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import torch
import torch.nn as nn
from functools import partial
from collections import OrderedDict
from .pos_embed import interpolate_pos_embed
class Mlp(nn.Module):
""" MLP as used in Vision Transformer, MLP-Mixer and related networks
"""
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer() if act_layer else nn.GELU()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class Attention(nn.Module):
''' Multi-head self-attention '''
def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
qkv = qkv.permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1,2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., mlp_drop=0., qkv_bias=False, attn_drop=0.,
proj_drop=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
super().__init__()
self.norm1 = norm_layer(dim)
self.norm2 = norm_layer(dim)
self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop,
proj_drop=proj_drop)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=mlp_drop)
def forward(self, x):
x = x + self.attn(self.norm1(x))
x = x + self.mlp(self.norm2(x))
return x
class PatchEmbed(nn.Module):
""" 2D Image to Patch Embedding
"""
def __init__(self, img_size=(224,224), patch_size=(16,16), in_chans=3, embed_dim=768,
norm_layer=None, flatten=True):
super().__init__()
self.in_chans = in_chans
self.img_size = img_size
self.patch_size = patch_size
self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
self.num_patches = self.grid_size[0] * self.grid_size[1]
self.flatten = flatten
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
def forward(self, x):
# B, C, H, W = x.shape
# assert H % self.img_size[0] == 0 and W == self.img_size[1], \
# f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
# assert C == self.in_chans, \
# f"Input image chanel ({C}) doesn't match model ({self.in_chans})"
x = self.proj(x)
if self.flatten:
x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
x = self.norm(x)
return x
class VisionTransformer(nn.Module):
""" Vision Transformer with support for global average pooling
"""
def __init__(self, img_size=(224,224), patch_size=(16, 16), in_chans=3, num_classes=1000, embed_dim=768,
depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, pos_drop_rate=0., attn_drop_rate=0.,
proj_drop_rate=0., norm_layer=None, act_layer=None, cls_feature_dim=None, global_pool=False, enable_gra=False):
super().__init__()
self.global_pool = global_pool
self.num_classes = num_classes
self.num_features = self.embed_dim = embed_dim
self.patch_embed = PatchEmbed(img_size=img_size, patch_size=patch_size, in_chans=in_chans,
embed_dim=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))
self.pos_drop = nn.Dropout(p=pos_drop_rate)
norm_layer = norm_layer if norm_layer else partial(nn.LayerNorm, eps=1e-6)
self.blocks = nn.Sequential(*[
Block(dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
attn_drop=attn_drop_rate, proj_drop=proj_drop_rate, norm_layer=norm_layer,
act_layer=act_layer)
for _ in range(depth)])
self.fc_norm = norm_layer(embed_dim)
self.enable_gra = enable_gra
if self.enable_gra:
self.gra_embed = nn.Embedding(10, embed_dim)
# feature representation for classification
if cls_feature_dim:
self.num_features = cls_feature_dim
self.pre_logits = nn.Sequential(OrderedDict([
('fc', nn.Linear(embed_dim, cls_feature_dim)),
('act', nn.Tanh())
]))
else:
self.pre_logits = nn.Identity()
# classification head(s)
self.head = nn.Linear(self.num_features, num_classes)
self.init_weights()
def init_weights_from_pretrained(self, pretrained_path):
if pretrained_path:
checkpoint = torch.load(pretrained_path, map_location='cpu')
print("Load pre-trained checkpoint from: %s" % pretrained_path)
checkpoint_model = checkpoint['model']
# interpolate position embedding
interpolate_pos_embed(self, checkpoint_model)
# load pre-trained model
msg = self.load_state_dict(checkpoint_model, strict=False)
print(msg)
def init_weights(self):
# initialize patch_embed like nn.Linear (instead of nn.Conv2d)
w = self.patch_embed.proj.weight.data
nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
# timm's trunc_normal_(std=.02) is effectively similar to normal_(std=0.02)
# as the default cutoff in trunc_normal_(std=.02) is too big (-2., 2.)
nn.init.normal_(self.cls_token, std=.02)
nn.init.normal_(self.pos_embed, 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:
nn.init.xavier_uniform_(m.weight)
if 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 no_weight_decay(self):
return {'pos_embed', 'cls_token', 'dist_token'}
def shuffle(self, x):
"""
in: x (B, N, C)
out: x_shuffle (B, N, C), ids_restore (B, N)
"""
B, N, C = x.shape
noise = torch.rand(B, N, device=x.device)
ids_shuffle = torch.argsort(noise, dim=1)
ids_restore = torch.argsort(ids_shuffle, dim=1)
x_shuffle = torch.gather(x, 1, index=ids_shuffle.unsqueeze(-1).repeat(1, 1, C))
return x_shuffle, ids_restore
def unshuffle(self, x, ids_restore):
B, N, C = x.shape
x_unshuffle = torch.gather(x, 1, index=ids_restore.unsqueeze(-1).repeat(1, 1, C))
return x_unshuffle
def split(self, x):
B, N, C = x.shape
num_tokens_per_split = 224 * 224
num_splits = max(1, N // num_tokens_per_split)
out = []
for i in range(num_splits):
if i == num_splits - 1:
out.append(x[:, i*num_tokens_per_split:])
return out
out.append(x[:, i*num_tokens_per_split:(i+1)*num_tokens_per_split])
# window split for finetuning on larger size (the pretraining size should be 224 x 224)
def patchify(self, x):
"""
in: (B, N, C)
out: (B*win_w*win_h, N//(win_w*win_h), C)
"""
B, N, C = x.shape
grid_h, grid_w = self.patch_embed.grid_size
win_h_grid = 224 // self.patch_embed.patch_size[0]
win_w_grid = 224 // self.patch_embed.patch_size[1]
win_h, win_w = grid_h // win_h_grid, grid_w // win_w_grid
x = x.view(B, win_h, grid_h // win_h, win_w, grid_w // win_w, C)
x_patchified = x.permute((0, 1, 3, 2, 4, 5)).contiguous()
x_patchified = x_patchified.view(B * win_h * win_w, grid_h * grid_w // (win_h * win_w), C)
return x_patchified
# recover the window split
def unpatchify(self, x):
"""
in: (B*win_h*win_w, N//(win_h*win_w), C)
out: (B, N, C)
"""
B, N, C = x.shape
grid_h, grid_w = self.patch_embed.grid_size
win_h_grid = 224 // self.patch_embed.patch_size[0]
win_w_grid = 224 // self.patch_embed.patch_size[1]
win_h, win_w = grid_h // win_h_grid, grid_w // win_w_grid
x = x.view(B // (win_h * win_w), win_h, win_w, grid_h // win_h, grid_w // win_w, C)
x = x.permute((0, 1, 3, 2, 4, 5)).contiguous().view(B // (win_h * win_w), win_h * win_w * N, C)
return x
def forward_backbone(self, x, additional_features=None, gra=None, shuffle=False):
x = self.patch_embed(x)
if additional_features is not None:
x += additional_features
if self.enable_gra and gra is not None:
gra_idx = torch.clamp(gra * 10 - 1, 0, 9).long()
x += self.gra_embed(gra_idx).repeat(1, x.shape[1], 1)
x = self.pos_drop(x + self.pos_embed[:, 1:])
num_blocks = len(self.blocks)
assert num_blocks % 4 == 0
if shuffle:
for i in range(1, num_blocks + 1):
x, ids_restore = self.shuffle(x)
x_split = self.split(x)
x_split = [self.blocks[i-1](x_split[j]) for j in range(len(x_split))]
x = torch.cat(x_split, dim=1)
x = self.unshuffle(x, ids_restore)
else:
num_blocks_per_group = 6 if num_blocks == 12 else num_blocks // 4
is_patchified = False
for i in range(1, num_blocks + 1):
if i % num_blocks_per_group:
if not is_patchified:
x = self.patchify(x)
is_patchified = True
else:
pass # do nothing
else:
x = self.unpatchify(x)
is_patchified = False
x = self.blocks[i-1](x)
return x
def forward(self, x):
x = self.patch_embed(x)
cls_token = self.cls_token.expand(x.shape[0], -1, -1)
x = torch.cat((cls_token, x), dim=1)
x = self.pos_drop(x + self.pos_embed)
x = self.blocks(x)
if self.global_pool:
x = x[:, 1:].mean(dim=1) # global pool without cls token
x = self.fc_norm(x)
else:
x = self.fc_norm(x)
x = x[:, 0]
x = self.pre_logits(x)
x = self.head(x)
return x
def vit_base_patch16(**kwargs):
model = VisionTransformer(
patch_size=(16, 16), embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True, **kwargs)
return model
def vit_large_patch16(**kwargs):
model = VisionTransformer(
patch_size=(16, 16), embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True, **kwargs)
return model
def vit_huge_patch14(**kwargs):
model = VisionTransformer(
patch_size=(14,14), embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4, qkv_bias=True, **kwargs)
return model |