Spaces:
Running
on
Zero
Running
on
Zero
File size: 7,942 Bytes
9b9e0ee |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# timm: https://github.com/rwightman/pytorch-image-models/tree/master/timm
# DeiT: https://github.com/facebookresearch/deit
# --------------------------------------------------------
from functools import partial
import torch
import torch.nn as nn
import numpy as np
import timm.models.vision_transformer
from timm.models.vision_transformer import PatchEmbed, Block
from qa_mdt.audioldm_train.modules.audiomae.util.patch_embed import (
PatchEmbed_new,
PatchEmbed3D_new,
)
class VisionTransformer(timm.models.vision_transformer.VisionTransformer):
"""Vision Transformer with support for global average pooling"""
def __init__(
self, global_pool=False, mask_2d=True, use_custom_patch=False, **kwargs
):
super(VisionTransformer, self).__init__(**kwargs)
self.global_pool = global_pool
if self.global_pool:
norm_layer = kwargs["norm_layer"]
embed_dim = kwargs["embed_dim"]
self.fc_norm = norm_layer(embed_dim)
del self.norm # remove the original norm
self.mask_2d = mask_2d
self.use_custom_patch = use_custom_patch
num_heads = 12
depth = 12
mlp_ratio = 4
def forward_features(self, x):
B = x.shape[0]
x = self.patch_embed(x)
x = x + self.pos_embed[:, 1:, :]
cls_token = self.cls_token + self.pos_embed[:, :1, :]
cls_tokens = cls_token.expand(
B, -1, -1
) # stole cls_tokens impl from Phil Wang, thanks
x = torch.cat((cls_tokens, x), dim=1)
x = self.pos_drop(x)
for blk in self.blocks:
x = blk(x)
if self.global_pool:
x = x[:, 1:, :].mean(dim=1) # global pool without cls token
outcome = self.fc_norm(x)
else:
x = self.norm(x)
outcome = x[:, 0]
return outcome
def random_masking(self, x, mask_ratio):
"""
Perform per-sample random masking by per-sample shuffling.
Per-sample shuffling is done by argsort random noise.
x: [N, L, D], sequence
"""
N, L, D = x.shape # batch, length, dim
len_keep = int(L * (1 - mask_ratio))
noise = torch.rand(N, L, device=x.device) # noise in [0, 1]
# sort noise for each sample
ids_shuffle = torch.argsort(
noise, dim=1
) # ascend: small is keep, large is remove
ids_restore = torch.argsort(ids_shuffle, dim=1)
# keep the first subset
ids_keep = ids_shuffle[:, :len_keep]
x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
# generate the binary mask: 0 is keep, 1 is remove
mask = torch.ones([N, L], device=x.device)
mask[:, :len_keep] = 0
# unshuffle to get the binary mask
mask = torch.gather(mask, dim=1, index=ids_restore)
return x_masked, mask, ids_restore
def random_masking_2d(self, x, mask_t_prob, mask_f_prob):
"""
2D: Spectrogram (msking t and f under mask_t_prob and mask_f_prob)
Perform per-sample random masking by per-sample shuffling.
Per-sample shuffling is done by argsort random noise.
x: [N, L, D], sequence
"""
N, L, D = x.shape # batch, length, dim
if self.use_custom_patch:
# # for AS
T = 101 # 64,101
F = 12 # 8,12
# # for ESC
# T=50
# F=12
# for SPC
# T=12
# F=12
else:
# ## for AS
T = 64
F = 8
# ## for ESC
# T=32
# F=8
## for SPC
# T=8
# F=8
# mask T
x = x.reshape(N, T, F, D)
len_keep_T = int(T * (1 - mask_t_prob))
noise = torch.rand(N, T, device=x.device) # noise in [0, 1]
# sort noise for each sample
ids_shuffle = torch.argsort(
noise, dim=1
) # ascend: small is keep, large is remove
ids_keep = ids_shuffle[:, :len_keep_T]
index = ids_keep.unsqueeze(-1).unsqueeze(-1).repeat(1, 1, F, D)
# x_masked = torch.gather(x, dim=1, index=index)
# x_masked = x_masked.reshape(N,len_keep_T*F,D)
x = torch.gather(x, dim=1, index=index) # N, len_keep_T(T'), F, D
# mask F
# x = x.reshape(N, T, F, D)
x = x.permute(0, 2, 1, 3) # N T' F D => N F T' D
len_keep_F = int(F * (1 - mask_f_prob))
noise = torch.rand(N, F, device=x.device) # noise in [0, 1]
# sort noise for each sample
ids_shuffle = torch.argsort(
noise, dim=1
) # ascend: small is keep, large is remove
ids_keep = ids_shuffle[:, :len_keep_F]
# index = ids_keep.unsqueeze(-1).unsqueeze(-1).repeat(1, 1, T, D)
index = ids_keep.unsqueeze(-1).unsqueeze(-1).repeat(1, 1, len_keep_T, D)
x_masked = torch.gather(x, dim=1, index=index)
x_masked = x_masked.permute(0, 2, 1, 3) # N F' T' D => N T' F' D
# x_masked = x_masked.reshape(N,len_keep*T,D)
x_masked = x_masked.reshape(N, len_keep_F * len_keep_T, D)
return x_masked, None, None
def forward_features_mask(self, x, mask_t_prob, mask_f_prob):
B = x.shape[0] # 4,1,1024,128
x = self.patch_embed(x) # 4, 512, 768
x = x + self.pos_embed[:, 1:, :]
if self.random_masking_2d:
x, mask, ids_restore = self.random_masking_2d(x, mask_t_prob, mask_f_prob)
else:
x, mask, ids_restore = self.random_masking(x, mask_t_prob)
cls_token = self.cls_token + self.pos_embed[:, :1, :]
cls_tokens = cls_token.expand(B, -1, -1)
x = torch.cat((cls_tokens, x), dim=1)
x = self.pos_drop(x)
# apply Transformer blocks
for blk in self.blocks:
x = blk(x)
if self.global_pool:
x = x[:, 1:, :].mean(dim=1) # global pool without cls token
outcome = self.fc_norm(x)
else:
x = self.norm(x)
outcome = x[:, 0]
return outcome
# overwrite original timm
def forward(self, x, v=None, mask_t_prob=0.0, mask_f_prob=0.0):
if mask_t_prob > 0.0 or mask_f_prob > 0.0:
x = self.forward_features_mask(
x, mask_t_prob=mask_t_prob, mask_f_prob=mask_f_prob
)
else:
x = self.forward_features(x)
x = self.head(x)
return x
def vit_small_patch16(**kwargs):
model = VisionTransformer(
patch_size=16,
embed_dim=384,
depth=12,
num_heads=6,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs
)
return model
def vit_base_patch16(**kwargs):
model = VisionTransformer(
patch_size=16,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs
)
return model
def vit_large_patch16(**kwargs):
model = VisionTransformer(
patch_size=16,
embed_dim=1024,
depth=24,
num_heads=16,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs
)
return model
def vit_huge_patch14(**kwargs):
model = VisionTransformer(
patch_size=14,
embed_dim=1280,
depth=32,
num_heads=16,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs
)
return model
|