forked from mindspore-Ecosystem/mindspore
!6335 add include_top parameter in googlenet for hub
Merge pull request !6335 from hzf/add_inctop
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defd74e261
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@ -81,7 +81,7 @@ class GoogleNet(nn.Cell):
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Googlenet architecture
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Googlenet architecture
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"""
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"""
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def __init__(self, num_classes):
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def __init__(self, num_classes, include_top=True):
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super(GoogleNet, self).__init__()
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super(GoogleNet, self).__init__()
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self.conv1 = Conv2dBlock(3, 64, kernel_size=7, stride=2, padding=0)
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self.conv1 = Conv2dBlock(3, 64, kernel_size=7, stride=2, padding=0)
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self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
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self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
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@ -104,8 +104,10 @@ class GoogleNet(nn.Cell):
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self.block5a = Inception(832, 256, 160, 320, 32, 128, 128)
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self.block5a = Inception(832, 256, 160, 320, 32, 128, 128)
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self.block5b = Inception(832, 384, 192, 384, 48, 128, 128)
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self.block5b = Inception(832, 384, 192, 384, 48, 128, 128)
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self.mean = P.ReduceMean(keep_dims=True)
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self.dropout = nn.Dropout(keep_prob=0.8)
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self.dropout = nn.Dropout(keep_prob=0.8)
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self.include_top = include_top
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if self.include_top:
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self.mean = P.ReduceMean(keep_dims=True)
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self.flatten = nn.Flatten()
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self.flatten = nn.Flatten()
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self.classifier = nn.Dense(1024, num_classes, weight_init=weight_variable(),
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self.classifier = nn.Dense(1024, num_classes, weight_init=weight_variable(),
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bias_init=weight_variable())
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bias_init=weight_variable())
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@ -133,6 +135,8 @@ class GoogleNet(nn.Cell):
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x = self.block5a(x)
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x = self.block5a(x)
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x = self.block5b(x)
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x = self.block5b(x)
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if not self.include_top:
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return x
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x = self.mean(x, (2, 3))
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x = self.mean(x, (2, 3))
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x = self.flatten(x)
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x = self.flatten(x)
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