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Revert "fix mv3 to adapt to paddle2.0"

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zhoujun 2021-01-26 15:04:15 +08:00 committed by GitHub
parent 95ad4d1032
commit 493027d39e
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 42 additions and 43 deletions
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47
ppocr/modeling/backbones/det_mobilenet_v3.py
View File

@ -58,15 +58,15 @@ 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],
] ]
cls_ch_squeeze = 960 cls_ch_squeeze = 960
elif model_name == "small": elif model_name == "small":
@ -75,14 +75,14 @@ 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],
] ]
cls_ch_squeeze = 576 cls_ch_squeeze = 576
else: else:
@ -102,7 +102,7 @@ class MobileNetV3(nn.Layer):
padding=1, padding=1,
groups=1, groups=1,
if_act=True, if_act=True,
act='hardswish', act='hard_swish',
name='conv1') name='conv1')
self.stages = [] self.stages = []
@ -137,7 +137,7 @@ class MobileNetV3(nn.Layer):
padding=0, padding=0,
groups=1, groups=1,
if_act=True, if_act=True,
act='hardswish', act='hard_swish',
name='conv_last')) name='conv_last'))
self.stages.append(nn.Sequential(*block_list)) self.stages.append(nn.Sequential(*block_list))
self.out_channels.append(make_divisible(scale * cls_ch_squeeze)) self.out_channels.append(make_divisible(scale * cls_ch_squeeze))
@ -191,11 +191,10 @@ 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 = F.hardswish(x) x = F.activation.hard_swish(x)
else: else:
print("The activation function({}) is selected incorrectly.". print("The activation function is selected incorrectly.")
format(self.act))
exit() exit()
return x return x
@ -282,5 +281,5 @@ 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 = F.hardsigmoid(outputs, slope=0.2, offset=0.5) outputs = F.activation.hard_sigmoid(outputs)
return inputs * outputs return inputs * outputs

38
ppocr/modeling/backbones/rec_mobilenet_v3.py
View File

@ -51,15 +51,15 @@ class MobileNetV3(nn.Layer):
[5, 72, 40, True, 'relu', (large_stride[2], 1)], [5, 72, 40, True, 'relu', (large_stride[2], 1)],
[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', 1], [3, 240, 80, False, 'hard_swish', 1],
[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', (large_stride[3], 1)], [5, 672, 160, True, 'hard_swish', (large_stride[3], 1)],
[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],
] ]
cls_ch_squeeze = 960 cls_ch_squeeze = 960
elif model_name == "small": elif model_name == "small":
@ -68,14 +68,14 @@ class MobileNetV3(nn.Layer):
[3, 16, 16, True, 'relu', (small_stride[0], 1)], [3, 16, 16, True, 'relu', (small_stride[0], 1)],
[3, 72, 24, False, 'relu', (small_stride[1], 1)], [3, 72, 24, False, 'relu', (small_stride[1], 1)],
[3, 88, 24, False, 'relu', 1], [3, 88, 24, False, 'relu', 1],
[5, 96, 40, True, 'hardswish', (small_stride[2], 1)], [5, 96, 40, True, 'hard_swish', (small_stride[2], 1)],
[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', (small_stride[3], 1)], [5, 288, 96, True, 'hard_swish', (small_stride[3], 1)],
[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],
] ]
cls_ch_squeeze = 576 cls_ch_squeeze = 576
else: else:
@ -96,7 +96,7 @@ class MobileNetV3(nn.Layer):
padding=1, padding=1,
groups=1, groups=1,
if_act=True, if_act=True,
act='hardswish', act='hard_swish',
name='conv1') name='conv1')
i = 0 i = 0
block_list = [] block_list = []
@ -124,7 +124,7 @@ class MobileNetV3(nn.Layer):
padding=0, padding=0,
groups=1, groups=1,
if_act=True, if_act=True,
act='hardswish', act='hard_swish',
name='conv_last') name='conv_last')
self.pool = nn.MaxPool2D(kernel_size=2, stride=2, padding=0) self.pool = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)