import torch
import torch.nn as nn
import torch.nn.functional as F
from torchlibrosa.stft import Spectrogram, LogmelFilterBank
from torchlibrosa.augmentation import SpecAugmentation
from audio_infer.pytorch.pytorch_utils import do_mixup, interpolate, pad_framewise_output
import os
import sys
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torchlibrosa.stft import Spectrogram, LogmelFilterBank
from torchlibrosa.augmentation import SpecAugmentation
from audio_infer.pytorch.pytorch_utils import do_mixup
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
import warnings
from functools import partial
#from mmdet.models.builder import BACKBONES
from mmdet.utils import get_root_logger
from mmcv.runner import load_checkpoint
os.environ['TORCH_HOME'] = '../pretrained_models'
from copy import deepcopy
from timm.models.helpers import load_pretrained
from torch.cuda.amp import autocast
from collections import OrderedDict
import io
import re
from mmcv.runner import _load_checkpoint, load_state_dict
import mmcv.runner
import copy
import random
from einops import rearrange
from einops.layers.torch import Rearrange, Reduce
from torch import nn, einsum
def load_checkpoint(model,
filename,
map_location=None,
strict=False,
logger=None,
revise_keys=[(r'^module\.', '')]):
"""Load checkpoint from a file or URI.
Args:
model (Module): Module to load checkpoint.
filename (str): Accept local filepath, URL, ``torchvision://xxx``,
``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
details.
map_location (str): Same as :func:`torch.load`.
strict (bool): Whether to allow different params for the model and
checkpoint.
logger (:mod:`logging.Logger` or None): The logger for error message.
revise_keys (list): A list of customized keywords to modify the
state_dict in checkpoint. Each item is a (pattern, replacement)
pair of the regular expression operations. Default: strip
the prefix 'module.' by [(r'^module\\.', '')].
Returns:
dict or OrderedDict: The loaded checkpoint.
"""
checkpoint = _load_checkpoint(filename, map_location, logger)
new_proj = torch.nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(4, 4), padding=(2, 2))
new_proj.weight = torch.nn.Parameter(torch.sum(checkpoint['patch_embed1.proj.weight'], dim=1).unsqueeze(1))
checkpoint['patch_embed1.proj.weight'] = new_proj.weight
# OrderedDict is a subclass of dict
if not isinstance(checkpoint, dict):
raise RuntimeError(
f'No state_dict found in checkpoint file {filename}')
# get state_dict from checkpoint
if 'state_dict' in checkpoint:
state_dict = checkpoint['state_dict']
else:
state_dict = checkpoint
# strip prefix of state_dict
metadata = getattr(state_dict, '_metadata', OrderedDict())
for p, r in revise_keys:
state_dict = OrderedDict(
{re.sub(p, r, k): v
for k, v in state_dict.items()})
state_dict = OrderedDict({k.replace('backbone.',''):v for k,v in state_dict.items()})
# Keep metadata in state_dict
state_dict._metadata = metadata
# load state_dict
load_state_dict(model, state_dict, strict, logger)
return checkpoint
def init_layer(layer):
"""Initialize a Linear or Convolutional layer. """
nn.init.xavier_uniform_(layer.weight)
if hasattr(layer, 'bias'):
if layer.bias is not None:
layer.bias.data.fill_(0.)
def init_bn(bn):
"""Initialize a Batchnorm layer. """
bn.bias.data.fill_(0.)
bn.weight.data.fill_(1.)
class TimeShift(nn.Module):
def __init__(self, mean, std):
super().__init__()
self.mean = mean
self.std = std
def forward(self, x):
if self.training:
shift = torch.empty(1).normal_(self.mean, self.std).int().item()
x = torch.roll(x, shift, dims=2)
return x
class LinearSoftPool(nn.Module):
"""LinearSoftPool
Linear softmax, takes logits and returns a probability, near to the actual maximum value.
Taken from the paper:
A Comparison of Five Multiple Instance Learning Pooling Functions for Sound Event Detection with Weak Labeling
https://arxiv.org/abs/1810.09050
"""
def __init__(self, pooldim=1):
super().__init__()
self.pooldim = pooldim
def forward(self, logits, time_decision):
return (time_decision**2).sum(self.pooldim) / time_decision.sum(
self.pooldim)
class PVT(nn.Module):
def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin,
fmax, classes_num):
super(PVT, self).__init__()
window = 'hann'
center = True
pad_mode = 'reflect'
ref = 1.0
amin = 1e-10
top_db = None
# Spectrogram extractor
self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size,
win_length=window_size, window=window, center=center, pad_mode=pad_mode,
freeze_parameters=True)
# Logmel feature extractor
self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size,
n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db,
freeze_parameters=True)
self.time_shift = TimeShift(0, 10)
# Spec augmenter
self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2,
freq_drop_width=8, freq_stripes_num=2)
self.bn0 = nn.BatchNorm2d(64)
self.pvt_transformer = PyramidVisionTransformerV2(tdim=1001,
fdim=64,
patch_size=7,
stride=4,
in_chans=1,
num_classes=classes_num,
embed_dims=[64, 128, 320, 512],
depths=[3, 4, 6, 3],
num_heads=[1, 2, 5, 8],
mlp_ratios=[8, 8, 4, 4],
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
drop_path_rate=0.1,
sr_ratios=[8, 4, 2, 1],
norm_layer=partial(nn.LayerNorm, eps=1e-6),
num_stages=4,
#pretrained='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b2.pth'
)
#self.temp_pool = LinearSoftPool()
self.avgpool = nn.AdaptiveAvgPool1d(1)
self.fc_audioset = nn.Linear(512, classes_num, bias=True)
self.init_weights()
def init_weights(self):
init_bn(self.bn0)
init_layer(self.fc_audioset)
def forward(self, input, mixup_lambda=None):
"""Input: (batch_size, times_steps, freq_bins)"""
interpolate_ratio = 32
x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins)
x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins)
frames_num = x.shape[2]
x = x.transpose(1, 3)
x = self.bn0(x)
x = x.transpose(1, 3)
if self.training:
x = self.time_shift(x)
x = self.spec_augmenter(x)
# Mixup on spectrogram
if self.training and mixup_lambda is not None:
x = do_mixup(x, mixup_lambda)
#print(x.shape) #torch.Size([10, 1, 1001, 64])
x = self.pvt_transformer(x)
#print(x.shape) #torch.Size([10, 800, 128])
x = torch.mean(x, dim=3)
x = x.transpose(1, 2).contiguous()
framewise_output = torch.sigmoid(self.fc_audioset(x))
#clipwise_output = torch.mean(framewise_output, dim=1)
#clipwise_output = self.temp_pool(x, framewise_output).clamp(1e-7, 1.).squeeze(1)
x = framewise_output.transpose(1, 2).contiguous()
x = self.avgpool(x)
clipwise_output = torch.flatten(x, 1)
#print(framewise_output.shape) #torch.Size([10, 100, 17])
framewise_output = interpolate(framewise_output, interpolate_ratio)
#framewise_output = framewise_output[:,:1000,:]
#framewise_output = pad_framewise_output(framewise_output, frames_num)
output_dict = {'framewise_output': framewise_output,
'clipwise_output': clipwise_output}
return output_dict
class PVT2(nn.Module):
def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin,
fmax, classes_num):
super(PVT2, self).__init__()
window = 'hann'
center = True
pad_mode = 'reflect'
ref = 1.0
amin = 1e-10
top_db = None
# Spectrogram extractor
self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size,
win_length=window_size, window=window, center=center, pad_mode=pad_mode,
freeze_parameters=True)
# Logmel feature extractor
self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size,
n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db,
freeze_parameters=True)
self.time_shift = TimeShift(0, 10)
# Spec augmenter
self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2,
freq_drop_width=8, freq_stripes_num=2)
self.bn0 = nn.BatchNorm2d(64)
self.pvt_transformer = PyramidVisionTransformerV2(tdim=1001,
fdim=64,
patch_size=7,
stride=4,
in_chans=1,
num_classes=classes_num,
embed_dims=[64, 128, 320, 512],
depths=[3, 4, 6, 3],
num_heads=[1, 2, 5, 8],
mlp_ratios=[8, 8, 4, 4],
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
drop_path_rate=0.1,
sr_ratios=[8, 4, 2, 1],
norm_layer=partial(nn.LayerNorm, eps=1e-6),
num_stages=4,
pretrained='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b2.pth'
)
#self.temp_pool = LinearSoftPool()
self.fc_audioset = nn.Linear(512, classes_num, bias=True)
self.init_weights()
def init_weights(self):
init_bn(self.bn0)
init_layer(self.fc_audioset)
def forward(self, input, mixup_lambda=None):
"""Input: (batch_size, times_steps, freq_bins)"""
interpolate_ratio = 32
x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins)
x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins)
frames_num = x.shape[2]
x = x.transpose(1, 3)
x = self.bn0(x)
x = x.transpose(1, 3)
if self.training:
#x = self.time_shift(x)
x = self.spec_augmenter(x)
# Mixup on spectrogram
if self.training and mixup_lambda is not None:
x = do_mixup(x, mixup_lambda)
#print(x.shape) #torch.Size([10, 1, 1001, 64])
x = self.pvt_transformer(x)
#print(x.shape) #torch.Size([10, 800, 128])
x = torch.mean(x, dim=3)
x = x.transpose(1, 2).contiguous()
framewise_output = torch.sigmoid(self.fc_audioset(x))
clipwise_output = torch.mean(framewise_output, dim=1)
#clipwise_output = self.temp_pool(x, framewise_output).clamp(1e-7, 1.).squeeze(1)
#print(framewise_output.shape) #torch.Size([10, 100, 17])
framewise_output = interpolate(framewise_output, interpolate_ratio)
#framewise_output = framewise_output[:,:1000,:]
#framewise_output = pad_framewise_output(framewise_output, frames_num)
output_dict = {'framewise_output': framewise_output,
'clipwise_output': clipwise_output}
return output_dict
class PVT_2layer(nn.Module):
def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin,
fmax, classes_num):
super(PVT_2layer, self).__init__()
window = 'hann'
center = True
pad_mode = 'reflect'
ref = 1.0
amin = 1e-10
top_db = None
# Spectrogram extractor
self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size,
win_length=window_size, window=window, center=center, pad_mode=pad_mode,
freeze_parameters=True)
# Logmel feature extractor
self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size,
n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db,
freeze_parameters=True)
self.time_shift = TimeShift(0, 10)
# Spec augmenter
self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2,
freq_drop_width=8, freq_stripes_num=2)
self.bn0 = nn.BatchNorm2d(64)
self.pvt_transformer = PyramidVisionTransformerV2(tdim=1001,
fdim=64,
patch_size=7,
stride=4,
in_chans=1,
num_classes=classes_num,
embed_dims=[64, 128],
depths=[3, 4],
num_heads=[1, 2],
mlp_ratios=[8, 8],
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
drop_path_rate=0.1,
sr_ratios=[8, 4],
norm_layer=partial(nn.LayerNorm, eps=1e-6),
num_stages=2,
pretrained='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b2.pth'
)
#self.temp_pool = LinearSoftPool()
self.avgpool = nn.AdaptiveAvgPool1d(1)
self.fc_audioset = nn.Linear(128, classes_num, bias=True)
self.init_weights()
def init_weights(self):
init_bn(self.bn0)
init_layer(self.fc_audioset)
def forward(self, input, mixup_lambda=None):
"""Input: (batch_size, times_steps, freq_bins)"""
interpolate_ratio = 8
x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins)
x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins)
frames_num = x.shape[2]
x = x.transpose(1, 3)
x = self.bn0(x)
x = x.transpose(1, 3)
if self.training:
x = self.time_shift(x)
x = self.spec_augmenter(x)
# Mixup on spectrogram
if self.training and mixup_lambda is not None:
x = do_mixup(x, mixup_lambda)
#print(x.shape) #torch.Size([10, 1, 1001, 64])
x = self.pvt_transformer(x)
#print(x.shape) #torch.Size([10, 800, 128])
x = torch.mean(x, dim=3)
x = x.transpose(1, 2).contiguous()
framewise_output = torch.sigmoid(self.fc_audioset(x))
#clipwise_output = torch.mean(framewise_output, dim=1)
#clipwise_output = self.temp_pool(x, framewise_output).clamp(1e-7, 1.).squeeze(1)
x = framewise_output.transpose(1, 2).contiguous()
x = self.avgpool(x)
clipwise_output = torch.flatten(x, 1)
#print(framewise_output.shape) #torch.Size([10, 100, 17])
framewise_output = interpolate(framewise_output, interpolate_ratio)
#framewise_output = framewise_output[:,:1000,:]
#framewise_output = pad_framewise_output(framewise_output, frames_num)
output_dict = {'framewise_output': framewise_output,
'clipwise_output': clipwise_output}
return output_dict
class PVT_lr(nn.Module):
def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin,
fmax, classes_num):
super(PVT_lr, self).__init__()
window = 'hann'
center = True
pad_mode = 'reflect'
ref = 1.0
amin = 1e-10
top_db = None
# Spectrogram extractor
self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size,
win_length=window_size, window=window, center=center, pad_mode=pad_mode,
freeze_parameters=True)
# Logmel feature extractor
self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size,
n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db,
freeze_parameters=True)
self.time_shift = TimeShift(0, 10)
# Spec augmenter
self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2,
freq_drop_width=8, freq_stripes_num=2)
self.bn0 = nn.BatchNorm2d(64)
self.pvt_transformer = PyramidVisionTransformerV2(tdim=1001,
fdim=64,
patch_size=7,
stride=4,
in_chans=1,
num_classes=classes_num,
embed_dims=[64, 128, 320, 512],
depths=[3, 4, 6, 3],
num_heads=[1, 2, 5, 8],
mlp_ratios=[8, 8, 4, 4],
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
drop_path_rate=0.1,
sr_ratios=[8, 4, 2, 1],
norm_layer=partial(nn.LayerNorm, eps=1e-6),
num_stages=4,
pretrained='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b2.pth'
)
self.temp_pool = LinearSoftPool()
self.fc_audioset = nn.Linear(512, classes_num, bias=True)
self.init_weights()
def init_weights(self):
init_bn(self.bn0)
init_layer(self.fc_audioset)
def forward(self, input, mixup_lambda=None):
"""Input: (batch_size, times_steps, freq_bins)"""
interpolate_ratio = 32
x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins)
x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins)
frames_num = x.shape[2]
x = x.transpose(1, 3)
x = self.bn0(x)
x = x.transpose(1, 3)
if self.training:
x = self.time_shift(x)
x = self.spec_augmenter(x)
# Mixup on spectrogram
if self.training and mixup_lambda is not None:
x = do_mixup(x, mixup_lambda)
#print(x.shape) #torch.Size([10, 1, 1001, 64])
x = self.pvt_transformer(x)
#print(x.shape) #torch.Size([10, 800, 128])
x = torch.mean(x, dim=3)
x = x.transpose(1, 2).contiguous()
framewise_output = torch.sigmoid(self.fc_audioset(x))
clipwise_output = self.temp_pool(x, framewise_output).clamp(1e-7, 1.).squeeze(1)
#print(framewise_output.shape) #torch.Size([10, 100, 17])
framewise_output = interpolate(framewise_output, interpolate_ratio)
#framewise_output = framewise_output[:,:1000,:]
#framewise_output = pad_framewise_output(framewise_output, frames_num)
output_dict = {'framewise_output': framewise_output,
'clipwise_output': clipwise_output}
return output_dict
class PVT_nopretrain(nn.Module):
def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin,
fmax, classes_num):
super(PVT_nopretrain, self).__init__()
window = 'hann'
center = True
pad_mode = 'reflect'
ref = 1.0
amin = 1e-10
top_db = None
# Spectrogram extractor
self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size,
win_length=window_size, window=window, center=center, pad_mode=pad_mode,
freeze_parameters=True)
# Logmel feature extractor
self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size,
n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db,
freeze_parameters=True)
self.time_shift = TimeShift(0, 10)
# Spec augmenter
self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2,
freq_drop_width=8, freq_stripes_num=2)
self.bn0 = nn.BatchNorm2d(64)
self.pvt_transformer = PyramidVisionTransformerV2(tdim=1001,
fdim=64,
patch_size=7,
stride=4,
in_chans=1,
num_classes=classes_num,
embed_dims=[64, 128, 320, 512],
depths=[3, 4, 6, 3],
num_heads=[1, 2, 5, 8],
mlp_ratios=[8, 8, 4, 4],
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
drop_path_rate=0.1,
sr_ratios=[8, 4, 2, 1],
norm_layer=partial(nn.LayerNorm, eps=1e-6),
num_stages=4,
#pretrained='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b2.pth'
)
self.temp_pool = LinearSoftPool()
self.fc_audioset = nn.Linear(512, classes_num, bias=True)
self.init_weights()
def init_weights(self):
init_bn(self.bn0)
init_layer(self.fc_audioset)
def forward(self, input, mixup_lambda=None):
"""Input: (batch_size, times_steps, freq_bins)"""
interpolate_ratio = 32
x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins)
x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins)
frames_num = x.shape[2]
x = x.transpose(1, 3)
x = self.bn0(x)
x = x.transpose(1, 3)
if self.training:
x = self.time_shift(x)
x = self.spec_augmenter(x)
# Mixup on spectrogram
if self.training and mixup_lambda is not None:
x = do_mixup(x, mixup_lambda)
#print(x.shape) #torch.Size([10, 1, 1001, 64])
x = self.pvt_transformer(x)
#print(x.shape) #torch.Size([10, 800, 128])
x = torch.mean(x, dim=3)
x = x.transpose(1, 2).contiguous()
framewise_output = torch.sigmoid(self.fc_audioset(x))
clipwise_output = self.temp_pool(x, framewise_output).clamp(1e-7, 1.).squeeze(1)
#print(framewise_output.shape) #torch.Size([10, 100, 17])
framewise_output = interpolate(framewise_output, interpolate_ratio)
framewise_output = framewise_output[:,:1000,:]
#framewise_output = pad_framewise_output(framewise_output, frames_num)
output_dict = {'framewise_output': framewise_output,
'clipwise_output': clipwise_output}
return output_dict
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0., linear=False):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.dwconv = DWConv(hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
self.linear = linear
if self.linear:
self.relu = nn.ReLU()
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x, H, W):
x = self.fc1(x)
if self.linear:
x = self.relu(x)
x = self.dwconv(x, H, W)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class Attention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1, linear=False):
super().__init__()
assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
self.dim = dim
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.q = nn.Linear(dim, dim, bias=qkv_bias)
self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.linear = linear
self.sr_ratio = sr_ratio
if not linear:
if sr_ratio > 1:
self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
self.norm = nn.LayerNorm(dim)
else:
self.pool = nn.AdaptiveAvgPool2d(7)
self.sr = nn.Conv2d(dim, dim, kernel_size=1, stride=1)
self.norm = nn.LayerNorm(dim)
self.act = nn.GELU()
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x, H, W):
B, N, C = x.shape
q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
if not self.linear:
if self.sr_ratio > 1:
x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
x_ = self.norm(x_)
kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
else:
kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
else:
x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
x_ = self.sr(self.pool(x_)).reshape(B, C, -1).permute(0, 2, 1)
x_ = self.norm(x_)
x_ = self.act(x_)
kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
k, v = kv[0], kv[1]
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Pooling(nn.Module):
"""
Implementation of pooling for PoolFormer
--pool_size: pooling size
"""
def __init__(self, pool_size=3):
super().__init__()
self.pool = nn.AvgPool2d(
pool_size, stride=1, padding=pool_size//2, count_include_pad=False)
def forward(self, x):
return self.pool(x) - x
class Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1, linear=False):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim,
num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio, linear=linear)
#self.norm3 = norm_layer(dim)
#self.token_mixer = Pooling(pool_size=3)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop, linear=linear)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x, H, W):
x = x + self.drop_path(self.attn(self.norm1(x), H, W))
x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
return x
class OverlapPatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, tdim, fdim, patch_size=7, stride=4, in_chans=3, embed_dim=768):
super().__init__()
img_size = (tdim, fdim)
patch_size = to_2tuple(patch_size)
self.img_size = img_size
self.patch_size = patch_size
self.H, self.W = img_size[0] // stride, img_size[1] // stride
self.num_patches = self.H * self.W
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride,
padding=(patch_size[0] // 3, patch_size[1] // 3))
self.norm = nn.LayerNorm(embed_dim)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x):
x = self.proj(x)
_, _, H, W = x.shape
x = x.flatten(2).transpose(1, 2)
x = self.norm(x)
return x, H, W
class PyramidVisionTransformerV2(nn.Module):
def __init__(self, tdim=1001, fdim=64, patch_size=16, stride=4, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512],
num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
attn_drop_rate=0., drop_path_rate=0.1, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3],
sr_ratios=[8, 4, 2, 1], num_stages=2, linear=False, pretrained=None):
super().__init__()
# self.num_classes = num_classes
self.depths = depths
self.num_stages = num_stages
self.linear = linear
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
cur = 0
for i in range(num_stages):
patch_embed = OverlapPatchEmbed(tdim=tdim if i == 0 else tdim // (2 ** (i + 1)),
fdim=fdim if i == 0 else tdim // (2 ** (i + 1)),
patch_size=7 if i == 0 else 3,
stride=stride if i == 0 else 2,
in_chans=in_chans if i == 0 else embed_dims[i - 1],
embed_dim=embed_dims[i])
block = nn.ModuleList([Block(
dim=embed_dims[i], num_heads=num_heads[i], mlp_ratio=mlp_ratios[i], qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + j], norm_layer=norm_layer,
sr_ratio=sr_ratios[i], linear=linear)
for j in range(depths[i])])
norm = norm_layer(embed_dims[i])
cur += depths[i]
setattr(self, f"patch_embed{i + 1}", patch_embed)
setattr(self, f"block{i + 1}", block)
setattr(self, f"norm{i + 1}", norm)
#self.n = nn.Linear(125, 250, bias=True)
# classification head
# self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else nn.Identity()
self.apply(self._init_weights)
self.init_weights(pretrained)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def init_weights(self, pretrained=None):
if isinstance(pretrained, str):
logger = get_root_logger()
load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger)
def freeze_patch_emb(self):
self.patch_embed1.requires_grad = False
@torch.jit.ignore
def no_weight_decay(self):
return {'pos_embed1', 'pos_embed2', 'pos_embed3', 'pos_embed4', 'cls_token'} # has pos_embed may be better
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=''):
self.num_classes = num_classes
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
B = x.shape[0]
for i in range(self.num_stages):
patch_embed = getattr(self, f"patch_embed{i + 1}")
block = getattr(self, f"block{i + 1}")
norm = getattr(self, f"norm{i + 1}")
x, H, W = patch_embed(x)
#print(x.shape)
for blk in block:
x = blk(x, H, W)
#print(x.shape)
x = norm(x)
#if i != self.num_stages - 1:
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
#print(x.shape)
return x
def forward(self, x):
x = self.forward_features(x)
# x = self.head(x)
return x
class DWConv(nn.Module):
def __init__(self, dim=768):
super(DWConv, self).__init__()
self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
def forward(self, x, H, W):
B, N, C = x.shape
x = x.transpose(1, 2).view(B, C, H, W)
x = self.dwconv(x)
x = x.flatten(2).transpose(1, 2)
return x
def _conv_filter(state_dict, patch_size=16):
""" convert patch embedding weight from manual patchify + linear proj to conv"""
out_dict = {}
for k, v in state_dict.items():
if 'patch_embed.proj.weight' in k:
v = v.reshape((v.shape[0], 3, patch_size, patch_size))
out_dict[k] = v
return out_dict