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lora-scripts/sd-scripts/finetune/blip/vit.py
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1 |
+
'''
|
2 |
+
* Copyright (c) 2022, salesforce.com, inc.
|
3 |
+
* All rights reserved.
|
4 |
+
* SPDX-License-Identifier: BSD-3-Clause
|
5 |
+
* For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
|
6 |
+
* By Junnan Li
|
7 |
+
* Based on timm code base
|
8 |
+
* https://github.com/rwightman/pytorch-image-models/tree/master/timm
|
9 |
+
'''
|
10 |
+
|
11 |
+
import torch
|
12 |
+
import torch.nn as nn
|
13 |
+
import torch.nn.functional as F
|
14 |
+
from functools import partial
|
15 |
+
|
16 |
+
from timm.models.vision_transformer import _cfg, PatchEmbed
|
17 |
+
from timm.models.registry import register_model
|
18 |
+
from timm.models.layers import trunc_normal_, DropPath
|
19 |
+
from timm.models.helpers import named_apply, adapt_input_conv
|
20 |
+
|
21 |
+
from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper
|
22 |
+
|
23 |
+
class Mlp(nn.Module):
|
24 |
+
""" MLP as used in Vision Transformer, MLP-Mixer and related networks
|
25 |
+
"""
|
26 |
+
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
|
27 |
+
super().__init__()
|
28 |
+
out_features = out_features or in_features
|
29 |
+
hidden_features = hidden_features or in_features
|
30 |
+
self.fc1 = nn.Linear(in_features, hidden_features)
|
31 |
+
self.act = act_layer()
|
32 |
+
self.fc2 = nn.Linear(hidden_features, out_features)
|
33 |
+
self.drop = nn.Dropout(drop)
|
34 |
+
|
35 |
+
def forward(self, x):
|
36 |
+
x = self.fc1(x)
|
37 |
+
x = self.act(x)
|
38 |
+
x = self.drop(x)
|
39 |
+
x = self.fc2(x)
|
40 |
+
x = self.drop(x)
|
41 |
+
return x
|
42 |
+
|
43 |
+
|
44 |
+
class Attention(nn.Module):
|
45 |
+
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
|
46 |
+
super().__init__()
|
47 |
+
self.num_heads = num_heads
|
48 |
+
head_dim = dim // num_heads
|
49 |
+
# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
|
50 |
+
self.scale = qk_scale or head_dim ** -0.5
|
51 |
+
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
52 |
+
self.attn_drop = nn.Dropout(attn_drop)
|
53 |
+
self.proj = nn.Linear(dim, dim)
|
54 |
+
self.proj_drop = nn.Dropout(proj_drop)
|
55 |
+
self.attn_gradients = None
|
56 |
+
self.attention_map = None
|
57 |
+
|
58 |
+
def save_attn_gradients(self, attn_gradients):
|
59 |
+
self.attn_gradients = attn_gradients
|
60 |
+
|
61 |
+
def get_attn_gradients(self):
|
62 |
+
return self.attn_gradients
|
63 |
+
|
64 |
+
def save_attention_map(self, attention_map):
|
65 |
+
self.attention_map = attention_map
|
66 |
+
|
67 |
+
def get_attention_map(self):
|
68 |
+
return self.attention_map
|
69 |
+
|
70 |
+
def forward(self, x, register_hook=False):
|
71 |
+
B, N, C = x.shape
|
72 |
+
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
73 |
+
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
|
74 |
+
|
75 |
+
attn = (q @ k.transpose(-2, -1)) * self.scale
|
76 |
+
attn = attn.softmax(dim=-1)
|
77 |
+
attn = self.attn_drop(attn)
|
78 |
+
|
79 |
+
if register_hook:
|
80 |
+
self.save_attention_map(attn)
|
81 |
+
attn.register_hook(self.save_attn_gradients)
|
82 |
+
|
83 |
+
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
|
84 |
+
x = self.proj(x)
|
85 |
+
x = self.proj_drop(x)
|
86 |
+
return x
|
87 |
+
|
88 |
+
|
89 |
+
class Block(nn.Module):
|
90 |
+
|
91 |
+
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
|
92 |
+
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_grad_checkpointing=False):
|
93 |
+
super().__init__()
|
94 |
+
self.norm1 = norm_layer(dim)
|
95 |
+
self.attn = Attention(
|
96 |
+
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
|
97 |
+
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
|
98 |
+
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
99 |
+
self.norm2 = norm_layer(dim)
|
100 |
+
mlp_hidden_dim = int(dim * mlp_ratio)
|
101 |
+
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
|
102 |
+
|
103 |
+
if use_grad_checkpointing:
|
104 |
+
self.attn = checkpoint_wrapper(self.attn)
|
105 |
+
self.mlp = checkpoint_wrapper(self.mlp)
|
106 |
+
|
107 |
+
def forward(self, x, register_hook=False):
|
108 |
+
x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook))
|
109 |
+
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
110 |
+
return x
|
111 |
+
|
112 |
+
|
113 |
+
class VisionTransformer(nn.Module):
|
114 |
+
""" Vision Transformer
|
115 |
+
A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` -
|
116 |
+
https://arxiv.org/abs/2010.11929
|
117 |
+
"""
|
118 |
+
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
|
119 |
+
num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None,
|
120 |
+
drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None,
|
121 |
+
use_grad_checkpointing=False, ckpt_layer=0):
|
122 |
+
"""
|
123 |
+
Args:
|
124 |
+
img_size (int, tuple): input image size
|
125 |
+
patch_size (int, tuple): patch size
|
126 |
+
in_chans (int): number of input channels
|
127 |
+
num_classes (int): number of classes for classification head
|
128 |
+
embed_dim (int): embedding dimension
|
129 |
+
depth (int): depth of transformer
|
130 |
+
num_heads (int): number of attention heads
|
131 |
+
mlp_ratio (int): ratio of mlp hidden dim to embedding dim
|
132 |
+
qkv_bias (bool): enable bias for qkv if True
|
133 |
+
qk_scale (float): override default qk scale of head_dim ** -0.5 if set
|
134 |
+
representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
|
135 |
+
drop_rate (float): dropout rate
|
136 |
+
attn_drop_rate (float): attention dropout rate
|
137 |
+
drop_path_rate (float): stochastic depth rate
|
138 |
+
norm_layer: (nn.Module): normalization layer
|
139 |
+
"""
|
140 |
+
super().__init__()
|
141 |
+
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
|
142 |
+
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
|
143 |
+
|
144 |
+
self.patch_embed = PatchEmbed(
|
145 |
+
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
|
146 |
+
|
147 |
+
num_patches = self.patch_embed.num_patches
|
148 |
+
|
149 |
+
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
|
150 |
+
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
|
151 |
+
self.pos_drop = nn.Dropout(p=drop_rate)
|
152 |
+
|
153 |
+
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
|
154 |
+
self.blocks = nn.ModuleList([
|
155 |
+
Block(
|
156 |
+
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
|
157 |
+
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
|
158 |
+
use_grad_checkpointing=(use_grad_checkpointing and i>=depth-ckpt_layer)
|
159 |
+
)
|
160 |
+
for i in range(depth)])
|
161 |
+
self.norm = norm_layer(embed_dim)
|
162 |
+
|
163 |
+
trunc_normal_(self.pos_embed, std=.02)
|
164 |
+
trunc_normal_(self.cls_token, std=.02)
|
165 |
+
self.apply(self._init_weights)
|
166 |
+
|
167 |
+
def _init_weights(self, m):
|
168 |
+
if isinstance(m, nn.Linear):
|
169 |
+
trunc_normal_(m.weight, std=.02)
|
170 |
+
if isinstance(m, nn.Linear) and m.bias is not None:
|
171 |
+
nn.init.constant_(m.bias, 0)
|
172 |
+
elif isinstance(m, nn.LayerNorm):
|
173 |
+
nn.init.constant_(m.bias, 0)
|
174 |
+
nn.init.constant_(m.weight, 1.0)
|
175 |
+
|
176 |
+
@torch.jit.ignore
|
177 |
+
def no_weight_decay(self):
|
178 |
+
return {'pos_embed', 'cls_token'}
|
179 |
+
|
180 |
+
def forward(self, x, register_blk=-1):
|
181 |
+
B = x.shape[0]
|
182 |
+
x = self.patch_embed(x)
|
183 |
+
|
184 |
+
cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
|
185 |
+
x = torch.cat((cls_tokens, x), dim=1)
|
186 |
+
|
187 |
+
x = x + self.pos_embed[:,:x.size(1),:]
|
188 |
+
x = self.pos_drop(x)
|
189 |
+
|
190 |
+
for i,blk in enumerate(self.blocks):
|
191 |
+
x = blk(x, register_blk==i)
|
192 |
+
x = self.norm(x)
|
193 |
+
|
194 |
+
return x
|
195 |
+
|
196 |
+
@torch.jit.ignore()
|
197 |
+
def load_pretrained(self, checkpoint_path, prefix=''):
|
198 |
+
_load_weights(self, checkpoint_path, prefix)
|
199 |
+
|
200 |
+
|
201 |
+
@torch.no_grad()
|
202 |
+
def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''):
|
203 |
+
""" Load weights from .npz checkpoints for official Google Brain Flax implementation
|
204 |
+
"""
|
205 |
+
import numpy as np
|
206 |
+
|
207 |
+
def _n2p(w, t=True):
|
208 |
+
if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:
|
209 |
+
w = w.flatten()
|
210 |
+
if t:
|
211 |
+
if w.ndim == 4:
|
212 |
+
w = w.transpose([3, 2, 0, 1])
|
213 |
+
elif w.ndim == 3:
|
214 |
+
w = w.transpose([2, 0, 1])
|
215 |
+
elif w.ndim == 2:
|
216 |
+
w = w.transpose([1, 0])
|
217 |
+
return torch.from_numpy(w)
|
218 |
+
|
219 |
+
w = np.load(checkpoint_path)
|
220 |
+
if not prefix and 'opt/target/embedding/kernel' in w:
|
221 |
+
prefix = 'opt/target/'
|
222 |
+
|
223 |
+
if hasattr(model.patch_embed, 'backbone'):
|
224 |
+
# hybrid
|
225 |
+
backbone = model.patch_embed.backbone
|
226 |
+
stem_only = not hasattr(backbone, 'stem')
|
227 |
+
stem = backbone if stem_only else backbone.stem
|
228 |
+
stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel'])))
|
229 |
+
stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale']))
|
230 |
+
stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias']))
|
231 |
+
if not stem_only:
|
232 |
+
for i, stage in enumerate(backbone.stages):
|
233 |
+
for j, block in enumerate(stage.blocks):
|
234 |
+
bp = f'{prefix}block{i + 1}/unit{j + 1}/'
|
235 |
+
for r in range(3):
|
236 |
+
getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel']))
|
237 |
+
getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale']))
|
238 |
+
getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias']))
|
239 |
+
if block.downsample is not None:
|
240 |
+
block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel']))
|
241 |
+
block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale']))
|
242 |
+
block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias']))
|
243 |
+
embed_conv_w = _n2p(w[f'{prefix}embedding/kernel'])
|
244 |
+
else:
|
245 |
+
embed_conv_w = adapt_input_conv(
|
246 |
+
model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel']))
|
247 |
+
model.patch_embed.proj.weight.copy_(embed_conv_w)
|
248 |
+
model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias']))
|
249 |
+
model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False))
|
250 |
+
pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False)
|
251 |
+
if pos_embed_w.shape != model.pos_embed.shape:
|
252 |
+
pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights
|
253 |
+
pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size)
|
254 |
+
model.pos_embed.copy_(pos_embed_w)
|
255 |
+
model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale']))
|
256 |
+
model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias']))
|
257 |
+
# if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]:
|
258 |
+
# model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel']))
|
259 |
+
# model.head.bias.copy_(_n2p(w[f'{prefix}head/bias']))
|
260 |
+
# if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w:
|
261 |
+
# model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel']))
|
262 |
+
# model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias']))
|
263 |
+
for i, block in enumerate(model.blocks.children()):
|
264 |
+
block_prefix = f'{prefix}Transformer/encoderblock_{i}/'
|
265 |
+
mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/'
|
266 |
+
block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale']))
|
267 |
+
block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias']))
|
268 |
+
block.attn.qkv.weight.copy_(torch.cat([
|
269 |
+
_n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')]))
|
270 |
+
block.attn.qkv.bias.copy_(torch.cat([
|
271 |
+
_n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')]))
|
272 |
+
block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1))
|
273 |
+
block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias']))
|
274 |
+
for r in range(2):
|
275 |
+
getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel']))
|
276 |
+
getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias']))
|
277 |
+
block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale']))
|
278 |
+
block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias']))
|
279 |
+
|
280 |
+
|
281 |
+
def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder):
|
282 |
+
# interpolate position embedding
|
283 |
+
embedding_size = pos_embed_checkpoint.shape[-1]
|
284 |
+
num_patches = visual_encoder.patch_embed.num_patches
|
285 |
+
num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches
|
286 |
+
# height (== width) for the checkpoint position embedding
|
287 |
+
orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
|
288 |
+
# height (== width) for the new position embedding
|
289 |
+
new_size = int(num_patches ** 0.5)
|
290 |
+
|
291 |
+
if orig_size!=new_size:
|
292 |
+
# class_token and dist_token are kept unchanged
|
293 |
+
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
|
294 |
+
# only the position tokens are interpolated
|
295 |
+
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
|
296 |
+
pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
|
297 |
+
pos_tokens = torch.nn.functional.interpolate(
|
298 |
+
pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
|
299 |
+
pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
|
300 |
+
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
|
301 |
+
print('reshape position embedding from %d to %d'%(orig_size ** 2,new_size ** 2))
|
302 |
+
|
303 |
+
return new_pos_embed
|
304 |
+
else:
|
305 |
+
return pos_embed_checkpoint
|