-
Notifications
You must be signed in to change notification settings - Fork 3
/
Copy pathmim_vit_pretrain.py
257 lines (201 loc) · 9.54 KB
/
mim_vit_pretrain.py
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
254
255
256
257
# 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 torch.nn.functional as F
from timm.models.vision_transformer import PatchEmbed, Block
from util.pos_embed import get_2d_sincos_pos_embed
from util.hog_layer import HOGLayerC
class MaskedAutoencoderViT(nn.Module):
""" Masked Autoencoder with VisionTransformer backbone
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3,
embed_dim=1024, depth=24, num_heads=16, block_size=16,
decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,
mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False,
mim_loss='l2', **kwargs):
super().__init__()
decoder_num_heads = int(decoder_embed_dim / 32)
# --------------------------------------------------------------------------
# MAE encoder specifics
self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, embed_dim)
num_patches = self.patch_embed.num_patches
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim), requires_grad=False) # fixed sin-cos embedding
self.blocks = nn.ModuleList([
Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer)
for i in range(depth)])
self.norm = norm_layer(embed_dim)
self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True) if decoder_depth > 0 else None
self.mask_token = nn.Parameter(torch.zeros(1, 3, 1, 1), requires_grad=True)
self.decoder_blocks = nn.ModuleList([
Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer)
for i in range(decoder_depth)])
self.decoder_norm = norm_layer(decoder_embed_dim)
if mim_loss == "HOG":
num_class = (patch_size//8)**2 * 9 * 3
else:
num_class = patch_size**2 * in_chans
self.decoder_pred = nn.Linear(decoder_embed_dim, num_class, bias=True)
self.norm_pix_loss = norm_pix_loss
self.img_size = img_size
self.block_size = block_size
self.patch_size = patch_size
self.mim_loss = mim_loss
self.initialize_weights()
def initialize_weights(self):
# initialization
# initialize (and freeze) pos_embed by sin-cos embedding
pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True)
self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
w = self.patch_embed.proj.weight.data
torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
torch.nn.init.normal_(self.cls_token, std=.02)
torch.nn.init.normal_(self.mask_token, std=.02)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
# we use xavier_uniform following official JAX ViT:
torch.nn.init.xavier_uniform_(m.weight)
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)
def patchify(self, imgs):
"""
imgs: (N, 3, H, W)
x: (N, L, patch_size**2 *3)
"""
p = self.patch_embed.patch_size[0]
assert imgs.shape[2] == imgs.shape[3] and imgs.shape[2] % p == 0
h = w = imgs.shape[2] // p
x = imgs.reshape(shape=(imgs.shape[0], 3, h, p, w, p))
x = torch.einsum('nchpwq->npqchw', x)
x = x.reshape(shape=(imgs.shape[0], p**2 * 3, h, w))
return x
def unpatchify(self, x):
"""
x: (N, L, patch_size**2 *3)
imgs: (N, 3, H, W)
"""
p = self.patch_embed.patch_size[0]
h = w = int(x.shape[1]**.5)
assert h * w == x.shape[1]
x = x.reshape(shape=(x.shape[0], h, w, p, p, 3))
x = torch.einsum('nhwpqc->nchpwq', x)
imgs = x.reshape(shape=(x.shape[0], 3, h * p, h * p))
return imgs
def mim_per_sample_block_masking(self, x, mask_ratio, block_size=16):
batch, channel, height, width = x.shape
input_size = self.img_size
assert height == width, f"Input height and width doesn't match ({height} != {width})."
mask_size = input_size // block_size
bw_ratio = height // mask_size
len_keep = int(mask_size**2 * (1 - mask_ratio))
noise = torch.rand(batch, mask_size**2, device=x.device) # noise in [0, 1]
ids_shuffle = torch.argsort(noise, dim=1)
ids_restore = torch.argsort(ids_shuffle, dim=1)
loss_mask = torch.ones([batch, mask_size**2], device=x.device)
loss_mask[:, :len_keep] = 0
loss_mask = torch.gather(loss_mask, dim=1, index=ids_restore)
loss_mask = loss_mask.reshape(batch, 1, mask_size, mask_size).long()
mask = loss_mask.repeat(1, bw_ratio**2, 1, 1)
mask = mask.reshape(batch, bw_ratio, bw_ratio, mask_size, mask_size).permute(
0, 3, 1, 4, 2).reshape(batch, 1, height, width)
if self.block_size > self.patch_size:
loss_mask = torch.repeat_interleave(loss_mask, self.block_size//self.patch_size, dim=2)
loss_mask = torch.repeat_interleave(loss_mask, self.block_size//self.patch_size, dim=3)
return mask, loss_mask
def forward_encoder(self, imgs, mask_ratio):
B, C, H, W = imgs.shape
mask, loss_mask = self.mim_per_sample_block_masking(imgs, mask_ratio, block_size=self.block_size)
x = imgs * (1-mask) + (mask) * self.mask_token.repeat(B, 1, H, W)
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(x.shape[0], -1, -1)
x = torch.cat((cls_tokens, x), dim=1)
# apply Transformer blocks
for blk in self.blocks:
x = blk(x)
x = self.norm(x)
return x, loss_mask
def forward_decoder(self, x):
if self.decoder_blocks:
x = self.decoder_embed(x)
for blk in self.decoder_blocks:
x = blk(x)
x = self.decoder_norm(x)
x = self.decoder_pred(x)
x = x[:, 1:, :]
return x
def forward_l2_loss(self, imgs, pred, mask):
"""
imgs: [N, 3, H, W]
pred: [N, L, p*p*3]
mask: [N, L], 0 is keep, 1 is remove,
"""
B, N, C = pred.shape
H = W = int(N**0.5)
pred = pred.transpose(-1,-2).reshape(B, C, H, W)
target = self.patchify(imgs)
if self.norm_pix_loss:
mean = target.mean(dim=1, keepdim=True)
var = target.var(dim=1, keepdim=True)
target = (target - mean) / (var + 1.e-6)**.5
# target = (target - mean) / torch.clip((var + 1.e-6)**.5, min=0.01)
mask = mask.repeat(1, C, 1, 1).bool()
loss = (pred[mask] - target[mask]) ** 2
loss = loss.mean()
return loss
def forward_hog_loss(self, imgs, pred, mask):
"""
imgs: [N, 3, H, W]
pred: [N, L, p*p*3]
mask: [N, L], 0 is keep, 1 is remove,
"""
B, N, C = pred.shape
H = W = int(N**0.5)
hogC = HOGLayerC(nbins=9, pool=8, norm_pix_loss=self.norm_pix_loss).cuda()
target = hogC(imgs)
mask_size = mask.shape[-1]
if mask_size > W:
target_size, target_channel = target.shape[3], target.shape[1]
target = target.permute(0, 2, 3, 1).flatten(1, 2)
mask = torch.repeat_interleave(mask, target_size//mask_size, dim=2)
mask = torch.repeat_interleave(mask, target_size//mask_size, dim=3)
pred = pred.reshape(B, H, W, -1, target_size//H, target_size//W).permute(0, 1, 4, 2, 5, 3).reshape(B, target_size**2, target_channel)
else:
unfold_size = target.shape[-1] // W
target = (
target.permute(0, 2, 3, 1)
.unfold(1, unfold_size, unfold_size)
.unfold(2, unfold_size, unfold_size)
.flatten(1, 2).flatten(2)
)
mask = mask.flatten(1).bool()
loss = (pred[mask] - target[mask]) ** 2
loss = loss.mean()
return loss
def forward(self, imgs, mask_ratio=0.75, mask_type='random'):
latent, mask = self.forward_encoder(imgs, mask_ratio)
pred = self.forward_decoder(latent)
if self.mim_loss == 'l2':
loss = self.forward_l2_loss(imgs, pred, mask)
elif self.mim_loss == 'HOG':
loss = self.forward_hog_loss(imgs, pred, mask)
return loss, pred, mask
def mim_vit_base(**kwargs):
model = MaskedAutoencoderViT(
patch_size=16, embed_dim=768, depth=12, num_heads=6,
mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
return model