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Update mobilenet_v3.py

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littletomatodonkey 2021-01-05 11:51:16 +08:00 committed by GitHub
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commit a138a6ee53
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1 changed files with 25 additions and 25 deletions
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ppcls/modeling/architectures/mobilenet_v3.py
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@ -21,7 +21,7 @@ import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn.functional import hardswish, hardsigmoid
from paddle.nn.functional.activation import hard_sigmoid, hard_swish
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.regularizer import L2Decay
@ -64,15 +64,15 @@ class MobileNetV3(nn.Layer):
[5, 72, 40, True, "relu", 2],
[5, 120, 40, True, "relu", 1],
[5, 120, 40, True, "relu", 1],
[3, 240, 80, False, "hardswish", 2],
[3, 200, 80, False, "hardswish", 1],
[3, 184, 80, False, "hardswish", 1],
[3, 184, 80, False, "hardswish", 1],
[3, 480, 112, True, "hardswish", 1],
[3, 672, 112, True, "hardswish", 1],
[5, 672, 160, True, "hardswish", 2],
[5, 960, 160, True, "hardswish", 1],
[5, 960, 160, True, "hardswish", 1],
[3, 240, 80, False, "hard_swish", 2],
[3, 200, 80, False, "hard_swish", 1],
[3, 184, 80, False, "hard_swish", 1],
[3, 184, 80, False, "hard_swish", 1],
[3, 480, 112, True, "hard_swish", 1],
[3, 672, 112, True, "hard_swish", 1],
[5, 672, 160, True, "hard_swish", 2],
[5, 960, 160, True, "hard_swish", 1],
[5, 960, 160, True, "hard_swish", 1],
]
self.cls_ch_squeeze = 960
self.cls_ch_expand = 1280
@ -82,14 +82,14 @@ class MobileNetV3(nn.Layer):
[3, 16, 16, True, "relu", 2],
[3, 72, 24, False, "relu", 2],
[3, 88, 24, False, "relu", 1],
[5, 96, 40, True, "hardswish", 2],
[5, 240, 40, True, "hardswish", 1],
[5, 240, 40, True, "hardswish", 1],
[5, 120, 48, True, "hardswish", 1],
[5, 144, 48, True, "hardswish", 1],
[5, 288, 96, True, "hardswish", 2],
[5, 576, 96, True, "hardswish", 1],
[5, 576, 96, True, "hardswish", 1],
[5, 96, 40, True, "hard_swish", 2],
[5, 240, 40, True, "hard_swish", 1],
[5, 240, 40, True, "hard_swish", 1],
[5, 120, 48, True, "hard_swish", 1],
[5, 144, 48, True, "hard_swish", 1],
[5, 288, 96, True, "hard_swish", 2],
[5, 576, 96, True, "hard_swish", 1],
[5, 576, 96, True, "hard_swish", 1],
]
self.cls_ch_squeeze = 576
self.cls_ch_expand = 1280
@ -105,7 +105,7 @@ class MobileNetV3(nn.Layer):
padding=1,
num_groups=1,
if_act=True,
act="hardswish",
act="hard_swish",
name="conv1")
self.block_list = []
@ -135,7 +135,7 @@ class MobileNetV3(nn.Layer):
padding=0,
num_groups=1,
if_act=True,
act="hardswish",
act="hard_swish",
name="conv_last")
self.pool = AdaptiveAvgPool2D(1)
@ -167,9 +167,9 @@ class MobileNetV3(nn.Layer):
x = self.pool(x)
x = self.last_conv(x)
x = hardswish(x)
x = hard_swish(x)
x = self.dropout(x)
x = paddle.flatten(x, start_axis=1, stop_axis=-1)
x = paddle.reshape(x, shape=[x.shape[0], x.shape[1]])
x = self.out(x)
return x
@ -215,8 +215,8 @@ class ConvBNLayer(nn.Layer):
if self.if_act:
if self.act == "relu":
x = F.relu(x)
elif self.act == "hardswish":
x = hardswish(x)
elif self.act == "hard_swish":
x = hard_swish(x)
else:
print("The activation function is selected incorrectly.")
exit()
@ -305,7 +305,7 @@ class SEModule(nn.Layer):
outputs = self.conv1(outputs)
outputs = F.relu(outputs)
outputs = self.conv2(outputs)
outputs = hardsigmoid(outputs, slope=0.2, offset=0.5)
outputs = hard_sigmoid(outputs)
return paddle.multiply(x=inputs, y=outputs)