forked from PaddlePaddle/PaddleSeg
-
Notifications
You must be signed in to change notification settings - Fork 0
/
mscan.py
383 lines (316 loc) · 12.5 KB
/
mscan.py
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
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
# copyright (c) 2023 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import numpy as np
import paddle
import paddle.nn as nn
from paddle.nn import Conv2D
from paddle.nn.initializer import Assign, Normal
from paddleseg.cvlibs import manager
from paddleseg.models.backbones.transformer_utils import (DropPath, ones_,
to_2tuple, zeros_)
from paddleseg.models.layers import SyncBatchNorm
from paddleseg.utils import utils
__all__ = ["MSCAN", "MSCAN_T", "MSCAN_S", "MSCAN_B", "MSCAN_L"]
def get_depthwise_conv(dim, kernel_size=3):
if isinstance(kernel_size, int):
kernel_size = to_2tuple(kernel_size)
padding = tuple([k // 2 for k in kernel_size])
return Conv2D(
dim, dim, kernel_size, padding=padding, bias_attr=True, groups=dim)
class Mlp(nn.Layer):
"""Multilayer perceptron."""
def __init__(self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
drop=0.0):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Conv2D(in_features, hidden_features, 1)
self.dwconv = get_depthwise_conv(hidden_features)
self.act = act_layer()
self.fc2 = nn.Conv2D(hidden_features, out_features, 1)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.dwconv(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class StemConv(nn.Layer):
def __init__(
self,
in_channels,
out_channels, ):
super().__init__()
self.proj = nn.Sequential(
nn.Conv2D(
in_channels,
out_channels // 2,
kernel_size=3,
stride=2,
padding=1),
SyncBatchNorm(out_channels // 2),
nn.GELU(),
nn.Conv2D(
out_channels // 2,
out_channels,
kernel_size=3,
stride=2,
padding=1),
SyncBatchNorm(out_channels))
def forward(self, x):
x = self.proj(x)
H, W = x.shape[2:]
x = x.flatten(2).transpose([0, 2, 1])
return x, H, W
class AttentionModule(nn.Layer):
"""
AttentionModule Layer, which contains some depth-wise strip convolutions.
Args:
dim (int): Number of input channels.
kernel_sizes (list[int], optional): The height or width of each strip convolution kernel. Default: [7, 11, 21].
"""
def __init__(self, dim, kernel_sizes=[7, 11, 21]):
super().__init__()
self.conv0 = nn.Conv2D(dim, dim, 5, padding=2, groups=dim)
self.dwconvs = nn.LayerList([
nn.Sequential((f"conv{i+1}_1", get_depthwise_conv(dim, (1, k))),
(f"conv{i+1}_2", get_depthwise_conv(dim, (k, 1))))
for i, k in enumerate(kernel_sizes)
])
self.conv_out = nn.Conv2D(dim, dim, 1)
def forward(self, x):
u = paddle.clone(x)
attn = self.conv0(x)
attns = [m(attn) for m in self.dwconvs]
attn += sum(attns)
attn = self.conv_out(attn)
return attn * u
class SpatialAttention(nn.Layer):
"""
SpatialAttention Layer.
Args:
d_model (int): Number of input channels.
atten_kernel_sizes (list[int], optional): The height or width of each strip convolution kernel in attention module.
Default: [7, 11, 21].
"""
def __init__(self, d_model, atten_kernel_sizes=[7, 11, 21]):
super().__init__()
self.d_model = d_model
self.proj_1 = nn.Conv2D(d_model, d_model, 1)
self.activation = nn.GELU()
self.spatial_gating_unit = AttentionModule(d_model, atten_kernel_sizes)
self.proj_2 = nn.Conv2D(d_model, d_model, 1)
def forward(self, x):
shorcut = paddle.clone(x)
x = self.proj_1(x)
x = self.activation(x)
x = self.spatial_gating_unit(x)
x = self.proj_2(x)
x = x + shorcut
return x
class Block(nn.Layer):
"""
MSCAN Block.
Args:
dim (int): Number of feature channels.
atten_kernel_sizes (list[int], optional): The height or width of each strip convolution kernel in attention module.
Default: [7, 11, 21].
mlp_ratio (float, optional): Ratio of mlp hidden dim to embedding dim. Default: 4.0.
drop (float, optional): Dropout rate. Default: 0.0
attn_drop (float, optional): Attention dropout rate. Default: 0.0.
drop_path (float, optional): Stochastic depth rate. Default: 0.0.
act_layer (nn.Layer, optional): Activation layer. Default: nn.GELU.
"""
def __init__(
self,
dim,
atten_kernel_sizes=[7, 11, 21],
mlp_ratio=4.0,
drop=0.0,
drop_path=0.0,
act_layer=nn.GELU, ):
super().__init__()
self.norm1 = SyncBatchNorm(dim)
self.attn = SpatialAttention(dim, atten_kernel_sizes)
self.drop_path = DropPath(
drop_path) if drop_path > 0.0 else nn.Identity()
self.norm2 = SyncBatchNorm(dim)
self.mlp = Mlp(in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
drop=drop)
layer_scale_init_value = paddle.full(
[dim, 1, 1], fill_value=1e-2, dtype="float32")
self.layer_scale_1 = paddle.create_parameter(
[dim, 1, 1], "float32", attr=Assign(layer_scale_init_value))
self.layer_scale_2 = paddle.create_parameter(
[dim, 1, 1], "float32", attr=Assign(layer_scale_init_value))
def forward(self, x, H, W):
B, N, C = x.shape
x = x.transpose([0, 2, 1]).reshape([B, C, H, W])
x = x + self.drop_path(self.layer_scale_1 * self.attn(self.norm1(x)))
x = x + self.drop_path(self.layer_scale_2 * self.mlp(self.norm2(x)))
x = x.reshape([B, C, N]).transpose([0, 2, 1])
return x
class OverlapPatchEmbed(nn.Layer):
"""
An Opverlaping Image to Patch Embedding Layer.
Args:
patch_size (int, optional): Patch token size. Default: 7.
stride (int, optional): Stride of Convolution in OverlapPatchEmbed. Default: 4.
in_chans (int, optional): Number of input image channels. Default: 3.
embed_dim (int, optional): Number of linear projection output channels. Default: 768.
"""
def __init__(self, patch_size=7, stride=4, in_chans=3, embed_dim=768):
super().__init__()
patch_size = to_2tuple(patch_size)
self.proj = nn.Conv2D(
in_chans,
embed_dim,
kernel_size=patch_size,
stride=stride,
padding=(patch_size[0] // 2, patch_size[1] // 2))
self.norm = SyncBatchNorm(embed_dim)
def forward(self, x):
x = self.proj(x)
H, W = x.shape[2:]
x = self.norm(x)
x = x.flatten(2).transpose([0, 2, 1])
return x, H, W
def _check_length(*args):
target_length = len(args[0])
for item in args:
if target_length != len(item):
return False
return True
@manager.BACKBONES.add_component
class MSCAN(nn.Layer):
"""
The MSCAN implementation based on PaddlePaddle.
The original article refers to
Guo, Meng-Hao, et al. "SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation"
(https://arxiv.org/pdf/2209.08575.pdf)
Args:
in_channels (int, optional): Number of input image channels. Default: 3.
embed_dims (list[int], optional): Number of each stage output channels. Default: [32, 64, 160, 256].
depths (list[int], optional): Depths of each MSCAN stage.
atten_kernel_sizes (list[int], optional): The height or width of each strip convolution kernel in attention module.
Default: [7, 11, 21].
mlp_ratio (float, optional): Ratio of mlp hidden dim to embedding dim. Default: 4.
drop_rate (float, optional): Dropout rate. Default: 0.0.
drop_path_rate (float, optional): Stochastic depth rate. Default: 0.1.
pretrained (str, optional): The path or url of pretrained model. Default: None.
"""
def __init__(self,
in_channels=3,
embed_dims=[32, 64, 160, 256],
depths=[3, 3, 5, 2],
mlp_ratios=[8, 8, 4, 4],
atten_kernel_sizes=[7, 11, 21],
drop_rate=0.0,
drop_path_rate=0.1,
pretrained=None):
super().__init__()
if not _check_length(embed_dims, mlp_ratios, depths):
raise ValueError(
"The length of aurgments 'embed_dims', 'mlp_ratios' and 'drop_path_rate' must be same."
)
self.depths = depths
self.num_stages = len(embed_dims)
self.feat_channels = embed_dims
drop_path_rates = [
x for x in np.linspace(0, drop_path_rate, sum(depths))
] # stochastic depth decay rule
cur = 0
for i in range(self.num_stages):
if i == 0:
patch_embed = StemConv(in_channels, embed_dims[0])
else:
patch_embed = OverlapPatchEmbed(
patch_size=3,
stride=2,
in_chans=embed_dims[i - 1],
embed_dim=embed_dims[i])
block = nn.LayerList([
Block(
dim=embed_dims[i],
atten_kernel_sizes=atten_kernel_sizes,
mlp_ratio=mlp_ratios[i],
drop=drop_rate,
drop_path=drop_path_rates[cur + j])
for j in range(depths[i])
])
norm = nn.LayerNorm(embed_dims[i])
cur += depths[i]
setattr(self, f"patch_embed{i + 1}", patch_embed)
setattr(self, f"block{i + 1}", block)
setattr(self, f"norm{i + 1}", norm)
self.init_weights(pretrained)
def init_weights(self, pretrained=None):
if pretrained is not None:
utils.load_pretrained_model(self, pretrained)
else:
for sublayer in self.sublayers():
if isinstance(sublayer, nn.LayerNorm):
zeros_(sublayer.bias)
ones_(sublayer.weight)
elif isinstance(sublayer, nn.Conv2D):
fan_out = (sublayer._kernel_size[0] *
sublayer._kernel_size[1] *
sublayer._out_channels)
fan_out //= sublayer._groups
initializer = Normal(mean=0, std=math.sqrt(2.0 / fan_out))
initializer(sublayer.weight)
zeros_(sublayer.bias)
def forward(self, x):
B = x.shape[0]
outs = []
for i in range(self.num_stages):
patch_embed = getattr(self, f"patch_embed{i + 1}")
block = getattr(self, f"block{i + 1}")
norm = getattr(self, f"norm{i + 1}")
x, H, W = patch_embed(x)
for blk in block:
x = blk(x, H, W)
x = norm(x)
x = x.reshape([B, H, W, -1]).transpose([0, 3, 1, 2])
outs.append(x)
return outs
@manager.BACKBONES.add_component
def MSCAN_T(**kwargs):
return MSCAN(**kwargs)
@manager.BACKBONES.add_component
def MSCAN_S(**kwargs):
return MSCAN(embed_dims=[64, 128, 320, 512], depths=[2, 2, 4, 2], **kwargs)
@manager.BACKBONES.add_component
def MSCAN_B(**kwargs):
return MSCAN(
embed_dims=[64, 128, 320, 512],
depths=[3, 3, 12, 3],
drop_path_rate=0.1,
**kwargs)
@manager.BACKBONES.add_component
def MSCAN_L(**kwargs):
return MSCAN(
embed_dims=[64, 128, 320, 512],
depths=[3, 5, 27, 3],
drop_path_rate=0.3,
**kwargs)