首先这次主要看CoaT-Lite small的代码。因为他还有CoaT代码，等下一步再看。
代码地址：代码每一步debug后的维度都批注在代码后面。
mlpc-ucsd/CoaT: (ICCV 2021 Oral) CoaT: Co-Scale Conv-Attentional Image Transformers (github.com)
"""
CoaT architecture.
Modified from timm/models/vision_transformer.py
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchsummary import summary
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.registry import register_model
from einops import rearrange
from functools import partial
from torch import nn, einsum
__all__ = [
"coat_tiny",
"coat_mini",
"coat_small",
"coat_lite_tiny",
"coat_lite_mini",
"coat_lite_small"
]
def _cfg_coat(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'crop_pct': .9, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'patch_embed.proj', 'classifier': 'head',
**kwargs
}
class Mlp(nn.Module):
""" Feed-forward network (FFN, a.k.a. MLP) class. """
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()
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 ConvRelPosEnc(nn.Module):
""" Convolutional relative position encoding. """
def __init__(self, Ch, h, window): #(8,8,window=crpe_window={3:2, 5:3, 7:3})
"""
Initialization.
Ch: Channels per head.
h: Number of heads.
window: Window size(s) in convolutional relative positional encoding. It can have two forms:
1. An integer of window size, which assigns all attention heads with the same window size in ConvRelPosEnc.
2. A dict mapping window size to #attention head splits (e.g. {window size 1: #attention head split 1, window size 2: #attention head split 2})
It will apply different window size to the attention head splits.
"""
#embed_dims=[64, 128, 320, 512], serial_depths=[3, 4, 6, 3], parallel_depth=0, num_heads=8, mlp_ratios=[8, 8, 4, 4], **kwargs
super().__init__()
if isinstance(window, int):
window = {window: h} # Set the same window size for all attention heads.
self.window = window
elif isinstance(window, dict):
self.window = window #{3:2, 5:3, 7:3}
else:
raise ValueError()
self.conv_list = nn.ModuleList()
self.head_splits = []
for cur_window, cur_head_split in window.items():#(3,2)/(5,3)/(7,3)
dilation = 1 # Use dilation=1 at default.
padding_size = (cur_window + (cur_window - 1) * (dilation - 1)) // 2 #(3+(2)*(0))//2 =1 # Determine padding size. Ref: https://discuss.pytorch.org/t/how-to-keep-the-shape-of-input-and-output-same-when-dilation-conv/14338
cur_conv = nn.Conv2d(cur_head_split*Ch, cur_head_split*Ch, #(16,16,k=3,p=1,1,16)/(18,18,k=5,p=2,1,16)
kernel_size=(cur_window, cur_window),
padding=(padding_size, padding_size),
dilation=(dilation, dilation),
groups=cur_head_split*Ch,
)
self.conv_list.append(cur_conv)
self.head_splits.append(cur_head_split)#(2,3,3)
self.channel_splits = [x*Ch for x in self.head_splits] #ch = 8 , head_splits=[2,3,3]
def forward(self, q, v, size): #size(q=v)=(1,8,19201,8)
B, h, N, Ch = q.shape # B:1 h:8 N:19201 Ch:8
H, W = size #(120,160)
assert N == 1 + H * W
# Convolutional relative position encoding.
q_img = q[:,:,1:,:]#(1,8,19200,8) # Shape: [B, h, H*W, Ch].
v_img = v[:,:,1:,:]#(1,8,19200,8) # Shape: [B, h, H*W, Ch].
v_img = rearrange(v_img, 'B h (H W) Ch -> B (h Ch) H W', H=H, W=W)#(1,64,120,160) # Shape: [B, h, H*W, Ch] -> [B, h*Ch, H, W].
v_img_list = torch.split(v_img, self.channel_splits, dim=1) #channel_splits=[16,24,24] # Split according to channels.
conv_v_img_list = [conv(x) for conv, x in zip(self.conv_list, v_img_list)]#[(1,16,120,160),(1,24,120,160),(1,24,120,160)]
conv_v_img = torch.cat(conv_v_img_list, dim=1)#(1,64,120,160)
conv_v_img = rearrange(conv_v_img, 'B (h Ch) H W -> B h (H W) Ch', h=h)#(1,8,19200,8) # Shape: [B, h*Ch, H, W] -> [B, h, H*W, Ch].
EV_hat_img = q_img * conv_v_img#(1,8,19200,8)
zero = torch.zeros((B, h, 1, Ch), dtype=q.dtype, layout=q.layout, device=q.device)#(1,8,1,8)
EV_hat = torch.cat((zero, EV_hat_img), dim=2) #(1,8,19201,8) # Shape: [B, h, N, Ch].
return EV_hat
class FactorAtt_ConvRelPosEnc(nn.Module):
""" Factorized attention with convolutional relative position encoding class. """
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., shared_crpe=None):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop) # Note: attn_drop is actually not used.
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
# Shared convolutional relative position encoding.
self.crpe = shared_crpe
def forward(self, x, size):
B, N, C = x.shape #(1,19201,64)
# Generate Q, K, V.
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) #(3,1,8,19201,8) # Shape: [3, B, h, N, Ch].
q, k, v = qkv[0], qkv[1], qkv[2] #(1,8,19201,8) # Shape: [B, h, N, Ch].
# Factorized attention.
k_softmax = k.softmax(dim=2) # Softmax on dim N.
k_softmax_T_dot_v = einsum('b h n k, b h n v -> b h k v', k_softmax, v) #(1,8,8,8) # Shape: [B, h, Ch, Ch].
factor_att = einsum('b h n k, b h k v -> b h n v', q, k_softmax_T_dot_v) #(1,8,19201,8) # Shape: [B, h, N, Ch].
# Convolutional relative position encoding.
crpe = self.crpe(q, v, size=size) #(1,8,19201,8) # Shape: [B, h, N, Ch].
# Merge and reshape.
x = self.scale * factor_att + crpe #(1,8,19201,8)
x = x.transpose(1, 2).reshape(B, N, C)#(1,19201,64) # Shape: [B, h, N, Ch] -> [B, N, h, Ch] -> [B, N, C].
# Output projection.
x = self.proj(x)#(1,19201,64)
x = self.proj_drop(x)
return x # Shape: [B, N, C].
class ConvPosEnc(nn.Module):
""" Convolutional Position Encoding.
Note: This module is similar to the conditional position encoding in CPVT.
"""
def __init__(self, dim, k=3):
super(ConvPosEnc, self).__init__()
self.proj = nn.Conv2d(dim, dim, k, 1, k//2, groups=dim)
def forward(self, x, size):
B, N, C = x.shape #(1,19201,64)
H, W = size #(120,160)
assert N == 1 + H * W
# Extract CLS token and image tokens.
cls_token, img_tokens = x[:, :1], x[:, 1:] #(1,1,64),(1,19200,64) # Shape: [B, 1, C], [B, H*W, C].
# Depthwise convolution.
feat = img_tokens.transpose(1, 2).view(B, C, H, W)#(1,64,120,160)
x = self.proj(feat) + feat#(1,64,120,160)
x = x.flatten(2).transpose(1, 2)
# Combine with CLS token.
x = torch.cat((cls_token, x), dim=1)#(1,19200,64)
return x
class SerialBlock(nn.Module):
""" Serial block class.
Note: In this implementation, each serial block only contains a conv-attention and a FFN (MLP) module. """
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm,
shared_cpe=None, shared_crpe=None):
# shared_cpe=self.cpe1, shared_crpe=self.crpe1
super().__init__()
# Conv-Attention.
self.cpe = shared_cpe
self.norm1 = norm_layer(dim)
self.factoratt_crpe = FactorAtt_ConvRelPosEnc(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop,
shared_crpe=shared_crpe)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
# MLP.
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
def forward(self, x, size):
# Conv-Attention.
x = self.cpe(x, size) #[(1,19201,64),(120,160)]/[(1,4801,128),(60,80)] # Apply convolutional position encoding.
cur = self.norm1(x)
cur = self.factoratt_crpe(cur, size) #(1,19201,64)/(1,4801,128) # Apply factorized attention and convolutional relative position encoding.
x = x + self.drop_path(cur) #(1,19201,64)/(1,4801,128)
# MLP.
cur = self.norm2(x)
cur = self.mlp(cur)
x = x + self.drop_path(cur)
return x
class ParallelBlock(nn.Module):
""" Parallel block class. """
def __init__(self, dims, num_heads, mlp_ratios=[], qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm,
shared_cpes=None, shared_crpes=None):
super().__init__()
# Conv-Attention.
self.cpes = shared_cpes
self.norm12 = norm_layer(dims[1])
self.norm13 = norm_layer(dims[2])
self.norm14 = norm_layer(dims[3])
self.factoratt_crpe2 = FactorAtt_ConvRelPosEnc(
dims[1], num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop,
shared_crpe=shared_crpes[1]
)
self.factoratt_crpe3 = FactorAtt_ConvRelPosEnc(
dims[2], num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop,
shared_crpe=shared_crpes[2]
)
self.factoratt_crpe4 = FactorAtt_ConvRelPosEnc(
dims[3], num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop,
shared_crpe=shared_crpes[3]
)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
# MLP.
self.norm22 = norm_layer(dims[1])
self.norm23 = norm_layer(dims[2])
self.norm24 = norm_layer(dims[3])
assert dims[1] == dims[2] == dims[3] # In parallel block, we assume dimensions are the same and share the linear transformation.
assert mlp_ratios[1] == mlp_ratios[2] == mlp_ratios[3]
mlp_hidden_dim = int(dims[1] * mlp_ratios[1])
self.mlp2 = self.mlp3 = self.mlp4 = Mlp(in_features=dims[1], hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
def upsample(self, x, output_size, size):
""" Feature map up-sampling. """
return self.interpolate(x, output_size=output_size, size=size)
def downsample(self, x, output_size, size):
""" Feature map down-sampling. """
return self.interpolate(x, output_size=output_size, size=size)
def interpolate(self, x, output_size, size):
""" Feature map interpolation. """
B, N, C = x.shape
H, W = size
assert N == 1 + H * W
cls_token = x[:, :1, :]
img_tokens = x[:, 1:, :]
img_tokens = img_tokens.transpose(1, 2).reshape(B, C, H, W)
img_tokens = F.interpolate(img_tokens, size=output_size, mode='bilinear') # FIXME: May have alignment issue.
img_tokens = img_tokens.reshape(B, C, -1).transpose(1, 2)
out = torch.cat((cls_token, img_tokens), dim=1)
return out
def forward(self, x1, x2, x3, x4, sizes):
_, (H2, W2), (H3, W3), (H4, W4) = sizes
# Conv-Attention.
x2 = self.cpes[1](x2, size=(H2, W2)) # Note: x1 is ignored.
x3 = self.cpes[2](x3, size=(H3, W3))
x4 = self.cpes[3](x4, size=(H4, W4))
cur2 = self.norm12(x2)
cur3 = self.norm13(x3)
cur4 = self.norm14(x4)
cur2 = self.factoratt_crpe2(cur2, size=(H2,W2))
cur3 = self.factoratt_crpe3(cur3, size=(H3,W3))
cur4 = self.factoratt_crpe4(cur4, size=(H4,W4))
upsample3_2 = self.upsample(cur3, output_size=(H2,W2), size=(H3,W3))
upsample4_3 = self.upsample(cur4, output_size=(H3,W3), size=(H4,W4))
upsample4_2 = self.upsample(cur4, output_size=(H2,W2), size=(H4,W4))
downsample2_3 = self.downsample(cur2, output_size=(H3,W3), size=(H2,W2))
downsample3_4 = self.downsample(cur3, output_size=(H4,W4), size=(H3,W3))
downsample2_4 = self.downsample(cur2, output_size=(H4,W4), size=(H2,W2))
cur2 = cur2 + upsample3_2 + upsample4_2
cur3 = cur3 + upsample4_3 + downsample2_3
cur4 = cur4 + downsample3_4 + downsample2_4
x2 = x2 + self.drop_path(cur2)
x3 = x3 + self.drop_path(cur3)
x4 = x4 + self.drop_path(cur4)
# MLP.
cur2 = self.norm22(x2)
cur3 = self.norm23(x3)
cur4 = self.norm24(x4)
cur2 = self.mlp2(cur2)
cur3 = self.mlp3(cur3)
cur4 = self.mlp4(cur4)
x2 = x2 + self.drop_path(cur2)
x3 = x3 + self.drop_path(cur3)
x4 = x4 + self.drop_path(cur4)
return x1, x2, x3, x4
class PatchEmbed(nn.Module):
""" Image to Patch Embedding """
def __init__(self, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
patch_size = to_2tuple(patch_size)
self.patch_size = patch_size #4
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)#(3,64,4,4)
self.norm = nn.LayerNorm(embed_dim)
def forward(self, x):
_, _, H, W = x.shape
out_H, out_W = H // self.patch_size[0], W // self.patch_size[1] #(120,160)/(80,60)
x = self.proj(x).flatten(2).transpose(1, 2)#(1,19200,64)/(1,4800,128)
out = self.norm(x)#(1,19200,64)
return out, (out_H, out_W)
class CoaT(nn.Module):
""" CoaT class. """
def __init__(self, patch_size=16, in_chans=3, num_classes=1000, embed_dims=[0, 0, 0, 0],
serial_depths=[3,4,6,3], parallel_depth=0,
num_heads=0, mlp_ratios=[0, 0, 0, 0], qkv_bias=True, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
drop_path_rate=0., norm_layer=partial(nn.LayerNorm, eps=1e-6),
return_interm_layers=False, out_features=None, crpe_window={3:2, 5:3, 7:3},
**kwargs):
super().__init__()
self.return_interm_layers = return_interm_layers
self.out_features = out_features
self.num_classes = num_classes #1000
# Patch embeddings.
self.patch_embed1 = PatchEmbed(patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dims[0])
self.patch_embed2 = PatchEmbed(patch_size=2, in_chans=embed_dims[0], embed_dim=embed_dims[1])
self.patch_embed3 = PatchEmbed(patch_size=2, in_chans=embed_dims[1], embed_dim=embed_dims[2])
self.patch_embed4 = PatchEmbed(patch_size=2, in_chans=embed_dims[2], embed_dim=embed_dims[3])
# Class tokens.
self.cls_token1 = nn.Parameter(torch.zeros(1, 1, embed_dims[0])) #(1,1,64)
self.cls_token2 = nn.Parameter(torch.zeros(1, 1, embed_dims[1]))#(1,1,128)
self.cls_token3 = nn.Parameter(torch.zeros(1, 1, embed_dims[2]))
self.cls_token4 = nn.Parameter(torch.zeros(1, 1, embed_dims[3]))
# Convolutional position encodings.
self.cpe1 = ConvPosEnc(dim=embed_dims[0], k=3) #(64,k=3)
self.cpe2 = ConvPosEnc(dim=embed_dims[1], k=3) #(128,k=3)
self.cpe3 = ConvPosEnc(dim=embed_dims[2], k=3) #(320,k=3)
self.cpe4 = ConvPosEnc(dim=embed_dims[3], k=3) #(512,k=3)
# Convolutional relative position encodings.
self.crpe1 = ConvRelPosEnc(Ch=embed_dims[0] // num_heads, h=num_heads, window=crpe_window)
self.crpe2 = ConvRelPosEnc(Ch=embed_dims[1] // num_heads, h=num_heads, window=crpe_window)
self.crpe3 = ConvRelPosEnc(Ch=embed_dims[2] // num_heads, h=num_heads, window=crpe_window)
self.crpe4 = ConvRelPosEnc(Ch=embed_dims[3] // num_heads, h=num_heads, window=crpe_window)
# Enable stochastic depth.
dpr = drop_path_rate
# Serial blocks 1.
self.serial_blocks1 = nn.ModuleList([
SerialBlock(
dim=embed_dims[0], num_heads=num_heads, mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr, norm_layer=norm_layer,
shared_cpe=self.cpe1, shared_crpe=self.crpe1
)
for _ in range(serial_depths[0])]
)
# Serial blocks 2.
self.serial_blocks2 = nn.ModuleList([
SerialBlock(
dim=embed_dims[1], num_heads=num_heads, mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr, norm_layer=norm_layer,
shared_cpe=self.cpe2, shared_crpe=self.crpe2
)
for _ in range(serial_depths[1])]
)
# Serial blocks 3.
self.serial_blocks3 = nn.ModuleList([
SerialBlock(
dim=embed_dims[2], num_heads=num_heads, mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr, norm_layer=norm_layer,
shared_cpe=self.cpe3, shared_crpe=self.crpe3
)
for _ in range(serial_depths[2])]
)
# Serial blocks 4.
self.serial_blocks4 = nn.ModuleList([
SerialBlock(
dim=embed_dims[3], num_heads=num_heads, mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr, norm_layer=norm_layer,
shared_cpe=self.cpe4, shared_crpe=self.crpe4
)
for _ in range(serial_depths[3])]
)
# Parallel blocks.
self.parallel_depth = parallel_depth
if self.parallel_depth > 0:
self.parallel_blocks = nn.ModuleList([
ParallelBlock(
dims=embed_dims, num_heads=num_heads, mlp_ratios=mlp_ratios, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr, norm_layer=norm_layer,
shared_cpes=[self.cpe1, self.cpe2, self.cpe3, self.cpe4],
shared_crpes=[self.crpe1, self.crpe2, self.crpe3, self.crpe4]
)
for _ in range(parallel_depth)]
)
# Classification head(s).
if not self.return_interm_layers:
self.norm1 = norm_layer(embed_dims[0])
self.norm2 = norm_layer(embed_dims[1])
self.norm3 = norm_layer(embed_dims[2])
self.norm4 = norm_layer(embed_dims[3])
if self.parallel_depth > 0: # CoaT series: Aggregate features of last three scales for classification.
assert embed_dims[1] == embed_dims[2] == embed_dims[3]
self.aggregate = torch.nn.Conv1d(in_channels=3, out_channels=1, kernel_size=1)
self.head = nn.Linear(embed_dims[3], num_classes)
else:
self.head = nn.Linear(embed_dims[3], num_classes) # CoaT-Lite series: Use feature of last scale for classification.
# Initialize weights.
trunc_normal_(self.cls_token1, std=.02)
trunc_normal_(self.cls_token2, std=.02)
trunc_normal_(self.cls_token3, std=.02)
trunc_normal_(self.cls_token4, std=.02)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
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)
@torch.jit.ignore
def no_weight_decay(self):
return {'cls_token1', 'cls_token2', 'cls_token3', 'cls_token4'}
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=''):
self.num_classes = num_classes
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def insert_cls(self, x, cls_token):
""" Insert CLS token. """
cls_tokens = cls_token.expand(x.shape[0], -1, -1)#(1,1,64)->(1,1,64)
x = torch.cat((cls_tokens, x), dim=1) #(1,19201,64)
return x
def remove_cls(self, x):
""" Remove CLS token. """
return x[:, 1:, :]
def forward_features(self, x0): #(1,3,482,640)
B = x0.shape[0]#1
# Serial blocks 1.
x1, (H1, W1) = self.patch_embed1(x0) ##(1,19200,64),(120,160)
x1 = self.insert_cls(x1, self.cls_token1) #(1,19201,64)
for blk in self.serial_blocks1:
x1 = blk(x1, size=(H1, W1)) #迭代四次(1,19201,64)
x1_nocls = self.remove_cls(x1) #(1,19200,64)
x1_nocls = x1_nocls.reshape(B, H1, W1, -1).permute(0, 3, 1, 2).contiguous() #(1,64,120,160)
# Serial blocks 2.
x2, (H2, W2) = self.patch_embed2(x1_nocls)#(1,4800,128),(60,80)
x2 = self.insert_cls(x2, self.cls_token2)#(1,4801,128)
for blk in self.serial_blocks2:
x2 = blk(x2, size=(H2, W2)) #(1,4801,128)
x2_nocls = self.remove_cls(x2) #(1,4800,128)
x2_nocls = x2_nocls.reshape(B, H2, W2, -1).permute(0, 3, 1, 2).contiguous() #(1,128,60,80)
# Serial blocks 3.
x3, (H3, W3) = self.patch_embed3(x2_nocls) #[(1,1200,320),(30,40)]
x3 = self.insert_cls(x3, self.cls_token3) #(1,1201,320)
for blk in self.serial_blocks3:
x3 = blk(x3, size=(H3, W3))#(1，1201，320)
x3_nocls = self.remove_cls(x3)#(1,1200,320)
x3_nocls = x3_nocls.reshape(B, H3, W3, -1).permute(0, 3, 1, 2).contiguous()#(1,320,30,40)
# Serial blocks 4.
x4, (H4, W4) = self.patch_embed4(x3_nocls)#[(1,300,512),(15,20)]
x4 = self.insert_cls(x4, self.cls_token4)#(1,301,512)
for blk in self.serial_blocks4:
x4 = blk(x4, size=(H4, W4))#(1,301,512)
x4_nocls = self.remove_cls(x4)#(1,300,512)
x4_nocls = x4_nocls.reshape(B, H4, W4, -1).permute(0, 3, 1, 2).contiguous()#(1,512,15,20)
# Only serial blocks: Early return.
if self.parallel_depth == 0:
if self.return_interm_layers: # Return intermediate features for down-stream tasks (e.g. Deformable DETR and Detectron2).
feat_out = {}
if 'x1_nocls' in self.out_features:
feat_out['x1_nocls'] = x1_nocls
if 'x2_nocls' in self.out_features:
feat_out['x2_nocls'] = x2_nocls
if 'x3_nocls' in self.out_features:
feat_out['x3_nocls'] = x3_nocls
if 'x4_nocls' in self.out_features:
feat_out['x4_nocls'] = x4_nocls
return feat_out
else: # Return features for classification.
x4 = self.norm4(x4) #(1,301,512)
x4_cls = x4[:, 0]#(1,512),取第一列所有行元素。
return x4_cls
# Parallel blocks.
for blk in self.parallel_blocks:
x1, x2, x3, x4 = blk(x1, x2, x3, x4, sizes=[(H1, W1), (H2, W2), (H3, W3), (H4, W4)])
if self.return_interm_layers: # Return intermediate features for down-stream tasks (e.g. Deformable DETR and Detectron2).
feat_out = {}
if 'x1_nocls' in self.out_features:
x1_nocls = self.remove_cls(x1)
x1_nocls = x1_nocls.reshape(B, H1, W1, -1).permute(0, 3, 1, 2).contiguous()
feat_out['x1_nocls'] = x1_nocls
if 'x2_nocls' in self.out_features:
x2_nocls = self.remove_cls(x2)
x2_nocls = x2_nocls.reshape(B, H2, W2, -1).permute(0, 3, 1, 2).contiguous()
feat_out['x2_nocls'] = x2_nocls
if 'x3_nocls' in self.out_features:
x3_nocls = self.remove_cls(x3)
x3_nocls = x3_nocls.reshape(B, H3, W3, -1).permute(0, 3, 1, 2).contiguous()
feat_out['x3_nocls'] = x3_nocls
if 'x4_nocls' in self.out_features:
x4_nocls = self.remove_cls(x4)
x4_nocls = x4_nocls.reshape(B, H4, W4, -1).permute(0, 3, 1, 2).contiguous()
feat_out['x4_nocls'] = x4_nocls
return feat_out
else:
x2 = self.norm2(x2)
x3 = self.norm3(x3)
x4 = self.norm4(x4)
x2_cls = x2[:, :1] # Shape: [B, 1, C].
x3_cls = x3[:, :1]
x4_cls = x4[:, :1]
merged_cls = torch.cat((x2_cls, x3_cls, x4_cls), dim=1) # Shape: [B, 3, C].
merged_cls = self.aggregate(merged_cls).squeeze(dim=1) # Shape: [B, C].
return merged_cls
def forward(self, x):
if self.return_interm_layers: # Return intermediate features (for down-stream tasks).
return self.forward_features(x)
else: # Return features for classification.
x = self.forward_features(x) #(1,512)
x = self.head(x)#(1,1000)
return x
# CoaT.
@register_model
def coat_tiny(**kwargs):
model = CoaT(patch_size=4, embed_dims=[152, 152, 152, 152], serial_depths=[2, 2, 2, 2], parallel_depth=6, num_heads=8, mlp_ratios=[4, 4, 4, 4], **kwargs)
model.default_cfg = _cfg_coat()
return model
@register_model
def coat_mini(**kwargs):
model = CoaT(patch_size=4, embed_dims=[152, 216, 216, 216], serial_depths=[2, 2, 2, 2], parallel_depth=6, num_heads=8, mlp_ratios=[4, 4, 4, 4], **kwargs)
model.default_cfg = _cfg_coat()
return model
@register_model
def coat_small(**kwargs):
model = CoaT(patch_size=4, embed_dims=[152, 320, 320, 320], serial_depths=[2, 2, 2, 2], parallel_depth=6, num_heads=8, mlp_ratios=[4, 4, 4, 4], **kwargs)
model.default_cfg = _cfg_coat()
return model
# CoaT-Lite.
@register_model
def coat_lite_tiny(**kwargs):
model = CoaT(patch_size=4, embed_dims=[64, 128, 256, 320], serial_depths=[2, 2, 2, 2], parallel_depth=0, num_heads=8, mlp_ratios=[8, 8, 4, 4], **kwargs)
model.default_cfg = _cfg_coat()
return model
@register_model
def coat_lite_mini(**kwargs):
model = CoaT(patch_size=4, embed_dims=[64, 128, 320, 512], serial_depths=[2, 2, 2, 2], parallel_depth=0, num_heads=8, mlp_ratios=[8, 8, 4, 4], **kwargs)
model.default_cfg = _cfg_coat()
return model
@register_model
def coat_lite_small(**kwargs):
model = CoaT(patch_size=4, embed_dims=[64, 128, 320, 512], serial_depths=[3, 4, 6, 3], parallel_depth=0, num_heads=8, mlp_ratios=[8, 8, 4, 4], **kwargs)
model.default_cfg = _cfg_coat()
return model
@register_model
def coat_lite_medium(**kwargs):
model = CoaT(patch_size=4, embed_dims=[128, 256, 320, 512], serial_depths=[3, 6, 10, 8], parallel_depth=0, num_heads=8, mlp_ratios=[4, 4, 4, 4], **kwargs)
model.default_cfg = _cfg_coat()
return model
def main():
model = coat_lite_small() # (传入参数)
# summary(model,input_size=(3,480,640),device='cpu')
model.eval()
rgb_image = torch.randn(1,3, 480, 640)
with torch.no_grad():
output = model(rgb_image)
print(output.shape)
if __name__ == '__main__':
main()
首先照例看一下框架图：
框架图的每一部分：
1：模型首先输入到serial block，在block内，图片首先进行patch embedding。对应于主函数 CoaT的forward_features函数。首先给出CoaT的一些参数，这样就替换掉原始的默认参数。
def coat_lite_small(**kwargs):
model = CoaT(patch_size=4, embed_dims=[64, 128, 320, 512], serial_depths=[3, 4, 6, 3], parallel_depth=0, num_heads=8, mlp_ratios=[8, 8, 4, 4], **kwargs)
model.default_cfg = _cfg_coat()
return model在第一个block阶段，patch=4，inchannel=3，embed_dims[0]=64。我们跳到patch embedding函数中。首先获得输出的H和W，原始输入为(1,3,480,640)。接着将输入维度3投射为64，展平，交换1，2位。再经过归一化，那么输出的维度为(1,19200,64)。
class PatchEmbed(nn.Module):
""" Image to Patch Embedding """
def __init__(self, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
patch_size = to_2tuple(patch_size)
self.patch_size = patch_size #4
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)#(3,64,4,4)
self.norm = nn.LayerNorm(embed_dim)
def forward(self, x):
_, _, H, W = x.shape
out_H, out_W = H // self.patch_size[0], W // self.patch_size[1] #(120,160)/(80,60)
x = self.proj(x).flatten(2).transpose(1, 2)#(1,19200,64)/(1,4800,128)
out = self.norm(x)#(1,19200,64)
return out, (out_H, out_W)然后插入classtoken，classtoken维度为(1,1,64)，新的维度为(1,19201,64)。接着就进入了conv-attention block。
2：在第一个阶段有三个serialblock。首先给出block的参数。有8个头，注意shared_cpe=self.cpe1, shared_crpe=self.crpe1这两个重要的函数。
self.serial_blocks1 = nn.ModuleList([
SerialBlock(
dim=embed_dims[0], num_heads=num_heads, mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr, norm_layer=norm_layer,
shared_cpe=self.cpe1, shared_crpe=self.crpe1
)
for _ in range(serial_depths[0])]
)
我们进入到conv-attention block中：输入的x首先进行卷积位置编码。
class SerialBlock(nn.Module):
""" Serial block class.
Note: In this implementation, each serial block only contains a conv-attention and a FFN (MLP) module. """
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm,
shared_cpe=None, shared_crpe=None):
# shared_cpe=self.cpe1, shared_crpe=self.crpe1
super().__init__()
# Conv-Attention.
self.cpe = shared_cpe
self.norm1 = norm_layer(dim)
self.factoratt_crpe = FactorAtt_ConvRelPosEnc(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop,
shared_crpe=shared_crpe)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
# MLP.
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
def forward(self, x, size):
# Conv-Attention.
x = self.cpe(x, size) #[(1,19201,64),(120,160)]/[(1,4801,128),(60,80)] # Apply convolutional position encoding.
cur = self.norm1(x)
cur = self.factoratt_crpe(cur, size) #(1,19201,64)/(1,4801,128) # Apply factorized attention and convolutional relative position encoding.
x = x + self.drop_path(cur) #(1,19201,64)/(1,4801,128)
# MLP.
cur = self.norm2(x)
cur = self.mlp(cur)
x = x + self.drop_path(cur)
return x
self.cpe1 = ConvPosEnc(dim=embed_dims[0], k=3) #(64,k=3)卷积位置编码对应于 ConvPosEnc函数。首先获得x的形状，然后取图像的token和class的token。因为在patch embed中我们插入了class token。两个token的维度分别为(1,1,64),(1,19200,64)。接着将图像reshape到原来的形状，进行逐深度卷积。然后再展平为token。与原始的token进行concat。
class ConvPosEnc(nn.Module):
""" Convolutional Position Encoding.
Note: This module is similar to the conditional position encoding in CPVT.
"""
def __init__(self, dim, k=3):
super(ConvPosEnc, self).__init__()
self.proj = nn.Conv2d(dim, dim, k, 1, k//2, groups=dim)
def forward(self, x, size):
B, N, C = x.shape #(1,19201,64)
H, W = size #(120,160)
assert N == 1 + H * W
# Extract CLS token and image tokens.
cls_token, img_tokens = x[:, :1], x[:, 1:] #(1,1,64),(1,19200,64) # Shape: [B, 1, C], [B, H*W, C].
# Depthwise convolution.
feat = img_tokens.transpose(1, 2).view(B, C, H, W)#(1,64,120,160)
x = self.proj(feat) + feat#(1,64,120,160)
x = x.flatten(2).transpose(1, 2)
# Combine with CLS token.
x = torch.cat((cls_token, x), dim=1)#(1,19201,64)
return x3：我们回到原SerialBlock函数中，接着进行归一化，再进行factorized attention mechanism。
self.factoratt_crpe = FactorAtt_ConvRelPosEnc(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop,
shared_crpe=shared_crpe)首先我们获得qkv。接着分别取第一个维度就是q，k，v。维度为(1,8,19201,8)。根据公式我们要求softmax(K)的转置，然后与V相乘，这里直接用einsum函数得到结果为(1,8,8,8)。然后Q乘以 k_softmax_T_dot_v ，结果再乘以scale函数，得到factor_att。
接着我们将q和v输入到crep函数。即卷积的相对位置编码。
class FactorAtt_ConvRelPosEnc(nn.Module):
""" Factorized attention with convolutional relative position encoding class. """
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., shared_crpe=None):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop) # Note: attn_drop is actually not used.
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
# Shared convolutional relative position encoding.
self.crpe = shared_crpe
def forward(self, x, size):
B, N, C = x.shape #(1,19201,64)
# Generate Q, K, V.
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) #(3,1,8,19201,8) # Shape: [3, B, h, N, Ch].
q, k, v = qkv[0], qkv[1], qkv[2] #(1,8,19201,8) # Shape: [B, h, N, Ch].
# Factorized attention.
k_softmax = k.softmax(dim=2) # Softmax on dim N.
k_softmax_T_dot_v = einsum('b h n k, b h n v -> b h k v', k_softmax, v) #(1,8,8,8) # Shape: [B, h, Ch, Ch].
factor_att = einsum('b h n k, b h k v -> b h n v', q, k_softmax_T_dot_v) #(1,8,19201,8) # Shape: [B, h, N, Ch].
# Convolutional relative position encoding.
crpe = self.crpe(q, v, size=size) #(1,8,19201,8) # Shape: [B, h, N, Ch].
# Merge and reshape.
x = self.scale * factor_att + crpe #(1,8,19201,8)
x = x.transpose(1, 2).reshape(B, N, C)#(1,19201,64) # Shape: [B, h, N, Ch] -> [B, N, h, Ch] -> [B, N, C].
# Output projection.
x = self.proj(x)#(1,19201,64)
x = self.proj_drop(x)
return x # Shape: [B, N, C].
self.crpe1 = ConvRelPosEnc(Ch=embed_dims[0] // num_heads, h=num_heads, window=crpe_window)在ConvRelPosEnc中，首先指定(Ch, h, window):(8,8,window=crpe_window={3:2, 5:3, 7:3})参数，首先window是一个字典形式，且注意这一句话：
A dict mapping window size to #attention head splits (e.g. {window size 1: #attention head split 1, window size 2: #attention head split 2})。It will apply different window size to the attention head splits.
这个字典将窗口大小映射为注意力头划分，window size1则注意力头划分为1，window size2则注意力头划分为2，对于注意力头的划分，将会使用不同的窗口大小。
遍历字典，我们获得窗口和头划分的大小，第一次遍历cur_window, cur_head_split分别为(3,2)。dialation=1，padding=1，然后cur_conv卷积输入通道16，输出通道16，kernel=3，group=16。第二次遍历：卷积(24,24,k=5,p=2,1,24)，第三次遍历：(24,24,k=5,p=2,1,24)。将生成的三个卷积按顺序添加到modul卷积的modulistist中。cur_head_split添加到head_splits空列表中。channel_splits=[16,24,24]。
回到forward函数中，首先获得不包含class token的q和v。然后将v调整为2d(1,64,120,160)。接着就是将v按通道进行划分。v_img_list包含三个list，维度分别为[(1,16,120,160),(1,24,120,160),(1,24,120,160)]。将每一个list输入到卷积list中的每一个卷积。维度不发生变换。接着将生成的结果按照维度拼接起来。经过reshape又重新回到原图像大小。
接着将q和逐深度2d卷积结果相乘。结果与0矩阵进行concat。就生成了EV_hat，维度为(1,8,19201,8)。
class ConvRelPosEnc(nn.Module):
""" Convolutional relative position encoding. """
def __init__(self, Ch, h, window): #(8,8,window=crpe_window={3:2, 5:3, 7:3})
"""
Initialization.
Ch: Channels per head.
h: Number of heads.
window: Window size(s) in convolutional relative positional encoding. It can have two forms:
1. An integer of window size, which assigns all attention heads with the same window size in ConvRelPosEnc.
2. A dict mapping window size to #attention head splits (e.g. {window size 1: #attention head split 1, window size 2: #attention head split 2})
It will apply different window size to the attention head splits.
"""
#embed_dims=[64, 128, 320, 512], serial_depths=[3, 4, 6, 3], parallel_depth=0, num_heads=8, mlp_ratios=[8, 8, 4, 4], **kwargs
super().__init__()
if isinstance(window, int):
window = {window: h} # Set the same window size for all attention heads.
self.window = window
elif isinstance(window, dict):
self.window = window #{3:2, 5:3, 7:3}
else:
raise ValueError()
self.conv_list = nn.ModuleList()
self.head_splits = []
for cur_window, cur_head_split in window.items():#(3,2)/(5,3)/(7,3)
dilation = 1 # Use dilation=1 at default.
padding_size = (cur_window + (cur_window - 1) * (dilation - 1)) // 2 #(3+(2)*(0))//2 =1 # Determine padding size. Ref: https://discuss.pytorch.org/t/how-to-keep-the-shape-of-input-and-output-same-when-dilation-conv/14338
cur_conv = nn.Conv2d(cur_head_split*Ch, cur_head_split*Ch, #(16,16,k=3,p=1,1,16)/(18,18,k=5,p=2,1,16)
kernel_size=(cur_window, cur_window),
padding=(padding_size, padding_size),
dilation=(dilation, dilation),
groups=cur_head_split*Ch,
)
self.conv_list.append(cur_conv)
self.head_splits.append(cur_head_split)#(2,3,3)
self.channel_splits = [x*Ch for x in self.head_splits] #ch = 8 , head_splits=[2,3,3]
def forward(self, q, v, size): #size(q=v)=(1,8,19201,8)
B, h, N, Ch = q.shape # B:1 h:8 N:19201 Ch:8
H, W = size #(120,160)
assert N == 1 + H * W
# Convolutional relative position encoding.
q_img = q[:,:,1:,:]#(1,8,19200,8) # Shape: [B, h, H*W, Ch].
v_img = v[:,:,1:,:]#(1,8,19200,8) # Shape: [B, h, H*W, Ch].
v_img = rearrange(v_img, 'B h (H W) Ch -> B (h Ch) H W', H=H, W=W)#(1,64,120,160) # Shape: [B, h, H*W, Ch] -> [B, h*Ch, H, W].
v_img_list = torch.split(v_img, self.channel_splits, dim=1) #channel_splits=[16,24,24] # Split according to channels.
conv_v_img_list = [conv(x) for conv, x in zip(self.conv_list, v_img_list)]#[(1,16,120,160),(1,24,120,160),(1,24,120,160)]
conv_v_img = torch.cat(conv_v_img_list, dim=1)#(1,64,120,160)
conv_v_img = rearrange(conv_v_img, 'B (h Ch) H W -> B h (H W) Ch', h=h)#(1,8,19200,8) # Shape: [B, h*Ch, H, W] -> [B, h, H*W, Ch].
EV_hat_img = q_img * conv_v_img#(1,8,19200,8)
zero = torch.zeros((B, h, 1, Ch), dtype=q.dtype, layout=q.layout, device=q.device)#(1,8,1,8)
EV_hat = torch.cat((zero, EV_hat_img), dim=2) #(1,8,19201,8) # Shape: [B, h, N, Ch].
return EV_hat
再回到FactorAtt_ConvRelPosEnc函数中，我们将factorized attention和卷积相对位置编码的结果相加。这样conv-attention就计算完毕。将(1,8,19201,8)的大小reshape到(1,19201,64)，经过proj。这样
FactorAtt_ConvRelPosEnc计算完毕
。
再回到SerialBlock，接着我们输送到前向传播模块，经过mlp层，维度由64-512-64。最终x输出为(1,19201,64)。这样
SerialBlock计算完毕。
在整体的CoaT函数中，self.serial_blocks1包含了三个SerialBlock，那么blk会迭代四次，最终的输出大小为(1,19201,64)。移除掉class token，维度变为(1,19200,64)。在reshape为2d图像大小。(1,64,120,160)。同理block的输出作为block的输入，处理流程和block1一样。最终的大小为(1,128,60,80)。block3最终大小为(1,320,30,40)。block4最终大小为(1,512,15,20)。
其中我们将还未移除classtoken的x4取出(1,301,512)，取其第一列所有元素(1,512)。然后经过一个线性层，输出最终的1000类。这样CoaT-lite就
计算完毕
。
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