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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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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 from paddle import ParamAttr
import paddle.nn as nn import paddle.nn as nn
import paddle.nn.functional as F 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 Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.regularizer import L2Decay from paddle.regularizer import L2Decay
@ -64,15 +64,15 @@ class MobileNetV3(nn.Layer):
[5, 72, 40, True, "relu", 2], [5, 72, 40, True, "relu", 2],
[5, 120, 40, True, "relu", 1], [5, 120, 40, True, "relu", 1],
[5, 120, 40, True, "relu", 1], [5, 120, 40, True, "relu", 1],
[3, 240, 80, False, "hardswish", 2], [3, 240, 80, False, "hard_swish", 2],
[3, 200, 80, False, "hardswish", 1], [3, 200, 80, False, "hard_swish", 1],
[3, 184, 80, False, "hardswish", 1], [3, 184, 80, False, "hard_swish", 1],
[3, 184, 80, False, "hardswish", 1], [3, 184, 80, False, "hard_swish", 1],
[3, 480, 112, True, "hardswish", 1], [3, 480, 112, True, "hard_swish", 1],
[3, 672, 112, True, "hardswish", 1], [3, 672, 112, True, "hard_swish", 1],
[5, 672, 160, True, "hardswish", 2], [5, 672, 160, True, "hard_swish", 2],
[5, 960, 160, True, "hardswish", 1], [5, 960, 160, True, "hard_swish", 1],
[5, 960, 160, True, "hardswish", 1], [5, 960, 160, True, "hard_swish", 1],
] ]
self.cls_ch_squeeze = 960 self.cls_ch_squeeze = 960
self.cls_ch_expand = 1280 self.cls_ch_expand = 1280
@ -82,14 +82,14 @@ class MobileNetV3(nn.Layer):
[3, 16, 16, True, "relu", 2], [3, 16, 16, True, "relu", 2],
[3, 72, 24, False, "relu", 2], [3, 72, 24, False, "relu", 2],
[3, 88, 24, False, "relu", 1], [3, 88, 24, False, "relu", 1],
[5, 96, 40, True, "hardswish", 2], [5, 96, 40, True, "hard_swish", 2],
[5, 240, 40, True, "hardswish", 1], [5, 240, 40, True, "hard_swish", 1],
[5, 240, 40, True, "hardswish", 1], [5, 240, 40, True, "hard_swish", 1],
[5, 120, 48, True, "hardswish", 1], [5, 120, 48, True, "hard_swish", 1],
[5, 144, 48, True, "hardswish", 1], [5, 144, 48, True, "hard_swish", 1],
[5, 288, 96, True, "hardswish", 2], [5, 288, 96, True, "hard_swish", 2],
[5, 576, 96, True, "hardswish", 1], [5, 576, 96, True, "hard_swish", 1],
[5, 576, 96, True, "hardswish", 1], [5, 576, 96, True, "hard_swish", 1],
] ]
self.cls_ch_squeeze = 576 self.cls_ch_squeeze = 576
self.cls_ch_expand = 1280 self.cls_ch_expand = 1280
@ -105,7 +105,7 @@ class MobileNetV3(nn.Layer):
padding=1, padding=1,
num_groups=1, num_groups=1,
if_act=True, if_act=True,
act="hardswish", act="hard_swish",
name="conv1") name="conv1")
self.block_list = [] self.block_list = []
@ -135,7 +135,7 @@ class MobileNetV3(nn.Layer):
padding=0, padding=0,
num_groups=1, num_groups=1,
if_act=True, if_act=True,
act="hardswish", act="hard_swish",
name="conv_last") name="conv_last")
self.pool = AdaptiveAvgPool2D(1) self.pool = AdaptiveAvgPool2D(1)
@ -167,9 +167,9 @@ class MobileNetV3(nn.Layer):
x = self.pool(x) x = self.pool(x)
x = self.last_conv(x) x = self.last_conv(x)
x = hardswish(x) x = hard_swish(x)
x = self.dropout(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) x = self.out(x)
return x return x
@ -215,8 +215,8 @@ class ConvBNLayer(nn.Layer):
if self.if_act: if self.if_act:
if self.act == "relu": if self.act == "relu":
x = F.relu(x) x = F.relu(x)
elif self.act == "hardswish": elif self.act == "hard_swish":
x = hardswish(x) x = hard_swish(x)
else: else:
print("The activation function is selected incorrectly.") print("The activation function is selected incorrectly.")
exit() exit()
@ -305,7 +305,7 @@ class SEModule(nn.Layer):
outputs = self.conv1(outputs) outputs = self.conv1(outputs)
outputs = F.relu(outputs) outputs = F.relu(outputs)
outputs = self.conv2(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) return paddle.multiply(x=inputs, y=outputs)