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Update inception_v3.py 

remove names and add nn.LayerList
pull/746/head
Felix 2021-05-26 19:43:27 +08:00 committed by GitHub
parent a62d5503fc
commit d9eeb2d1bb
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1 changed files with 56 additions and 105 deletions
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161
ppcls/arch/backbone/legendary_models/inception_v3.py
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@ -52,8 +52,7 @@ class ConvBNLayer(TheseusLayer):
stride=1,
padding=0,
groups=1,
act="relu",
name=None):
act="relu"):
super(ConvBNLayer, self).__init__()
self.conv = Conv2D(
@ -63,15 +62,10 @@ class ConvBNLayer(TheseusLayer):
stride=stride,
padding=padding,
groups=groups,
weight_attr=ParamAttr(name=name+"_weights"),
bias_attr=False)
self.batch_norm = BatchNorm(
num_filters,
act=act,
param_attr=ParamAttr(name=name+"_bn_scale"),
bias_attr=ParamAttr(name=name+"_bn_offset"),
moving_mean_name=name+"_bn_mean",
moving_variance_name=name+"_bn_variance")
act=act)
def forward(self, inputs):
y = self.conv(inputs)
@ -85,32 +79,27 @@ class InceptionStem(TheseusLayer):
num_filters=32,
filter_size=3,
stride=2,
act="relu",
name="conv_1a_3x3")
act="relu")
self.conv_2a_3x3 = ConvBNLayer(num_channels=32,
num_filters=32,
filter_size=3,
stride=1,
act="relu",
name="conv_2a_3x3")
act="relu")
self.conv_2b_3x3 = ConvBNLayer(num_channels=32,
num_filters=64,
filter_size=3,
padding=1,
act="relu",
name="conv_2b_3x3")
act="relu")
self.maxpool = MaxPool2D(kernel_size=3, stride=2, padding=0)
self.conv_3b_1x1 = ConvBNLayer(num_channels=64,
num_filters=80,
filter_size=1,
act="relu",
name="conv_3b_1x1")
act="relu")
self.conv_4a_3x3 = ConvBNLayer(num_channels=80,
num_filters=192,
filter_size=3,
act="relu",
name="conv_4a_3x3")
act="relu")
def forward(self, x):
y = self.conv_1a_3x3(x)
y = self.conv_2a_3x3(y)
@ -123,48 +112,41 @@ class InceptionStem(TheseusLayer):
class InceptionA(TheseusLayer):
def __init__(self, num_channels, pool_features, name=None):
def __init__(self, num_channels, pool_features):
super(InceptionA, self).__init__()
self.branch1x1 = ConvBNLayer(num_channels=num_channels,
num_filters=64,
filter_size=1,
act="relu",
name="inception_a_branch1x1_"+name)
act="relu")
self.branch5x5_1 = ConvBNLayer(num_channels=num_channels,
num_filters=48,
filter_size=1,
act="relu",
name="inception_a_branch5x5_1_"+name)
act="relu")
self.branch5x5_2 = ConvBNLayer(num_channels=48,
num_filters=64,
filter_size=5,
padding=2,
act="relu",
name="inception_a_branch5x5_2_"+name)
act="relu")
self.branch3x3dbl_1 = ConvBNLayer(num_channels=num_channels,
num_filters=64,
filter_size=1,
act="relu",
name="inception_a_branch3x3dbl_1_"+name)
act="relu")
self.branch3x3dbl_2 = ConvBNLayer(num_channels=64,
num_filters=96,
filter_size=3,
padding=1,
act="relu",
name="inception_a_branch3x3dbl_2_"+name)
act="relu")
self.branch3x3dbl_3 = ConvBNLayer(num_channels=96,
num_filters=96,
filter_size=3,
padding=1,
act="relu",
name="inception_a_branch3x3dbl_3_"+name)
act="relu")
self.branch_pool = AvgPool2D(kernel_size=3, stride=1, padding=1, exclusive=False)
self.branch_pool_conv = ConvBNLayer(num_channels=num_channels,
num_filters=pool_features,
filter_size=1,
act="relu",
name="inception_a_branch_pool_"+name)
act="relu")
def forward(self, x):
branch1x1 = self.branch1x1(x)
@ -183,31 +165,27 @@ class InceptionA(TheseusLayer):
class InceptionB(TheseusLayer):
def __init__(self, num_channels, name=None):
def __init__(self, num_channels):
super(InceptionB, self).__init__()
self.branch3x3 = ConvBNLayer(num_channels=num_channels,
num_filters=384,
filter_size=3,
stride=2,
act="relu",
name="inception_b_branch3x3_"+name)
act="relu")
self.branch3x3dbl_1 = ConvBNLayer(num_channels=num_channels,
num_filters=64,
filter_size=1,
act="relu",
name="inception_b_branch3x3dbl_1_"+name)
act="relu")
self.branch3x3dbl_2 = ConvBNLayer(num_channels=64,
num_filters=96,
filter_size=3,
padding=1,
act="relu",
name="inception_b_branch3x3dbl_2_"+name)
act="relu")
self.branch3x3dbl_3 = ConvBNLayer(num_channels=96,
num_filters=96,
filter_size=3,
stride=2,
act="relu",
name="inception_b_branch3x3dbl_3_"+name)
act="relu")
self.branch_pool = MaxPool2D(kernel_size=3, stride=2)
def forward(self, x):
@ -224,70 +202,60 @@ class InceptionB(TheseusLayer):
return outputs
class InceptionC(TheseusLayer):
def __init__(self, num_channels, channels_7x7, name=None):
def __init__(self, num_channels, channels_7x7):
super(InceptionC, self).__init__()
self.branch1x1 = ConvBNLayer(num_channels=num_channels,
num_filters=192,
filter_size=1,
act="relu",
name="inception_c_branch1x1_"+name)
act="relu")
self.branch7x7_1 = ConvBNLayer(num_channels=num_channels,
num_filters=channels_7x7,
filter_size=1,
stride=1,
act="relu",
name="inception_c_branch7x7_1_"+name)
act="relu")
self.branch7x7_2 = ConvBNLayer(num_channels=channels_7x7,
num_filters=channels_7x7,
filter_size=(1, 7),
stride=1,
padding=(0, 3),
act="relu",
name="inception_c_branch7x7_2_"+name)
act="relu")
self.branch7x7_3 = ConvBNLayer(num_channels=channels_7x7,
num_filters=192,
filter_size=(7, 1),
stride=1,
padding=(3, 0),
act="relu",
name="inception_c_branch7x7_3_"+name)
act="relu")
self.branch7x7dbl_1 = ConvBNLayer(num_channels=num_channels,
num_filters=channels_7x7,
filter_size=1,
act="relu",
name="inception_c_branch7x7dbl_1_"+name)
act="relu")
self.branch7x7dbl_2 = ConvBNLayer(num_channels=channels_7x7,
num_filters=channels_7x7,
filter_size=(7, 1),
padding = (3, 0),
act="relu",
name="inception_c_branch7x7dbl_2_"+name)
act="relu")
self.branch7x7dbl_3 = ConvBNLayer(num_channels=channels_7x7,
num_filters=channels_7x7,
filter_size=(1, 7),
padding = (0, 3),
act="relu",
name="inception_c_branch7x7dbl_3_"+name)
act="relu")
self.branch7x7dbl_4 = ConvBNLayer(num_channels=channels_7x7,
num_filters=channels_7x7,
filter_size=(7, 1),
padding = (3, 0),
act="relu",
name="inception_c_branch7x7dbl_4_"+name)
act="relu")
self.branch7x7dbl_5 = ConvBNLayer(num_channels=channels_7x7,
num_filters=192,
filter_size=(1, 7),
padding = (0, 3),
act="relu",
name="inception_c_branch7x7dbl_5_"+name)
act="relu")
self.branch_pool = AvgPool2D(kernel_size=3, stride=1, padding=1, exclusive=False)
self.branch_pool_conv = ConvBNLayer(num_channels=num_channels,
num_filters=192,
filter_size=1,
act="relu",
name="inception_c_branch_pool_"+name)
act="relu")
def forward(self, x):
branch1x1 = self.branch1x1(x)
@ -310,42 +278,36 @@ class InceptionC(TheseusLayer):
return outputs
class InceptionD(TheseusLayer):
def __init__(self, num_channels, name=None):
def __init__(self, num_channels):
super(InceptionD, self).__init__()
self.branch3x3_1 = ConvBNLayer(num_channels=num_channels,
num_filters=192,
filter_size=1,
act="relu",
name="inception_d_branch3x3_1_"+name)
act="relu")
self.branch3x3_2 = ConvBNLayer(num_channels=192,
num_filters=320,
filter_size=3,
stride=2,
act="relu",
name="inception_d_branch3x3_2_"+name)
act="relu")
self.branch7x7x3_1 = ConvBNLayer(num_channels=num_channels,
num_filters=192,
filter_size=1,
act="relu",
name="inception_d_branch7x7x3_1_"+name)
act="relu")
self.branch7x7x3_2 = ConvBNLayer(num_channels=192,
num_filters=192,
filter_size=(1, 7),
padding=(0, 3),
act="relu",
name="inception_d_branch7x7x3_2_"+name)
act="relu")
self.branch7x7x3_3 = ConvBNLayer(num_channels=192,
num_filters=192,
filter_size=(7, 1),
padding=(3, 0),
act="relu",
name="inception_d_branch7x7x3_3_"+name)
act="relu")
self.branch7x7x3_4 = ConvBNLayer(num_channels=192,
num_filters=192,
filter_size=3,
stride=2,
act="relu",
name="inception_d_branch7x7x3_4_"+name)
act="relu")
self.branch_pool = MaxPool2D(kernel_size=3, stride=2)
def forward(self, x):
@ -363,60 +325,51 @@ class InceptionD(TheseusLayer):
return outputs
class InceptionE(TheseusLayer):
def __init__(self, num_channels, name=None):
def __init__(self, num_channels):
super(InceptionE, self).__init__()
self.branch1x1 = ConvBNLayer(num_channels=num_channels,
num_filters=320,
filter_size=1,
act="relu",
name="inception_e_branch1x1_"+name)
act="relu")
self.branch3x3_1 = ConvBNLayer(num_channels=num_channels,
num_filters=384,
filter_size=1,
act="relu",
name="inception_e_branch3x3_1_"+name)
act="relu")
self.branch3x3_2a = ConvBNLayer(num_channels=384,
num_filters=384,
filter_size=(1, 3),
padding=(0, 1),
act="relu",
name="inception_e_branch3x3_2a_"+name)
act="relu")
self.branch3x3_2b = ConvBNLayer(num_channels=384,
num_filters=384,
filter_size=(3, 1),
padding=(1, 0),
act="relu",
name="inception_e_branch3x3_2b_"+name)
act="relu")
self.branch3x3dbl_1 = ConvBNLayer(num_channels=num_channels,
num_filters=448,
filter_size=1,
act="relu",
name="inception_e_branch3x3dbl_1_"+name)
act="relu")
self.branch3x3dbl_2 = ConvBNLayer(num_channels=448,
num_filters=384,
filter_size=3,
padding=1,
act="relu",
name="inception_e_branch3x3dbl_2_"+name)
act="relu")
self.branch3x3dbl_3a = ConvBNLayer(num_channels=384,
num_filters=384,
filter_size=(1, 3),
padding=(0, 1),
act="relu",
name="inception_e_branch3x3dbl_3a_"+name)
act="relu")
self.branch3x3dbl_3b = ConvBNLayer(num_channels=384,
num_filters=384,
filter_size=(3, 1),
padding=(1, 0),
act="relu",
name="inception_e_branch3x3dbl_3b_"+name)
act="relu")
self.branch_pool = AvgPool2D(kernel_size=3, stride=1, padding=1, exclusive=False)
self.branch_pool_conv = ConvBNLayer(num_channels=num_channels,
num_filters=192,
filter_size=1,
act="relu",
name="inception_e_branch_pool_"+name)
act="relu")
def forward(self, x):
branch1x1 = self.branch1x1(x)
@ -454,29 +407,27 @@ class Inception_V3(TheseusLayer):
self.inception_stem = InceptionStem()
self.inception_block_list = []
self.inception_block_list = nn.LayerList()
for i in range(len(self.inception_a_list[0])):
inception_a = InceptionA(self.inception_a_list[0][i],
self.inception_a_list[1][i],
name=str(i+1))
self.inception_a_list[1][i])
self.inception_block_list.append(inception_a)
for i in range(len(self.inception_b_list)):
inception_b = InceptionB(self.inception_b_list[i], name=str(i+1))
inception_b = InceptionB(self.inception_b_list[i])
self.inception_block_list.append(inception_b)
for i in range(len(self.inception_c_list[0])):
inception_c = InceptionC(self.inception_c_list[0][i],
self.inception_c_list[1][i],
name=str(i+1))
self.inception_c_list[1][i])
self.inception_block_list.append(inception_c)
for i in range(len(self.inception_d_list)):
inception_d = InceptionD(self.inception_d_list[i], name=str(i+1))
inception_d = InceptionD(self.inception_d_list[i])
self.inception_block_list.append(inception_d)
for i in range(len(self.inception_e_list)):
inception_e = InceptionE(self.inception_e_list[i], name=str(i+1))
inception_e = InceptionE(self.inception_e_list[i])
self.inception_block_list.append(inception_e)
self.gap = AdaptiveAvgPool2D(1)
@ -486,8 +437,8 @@ class Inception_V3(TheseusLayer):
2048,
class_num,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="fc_weights"),
bias_attr=ParamAttr(name="fc_offset"))
initializer=Uniform(-stdv, stdv)),
bias_attr=ParamAttr())
def forward(self, x):
y = self.inception_stem(x)