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@@ -208,7 +208,7 @@ class _DepthwiseConv2dNative(nn.Cell): |
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self.padding = padding |
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self.dilation = dilation |
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self.group = group |
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if not (isinstance(in_channels, int) and in_channels > 0): |
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if not (isinstance(in_channels, int) and in_channels > 0): |
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raise ValueError('Attr \'in_channels\' of \'DepthwiseConv2D\' Op passed ' |
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+ str(in_channels) + ', should be a int and greater than 0.') |
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if (not isinstance(kernel_size, tuple)) or len(kernel_size) != 2 or \ |
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@@ -526,12 +526,12 @@ class RootBlockBeta(nn.Cell): |
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super(RootBlockBeta, self).__init__() |
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self.conv1 = _conv_bn_relu(3, 64, ksize=3, stride=2, padding=0, pad_mode="valid", use_batch_statistics=fine_tune_batch_norm) |
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self.conv2 = _conv_bn_relu(64, 64, ksize=3, stride=1, padding=0, pad_mode="same", use_batch_statistics=fine_tune_batch_norm) |
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self.conv3 = _conv_bn_relu(64, 128, ksize=3, stride=1, padding=0, pad_mode="same", use_batch_statistics=fine_tune_batch_norm) |
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self.conv3 = _conv_bn_relu(64, 128, ksize=3, stride=1, padding=0, pad_m ode="same", use_batch_statistics=fine_tune_batch_norm) |
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def construct(self, x): |
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x = self.conv1(x) |
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x = self.conv2(x) |
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x = self.conv3(x) |
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return x |
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class resnet50_dl(fine_tune_batch_norm=False): |
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def resnet50_dl(fine_tune_batch_norm=False): |
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return ResNetV1(fine_tune_batch_norm) |
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