Add the Inception-ResNet-v2 model #510
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| import paddle.v2 as paddle | ||
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| def conv_bn_layer(input, | ||
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There was a problem hiding this comment. 这个文件的函数都注释一下功能，输入和输出，否则对一般的用户，很难理解和使用。 |
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| ch_out, | ||
| filter_size, | ||
| stride, | ||
| padding=0, | ||
| active_type=paddle.activation.Relu(), | ||
| ch_in=None): | ||
| tmp = paddle.layer.img_conv( | ||
| input=input, | ||
| filter_size=filter_size, | ||
| num_channels=ch_in, | ||
| num_filters=ch_out, | ||
| stride=stride, | ||
| padding=padding, | ||
| act=paddle.activation.Linear(), | ||
| bias_attr=False) | ||
| return paddle.layer.batch_norm(input=tmp, epsilon=0.001, act=active_type) | ||
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| def sequential_block(input, *layers): | ||
| for layer in layers: | ||
| layer_func, layer_conf = layer | ||
| input = layer_func(input, **layer_conf) | ||
| return input | ||
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| def mixed_5b_block(input): | ||
| branch0 = conv_bn_layer( | ||
| input, ch_in=192, ch_out=96, filter_size=1, stride=1) | ||
| branch1 = sequential_block(input, (conv_bn_layer, { | ||
| "ch_in": 192, | ||
| "ch_out": 48, | ||
| "filter_size": 1, | ||
| "stride": 1 | ||
| }), (conv_bn_layer, { | ||
| "ch_in": 48, | ||
| "ch_out": 64, | ||
| "filter_size": 5, | ||
| "stride": 1, | ||
| "padding": 2 | ||
| })) | ||
| branch2 = sequential_block(input, (conv_bn_layer, { | ||
| "ch_in": 192, | ||
| "ch_out": 64, | ||
| "filter_size": 1, | ||
| "stride": 1 | ||
| }), (conv_bn_layer, { | ||
| "ch_in": 64, | ||
| "ch_out": 96, | ||
| "filter_size": 3, | ||
| "stride": 1, | ||
| "padding": 1 | ||
| }), (conv_bn_layer, { | ||
| "ch_in": 96, | ||
| "ch_out": 96, | ||
| "filter_size": 3, | ||
| "stride": 1, | ||
| "padding": 1 | ||
| })) | ||
| branch3 = sequential_block( | ||
| input, | ||
| (paddle.layer.img_pool, { | ||
| "pool_size": 3, | ||
| "stride": 1, | ||
| "padding": 1, | ||
| "pool_type": paddle.pooling.Avg(), | ||
| "exclude_mode": False | ||
| }), | ||
| (conv_bn_layer, { | ||
| "ch_in": 192, | ||
| "ch_out": 64, | ||
| "filter_size": 1, | ||
| "stride": 1 | ||
| }), ) | ||
| out = paddle.layer.concat(input=[branch0, branch1, branch2, branch3]) | ||
| return out | ||
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| def block35(input, scale=1.0): | ||
| branch0 = conv_bn_layer( | ||
| input, ch_in=320, ch_out=32, filter_size=1, stride=1) | ||
| branch1 = sequential_block(input, (conv_bn_layer, { | ||
| "ch_in": 320, | ||
| "ch_out": 32, | ||
| "filter_size": 1, | ||
| "stride": 1 | ||
| }), (conv_bn_layer, { | ||
| "ch_in": 32, | ||
| "ch_out": 32, | ||
| "filter_size": 3, | ||
| "stride": 1, | ||
| "padding": 1 | ||
| })) | ||
| branch2 = sequential_block(input, (conv_bn_layer, { | ||
| "ch_in": 320, | ||
| "ch_out": 32, | ||
| "filter_size": 1, | ||
| "stride": 1 | ||
| }), (conv_bn_layer, { | ||
| "ch_in": 32, | ||
| "ch_out": 48, | ||
| "filter_size": 3, | ||
| "stride": 1, | ||
| "padding": 1 | ||
| }), (conv_bn_layer, { | ||
| "ch_in": 48, | ||
| "ch_out": 64, | ||
| "filter_size": 3, | ||
| "stride": 1, | ||
| "padding": 1 | ||
| })) | ||
| out = paddle.layer.concat(input=[branch0, branch1, branch2]) | ||
| out = paddle.layer.img_conv( | ||
| input=out, | ||
| filter_size=1, | ||
| num_channels=128, | ||
| num_filters=320, | ||
| stride=1, | ||
| padding=0, | ||
| act=paddle.activation.Linear(), | ||
| bias_attr=None) | ||
| out = paddle.layer.slope_intercept(out, slope=scale, intercept=0.0) | ||
| out = paddle.layer.addto(input=[input, out], act=paddle.activation.Relu()) | ||
| return out | ||
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| def mixed_6a_block(input): | ||
| branch0 = conv_bn_layer( | ||
| input, ch_in=320, ch_out=384, filter_size=3, stride=2) | ||
| branch1 = sequential_block(input, (conv_bn_layer, { | ||
| "ch_in": 320, | ||
| "ch_out": 256, | ||
| "filter_size": 1, | ||
| "stride": 1 | ||
| }), (conv_bn_layer, { | ||
| "ch_in": 256, | ||
| "ch_out": 256, | ||
| "filter_size": 3, | ||
| "stride": 1, | ||
| "padding": 1 | ||
| }), (conv_bn_layer, { | ||
| "ch_in": 256, | ||
| "ch_out": 384, | ||
| "filter_size": 3, | ||
| "stride": 2 | ||
| })) | ||
| branch2 = paddle.layer.img_pool( | ||
| input, | ||
| num_channels=320, | ||
| pool_size=3, | ||
| stride=2, | ||
| pool_type=paddle.pooling.Max()) | ||
| out = paddle.layer.concat(input=[branch0, branch1, branch2]) | ||
| return out | ||
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| def block17(input, scale=1.0): | ||
| branch0 = conv_bn_layer( | ||
| input, ch_in=1088, ch_out=192, filter_size=1, stride=1) | ||
| branch1 = sequential_block(input, (conv_bn_layer, { | ||
| "ch_in": 1088, | ||
| "ch_out": 128, | ||
| "filter_size": 1, | ||
| "stride": 1 | ||
| }), (conv_bn_layer, { | ||
| "ch_in": 128, | ||
| "ch_out": 160, | ||
| "filter_size": [7, 1], | ||
| "stride": 1, | ||
| "padding": [3, 0] | ||
| }), (conv_bn_layer, { | ||
| "ch_in": 160, | ||
| "ch_out": 192, | ||
| "filter_size": [1, 7], | ||
| "stride": 1, | ||
| "padding": [0, 3] | ||
| })) | ||
| out = paddle.layer.concat(input=[branch0, branch1]) | ||
| out = paddle.layer.img_conv( | ||
| input=out, | ||
| filter_size=1, | ||
| num_channels=384, | ||
| num_filters=1088, | ||
| stride=1, | ||
| padding=0, | ||
| act=paddle.activation.Linear(), | ||
| bias_attr=None) | ||
| out = paddle.layer.slope_intercept(out, slope=scale, intercept=0.0) | ||
| out = paddle.layer.addto(input=[input, out], act=paddle.activation.Relu()) | ||
| return out | ||
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| def mixed_7a_block(input): | ||
| branch0 = sequential_block( | ||
| input, | ||
| (conv_bn_layer, { | ||
| "ch_in": 1088, | ||
| "ch_out": 256, | ||
| "filter_size": 1, | ||
| "stride": 1 | ||
| }), | ||
| (conv_bn_layer, { | ||
| "ch_in": 256, | ||
| "ch_out": 384, | ||
| "filter_size": 3, | ||
| "stride": 2 | ||
| }), ) | ||
| branch1 = sequential_block( | ||
| input, | ||
| (conv_bn_layer, { | ||
| "ch_in": 1088, | ||
| "ch_out": 256, | ||
| "filter_size": 1, | ||
| "stride": 1 | ||
| }), | ||
| (conv_bn_layer, { | ||
| "ch_in": 256, | ||
| "ch_out": 288, | ||
| "filter_size": 3, | ||
| "stride": 2 | ||
| }), ) | ||
| branch2 = sequential_block(input, (conv_bn_layer, { | ||
| "ch_in": 1088, | ||
| "ch_out": 256, | ||
| "filter_size": 1, | ||
| "stride": 1 | ||
| }), (conv_bn_layer, { | ||
| "ch_in": 256, | ||
| "ch_out": 288, | ||
| "filter_size": 3, | ||
| "stride": 1, | ||
| "padding": 1 | ||
| }), (conv_bn_layer, { | ||
| "ch_in": 288, | ||
| "ch_out": 320, | ||
| "filter_size": 3, | ||
| "stride": 2 | ||
| })) | ||
| branch3 = paddle.layer.img_pool( | ||
| input, | ||
| num_channels=1088, | ||
| pool_size=3, | ||
| stride=2, | ||
| pool_type=paddle.pooling.Max()) | ||
| out = paddle.layer.concat(input=[branch0, branch1, branch2, branch3]) | ||
| return out | ||
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| def block8(input, scale=1.0, no_relu=False): | ||
| branch0 = conv_bn_layer( | ||
| input, ch_in=2080, ch_out=192, filter_size=1, stride=1) | ||
| branch1 = sequential_block(input, (conv_bn_layer, { | ||
| "ch_in": 2080, | ||
| "ch_out": 192, | ||
| "filter_size": 1, | ||
| "stride": 1 | ||
| }), (conv_bn_layer, { | ||
| "ch_in": 192, | ||
| "ch_out": 224, | ||
| "filter_size": [3, 1], | ||
| "stride": 1, | ||
| "padding": [1, 0] | ||
| }), (conv_bn_layer, { | ||
| "ch_in": 224, | ||
| "ch_out": 256, | ||
| "filter_size": [1, 3], | ||
| "stride": 1, | ||
| "padding": [0, 1] | ||
| })) | ||
| out = paddle.layer.concat(input=[branch0, branch1]) | ||
| out = paddle.layer.img_conv( | ||
| input=out, | ||
| filter_size=1, | ||
| num_channels=448, | ||
| num_filters=2080, | ||
| stride=1, | ||
| padding=0, | ||
| act=paddle.activation.Linear(), | ||
| bias_attr=None) | ||
| out = paddle.layer.slope_intercept(out, slope=scale, intercept=0.0) | ||
| out = paddle.layer.addto( | ||
| input=[input, out], | ||
| act=paddle.activation.Linear() if no_relu else paddle.activation.Relu()) | ||
| return out | ||
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| def inception_resnet_v2(input, | ||
| class_dim, | ||
| dropout_rate=0.5, | ||
| data_dim=3 * 331 * 331): | ||
| conv2d_1a = conv_bn_layer( | ||
| input, ch_in=3, ch_out=32, filter_size=3, stride=2) | ||
| conv2d_2a = conv_bn_layer( | ||
| conv2d_1a, ch_in=32, ch_out=32, filter_size=3, stride=1) | ||
| conv2d_2b = conv_bn_layer( | ||
| conv2d_2a, ch_in=32, ch_out=64, filter_size=3, stride=1, padding=1) | ||
| maxpool_3a = paddle.layer.img_pool( | ||
| input=conv2d_2b, pool_size=3, stride=2, pool_type=paddle.pooling.Max()) | ||
| conv2d_3b = conv_bn_layer( | ||
| maxpool_3a, ch_in=64, ch_out=80, filter_size=1, stride=1) | ||
| conv2d_4a = conv_bn_layer( | ||
| conv2d_3b, ch_in=80, ch_out=192, filter_size=3, stride=1) | ||
| maxpool_5a = paddle.layer.img_pool( | ||
| input=conv2d_4a, pool_size=3, stride=2, pool_type=paddle.pooling.Max()) | ||
| mixed_5b = mixed_5b_block(maxpool_5a) | ||
| repeat = sequential_block(mixed_5b, *([(block35, {"scale": 0.17})] * 10)) | ||
| mixed_6a = mixed_6a_block(repeat) | ||
| repeat1 = sequential_block(mixed_6a, *([(block17, {"scale": 0.10})] * 20)) | ||
| mixed_7a = mixed_7a_block(repeat1) | ||
| repeat2 = sequential_block(mixed_7a, *([(block8, {"scale": 0.20})] * 9)) | ||
| block_8 = block8(repeat2, no_relu=True) | ||
| conv2d_7b = conv_bn_layer( | ||
| block_8, ch_in=2080, ch_out=1536, filter_size=1, stride=1) | ||
| avgpool_1a = paddle.layer.img_pool( | ||
| input=conv2d_7b, | ||
| pool_size=8 if data_dim == 3 * 299 * 299 else 9, | ||
| stride=1, | ||
| pool_type=paddle.pooling.Avg(), | ||
| exclude_mode=False) | ||
| drop_out = paddle.layer.dropout(input=avgpool_1a, dropout_rate=dropout_rate) | ||
| out = paddle.layer.fc( | ||
| input=drop_out, size=class_dim, act=paddle.activation.Softmax()) | ||
| return out | ||
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这里做一个折行，超过 80 个字符了。