indent_str}scales={self.scales},\n'
-        repr_str += f'{indent_str}ratios={self.ratios},\n'
-        repr_str += f'{indent_str}num_levels={self.num_levels},\n'
-        repr_str += f'{indent_str}base_sizes={self.base_sizes},\n'
-        repr_str += f'{indent_str}basesize_ratio_range='
-        repr_str += f'{self.basesize_ratio_range})'
-        return repr_str
-
-
-@ANCHOR_GENERATORS.register_module()
-class LegacyAnchorGenerator(AnchorGenerator):
-    """Legacy anchor generator used in MMDetection V1.x.
-
-    Note:
-        Difference to the V2.0 anchor generator:
-
-        1. The center offset of V1.x anchors are set to be 0.5 rather than 0.
-        2. The width/height are minused by 1 when calculating the anchors' \
-            centers and corners to meet the V1.x coordinate system.
-        3. The anchors' corners are quantized.
-
-    Args:
-        strides (list[int] | list[tuple[int]]): Strides of anchors
-            in multiple feature levels.
-        ratios (list[float]): The list of ratios between the height and width
-            of anchors in a single level.
-        scales (list[int] | None): Anchor scales for anchors in a single level.
-            It cannot be set at the same time if `octave_base_scale` and
-            `scales_per_octave` are set.
-        base_sizes (list[int]): The basic sizes of anchors in multiple levels.
-            If None is given, strides will be used to generate base_sizes.
-        scale_major (bool): Whether to multiply scales first when generating
-            base anchors. If true, the anchors in the same row will have the
-            same scales. By default it is True in V2.0
-        octave_base_scale (int): The base scale of octave.
-        scales_per_octave (int): Number of scales for each octave.
-            `octave_base_scale` and `scales_per_octave` are usually used in
-            retinanet and the `scales` should be None when they are set.
-        centers (list[tuple[float, float]] | None): The centers of the anchor
-            relative to the feature grid center in multiple feature levels.
-            By default it is set to be None and not used. It a list of float
-            is given, this list will be used to shift the centers of anchors.
-        center_offset (float): The offset of center in propotion to anchors'
-            width and height. By default it is 0.5 in V2.0 but it should be 0.5
-            in v1.x models.
-
-    Examples:
-        >>> from mmdet.core import LegacyAnchorGenerator
-        >>> self = LegacyAnchorGenerator(
-        >>>     [16], [1.], [1.], [9], center_offset=0.5)
-        >>> all_anchors = self.grid_anchors(((2, 2),), device='cpu')
-        >>> print(all_anchors)
-        [tensor([[ 0.,  0.,  8.,  8.],
-                [16.,  0., 24.,  8.],
-                [ 0., 16.,  8., 24.],
-                [16., 16., 24., 24.]])]
-    """
-
-    def gen_single_level_base_anchors(self,
-                                      base_size,
-                                      scales,
-                                      ratios,
-                                      center=None):
-        """Generate base anchors of a single level.
-
-        Note:
-            The width/height of anchors are minused by 1 when calculating \
-                the centers and corners to meet the V1.x coordinate system.
-
-        Args:
-            base_size (int | float): Basic size of an anchor.
-            scales (torch.Tensor): Scales of the anchor.
-            ratios (torch.Tensor): The ratio between between the height.
-                and width of anchors in a single level.
-            center (tuple[float], optional): The center of the base anchor
-                related to a single feature grid. Defaults to None.
-
-        Returns:
-            torch.Tensor: Anchors in a single-level feature map.
-        """
-        w = base_size
-        h = base_size
-        if center is None:
-            x_center = self.center_offset * (w - 1)
-            y_center = self.center_offset * (h - 1)
-        else:
-            x_center, y_center = center
-
-        h_ratios = torch.sqrt(ratios)
-        w_ratios = 1 / h_ratios
-        if self.scale_major:
-            ws = (w * w_ratios[:, None] * scales[None, :]).view(-1)
-            hs = (h * h_ratios[:, None] * scales[None, :]).view(-1)
-        else:
-            ws = (w * scales[:, None] * w_ratios[None, :]).view(-1)
-            hs = (h * scales[:, None] * h_ratios[None, :]).view(-1)
-
-        # use float anchor and the anchor's center is aligned with the
-        # pixel center
-        base_anchors = [
-            x_center - 0.5 * (ws - 1), y_center - 0.5 * (hs - 1),
-            x_center + 0.5 * (ws - 1), y_center + 0.5 * (hs - 1)
-        ]
-        base_anchors = torch.stack(base_anchors, dim=-1).round()
-
-        return base_anchors
-
-
-@ANCHOR_GENERATORS.register_module()
-class LegacySSDAnchorGenerator(SSDAnchorGenerator, LegacyAnchorGenerator):
-    """Legacy anchor generator used in MMDetection V1.x.
-
-    The difference between `LegacySSDAnchorGenerator` and `SSDAnchorGenerator`
-    can be found in `LegacyAnchorGenerator`.
-    """
-
-    def __init__(self,
-                 strides,
-                 ratios,
-                 basesize_ratio_range,
-                 input_size=300,
-                 scale_major=True):
-        super(LegacySSDAnchorGenerator,
-              self).__init__(strides, ratios, basesize_ratio_range, input_size,
-                             scale_major)
-        self.centers = [((stride - 1) / 2., (stride - 1) / 2.)
-                        for stride in strides]
-        self.base_anchors = self.gen_base_anchors()
-
-
-@ANCHOR_GENERATORS.register_module()
-class YOLOAnchorGenerator(AnchorGenerator):
-    """Anchor generator for YOLO.
-
-    Args:
-        strides (list[int] | list[tuple[int, int]]): Strides of anchors
-            in multiple feature levels.
-        base_sizes (list[list[tuple[int, int]]]): The basic sizes
-            of anchors in multiple levels.
-    """
-
-    def __init__(self, strides, base_sizes):
-        self.strides = [_pair(stride) for stride in strides]
-        self.centers = [(stride[0] / 2., stride[1] / 2.)
-                        for stride in self.strides]
-        self.base_sizes = []
-        num_anchor_per_level = len(base_sizes[0])
-        for base_sizes_per_level in base_sizes:
-            assert num_anchor_per_level == len(base_sizes_per_level)
-            self.base_sizes.append(
-                [_pair(base_size) for base_size in base_sizes_per_level])
-        self.base_anchors = self.gen_base_anchors()
-
-    @property
-    def num_levels(self):
-        """int: number of feature levels that the generator will be applied"""
-        return len(self.base_sizes)
-
-    def gen_base_anchors(self):
-        """Generate base anchors.
-
-        Returns:
-            list(torch.Tensor): Base anchors of a feature grid in multiple \
-                feature levels.
-        """
-        multi_level_base_anchors = []
-        for i, base_sizes_per_level in enumerate(self.base_sizes):
-            center = None
-            if self.centers is not None:
-                center = self.centers[i]
-            multi_level_base_anchors.append(
-                self.gen_single_level_base_anchors(base_sizes_per_level,
-                                                   center))
-        return multi_level_base_anchors
-
-    def gen_single_level_base_anchors(self, base_sizes_per_level, center=None):
-        """Generate base anchors of a single level.
-
-        Args:
-            base_sizes_per_level (list[tuple[int, int]]): Basic sizes of
-                anchors.
-            center (tuple[float], optional): The center of the base anchor
-                related to a single feature grid. Defaults to None.
-
-        Returns:
-            torch.Tensor: Anchors in a single-level feature maps.
-        """
-        x_center, y_center = center
-        base_anchors = []
-        for base_size in base_sizes_per_level:
-            w, h = base_size
-
-            # use float anchor and the anchor's center is aligned with the
-            # pixel center
-            base_anchor = torch.Tensor([
-                x_center - 0.5 * w, y_center - 0.5 * h, x_center + 0.5 * w,
-                y_center + 0.5 * h
-            ])
-            base_anchors.append(base_anchor)
-        base_anchors = torch.stack(base_anchors, dim=0)
-
-        return base_anchors
-
-    def responsible_flags(self, featmap_sizes, gt_bboxes, device='cuda'):
-        """Generate responsible anchor flags of grid cells in multiple scales.
-
-        Args:
-            featmap_sizes (list(tuple)): List of feature map sizes in multiple
-                feature levels.
-            gt_bboxes (Tensor): Ground truth boxes, shape (n, 4).
-            device (str): Device where the anchors will be put on.
-
-        Return:
-            list(torch.Tensor): responsible flags of anchors in multiple level
-        """
-        assert self.num_levels == len(featmap_sizes)
-        multi_level_responsible_flags = []
-        for i in range(self.num_levels):
-            anchor_stride = self.strides[i]
-            flags = self.single_level_responsible_flags(
-                featmap_sizes[i],
-                gt_bboxes,
-                anchor_stride,
-                self.num_base_anchors[i],
-                device=device)
-            multi_level_responsible_flags.append(flags)
-        return multi_level_responsible_flags
-
-    def single_level_responsible_flags(self,
-                                       featmap_size,
-                                       gt_bboxes,
-                                       stride,
-                                       num_base_anchors,
-                                       device='cuda'):
-        """Generate the responsible flags of anchor in a single feature map.
-
-        Args:
-            featmap_size (tuple[int]): The size of feature maps.
-            gt_bboxes (Tensor): Ground truth boxes, shape (n, 4).
-            stride (tuple(int)): stride of current level
-            num_base_anchors (int): The number of base anchors.
-            device (str, optional): Device where the flags will be put on.
-                Defaults to 'cuda'.
-
-        Returns:
-            torch.Tensor: The valid flags of each anchor in a single level \
-                feature map.
-        """
-        feat_h, feat_w = featmap_size
-        gt_bboxes_cx = ((gt_bboxes[:, 0] + gt_bboxes[:, 2]) * 0.5).to(device)
-        gt_bboxes_cy = ((gt_bboxes[:, 1] + gt_bboxes[:, 3]) * 0.5).to(device)
-        gt_bboxes_grid_x = torch.floor(gt_bboxes_cx / stride[0]).long()
-        gt_bboxes_grid_y = torch.floor(gt_bboxes_cy / stride[1]).long()
-
-        # row major indexing
-        gt_bboxes_grid_idx = gt_bboxes_grid_y * feat_w + gt_bboxes_grid_x
-
-        responsible_grid = torch.zeros(
-            feat_h * feat_w, dtype=torch.uint8, device=device)
-        responsible_grid[gt_bboxes_grid_idx] = 1
-
-        responsible_grid = responsible_grid[:, None].expand(
-            responsible_grid.size(0), num_base_anchors).contiguous().view(-1)
-        return responsible_grid

spaces/CVPR/WALT/mmdet/models/dense_heads/rpn_test_mixin.py

@@ -1,59 +0,0 @@
-import sys
-
-from mmdet.core import merge_aug_proposals
-
-if sys.version_info >= (3, 7):
-    from mmdet.utils.contextmanagers import completed
-
-
-class RPNTestMixin(object):
-    """Test methods of RPN."""
-
-    if sys.version_info >= (3, 7):
-
-        async def async_simple_test_rpn(self, x, img_metas):
-            sleep_interval = self.test_cfg.pop('async_sleep_interval', 0.025)
-            async with completed(
-                    __name__, 'rpn_head_forward',
-                    sleep_interval=sleep_interval):
-                rpn_outs = self(x)
-
-            proposal_list = self.get_bboxes(*rpn_outs, img_metas)
-            return proposal_list
-
-    def simple_test_rpn(self, x, img_metas):
-        """Test without augmentation.
-
-        Args:
-            x (tuple[Tensor]): Features from the upstream network, each is
-                a 4D-tensor.
-            img_metas (list[dict]): Meta info of each image.
-
-        Returns:
-            list[Tensor]: Proposals of each image.
-        """
-        rpn_outs = self(x)
-        proposal_list = self.get_bboxes(*rpn_outs, img_metas)
-        return proposal_list
-
-    def aug_test_rpn(self, feats, img_metas):
-        samples_per_gpu = len(img_metas[0])
-        aug_proposals = [[] for _ in range(samples_per_gpu)]
-        for x, img_meta in zip(feats, img_metas):
-            proposal_list = self.simple_test_rpn(x, img_meta)
-            for i, proposals in enumerate(proposal_list):
-                aug_proposals[i].append(proposals)
-        # reorganize the order of 'img_metas' to match the dimensions
-        # of 'aug_proposals'
-        aug_img_metas = []
-        for i in range(samples_per_gpu):
-            aug_img_meta = []
-            for j in range(len(img_metas)):
-                aug_img_meta.append(img_metas[j][i])
-            aug_img_metas.append(aug_img_meta)
-        # after merging, proposals will be rescaled to the original image size
-        merged_proposals = [
-            merge_aug_proposals(proposals, aug_img_meta, self.test_cfg)
-            for proposals, aug_img_meta in zip(aug_proposals, aug_img_metas)
-        ]
-        return merged_proposals

spaces/CVPR/transfiner/configs/Detectron1-Comparisons/README.md

@@ -1,84 +0,0 @@
-
-Detectron2 model zoo's experimental settings and a few implementation details are different from Detectron.
-
-The differences in implementation details are shared in
-[Compatibility with Other Libraries](../../docs/notes/compatibility.md).
-
-The differences in model zoo's experimental settings include:
-* Use scale augmentation during training. This improves AP with lower training cost.
-* Use L1 loss instead of smooth L1 loss for simplicity. This sometimes improves box AP but may
-  affect other AP.
-* Use `POOLER_SAMPLING_RATIO=0` instead of 2. This does not significantly affect AP.
-* Use `ROIAlignV2`. This does not significantly affect AP.
-
-In this directory, we provide a few configs that __do not__ have the above changes.
-They mimic Detectron's behavior as close as possible,
-and provide a fair comparison of accuracy and speed against Detectron.
-
-<!--
-./gen_html_table.py --config 'Detectron1-Comparisons/*.yaml' --name "Faster R-CNN" "Keypoint R-CNN" "Mask R-CNN" --fields lr_sched train_speed inference_speed mem box_AP mask_AP keypoint_AP --base-dir ../../../configs/Detectron1-Comparisons
--->
-
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">mask<br/>AP</th>
-<th valign="bottom">kp.<br/>AP</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: faster_rcnn_R_50_FPN_noaug_1x -->
- <tr><td align="left"><a href="faster_rcnn_R_50_FPN_noaug_1x.yaml">Faster R-CNN</a></td>
-<td align="center">1x</td>
-<td align="center">0.219</td>
-<td align="center">0.038</td>
-<td align="center">3.1</td>
-<td align="center">36.9</td>
-<td align="center"></td>
-<td align="center"></td>
-<td align="center">137781054</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x/137781054/model_final_7ab50c.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x/137781054/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: keypoint_rcnn_R_50_FPN_1x -->
- <tr><td align="left"><a href="keypoint_rcnn_R_50_FPN_1x.yaml">Keypoint R-CNN</a></td>
-<td align="center">1x</td>
-<td align="center">0.313</td>
-<td align="center">0.071</td>
-<td align="center">5.0</td>
-<td align="center">53.1</td>
-<td align="center"></td>
-<td align="center">64.2</td>
-<td align="center">137781195</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x/137781195/model_final_cce136.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x/137781195/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_50_FPN_noaug_1x -->
- <tr><td align="left"><a href="mask_rcnn_R_50_FPN_noaug_1x.yaml">Mask R-CNN</a></td>
-<td align="center">1x</td>
-<td align="center">0.273</td>
-<td align="center">0.043</td>
-<td align="center">3.4</td>
-<td align="center">37.8</td>
-<td align="center">34.9</td>
-<td align="center"></td>
-<td align="center">137781281</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x/137781281/model_final_62ca52.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x/137781281/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-## Comparisons:
-
-* Faster R-CNN: Detectron's AP is 36.7, similar to ours.
-* Keypoint R-CNN: Detectron's AP is box 53.6, keypoint 64.2. Fixing a Detectron's
-  [bug](https://github.com/facebookresearch/Detectron/issues/459) lead to a drop in box AP, and can be
-	compensated back by some parameter tuning.
-* Mask R-CNN: Detectron's AP is box 37.7, mask 33.9. We're 1 AP better in mask AP, due to more correct implementation.
-  See [this article](https://ppwwyyxx.com/blog/2021/Where-are-Pixels/) for details.
-
-For speed comparison, see [benchmarks](https://detectron2.readthedocs.io/notes/benchmarks.html).

spaces/Caoyunkang/Segment-Any-Anomaly/GroundingDINO/groundingdino/models/registry.py

@@ -1,66 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# -*- coding: utf-8 -*-
-# @Author: Yihao Chen
-# @Date:   2021-08-16 16:03:17
-# @Last Modified by:   Shilong Liu
-# @Last Modified time: 2022-01-23 15:26
-# modified from mmcv
-
-import inspect
-from functools import partial
-
-
-class Registry(object):
-    def __init__(self, name):
-        self._name = name
-        self._module_dict = dict()
-
-    def __repr__(self):
-        format_str = self.__class__.__name__ + "(name={}, items={})".format(
-            self._name, list(self._module_dict.keys())
-        )
-        return format_str
-
-    def __len__(self):
-        return len(self._module_dict)
-
-    @property
-    def name(self):
-        return self._name
-
-    @property
-    def module_dict(self):
-        return self._module_dict
-
-    def get(self, key):
-        return self._module_dict.get(key, None)
-
-    def registe_with_name(self, module_name=None, force=False):
-        return partial(self.register, module_name=module_name, force=force)
-
-    def register(self, module_build_function, module_name=None, force=False):
-        """Register a module build function.
-        Args:
-            module (:obj:`nn.Module`): Module to be registered.
-        """
-        if not inspect.isfunction(module_build_function):
-            raise TypeError(
-                "module_build_function must be a function, but got {}".format(
-                    type(module_build_function)
-                )
-            )
-        if module_name is None:
-            module_name = module_build_function.__name__
-        if not force and module_name in self._module_dict:
-            raise KeyError("{} is already registered in {}".format(module_name, self.name))
-        self._module_dict[module_name] = module_build_function
-
-        return module_build_function
-
-
-MODULE_BUILD_FUNCS = Registry("model build functions")

spaces/Caoyunkang/Segment-Any-Anomaly/SAM/segment_anything/predictor.py

@@ -1,269 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import numpy as np
-import torch
-
-from segment_anything.modeling import Sam
-
-from typing import Optional, Tuple
-
-from .utils.transforms import ResizeLongestSide
-
-
-class SamPredictor:
-    def __init__(
-        self,
-        sam_model: Sam,
-    ) -> None:
-        """
-        Uses SAM to calculate the image embedding for an image, and then
-        allow repeated, efficient mask prediction given prompts.
-
-        Arguments:
-          sam_model (Sam): The model to use for mask prediction.
-        """
-        super().__init__()
-        self.model = sam_model
-        self.transform = ResizeLongestSide(sam_model.image_encoder.img_size)
-        self.reset_image()
-
-    def set_image(
-        self,
-        image: np.ndarray,
-        image_format: str = "RGB",
-    ) -> None:
-        """
-        Calculates the image embeddings for the provided image, allowing
-        masks to be predicted with the 'predict' method.
-
-        Arguments:
-          image (np.ndarray): The image for calculating masks. Expects an
-            image in HWC uint8 format, with pixel values in [0, 255].
-          image_format (str): The color format of the image, in ['RGB', 'BGR'].
-        """
-        assert image_format in [
-            "RGB",
-            "BGR",
-        ], f"image_format must be in ['RGB', 'BGR'], is {image_format}."
-        if image_format != self.model.image_format:
-            image = image[..., ::-1]
-
-        # Transform the image to the form expected by the model
-        input_image = self.transform.apply_image(image)
-        input_image_torch = torch.as_tensor(input_image, device=self.device)
-        input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]
-
-        self.set_torch_image(input_image_torch, image.shape[:2])
-
-    @torch.no_grad()
-    def set_torch_image(
-        self,
-        transformed_image: torch.Tensor,
-        original_image_size: Tuple[int, ...],
-    ) -> None:
-        """
-        Calculates the image embeddings for the provided image, allowing
-        masks to be predicted with the 'predict' method. Expects the input
-        image to be already transformed to the format expected by the model.
-
-        Arguments:
-          transformed_image (torch.Tensor): The input image, with shape
-            1x3xHxW, which has been transformed with ResizeLongestSide.
-          original_image_size (tuple(int, int)): The size of the image
-            before transformation, in (H, W) format.
-        """
-        assert (
-            len(transformed_image.shape) == 4
-            and transformed_image.shape[1] == 3
-            and max(*transformed_image.shape[2:]) == self.model.image_encoder.img_size
-        ), f"set_torch_image input must be BCHW with long side {self.model.image_encoder.img_size}."
-        self.reset_image()
-
-        self.original_size = original_image_size
-        self.input_size = tuple(transformed_image.shape[-2:])
-        input_image = self.model.preprocess(transformed_image)
-        self.features = self.model.image_encoder(input_image)
-        self.is_image_set = True
-
-    def predict(
-        self,
-        point_coords: Optional[np.ndarray] = None,
-        point_labels: Optional[np.ndarray] = None,
-        box: Optional[np.ndarray] = None,
-        mask_input: Optional[np.ndarray] = None,
-        multimask_output: bool = True,
-        return_logits: bool = False,
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """
-        Predict masks for the given input prompts, using the currently set image.
-
-        Arguments:
-          point_coords (np.ndarray or None): A Nx2 array of point prompts to the
-            model. Each point is in (X,Y) in pixels.
-          point_labels (np.ndarray or None): A length N array of labels for the
-            point prompts. 1 indicates a foreground point and 0 indicates a
-            background point.
-          box (np.ndarray or None): A length 4 array given a box prompt to the
-            model, in XYXY format.
-          mask_input (np.ndarray): A low resolution mask input to the model, typically
-            coming from a previous prediction iteration. Has form 1xHxW, where
-            for SAM, H=W=256.
-          multimask_output (bool): If true, the model will return three masks.
-            For ambiguous input prompts (such as a single click), this will often
-            produce better masks than a single prediction. If only a single
-            mask is needed, the model's predicted quality score can be used
-            to select the best mask. For non-ambiguous prompts, such as multiple
-            input prompts, multimask_output=False can give better results.
-          return_logits (bool): If true, returns un-thresholded masks logits
-            instead of a binary mask.
-
-        Returns:
-          (np.ndarray): The output masks in CxHxW format, where C is the
-            number of masks, and (H, W) is the original image size.
-          (np.ndarray): An array of length C containing the model's
-            predictions for the quality of each mask.
-          (np.ndarray): An array of shape CxHxW, where C is the number
-            of masks and H=W=256. These low resolution logits can be passed to
-            a subsequent iteration as mask input.
-        """
-        if not self.is_image_set:
-            raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")
-
-        # Transform input prompts
-        coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None
-        if point_coords is not None:
-            assert (
-                point_labels is not None
-            ), "point_labels must be supplied if point_coords is supplied."
-            point_coords = self.transform.apply_coords(point_coords, self.original_size)
-            coords_torch = torch.as_tensor(point_coords, dtype=torch.float, device=self.device)
-            labels_torch = torch.as_tensor(point_labels, dtype=torch.int, device=self.device)
-            coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :]
-        if box is not None:
-            box = self.transform.apply_boxes(box, self.original_size)
-            box_torch = torch.as_tensor(box, dtype=torch.float, device=self.device)
-            box_torch = box_torch[None, :]
-        if mask_input is not None:
-            mask_input_torch = torch.as_tensor(mask_input, dtype=torch.float, device=self.device)
-            mask_input_torch = mask_input_torch[None, :, :, :]
-
-        masks, iou_predictions, low_res_masks = self.predict_torch(
-            coords_torch,
-            labels_torch,
-            box_torch,
-            mask_input_torch,
-            multimask_output,
-            return_logits=return_logits,
-        )
-
-        masks = masks[0].detach().cpu().numpy()
-        iou_predictions = iou_predictions[0].detach().cpu().numpy()
-        low_res_masks = low_res_masks[0].detach().cpu().numpy()
-        return masks, iou_predictions, low_res_masks
-
-    @torch.no_grad()
-    def predict_torch(
-        self,
-        point_coords: Optional[torch.Tensor],
-        point_labels: Optional[torch.Tensor],
-        boxes: Optional[torch.Tensor] = None,
-        mask_input: Optional[torch.Tensor] = None,
-        multimask_output: bool = True,
-        return_logits: bool = False,
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """
-        Predict masks for the given input prompts, using the currently set image.
-        Input prompts are batched torch tensors and are expected to already be
-        transformed to the input frame using ResizeLongestSide.
-
-        Arguments:
-          point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the
-            model. Each point is in (X,Y) in pixels.
-          point_labels (torch.Tensor or None): A BxN array of labels for the
-            point prompts. 1 indicates a foreground point and 0 indicates a
-            background point.
-          box (np.ndarray or None): A Bx4 array given a box prompt to the
-            model, in XYXY format.
-          mask_input (np.ndarray): A low resolution mask input to the model, typically
-            coming from a previous prediction iteration. Has form Bx1xHxW, where
-            for SAM, H=W=256. Masks returned by a previous iteration of the
-            predict method do not need further transformation.
-          multimask_output (bool): If true, the model will return three masks.
-            For ambiguous input prompts (such as a single click), this will often
-            produce better masks than a single prediction. If only a single
-            mask is needed, the model's predicted quality score can be used
-            to select the best mask. For non-ambiguous prompts, such as multiple
-            input prompts, multimask_output=False can give better results.
-          return_logits (bool): If true, returns un-thresholded masks logits
-            instead of a binary mask.
-
-        Returns:
-          (torch.Tensor): The output masks in BxCxHxW format, where C is the
-            number of masks, and (H, W) is the original image size.
-          (torch.Tensor): An array of shape BxC containing the model's
-            predictions for the quality of each mask.
-          (torch.Tensor): An array of shape BxCxHxW, where C is the number
-            of masks and H=W=256. These low res logits can be passed to
-            a subsequent iteration as mask input.
-        """
-        if not self.is_image_set:
-            raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")
-
-        if point_coords is not None:
-            points = (point_coords, point_labels)
-        else:
-            points = None
-
-        # Embed prompts
-        sparse_embeddings, dense_embeddings = self.model.prompt_encoder(
-            points=points,
-            boxes=boxes,
-            masks=mask_input,
-        )
-
-        # Predict masks
-        low_res_masks, iou_predictions = self.model.mask_decoder(
-            image_embeddings=self.features,
-            image_pe=self.model.prompt_encoder.get_dense_pe(),
-            sparse_prompt_embeddings=sparse_embeddings,
-            dense_prompt_embeddings=dense_embeddings,
-            multimask_output=multimask_output,
-        )
-
-        # Upscale the masks to the original image resolution
-        masks = self.model.postprocess_masks(low_res_masks, self.input_size, self.original_size)
-
-        if not return_logits:
-            masks = masks > self.model.mask_threshold
-
-        return masks, iou_predictions, low_res_masks
-
-    def get_image_embedding(self) -> torch.Tensor:
-        """
-        Returns the image embeddings for the currently set image, with
-        shape 1xCxHxW, where C is the embedding dimension and (H,W) are
-        the embedding spatial dimension of SAM (typically C=256, H=W=64).
-        """
-        if not self.is_image_set:
-            raise RuntimeError(
-                "An image must be set with .set_image(...) to generate an embedding."
-            )
-        assert self.features is not None, "Features must exist if an image has been set."
-        return self.features
-
-    @property
-    def device(self) -> torch.device:
-        return self.model.device
-
-    def reset_image(self) -> None:
-        """Resets the currently set image."""
-        self.is_image_set = False
-        self.features = None
-        self.orig_h = None
-        self.orig_w = None
-        self.input_h = None
-        self.input_w = None

spaces/Casio991ms/MathBot/README.md

@@ -1,13 +0,0 @@
----
-title: Math_Word_Problem_Solver
-emoji: 🏃
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-sdk_version: 2.9.4
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

spaces/ClementBM/connectfour/connectfour/app.py

@@ -1,186 +0,0 @@
-import time
-
-import gradio as gr
-import numpy as np
-import onnxruntime as ort
-from pettingzoo.classic import connect_four_v3
-
-from connectfour import ERROR_SCREEN
-from models import MODEL_PATH
-
-# poetry export -f requirements.txt --output requirements.txt --without-hashes
-# gradio connectfour/app.py
-
-session = ort.InferenceSession(str(MODEL_PATH), None)
-demo = gr.Blocks()
-
-column_count = 7
-game_on_msg = "Game On"
-
-
-def flatten_observation(obs):
-    flatten_action_mask = np.array(obs["action_mask"])
-    flatten_observation = np.reshape(obs["observation"], 2 * 6 * column_count)
-    flatten_obs = np.concatenate([flatten_action_mask, flatten_observation])
-    return flatten_obs[np.newaxis, ...].astype(np.float32)
-
-
-def legal_moves(env, player_id):
-    return np.arange(column_count)[env.observe(player_id)["action_mask"] == 1]
-
-
-def done(env):
-    return np.any(list(env.terminations.values()) + list(env.truncations.values()))
-
-
-def get_state_msg(env, human):
-    if done(env):
-        end_message = "End of the game"
-        if env.rewards[human] > 0:
-            end_message += ": You WIN !!"
-        elif env.rewards[human] < 0:
-            end_message += ": You LOSE !!"
-        return end_message
-
-    return game_on_msg
-
-
-def play(env, human, action=None):
-    try:
-        if human != env.agent_selection:
-            action = session.run(
-                ["output"],
-                {
-                    "obs": flatten_observation(env.observe(env.agent_selection)),
-                    "state_ins": [],
-                },
-            )
-            action = int(np.argmax(action[0]))
-
-        if action not in legal_moves(env, env.agent_selection):
-            action = np.random.choice(legal_moves(env, env.agent_selection))
-
-        env.step(action)
-        return env, get_state_msg(env, human)
-    except Exception as e:
-        return env, f"Restart the Game"
-
-
-def init_env(env, who_plays_first, human):
-    env.reset()
-
-    if who_plays_first != "You":
-        play(env, human)
-
-    return env
-
-
-def error_screen():
-    with open(ERROR_SCREEN, "rb") as f:
-        error_screen = np.load(f)
-    return error_screen
-
-
-def create_env():
-    return init_env(connect_four_v3.env(render_mode="rgb_array"), "You", "player_0")
-
-
-with demo:
-    human = gr.State("player_0")
-    env = gr.State(create_env())
-
-    drop_token_btns = []
-
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown("# Let's Play Connect Four !")
-
-            who_plays_first = gr.Radio(
-                label="Who plays first", choices=["You", "Bot"], value="You"
-            )
-            reinitialize = gr.Button("New Game")
-            game_state = gr.Text(value=game_on_msg, interactive=False, label="Status")
-
-        with gr.Column(scale=1):
-            output = gr.Image(
-                label="Connect Four Grid",
-                type="numpy",
-                show_label=False,
-                value=error_screen(),
-            )
-
-            with gr.Row():
-                for i in range(column_count):
-                    with gr.Column(min_width=20):
-                        drop_token_btns.append(gr.Button("X", elem_id=i))
-
-    def reinit_game(env, who_plays_first, human):
-        env = init_env(env, who_plays_first, human)
-        return [
-            env,
-            env.agent_selection,  # human
-            get_state_msg(env, human),  # state_msg
-            gr.Checkbox.update(interactive=True),  # who_plays_first
-        ]
-
-    def on_render_change(env):
-        return env.render()
-
-    def wait(game_state_value):
-        if game_state_value == game_on_msg:
-            time.sleep(0.7)
-            return gr.Checkbox.update(interactive=False)
-        else:
-            return gr.Checkbox.update(interactive=True)
-
-    def bot(env, game_state_value, human):
-        if game_state_value == game_on_msg:
-            env, state_msg = play(env, human)
-            if state_msg == game_on_msg:
-                return state_msg, gr.Checkbox.update(interactive=False)
-            else:
-                return state_msg, gr.Checkbox.update(interactive=True)
-        return (
-            game_state_value,
-            gr.Checkbox.update(interactive=True),
-        )
-
-    def click_column(env, human, evt: gr.EventData):
-        env, state_msg = play(env, human, int(evt.target.elem_id))
-        return env, state_msg
-
-    def game_state_change(value):
-        return [gr.Button.update(interactive=value == game_on_msg)] * column_count
-
-    who_plays_first.change(
-        reinit_game,
-        [env, who_plays_first, human],
-        outputs=[env, human, game_state, who_plays_first],
-    ).then(on_render_change, inputs=[env], outputs=[output])
-
-    reinitialize.click(
-        reinit_game,
-        [env, who_plays_first, human],
-        outputs=[env, human, game_state, who_plays_first],
-    ).then(on_render_change, inputs=[env], outputs=[output])
-
-    for i in range(column_count):
-        drop_token_btns[i].click(
-            click_column,
-            inputs=[env, human],
-            outputs=[env, game_state],
-        ).then(on_render_change, inputs=[env], outputs=[output]).then(
-            wait, inputs=[game_state], outputs=[who_plays_first]
-        ).then(
-            bot, inputs=[env, game_state, human], outputs=[game_state, who_plays_first]
-        ).then(
-            on_render_change, inputs=[env], outputs=[output]
-        )
-
-    game_state.change(
-        game_state_change,
-        game_state,
-        outputs=drop_token_btns,
-    )
-
-demo.launch()

spaces/Cpp4App/Cpp4App/SEM/P1_PP_processing.py

@@ -1,120 +0,0 @@
-import os
-import time
-import shutil
-
-from bs4 import BeautifulSoup
-
-from find_subtitle import find_title_Label
-from get_text import write_text, write_text_without_label, removeUnneccessaryElements, makeCoarseSegments
-from types_pp_processing import caculateSim, getSentences, getSentences_no_classifier, getSentences_with_classifier
-# from children_pp_processing import process_specialGroup
-# from region_pp_processing import get_alifornia
-# from retention_pp_processing import retention_process
-# from clean_txt import cleaning_txt
-
-if __name__ == '__main__':
-    # INPUT = "../dataset/privacy_policies_html/"
-    INPUT = "./pp_example/"
-    # cleaning_txt("./txt")
-    # os.mkdir("./txt")
-    if os.path.exists("./txt"):
-        shutil.rmtree("./txt")
-    os.makedirs("./txt")
-
-    for file in os.listdir(INPUT):
-
-        segmentation_start_time = time.clock()
-
-        pathName = os.path.basename(file)
-        if pathName == ".DS_Store":
-            continue
-        path = INPUT+pathName
-        label = find_title_Label(path)
-        print("The current file is:" + pathName)
-
-        # if pathName != '20.html':
-        #     continue
-
-        para_start_time = time.clock()
-        soup = BeautifulSoup(open(path,encoding='utf-8'), features="html.parser")
-        title_list = soup.find_all(label)
-        # cleaning_txt()
-
-        if not os.path.exists('./txt/' + pathName[:-5]):
-            os.mkdir('./txt/' + pathName[:-5])
-
-        if len(title_list) == 0 or pathName == '20.html' or pathName == '29.html' or pathName == '25.html' or pathName == '8.html' or pathName == '27.html' or pathName == '28.html':
-            # write_text_without_label(soup.getText(), pathName)
-            removeUnneccessaryElements(soup)
-            result = makeCoarseSegments(soup)
-            for seg in result:
-                with open('./txt/' + pathName[:-5] + '/data_types.txt', "a", encoding='utf-8') as f:
-
-                    f.write(seg)
-                    f.write("\n")
-        else:
-            write_text(title_list, pathName)
-        print("Paragraph level processing time: %2.2f s" % (time.clock() - para_start_time))
-
-        for t in title_list:
-            with open('./txt/' + pathName[:-5] + '/headings.txt', "a", encoding='utf-8') as g:
-                g.write(str(t))
-                g.write("\n")
-
-        # data types
-        if not os.path.exists("./txt/"+pathName[:-5]+"/data_types.txt"):
-            print("No information about data types!")
-        else:
-            sen_start_time = time.clock()
-            # all_types = caculateSim("./txt/"+pathName[:-5]+"/data_types.txt")
-            dict_sentences, dict_index = getSentences_with_classifier("./txt/" + pathName[:-5] + "/data_types.txt")
-            print("sentence level processing time: %2.2f s" % (time.clock() - sen_start_time))
-
-            os.makedirs("./txt/"+pathName[:-5]+"/classified_sentences")
-            for key in dict_sentences:
-
-                if dict_sentences[key] == "":
-                    continue
-                with open('./txt/' + pathName[:-5] + "/classified_sentences/" + key + ".txt", "a", encoding='utf-8') as g:
-                    g.write(dict_sentences[key])
-
-            for key in dict_index:
-                with open('./txt/' + pathName[:-5] + "/classified_sentences/keyword_index.txt", "a", encoding='utf-8') as f:
-                    f.write(key + ":" + str(dict_index[key]) + "\n")
-
-
-        # #children
-        # if not os.path.exists("./txt/"+pathName[:-5]+"/children.txt"):
-        #     print("No information about children!")
-        # else:
-        #     age , rule, childUse, specialGroup = process_specialGroup("./txt/"+pathName[:-5]+"/children.txt")
-        #     # print("children age is :")
-        #     print("D.CHILDREN.age : " + str(age))
-        #     if childUse == 1:
-        #         print(" the skill’s privacy policy states that it does not collect any information from children")
-        #         print("D.CHILDREN.[CTypes] = [ ]")
-        #     else:
-        #         # print("D.CHILDREN.[CTypes] :" + str(all_types))
-        #         None
-        # #region
-        # if not os.path.exists("./txt/"+pathName[:-5]+"/region.txt"):
-        #     print("No information about region!")
-        # else:
-        #     specialArea,california = get_alifornia("./txt/"+pathName[:-5]+"/region.txt")
-        #     if california == 1:
-        #         print("D.REGIONS.region :California")
-        #         print("D.REGIONS.delete : Yes")
-        #     else:
-        #         print("D.REGIONS.region :No mention")
-        #         print("D.REGIONS.delete : No")
-        #
-        # #retention
-        # if not os.path.exists("./txt/"+pathName[:-5]+"/data_retention.txt"):
-        #     print("No information about data retention!")
-        # else:
-        #     retention_time, text = retention_process("./txt/"+pathName[:-5]+"/data_retention.txt")
-        #     print("D.RETENTION.period :"+ retention_time)
-        #     # cleaning_txt()
-        #     print("-------------------------------------------------------")
-
-        print("time cost for segmentation: %2.2f s" % (time.clock() - segmentation_start_time))

spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/matcher.py

@@ -1,112 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import torch
-
-
-class Matcher(object):
-    """
-    This class assigns to each predicted "element" (e.g., a box) a ground-truth
-    element. Each predicted element will have exactly zero or one matches; each
-    ground-truth element may be assigned to zero or more predicted elements.
-
-    Matching is based on the MxN match_quality_matrix, that characterizes how well
-    each (ground-truth, predicted)-pair match. For example, if the elements are
-    boxes, the matrix may contain box IoU overlap values.
-
-    The matcher returns a tensor of size N containing the index of the ground-truth
-    element m that matches to prediction n. If there is no match, a negative value
-    is returned.
-    """
-
-    BELOW_LOW_THRESHOLD = -1
-    BETWEEN_THRESHOLDS = -2
-
-    def __init__(self, high_threshold, low_threshold, allow_low_quality_matches=False):
-        """
-        Args:
-            high_threshold (float): quality values greater than or equal to
-                this value are candidate matches.
-            low_threshold (float): a lower quality threshold used to stratify
-                matches into three levels:
-                1) matches >= high_threshold
-                2) BETWEEN_THRESHOLDS matches in [low_threshold, high_threshold)
-                3) BELOW_LOW_THRESHOLD matches in [0, low_threshold)
-            allow_low_quality_matches (bool): if True, produce additional matches
-                for predictions that have only low-quality match candidates. See
-                set_low_quality_matches_ for more details.
-        """
-        assert low_threshold <= high_threshold
-        self.high_threshold = high_threshold
-        self.low_threshold = low_threshold
-        self.allow_low_quality_matches = allow_low_quality_matches
-
-    def __call__(self, match_quality_matrix):
-        """
-        Args:
-            match_quality_matrix (Tensor[float]): an MxN tensor, containing the
-            pairwise quality between M ground-truth elements and N predicted elements.
-
-        Returns:
-            matches (Tensor[int64]): an N tensor where N[i] is a matched gt in
-            [0, M - 1] or a negative value indicating that prediction i could not
-            be matched.
-        """
-        if match_quality_matrix.numel() == 0:
-            # empty targets or proposals not supported during training
-            if match_quality_matrix.shape[0] == 0:
-                raise ValueError(
-                    "No ground-truth boxes available for one of the images "
-                    "during training")
-            else:
-                raise ValueError(
-                    "No proposal boxes available for one of the images "
-                    "during training")
-
-        # match_quality_matrix is M (gt) x N (predicted)
-        # Max over gt elements (dim 0) to find best gt candidate for each prediction
-        matched_vals, matches = match_quality_matrix.max(dim=0)
-        if self.allow_low_quality_matches:
-            all_matches = matches.clone()
-
-        # Assign candidate matches with low quality to negative (unassigned) values
-        below_low_threshold = matched_vals < self.low_threshold
-        between_thresholds = (matched_vals >= self.low_threshold) & (
-            matched_vals < self.high_threshold
-        )
-        matches[below_low_threshold] = Matcher.BELOW_LOW_THRESHOLD
-        matches[between_thresholds] = Matcher.BETWEEN_THRESHOLDS
-
-        if self.allow_low_quality_matches:
-            self.set_low_quality_matches_(matches, all_matches, match_quality_matrix)
-
-        return matches
-
-    def set_low_quality_matches_(self, matches, all_matches, match_quality_matrix):
-        """
-        Produce additional matches for predictions that have only low-quality matches.
-        Specifically, for each ground-truth find the set of predictions that have
-        maximum overlap with it (including ties); for each prediction in that set, if
-        it is unmatched, then match it to the ground-truth with which it has the highest
-        quality value.
-        """
-        # For each gt, find the prediction with which it has highest quality
-        highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1)
-        # Find highest quality match available, even if it is low, including ties
-        gt_pred_pairs_of_highest_quality = torch.nonzero(
-            match_quality_matrix == highest_quality_foreach_gt[:, None]
-        )
-        # Example gt_pred_pairs_of_highest_quality:
-        #   tensor([[    0, 39796],
-        #           [    1, 32055],
-        #           [    1, 32070],
-        #           [    2, 39190],
-        #           [    2, 40255],
-        #           [    3, 40390],
-        #           [    3, 41455],
-        #           [    4, 45470],
-        #           [    5, 45325],
-        #           [    5, 46390]])
-        # Each row is a (gt index, prediction index)
-        # Note how gt items 1, 2, 3, and 5 each have two ties
-
-        pred_inds_to_update = gt_pred_pairs_of_highest_quality[:, 1]
-        matches[pred_inds_to_update] = all_matches[pred_inds_to_update]

spaces/Cyril666/my_abi/docker/Dockerfile

@@ -1,25 +0,0 @@
-FROM anibali/pytorch:cuda-9.0
-MAINTAINER fangshancheng <[email protected]>
-RUN sudo rm -rf /etc/apt/sources.list.d && \
-    sudo apt update && \
-    sudo apt install -y build-essential vim && \
-    conda config --add channels https://mirrors.ustc.edu.cn/anaconda/pkgs/free/ && \
-    conda config --add channels https://mirrors.ustc.edu.cn/anaconda/pkgs/main/ && \
-    conda config --set show_channel_urls yes && \
-    pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/ && \
-    pip install torch==1.1.0 torchvision==0.3.0 && \
-    pip install fastai==1.0.60 && \
-    pip install ipdb jupyter ipython lmdb editdistance tensorboardX natsort nltk && \
-    conda uninstall -y --force pillow pil jpeg libtiff libjpeg-turbo && \
-    pip uninstall -y pillow pil jpeg libtiff libjpeg-turbo && \
-    conda install -yc conda-forge libjpeg-turbo && \
-    CFLAGS="${CFLAGS} -mavx2" pip install --no-cache-dir --force-reinstall --no-binary :all: --compile pillow-simd==6.2.2.post1 && \
-    conda install -y jpeg libtiff opencv && \
-    sudo rm -rf /var/lib/apt/lists/* && \
-    sudo rm -rf /tmp/* && \
-    sudo rm -rf ~/.cache && \
-    sudo apt clean all && \
-    conda clean -y -a
-EXPOSE 8888
-ENV LANG C.UTF-8
-ENV LC_ALL C.UTF-8

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/QoiImagePlugin.py

@@ -1,105 +0,0 @@
-#
-# The Python Imaging Library.
-#
-# QOI support for PIL
-#
-# See the README file for information on usage and redistribution.
-#
-
-import os
-
-from . import Image, ImageFile
-from ._binary import i32be as i32
-from ._binary import o8
-
-
-def _accept(prefix):
-    return prefix[:4] == b"qoif"
-
-
-class QoiImageFile(ImageFile.ImageFile):
-    format = "QOI"
-    format_description = "Quite OK Image"
-
-    def _open(self):
-        if not _accept(self.fp.read(4)):
-            msg = "not a QOI file"
-            raise SyntaxError(msg)
-
-        self._size = tuple(i32(self.fp.read(4)) for i in range(2))
-
-        channels = self.fp.read(1)[0]
-        self.mode = "RGB" if channels == 3 else "RGBA"
-
-        self.fp.seek(1, os.SEEK_CUR)  # colorspace
-        self.tile = [("qoi", (0, 0) + self._size, self.fp.tell(), None)]
-
-
-class QoiDecoder(ImageFile.PyDecoder):
-    _pulls_fd = True
-
-    def _add_to_previous_pixels(self, value):
-        self._previous_pixel = value
-
-        r, g, b, a = value
-        hash_value = (r * 3 + g * 5 + b * 7 + a * 11) % 64
-        self._previously_seen_pixels[hash_value] = value
-
-    def decode(self, buffer):
-        self._previously_seen_pixels = {}
-        self._previous_pixel = None
-        self._add_to_previous_pixels(b"".join(o8(i) for i in (0, 0, 0, 255)))
-
-        data = bytearray()
-        bands = Image.getmodebands(self.mode)
-        while len(data) < self.state.xsize * self.state.ysize * bands:
-            byte = self.fd.read(1)[0]
-            if byte == 0b11111110:  # QOI_OP_RGB
-                value = self.fd.read(3) + o8(255)
-            elif byte == 0b11111111:  # QOI_OP_RGBA
-                value = self.fd.read(4)
-            else:
-                op = byte >> 6
-                if op == 0:  # QOI_OP_INDEX
-                    op_index = byte & 0b00111111
-                    value = self._previously_seen_pixels.get(op_index, (0, 0, 0, 0))
-                elif op == 1:  # QOI_OP_DIFF
-                    value = (
-                        (self._previous_pixel[0] + ((byte & 0b00110000) >> 4) - 2)
-                        % 256,
-                        (self._previous_pixel[1] + ((byte & 0b00001100) >> 2) - 2)
-                        % 256,
-                        (self._previous_pixel[2] + (byte & 0b00000011) - 2) % 256,
-                    )
-                    value += (self._previous_pixel[3],)
-                elif op == 2:  # QOI_OP_LUMA
-                    second_byte = self.fd.read(1)[0]
-                    diff_green = (byte & 0b00111111) - 32
-                    diff_red = ((second_byte & 0b11110000) >> 4) - 8
-                    diff_blue = (second_byte & 0b00001111) - 8
-
-                    value = tuple(
-                        (self._previous_pixel[i] + diff_green + diff) % 256
-                        for i, diff in enumerate((diff_red, 0, diff_blue))
-                    )
-                    value += (self._previous_pixel[3],)
-                elif op == 3:  # QOI_OP_RUN
-                    run_length = (byte & 0b00111111) + 1
-                    value = self._previous_pixel
-                    if bands == 3:
-                        value = value[:3]
-                    data += value * run_length
-                    continue
-                value = b"".join(o8(i) for i in value)
-            self._add_to_previous_pixels(value)
-
-            if bands == 3:
-                value = value[:3]
-            data += value
-        self.set_as_raw(bytes(data))
-        return -1, 0
-
-
-Image.register_open(QoiImageFile.format, QoiImageFile, _accept)
-Image.register_decoder("qoi", QoiDecoder)
-Image.register_extension(QoiImageFile.format, ".qoi")