Srikumar26
commited on
Commit
•
792e911
1
Parent(s):
fdf4051
Upload model.py with huggingface_hub
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model.py
ADDED
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1 |
+
# --------------------------------------------------------
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2 |
+
# References:
|
3 |
+
# MAE: https://github.com/facebookresearch/mae
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4 |
+
# timm: https://github.com/rwightman/pytorch-image-models/tree/master/timm
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5 |
+
# DeiT: https://github.com/facebookresearch/deit
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6 |
+
# --------------------------------------------------------
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7 |
+
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8 |
+
from functools import partial
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9 |
+
import numpy as np
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10 |
+
import torch
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11 |
+
import torch.nn as nn
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12 |
+
import torch.nn.functional as F
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13 |
+
from timm.models.vision_transformer import PatchEmbed, Block
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14 |
+
from huggingface_hub import PyTorchModelHubMixin
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+
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16 |
+
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17 |
+
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
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+
"""
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+
grid_size: int of the grid height and width
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+
return:
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21 |
+
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
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22 |
+
"""
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23 |
+
grid_h = np.arange(grid_size, dtype=np.float32)
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24 |
+
grid_w = np.arange(grid_size, dtype=np.float32)
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25 |
+
grid = np.meshgrid(grid_w, grid_h) # here w goes first
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26 |
+
grid = np.stack(grid, axis=0)
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27 |
+
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28 |
+
grid = grid.reshape([2, 1, grid_size, grid_size])
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29 |
+
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
|
30 |
+
if cls_token:
|
31 |
+
pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
|
32 |
+
return pos_embed
|
33 |
+
|
34 |
+
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
|
35 |
+
assert embed_dim % 2 == 0
|
36 |
+
|
37 |
+
# use half of dimensions to encode grid_h
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38 |
+
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
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39 |
+
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
|
40 |
+
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41 |
+
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
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42 |
+
return emb
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43 |
+
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44 |
+
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45 |
+
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
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46 |
+
"""
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47 |
+
embed_dim: output dimension for each position
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48 |
+
pos: a list of positions to be encoded: size (M,)
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49 |
+
out: (M, D)
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50 |
+
"""
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51 |
+
assert embed_dim % 2 == 0
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52 |
+
omega = np.arange(embed_dim // 2, dtype=np.float32)
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53 |
+
omega /= embed_dim / 2.
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54 |
+
omega = 1. / 10000**omega # (D/2,)
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55 |
+
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56 |
+
pos = pos.reshape(-1) # (M,)
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57 |
+
out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
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58 |
+
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59 |
+
emb_sin = np.sin(out) # (M, D/2)
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60 |
+
emb_cos = np.cos(out) # (M, D/2)
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61 |
+
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62 |
+
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
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63 |
+
return emb
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64 |
+
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65 |
+
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66 |
+
################################################################################
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67 |
+
# Upsample Block Modules
|
68 |
+
################################################################################
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69 |
+
class LayerNorm(nn.Module):
|
70 |
+
def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
|
71 |
+
super().__init__()
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72 |
+
self.weight = nn.Parameter(torch.ones(normalized_shape))
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73 |
+
self.bias = nn.Parameter(torch.zeros(normalized_shape))
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74 |
+
self.eps = eps
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75 |
+
self.data_format = data_format
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76 |
+
if self.data_format not in ["channels_last", "channels_first"]:
|
77 |
+
raise NotImplementedError
|
78 |
+
self.normalized_shape = (normalized_shape,)
|
79 |
+
|
80 |
+
def forward(self, x):
|
81 |
+
if self.data_format == "channels_last":
|
82 |
+
return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
|
83 |
+
elif self.data_format == "channels_first":
|
84 |
+
u = x.mean(1, keepdim=True)
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85 |
+
s = (x - u).pow(2).mean(1, keepdim=True)
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86 |
+
x = (x - u) / torch.sqrt(s + self.eps)
|
87 |
+
x = self.weight[:, None, None] * x + self.bias[:, None, None]
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88 |
+
return x
|
89 |
+
|
90 |
+
class ResidualBlock(torch.nn.Module):
|
91 |
+
"""
|
92 |
+
Utilized in upsample block
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93 |
+
"""
|
94 |
+
def __init__(self, channels):
|
95 |
+
super(ResidualBlock, self).__init__()
|
96 |
+
self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, stride=1, padding=1)
|
97 |
+
self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, stride=1, padding=1)
|
98 |
+
self.relu = nn.ReLU()
|
99 |
+
|
100 |
+
def forward(self, x):
|
101 |
+
"""
|
102 |
+
x: tensor of shape (B,C,H,W)
|
103 |
+
"""
|
104 |
+
residual = x
|
105 |
+
out = self.relu(self.conv1(x))
|
106 |
+
out = self.conv2(out) * 0.5
|
107 |
+
out = out + residual
|
108 |
+
|
109 |
+
return out
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110 |
+
|
111 |
+
|
112 |
+
class UpsampleBlock(nn.Module):
|
113 |
+
def __init__(self, in_channels, out_channels):
|
114 |
+
super(UpsampleBlock, self).__init__()
|
115 |
+
|
116 |
+
self.up_conv = nn.ConvTranspose2d(in_channels, in_channels, kernel_size=4, stride=2, padding=1)
|
117 |
+
self.up_norm = LayerNorm(in_channels, eps=1e-6, data_format="channels_first")
|
118 |
+
|
119 |
+
self.res_block = ResidualBlock(in_channels)
|
120 |
+
self.res_norm = LayerNorm(in_channels, eps=1e-6, data_format="channels_first")
|
121 |
+
|
122 |
+
self.proj_out = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
|
123 |
+
|
124 |
+
self.apply(self._init_weights)
|
125 |
+
|
126 |
+
def _init_weights(self, m):
|
127 |
+
if isinstance(m, nn.Linear):
|
128 |
+
torch.nn.init.xavier_uniform_(m.weight)
|
129 |
+
if isinstance(m, nn.Linear) and m.bias is not None:
|
130 |
+
nn.init.constant_(m.bias, 0)
|
131 |
+
|
132 |
+
elif isinstance(m, nn.Conv2d):
|
133 |
+
nn.init.constant_(m.bias, 0)
|
134 |
+
nn.init.xavier_uniform_(m.weight)
|
135 |
+
|
136 |
+
elif isinstance(m, nn.LayerNorm):
|
137 |
+
nn.init.constant_(m.bias, 0)
|
138 |
+
nn.init.constant_(m.weight, 1.0)
|
139 |
+
|
140 |
+
def forward(self, x):
|
141 |
+
## upsample 2x
|
142 |
+
x = self.up_conv(x)
|
143 |
+
x = self.up_norm(x)
|
144 |
+
x = torch.nn.functional.leaky_relu(x)
|
145 |
+
|
146 |
+
# residual block
|
147 |
+
x = self.res_block(x)
|
148 |
+
x = self.res_norm(x)
|
149 |
+
|
150 |
+
out = self.proj_out(x)
|
151 |
+
|
152 |
+
return x, out
|
153 |
+
|
154 |
+
################################################################################
|
155 |
+
|
156 |
+
class MaskedAutoencoderViT(nn.Module, PyTorchModelHubMixin):
|
157 |
+
""" Masked Autoencoder with VisionTransformer backbone
|
158 |
+
"""
|
159 |
+
def __init__(self, img_size=224, patch_size=16, in_chans=3,
|
160 |
+
embed_dim=1024, depth=24, num_heads=16,
|
161 |
+
decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,
|
162 |
+
mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False,
|
163 |
+
proj_ratio=4):
|
164 |
+
super().__init__()
|
165 |
+
|
166 |
+
self.in_c = in_chans
|
167 |
+
|
168 |
+
######################################################
|
169 |
+
# create upsample block layers
|
170 |
+
ms_dim = self.in_c*proj_ratio
|
171 |
+
self.proj_up_conv = nn.Conv2d(self.in_c, ms_dim, kernel_size=1, stride=1, padding=0)
|
172 |
+
self.proj_up_norm = LayerNorm(ms_dim, eps=1e-6, data_format="channels_first")
|
173 |
+
|
174 |
+
self.up_block = UpsampleBlock(ms_dim, self.in_c)
|
175 |
+
|
176 |
+
######################################################
|
177 |
+
# MAE encoder specifics
|
178 |
+
self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, embed_dim)
|
179 |
+
num_patches = self.patch_embed.num_patches
|
180 |
+
|
181 |
+
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
|
182 |
+
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim), requires_grad=False) # fixed sin-cos embedding
|
183 |
+
|
184 |
+
self.blocks = nn.ModuleList([
|
185 |
+
Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer)
|
186 |
+
for i in range(depth)])
|
187 |
+
self.norm = norm_layer(embed_dim)
|
188 |
+
|
189 |
+
######################################################
|
190 |
+
# MAE decoder specifics
|
191 |
+
self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True)
|
192 |
+
|
193 |
+
self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim))
|
194 |
+
|
195 |
+
self.decoder_pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim), requires_grad=False) # fixed sin-cos embedding
|
196 |
+
|
197 |
+
self.decoder_blocks = nn.ModuleList([
|
198 |
+
Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer)
|
199 |
+
for i in range(decoder_depth)])
|
200 |
+
|
201 |
+
self.decoder_norm = norm_layer(decoder_embed_dim)
|
202 |
+
self.decoder_pred = nn.Linear(decoder_embed_dim, patch_size**2 * in_chans, bias=True) # decoder to patch
|
203 |
+
|
204 |
+
|
205 |
+
self.norm_pix_loss = norm_pix_loss
|
206 |
+
|
207 |
+
self.initialize_weights()
|
208 |
+
|
209 |
+
def initialize_weights(self):
|
210 |
+
# initialization
|
211 |
+
# initialize (and freeze) pos_embed by sin-cos embedding
|
212 |
+
pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True)
|
213 |
+
self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
|
214 |
+
|
215 |
+
decoder_pos_embed = get_2d_sincos_pos_embed(self.decoder_pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True)
|
216 |
+
self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0))
|
217 |
+
|
218 |
+
# initialize patch_embed like nn.Linear (instead of nn.Conv2d)
|
219 |
+
w = self.patch_embed.proj.weight.data
|
220 |
+
torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
|
221 |
+
|
222 |
+
# timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.)
|
223 |
+
torch.nn.init.normal_(self.cls_token, std=.02)
|
224 |
+
torch.nn.init.normal_(self.mask_token, std=.02)
|
225 |
+
|
226 |
+
# initialize nn.Linear and nn.LayerNorm
|
227 |
+
self.apply(self._init_weights)
|
228 |
+
|
229 |
+
def _init_weights(self, m):
|
230 |
+
if isinstance(m, nn.Linear):
|
231 |
+
# we use xavier_uniform following official JAX ViT:
|
232 |
+
torch.nn.init.xavier_uniform_(m.weight)
|
233 |
+
if isinstance(m, nn.Linear) and m.bias is not None:
|
234 |
+
nn.init.constant_(m.bias, 0)
|
235 |
+
elif isinstance(m, nn.LayerNorm):
|
236 |
+
nn.init.constant_(m.bias, 0)
|
237 |
+
nn.init.constant_(m.weight, 1.0)
|
238 |
+
|
239 |
+
|
240 |
+
def patchify(self, imgs, p, c):
|
241 |
+
"""
|
242 |
+
imgs: (N, C, H, W)
|
243 |
+
p: Patch embed patch size
|
244 |
+
c: Number of channels
|
245 |
+
x: (N, L, patch_size**2 *C)
|
246 |
+
"""
|
247 |
+
# p = self.patch_embed.patch_size[0]
|
248 |
+
assert imgs.shape[2] == imgs.shape[3] and imgs.shape[2] % p == 0
|
249 |
+
|
250 |
+
# c = self.in_c
|
251 |
+
h = w = imgs.shape[2] // p
|
252 |
+
x = imgs.reshape(shape=(imgs.shape[0], c, h, p, w, p))
|
253 |
+
x = torch.einsum('nchpwq->nhwpqc', x)
|
254 |
+
x = x.reshape(shape=(imgs.shape[0], h * w, p**2 * c))
|
255 |
+
return x
|
256 |
+
|
257 |
+
def unpatchify(self, x, p, c):
|
258 |
+
"""
|
259 |
+
x: (N, L, patch_size**2 *C)
|
260 |
+
p: Patch embed patch size
|
261 |
+
c: Number of channels
|
262 |
+
imgs: (N, C, H, W)
|
263 |
+
"""
|
264 |
+
h = w = int(x.shape[1]**.5)
|
265 |
+
assert h * w == x.shape[1]
|
266 |
+
|
267 |
+
x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
|
268 |
+
x = torch.einsum('nhwpqc->nchpwq', x)
|
269 |
+
imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p))
|
270 |
+
return imgs
|
271 |
+
|
272 |
+
def random_masking(self, x, mask_ratio):
|
273 |
+
"""
|
274 |
+
Perform per-sample random masking by per-sample shuffling.
|
275 |
+
Per-sample shuffling is done by argsort random noise.
|
276 |
+
x: [N, L, D], sequence
|
277 |
+
"""
|
278 |
+
N, L, D = x.shape # batch, length, dim
|
279 |
+
len_keep = int(L * (1 - mask_ratio))
|
280 |
+
|
281 |
+
noise = torch.rand(N, L, device=x.device) # noise in [0, 1]
|
282 |
+
|
283 |
+
# sort noise for each sample
|
284 |
+
ids_shuffle = torch.argsort(noise, dim=1) # ascend: small is keep, large is remove
|
285 |
+
ids_restore = torch.argsort(ids_shuffle, dim=1)
|
286 |
+
|
287 |
+
# keep the first subset
|
288 |
+
ids_keep = ids_shuffle[:, :len_keep]
|
289 |
+
x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
|
290 |
+
|
291 |
+
# generate the binary mask: 0 is keep, 1 is remove
|
292 |
+
mask = torch.ones([N, L], device=x.device)
|
293 |
+
mask[:, :len_keep] = 0
|
294 |
+
# unshuffle to get the binary mask
|
295 |
+
mask = torch.gather(mask, dim=1, index=ids_restore)
|
296 |
+
|
297 |
+
return x_masked, mask, ids_restore
|
298 |
+
|
299 |
+
def forward_encoder(self, x, mask_ratio):
|
300 |
+
# embed patches
|
301 |
+
x = self.patch_embed(x)
|
302 |
+
|
303 |
+
# add pos embed w/o cls token
|
304 |
+
x = x + self.pos_embed[:, 1:, :]
|
305 |
+
|
306 |
+
# masking: length -> length * mask_ratio
|
307 |
+
x, mask, ids_restore = self.random_masking(x, mask_ratio)
|
308 |
+
|
309 |
+
# append cls token
|
310 |
+
cls_token = self.cls_token + self.pos_embed[:, :1, :]
|
311 |
+
cls_tokens = cls_token.expand(x.shape[0], -1, -1)
|
312 |
+
x = torch.cat((cls_tokens, x), dim=1)
|
313 |
+
|
314 |
+
# apply Transformer blocks
|
315 |
+
for blk in self.blocks:
|
316 |
+
x = blk(x)
|
317 |
+
x = self.norm(x)
|
318 |
+
|
319 |
+
return x, mask, ids_restore
|
320 |
+
|
321 |
+
def forward_decoder(self, x, ids_restore):
|
322 |
+
# embed tokens
|
323 |
+
x = self.decoder_embed(x)
|
324 |
+
|
325 |
+
# append mask tokens to sequence
|
326 |
+
mask_tokens = self.mask_token.repeat(x.shape[0], ids_restore.shape[1] + 1 - x.shape[1], 1)
|
327 |
+
x_ = torch.cat([x[:, 1:, :], mask_tokens], dim=1) # no cls token
|
328 |
+
x_ = torch.gather(x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2])) # unshuffle
|
329 |
+
x = torch.cat([x[:, :1, :], x_], dim=1) # append cls token
|
330 |
+
|
331 |
+
# add pos embed
|
332 |
+
x = x + self.decoder_pos_embed
|
333 |
+
|
334 |
+
# apply Transformer blocks
|
335 |
+
for blk in self.decoder_blocks:
|
336 |
+
x = blk(x)
|
337 |
+
x = self.decoder_norm(x)
|
338 |
+
|
339 |
+
# predictor projection
|
340 |
+
x = self.decoder_pred(x)
|
341 |
+
|
342 |
+
# remove cls token
|
343 |
+
x = x[:, 1:, :]
|
344 |
+
|
345 |
+
return x
|
346 |
+
|
347 |
+
def forward_loss(self, imgs, pred, mask):
|
348 |
+
"""
|
349 |
+
imgs: [N, 3, H, W]
|
350 |
+
pred: [N, L, p*p*3]
|
351 |
+
mask: [N, L], 0 is keep, 1 is remove,
|
352 |
+
"""
|
353 |
+
target = self.patchify(imgs, self.patch_embed.patch_size[0], self.in_c)
|
354 |
+
|
355 |
+
if self.norm_pix_loss:
|
356 |
+
mean = target.mean(dim=-1, keepdim=True)
|
357 |
+
var = target.var(dim=-1, keepdim=True)
|
358 |
+
target = (target - mean) / (var + 1.e-6)**.5
|
359 |
+
|
360 |
+
loss = (pred - target) ** 2
|
361 |
+
loss = loss.mean(dim=-1) # [N, L], mean loss per patch
|
362 |
+
|
363 |
+
loss = (loss * mask).sum() / mask.sum() # mean loss on removed patches
|
364 |
+
return loss
|
365 |
+
|
366 |
+
def forward_multiscale(self, x):
|
367 |
+
"""
|
368 |
+
x: (N, L, p*p*3)
|
369 |
+
"""
|
370 |
+
x = self.unpatchify(x, self.patch_embed.patch_size[0], self.in_c)
|
371 |
+
|
372 |
+
x = self.proj_up_conv(x)
|
373 |
+
x = F.gelu(x)
|
374 |
+
x = self.proj_up_norm(x)
|
375 |
+
|
376 |
+
_, x = self.up_block(x)
|
377 |
+
|
378 |
+
return x
|
379 |
+
|
380 |
+
def forward(self, imgs, imgs_up, mask_ratio=0.75):
|
381 |
+
latent, mask, ids_restore = self.forward_encoder(imgs, mask_ratio)
|
382 |
+
pred = self.forward_decoder(latent, ids_restore) # [N, L, p*p*3]
|
383 |
+
pred_ms = self.forward_multiscale(pred)
|
384 |
+
|
385 |
+
loss = self.forward_loss(imgs, pred, mask) # MSE loss
|
386 |
+
ms_loss = F.l1_loss(pred_ms, imgs_up) # compute multiscale loss (L1 loss)
|
387 |
+
|
388 |
+
return loss, ms_loss, pred, mask
|