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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Acronis Backup 12.5.1 Build 14240 Crack !EXCLUSIVE!.md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Navisworks Exporter for Revit and Boost Your Collaboration and Coordination.md

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-<h1>How to Download and Install Navisworks Exporter for Revit</h1>
-<p>Navisworks Exporter for Revit is a plug-in that allows you to export Revit models as NWC files that can be opened and viewed in Navisworks. NWC files are optimized for performance and collaboration, and can be used for clash detection, coordination, and simulation.</p>
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-<p>To use Acronis True Image 2018, you need to have a device that meets the following minimum system requirements:</p>
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- <h2>How to purchase and activate Acronis True Image 2018</h2>
-<p>To use Acronis True Image 2018, you need to purchase a subscription plan and activate the software with a license key. Here is how you can do that:</p>
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-<p>To activate Acronis True Image 2018, you need to have a license key that corresponds to your subscription plan. You can get the license key in one of the following ways:</p>
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spaces/1phancelerku/anime-remove-background/Download Very Very Very by I.O.I - The Song That Broke the Charts.md

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-<h1>How to Download I.O.I's Very Very Very Song and Enjoy Its Catchy Melody</h1>
-<p>If you are a fan of K-pop, you might have heard of I.O.I, a girl group project that was formed through a survival reality show called Produce 101. The group consisted of 11 members who were selected from different agencies and debuted in 2016. They released two mini-albums and several singles before disbanding in 2017.</p>
-<p>One of their most popular songs is Very Very Very, which was released as the title track of their second mini-album Miss Me? in October 2016. The song was composed by Park Jin-young, the founder of JYP Entertainment, and has a catchy melody and lyrics that express a girl's feelings for a boy. The song topped various music charts in South Korea and won several music awards.</p>
-<h2>download ioi very very very</h2><br /><p><b><b>Download File</b> &#9989; <a href="https://jinyurl.com/2uNULO">https://jinyurl.com/2uNULO</a></b></p><br /><br />
-<p>If you love this song and want to listen to it anytime and anywhere, you might want to download it and enjoy it offline. Downloading the song can save you data and battery, as well as allow you to play it on different devices without internet connection. In this article, we will show you how to download I.O.I's Very Very Very song from different platforms, and how to enjoy it offline.</p>
-<h2>How to Download the Song from Different Platforms</h2>
-<p>There are many platforms where you can stream or download I.O.I's Very Very Very song, such as YouTube, Spotify, Apple Music, etc. However, not all of them offer free downloads or easy access. Here are some ways you can download the song from these platforms:</p>
-<h3>YouTube</h3>
-<p>YouTube is one of the most popular platforms where you can watch I.O.I's Very Very Very music video and listen to their song. However, if you want to download the song from YouTube, you have two options:</p>
-<ul>
-<li>Use YouTube Premium or YouTube Music. These are subscription services that allow you to download videos and songs from YouTube and play them offline. You can sign up for a free trial or pay a monthly fee to use these services. To download I.O.I's Very Very Very song from YouTube Premium or YouTube Music, you need to:</li>
-<ol>
-<li>Open the YouTube app on your device and search for I.O.I's Very Very Very music video or song.</li>
-<li>Tap on the download icon below the video or song and choose the quality you want.</li>
-<li>Wait for the download to finish and then go to your library or downloads section to find your downloaded video or song.</li>
-</ol>
-<li>Use third-party tools or websites. These are free or paid tools or websites that allow you to download videos and songs from YouTube by copying and pasting their URLs. However, these tools or websites may not be safe, legal, or reliable. They may also have limited features, quality, or speed. To download I.O.I's Very Very Very song from third-party tools or websites, you need to:</li>
-<ol>
-<li>Open the YouTube app or website on your device and search for I.O.I's Very Very Very music video or song.</li>
-<li>Copy the URL of the video or song from the address bar or the share option.</li>
-<li>Open a third-party tool or website that can download YouTube videos or songs, such as Y2mate, 4K Video Downloader, MP3Juices, etc.</li>
-<li>Paste the URL of the video or song into the tool or website and choose the format and quality you want.</li>
-<li>Click on the download button and wait for the download to finish.</li>
-<li>Find your downloaded file in your device's storage or downloads folder.</li>
-</ol>
-</ul>
-<h3>Spotify</h3>
-<p>Spotify is another popular platform where you can stream or download I.O.I's Very Very Very song, as well as other songs from their albums and playlists. However, if you want to download the song from Spotify, you also have two options:</p>
-<ul>
-<li>Use Spotify Premium. This is a subscription service that allows you to download songs from Spotify and play them offline. You can sign up for a free trial or pay a monthly fee to use this service. To download I.O.I's Very Very Very song from Spotify Premium, you need to:</li>
-<ol>
-<li>Open the Spotify app on your device and search for I.O.I's Very Very Very song or their Miss Me? album.</li>
-<li>Tap on the heart icon next to the song or the album to add it to your library.</li>
-<li>Go to your library and tap on the download toggle next to the song or the album.</li>
-<li>Wait for the download to finish and then go to your library or downloads section to find your downloaded song or album.</li>
-</ol>
-<li>Use third-party tools or websites. These are free or paid tools or websites that allow you to download songs from Spotify by copying and pasting their URLs. However, these tools or websites may not be safe, legal, or reliable. They may also have limited features, quality, or speed. To download I.O.I's Very Very Very song from third-party tools or websites, you need to:</li>
-<ol>
-<li>Open the Spotify app or website on your device and search for I.O.I's Very Very Very song or their Miss Me? album.</li>
-<li>Copy the URL of the song or the album from the address bar or the share option.</li>
-<li>Open a third-party tool or website that can download Spotify songs, such as Sidify, TuneFab, AudFree, etc.</li>
-<li>Paste the URL of the song or the album into the tool or website and choose the format and quality you want.</li>
-<li>Click on the download button and wait for the download to finish.</li>
-<li>Find your downloaded file in your device's storage or downloads folder.</li>
-</ol>
-</ul> <h3>Apple Music</h3>
-<p>Apple Music is another popular platform where you can stream or download I.O.I's Very Very Very song, as well as other songs from their albums and playlists. However, if you want to download the song from Apple Music, you also have two options:</p>
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-<ul>
-<li>Use Apple Music subscription. This is a subscription service that allows you to download songs from Apple Music and play them offline. You can sign up for a free trial or pay a monthly fee to use this service. To download I.O.I's Very Very Very song from Apple Music, you need to:</li>
-<ol>
-<li>Open the Apple Music app on your device and search for I.O.I's Very Very Very song or their Miss Me? album.</li>
-<li>Tap on the plus icon next to the song or the album to add it to your library.</li>
-<li>Go to your library and tap on the cloud icon next to the song or the album.</li>
-<li>Wait for the download to finish and then go to your library or downloads section to find your downloaded song or album.</li>
-</ol>
-<li>Use third-party tools or websites. These are free or paid tools or websites that allow you to download songs from Apple Music by copying and pasting their URLs. However, these tools or websites may not be safe, legal, or reliable. They may also have limited features, quality, or speed. To download I.O.I's Very Very Very song from third-party tools or websites, you need to:</li>
-<ol>
-<li>Open the Apple Music app or website on your device and search for I.O.I's Very Very Very song or their Miss Me? album.</li>
-<li>Copy the URL of the song or the album from the address bar or the share option.</li>
-<li>Open a third-party tool or website that can download Apple Music songs, such as NoteBurner, TunesKit, DRmare, etc.</li>
-<li>Paste the URL of the song or the album into the tool or website and choose the format and quality you want.</li>
-<li>Click on the download button and wait for the download to finish.</li>
-<li>Find your downloaded file in your device's storage or downloads folder.</li>
-</ol>
-</ul>
-<h2>How to Enjoy the Song Offline</h2>
-<p>Now that you have downloaded I.O.I's Very Very Very song from your preferred platform, you can enjoy it offline anytime and anywhere. Here are some ways you can enjoy the song offline:</p>
-<h3>Transfer the Song to Your Devices</h3>
-<p>If you want to listen to the song on different devices, such as your phone, tablet, laptop, etc., you need to transfer the song from your original device to your other devices. There are several ways you can do this:</p>
-<ul>
-<li>Use USB cables. You can connect your devices with USB cables and copy and paste the song file from one device to another. This is a simple and fast way to transfer files, but it may require different types of cables for different devices.</li>
-<li>Use Bluetooth. You can pair your devices with Bluetooth and send and receive the song file wirelessly. This is a convenient and cordless way to transfer files, but it may take longer time and consume more battery.</li>
-<li>Use cloud services. You can upload your song file to a cloud service, such as Google Drive, Dropbox, iCloud, etc., and then download it to your other devices. This is a secure and accessible way to transfer files, but it may require internet connection and storage space.</li>
-</ul>
-<h3>Play the Song with Your Favorite Music Player</h3>
-<p>If you want to listen to the song with your favorite music player, such as VLC, Winamp, iTunes, etc., you need to open the song file with your music player and enjoy its features and settings. Here are some tips you can follow:</p>
-<ul>
-<li>Choose a music player that suits your preferences and needs. There are many music players available for different devices and platforms, each with its own advantages and disadvantages. You can compare their features, functions, compatibility, interface, etc., and choose one that meets your expectations.</li>
-<li>Adjust the settings and features of the music player to enhance your listening experience. You can customize various aspects of your music player, such as volume, equalizer, playback mode, playlist, etc., to suit your mood and taste. You can also explore other features of your music player, such as lyrics display, visualizer, sound effects, etc., to make your listening more fun and enjoyable.</li>
-</ul>
- <h3>Sing Along with the Lyrics and Learn Some Korean Words</h3>
- <p>If you want to sing along with I.O.I's Very Very Very song and learn some Korean words from it, you need to find the lyrics of the song online or offline. You can use the following table to compare the sources of the lyrics and their features: | Source | Features | | ------ | -------- | | [Color Coded Lyrics](^1^) | Provides the lyrics in Korean, Romanization, and English translation. Also provides the color codes for each member's parts and some background information about the song. | | [Genius](^2^) | Provides the lyrics in Korean and English translation. Also provides some annotations, explanations, and trivia about the song. | | [AZLyrics](^3^) | Provides the lyrics in English translation only. | You can choose the source that suits your preference and needs, and then follow these steps to sing along with the lyrics and learn some Korean words: - Open the source of the lyrics on your device and search for I.O.I's Very Very Very song. - Play the song with your music player and follow the lyrics on your screen. - Try to sing along with the song and pronounce the Korean words correctly. You can also use the Romanization or the English translation to help you understand the meaning of the words. - Pay attention to some common or useful Korean words and phrases from the song, such as 너무 (very), 좋아하다 (to like), 말해줘 (tell me), 자꾸 (keep), 떠오르다 (to come to mind), 조심하다 (to be careful), etc. You can also use a dictionary or a translator to look up more words or phrases that interest you. - Repeat the steps until you can sing along with the song confidently and learn some Korean words fluently. <h2>Conclusion</h2>
-<p>In this article, we have shown you how to download I.O.I's Very Very Very song from different platforms, such as YouTube, Spotify, Apple Music, etc., and how to enjoy it offline, such as transferring it to your devices, playing it with your favorite music player, singing along with the lyrics, and learning some Korean words. We hope you have found this article helpful and informative, and that you have enjoyed listening to I.O.I's Very Very Very song.</p>
-<p>I.O.I was a talented and charming girl group that left a lasting impression on many fans with their songs and performances. Although they have disbanded, their music lives on and can still bring joy and happiness to many listeners. If you are one of them, we encourage you to download and enjoy their Very Very Very song offline, as well as their other songs from their albums and playlists.</p>
-<p>Thank you for reading this article. If you have any questions or feedback, please feel free to leave them in the comments section below. We would love to hear from you.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about I.O.I's Very Very Very song and how to download and enjoy it offline:</p>
-<ol>
-<li>Q: When was I.O.I's Very Very Very song released?<br>
-A: I.O.I's Very Very Very song was released on October 17, 2016 as the title track of their second mini-album Miss Me?</li>
-<li>Q: Who composed I.O.I's Very Very Very song?<br>
-A: I.O.I's Very Very Very song was composed by Park Jin-young, the founder of JYP Entertainment, who also produced other songs for I.O.I.</li>
-<li>Q: How many members were in I.O.I?<br>
-A: I.O.I had 11 members who were selected from different agencies through a survival reality show called Produce 101. They were Nayoung, Chungha, Sejeong, Chaeyeon, Kyulkyung, Sohye, Yeonjung, Yoojung, Mina, Doyeon, and Somi.</li>
-<li>Q: Why did I.O.I disband?<br>
-A: I.O.I disbanded in 2017 because they were a project group that had a limited contract period. The members returned to their original agencies and pursued their individual careers.</li>
-<li>Q: Where can I find more songs by I.O.I?<br>
-A: You can find more songs by I.O.I on various platforms, such as YouTube, Spotify, Apple Music, etc. You can also check out their albums and playlists, such as Chrysalis, Miss Me?, Whatta Man, etc.</li>
-</ol></p> 401be4b1e0<br />
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spaces/2023Liu2023/bingo/src/components/markdown.tsx

@@ -1,9 +0,0 @@
-import { FC, memo } from 'react'
-import ReactMarkdown, { Options } from 'react-markdown'
-
-export const MemoizedReactMarkdown: FC<Options> = memo(
-  ReactMarkdown,
-  (prevProps, nextProps) =>
-    prevProps.children === nextProps.children &&
-    prevProps.className === nextProps.className
-)

spaces/AIGC-Audio/AudioGPT/NeuralSeq/inference/svs/base_svs_infer.py

@@ -1,265 +0,0 @@
-import os
-
-import torch
-import numpy as np
-from modules.hifigan.hifigan import HifiGanGenerator
-from vocoders.hifigan import HifiGAN
-from inference.svs.opencpop.map import cpop_pinyin2ph_func
-
-from utils import load_ckpt
-from utils.hparams import set_hparams, hparams
-from utils.text_encoder import TokenTextEncoder
-from pypinyin import pinyin, lazy_pinyin, Style
-import librosa
-import glob
-import re
-
-
-class BaseSVSInfer:
-    def __init__(self, hparams, device=None):
-        if device is None:
-            device = 'cuda' if torch.cuda.is_available() else 'cpu'
-        self.hparams = hparams
-        self.device = device
-
-        phone_list = ["AP", "SP", "a", "ai", "an", "ang", "ao", "b", "c", "ch", "d", "e", "ei", "en", "eng", "er", "f", "g",
-                  "h", "i", "ia", "ian", "iang", "iao", "ie", "in", "ing", "iong", "iu", "j", "k", "l", "m", "n", "o",
-                  "ong", "ou", "p", "q", "r", "s", "sh", "t", "u", "ua", "uai", "uan", "uang", "ui", "un", "uo", "v",
-                  "van", "ve", "vn", "w", "x", "y", "z", "zh"]
-        self.ph_encoder = TokenTextEncoder(None, vocab_list=phone_list, replace_oov=',')
-        self.pinyin2phs = cpop_pinyin2ph_func()
-        self.spk_map = {'opencpop': 0}
-
-        self.model = self.build_model()
-        self.model.eval()
-        self.model.to(self.device)
-        self.vocoder = self.build_vocoder()
-        self.vocoder.eval()
-        self.vocoder.to(self.device)
-
-    def build_model(self):
-        raise NotImplementedError
-
-    def forward_model(self, inp):
-        raise NotImplementedError
-
-    def build_vocoder(self):
-        base_dir = hparams['vocoder_ckpt']
-        config_path = f'{base_dir}/config.yaml'
-        ckpt = sorted(glob.glob(f'{base_dir}/model_ckpt_steps_*.ckpt'), key=
-        lambda x: int(re.findall(f'{base_dir}/model_ckpt_steps_(\d+).ckpt', x)[0]))[-1]
-        print('| load HifiGAN: ', ckpt)
-        ckpt_dict = torch.load(ckpt, map_location="cpu")
-        config = set_hparams(config_path, global_hparams=False)
-        state = ckpt_dict["state_dict"]["model_gen"]
-        vocoder = HifiGanGenerator(config)
-        vocoder.load_state_dict(state, strict=True)
-        vocoder.remove_weight_norm()
-        vocoder = vocoder.eval().to(self.device)
-        return vocoder
-
-    def run_vocoder(self, c, **kwargs):
-        c = c.transpose(2, 1)  # [B, 80, T]
-        f0 = kwargs.get('f0')  # [B, T]
-        if f0 is not None and hparams.get('use_nsf'):
-            # f0 = torch.FloatTensor(f0).to(self.device)
-            y = self.vocoder(c, f0).view(-1)
-        else:
-            y = self.vocoder(c).view(-1)
-            # [T]
-        return y[None]
-
-    def preprocess_word_level_input(self, inp):
-        # Pypinyin can't solve polyphonic words
-        text_raw = inp['text'].replace('最长', '最常').replace('长睫毛', '常睫毛') \
-            .replace('那么长', '那么常').replace('多长', '多常') \
-            .replace('很长', '很常')  # We hope someone could provide a better g2p module for us by opening pull requests.
-
-        # lyric
-        pinyins = lazy_pinyin(text_raw, strict=False)
-        ph_per_word_lst = [self.pinyin2phs[pinyin.strip()] for pinyin in pinyins if pinyin.strip() in self.pinyin2phs]
-
-        # Note
-        note_per_word_lst = [x.strip() for x in inp['notes'].split('|') if x.strip() != '']
-        mididur_per_word_lst = [x.strip() for x in inp['notes_duration'].split('|') if x.strip() != '']
-
-        if len(note_per_word_lst) == len(ph_per_word_lst) == len(mididur_per_word_lst):
-            print('Pass word-notes check.')
-        else:
-            print('The number of words does\'t match the number of notes\' windows. ',
-                  'You should split the note(s) for each word by | mark.')
-            print(ph_per_word_lst, note_per_word_lst, mididur_per_word_lst)
-            print(len(ph_per_word_lst), len(note_per_word_lst), len(mididur_per_word_lst))
-            return None
-
-        note_lst = []
-        ph_lst = []
-        midi_dur_lst = []
-        is_slur = []
-        for idx, ph_per_word in enumerate(ph_per_word_lst):
-            # for phs in one word:
-            # single ph like ['ai']  or multiple phs like ['n', 'i']
-            ph_in_this_word = ph_per_word.split()
-
-            # for notes in one word:
-            # single note like ['D4'] or multiple notes like ['D4', 'E4'] which means a 'slur' here.
-            note_in_this_word = note_per_word_lst[idx].split()
-            midi_dur_in_this_word = mididur_per_word_lst[idx].split()
-            # process for the model input
-            # Step 1.
-            #  Deal with note of 'not slur' case or the first note of 'slur' case
-            #  j        ie
-            #  F#4/Gb4  F#4/Gb4
-            #  0        0
-            for ph in ph_in_this_word:
-                ph_lst.append(ph)
-                note_lst.append(note_in_this_word[0])
-                midi_dur_lst.append(midi_dur_in_this_word[0])
-                is_slur.append(0)
-            # step 2.
-            #  Deal with the 2nd, 3rd... notes of 'slur' case
-            #  j        ie         ie
-            #  F#4/Gb4  F#4/Gb4    C#4/Db4
-            #  0        0          1
-            if len(note_in_this_word) > 1:  # is_slur = True, we should repeat the YUNMU to match the 2nd, 3rd... notes.
-                for idx in range(1, len(note_in_this_word)):
-                    ph_lst.append(ph_in_this_word[-1])
-                    note_lst.append(note_in_this_word[idx])
-                    midi_dur_lst.append(midi_dur_in_this_word[idx])
-                    is_slur.append(1)
-        ph_seq = ' '.join(ph_lst)
-
-        if len(ph_lst) == len(note_lst) == len(midi_dur_lst):
-            print(len(ph_lst), len(note_lst), len(midi_dur_lst))
-            print('Pass word-notes check.')
-        else:
-            print('The number of words does\'t match the number of notes\' windows. ',
-                  'You should split the note(s) for each word by | mark.')
-            return None
-        return ph_seq, note_lst, midi_dur_lst, is_slur
-
-    def preprocess_phoneme_level_input(self, inp):
-        ph_seq = inp['ph_seq']
-        note_lst = inp['note_seq'].split()
-        midi_dur_lst = inp['note_dur_seq'].split()
-        is_slur = [float(x) for x in inp['is_slur_seq'].split()]
-        print(len(note_lst), len(ph_seq.split()), len(midi_dur_lst))
-        if len(note_lst) == len(ph_seq.split()) == len(midi_dur_lst):
-            print('Pass word-notes check.')
-        else:
-            print('The number of words does\'t match the number of notes\' windows. ',
-                  'You should split the note(s) for each word by | mark.')
-            return None
-        return ph_seq, note_lst, midi_dur_lst, is_slur
-
-    def preprocess_input(self, inp, input_type='word'):
-        """
-
-        :param inp: {'text': str, 'item_name': (str, optional), 'spk_name': (str, optional)}
-        :return:
-        """
-
-        item_name = inp.get('item_name', '<ITEM_NAME>')
-        spk_name = inp.get('spk_name', 'opencpop')
-
-        # single spk
-        spk_id = self.spk_map[spk_name]
-
-        # get ph seq, note lst, midi dur lst, is slur lst.
-        if input_type == 'word':
-            ret = self.preprocess_word_level_input(inp)
-        elif input_type == 'phoneme':  # like transcriptions.txt in Opencpop dataset.
-            ret = self.preprocess_phoneme_level_input(inp)
-        else:
-            print('Invalid input type.')
-            return None
-
-        if ret:
-            ph_seq, note_lst, midi_dur_lst, is_slur = ret
-        else:
-            print('==========> Preprocess_word_level or phone_level input wrong.')
-            return None
-
-        # convert note lst to midi id; convert note dur lst to midi duration
-        try:
-            midis = [librosa.note_to_midi(x.split("/")[0]) if x != 'rest' else 0
-                     for x in note_lst]
-            midi_dur_lst = [float(x) for x in midi_dur_lst]
-        except Exception as e:
-            print(e)
-            print('Invalid Input Type.')
-            return None
-
-        ph_token = self.ph_encoder.encode(ph_seq)
-        item = {'item_name': item_name, 'text': inp['text'], 'ph': ph_seq, 'spk_id': spk_id,
-                'ph_token': ph_token, 'pitch_midi': np.asarray(midis), 'midi_dur': np.asarray(midi_dur_lst),
-                'is_slur': np.asarray(is_slur), }
-        item['ph_len'] = len(item['ph_token'])
-        return item
-
-    def input_to_batch(self, item):
-        item_names = [item['item_name']]
-        text = [item['text']]
-        ph = [item['ph']]
-        txt_tokens = torch.LongTensor(item['ph_token'])[None, :].to(self.device)
-        txt_lengths = torch.LongTensor([txt_tokens.shape[1]]).to(self.device)
-        spk_ids = torch.LongTensor(item['spk_id'])[None, :].to(self.device)
-
-        pitch_midi = torch.LongTensor(item['pitch_midi'])[None, :hparams['max_frames']].to(self.device)
-        midi_dur = torch.FloatTensor(item['midi_dur'])[None, :hparams['max_frames']].to(self.device)
-        is_slur = torch.LongTensor(item['is_slur'])[None, :hparams['max_frames']].to(self.device)
-
-        batch = {
-            'item_name': item_names,
-            'text': text,
-            'ph': ph,
-            'txt_tokens': txt_tokens,
-            'txt_lengths': txt_lengths,
-            'spk_ids': spk_ids,
-            'pitch_midi': pitch_midi,
-            'midi_dur': midi_dur,
-            'is_slur': is_slur
-        }
-        return batch
-
-    def postprocess_output(self, output):
-        return output
-
-    def infer_once(self, inp):
-        inp = self.preprocess_input(inp, input_type=inp['input_type'] if inp.get('input_type') else 'word')
-        output = self.forward_model(inp)
-        output = self.postprocess_output(output)
-        return output
-
-    @classmethod
-    def example_run(cls, inp):
-        from utils.audio import save_wav
-        set_hparams(print_hparams=False)
-        infer_ins = cls(hparams)
-        out = infer_ins.infer_once(inp)
-        os.makedirs('infer_out', exist_ok=True)
-        save_wav(out, f'infer_out/example_out.wav', hparams['audio_sample_rate'])
-
-
-# if __name__ == '__main__':
-    # debug
-    # a = BaseSVSInfer(hparams)
-    # a.preprocess_input({'text': '你 说 你 不 SP 懂 为 何 在 这 时 牵 手 AP',
-    #                     'notes': 'D#4/Eb4 | D#4/Eb4 | D#4/Eb4 | D#4/Eb4 | rest | D#4/Eb4 | D4 | D4 | D4 | D#4/Eb4 | F4 | D#4/Eb4 | D4 | rest',
-    #                     'notes_duration': '0.113740 | 0.329060 | 0.287950 | 0.133480 | 0.150900 | 0.484730 | 0.242010 | 0.180820 | 0.343570 | 0.152050 | 0.266720 | 0.280310 | 0.633300 | 0.444590'
-    #                     })
-
-    # b = {
-    #     'text': '小酒窝长睫毛AP是你最美的记号',
-    #     'notes': 'C#4/Db4 | F#4/Gb4 | G#4/Ab4 | A#4/Bb4 F#4/Gb4 | F#4/Gb4 C#4/Db4 | C#4/Db4 | rest | C#4/Db4 | A#4/Bb4 | G#4/Ab4 | A#4/Bb4 | G#4/Ab4 | F4 | C#4/Db4',
-    #     'notes_duration': '0.407140 | 0.376190 | 0.242180 | 0.509550 0.183420 | 0.315400 0.235020 | 0.361660 | 0.223070 | 0.377270 | 0.340550 | 0.299620 | 0.344510 | 0.283770 | 0.323390 | 0.360340'
-    # }
-    # c = {
-    #     'text': '小酒窝长睫毛AP是你最美的记号',
-    #     'ph_seq': 'x iao j iu w o ch ang ang j ie ie m ao AP sh i n i z ui m ei d e j i h ao',
-    #     'note_seq': 'C#4/Db4 C#4/Db4 F#4/Gb4 F#4/Gb4 G#4/Ab4 G#4/Ab4 A#4/Bb4 A#4/Bb4 F#4/Gb4 F#4/Gb4 F#4/Gb4 C#4/Db4 C#4/Db4 C#4/Db4 rest C#4/Db4 C#4/Db4 A#4/Bb4 A#4/Bb4 G#4/Ab4 G#4/Ab4 A#4/Bb4 A#4/Bb4 G#4/Ab4 G#4/Ab4 F4 F4 C#4/Db4 C#4/Db4',
-    #     'note_dur_seq': '0.407140 0.407140 0.376190 0.376190 0.242180 0.242180 0.509550 0.509550 0.183420 0.315400 0.315400 0.235020 0.361660 0.361660 0.223070 0.377270 0.377270 0.340550 0.340550 0.299620 0.299620 0.344510 0.344510 0.283770 0.283770 0.323390 0.323390 0.360340 0.360340',
-    #     'is_slur_seq': '0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0'
-    # }  # input like Opencpop dataset.
-    # a.preprocess_input(b)
-    # a.preprocess_input(c, input_type='phoneme')

spaces/AgentVerse/agentVerse/agentverse/environments/tasksolving_env/rules/decision_maker/brainstorming.py

@@ -1,67 +0,0 @@
-from __future__ import annotations
-import asyncio
-from colorama import Fore
-
-from typing import TYPE_CHECKING, List
-
-from . import decision_maker_registry
-from .base import BaseDecisionMaker
-from agentverse.logging import logger
-
-from agentverse.message import Message
-
-if TYPE_CHECKING:
-    from agentverse.agents.base import BaseAgent
-    from agentverse.message import CriticMessage
-
-
-@decision_maker_registry.register("brainstorming")
-class BrainstormingDecisionMaker(BaseDecisionMaker):
-    """
-    Much like the horizontal decision maker, but with some twists:
-    (1) Solver acts as a summarizer, summarizing the discussion of this turn
-    (2) After summarizing, all the agents' memory are cleared, and replaced with
-    the summary (to avoid exceeding maximum context length of the model too fast)
-    """
-
-    name: str = "brainstorming"
-
-    async def astep(
-        self,
-        agents: List[BaseAgent],
-        task_description: str,
-        previous_plan: str = "No solution yet.",
-        advice: str = "No advice yet.",
-        *args,
-        **kwargs,
-    ) -> List[str]:
-        if advice != "No advice yet.":
-            self.broadcast_messages(
-                agents, [Message(content=advice, sender="Evaluator")]
-            )
-        for agent in agents[1:]:
-            review: CriticMessage = await agent.astep(
-                previous_plan, advice, task_description
-            )
-            if review.content != "":
-                self.broadcast_messages(agents, [review])
-
-            logger.info("", "Reviews:", Fore.YELLOW)
-            logger.info(
-                "",
-                f"[{review.sender}]: {review.content}",
-                Fore.YELLOW,
-            )
-
-        result = agents[0].step(previous_plan, advice, task_description)
-        for agent in agents:
-            agent.memory.reset()
-        self.broadcast_messages(
-            agents,
-            [
-                Message(
-                    content=result.content, sender="Summary From Previous Discussion"
-                )
-            ],
-        )
-        return [result]

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridsizer/AddChildMethods.js

@@ -1,112 +0,0 @@
-import AddChild from '../basesizer/utils/AddChild.js';
-import GetBoundsConfig from '../utils/GetBoundsConfig.js';
-import ALIGNMODE from '../utils/AlignConst.js';
-
-const IsPlainObject = Phaser.Utils.Objects.IsPlainObject;
-const GetValue = Phaser.Utils.Objects.GetValue;
-const ALIGN_CENTER = Phaser.Display.Align.CENTER;
-
-
-var GetEmptyCellIndex = function (columnIndex, rowIndex, cells, columnCount, rowCount) {
-    if ((typeof (columnIndex) === 'number') || (typeof (rowIndex) === 'number')) {
-        if (columnIndex === undefined) {
-            var idx;
-            for (var i = 0; i < columnCount; i++) {
-                idx = (rowIndex * columnCount) + i;
-                if (!cells[idx]) {
-                    return idx;
-                }
-            }
-        } else if (rowIndex === undefined) {
-            var idx;
-            for (var i = 0; i < rowCount; i++) {
-                idx = (i * columnCount) + columnIndex;
-                if (!cells[idx]) {
-                    return idx;
-                }
-            }
-        } else {
-            var idx = (rowIndex * columnCount) + columnIndex;
-            if (!cells[idx]) {
-                return idx;
-            }
-        }
-
-    } else if (rowIndex === true) {
-        var idx;
-        for (var i = 0; i < columnCount; i++) {
-            for (var j = 0; j < rowCount; j++) {
-                idx = (j * columnCount) + i;
-                if (!cells[idx]) {
-                    return idx;
-                }
-            }
-        }
-    } else {
-        for (var i = 0, cnt = cells.length; i < cnt; i++) {
-            if (!cells[i]) {
-                return i;
-            }
-        }
-    }
-    return null;
-}
-
-var Add = function (gameObject, columnIndex, rowIndex, align, paddingConfig, expand, childKey) {
-    AddChild.call(this, gameObject);
-    if (IsPlainObject(columnIndex)) {
-        var config = columnIndex;
-        columnIndex = GetValue(config, 'column', undefined);
-        rowIndex = GetValue(config, 'row', undefined);
-        align = GetValue(config, 'align', ALIGN_CENTER);
-        paddingConfig = GetValue(config, 'padding', 0);
-        expand = GetValue(config, 'expand', false);
-        childKey = GetValue(config, 'key', undefined);
-    }
-
-    // Get insert index
-    var itemIndex = GetEmptyCellIndex(columnIndex, rowIndex, this.sizerChildren, this.columnCount, this.rowCount);
-    if (itemIndex === null) {
-        // Specific index mode
-        if ((typeof (columnIndex) === 'number') && (typeof (rowIndex) === 'number')) {
-            return this;
-        }
-
-        if ((rowIndex === true) || (typeof (rowIndex) === 'number')) {
-            this.addEmptyColumn();
-        } else {            
-            this.addEmptyRow();
-        }
-
-        // Get insert index again
-        itemIndex = GetEmptyCellIndex(columnIndex, rowIndex, this.sizerChildren, this.columnCount, this.rowCount);
-    }
-
-    if (typeof (align) === 'string') {
-        align = ALIGNMODE[align];
-    }
-    if (align === undefined) {
-        align = ALIGN_CENTER;
-    }
-    if (paddingConfig === undefined) {
-        paddingConfig = 0;
-    }
-    if (expand === undefined) {
-        expand = true;
-    }
-
-    var config = this.getSizerConfig(gameObject);
-    config.align = align;
-    config.padding = GetBoundsConfig(paddingConfig);
-    config.expand = expand;
-    this.sizerChildren[itemIndex] = gameObject;
-
-    if (childKey !== undefined) {
-        this.addChildrenMap(childKey, gameObject)
-    }
-    return this;
-}
-
-export default {
-    add: Add
-}

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

@@ -1,13 +0,0 @@
-import LineProgressCanvas from './LineProgressCanvas.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('circularProgressCanvas', function (x, y, width, height, barColor, value, config) {
-    var gameObject = new LineProgressCanvas(this.scene, x, y, width, height, barColor, value, config);
-    this.scene.add.existing(gameObject);
-    return gameObject;
-});
-
-SetValue(window, 'RexPlugins.UI.LineProgressCanvas', LineProgressCanvas);
-
-export default LineProgressCanvas;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/menu/methods/ParseEaseConfig.js

@@ -1,18 +0,0 @@
-import GetOrientationMode from '../../utils/GetOrientationMode.js';
-var ParseEaseConfig = function (menu, easeConfig) {
-    if (typeof (easeConfig) === 'number') {
-        easeConfig = {
-            duration: easeConfig
-        };
-    }
-
-    if (easeConfig.hasOwnProperty('orientation') && (easeConfig.orientation !== undefined)) {
-        easeConfig.sameOrientation = GetOrientationMode(easeConfig.orientation) === menu.orientation;
-    } else {
-        easeConfig.sameOrientation = true;
-    }
-    easeConfig.destroy = false;
-    return easeConfig;
-}
-
-export default ParseEaseConfig;

spaces/Akshat-1812/Dog-Vision/README.md

@@ -1,13 +0,0 @@
----
-title: Dog Vision
-emoji: 📉
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: unknown
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AlexWang/lama/saicinpainting/training/visualizers/noop.py

@@ -1,9 +0,0 @@
-from saicinpainting.training.visualizers.base import BaseVisualizer
-
-
-class NoopVisualizer(BaseVisualizer):
-    def __init__(self, *args, **kwargs):
-        pass
-
-    def __call__(self, epoch_i, batch_i, batch, suffix='', rank=None):
-        pass

spaces/Alpaca233/ChatGPT-PPT-Generate/app.py

@@ -1,245 +0,0 @@
-import glob
-import os
-import random
-import re
-import string
-
-import gradio as gr
-
-import openai
-from icrawler import ImageDownloader
-from icrawler.builtin import GoogleImageCrawler, BingImageCrawler
-from uuid import uuid4
-from pptx import Presentation
-
-bad_coding_practice = ''.join(random.choice(string.ascii_uppercase + string.ascii_lowercase + string.digits) for _ in
-                              range(16))
-
-
-def refresh_bad_coding_practice():
-    global bad_coding_practice
-    bad_coding_practice = ''.join(random.choice(string.ascii_uppercase + string.ascii_lowercase + string.digits)
-                                  for _ in range(16))
-    return
-
-
-class PrefixNameDownloader(ImageDownloader):
-
-    def get_filename(self, task, default_ext):
-        filename = super(PrefixNameDownloader, self).get_filename(
-            task, default_ext)
-        print(bad_coding_practice)
-        return 'prefix_' + bad_coding_practice + filename
-
-
-def generate_ppt(file, topic, slide_length, api_key):
-    print(file.name)
-
-    root = Presentation(file.name)
-
-    openai.api_key = api_key
-
-    message = f"""
-    Create content for a slideshow presentation.
-    The content's topic is {topic}. 
-    The slideshow is {slide_length} slides long. 
-    The content is written in the language of the content I give you above.
-    
-    
-    You are allowed to use the following slide types:
-    
-    Slide types:
-    Title Slide - (Title, Subtitle)
-    Content Slide - (Title, Content)
-    Image Slide - (Title, Content, Image)
-    Thanks Slide - (Title)
-    
-    Put this tag before the Title Slide: [L_TS]
-    Put this tag before the Content Slide: [L_CS]
-    Put this tag before the Image Slide: [L_IS]
-    Put this tag before the Thanks Slide: [L_THS]
-    
-    Put "[SLIDEBREAK]" after each slide 
-    
-    For example:
-    [L_TS]
-    [TITLE]Mental Health[/TITLE]
-    
-    [SLIDEBREAK]
-    
-    [L_CS] 
-    [TITLE]Mental Health Definition[/TITLE]
-    [CONTENT]
-    1. Definition: A person’s condition with regard to their psychological and emotional well-being
-    2. Can impact one's physical health
-    3. Stigmatized too often.
-    [/CONTENT]
-    
-    [SLIDEBREAK]
-    
-    Put this tag before the Title: [TITLE]
-    Put this tag after the Title: [/TITLE]
-    Put this tag before the Subitle: [SUBTITLE]
-    Put this tag after the Subtitle: [/SUBTITLE]
-    Put this tag before the Content: [CONTENT]
-    Put this tag after the Content: [/CONTENT]
-    Put this tag before the Image: [IMAGE]
-    Put this tag after the Image: [/IMAGE]
-    
-    Elaborate on the Content, provide as much information as possible.
-    You put a [/CONTENT] at the end of the Content.
-    Do not reply as if you are talking about the slideshow itself. (ex. "Include pictures here about...")
-    Do not include any special characters (?, !, ., :, ) in the Title.
-    Do not include any additional information in your response and stick to the format."""
-
-    response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        messages=[
-            {"role": "user", "content": message}
-        ]
-    )
-
-    # """ Ref for slide types:
-    # 0 -> title and subtitle
-    # 1 -> title and content
-    # 2 -> section header
-    # 3 -> two content
-    # 4 -> Comparison
-    # 5 -> Title only
-    # 6 -> Blank
-    # 7 -> Content with caption
-    # 8 -> Pic with caption
-    # """
-
-    def delete_all_slides():
-        for i in range(len(root.slides) - 1, -1, -1):
-            r_id = root.slides._sldIdLst[i].rId
-            root.part.drop_rel(r_id)
-            del root.slides._sldIdLst[i]
-
-    def create_title_slide(title, subtitle):
-        layout = root.slide_layouts[0]
-        slide = root.slides.add_slide(layout)
-        slide.shapes.title.text = title
-        slide.placeholders[1].text = subtitle
-
-    def create_section_header_slide(title):
-        layout = root.slide_layouts[2]
-        slide = root.slides.add_slide(layout)
-        slide.shapes.title.text = title
-
-    def create_title_and_content_slide(title, content):
-        layout = root.slide_layouts[1]
-        slide = root.slides.add_slide(layout)
-        slide.shapes.title.text = title
-        slide.placeholders[1].text = content
-
-    def create_title_and_content_and_image_slide(title, content, image_query):
-        layout = root.slide_layouts[8]
-        slide = root.slides.add_slide(layout)
-        slide.shapes.title.text = title
-        slide.placeholders[2].text = content
-        refresh_bad_coding_practice()
-        bing_crawler = GoogleImageCrawler(downloader_cls=PrefixNameDownloader, storage={'root_dir': os.getcwd()})
-        bing_crawler.crawl(keyword=image_query, max_num=1)
-        dir_path = os.path.dirname(os.path.realpath(__file__))
-        file_name = glob.glob(f"prefix_{bad_coding_practice}*")
-        print(file_name)
-        img_path = os.path.join(dir_path, file_name[0])
-        slide.shapes.add_picture(img_path, slide.placeholders[1].left, slide.placeholders[1].top,
-                                 slide.placeholders[1].width, slide.placeholders[1].height)
-
-    def find_text_in_between_tags(text, start_tag, end_tag):
-        start_pos = text.find(start_tag)
-        end_pos = text.find(end_tag)
-        result = []
-        while start_pos > -1 and end_pos > -1:
-            text_between_tags = text[start_pos + len(start_tag):end_pos]
-            result.append(text_between_tags)
-            start_pos = text.find(start_tag, end_pos + len(end_tag))
-            end_pos = text.find(end_tag, start_pos)
-        res1 = "".join(result)
-        res2 = re.sub(r"\[IMAGE\].*?\[/IMAGE\]", '', res1)
-        if len(result) > 0:
-            return res2
-        else:
-            return ""
-
-    def search_for_slide_type(text):
-        tags = ["[L_TS]", "[L_CS]", "[L_IS]", "[L_THS]"]
-        found_text = next((s for s in tags if s in text), None)
-        return found_text
-
-    def parse_response(reply):
-        list_of_slides = reply.split("[SLIDEBREAK]")
-        for slide in list_of_slides:
-            slide_type = search_for_slide_type(slide)
-            if slide_type == "[L_TS]":
-                create_title_slide(find_text_in_between_tags(str(slide), "[TITLE]", "[/TITLE]"),
-                                   find_text_in_between_tags(str(slide), "[SUBTITLE]", "[/SUBTITLE]"))
-            elif slide_type == "[L_CS]":
-                create_title_and_content_slide("".join(find_text_in_between_tags(str(slide), "[TITLE]", "[/TITLE]")),
-                                               "".join(find_text_in_between_tags(str(slide), "[CONTENT]",
-                                                                                 "[/CONTENT]")))
-            elif slide_type == "[L_IS]":
-                create_title_and_content_and_image_slide("".join(find_text_in_between_tags(str(slide), "[TITLE]",
-                                                                                           "[/TITLE]")),
-                                                         "".join(find_text_in_between_tags(str(slide), "[CONTENT]",
-                                                                                           "[/CONTENT]")),
-                                                         "".join(find_text_in_between_tags(str(slide), "[IMAGE]",
-                                                                                           "[/IMAGE]")))
-            elif slide_type == "[L_THS]":
-                create_section_header_slide("".join(find_text_in_between_tags(str(slide), "[TITLE]", "[/TITLE]")))
-
-    def find_title():
-        return root.slides[0].shapes.title.text
-
-    delete_all_slides()
-
-    print(response)
-
-    parse_response(response['choices'][0]['message']['content'])
-
-    name_ = str(uuid4()).replace('-', '')
-
-    root.save(f"./{name_}.pptx")
-
-    print("done")
-
-    dir_path = "./"
-    prefix = "prefix_"
-
-    for file_name in os.listdir(dir_path):
-        if file_name.startswith(prefix):
-            file_path = os.path.join(dir_path, file_name)
-            if os.path.isfile(file_path):
-                os.remove(file_path)
-
-    return f"./{name_}.pptx"
-
-
-with gr.Blocks(title="ChatGPT PPT框架生成") as demo:
-    gr.Markdown("""<h1><center>ChatGPT PPT框架生成</center></h1>""")
-    with gr.Row():
-        with gr.Column():
-            openai_token = gr.Textbox(label="OpenAI API Key")
-            topic = gr.Textbox(label="PPT的主题或内容")
-            length = gr.Slider(minimum=1, maximum=20, value=6, label="生成的PPT页数", step=1)
-            theme = gr.File(value="./theme.pptx", file_types=['pptx', 'ppt'], label="PPT模版")
-            output_file = gr.File(interactive=False)
-
-            topic.submit(
-                fn=generate_ppt,
-                inputs=[theme, topic, length, openai_token],
-                outputs=[output_file]
-            )
-
-            submit = gr.Button("生成")
-            submit.click(
-                fn=generate_ppt,
-                inputs=[theme, topic, length, openai_token],
-                outputs=[output_file]
-            )
-
-if __name__ == "__main__":
-    demo.launch()

spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/configs/glint360k_mbf.py

@@ -1,26 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G  tmpfs /train_tmp
-
-config = edict()
-config.loss = "cosface"
-config.network = "mbf"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 0.1
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 2e-4
-config.batch_size = 128
-config.lr = 0.1  # batch size is 512
-
-config.rec = "/train_tmp/glint360k"
-config.num_classes = 360232
-config.num_image = 17091657
-config.num_epoch = 20
-config.warmup_epoch = -1
-config.decay_epoch = [8, 12, 15, 18]
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]

spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_mstrain_2x.py

@@ -1,167 +0,0 @@
-_base_ = '../_base_/default_runtime.py'
-
-# model settings
-model = dict(
-    type='RetinaNet',
-    pretrained='open-mmlab://detectron2/resnet101_caffe',
-    backbone=dict(
-        type='ResNet',
-        depth=101,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=False),
-        norm_eval=True,
-        style='caffe'),
-    neck=dict(
-        type='FPN',
-        in_channels=[256, 512, 1024, 2048],
-        out_channels=256,
-        start_level=1,
-        add_extra_convs=True,
-        num_outs=5),
-    bbox_head=dict(
-        type='GARetinaHead',
-        num_classes=80,
-        in_channels=256,
-        stacked_convs=4,
-        feat_channels=256,
-        approx_anchor_generator=dict(
-            type='AnchorGenerator',
-            octave_base_scale=4,
-            scales_per_octave=3,
-            ratios=[0.5, 1.0, 2.0],
-            strides=[8, 16, 32, 64, 128]),
-        square_anchor_generator=dict(
-            type='AnchorGenerator',
-            ratios=[1.0],
-            scales=[4],
-            strides=[8, 16, 32, 64, 128]),
-        anchor_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[1.0, 1.0, 1.0, 1.0]),
-        bbox_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[1.0, 1.0, 1.0, 1.0]),
-        loc_filter_thr=0.01,
-        loss_loc=dict(
-            type='FocalLoss',
-            use_sigmoid=True,
-            gamma=2.0,
-            alpha=0.25,
-            loss_weight=1.0),
-        loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
-        loss_cls=dict(
-            type='FocalLoss',
-            use_sigmoid=True,
-            gamma=2.0,
-            alpha=0.25,
-            loss_weight=1.0),
-        loss_bbox=dict(type='SmoothL1Loss', beta=0.04, loss_weight=1.0)))
-# training and testing settings
-train_cfg = dict(
-    ga_assigner=dict(
-        type='ApproxMaxIoUAssigner',
-        pos_iou_thr=0.5,
-        neg_iou_thr=0.4,
-        min_pos_iou=0.4,
-        ignore_iof_thr=-1),
-    ga_sampler=dict(
-        type='RandomSampler',
-        num=256,
-        pos_fraction=0.5,
-        neg_pos_ub=-1,
-        add_gt_as_proposals=False),
-    assigner=dict(
-        type='MaxIoUAssigner',
-        pos_iou_thr=0.5,
-        neg_iou_thr=0.5,
-        min_pos_iou=0.0,
-        ignore_iof_thr=-1),
-    allowed_border=-1,
-    pos_weight=-1,
-    center_ratio=0.2,
-    ignore_ratio=0.5,
-    debug=False)
-test_cfg = dict(
-    nms_pre=1000,
-    min_bbox_size=0,
-    score_thr=0.05,
-    nms=dict(type='nms', iou_threshold=0.5),
-    max_per_img=100)
-# dataset settings
-dataset_type = 'CocoDataset'
-data_root = 'data/coco/'
-img_norm_cfg = dict(
-    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(
-        type='Resize',
-        img_scale=[(1333, 480), (1333, 960)],
-        keep_ratio=True,
-        multiscale_mode='range'),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    samples_per_gpu=2,
-    workers_per_gpu=2,
-    train=dict(
-        type=dataset_type,
-        ann_file=data_root + 'annotations/instances_train2017.json',
-        img_prefix=data_root + 'train2017/',
-        pipeline=train_pipeline),
-    val=dict(
-        type=dataset_type,
-        ann_file=data_root + 'annotations/instances_val2017.json',
-        img_prefix=data_root + 'val2017/',
-        pipeline=test_pipeline),
-    test=dict(
-        type=dataset_type,
-        ann_file=data_root + 'annotations/instances_val2017.json',
-        img_prefix=data_root + 'val2017/',
-        pipeline=test_pipeline))
-evaluation = dict(interval=1, metric='bbox')
-# optimizer
-optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
-optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
-# learning policy
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=500,
-    warmup_ratio=1.0 / 3,
-    step=[16, 22])
-checkpoint_config = dict(interval=1)
-# yapf:disable
-log_config = dict(
-    interval=50,
-    hooks=[
-        dict(type='TextLoggerHook'),
-        # dict(type='TensorboardLoggerHook')
-    ])
-# yapf:enable
-# runtime settings
-runner = dict(type='EpochBasedRunner', max_epochs=24)

spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/deeplabv3_r50-d8.py

@@ -1,44 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained='open-mmlab://resnet50_v1c',
-    backbone=dict(
-        type='ResNetV1c',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        dilations=(1, 1, 2, 4),
-        strides=(1, 2, 1, 1),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    decode_head=dict(
-        type='ASPPHead',
-        in_channels=2048,
-        in_index=3,
-        channels=512,
-        dilations=(1, 12, 24, 36),
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=1024,
-        in_index=2,
-        channels=256,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))

spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug.py

@@ -1,7 +0,0 @@
-_base_ = [
-    '../_base_/models/deeplabv3plus_r50-d8.py',
-    '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
-    '../_base_/schedules/schedule_20k.py'
-]
-model = dict(
-    decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21))

spaces/Andy1621/uniformer_image_segmentation/configs/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes.py

@@ -1,5 +0,0 @@
-_base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py'
-# fp16 settings
-optimizer_config = dict(type='Fp16OptimizerHook', loss_scale=512.)
-# fp16 placeholder
-fp16 = dict()

spaces/Apex-X/ROOPOK/roop/metadata.py

@@ -1,2 +0,0 @@
-name = 'roop'
-version = '1.3.2'

spaces/ArcanAlt/arcanDream/README.md

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

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/tenacity/nap.py

@@ -1,43 +0,0 @@
-# Copyright 2016 Étienne Bersac
-# Copyright 2016 Julien Danjou
-# Copyright 2016 Joshua Harlow
-# Copyright 2013-2014 Ray Holder
-#
-# 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.
-
-import time
-import typing
-
-if typing.TYPE_CHECKING:
-    import threading
-
-
-def sleep(seconds: float) -> None:
-    """
-    Sleep strategy that delays execution for a given number of seconds.
-
-    This is the default strategy, and may be mocked out for unit testing.
-    """
-    time.sleep(seconds)
-
-
-class sleep_using_event:
-    """Sleep strategy that waits on an event to be set."""
-
-    def __init__(self, event: "threading.Event") -> None:
-        self.event = event
-
-    def __call__(self, timeout: typing.Optional[float]) -> None:
-        # NOTE(harlowja): this may *not* actually wait for timeout
-        # seconds if the event is set (ie this may eject out early).
-        self.event.wait(timeout=timeout)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/more_itertools/__init__.py

@@ -1,4 +0,0 @@
-from .more import *  # noqa
-from .recipes import *  # noqa
-
-__version__ = '8.12.0'

spaces/Audio-AGI/AudioSep/utils.py

@@ -1,384 +0,0 @@
-import os
-import datetime
-import json
-import logging
-import librosa
-import pickle
-from typing import Dict
-import numpy as np
-import torch
-import torch.nn as nn
-import yaml
-from models.audiosep import AudioSep, get_model_class
-
-
-def ignore_warnings():
-    import warnings
-    # Ignore UserWarning from torch.meshgrid
-    warnings.filterwarnings('ignore', category=UserWarning, module='torch.functional')
-
-    # Refined regex pattern to capture variations in the warning message
-    pattern = r"Some weights of the model checkpoint at roberta-base were not used when initializing RobertaModel: \['lm_head\..*'\].*"
-    warnings.filterwarnings('ignore', message=pattern)
-
-
-
-def create_logging(log_dir, filemode):
-    os.makedirs(log_dir, exist_ok=True)
-    i1 = 0
-
-    while os.path.isfile(os.path.join(log_dir, "{:04d}.log".format(i1))):
-        i1 += 1
-
-    log_path = os.path.join(log_dir, "{:04d}.log".format(i1))
-    logging.basicConfig(
-        level=logging.DEBUG,
-        format="%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s",
-        datefmt="%a, %d %b %Y %H:%M:%S",
-        filename=log_path,
-        filemode=filemode,
-    )
-
-    # Print to console
-    console = logging.StreamHandler()
-    console.setLevel(logging.INFO)
-    formatter = logging.Formatter("%(name)-12s: %(levelname)-8s %(message)s")
-    console.setFormatter(formatter)
-    logging.getLogger("").addHandler(console)
-
-    return logging
-
-
-def float32_to_int16(x: float) -> int:
-    x = np.clip(x, a_min=-1, a_max=1)
-    return (x * 32767.0).astype(np.int16)
-
-
-def int16_to_float32(x: int) -> float:
-    return (x / 32767.0).astype(np.float32)
-
-
-def parse_yaml(config_yaml: str) -> Dict:
-    r"""Parse yaml file.
-
-    Args:
-        config_yaml (str): config yaml path
-
-    Returns:
-        yaml_dict (Dict): parsed yaml file
-    """
-
-    with open(config_yaml, "r") as fr:
-        return yaml.load(fr, Loader=yaml.FullLoader)
-
-
-def get_audioset632_id_to_lb(ontology_path: str) -> Dict:
-    r"""Get AudioSet 632 classes ID to label mapping."""
-    
-    audioset632_id_to_lb = {}
-
-    with open(ontology_path) as f:
-        data_list = json.load(f)
-
-    for e in data_list:
-        audioset632_id_to_lb[e["id"]] = e["name"]
-
-    return audioset632_id_to_lb
-
-
-def load_pretrained_panns(
-    model_type: str,
-    checkpoint_path: str,
-    freeze: bool
-) -> nn.Module:
-    r"""Load pretrained pretrained audio neural networks (PANNs).
-
-    Args:
-        model_type: str, e.g., "Cnn14"
-        checkpoint_path, str, e.g., "Cnn14_mAP=0.431.pth"
-        freeze: bool
-
-    Returns:
-        model: nn.Module
-    """
-
-    if model_type == "Cnn14":
-        Model = Cnn14
-
-    elif model_type == "Cnn14_DecisionLevelMax":
-        Model = Cnn14_DecisionLevelMax
-
-    else:
-        raise NotImplementedError
-
-    model = Model(sample_rate=32000, window_size=1024, hop_size=320,
-                  mel_bins=64, fmin=50, fmax=14000, classes_num=527)
-
-    if checkpoint_path:
-        checkpoint = torch.load(checkpoint_path, map_location="cpu")
-        model.load_state_dict(checkpoint["model"])
-
-    if freeze:
-        for param in model.parameters():
-            param.requires_grad = False
-
-    return model
-
-
-def energy(x):
-    return torch.mean(x ** 2)
-
-
-def magnitude_to_db(x):
-    eps = 1e-10
-    return 20. * np.log10(max(x, eps))
-
-
-def db_to_magnitude(x):
-    return 10. ** (x / 20)
-
-
-def ids_to_hots(ids, classes_num, device):
-    hots = torch.zeros(classes_num).to(device)
-    for id in ids:
-        hots[id] = 1
-    return hots
-
-
-def calculate_sdr(
-    ref: np.ndarray,
-    est: np.ndarray,
-    eps=1e-10
-) -> float:
-    r"""Calculate SDR between reference and estimation.
-
-    Args:
-        ref (np.ndarray), reference signal
-        est (np.ndarray), estimated signal
-    """
-    reference = ref
-    noise = est - reference
-
-
-    numerator = np.clip(a=np.mean(reference ** 2), a_min=eps, a_max=None)
-
-    denominator = np.clip(a=np.mean(noise ** 2), a_min=eps, a_max=None)
-
-    sdr = 10. * np.log10(numerator / denominator)
-
-    return sdr
-
-
-def calculate_sisdr(ref, est):
-    r"""Calculate SDR between reference and estimation.
-
-    Args:
-        ref (np.ndarray), reference signal
-        est (np.ndarray), estimated signal
-    """
-
-    eps = np.finfo(ref.dtype).eps
-
-    reference = ref.copy()
-    estimate = est.copy()
-    
-    reference = reference.reshape(reference.size, 1)
-    estimate = estimate.reshape(estimate.size, 1)
-
-    Rss = np.dot(reference.T, reference)
-    # get the scaling factor for clean sources
-    a = (eps + np.dot(reference.T, estimate)) / (Rss + eps)
-
-    e_true = a * reference
-    e_res = estimate - e_true
-
-    Sss = (e_true**2).sum()
-    Snn = (e_res**2).sum()
-
-    sisdr = 10 * np.log10((eps+ Sss)/(eps + Snn))
-
-    return sisdr 
-
-
-class StatisticsContainer(object):
-    def __init__(self, statistics_path):
-        self.statistics_path = statistics_path
-
-        self.backup_statistics_path = "{}_{}.pkl".format(
-            os.path.splitext(self.statistics_path)[0],
-            datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S"),
-        )
-
-        self.statistics_dict = {"balanced_train": [], "test": []}
-
-    def append(self, steps, statistics, split, flush=True):
-        statistics["steps"] = steps
-        self.statistics_dict[split].append(statistics)
-
-        if flush:
-            self.flush()
-
-    def flush(self):
-        pickle.dump(self.statistics_dict, open(self.statistics_path, "wb"))
-        pickle.dump(self.statistics_dict, open(self.backup_statistics_path, "wb"))
-        logging.info("    Dump statistics to {}".format(self.statistics_path))
-        logging.info("    Dump statistics to {}".format(self.backup_statistics_path))
-
-
-def get_mean_sdr_from_dict(sdris_dict):
-    mean_sdr = np.nanmean(list(sdris_dict.values()))
-    return mean_sdr
-
-
-def remove_silence(audio: np.ndarray, sample_rate: int) -> np.ndarray:
-    r"""Remove silent frames."""
-    window_size = int(sample_rate * 0.1)
-    threshold = 0.02
-
-    frames = librosa.util.frame(x=audio, frame_length=window_size, hop_length=window_size).T
-    # shape: (frames_num, window_size)
-
-    new_frames = get_active_frames(frames, threshold)
-    # shape: (new_frames_num, window_size)
-
-    new_audio = new_frames.flatten()
-    # shape: (new_audio_samples,)
-
-    return new_audio
-
-
-def get_active_frames(frames: np.ndarray, threshold: float) -> np.ndarray:
-    r"""Get active frames."""
-
-    energy = np.max(np.abs(frames), axis=-1)
-    # shape: (frames_num,)
-
-    active_indexes = np.where(energy > threshold)[0]
-    # shape: (new_frames_num,)
-
-    new_frames = frames[active_indexes]
-    # shape: (new_frames_num,)
-
-    return new_frames
-
-
-def repeat_to_length(audio: np.ndarray, segment_samples: int) -> np.ndarray:
-    r"""Repeat audio to length."""
-    
-    repeats_num = (segment_samples // audio.shape[-1]) + 1
-    audio = np.tile(audio, repeats_num)[0 : segment_samples]
-
-    return audio
-
-def calculate_segmentwise_sdr(ref, est, hop_samples, return_sdr_list=False):
-    min_len = min(ref.shape[-1], est.shape[-1])
-    pointer = 0
-    sdrs = []
-    while pointer + hop_samples < min_len:
-        sdr = calculate_sdr(
-            ref=ref[:, pointer : pointer + hop_samples], 
-            est=est[:, pointer : pointer + hop_samples],
-        )
-        sdrs.append(sdr)
-        pointer += hop_samples
-
-    sdr = np.nanmedian(sdrs)
-
-    if return_sdr_list:
-        return sdr, sdrs
-    else:
-        return sdr
-
-
-def loudness(data, input_loudness, target_loudness):
-    """ Loudness normalize a signal.
-    
-    Normalize an input signal to a user loudness in dB LKFS.   
-
-    Params
-    -------
-    data : torch.Tensor
-        Input multichannel audio data.
-    input_loudness : float
-        Loudness of the input in dB LUFS. 
-    target_loudness : float
-        Target loudness of the output in dB LUFS.
-        
-    Returns
-    -------
-    output : torch.Tensor
-        Loudness normalized output data.
-    """    
-        
-    # calculate the gain needed to scale to the desired loudness level
-    delta_loudness = target_loudness - input_loudness
-    gain = torch.pow(10.0, delta_loudness / 20.0)
-
-    output = gain * data
-
-    # check for potentially clipped samples
-    # if torch.max(torch.abs(output)) >= 1.0:
-    #     warnings.warn("Possible clipped samples in output.")
-
-    return output
-
-
-def load_ss_model(
-    configs: Dict,
-    checkpoint_path: str,
-    query_encoder: nn.Module
-) -> nn.Module:
-    r"""Load trained universal source separation model.
-
-    Args:
-        configs (Dict)
-        checkpoint_path (str): path of the checkpoint to load
-        device (str): e.g., "cpu" | "cuda"
-
-    Returns:
-        pl_model: pl.LightningModule
-    """
-
-    ss_model_type = configs["model"]["model_type"]
-    input_channels = configs["model"]["input_channels"]
-    output_channels = configs["model"]["output_channels"]
-    condition_size = configs["model"]["condition_size"]
-    
-    # Initialize separation model
-    SsModel = get_model_class(model_type=ss_model_type)
-
-    ss_model = SsModel(
-        input_channels=input_channels,
-        output_channels=output_channels,
-        condition_size=condition_size,
-    )
-
-    # Load PyTorch Lightning model
-    pl_model = AudioSep.load_from_checkpoint(
-        checkpoint_path=checkpoint_path,
-        strict=False,
-        ss_model=ss_model,
-        waveform_mixer=None,
-        query_encoder=query_encoder,
-        loss_function=None,
-        optimizer_type=None,
-        learning_rate=None,
-        lr_lambda_func=None,
-        map_location=torch.device('cpu'),
-    )
-
-    return pl_model
-
-
-def parse_yaml(config_yaml: str) -> Dict:
-    r"""Parse yaml file.
-
-    Args:
-        config_yaml (str): config yaml path
-
-    Returns:
-        yaml_dict (Dict): parsed yaml file
-    """
-
-    with open(config_yaml, "r") as fr:
-        return yaml.load(fr, Loader=yaml.FullLoader)

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/test_model_zoo.py

@@ -1,50 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import unittest
-
-from detectron2 import model_zoo
-from detectron2.config import instantiate
-from detectron2.modeling import FPN, GeneralizedRCNN
-
-logger = logging.getLogger(__name__)
-
-
-class TestModelZoo(unittest.TestCase):
-    def test_get_returns_model(self):
-        model = model_zoo.get("Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml", trained=False)
-        self.assertIsInstance(model, GeneralizedRCNN)
-        self.assertIsInstance(model.backbone, FPN)
-
-    def test_get_invalid_model(self):
-        self.assertRaises(RuntimeError, model_zoo.get, "Invalid/config.yaml")
-
-    def test_get_url(self):
-        url = model_zoo.get_checkpoint_url("Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml")
-        self.assertEqual(
-            url,
-            "https://dl.fbaipublicfiles.com/detectron2/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn/138602908/model_final_01ca85.pkl",  # noqa
-        )
-        url2 = model_zoo.get_checkpoint_url("Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.py")
-        self.assertEqual(url, url2)
-
-    def _build_lazy_model(self, name):
-        cfg = model_zoo.get_config("common/models/" + name)
-        instantiate(cfg.model)
-
-    def test_mask_rcnn_fpn(self):
-        self._build_lazy_model("mask_rcnn_fpn.py")
-
-    def test_mask_rcnn_c4(self):
-        self._build_lazy_model("mask_rcnn_c4.py")
-
-    def test_panoptic_fpn(self):
-        self._build_lazy_model("panoptic_fpn.py")
-
-    def test_schedule(self):
-        cfg = model_zoo.get_config("common/coco_schedule.py")
-        for _, v in cfg.items():
-            instantiate(v)
-
-
-if __name__ == "__main__":
-    unittest.main()

spaces/Bart92/RVC_HF/infer_batch_rvc.py

@@ -1,215 +0,0 @@
-"""
-v1
-runtime\python.exe myinfer-v2-0528.py 0 "E:\codes\py39\RVC-beta\todo-songs" "E:\codes\py39\logs\mi-test\added_IVF677_Flat_nprobe_7.index" harvest "E:\codes\py39\RVC-beta\output" "E:\codes\py39\test-20230416b\weights\mi-test.pth" 0.66 cuda:0 True 3 0 1 0.33
-v2
-runtime\python.exe myinfer-v2-0528.py 0 "E:\codes\py39\RVC-beta\todo-songs" "E:\codes\py39\test-20230416b\logs\mi-test-v2\aadded_IVF677_Flat_nprobe_1_v2.index" harvest "E:\codes\py39\RVC-beta\output_v2" "E:\codes\py39\test-20230416b\weights\mi-test-v2.pth" 0.66 cuda:0 True 3 0 1 0.33
-"""
-import os, sys, pdb, torch
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-import sys
-import torch
-import tqdm as tq
-from multiprocessing import cpu_count
-
-
-class Config:
-    def __init__(self, device, is_half):
-        self.device = device
-        self.is_half = is_half
-        self.n_cpu = 0
-        self.gpu_name = None
-        self.gpu_mem = None
-        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
-
-    def device_config(self) -> tuple:
-        if torch.cuda.is_available():
-            i_device = int(self.device.split(":")[-1])
-            self.gpu_name = torch.cuda.get_device_name(i_device)
-            if (
-                ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
-                or "P40" in self.gpu_name.upper()
-                or "1060" in self.gpu_name
-                or "1070" in self.gpu_name
-                or "1080" in self.gpu_name
-            ):
-                print("16系/10系显卡和P40强制单精度")
-                self.is_half = False
-                for config_file in ["32k.json", "40k.json", "48k.json"]:
-                    with open(f"configs/{config_file}", "r") as f:
-                        strr = f.read().replace("true", "false")
-                    with open(f"configs/{config_file}", "w") as f:
-                        f.write(strr)
-                with open("infer/modules/train/preprocess.py", "r") as f:
-                    strr = f.read().replace("3.7", "3.0")
-                with open("infer/modules/train/preprocess.py", "w") as f:
-                    f.write(strr)
-            else:
-                self.gpu_name = None
-            self.gpu_mem = int(
-                torch.cuda.get_device_properties(i_device).total_memory
-                / 1024
-                / 1024
-                / 1024
-                + 0.4
-            )
-            if self.gpu_mem <= 4:
-                with open("infer/modules/train/preprocess.py", "r") as f:
-                    strr = f.read().replace("3.7", "3.0")
-                with open("infer/modules/train/preprocess.py", "w") as f:
-                    f.write(strr)
-        elif torch.backends.mps.is_available():
-            print("没有发现支持的N卡, 使用MPS进行推理")
-            self.device = "mps"
-        else:
-            print("没有发现支持的N卡, 使用CPU进行推理")
-            self.device = "cpu"
-            self.is_half = True
-
-        if self.n_cpu == 0:
-            self.n_cpu = cpu_count()
-
-        if self.is_half:
-            # 6G显存配置
-            x_pad = 3
-            x_query = 10
-            x_center = 60
-            x_max = 65
-        else:
-            # 5G显存配置
-            x_pad = 1
-            x_query = 6
-            x_center = 38
-            x_max = 41
-
-        if self.gpu_mem != None and self.gpu_mem <= 4:
-            x_pad = 1
-            x_query = 5
-            x_center = 30
-            x_max = 32
-
-        return x_pad, x_query, x_center, x_max
-
-
-f0up_key = sys.argv[1]
-input_path = sys.argv[2]
-index_path = sys.argv[3]
-f0method = sys.argv[4]  # harvest or pm
-opt_path = sys.argv[5]
-model_path = sys.argv[6]
-index_rate = float(sys.argv[7])
-device = sys.argv[8]
-is_half = sys.argv[9].lower() != "false"
-filter_radius = int(sys.argv[10])
-resample_sr = int(sys.argv[11])
-rms_mix_rate = float(sys.argv[12])
-protect = float(sys.argv[13])
-print(sys.argv)
-config = Config(device, is_half)
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-from infer.modules.vc.modules import VC
-from lib.infer_pack.models import (
-    SynthesizerTrnMs256NSFsid,
-    SynthesizerTrnMs256NSFsid_nono,
-    SynthesizerTrnMs768NSFsid,
-    SynthesizerTrnMs768NSFsid_nono,
-)
-from infer.lib.audio import load_audio
-from fairseq import checkpoint_utils
-from scipy.io import wavfile
-
-hubert_model = None
-
-
-def load_hubert():
-    global hubert_model
-    models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-        ["hubert_base.pt"],
-        suffix="",
-    )
-    hubert_model = models[0]
-    hubert_model = hubert_model.to(device)
-    if is_half:
-        hubert_model = hubert_model.half()
-    else:
-        hubert_model = hubert_model.float()
-    hubert_model.eval()
-
-
-def vc_single(sid, input_audio, f0_up_key, f0_file, f0_method, file_index, index_rate):
-    global tgt_sr, net_g, vc, hubert_model, version
-    if input_audio is None:
-        return "You need to upload an audio", None
-    f0_up_key = int(f0_up_key)
-    audio = load_audio(input_audio, 16000)
-    times = [0, 0, 0]
-    if hubert_model == None:
-        load_hubert()
-    if_f0 = cpt.get("f0", 1)
-    # audio_opt=vc.pipeline(hubert_model,net_g,sid,audio,times,f0_up_key,f0_method,file_index,file_big_npy,index_rate,if_f0,f0_file=f0_file)
-    audio_opt = vc.pipeline(
-        hubert_model,
-        net_g,
-        sid,
-        audio,
-        input_audio,
-        times,
-        f0_up_key,
-        f0_method,
-        file_index,
-        index_rate,
-        if_f0,
-        filter_radius,
-        tgt_sr,
-        resample_sr,
-        rms_mix_rate,
-        version,
-        protect,
-        f0_file=f0_file,
-    )
-    print(times)
-    return audio_opt
-
-
-def get_vc(model_path):
-    global n_spk, tgt_sr, net_g, vc, cpt, device, is_half, version
-    print("loading pth %s" % model_path)
-    cpt = torch.load(model_path, map_location="cpu")
-    tgt_sr = cpt["config"][-1]
-    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
-    if_f0 = cpt.get("f0", 1)
-    version = cpt.get("version", "v1")
-    if version == "v1":
-        if if_f0 == 1:
-            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
-        else:
-            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-    elif version == "v2":
-        if if_f0 == 1:  #
-            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=is_half)
-        else:
-            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
-    del net_g.enc_q
-    print(net_g.load_state_dict(cpt["weight"], strict=False))  # 不加这一行清不干净，真奇葩
-    net_g.eval().to(device)
-    if is_half:
-        net_g = net_g.half()
-    else:
-        net_g = net_g.float()
-    vc = VC(tgt_sr, config)
-    n_spk = cpt["config"][-3]
-    # return {"visible": True,"maximum": n_spk, "__type__": "update"}
-
-
-get_vc(model_path)
-audios = os.listdir(input_path)
-for file in tq.tqdm(audios):
-    if file.endswith(".wav"):
-        file_path = input_path + "/" + file
-        wav_opt = vc_single(
-            0, file_path, f0up_key, None, f0method, index_path, index_rate
-        )
-        out_path = opt_path + "/" + file
-        wavfile.write(out_path, tgt_sr, wav_opt)

spaces/Bart92/RVC_HF/lib/uvr5_pack/lib_v5/dataset.py

@@ -1,183 +0,0 @@
-import os
-import random
-
-import numpy as np
-import torch
-import torch.utils.data
-from tqdm import tqdm
-
-from . import spec_utils
-
-
-class VocalRemoverValidationSet(torch.utils.data.Dataset):
-    def __init__(self, patch_list):
-        self.patch_list = patch_list
-
-    def __len__(self):
-        return len(self.patch_list)
-
-    def __getitem__(self, idx):
-        path = self.patch_list[idx]
-        data = np.load(path)
-
-        X, y = data["X"], data["y"]
-
-        X_mag = np.abs(X)
-        y_mag = np.abs(y)
-
-        return X_mag, y_mag
-
-
-def make_pair(mix_dir, inst_dir):
-    input_exts = [".wav", ".m4a", ".mp3", ".mp4", ".flac"]
-
-    X_list = sorted(
-        [
-            os.path.join(mix_dir, fname)
-            for fname in os.listdir(mix_dir)
-            if os.path.splitext(fname)[1] in input_exts
-        ]
-    )
-    y_list = sorted(
-        [
-            os.path.join(inst_dir, fname)
-            for fname in os.listdir(inst_dir)
-            if os.path.splitext(fname)[1] in input_exts
-        ]
-    )
-
-    filelist = list(zip(X_list, y_list))
-
-    return filelist
-
-
-def train_val_split(dataset_dir, split_mode, val_rate, val_filelist):
-    if split_mode == "random":
-        filelist = make_pair(
-            os.path.join(dataset_dir, "mixtures"),
-            os.path.join(dataset_dir, "instruments"),
-        )
-
-        random.shuffle(filelist)
-
-        if len(val_filelist) == 0:
-            val_size = int(len(filelist) * val_rate)
-            train_filelist = filelist[:-val_size]
-            val_filelist = filelist[-val_size:]
-        else:
-            train_filelist = [
-                pair for pair in filelist if list(pair) not in val_filelist
-            ]
-    elif split_mode == "subdirs":
-        if len(val_filelist) != 0:
-            raise ValueError(
-                "The `val_filelist` option is not available in `subdirs` mode"
-            )
-
-        train_filelist = make_pair(
-            os.path.join(dataset_dir, "training/mixtures"),
-            os.path.join(dataset_dir, "training/instruments"),
-        )
-
-        val_filelist = make_pair(
-            os.path.join(dataset_dir, "validation/mixtures"),
-            os.path.join(dataset_dir, "validation/instruments"),
-        )
-
-    return train_filelist, val_filelist
-
-
-def augment(X, y, reduction_rate, reduction_mask, mixup_rate, mixup_alpha):
-    perm = np.random.permutation(len(X))
-    for i, idx in enumerate(tqdm(perm)):
-        if np.random.uniform() < reduction_rate:
-            y[idx] = spec_utils.reduce_vocal_aggressively(
-                X[idx], y[idx], reduction_mask
-            )
-
-        if np.random.uniform() < 0.5:
-            # swap channel
-            X[idx] = X[idx, ::-1]
-            y[idx] = y[idx, ::-1]
-        if np.random.uniform() < 0.02:
-            # mono
-            X[idx] = X[idx].mean(axis=0, keepdims=True)
-            y[idx] = y[idx].mean(axis=0, keepdims=True)
-        if np.random.uniform() < 0.02:
-            # inst
-            X[idx] = y[idx]
-
-        if np.random.uniform() < mixup_rate and i < len(perm) - 1:
-            lam = np.random.beta(mixup_alpha, mixup_alpha)
-            X[idx] = lam * X[idx] + (1 - lam) * X[perm[i + 1]]
-            y[idx] = lam * y[idx] + (1 - lam) * y[perm[i + 1]]
-
-    return X, y
-
-
-def make_padding(width, cropsize, offset):
-    left = offset
-    roi_size = cropsize - left * 2
-    if roi_size == 0:
-        roi_size = cropsize
-    right = roi_size - (width % roi_size) + left
-
-    return left, right, roi_size
-
-
-def make_training_set(filelist, cropsize, patches, sr, hop_length, n_fft, offset):
-    len_dataset = patches * len(filelist)
-
-    X_dataset = np.zeros((len_dataset, 2, n_fft // 2 + 1, cropsize), dtype=np.complex64)
-    y_dataset = np.zeros((len_dataset, 2, n_fft // 2 + 1, cropsize), dtype=np.complex64)
-
-    for i, (X_path, y_path) in enumerate(tqdm(filelist)):
-        X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft)
-        coef = np.max([np.abs(X).max(), np.abs(y).max()])
-        X, y = X / coef, y / coef
-
-        l, r, roi_size = make_padding(X.shape[2], cropsize, offset)
-        X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode="constant")
-        y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode="constant")
-
-        starts = np.random.randint(0, X_pad.shape[2] - cropsize, patches)
-        ends = starts + cropsize
-        for j in range(patches):
-            idx = i * patches + j
-            X_dataset[idx] = X_pad[:, :, starts[j] : ends[j]]
-            y_dataset[idx] = y_pad[:, :, starts[j] : ends[j]]
-
-    return X_dataset, y_dataset
-
-
-def make_validation_set(filelist, cropsize, sr, hop_length, n_fft, offset):
-    patch_list = []
-    patch_dir = "cs{}_sr{}_hl{}_nf{}_of{}".format(
-        cropsize, sr, hop_length, n_fft, offset
-    )
-    os.makedirs(patch_dir, exist_ok=True)
-
-    for i, (X_path, y_path) in enumerate(tqdm(filelist)):
-        basename = os.path.splitext(os.path.basename(X_path))[0]
-
-        X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft)
-        coef = np.max([np.abs(X).max(), np.abs(y).max()])
-        X, y = X / coef, y / coef
-
-        l, r, roi_size = make_padding(X.shape[2], cropsize, offset)
-        X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode="constant")
-        y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode="constant")
-
-        len_dataset = int(np.ceil(X.shape[2] / roi_size))
-        for j in range(len_dataset):
-            outpath = os.path.join(patch_dir, "{}_p{}.npz".format(basename, j))
-            start = j * roi_size
-            if not os.path.exists(outpath):
-                np.savez(
-                    outpath,
-                    X=X_pad[:, :, start : start + cropsize],
-                    y=y_pad[:, :, start : start + cropsize],
-                )
-            patch_list.append(outpath)
-
-    return VocalRemoverValidationSet(patch_list)

spaces/Belshia/shia/README.md

@@ -1,13 +0,0 @@
----
-title: Shia
-emoji: 🌍
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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

@@ -1,5 +0,0 @@
-# coding: utf-8
-# file generated by setuptools_scm
-# don't change, don't track in version control
-version = '2.8.2'
-version_tuple = (2, 8, 2)

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/poolmanager.py

@@ -1,537 +0,0 @@
-from __future__ import absolute_import
-
-import collections
-import functools
-import logging
-
-from ._collections import RecentlyUsedContainer
-from .connectionpool import HTTPConnectionPool, HTTPSConnectionPool, port_by_scheme
-from .exceptions import (
-    LocationValueError,
-    MaxRetryError,
-    ProxySchemeUnknown,
-    ProxySchemeUnsupported,
-    URLSchemeUnknown,
-)
-from .packages import six
-from .packages.six.moves.urllib.parse import urljoin
-from .request import RequestMethods
-from .util.proxy import connection_requires_http_tunnel
-from .util.retry import Retry
-from .util.url import parse_url
-
-__all__ = ["PoolManager", "ProxyManager", "proxy_from_url"]
-
-
-log = logging.getLogger(__name__)
-
-SSL_KEYWORDS = (
-    "key_file",
-    "cert_file",
-    "cert_reqs",
-    "ca_certs",
-    "ssl_version",
-    "ca_cert_dir",
-    "ssl_context",
-    "key_password",
-    "server_hostname",
-)
-
-# All known keyword arguments that could be provided to the pool manager, its
-# pools, or the underlying connections. This is used to construct a pool key.
-_key_fields = (
-    "key_scheme",  # str
-    "key_host",  # str
-    "key_port",  # int
-    "key_timeout",  # int or float or Timeout
-    "key_retries",  # int or Retry
-    "key_strict",  # bool
-    "key_block",  # bool
-    "key_source_address",  # str
-    "key_key_file",  # str
-    "key_key_password",  # str
-    "key_cert_file",  # str
-    "key_cert_reqs",  # str
-    "key_ca_certs",  # str
-    "key_ssl_version",  # str
-    "key_ca_cert_dir",  # str
-    "key_ssl_context",  # instance of ssl.SSLContext or urllib3.util.ssl_.SSLContext
-    "key_maxsize",  # int
-    "key_headers",  # dict
-    "key__proxy",  # parsed proxy url
-    "key__proxy_headers",  # dict
-    "key__proxy_config",  # class
-    "key_socket_options",  # list of (level (int), optname (int), value (int or str)) tuples
-    "key__socks_options",  # dict
-    "key_assert_hostname",  # bool or string
-    "key_assert_fingerprint",  # str
-    "key_server_hostname",  # str
-)
-
-#: The namedtuple class used to construct keys for the connection pool.
-#: All custom key schemes should include the fields in this key at a minimum.
-PoolKey = collections.namedtuple("PoolKey", _key_fields)
-
-_proxy_config_fields = ("ssl_context", "use_forwarding_for_https")
-ProxyConfig = collections.namedtuple("ProxyConfig", _proxy_config_fields)
-
-
-def _default_key_normalizer(key_class, request_context):
-    """
-    Create a pool key out of a request context dictionary.
-
-    According to RFC 3986, both the scheme and host are case-insensitive.
-    Therefore, this function normalizes both before constructing the pool
-    key for an HTTPS request. If you wish to change this behaviour, provide
-    alternate callables to ``key_fn_by_scheme``.
-
-    :param key_class:
-        The class to use when constructing the key. This should be a namedtuple
-        with the ``scheme`` and ``host`` keys at a minimum.
-    :type  key_class: namedtuple
-    :param request_context:
-        A dictionary-like object that contain the context for a request.
-    :type  request_context: dict
-
-    :return: A namedtuple that can be used as a connection pool key.
-    :rtype:  PoolKey
-    """
-    # Since we mutate the dictionary, make a copy first
-    context = request_context.copy()
-    context["scheme"] = context["scheme"].lower()
-    context["host"] = context["host"].lower()
-
-    # These are both dictionaries and need to be transformed into frozensets
-    for key in ("headers", "_proxy_headers", "_socks_options"):
-        if key in context and context[key] is not None:
-            context[key] = frozenset(context[key].items())
-
-    # The socket_options key may be a list and needs to be transformed into a
-    # tuple.
-    socket_opts = context.get("socket_options")
-    if socket_opts is not None:
-        context["socket_options"] = tuple(socket_opts)
-
-    # Map the kwargs to the names in the namedtuple - this is necessary since
-    # namedtuples can't have fields starting with '_'.
-    for key in list(context.keys()):
-        context["key_" + key] = context.pop(key)
-
-    # Default to ``None`` for keys missing from the context
-    for field in key_class._fields:
-        if field not in context:
-            context[field] = None
-
-    return key_class(**context)
-
-
-#: A dictionary that maps a scheme to a callable that creates a pool key.
-#: This can be used to alter the way pool keys are constructed, if desired.
-#: Each PoolManager makes a copy of this dictionary so they can be configured
-#: globally here, or individually on the instance.
-key_fn_by_scheme = {
-    "http": functools.partial(_default_key_normalizer, PoolKey),
-    "https": functools.partial(_default_key_normalizer, PoolKey),
-}
-
-pool_classes_by_scheme = {"http": HTTPConnectionPool, "https": HTTPSConnectionPool}
-
-
-class PoolManager(RequestMethods):
-    """
-    Allows for arbitrary requests while transparently keeping track of
-    necessary connection pools for you.
-
-    :param num_pools:
-        Number of connection pools to cache before discarding the least
-        recently used pool.
-
-    :param headers:
-        Headers to include with all requests, unless other headers are given
-        explicitly.
-
-    :param \\**connection_pool_kw:
-        Additional parameters are used to create fresh
-        :class:`urllib3.connectionpool.ConnectionPool` instances.
-
-    Example::
-
-        >>> manager = PoolManager(num_pools=2)
-        >>> r = manager.request('GET', 'http://google.com/')
-        >>> r = manager.request('GET', 'http://google.com/mail')
-        >>> r = manager.request('GET', 'http://yahoo.com/')
-        >>> len(manager.pools)
-        2
-
-    """
-
-    proxy = None
-    proxy_config = None
-
-    def __init__(self, num_pools=10, headers=None, **connection_pool_kw):
-        RequestMethods.__init__(self, headers)
-        self.connection_pool_kw = connection_pool_kw
-        self.pools = RecentlyUsedContainer(num_pools, dispose_func=lambda p: p.close())
-
-        # Locally set the pool classes and keys so other PoolManagers can
-        # override them.
-        self.pool_classes_by_scheme = pool_classes_by_scheme
-        self.key_fn_by_scheme = key_fn_by_scheme.copy()
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        self.clear()
-        # Return False to re-raise any potential exceptions
-        return False
-
-    def _new_pool(self, scheme, host, port, request_context=None):
-        """
-        Create a new :class:`urllib3.connectionpool.ConnectionPool` based on host, port, scheme, and
-        any additional pool keyword arguments.
-
-        If ``request_context`` is provided, it is provided as keyword arguments
-        to the pool class used. This method is used to actually create the
-        connection pools handed out by :meth:`connection_from_url` and
-        companion methods. It is intended to be overridden for customization.
-        """
-        pool_cls = self.pool_classes_by_scheme[scheme]
-        if request_context is None:
-            request_context = self.connection_pool_kw.copy()
-
-        # Although the context has everything necessary to create the pool,
-        # this function has historically only used the scheme, host, and port
-        # in the positional args. When an API change is acceptable these can
-        # be removed.
-        for key in ("scheme", "host", "port"):
-            request_context.pop(key, None)
-
-        if scheme == "http":
-            for kw in SSL_KEYWORDS:
-                request_context.pop(kw, None)
-
-        return pool_cls(host, port, **request_context)
-
-    def clear(self):
-        """
-        Empty our store of pools and direct them all to close.
-
-        This will not affect in-flight connections, but they will not be
-        re-used after completion.
-        """
-        self.pools.clear()
-
-    def connection_from_host(self, host, port=None, scheme="http", pool_kwargs=None):
-        """
-        Get a :class:`urllib3.connectionpool.ConnectionPool` based on the host, port, and scheme.
-
-        If ``port`` isn't given, it will be derived from the ``scheme`` using
-        ``urllib3.connectionpool.port_by_scheme``. If ``pool_kwargs`` is
-        provided, it is merged with the instance's ``connection_pool_kw``
-        variable and used to create the new connection pool, if one is
-        needed.
-        """
-
-        if not host:
-            raise LocationValueError("No host specified.")
-
-        request_context = self._merge_pool_kwargs(pool_kwargs)
-        request_context["scheme"] = scheme or "http"
-        if not port:
-            port = port_by_scheme.get(request_context["scheme"].lower(), 80)
-        request_context["port"] = port
-        request_context["host"] = host
-
-        return self.connection_from_context(request_context)
-
-    def connection_from_context(self, request_context):
-        """
-        Get a :class:`urllib3.connectionpool.ConnectionPool` based on the request context.
-
-        ``request_context`` must at least contain the ``scheme`` key and its
-        value must be a key in ``key_fn_by_scheme`` instance variable.
-        """
-        scheme = request_context["scheme"].lower()
-        pool_key_constructor = self.key_fn_by_scheme.get(scheme)
-        if not pool_key_constructor:
-            raise URLSchemeUnknown(scheme)
-        pool_key = pool_key_constructor(request_context)
-
-        return self.connection_from_pool_key(pool_key, request_context=request_context)
-
-    def connection_from_pool_key(self, pool_key, request_context=None):
-        """
-        Get a :class:`urllib3.connectionpool.ConnectionPool` based on the provided pool key.
-
-        ``pool_key`` should be a namedtuple that only contains immutable
-        objects. At a minimum it must have the ``scheme``, ``host``, and
-        ``port`` fields.
-        """
-        with self.pools.lock:
-            # If the scheme, host, or port doesn't match existing open
-            # connections, open a new ConnectionPool.
-            pool = self.pools.get(pool_key)
-            if pool:
-                return pool
-
-            # Make a fresh ConnectionPool of the desired type
-            scheme = request_context["scheme"]
-            host = request_context["host"]
-            port = request_context["port"]
-            pool = self._new_pool(scheme, host, port, request_context=request_context)
-            self.pools[pool_key] = pool
-
-        return pool
-
-    def connection_from_url(self, url, pool_kwargs=None):
-        """
-        Similar to :func:`urllib3.connectionpool.connection_from_url`.
-
-        If ``pool_kwargs`` is not provided and a new pool needs to be
-        constructed, ``self.connection_pool_kw`` is used to initialize
-        the :class:`urllib3.connectionpool.ConnectionPool`. If ``pool_kwargs``
-        is provided, it is used instead. Note that if a new pool does not
-        need to be created for the request, the provided ``pool_kwargs`` are
-        not used.
-        """
-        u = parse_url(url)
-        return self.connection_from_host(
-            u.host, port=u.port, scheme=u.scheme, pool_kwargs=pool_kwargs
-        )
-
-    def _merge_pool_kwargs(self, override):
-        """
-        Merge a dictionary of override values for self.connection_pool_kw.
-
-        This does not modify self.connection_pool_kw and returns a new dict.
-        Any keys in the override dictionary with a value of ``None`` are
-        removed from the merged dictionary.
-        """
-        base_pool_kwargs = self.connection_pool_kw.copy()
-        if override:
-            for key, value in override.items():
-                if value is None:
-                    try:
-                        del base_pool_kwargs[key]
-                    except KeyError:
-                        pass
-                else:
-                    base_pool_kwargs[key] = value
-        return base_pool_kwargs
-
-    def _proxy_requires_url_absolute_form(self, parsed_url):
-        """
-        Indicates if the proxy requires the complete destination URL in the
-        request.  Normally this is only needed when not using an HTTP CONNECT
-        tunnel.
-        """
-        if self.proxy is None:
-            return False
-
-        return not connection_requires_http_tunnel(
-            self.proxy, self.proxy_config, parsed_url.scheme
-        )
-
-    def _validate_proxy_scheme_url_selection(self, url_scheme):
-        """
-        Validates that were not attempting to do TLS in TLS connections on
-        Python2 or with unsupported SSL implementations.
-        """
-        if self.proxy is None or url_scheme != "https":
-            return
-
-        if self.proxy.scheme != "https":
-            return
-
-        if six.PY2 and not self.proxy_config.use_forwarding_for_https:
-            raise ProxySchemeUnsupported(
-                "Contacting HTTPS destinations through HTTPS proxies "
-                "'via CONNECT tunnels' is not supported in Python 2"
-            )
-
-    def urlopen(self, method, url, redirect=True, **kw):
-        """
-        Same as :meth:`urllib3.HTTPConnectionPool.urlopen`
-        with custom cross-host redirect logic and only sends the request-uri
-        portion of the ``url``.
-
-        The given ``url`` parameter must be absolute, such that an appropriate
-        :class:`urllib3.connectionpool.ConnectionPool` can be chosen for it.
-        """
-        u = parse_url(url)
-        self._validate_proxy_scheme_url_selection(u.scheme)
-
-        conn = self.connection_from_host(u.host, port=u.port, scheme=u.scheme)
-
-        kw["assert_same_host"] = False
-        kw["redirect"] = False
-
-        if "headers" not in kw:
-            kw["headers"] = self.headers.copy()
-
-        if self._proxy_requires_url_absolute_form(u):
-            response = conn.urlopen(method, url, **kw)
-        else:
-            response = conn.urlopen(method, u.request_uri, **kw)
-
-        redirect_location = redirect and response.get_redirect_location()
-        if not redirect_location:
-            return response
-
-        # Support relative URLs for redirecting.
-        redirect_location = urljoin(url, redirect_location)
-
-        # RFC 7231, Section 6.4.4
-        if response.status == 303:
-            method = "GET"
-
-        retries = kw.get("retries")
-        if not isinstance(retries, Retry):
-            retries = Retry.from_int(retries, redirect=redirect)
-
-        # Strip headers marked as unsafe to forward to the redirected location.
-        # Check remove_headers_on_redirect to avoid a potential network call within
-        # conn.is_same_host() which may use socket.gethostbyname() in the future.
-        if retries.remove_headers_on_redirect and not conn.is_same_host(
-            redirect_location
-        ):
-            headers = list(six.iterkeys(kw["headers"]))
-            for header in headers:
-                if header.lower() in retries.remove_headers_on_redirect:
-                    kw["headers"].pop(header, None)
-
-        try:
-            retries = retries.increment(method, url, response=response, _pool=conn)
-        except MaxRetryError:
-            if retries.raise_on_redirect:
-                response.drain_conn()
-                raise
-            return response
-
-        kw["retries"] = retries
-        kw["redirect"] = redirect
-
-        log.info("Redirecting %s -> %s", url, redirect_location)
-
-        response.drain_conn()
-        return self.urlopen(method, redirect_location, **kw)
-
-
-class ProxyManager(PoolManager):
-    """
-    Behaves just like :class:`PoolManager`, but sends all requests through
-    the defined proxy, using the CONNECT method for HTTPS URLs.
-
-    :param proxy_url:
-        The URL of the proxy to be used.
-
-    :param proxy_headers:
-        A dictionary containing headers that will be sent to the proxy. In case
-        of HTTP they are being sent with each request, while in the
-        HTTPS/CONNECT case they are sent only once. Could be used for proxy
-        authentication.
-
-    :param proxy_ssl_context:
-        The proxy SSL context is used to establish the TLS connection to the
-        proxy when using HTTPS proxies.
-
-    :param use_forwarding_for_https:
-        (Defaults to False) If set to True will forward requests to the HTTPS
-        proxy to be made on behalf of the client instead of creating a TLS
-        tunnel via the CONNECT method. **Enabling this flag means that request
-        and response headers and content will be visible from the HTTPS proxy**
-        whereas tunneling keeps request and response headers and content
-        private.  IP address, target hostname, SNI, and port are always visible
-        to an HTTPS proxy even when this flag is disabled.
-
-    Example:
-        >>> proxy = urllib3.ProxyManager('http://localhost:3128/')
-        >>> r1 = proxy.request('GET', 'http://google.com/')
-        >>> r2 = proxy.request('GET', 'http://httpbin.org/')
-        >>> len(proxy.pools)
-        1
-        >>> r3 = proxy.request('GET', 'https://httpbin.org/')
-        >>> r4 = proxy.request('GET', 'https://twitter.com/')
-        >>> len(proxy.pools)
-        3
-
-    """
-
-    def __init__(
-        self,
-        proxy_url,
-        num_pools=10,
-        headers=None,
-        proxy_headers=None,
-        proxy_ssl_context=None,
-        use_forwarding_for_https=False,
-        **connection_pool_kw
-    ):
-
-        if isinstance(proxy_url, HTTPConnectionPool):
-            proxy_url = "%s://%s:%i" % (
-                proxy_url.scheme,
-                proxy_url.host,
-                proxy_url.port,
-            )
-        proxy = parse_url(proxy_url)
-
-        if proxy.scheme not in ("http", "https"):
-            raise ProxySchemeUnknown(proxy.scheme)
-
-        if not proxy.port:
-            port = port_by_scheme.get(proxy.scheme, 80)
-            proxy = proxy._replace(port=port)
-
-        self.proxy = proxy
-        self.proxy_headers = proxy_headers or {}
-        self.proxy_ssl_context = proxy_ssl_context
-        self.proxy_config = ProxyConfig(proxy_ssl_context, use_forwarding_for_https)
-
-        connection_pool_kw["_proxy"] = self.proxy
-        connection_pool_kw["_proxy_headers"] = self.proxy_headers
-        connection_pool_kw["_proxy_config"] = self.proxy_config
-
-        super(ProxyManager, self).__init__(num_pools, headers, **connection_pool_kw)
-
-    def connection_from_host(self, host, port=None, scheme="http", pool_kwargs=None):
-        if scheme == "https":
-            return super(ProxyManager, self).connection_from_host(
-                host, port, scheme, pool_kwargs=pool_kwargs
-            )
-
-        return super(ProxyManager, self).connection_from_host(
-            self.proxy.host, self.proxy.port, self.proxy.scheme, pool_kwargs=pool_kwargs
-        )
-
-    def _set_proxy_headers(self, url, headers=None):
-        """
-        Sets headers needed by proxies: specifically, the Accept and Host
-        headers. Only sets headers not provided by the user.
-        """
-        headers_ = {"Accept": "*/*"}
-
-        netloc = parse_url(url).netloc
-        if netloc:
-            headers_["Host"] = netloc
-
-        if headers:
-            headers_.update(headers)
-        return headers_
-
-    def urlopen(self, method, url, redirect=True, **kw):
-        "Same as HTTP(S)ConnectionPool.urlopen, ``url`` must be absolute."
-        u = parse_url(url)
-        if not connection_requires_http_tunnel(self.proxy, self.proxy_config, u.scheme):
-            # For connections using HTTP CONNECT, httplib sets the necessary
-            # headers on the CONNECT to the proxy. If we're not using CONNECT,
-            # we'll definitely need to set 'Host' at the very least.
-            headers = kw.get("headers", self.headers)
-            kw["headers"] = self._set_proxy_headers(url, headers)
-
-        return super(ProxyManager, self).urlopen(method, url, redirect=redirect, **kw)
-
-
-def proxy_from_url(url, **kw):
-    return ProxyManager(proxy_url=url, **kw)

spaces/Boadiwaa/Recipes/openai/api_resources/experimental/completion_config.py

@@ -1,11 +0,0 @@
-from openai.api_resources.abstract import (
-    CreateableAPIResource,
-    DeletableAPIResource,
-    ListableAPIResource,
-)
-
-
-class CompletionConfig(
-    CreateableAPIResource, ListableAPIResource, DeletableAPIResource
-):
-    OBJECT_NAME = "experimental.completion_configs"

spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/unique_by_key.h

@@ -1,67 +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/tbb/detail/execution_policy.h>
-#include <thrust/pair.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace tbb
-{
-namespace detail
-{
-
-
-template<typename DerivedPolicy,
-         typename ForwardIterator1,
-         typename ForwardIterator2,
-         typename BinaryPredicate>
-  thrust::pair<ForwardIterator1,ForwardIterator2>
-    unique_by_key(execution_policy<DerivedPolicy> &exec,
-                  ForwardIterator1 keys_first, 
-                  ForwardIterator1 keys_last,
-                  ForwardIterator2 values_first,
-                  BinaryPredicate binary_pred);
-
-
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator1,
-         typename OutputIterator2,
-         typename BinaryPredicate>
-  thrust::pair<OutputIterator1,OutputIterator2>
-    unique_by_key_copy(execution_policy<DerivedPolicy> &exec,
-                       InputIterator1 keys_first, 
-                       InputIterator1 keys_last,
-                       InputIterator2 values_first,
-                       OutputIterator1 keys_output,
-                       OutputIterator2 values_output,
-                       BinaryPredicate binary_pred);
-
-
-} // end namespace detail
-} // end namespace tbb 
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/tbb/detail/unique_by_key.inl>
-

spaces/CVPR/unicl-zero-shot-img-recog/model/text_encoder/build.py

@@ -1,31 +0,0 @@
-import os
-
-from transformers import CLIPTokenizer
-from transformers import AutoTokenizer
-
-from .registry import lang_encoders
-from .registry import is_lang_encoder
-
-
-def build_lang_encoder(config_encoder, tokenizer, verbose, **kwargs):
-    model_name = config_encoder['NAME']
-
-    if not is_lang_encoder(model_name):
-        raise ValueError(f'Unknown model: {model_name}')
-
-    return lang_encoders(model_name)(config_encoder, tokenizer, verbose, **kwargs)
-
-
-def build_tokenizer(config_encoder):
-    tokenizer = None
-    os.environ['TOKENIZERS_PARALLELISM'] = 'true'
-    if config_encoder['TOKENIZER'] == 'clip':
-        pretrained_tokenizer = config_encoder.get(
-            'PRETRAINED_TOKENIZER', 'openai/clip-vit-base-patch32'
-        )
-        tokenizer = CLIPTokenizer.from_pretrained(pretrained_tokenizer)
-        tokenizer.add_special_tokens({'cls_token': tokenizer.eos_token})
-    else:
-        tokenizer = AutoTokenizer.from_pretrained(config_encoder['TOKENIZER'])
-
-    return tokenizer

spaces/CognitiveLabs/GPT-auto-webscraping/app.py

@@ -1,107 +0,0 @@
-from AssistantService import GPTAssistant
-from openai.error import AuthenticationError
-import streamlit as st
-from langsmith.run_helpers import traceable
-import configparser
-import os
-
-config = configparser.ConfigParser()
-config.read('config.ini')
-if 'DEFAULT' in config:
-    assistant_api_key = config['DEFAULT'].get('API-KEY', '')
-
-os.environ["LANGCHAIN_TRACING_V2"]="true"
-os.environ["LANGCHAIN_ENDPOINT"]="https://api.smith.langchain.com"
-os.environ["LANGCHAIN_API_KEY"]=st.secrets["LANGCHAIN_API_KEY"]
-os.environ["LANGCHAIN_PROJECT"]=st.secrets["LANGCHAIN_PROJECT"]
-
-@traceable(run_type="tool")
-def start_session(session_started):
-    st.session_state['session_started'] = session_started
-    return session_started
-
-# change session_started to True
-if 'session_started' not in st.session_state:
-    start_session(True)
-
-st.write("This app helps you to extract data from HTML code using web scraping. It uses *GPT-3.5-turbo-16k* to generate the code for you. \n *Contribute to this project on [GitHub](https://github.com/CognitiveLabs/GPT-auto-webscraping)*")
-
-with st.expander(label="Check out the video demo"):
-    yt_video = st.video("https://www.youtube.com/watch?v=_zeCun4OlCc")
-
-info_text = """
-**Quick start** \n
-Fill the input with <HTML code>.
-- Choose a repeating element on the page, like a product on a list.
-- Inspect the HTML code and copy the element.
-- After generating the "output format" and the code, paste the complete HTML code of the page in the last input to test it
-"""
-st.write(info_text)
-st.image("https://j.gifs.com/gpqvPl.gif", width=600)
-
-
-
-if assistant_api_key == '':
-    assistant_api_key = st.secrets["API_KEY"]
-    if assistant_api_key:
-        gpt_assistant = GPTAssistant(assistant_api_key)
-else:
-    gpt_assistant = GPTAssistant(assistant_api_key)
-
-# get the html content
-html_content = st.text_input("Paste the HTML tags of the item you want to extract:", max_chars=10000, help="example: <li>Product 1 </li>, watch the video above")
-# check if html_content is an url, and show error if it is
-if html_content:
-    if html_content.startswith("http"):
-        st.write("Please paste the HTML piece code, not the URL")
-        html_content = None
-
-extract_button = st.button("Generate output format & code")
-
-
-if html_content and extract_button:
-    try:
-        st.write("1/2: Generating the output format...")
-        output = gpt_assistant.chain_response_format(html_content)
-        st.session_state['output_format'] = output
-    except NameError:
-        st.write("Complete the API key field")
-    except AuthenticationError:
-        st.write("Invalid API key")
-
-if 'output_format' in st.session_state:
-    output_format = st.code(st.session_state['output_format'], language="json")
-    
-    try:
-        st.write("2/2: Generating the code...")
-        python_code = gpt_assistant.chain_code_generator(st.session_state['output_format'], html_content)
-        st.session_state['code_generated'] = python_code
-        st.session_state['code_generated_exec'] = python_code + "\nresult = extract_info(html_data)"
-
-    except NameError:
-        st.write("Complete the API key field")
-    except AuthenticationError:
-        st.write("Invalid API key")
-            
-@traceable(run_type="tool")
-def test_the_code(code, full_content):
-    exec(code, globals())
-    if result:
-        st.write("data extracted successfully")
-        # show data in table
-        st.table(result)
-    else:
-        st.write("error extracting data")
-        
-    return result or "error"
-    
-
-if 'code_generated' in st.session_state:
-    python_function_label = st.write("Here is your python function:")
-    code_generated = st.code(st.session_state['code_generated'],language="python")
-    full_content = st.text_input("Paste your complete HTML here:")
-    test_code = st.button("Test the code")
-    if full_content and test_code:
-        html_data = full_content
-        result = None
-        test_the_code(st.session_state['code_generated_exec'], full_content=full_content)

spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/datasets/evaluation/word/util/tf.py

@@ -1,269 +0,0 @@
-from __future__ import print_function
-
-
-try:
-    import tensorflow as tf
-    from tensorflow.python.ops import nn
-    relu = nn.relu
-    slim = tf.contrib.slim
-    sigmoid = nn.sigmoid
-    softmax = nn.softmax
-except:
-    print("tensorflow is not installed, util.tf can not be used.")
-
-def is_gpu_available(cuda_only=True):
-  """
-  code from https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/platform/test.py
-  Returns whether TensorFlow can access a GPU.
-  Args:
-    cuda_only: limit the search to CUDA gpus.
-  Returns:
-    True iff a gpu device of the requested kind is available.
-  """
-  from tensorflow.python.client import device_lib as _device_lib
-
-  if cuda_only:
-    return any((x.device_type == 'GPU')
-               for x in _device_lib.list_local_devices())
-  else:
-    return any((x.device_type == 'GPU' or x.device_type == 'SYCL')
-               for x in _device_lib.list_local_devices())
-
-
-
-def get_available_gpus(num_gpus = None):
-    """
-    Modified on http://stackoverflow.com/questions/38559755/how-to-get-current-available-gpus-in-tensorflow
-    However, the original code will occupy all available gpu memory.
-    The modified code need a parameter: num_gpus. It does nothing but return the device handler name
-    It will work well on single-maching-training, but I don't know whether it will work well on a cluster.
-    """
-    if num_gpus == None:
-        from tensorflow.python.client import device_lib as _device_lib
-        local_device_protos = _device_lib.list_local_devices()
-        return [x.name for x in local_device_protos if x.device_type == 'GPU']
-    else:
-        return ['/gpu:%d'%(idx) for idx in xrange(num_gpus)]
-
-def get_latest_ckpt(path):
-# tf.train.latest_checkpoint
-    import util
-    path = util.io.get_absolute_path(path)
-    if util.io.is_dir(path):
-        ckpt = tf.train.get_checkpoint_state(path)
-        if ckpt is not None:
-            ckpt_path = ckpt.model_checkpoint_path
-        else:
-            ckpt_path = None
-    else:
-        ckpt_path = path;
-    return ckpt_path
-
-def get_all_ckpts(path):
-    ckpt = tf.train.get_checkpoint_state(path)
-    all_ckpts = ckpt.all_model_checkpoint_paths
-    ckpts = [str(c) for c in all_ckpts]
-    return ckpts
-
-def get_iter(ckpt):
-    import util
-    iter_ = int(util.str.find_all(ckpt, '.ckpt-\d+')[0].split('-')[-1])
-    return iter_
-
-def get_init_fn(checkpoint_path, train_dir, ignore_missing_vars = False,
-                checkpoint_exclude_scopes = None, model_name = None, checkpoint_model_scope = None):
-    """
-    code from github/SSD-tensorflow/tf_utils.py
-    Returns a function run by the chief worker to warm-start the training.
-    Note that the init_fn is only run when initializing the model during the very
-    first global step.
-
-    checkpoint_path: the checkpoint to be restored
-    train_dir: the directory where checkpoints are stored during training.
-    ignore_missing_vars: if False and there are variables in the model but not in the checkpoint, an error will be raised.
-    checkpoint_model_scope and model_name: if the root scope of checkpoints and the model in session is different,
-            (but the sub-scopes are all the same), specify them clearly
-    checkpoint_exclude_scopes: variables to be excluded when restoring from checkpoint_path.
-    Returns:
-      An init function run by the supervisor.
-    """
-    import util
-    if util.str.is_none_or_empty(checkpoint_path):
-        return None
-    # Warn the user if a checkpoint exists in the train_dir. Then ignore.
-    if tf.train.latest_checkpoint(train_dir):
-        tf.logging.info(
-            'Ignoring --checkpoint_path because a checkpoint already exists in %s'
-            % train_dir)
-        return None
-
-    exclusions = []
-    if checkpoint_exclude_scopes:
-        exclusions = [scope.strip()
-                      for scope in checkpoint_exclude_scopes.split(',')]
-
-    # TODO(sguada) variables.filter_variables()
-    variables_to_restore = []
-    for var in slim.get_model_variables():
-        excluded = False
-        for exclusion in exclusions:
-            if var.op.name.startswith(exclusion):
-                excluded = True
-                break
-        if not excluded:
-            variables_to_restore.append(var)
-    # Change model scope if necessary.
-    if checkpoint_model_scope is not None:
-        variables_to_restore = {checkpoint_model_scope + '/' + var.op.name : var for var in variables_to_restore}
-        tf.logging.info('variables_to_restore: %r'%(variables_to_restore))
-    checkpoint_path = get_latest_ckpt(checkpoint_path)
-    tf.logging.info('Fine-tuning from %s. Ignoring missing vars: %s' % (checkpoint_path, ignore_missing_vars))
-    print ('checkpoint_path', checkpoint_path)
-    return slim.assign_from_checkpoint_fn(
-        checkpoint_path,
-        variables_to_restore,
-        ignore_missing_vars=ignore_missing_vars)
-
-
-def get_variables_to_train(flags = None):
-    """code from github/SSD-tensorflow/tf_utils.py
-    Returns a list of variables to train.
-
-    Returns:
-      A list of variables to train by the optimizer.
-    """
-    if flags is None or flags.trainable_scopes is None:
-        return tf.trainable_variables()
-    else:
-        scopes = [scope.strip() for scope in flags.trainable_scopes.split(',')]
-
-    variables_to_train = []
-    for scope in scopes:
-        variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope)
-        variables_to_train.extend(variables)
-    return variables_to_train
-
-def Print(tensor, data, msg = '', file = None, mode = 'w'):
-    from tensorflow.python.ops import control_flow_ops
-    import util
-    def np_print(*args):
-        if util.str.contains(msg, '%'):
-            message = msg%tuple(args)
-        else:
-            message = msg + ' %'*len(args)%tuple(args)
-        if file is not None:
-            file_path = util.io.get_absolute_path(file)
-            print('writting message to file(%s):'%(file_path), message)
-            with open(file_path, mode) as f:
-                print(message, file = f)
-        else:
-            print(message)
-    return control_flow_ops.with_dependencies([tf.py_func(np_print, data, [])], tensor)
-
-def get_variable_names_in_checkpoint(path, return_shapes = False, return_reader = False):
-    """
-    Args:
-        path: the path to training directory containing checkpoints,
-            or path to checkpoint
-    Return:
-        a list of variable names in the checkpoint
-    """
-    import util
-    ckpt = get_latest_ckpt(path)
-    ckpt_reader = tf.train.NewCheckpointReader(ckpt)
-    ckpt_vars = ckpt_reader.get_variable_to_shape_map()
-    names = [var for var in ckpt_vars]
-    if return_shapes:
-        return names, ckpt_vars
-    def get(name):
-        return ckpt_reader.get_tensor(name)
-    if return_reader:
-        return names, get
-    return names
-
-
-
-def min_area_rect(xs, ys):
-    import util
-    rects = tf.py_func(util.img.min_area_rect, [xs, ys], xs.dtype)
-    rects.set_shape([None, 5])
-    return rects
-
-
-def gpu_config(config = None, allow_growth = None, gpu_memory_fraction = None):
-    if config is None:
-        config = tf.ConfigProto()
-
-    if allow_growth is not None:
-        config.gpu_options.allow_growth = allow_growth
-
-    if gpu_memory_fraction is not None:
-        config.gpu_options.per_process_gpu_memory_fraction = gpu_memory_fraction
-
-    return config
-
-def wait_for_checkpoint(path):
-    from tensorflow.contrib.training.python.training import evaluation
-    return evaluation.checkpoints_iterator(path)
-    
-def focal_loss(labels, logits, gamma = 2.0, alpha = 0.75, normalize = True):
-    labels = tf.where(labels > 0, tf.ones_like(labels), tf.zeros_like(labels))
-    labels = tf.cast(labels, tf.float32)
-    probs = tf.sigmoid(logits)
-    CE = tf.nn.sigmoid_cross_entropy_with_logits(labels = labels, logits = logits)
-
-    alpha_t = tf.ones_like(logits) * alpha
-    alpha_t = tf.where(labels > 0, alpha_t, 1.0 - alpha_t)
-    probs_t = tf.where(labels > 0, probs, 1.0 - probs)
-
-    focal_matrix = alpha_t * tf.pow((1.0 - probs_t), gamma)
-    fl = focal_matrix * CE
-
-    fl = tf.reduce_sum(fl)
-    if normalize:
-        #n_pos = tf.reduce_sum(labels)
-        #fl = fl / tf.cast(n_pos, tf.float32)
-        total_weights = tf.stop_gradient(tf.reduce_sum(focal_matrix))
-        fl = fl / total_weights
-    return fl
-
-
-def focal_loss_layer_initializer(sigma = 0.01, pi = 0.01):
-    import numpy as np
-    b0 = - np.log((1 - pi) / pi)
-    return tf.random_normal_initializer(stddev = sigma), \
-            tf.constant_initializer(b0)
-
-
-def sum_gradients(clone_grads, do_summary = False):                        
-    averaged_grads = []
-    for grad_and_vars in zip(*clone_grads):
-        grads = []
-        var = grad_and_vars[0][1]
-        try:
-            for g, v in grad_and_vars:
-                assert v == var
-                grads.append(g)
-            grad = tf.add_n(grads, name = v.op.name + '_summed_gradients')
-        except:
-            import pdb
-            pdb.set_trace()
-        
-        averaged_grads.append((grad, v))
-        
-        if do_summary:
-            tf.summary.histogram("variables_and_gradients_" + grad.op.name, grad)
-            tf.summary.histogram("variables_and_gradients_" + v.op.name, v)
-            tf.summary.scalar("variables_and_gradients_" + grad.op.name+\
-                  '_mean/var_mean', tf.reduce_mean(grad)/tf.reduce_mean(var))
-            tf.summary.scalar("variables_and_gradients_" + v.op.name+'_mean',tf.reduce_mean(var))
-    return averaged_grads
-
-def get_update_op():
-    """
-    Extremely important for BatchNorm
-    """
-    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
-    if update_ops:
-        return tf.group(*update_ops)
-    return None

spaces/DHEIVER/ImageClassifierCataract/README.md

@@ -1,12 +0,0 @@
----
-title: ImageClassifierCataract
-emoji: 📊
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.44.4
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/aiohttp/http.py

@@ -1,70 +0,0 @@
-import http.server
-import sys
-from typing import Mapping, Tuple
-
-from . import __version__
-from .http_exceptions import HttpProcessingError as HttpProcessingError
-from .http_parser import (
-    HeadersParser as HeadersParser,
-    HttpParser as HttpParser,
-    HttpRequestParser as HttpRequestParser,
-    HttpResponseParser as HttpResponseParser,
-    RawRequestMessage as RawRequestMessage,
-    RawResponseMessage as RawResponseMessage,
-)
-from .http_websocket import (
-    WS_CLOSED_MESSAGE as WS_CLOSED_MESSAGE,
-    WS_CLOSING_MESSAGE as WS_CLOSING_MESSAGE,
-    WS_KEY as WS_KEY,
-    WebSocketError as WebSocketError,
-    WebSocketReader as WebSocketReader,
-    WebSocketWriter as WebSocketWriter,
-    WSCloseCode as WSCloseCode,
-    WSMessage as WSMessage,
-    WSMsgType as WSMsgType,
-    ws_ext_gen as ws_ext_gen,
-    ws_ext_parse as ws_ext_parse,
-)
-from .http_writer import (
-    HttpVersion as HttpVersion,
-    HttpVersion10 as HttpVersion10,
-    HttpVersion11 as HttpVersion11,
-    StreamWriter as StreamWriter,
-)
-
-__all__ = (
-    "HttpProcessingError",
-    "RESPONSES",
-    "SERVER_SOFTWARE",
-    # .http_writer
-    "StreamWriter",
-    "HttpVersion",
-    "HttpVersion10",
-    "HttpVersion11",
-    # .http_parser
-    "HeadersParser",
-    "HttpParser",
-    "HttpRequestParser",
-    "HttpResponseParser",
-    "RawRequestMessage",
-    "RawResponseMessage",
-    # .http_websocket
-    "WS_CLOSED_MESSAGE",
-    "WS_CLOSING_MESSAGE",
-    "WS_KEY",
-    "WebSocketReader",
-    "WebSocketWriter",
-    "ws_ext_gen",
-    "ws_ext_parse",
-    "WSMessage",
-    "WebSocketError",
-    "WSMsgType",
-    "WSCloseCode",
-)
-
-
-SERVER_SOFTWARE: str = "Python/{0[0]}.{0[1]} aiohttp/{1}".format(
-    sys.version_info, __version__
-)
-
-RESPONSES: Mapping[int, Tuple[str, str]] = http.server.BaseHTTPRequestHandler.responses

spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/models/modules/patch_feature_extractor.py

@@ -1,57 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-from einops.layers.torch import Rearrange
-
-
-class PatchFeatureExtractor(nn.Module):
-    x_mean = torch.FloatTensor(np.array([0.485, 0.456, 0.406])[None, :, None, None])
-    x_std = torch.FloatTensor(np.array([0.229, 0.224, 0.225])[None, :, None, None])
-
-    def __init__(self, patch_num=256, input_shape=None):
-        super(PatchFeatureExtractor, self).__init__()
-
-        if input_shape is None:
-            input_shape = [3, 512, 1024]
-        self.patch_dim = 1024
-        self.patch_num = patch_num
-
-        img_channel = input_shape[0]
-        img_h = input_shape[1]
-        img_w = input_shape[2]
-
-        p_h, p_w = img_h, img_w // self.patch_num
-        p_dim = p_h * p_w * img_channel
-
-        self.patch_embedding = nn.Sequential(
-            Rearrange('b c h (p_n p_w) -> b p_n (h p_w c)', p_w=p_w),
-            nn.Linear(p_dim, self.patch_dim)
-        )
-
-        self.x_mean.requires_grad = False
-        self.x_std.requires_grad = False
-
-    def _prepare_x(self, x):
-        x = x.clone()
-        if self.x_mean.device != x.device:
-            self.x_mean = self.x_mean.to(x.device)
-            self.x_std = self.x_std.to(x.device)
-        x[:, :3] = (x[:, :3] - self.x_mean) / self.x_std
-
-        return x
-
-    def forward(self, x):
-        # x [b 3 512 1024]
-        x = self._prepare_x(x)  # [b 3 512 1024]
-        x = self.patch_embedding(x)  # [b 256(patch_num) 1024(d)]
-        x = x.permute(0, 2, 1)  # [b 1024(d) 256(patch_num)]
-        return x
-
-
-if __name__ == '__main__':
-    from PIL import Image
-    extractor = PatchFeatureExtractor()
-    img = np.array(Image.open("../../src/demo.png")).transpose((2, 0, 1))
-    input = torch.Tensor([img])  # 1 3 512 1024
-    feature = extractor(input)
-    print(feature.shape)  # 1, 1024, 256

spaces/DeepDrivePL/PaddleSeg-Matting/README.md

@@ -1,37 +0,0 @@
----
-title: PaddleSeg Matting
-emoji: 📊
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# 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` or `streamlit`
-
-`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).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.

spaces/Dinoking/Guccio-AI-Designer/netdissect/segmodel/models.py

@@ -1,558 +0,0 @@
-import torch
-import torch.nn as nn
-import torchvision
-from . import resnet, resnext
-try:
-    from lib.nn import SynchronizedBatchNorm2d
-except ImportError:
-    from torch.nn import BatchNorm2d as SynchronizedBatchNorm2d
-
-
-class SegmentationModuleBase(nn.Module):
-    def __init__(self):
-        super(SegmentationModuleBase, self).__init__()
-
-    def pixel_acc(self, pred, label):
-        _, preds = torch.max(pred, dim=1)
-        valid = (label >= 0).long()
-        acc_sum = torch.sum(valid * (preds == label).long())
-        pixel_sum = torch.sum(valid)
-        acc = acc_sum.float() / (pixel_sum.float() + 1e-10)
-        return acc
-
-
-class SegmentationModule(SegmentationModuleBase):
-    def __init__(self, net_enc, net_dec, crit, deep_sup_scale=None):
-        super(SegmentationModule, self).__init__()
-        self.encoder = net_enc
-        self.decoder = net_dec
-        self.crit = crit
-        self.deep_sup_scale = deep_sup_scale
-
-    def forward(self, feed_dict, *, segSize=None):
-        if segSize is None: # training
-            if self.deep_sup_scale is not None: # use deep supervision technique
-                (pred, pred_deepsup) = self.decoder(self.encoder(feed_dict['img_data'], return_feature_maps=True))
-            else:
-                pred = self.decoder(self.encoder(feed_dict['img_data'], return_feature_maps=True))
-
-            loss = self.crit(pred, feed_dict['seg_label'])
-            if self.deep_sup_scale is not None:
-                loss_deepsup = self.crit(pred_deepsup, feed_dict['seg_label'])
-                loss = loss + loss_deepsup * self.deep_sup_scale
-
-            acc = self.pixel_acc(pred, feed_dict['seg_label'])
-            return loss, acc
-        else: # inference
-            pred = self.decoder(self.encoder(feed_dict['img_data'], return_feature_maps=True), segSize=segSize)
-            return pred
-
-
-def conv3x3(in_planes, out_planes, stride=1, has_bias=False):
-    "3x3 convolution with padding"
-    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
-                     padding=1, bias=has_bias)
-
-
-def conv3x3_bn_relu(in_planes, out_planes, stride=1):
-    return nn.Sequential(
-            conv3x3(in_planes, out_planes, stride),
-            SynchronizedBatchNorm2d(out_planes),
-            nn.ReLU(inplace=True),
-            )
-
-
-class ModelBuilder():
-    # custom weights initialization
-    def weights_init(self, m):
-        classname = m.__class__.__name__
-        if classname.find('Conv') != -1:
-            nn.init.kaiming_normal_(m.weight.data)
-        elif classname.find('BatchNorm') != -1:
-            m.weight.data.fill_(1.)
-            m.bias.data.fill_(1e-4)
-        #elif classname.find('Linear') != -1:
-        #    m.weight.data.normal_(0.0, 0.0001)
-
-    def build_encoder(self, arch='resnet50_dilated8', fc_dim=512, weights=''):
-        pretrained = True if len(weights) == 0 else False
-        if arch == 'resnet34':
-            raise NotImplementedError
-            orig_resnet = resnet.__dict__['resnet34'](pretrained=pretrained)
-            net_encoder = Resnet(orig_resnet)
-        elif arch == 'resnet34_dilated8':
-            raise NotImplementedError
-            orig_resnet = resnet.__dict__['resnet34'](pretrained=pretrained)
-            net_encoder = ResnetDilated(orig_resnet,
-                                        dilate_scale=8)
-        elif arch == 'resnet34_dilated16':
-            raise NotImplementedError
-            orig_resnet = resnet.__dict__['resnet34'](pretrained=pretrained)
-            net_encoder = ResnetDilated(orig_resnet,
-                                        dilate_scale=16)
-        elif arch == 'resnet50':
-            orig_resnet = resnet.__dict__['resnet50'](pretrained=pretrained)
-            net_encoder = Resnet(orig_resnet)
-        elif arch == 'resnet50_dilated8':
-            orig_resnet = resnet.__dict__['resnet50'](pretrained=pretrained)
-            net_encoder = ResnetDilated(orig_resnet,
-                                        dilate_scale=8)
-        elif arch == 'resnet50_dilated16':
-            orig_resnet = resnet.__dict__['resnet50'](pretrained=pretrained)
-            net_encoder = ResnetDilated(orig_resnet,
-                                        dilate_scale=16)
-        elif arch == 'resnet101':
-            orig_resnet = resnet.__dict__['resnet101'](pretrained=pretrained)
-            net_encoder = Resnet(orig_resnet)
-        elif arch == 'resnet101_dilated8':
-            orig_resnet = resnet.__dict__['resnet101'](pretrained=pretrained)
-            net_encoder = ResnetDilated(orig_resnet,
-                                        dilate_scale=8)
-        elif arch == 'resnet101_dilated16':
-            orig_resnet = resnet.__dict__['resnet101'](pretrained=pretrained)
-            net_encoder = ResnetDilated(orig_resnet,
-                                        dilate_scale=16)
-        elif arch == 'resnext101':
-            orig_resnext = resnext.__dict__['resnext101'](pretrained=pretrained)
-            net_encoder = Resnet(orig_resnext) # we can still use class Resnet
-        else:
-            raise Exception('Architecture undefined!')
-
-        # net_encoder.apply(self.weights_init)
-        if len(weights) > 0:
-            # print('Loading weights for net_encoder')
-            net_encoder.load_state_dict(
-                torch.load(weights, map_location=lambda storage, loc: storage), strict=False)
-        return net_encoder
-
-    def build_decoder(self, arch='ppm_bilinear_deepsup',
-                      fc_dim=512, num_class=150,
-                      weights='', inference=False, use_softmax=False):
-        if arch == 'c1_bilinear_deepsup':
-            net_decoder = C1BilinearDeepSup(
-                num_class=num_class,
-                fc_dim=fc_dim,
-                inference=inference,
-                use_softmax=use_softmax)
-        elif arch == 'c1_bilinear':
-            net_decoder = C1Bilinear(
-                num_class=num_class,
-                fc_dim=fc_dim,
-                inference=inference,
-                use_softmax=use_softmax)
-        elif arch == 'ppm_bilinear':
-            net_decoder = PPMBilinear(
-                num_class=num_class,
-                fc_dim=fc_dim,
-                inference=inference,
-                use_softmax=use_softmax)
-        elif arch == 'ppm_bilinear_deepsup':
-            net_decoder = PPMBilinearDeepsup(
-                num_class=num_class,
-                fc_dim=fc_dim,
-                inference=inference,
-                use_softmax=use_softmax)
-        elif arch == 'upernet_lite':
-            net_decoder = UPerNet(
-                num_class=num_class,
-                fc_dim=fc_dim,
-                inference=inference,
-                use_softmax=use_softmax,
-                fpn_dim=256)
-        elif arch == 'upernet':
-            net_decoder = UPerNet(
-                num_class=num_class,
-                fc_dim=fc_dim,
-                inference=inference,
-                use_softmax=use_softmax,
-                fpn_dim=512)
-        elif arch == 'upernet_tmp':
-            net_decoder = UPerNetTmp(
-                num_class=num_class,
-                fc_dim=fc_dim,
-                inference=inference,
-                use_softmax=use_softmax,
-                fpn_dim=512)
-        else:
-            raise Exception('Architecture undefined!')
-
-        net_decoder.apply(self.weights_init)
-        if len(weights) > 0:
-            # print('Loading weights for net_decoder')
-            net_decoder.load_state_dict(
-                torch.load(weights, map_location=lambda storage, loc: storage), strict=False)
-        return net_decoder
-
-
-class Resnet(nn.Module):
-    def __init__(self, orig_resnet):
-        super(Resnet, self).__init__()
-
-        # take pretrained resnet, except AvgPool and FC
-        self.conv1 = orig_resnet.conv1
-        self.bn1 = orig_resnet.bn1
-        self.relu1 = orig_resnet.relu1
-        self.conv2 = orig_resnet.conv2
-        self.bn2 = orig_resnet.bn2
-        self.relu2 = orig_resnet.relu2
-        self.conv3 = orig_resnet.conv3
-        self.bn3 = orig_resnet.bn3
-        self.relu3 = orig_resnet.relu3
-        self.maxpool = orig_resnet.maxpool
-        self.layer1 = orig_resnet.layer1
-        self.layer2 = orig_resnet.layer2
-        self.layer3 = orig_resnet.layer3
-        self.layer4 = orig_resnet.layer4
-
-    def forward(self, x, return_feature_maps=False):
-        conv_out = []
-
-        x = self.relu1(self.bn1(self.conv1(x)))
-        x = self.relu2(self.bn2(self.conv2(x)))
-        x = self.relu3(self.bn3(self.conv3(x)))
-        x = self.maxpool(x)
-
-        x = self.layer1(x); conv_out.append(x);
-        x = self.layer2(x); conv_out.append(x);
-        x = self.layer3(x); conv_out.append(x);
-        x = self.layer4(x); conv_out.append(x);
-
-        if return_feature_maps:
-            return conv_out
-        return [x]
-
-
-class ResnetDilated(nn.Module):
-    def __init__(self, orig_resnet, dilate_scale=8):
-        super(ResnetDilated, self).__init__()
-        from functools import partial
-
-        if dilate_scale == 8:
-            orig_resnet.layer3.apply(
-                partial(self._nostride_dilate, dilate=2))
-            orig_resnet.layer4.apply(
-                partial(self._nostride_dilate, dilate=4))
-        elif dilate_scale == 16:
-            orig_resnet.layer4.apply(
-                partial(self._nostride_dilate, dilate=2))
-
-        # take pretrained resnet, except AvgPool and FC
-        self.conv1 = orig_resnet.conv1
-        self.bn1 = orig_resnet.bn1
-        self.relu1 = orig_resnet.relu1
-        self.conv2 = orig_resnet.conv2
-        self.bn2 = orig_resnet.bn2
-        self.relu2 = orig_resnet.relu2
-        self.conv3 = orig_resnet.conv3
-        self.bn3 = orig_resnet.bn3
-        self.relu3 = orig_resnet.relu3
-        self.maxpool = orig_resnet.maxpool
-        self.layer1 = orig_resnet.layer1
-        self.layer2 = orig_resnet.layer2
-        self.layer3 = orig_resnet.layer3
-        self.layer4 = orig_resnet.layer4
-
-    def _nostride_dilate(self, m, dilate):
-        classname = m.__class__.__name__
-        if classname.find('Conv') != -1:
-            # the convolution with stride
-            if m.stride == (2, 2):
-                m.stride = (1, 1)
-                if m.kernel_size == (3, 3):
-                    m.dilation = (dilate//2, dilate//2)
-                    m.padding = (dilate//2, dilate//2)
-            # other convoluions
-            else:
-                if m.kernel_size == (3, 3):
-                    m.dilation = (dilate, dilate)
-                    m.padding = (dilate, dilate)
-
-    def forward(self, x, return_feature_maps=False):
-        conv_out = []
-
-        x = self.relu1(self.bn1(self.conv1(x)))
-        x = self.relu2(self.bn2(self.conv2(x)))
-        x = self.relu3(self.bn3(self.conv3(x)))
-        x = self.maxpool(x)
-
-        x = self.layer1(x); conv_out.append(x);
-        x = self.layer2(x); conv_out.append(x);
-        x = self.layer3(x); conv_out.append(x);
-        x = self.layer4(x); conv_out.append(x);
-
-        if return_feature_maps:
-            return conv_out
-        return [x]
-
-
-# last conv, bilinear upsample
-class C1BilinearDeepSup(nn.Module):
-    def __init__(self, num_class=150, fc_dim=2048, inference=False, use_softmax=False):
-        super(C1BilinearDeepSup, self).__init__()
-        self.use_softmax = use_softmax
-        self.inference = inference
-
-        self.cbr = conv3x3_bn_relu(fc_dim, fc_dim // 4, 1)
-        self.cbr_deepsup = conv3x3_bn_relu(fc_dim // 2, fc_dim // 4, 1)
-
-        # last conv
-        self.conv_last = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0)
-        self.conv_last_deepsup = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0)
-
-    def forward(self, conv_out, segSize=None):
-        conv5 = conv_out[-1]
-
-        x = self.cbr(conv5)
-        x = self.conv_last(x)
-
-        if self.inference or self.use_softmax:  # is True during inference
-            x = nn.functional.interpolate(
-                x, size=segSize, mode='bilinear', align_corners=False)
-            if self.use_softmax:
-                x = nn.functional.softmax(x, dim=1)
-            return x
-
-        # deep sup
-        conv4 = conv_out[-2]
-        _ = self.cbr_deepsup(conv4)
-        _ = self.conv_last_deepsup(_)
-
-        x = nn.functional.log_softmax(x, dim=1)
-        _ = nn.functional.log_softmax(_, dim=1)
-
-        return (x, _)
-
-
-# last conv, bilinear upsample
-class C1Bilinear(nn.Module):
-    def __init__(self, num_class=150, fc_dim=2048, inference=False, use_softmax=False):
-        super(C1Bilinear, self).__init__()
-        self.use_softmax = use_softmax
-        self.inference = inference
-
-        self.cbr = conv3x3_bn_relu(fc_dim, fc_dim // 4, 1)
-
-        # last conv
-        self.conv_last = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0)
-
-    def forward(self, conv_out, segSize=None):
-        conv5 = conv_out[-1]
-        x = self.cbr(conv5)
-        x = self.conv_last(x)
-
-        if self.inference or self.use_softmax:  # is True during inference
-            x = nn.functional.interpolate(
-                x, size=segSize, mode='bilinear', align_corners=False)
-            if self.use_softmax:
-                x = nn.functional.softmax(x, dim=1)
-        else:
-            x = nn.functional.log_softmax(x, dim=1)
-
-        return x
-
-
-# pyramid pooling, bilinear upsample
-class PPMBilinear(nn.Module):
-    def __init__(self, num_class=150, fc_dim=4096,
-                 inference=False, use_softmax=False, pool_scales=(1, 2, 3, 6)):
-        super(PPMBilinear, self).__init__()
-        self.use_softmax = use_softmax
-        self.inference = inference
-
-        self.ppm = []
-        for scale in pool_scales:
-            self.ppm.append(nn.Sequential(
-                nn.AdaptiveAvgPool2d(scale),
-                nn.Conv2d(fc_dim, 512, kernel_size=1, bias=False),
-                SynchronizedBatchNorm2d(512),
-                nn.ReLU(inplace=True)
-            ))
-        self.ppm = nn.ModuleList(self.ppm)
-
-        self.conv_last = nn.Sequential(
-            nn.Conv2d(fc_dim+len(pool_scales)*512, 512,
-                      kernel_size=3, padding=1, bias=False),
-            SynchronizedBatchNorm2d(512),
-            nn.ReLU(inplace=True),
-            nn.Dropout2d(0.1),
-            nn.Conv2d(512, num_class, kernel_size=1)
-        )
-
-    def forward(self, conv_out, segSize=None):
-        conv5 = conv_out[-1]
-
-        input_size = conv5.size()
-        ppm_out = [conv5]
-        for pool_scale in self.ppm:
-            ppm_out.append(nn.functional.interpolate(
-                pool_scale(conv5),
-                (input_size[2], input_size[3]),
-                mode='bilinear', align_corners=False))
-        ppm_out = torch.cat(ppm_out, 1)
-
-        x = self.conv_last(ppm_out)
-
-        if self.inference or self.use_softmax:  # is True during inference
-            x = nn.functional.interpolate(
-                x, size=segSize, mode='bilinear', align_corners=False)
-            if self.use_softmax:
-                x = nn.functional.softmax(x, dim=1)
-        else:
-            x = nn.functional.log_softmax(x, dim=1)
-        return x
-
-
-# pyramid pooling, bilinear upsample
-class PPMBilinearDeepsup(nn.Module):
-    def __init__(self, num_class=150, fc_dim=4096,
-                 inference=False, use_softmax=False, pool_scales=(1, 2, 3, 6)):
-        super(PPMBilinearDeepsup, self).__init__()
-        self.use_softmax = use_softmax
-        self.inference = inference
-
-        self.ppm = []
-        for scale in pool_scales:
-            self.ppm.append(nn.Sequential(
-                nn.AdaptiveAvgPool2d(scale),
-                nn.Conv2d(fc_dim, 512, kernel_size=1, bias=False),
-                SynchronizedBatchNorm2d(512),
-                nn.ReLU(inplace=True)
-            ))
-        self.ppm = nn.ModuleList(self.ppm)
-        self.cbr_deepsup = conv3x3_bn_relu(fc_dim // 2, fc_dim // 4, 1)
-
-        self.conv_last = nn.Sequential(
-            nn.Conv2d(fc_dim+len(pool_scales)*512, 512,
-                      kernel_size=3, padding=1, bias=False),
-            SynchronizedBatchNorm2d(512),
-            nn.ReLU(inplace=True),
-            nn.Dropout2d(0.1),
-            nn.Conv2d(512, num_class, kernel_size=1)
-        )
-        self.conv_last_deepsup = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0)
-        self.dropout_deepsup = nn.Dropout2d(0.1)
-
-    def forward(self, conv_out, segSize=None):
-        conv5 = conv_out[-1]
-
-        input_size = conv5.size()
-        ppm_out = [conv5]
-        for pool_scale in self.ppm:
-            ppm_out.append(nn.functional.interpolate(
-                pool_scale(conv5),
-                (input_size[2], input_size[3]),
-                mode='bilinear', align_corners=False))
-        ppm_out = torch.cat(ppm_out, 1)
-
-        x = self.conv_last(ppm_out)
-
-        if self.inference or self.use_softmax:  # is True during inference
-            x = nn.functional.interpolate(
-                x, size=segSize, mode='bilinear', align_corners=False)
-            if self.use_softmax:
-                x = nn.functional.softmax(x, dim=1)
-            return x
-
-        # deep sup
-        conv4 = conv_out[-2]
-        _ = self.cbr_deepsup(conv4)
-        _ = self.dropout_deepsup(_)
-        _ = self.conv_last_deepsup(_)
-
-        x = nn.functional.log_softmax(x, dim=1)
-        _ = nn.functional.log_softmax(_, dim=1)
-
-        return (x, _)
-
-
-# upernet
-class UPerNet(nn.Module):
-    def __init__(self, num_class=150, fc_dim=4096,
-                 inference=False, use_softmax=False, pool_scales=(1, 2, 3, 6),
-                 fpn_inplanes=(256,512,1024,2048), fpn_dim=256):
-        super(UPerNet, self).__init__()
-        self.use_softmax = use_softmax
-        self.inference = inference
-
-        # PPM Module
-        self.ppm_pooling = []
-        self.ppm_conv = []
-
-        for scale in pool_scales:
-            self.ppm_pooling.append(nn.AdaptiveAvgPool2d(scale))
-            self.ppm_conv.append(nn.Sequential(
-                nn.Conv2d(fc_dim, 512, kernel_size=1, bias=False),
-                SynchronizedBatchNorm2d(512),
-                nn.ReLU(inplace=True)
-            ))
-        self.ppm_pooling = nn.ModuleList(self.ppm_pooling)
-        self.ppm_conv = nn.ModuleList(self.ppm_conv)
-        self.ppm_last_conv = conv3x3_bn_relu(fc_dim + len(pool_scales)*512, fpn_dim, 1)
-
-        # FPN Module
-        self.fpn_in = []
-        for fpn_inplane in fpn_inplanes[:-1]: # skip the top layer
-            self.fpn_in.append(nn.Sequential(
-                nn.Conv2d(fpn_inplane, fpn_dim, kernel_size=1, bias=False),
-                SynchronizedBatchNorm2d(fpn_dim),
-                nn.ReLU(inplace=True)
-            ))
-        self.fpn_in = nn.ModuleList(self.fpn_in)
-
-        self.fpn_out = []
-        for i in range(len(fpn_inplanes) - 1): # skip the top layer
-            self.fpn_out.append(nn.Sequential(
-                conv3x3_bn_relu(fpn_dim, fpn_dim, 1),
-            ))
-        self.fpn_out = nn.ModuleList(self.fpn_out)
-
-        self.conv_last = nn.Sequential(
-            conv3x3_bn_relu(len(fpn_inplanes) * fpn_dim, fpn_dim, 1),
-            nn.Conv2d(fpn_dim, num_class, kernel_size=1)
-        )
-
-    def forward(self, conv_out, segSize=None):
-        conv5 = conv_out[-1]
-
-        input_size = conv5.size()
-        ppm_out = [conv5]
-        for pool_scale, pool_conv in zip(self.ppm_pooling, self.ppm_conv):
-            ppm_out.append(pool_conv(nn.functional.interploate(
-                pool_scale(conv5),
-                (input_size[2], input_size[3]),
-                mode='bilinear', align_corners=False)))
-        ppm_out = torch.cat(ppm_out, 1)
-        f = self.ppm_last_conv(ppm_out)
-
-        fpn_feature_list = [f]
-        for i in reversed(range(len(conv_out) - 1)):
-            conv_x = conv_out[i]
-            conv_x = self.fpn_in[i](conv_x) # lateral branch
-
-            f = nn.functional.interpolate(
-                f, size=conv_x.size()[2:], mode='bilinear', align_corners=False) # top-down branch
-            f = conv_x + f
-
-            fpn_feature_list.append(self.fpn_out[i](f))
-
-        fpn_feature_list.reverse() # [P2 - P5]
-        output_size = fpn_feature_list[0].size()[2:]
-        fusion_list = [fpn_feature_list[0]]
-        for i in range(1, len(fpn_feature_list)):
-            fusion_list.append(nn.functional.interpolate(
-                fpn_feature_list[i],
-                output_size,
-                mode='bilinear', align_corners=False))
-        fusion_out = torch.cat(fusion_list, 1)
-        x = self.conv_last(fusion_out)
-
-        if self.inference or self.use_softmax:  # is True during inference
-            x = nn.functional.interpolate(
-                x, size=segSize, mode='bilinear', align_corners=False)
-            if self.use_softmax:
-                x = nn.functional.softmax(x, dim=1)
-            return x
-
-        x = nn.functional.log_softmax(x, dim=1)
-
-        return x

spaces/DragGan/DragGan-Inversion/PTI/torch_utils/ops/__init__.py

@@ -1,9 +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.
-
-# empty

spaces/ECCV2022/PSG/OpenPSG/configs/motifs/panoptic_fpn_r101_fpn_1x_predcls_psg.py

@@ -1,28 +0,0 @@
-_base_ = './panoptic_fpn_r50_fpn_1x_predcls_psg.py'
-
-model = dict(backbone=dict(
-    depth=101,
-    init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101')))
-
-# Log config
-project_name = 'openpsg'
-expt_name = 'motifs_panoptic_fpn_r101_fpn_1x_predcls_psg'
-work_dir = f'./work_dirs/{expt_name}'
-
-log_config = dict(
-    interval=50,
-    hooks=[
-        dict(type='TextLoggerHook'),
-        # dict(type='TensorboardLoggerHook')
-        dict(
-            type='WandbLoggerHook',
-            init_kwargs=dict(
-                project=project_name,
-                name=expt_name,
-                # config=work_dir + "/cfg.yaml"
-            ),
-        ),
-    ],
-)
-
-load_from = 'work_dirs/checkpoints/panoptic_fpn_r101_fpn_1x_coco_20210820_193950-ab9157a2.pth'

spaces/ECE1786-AG/ArtIstic-GENREator/app.py

@@ -1,91 +0,0 @@
-import torch
-import gradio as gr
-from transformers import pipeline, T5ForConditionalGeneration, T5Tokenizer
-from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
-
-# generate lyrics
-lyrics_generator = pipeline("text-generation", "ECE1786-AG/lyrics-generator")
-
-# summarize lyrics
-model = T5ForConditionalGeneration.from_pretrained("Michau/t5-base-en-generate-headline")
-tokenizer = T5Tokenizer.from_pretrained("Michau/t5-base-en-generate-headline")
-
-# generate single cover
-scheduler = EulerDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-2", subfolder="scheduler")
-pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", scheduler=scheduler, revision="fp16", torch_dtype=torch.float16)
-device = "cuda" if torch.cuda.is_available() else "cpu"
-pipe = pipe.to(device)
-
-def generate_lyrics(genre, prompt):
-  complete_prompt = "<BOS> <{0}>\n{1}".format(genre, prompt)
-  lyrics = lyrics_generator(complete_prompt, max_length=1024)
-  lyrics = lyrics[0]['generated_text']
-  lyrics = lyrics.split('\n', 1)[1] # remove first line from the generated lyrics
-  
-  return lyrics
-
-def summarize_lyrics(lyrics):
-  text =  "headline: " + lyrics
-  encoding = tokenizer.encode_plus(text, return_tensors = "pt")
-  input_ids = encoding["input_ids"]
-  attention_masks = encoding["attention_mask"]
-  beam_outputs = model.generate(
-      input_ids = input_ids,
-      attention_mask = attention_masks,
-      max_length = 100,
-      num_beams = 5,
-      early_stopping = True,
-  )
-  result = tokenizer.decode(beam_outputs[0])
-  result = result.replace('<pad>', '')
-  result = result.replace('</s>', '')
-
-  return result
-
-def generate_cover(prompt, style, effect):
-  prompt = summarize_lyrics(prompt) # call function summarize_lyrics to summarize lyrics
-  prompt = prompt + ", " + effect + ", album cover, artistic, " + style
-  print(prompt)
-  image = pipe(prompt).images[0]
-  return image
-
-demo = gr.Blocks()
-with demo:
-    gr.HTML(
-        """
-            <div style="text-align: center; max-width: 700px; margin: 0 auto;">
-              <div style="display: inline-flex; align-items: center; gap: 0.8rem; font-size: 1.75rem;">
-                <h1 style="font-weight: 900; margin-bottom: 7px;">ArtIstic GENREator</h1>
-              </div>
-              <p style="margin-bottom: 10px; font-size: 94%">Generate Inspirational Lyrics and Single Cover</p>
-            </div>
-        """
-    )
-
-    with gr.Row():
-    
-        # Left column (lyrics generation)
-        with gr.Column():
-            with gr.Accordion("Step 1. Generate Lyrics"):
-              gr.Markdown("Enter the starting text and select genre to generate lyrics")
-            with gr.Row():
-              input_start_text = gr.Textbox(placeholder='I am', label="Starting Text")
-              input_lyrics_type = gr.Radio(choices=['pop', 'rap', 'country', 'rock', 'r&b'], value='pop', label="Lyrics Genre")
-            button_gen_lyrics = gr.Button("Generate Lyrics", variant="primary")
-            output_generated_lyrics = gr.Textbox(label="Generated Lyrics", lines=8)
-
-        # Right column (single cover generation)
-        with gr.Column():
-          with gr.Accordion("Step 2. Generate Single Cover"):
-            gr.Markdown("Cover will be generated based on style, effect and generated lyrics")
-          with gr.Row():
-            input_cover_style = gr.Dropdown(choices=['painted', 'abstract', 'minimalist', 'illustrated', 'photographic', 'vintage'], value='painted', label="Track Cover Style")
-            input_cover_effect = gr.Radio(choices=['black and white', 'highly detailed', 'blurred'], value='highly detailed', label="Track Cover Effect")
-          button_gen_cover = gr.Button("Generate Cover", variant="primary")
-          output_generated_cover = gr.Image(label="Generated Cover")
-            
-    # Bind functions to buttons
-    button_gen_lyrics.click(fn=generate_lyrics, inputs=[input_lyrics_type , input_start_text], outputs=output_generated_lyrics)
-    button_gen_cover.click(fn=generate_cover, inputs=[output_generated_lyrics, input_cover_style, input_cover_effect], outputs=output_generated_cover)
-
-demo.launch(debug=True)

spaces/FridaZuley/RVC_HFKawaii/infer/lib/infer_pack/modules.py

@@ -1,521 +0,0 @@
-import copy
-import math
-
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
-from torch.nn import functional as F
-from torch.nn.utils import remove_weight_norm, weight_norm
-
-from infer.lib.infer_pack import commons
-from infer.lib.infer_pack.commons import get_padding, init_weights
-from infer.lib.infer_pack.transforms import piecewise_rational_quadratic_transform
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-5):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        x = x.transpose(1, -1)
-        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-        return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        hidden_channels,
-        out_channels,
-        kernel_size,
-        n_layers,
-        p_dropout,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_channels = hidden_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-        assert n_layers > 1, "Number of layers should be larger than 0."
-
-        self.conv_layers = nn.ModuleList()
-        self.norm_layers = nn.ModuleList()
-        self.conv_layers.append(
-            nn.Conv1d(
-                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
-            )
-        )
-        self.norm_layers.append(LayerNorm(hidden_channels))
-        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
-        for _ in range(n_layers - 1):
-            self.conv_layers.append(
-                nn.Conv1d(
-                    hidden_channels,
-                    hidden_channels,
-                    kernel_size,
-                    padding=kernel_size // 2,
-                )
-            )
-            self.norm_layers.append(LayerNorm(hidden_channels))
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask):
-        x_org = x
-        for i in range(self.n_layers):
-            x = self.conv_layers[i](x * x_mask)
-            x = self.norm_layers[i](x)
-            x = self.relu_drop(x)
-        x = x_org + self.proj(x)
-        return x * x_mask
-
-
-class DDSConv(nn.Module):
-    """
-    Dialted and Depth-Separable Convolution
-    """
-
-    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
-        super().__init__()
-        self.channels = channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-
-        self.drop = nn.Dropout(p_dropout)
-        self.convs_sep = nn.ModuleList()
-        self.convs_1x1 = nn.ModuleList()
-        self.norms_1 = nn.ModuleList()
-        self.norms_2 = nn.ModuleList()
-        for i in range(n_layers):
-            dilation = kernel_size**i
-            padding = (kernel_size * dilation - dilation) // 2
-            self.convs_sep.append(
-                nn.Conv1d(
-                    channels,
-                    channels,
-                    kernel_size,
-                    groups=channels,
-                    dilation=dilation,
-                    padding=padding,
-                )
-            )
-            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-            self.norms_1.append(LayerNorm(channels))
-            self.norms_2.append(LayerNorm(channels))
-
-    def forward(self, x, x_mask, g=None):
-        if g is not None:
-            x = x + g
-        for i in range(self.n_layers):
-            y = self.convs_sep[i](x * x_mask)
-            y = self.norms_1[i](y)
-            y = F.gelu(y)
-            y = self.convs_1x1[i](y)
-            y = self.norms_2[i](y)
-            y = F.gelu(y)
-            y = self.drop(y)
-            x = x + y
-        return x * x_mask
-
-
-class WN(torch.nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-        p_dropout=0,
-    ):
-        super(WN, self).__init__()
-        assert kernel_size % 2 == 1
-        self.hidden_channels = hidden_channels
-        self.kernel_size = (kernel_size,)
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-        self.p_dropout = p_dropout
-
-        self.in_layers = torch.nn.ModuleList()
-        self.res_skip_layers = torch.nn.ModuleList()
-        self.drop = nn.Dropout(p_dropout)
-
-        if gin_channels != 0:
-            cond_layer = torch.nn.Conv1d(
-                gin_channels, 2 * hidden_channels * n_layers, 1
-            )
-            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
-
-        for i in range(n_layers):
-            dilation = dilation_rate**i
-            padding = int((kernel_size * dilation - dilation) / 2)
-            in_layer = torch.nn.Conv1d(
-                hidden_channels,
-                2 * hidden_channels,
-                kernel_size,
-                dilation=dilation,
-                padding=padding,
-            )
-            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
-            self.in_layers.append(in_layer)
-
-            # last one is not necessary
-            if i < n_layers - 1:
-                res_skip_channels = 2 * hidden_channels
-            else:
-                res_skip_channels = hidden_channels
-
-            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
-            self.res_skip_layers.append(res_skip_layer)
-
-    def forward(self, x, x_mask, g=None, **kwargs):
-        output = torch.zeros_like(x)
-        n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-        if g is not None:
-            g = self.cond_layer(g)
-
-        for i in range(self.n_layers):
-            x_in = self.in_layers[i](x)
-            if g is not None:
-                cond_offset = i * 2 * self.hidden_channels
-                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
-            else:
-                g_l = torch.zeros_like(x_in)
-
-            acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
-            acts = self.drop(acts)
-
-            res_skip_acts = self.res_skip_layers[i](acts)
-            if i < self.n_layers - 1:
-                res_acts = res_skip_acts[:, : self.hidden_channels, :]
-                x = (x + res_acts) * x_mask
-                output = output + res_skip_acts[:, self.hidden_channels :, :]
-            else:
-                output = output + res_skip_acts
-        return output * x_mask
-
-    def remove_weight_norm(self):
-        if self.gin_channels != 0:
-            torch.nn.utils.remove_weight_norm(self.cond_layer)
-        for l in self.in_layers:
-            torch.nn.utils.remove_weight_norm(l)
-        for l in self.res_skip_layers:
-            torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[2],
-                        padding=get_padding(kernel_size, dilation[2]),
-                    )
-                ),
-            ]
-        )
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-            ]
-        )
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-            ]
-        )
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-            logdet = torch.sum(-y, [1, 2])
-            return y, logdet
-        else:
-            x = torch.exp(x) * x_mask
-            return x
-
-
-class Flip(nn.Module):
-    def forward(self, x, *args, reverse=False, **kwargs):
-        x = torch.flip(x, [1])
-        if not reverse:
-            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-            return x, logdet
-        else:
-            return x
-
-
-class ElementwiseAffine(nn.Module):
-    def __init__(self, channels):
-        super().__init__()
-        self.channels = channels
-        self.m = nn.Parameter(torch.zeros(channels, 1))
-        self.logs = nn.Parameter(torch.zeros(channels, 1))
-
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = self.m + torch.exp(self.logs) * x
-            y = y * x_mask
-            logdet = torch.sum(self.logs * x_mask, [1, 2])
-            return y, logdet
-        else:
-            x = (x - self.m) * torch.exp(-self.logs) * x_mask
-            return x
-
-
-class ResidualCouplingLayer(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        p_dropout=0,
-        gin_channels=0,
-        mean_only=False,
-    ):
-        assert channels % 2 == 0, "channels should be divisible by 2"
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.half_channels = channels // 2
-        self.mean_only = mean_only
-
-        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-        self.enc = WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            p_dropout=p_dropout,
-            gin_channels=gin_channels,
-        )
-        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-        self.post.weight.data.zero_()
-        self.post.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0) * x_mask
-        h = self.enc(h, x_mask, g=g)
-        stats = self.post(h) * x_mask
-        if not self.mean_only:
-            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
-        else:
-            m = stats
-            logs = torch.zeros_like(m)
-
-        if not reverse:
-            x1 = m + x1 * torch.exp(logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            logdet = torch.sum(logs, [1, 2])
-            return x, logdet
-        else:
-            x1 = (x1 - m) * torch.exp(-logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            return x
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class ConvFlow(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        filter_channels,
-        kernel_size,
-        n_layers,
-        num_bins=10,
-        tail_bound=5.0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.num_bins = num_bins
-        self.tail_bound = tail_bound
-        self.half_channels = in_channels // 2
-
-        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
-        self.proj = nn.Conv1d(
-            filter_channels, self.half_channels * (num_bins * 3 - 1), 1
-        )
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0)
-        h = self.convs(h, x_mask, g=g)
-        h = self.proj(h) * x_mask
-
-        b, c, t = x0.shape
-        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
-
-        unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
-        unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
-            self.filter_channels
-        )
-        unnormalized_derivatives = h[..., 2 * self.num_bins :]
-
-        x1, logabsdet = piecewise_rational_quadratic_transform(
-            x1,
-            unnormalized_widths,
-            unnormalized_heights,
-            unnormalized_derivatives,
-            inverse=reverse,
-            tails="linear",
-            tail_bound=self.tail_bound,
-        )
-
-        x = torch.cat([x0, x1], 1) * x_mask
-        logdet = torch.sum(logabsdet * x_mask, [1, 2])
-        if not reverse:
-            return x, logdet
-        else:
-            return x

spaces/FridaZuley/RVC_HFKawaii/infer/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py

@@ -1,98 +0,0 @@
-import numpy as np
-import parselmouth
-
-from infer.lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-
-
-class PMF0Predictor(F0Predictor):
-    def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    def interpolate_f0(self, f0):
-        """
-        对F0进行插值处理
-        """
-
-        data = np.reshape(f0, (f0.size, 1))
-
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-
-        ip_data = data
-
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i]  # 这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-
-        return ip_data[:, 0], vuv_vector[:, 0]
-
-    def compute_f0(self, wav, p_len=None):
-        x = wav
-        if p_len is None:
-            p_len = x.shape[0] // self.hop_length
-        else:
-            assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error"
-        time_step = self.hop_length / self.sampling_rate * 1000
-        f0 = (
-            parselmouth.Sound(x, self.sampling_rate)
-            .to_pitch_ac(
-                time_step=time_step / 1000,
-                voicing_threshold=0.6,
-                pitch_floor=self.f0_min,
-                pitch_ceiling=self.f0_max,
-            )
-            .selected_array["frequency"]
-        )
-
-        pad_size = (p_len - len(f0) + 1) // 2
-        if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-            f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
-        f0, uv = self.interpolate_f0(f0)
-        return f0
-
-    def compute_f0_uv(self, wav, p_len=None):
-        x = wav
-        if p_len is None:
-            p_len = x.shape[0] // self.hop_length
-        else:
-            assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error"
-        time_step = self.hop_length / self.sampling_rate * 1000
-        f0 = (
-            parselmouth.Sound(x, self.sampling_rate)
-            .to_pitch_ac(
-                time_step=time_step / 1000,
-                voicing_threshold=0.6,
-                pitch_floor=self.f0_min,
-                pitch_ceiling=self.f0_max,
-            )
-            .selected_array["frequency"]
-        )
-
-        pad_size = (p_len - len(f0) + 1) // 2
-        if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-            f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
-        f0, uv = self.interpolate_f0(f0)
-        return f0, uv

spaces/GT-RIPL/GPT-K/knowledge/retrieve.py

@@ -1,105 +0,0 @@
-import h5py
-import numpy as np
-from tqdm import tqdm
-import torch
-from knowledge import TextDB
-
-
-class ImageCropsIdx:
-    def __init__(self, knowledge_idx, topk_w, topk_f, topk_n):
-        topk = {"whole": topk_w, "five": topk_f, "nine": topk_n}
-        self.topk = {k: v for k, v in topk.items() if v > 0}
-
-        self.knowledge_idx, self.fdim, self.file_hash = self.load(knowledge_idx, self.topk)
-
-    def load(self, knowledge_idx, topk):
-        with h5py.File(knowledge_idx, "r") as f:
-            fdim = f.attrs["fdim"]
-            file_hash = f.attrs["file_hash"]
-
-            knowledge_idx_ = {}
-            for i in tqdm(range(len(f)), desc="Load sentence idx", dynamic_ncols=True, mininterval=1.0):
-                knowledge_idx_[str(i)] = {"image_ids": f[f"{i}/image_ids"][:]}
-                for k, v in topk.items():
-                    knowledge_idx_[str(i)][k] = {
-                        "index": f[f"{i}/{k}/index"][:, :, :v],
-                        "score": f[f"{i}/{k}/score"][:, :, :v],
-                        "query": f[f"{i}/{k}/query"][:]
-                    }
-        
-        knowledge_idx = {}
-        for i in knowledge_idx_.keys():
-            for j, id in enumerate(knowledge_idx_[i]["image_ids"]):
-                knowledge_idx[id] = {}
-                for k in topk.keys():
-                    knowledge_idx[id][k] = {
-                        "index": knowledge_idx_[i][k]["index"][j],
-                        "score": knowledge_idx_[i][k]["score"][j],
-                        "query": knowledge_idx_[i][k]["query"][j],
-                    }
-
-        return knowledge_idx, fdim, file_hash
-    
-    def __getitem__(self, image_id):
-        return self.knowledge_idx[image_id]
-        
-
-class KnowAugImageCrops:
-    def __init__(self, knowledge_db: TextDB, knowledge_idx: ImageCropsIdx, return_txt=False):
-        self.knowledge_db = knowledge_db
-        self.knowledge_idx = knowledge_idx
-        assert knowledge_db.file_hash == knowledge_idx.file_hash
-
-        self.ncrop = {"whole": 1, "five": 5, "nine": 9}
-        self.topk = knowledge_idx.topk
-        self.fdim = knowledge_idx.fdim
-
-        self.return_txt = return_txt
-    
-    def __call__(self, image_id):
-        ret = {}
-        for k in self.topk.keys():
-            ki = self.knowledge_idx[image_id][k]["index"].flatten()
-            ke, kt = self.knowledge_db[ki]
-            kq = self.knowledge_idx[image_id][k]["query"]
-            kp = np.tile(np.arange(self.ncrop[k])[:, None], (1, self.topk[k])).flatten()
-            ks = self.knowledge_idx[image_id][k]["score"].flatten()
-
-            ke = torch.FloatTensor(ke)
-            kq = torch.FloatTensor(kq)
-            kp = torch.LongTensor(kp)
-            ks = torch.FloatTensor(ks)
-
-            ret[k] = {"embed": ke, "query": kq, "pos": kp, "score": ks}
-            if self.return_txt:
-                ret[k]["text"] = kt
-        
-        return ret
-
-
-class KnowAugImageCropsCombined:
-    def __init__(
-        self, 
-        knwl_aug_obj: KnowAugImageCrops,
-        knwl_aug_attr: KnowAugImageCrops,
-        knwl_aug_act: KnowAugImageCrops
-    ):
-        self.knwl_aug_obj = knwl_aug_obj
-        self.knwl_aug_act = knwl_aug_act
-        self.knwl_aug_attr = knwl_aug_attr
-        self.fdim = knwl_aug_obj.fdim
-
-    def __call__(self, image_id):
-        knwl_obj = self.knwl_aug_obj(image_id)
-        knwl_attr = self.knwl_aug_attr(image_id)
-        knwl_act = self.knwl_aug_act(image_id)
-
-        ret = {}
-        for k in knwl_obj.keys():
-            ret[k] = {
-                "obj": knwl_obj[k],
-                "attr": knwl_attr[k],
-                "act": knwl_act[k]
-            }
-
-        return ret

spaces/Gen-Sim/Gen-Sim/cliport/models/streams/two_stream_transport_lang_fusion.py

@@ -1,196 +0,0 @@
-import torch
-import numpy as np
-
-import cliport.models as models
-import cliport.models.core.fusion as fusion
-from cliport.models.core.transport import Transport
-
-
-class TwoStreamTransportLangFusion(Transport):
-    """Two Stream Transport (a.k.a Place) module"""
-
-    def __init__(self, stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device):
-        self.fusion_type = cfg['train']['trans_stream_fusion_type']
-        super().__init__(stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device)
-
-    def _build_nets(self):
-        stream_one_fcn, stream_two_fcn = self.stream_fcn
-        stream_one_model = models.names[stream_one_fcn]
-        stream_two_model = models.names[stream_two_fcn]
-
-        self.key_stream_one = stream_one_model(self.in_shape, self.output_dim, self.cfg, self.device, self.preprocess)
-        self.key_stream_two = stream_two_model(self.in_shape, self.output_dim, self.cfg, self.device, self.preprocess)
-        self.query_stream_one = stream_one_model(self.kernel_shape, self.kernel_dim, self.cfg, self.device, self.preprocess)
-        self.query_stream_two = stream_two_model(self.kernel_shape, self.kernel_dim, self.cfg, self.device, self.preprocess)
-        self.fusion_key = fusion.names[self.fusion_type](input_dim=self.kernel_dim)
-        self.fusion_query = fusion.names[self.fusion_type](input_dim=self.kernel_dim)
-
-        print(f"Transport FCN - Stream One: {stream_one_fcn}, Stream Two: {stream_two_fcn}, Stream Fusion: {self.fusion_type}")
-
-    def transport2(self, in_tensor, crop, l):
-        logits = self.fusion_key(self.key_stream_one(in_tensor), self.key_stream_two(in_tensor, l))
-        kernel = self.fusion_query(self.query_stream_one(crop), self.query_stream_two(crop, l))
-        return logits, kernel
-
-    def forward(self, inp_img, p, lang_goal, softmax=True):
-        """Forward pass."""
-        if len(inp_img.shape) < 4:
-            inp_img = inp_img[None]
-
-        if type(inp_img) is not torch.Tensor:
-            in_data = inp_img # .reshape(in_shape)
-            in_tens = torch.from_numpy(in_data).to(dtype=torch.float, device=self.device)  # [B W H 6]
-        else:
-            in_data = inp_img
-            in_tens = in_data
-
-        in_tensor = torch.nn.functional.pad(in_tens, tuple(self.padding[[2,1,0]].reshape(-1)), mode='constant')
-        if type(p[0]) is not torch.Tensor:
-            p = torch.FloatTensor(p)[None]
-
-        in_tensors = []
-        crops = []
-
-        # this for loop is fast.
-        for i in range(len(in_tensor)):
-            in_tensor_i = in_tensor[[i]]           
-            # Rotation pivot.
-            pv = p[i] + self.pad_size
-
-            # Crop before network (default for Transporters CoRL 2020).
-            hcrop = self.pad_size
-            in_tensor_i = in_tensor_i.permute(0, 3, 1, 2)
-
-            crop = [in_tensor_i] * self.n_rotations
-            crop = self.rotator(crop, pivot=pv.float())
-            crop = torch.cat(crop, dim=0)
-            crop = crop[:, :, int(pv[0]-hcrop):int(pv[0]+hcrop), int(pv[1]-hcrop):int(pv[1]+hcrop)]
-
-            in_tensors.append(in_tensor_i)
-            crops.append(crop)
-
-        logits, kernels = self.transport(torch.cat(in_tensors,dim=0), torch.cat(crops, dim=0), lang_goal) #crops.shape:(8, 36, 6, 64, 64)
-        res =  self.correlate(logits, kernels, softmax)
-        return res
-        
-class TwoStreamTransportLangFusionLat(TwoStreamTransportLangFusion):
-    """Two Stream Transport (a.k.a Place) module with lateral connections"""
-
-    def __init__(self, stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device):
-
-        self.fusion_type = cfg['train']['trans_stream_fusion_type']
-        super().__init__(stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device)
-
-    def transport(self, in_tensor, crop, l):
-        key_out_one, key_lat_one = self.key_stream_one(in_tensor)
-        key_out_two = self.key_stream_two(in_tensor, key_lat_one, l)
-        logits = self.fusion_key(key_out_one, key_out_two)
-
-        query_out_one, query_lat_one = self.query_stream_one(crop)
-        query_out_two = self.query_stream_two(crop, query_lat_one, l)
-        kernel = self.fusion_query(query_out_one, query_out_two)
-
-        return logits, kernel
-    
-    
-class TwoStreamTransportLangFusionLatReduce(TwoStreamTransportLangFusionLat):
-    """Two Stream Transport (a.k.a Place) module with lateral connections"""
-
-    def __init__(self, stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device):
-
-        self.fusion_type = cfg['train']['trans_stream_fusion_type']
-        super().__init__(stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device)
-        
-        del self.query_stream_one
-        del self.query_stream_two
-        # del self.key_stream_one
-        # del self.key_stream_two
-
-        stream_one_fcn = 'plain_resnet_reduce_lat'
-        stream_one_model = models.names[stream_one_fcn]
-        stream_two_fcn = 'clip_ling'
-        stream_two_model = models.names[stream_two_fcn]
-        
-        
-        
-        # self.key_stream_one = stream_one_model(self.in_shape, self.output_dim, self.cfg, self.device, self.preprocess)
-        # self.key_stream_two = stream_two_model(self.in_shape, self.output_dim, self.cfg, self.device, self.preprocess)
-        
-        self.query_stream_one = stream_one_model(self.kernel_shape, self.kernel_dim, self.cfg, self.device, self.preprocess)
-        self.query_stream_two = stream_two_model(self.kernel_shape, self.kernel_dim, self.cfg, self.device, self.preprocess)
-
-    def transport(self, in_tensor, crop, l):
-        key_out_one, key_lat_one = self.key_stream_one(in_tensor)
-        key_out_two = self.key_stream_two(in_tensor, key_lat_one, l)
-        logits = self.fusion_key(key_out_one, key_out_two)
-
-        query_out_one, query_lat_one = self.query_stream_one(crop)
-        query_out_two = self.query_stream_two(crop, query_lat_one, l)
-        kernel = self.fusion_query(query_out_one, query_out_two)
-
-        return logits, kernel
-
-
-
-
-
-class TwoStreamTransportLangFusionLatReduceOneStream(TwoStreamTransportLangFusionLatReduce):
-    """Two Stream Transport (a.k.a Place) module with lateral connections"""
-
-    def __init__(self, stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device):
-
-        self.fusion_type = cfg['train']['trans_stream_fusion_type']
-        super().__init__(stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device)
-        
-        del self.query_stream_one
-        del self.query_stream_two
-
-        
-
-    def transport(self, in_tensor, crop, l):
-        key_out_one, key_lat_one = self.key_stream_one(in_tensor)
-        key_out_two = self.key_stream_two(in_tensor, key_lat_one, l)
-        logits = self.fusion_key(key_out_one, key_out_two)
-
-        query_out_one, query_lat_one = self.key_stream_one(crop)
-        query_out_two = self.key_stream_two(crop, query_lat_one, l)
-        kernel = self.fusion_query(query_out_one, query_out_two)
-
-        return logits, kernel
-
-
-
-
-class TwoStreamTransportLangFusionLatPretrained18(TwoStreamTransportLangFusionLat):
-    """Two Stream Transport (a.k.a Place) module with lateral connections"""
-
-    def __init__(self, stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device):
-
-        self.fusion_type = cfg['train']['trans_stream_fusion_type']
-        super().__init__(stream_fcn, in_shape, n_rotations, crop_size, preprocess, cfg, device)
-        
-        del self.query_stream_one
-        del self.query_stream_two
-        # del self.key_stream_one
-        # del self.key_stream_two
-        stream_one_fcn = 'pretrained_resnet18'
-        stream_one_model = models.names[stream_one_fcn]
-        stream_two_fcn = 'clip_ling'
-        stream_two_model = models.names[stream_two_fcn]
-        
-        # self.key_stream_one = stream_one_model(self.in_shape, self.output_dim, self.cfg, self.device, self.preprocess)
-        # self.key_stream_two = stream_two_model(self.in_shape, self.output_dim, self.cfg, self.device, self.preprocess)
-        
-        self.query_stream_one = stream_one_model(self.kernel_shape, self.kernel_dim, self.cfg, self.device, self.preprocess)
-        self.query_stream_two = stream_two_model(self.kernel_shape, self.kernel_dim, self.cfg, self.device, self.preprocess)
-
-    def transport(self, in_tensor, crop, l):
-        key_out_one, key_lat_one = self.key_stream_one(in_tensor)
-        key_out_two = self.key_stream_two(in_tensor, key_lat_one, l)
-        logits = self.fusion_key(key_out_one, key_out_two)
-
-        query_out_one, query_lat_one = self.query_stream_one(crop)
-        query_out_two = self.query_stream_two(crop, query_lat_one, l)
-        kernel = self.fusion_query(query_out_one, query_out_two)
-
-        return logits, kernel

spaces/GeorgeOrville/bingo/postcss.config.js

@@ -1,6 +0,0 @@
-module.exports = {
-  plugins: {
-    tailwindcss: {},
-    autoprefixer: {},
-  },
-}