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Merge pull request #555 from will-am/xception
Add Xception model for image classification
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,193 @@ | ||
| import paddle.v2 as paddle | ||
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| __all__ = ['xception'] | ||
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| def img_separable_conv_bn(name, input, num_channels, num_out_channels, | ||
| filter_size, stride, padding, act): | ||
| conv = paddle.networks.img_separable_conv( | ||
| name=name, | ||
| input=input, | ||
| num_channels=num_channels, | ||
| num_out_channels=num_out_channels, | ||
| filter_size=filter_size, | ||
| stride=stride, | ||
| padding=padding, | ||
| act=paddle.activation.Linear()) | ||
| norm = paddle.layer.batch_norm(name=name + '_norm', input=conv, act=act) | ||
| return norm | ||
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| def img_conv_bn(name, input, num_channels, num_filters, filter_size, stride, | ||
| padding, act): | ||
| conv = paddle.layer.img_conv( | ||
| name=name, | ||
| input=input, | ||
| num_channels=num_channels, | ||
| num_filters=num_filters, | ||
| filter_size=filter_size, | ||
| stride=stride, | ||
| padding=padding, | ||
| act=paddle.activation.Linear()) | ||
| norm = paddle.layer.batch_norm(name=name + '_norm', input=conv, act=act) | ||
| return norm | ||
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| def conv_block0(input, | ||
| group, | ||
| num_channels, | ||
| num_filters, | ||
| num_filters2=None, | ||
| filter_size=3, | ||
| pool_padding=0, | ||
| entry_relu=True): | ||
| if num_filters2 is None: | ||
| num_filters2 = num_filters | ||
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| if entry_relu: | ||
| act_input = paddle.layer.mixed( | ||
| input=paddle.layer.identity_projection(input=input), | ||
| act=paddle.activation.Relu()) | ||
| else: | ||
| act_input = input | ||
| conv0 = img_separable_conv_bn( | ||
| name='xception_block{0}_conv0'.format(group), | ||
| input=act_input, | ||
| num_channels=num_channels, | ||
| num_out_channels=num_filters, | ||
| filter_size=filter_size, | ||
| stride=1, | ||
| padding=(filter_size - 1) / 2, | ||
| act=paddle.activation.Relu()) | ||
| conv1 = img_separable_conv_bn( | ||
| name='xception_block{0}_conv1'.format(group), | ||
| input=conv0, | ||
| num_channels=num_filters, | ||
| num_out_channels=num_filters2, | ||
| filter_size=filter_size, | ||
| stride=1, | ||
| padding=(filter_size - 1) / 2, | ||
| act=paddle.activation.Linear()) | ||
| pool0 = paddle.layer.img_pool( | ||
| name='xception_block{0}_pool'.format(group), | ||
| input=conv1, | ||
| pool_size=3, | ||
| stride=2, | ||
| padding=pool_padding, | ||
| num_channels=num_filters2, | ||
| pool_type=paddle.pooling.CudnnMax()) | ||
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| shortcut = img_conv_bn( | ||
| name='xception_block{0}_shortcut'.format(group), | ||
| input=input, | ||
| num_channels=num_channels, | ||
| num_filters=num_filters2, | ||
| filter_size=1, | ||
| stride=2, | ||
| padding=0, | ||
| act=paddle.activation.Linear()) | ||
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| return paddle.layer.addto( | ||
| input=[pool0, shortcut], act=paddle.activation.Linear()) | ||
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| def conv_block1(input, group, num_channels, num_filters, filter_size=3): | ||
| act_input = paddle.layer.mixed( | ||
| input=paddle.layer.identity_projection(input=input), | ||
| act=paddle.activation.Relu()) | ||
| conv0 = img_separable_conv_bn( | ||
| name='xception_block{0}_conv0'.format(group), | ||
| input=act_input, | ||
| num_channels=num_channels, | ||
| num_out_channels=num_filters, | ||
| filter_size=filter_size, | ||
| stride=1, | ||
| padding=(filter_size - 1) / 2, | ||
| act=paddle.activation.Relu()) | ||
| conv1 = img_separable_conv_bn( | ||
| name='xception_block{0}_conv1'.format(group), | ||
| input=conv0, | ||
| num_channels=num_filters, | ||
| num_out_channels=num_filters, | ||
| filter_size=filter_size, | ||
| stride=1, | ||
| padding=(filter_size - 1) / 2, | ||
| act=paddle.activation.Relu()) | ||
| conv2 = img_separable_conv_bn( | ||
| name='xception_block{0}_conv2'.format(group), | ||
| input=conv1, | ||
| num_channels=num_filters, | ||
| num_out_channels=num_filters, | ||
| filter_size=filter_size, | ||
| stride=1, | ||
| padding=(filter_size - 1) / 2, | ||
| act=paddle.activation.Linear()) | ||
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| shortcut = input | ||
| return paddle.layer.addto( | ||
| input=[conv2, shortcut], act=paddle.activation.Linear()) | ||
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| def xception(input, class_dim): | ||
| conv = img_conv_bn( | ||
| name='xception_conv0', | ||
| input=input, | ||
| num_channels=3, | ||
| num_filters=32, | ||
| filter_size=3, | ||
| stride=2, | ||
| padding=1, | ||
| act=paddle.activation.Relu()) | ||
| conv = img_conv_bn( | ||
| name='xception_conv1', | ||
| input=conv, | ||
| num_channels=32, | ||
| num_filters=64, | ||
| filter_size=3, | ||
| stride=1, | ||
| padding=1, | ||
| act=paddle.activation.Relu()) | ||
| conv = conv_block0( | ||
| input=conv, group=2, num_channels=64, num_filters=128, entry_relu=False) | ||
| conv = conv_block0(input=conv, group=3, num_channels=128, num_filters=256) | ||
| conv = conv_block0(input=conv, group=4, num_channels=256, num_filters=728) | ||
| for group in range(5, 13): | ||
| conv = conv_block1( | ||
| input=conv, group=group, num_channels=728, num_filters=728) | ||
| conv = conv_block0( | ||
| input=conv, | ||
| group=13, | ||
| num_channels=728, | ||
| num_filters=728, | ||
| num_filters2=1024) | ||
| conv = img_separable_conv_bn( | ||
| name='xception_conv14', | ||
| input=conv, | ||
| num_channels=1024, | ||
| num_out_channels=1536, | ||
| filter_size=3, | ||
| stride=1, | ||
| padding=1, | ||
| act=paddle.activation.Relu()) | ||
| conv = img_separable_conv_bn( | ||
| name='xception_conv15', | ||
| input=conv, | ||
| num_channels=1536, | ||
| num_out_channels=2048, | ||
| filter_size=3, | ||
| stride=1, | ||
| padding=1, | ||
| act=paddle.activation.Relu()) | ||
| pool = paddle.layer.img_pool( | ||
| name='xception_global_pool', | ||
| input=conv, | ||
| pool_size=7, | ||
| stride=1, | ||
| num_channels=2048, | ||
| pool_type=paddle.pooling.CudnnAvg()) | ||
| out = paddle.layer.fc( | ||
| name='xception_fc', | ||
| input=pool, | ||
| size=class_dim, | ||
| act=paddle.activation.Softmax()) | ||
| return out |