Spaces:
Sleeping
Sleeping
File size: 8,761 Bytes
929f451 |
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 |
# --------------------------------------------------------
# References:
# MAE: https://github.com/IcarusWizard/MAE
# --------------------------------------------------------
import torch
import timm
import numpy as np
from einops import repeat, rearrange
from einops.layers.torch import Rearrange
from timm.models.layers import trunc_normal_
from timm.models.vision_transformer import Block
def random_indexes(size : int):
forward_indexes = np.arange(size)
np.random.shuffle(forward_indexes)
backward_indexes = np.argsort(forward_indexes)
return forward_indexes, backward_indexes
def take_indexes(sequences, indexes):
return torch.gather(sequences, 0, repeat(indexes, 't b -> t b c', c=sequences.shape[-1]))
class PatchShuffle(torch.nn.Module):
def __init__(self, ratio) -> None:
super().__init__()
self.ratio = ratio
def forward(self, patches : torch.Tensor):
T, B, C = patches.shape
remain_T = int(T * (1 - self.ratio))
indexes = [random_indexes(T) for _ in range(B)]
forward_indexes = torch.as_tensor(np.stack([i[0] for i in indexes], axis=-1), dtype=torch.long).to(patches.device)
backward_indexes = torch.as_tensor(np.stack([i[1] for i in indexes], axis=-1), dtype=torch.long).to(patches.device)
patches = take_indexes(patches, forward_indexes)
patches = patches[:remain_T]
return patches, forward_indexes, backward_indexes
class MAE_Encoder(torch.nn.Module):
def __init__(self,
image_size=32,
patch_size=2,
emb_dim=192,
num_layer=12,
num_head=3,
mask_ratio=0.75,
) -> None:
super().__init__()
self.cls_token = torch.nn.Parameter(torch.zeros(1, 1, emb_dim))
self.pos_embedding = torch.nn.Parameter(torch.zeros((image_size // patch_size) ** 2, 1, emb_dim))
self.shuffle = PatchShuffle(mask_ratio)
self.patchify = torch.nn.Conv2d(3, emb_dim, patch_size, patch_size)
self.transformer = torch.nn.Sequential(*[Block(emb_dim, num_head) for _ in range(num_layer)])
self.layer_norm = torch.nn.LayerNorm(emb_dim)
self.init_weight()
def init_weight(self):
trunc_normal_(self.cls_token, std=.02)
trunc_normal_(self.pos_embedding, std=.02)
def forward(self, img):
patches = self.patchify(img)
patches = rearrange(patches, 'b c h w -> (h w) b c')
patches = patches + self.pos_embedding
patches, forward_indexes, backward_indexes = self.shuffle(patches)
patches = torch.cat([self.cls_token.expand(-1, patches.shape[1], -1), patches], dim=0)
patches = rearrange(patches, 't b c -> b t c')
features = self.layer_norm(self.transformer(patches))
features = rearrange(features, 'b t c -> t b c')
return features, backward_indexes
class MAE_Decoder(torch.nn.Module):
def __init__(self,
image_size=32,
patch_size=2,
emb_dim=192,
num_layer=4,
num_head=3,
) -> None:
super().__init__()
self.mask_token = torch.nn.Parameter(torch.zeros(1, 1, emb_dim))
self.pos_embedding = torch.nn.Parameter(torch.zeros((image_size // patch_size) ** 2 + 1, 1, emb_dim))
self.transformer = torch.nn.Sequential(*[Block(emb_dim, num_head) for _ in range(num_layer)])
self.head = torch.nn.Linear(emb_dim, 3 * patch_size ** 2)
self.patch2img = Rearrange('(h w) b (c p1 p2) -> b c (h p1) (w p2)', p1=patch_size, p2=patch_size, h=image_size//patch_size)
self.init_weight()
def init_weight(self):
trunc_normal_(self.mask_token, std=.02)
trunc_normal_(self.pos_embedding, std=.02)
def forward(self, features, backward_indexes):
T = features.shape[0]
backward_indexes = torch.cat([torch.zeros(1, backward_indexes.shape[1]).to(backward_indexes), backward_indexes + 1], dim=0)
features = torch.cat([features, self.mask_token.expand(backward_indexes.shape[0] - features.shape[0], features.shape[1], -1)], dim=0)
features = take_indexes(features, backward_indexes)
features = features + self.pos_embedding
features = rearrange(features, 't b c -> b t c')
features = self.transformer(features)
features = rearrange(features, 'b t c -> t b c')
features = features[1:] # remove global feature
patches = self.head(features)
mask = torch.zeros_like(patches)
mask[T-1:] = 1
mask = take_indexes(mask, backward_indexes[1:] - 1)
img = self.patch2img(patches)
mask = self.patch2img(mask)
return img, mask
class MAE_ViT(torch.nn.Module):
def __init__(self,
image_size=32,
patch_size=2,
emb_dim=192,
encoder_layer=12,
encoder_head=3,
decoder_layer=4,
decoder_head=3,
mask_ratio=0.75,
) -> None:
super().__init__()
self.encoder = MAE_Encoder(image_size, patch_size, emb_dim, encoder_layer, encoder_head, mask_ratio)
self.decoder = MAE_Decoder(image_size, patch_size, emb_dim, decoder_layer, decoder_head)
def forward(self, img):
features, backward_indexes = self.encoder(img)
predicted_img, mask = self.decoder(features, backward_indexes)
return predicted_img, mask
class ViT_Classifier(torch.nn.Module):
'''
A simple image classification task acts as a head for ViT, allowing fine-tuning on downstream tasks.
We didn't directly use the MAE_ViT encoder because we need to add a classification head.
The Masked Autoencoder uses only some patches as input, which means it lacks the global information of the image,
making it unsuitable for classification.
'''
def __init__(self, encoder : MAE_Encoder, dropout_p, num_classes=10) -> None:
super().__init__()
self.dropout_p = dropout_p
self.cls_token = encoder.cls_token
self.pos_embedding = encoder.pos_embedding
self.patchify = encoder.patchify
self.transformer = encoder.transformer
self.layer_norm = encoder.layer_norm
self.dropout = torch.nn.Dropout(dropout_p) # Add dropout layer
self.head = torch.nn.Linear(self.pos_embedding.shape[-1], num_classes)
def forward(self, img):
patches = self.patchify(img)
patches = rearrange(patches, 'b c h w -> (h w) b c')
patches = patches + self.pos_embedding
patches = torch.cat([self.cls_token.expand(-1, patches.shape[1], -1), patches], dim=0)
patches = rearrange(patches, 't b c -> b t c')
features = self.layer_norm(self.transformer(patches))
# t is the number of patches, b is the batch size, c is the number of features
features = rearrange(features, 'b t c -> t b c')
if self.dropout_p > 0:
features = self.dropout(features) # Apply dropout before the final head
logits = self.head(features[0]) # only use the cls token
return logits
class MAE_Encoder_FeatureExtractor(torch.nn.Module):
'''
A feature extractor that extracts features from the encoder of the Masked Autoencoder.
'''
def __init__(self, encoder : MAE_Encoder) -> None:
super().__init__()
self.cls_token = encoder.cls_token
self.pos_embedding = encoder.pos_embedding
self.patchify = encoder.patchify
self.transformer = encoder.transformer
self.layer_norm = encoder.layer_norm
def forward(self, img):
patches = self.patchify(img)
patches = rearrange(patches, 'b c h w -> (h w) b c')
patches = patches + self.pos_embedding
patches = torch.cat([self.cls_token.expand(-1, patches.shape[1], -1), patches], dim=0)
patches = rearrange(patches, 't b c -> b t c')
features = self.layer_norm(self.transformer(patches))
# t is the number of patches, b is the batch size, c is the number of features
features = rearrange(features, 'b t c -> t b c')
return features
if __name__ == '__main__':
shuffle = PatchShuffle(0.75)
a = torch.rand(16, 2, 10)
b, forward_indexes, backward_indexes = shuffle(a)
print(b.shape)
img = torch.rand(2, 3, 32, 32)
encoder = MAE_Encoder()
decoder = MAE_Decoder()
features, backward_indexes = encoder(img)
print(forward_indexes.shape)
predicted_img, mask = decoder(features, backward_indexes)
print(predicted_img.shape)
loss = torch.mean((predicted_img - img) ** 2 * mask / 0.75)
print(loss) |