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Inception V3 for MRCNN
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| # -*- coding: utf-8 -*- | |
| from keras import layers | |
| from keras.layers import Activation | |
| from keras.layers import BatchNormalization | |
| from keras.layers import Conv2D | |
| from keras.layers import MaxPooling2D | |
| from keras.layers import AveragePooling2D | |
| from keras import backend as K | |
| def conv2d_bn(x, | |
| filters, | |
| num_row, | |
| num_col, | |
| padding='same', | |
| strides=(1, 1), | |
| name=None): | |
| """ | |
| Utility function to apply conv + BN. | |
| Arguments: | |
| x: input tensor. | |
| filters: filters in `Conv2D`. | |
| num_row: height of the convolution kernel. | |
| num_col: width of the convolution kernel. | |
| padding: padding mode in `Conv2D`. | |
| strides: strides in `Conv2D`. | |
| name: name of the ops; will become `name + '_conv'` | |
| for the convolution and `name + '_bn'` for the | |
| batch norm layer. | |
| Returns: | |
| Output tensor after applying `Conv2D` and `BatchNormalization`. | |
| """ | |
| if name is not None: | |
| bn_name = name + '_bn' | |
| conv_name = name + '_conv' | |
| else: | |
| bn_name = None | |
| conv_name = None | |
| if K.image_data_format() == 'channels_first': | |
| bn_axis = 1 | |
| else: | |
| bn_axis = 3 | |
| x = Conv2D( | |
| filters, (num_row, num_col), | |
| strides=strides, | |
| padding=padding, | |
| use_bias=False, | |
| name=conv_name)(x) | |
| x = BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x) | |
| x = Activation('relu', name=name)(x) | |
| return x | |
| def build_inception_graph(img_input, stage5=False): | |
| if K.image_data_format() == 'channels_first': | |
| channel_axis = 1 | |
| else: | |
| channel_axis = 3 | |
| x = InceptionV3.conv2d_bn(img_input, 32, 3, 3, strides=(2, 2), padding='valid') | |
| x = InceptionV3.conv2d_bn(x, 32, 3, 3, padding='valid') | |
| x = InceptionV3.conv2d_bn(x, 64, 3, 3) | |
| x = MaxPooling2D((3, 3), strides=(2, 2))(x) | |
| x = InceptionV3.conv2d_bn(x, 80, 1, 1, padding='valid') | |
| C2 = x = InceptionV3.conv2d_bn(x, 192, 3, 3, padding='valid') | |
| x = MaxPooling2D((3, 3), strides=(2, 2))(x) | |
| # mixed 0: 35 x 35 x 256 | |
| branch1x1 = InceptionV3.conv2d_bn(x, 64, 1, 1) | |
| branch5x5 = InceptionV3.conv2d_bn(x, 48, 1, 1) | |
| branch5x5 = InceptionV3.conv2d_bn(branch5x5, 64, 5, 5) | |
| branch3x3dbl = InceptionV3.conv2d_bn(x, 64, 1, 1) | |
| branch3x3dbl = InceptionV3.conv2d_bn(branch3x3dbl, 96, 3, 3) | |
| branch3x3dbl = InceptionV3.conv2d_bn(branch3x3dbl, 96, 3, 3) | |
| branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) | |
| branch_pool = InceptionV3.conv2d_bn(branch_pool, 32, 1, 1) | |
| x = layers.concatenate( | |
| [branch1x1, branch5x5, branch3x3dbl, branch_pool], | |
| axis=channel_axis, | |
| name='mixed0') | |
| # mixed 1: 35 x 35 x 256 | |
| branch1x1 = InceptionV3.conv2d_bn(x, 64, 1, 1) | |
| branch5x5 = InceptionV3.conv2d_bn(x, 48, 1, 1) | |
| branch5x5 = InceptionV3.conv2d_bn(branch5x5, 64, 5, 5) | |
| branch3x3dbl = InceptionV3.conv2d_bn(x, 64, 1, 1) | |
| branch3x3dbl = InceptionV3.conv2d_bn(branch3x3dbl, 96, 3, 3) | |
| branch3x3dbl = InceptionV3.conv2d_bn(branch3x3dbl, 96, 3, 3) | |
| branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) | |
| branch_pool = InceptionV3.conv2d_bn(branch_pool, 64, 1, 1) | |
| x = layers.concatenate( | |
| [branch1x1, branch5x5, branch3x3dbl, branch_pool], | |
| axis=channel_axis, | |
| name='mixed1') | |
| # mixed 2: 35 x 35 x 256 | |
| branch1x1 = InceptionV3.conv2d_bn(x, 64, 1, 1) | |
| branch5x5 = InceptionV3.conv2d_bn(x, 48, 1, 1) | |
| branch5x5 = InceptionV3.conv2d_bn(branch5x5, 64, 5, 5) | |
| branch3x3dbl = InceptionV3.conv2d_bn(x, 64, 1, 1) | |
| branch3x3dbl = InceptionV3.conv2d_bn(branch3x3dbl, 96, 3, 3) | |
| branch3x3dbl = InceptionV3.conv2d_bn(branch3x3dbl, 96, 3, 3) | |
| branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) | |
| branch_pool = InceptionV3.conv2d_bn(branch_pool, 64, 1, 1) | |
| C3 = x = layers.concatenate( | |
| [branch1x1, branch5x5, branch3x3dbl, branch_pool], | |
| axis=channel_axis, | |
| name='mixed2') | |
| # mixed 3: 17 x 17 x 768 | |
| branch3x3 = InceptionV3.conv2d_bn(x, 384, 3, 3, strides=(2, 2), padding='valid') | |
| branch3x3dbl = InceptionV3.conv2d_bn(x, 64, 1, 1) | |
| branch3x3dbl = InceptionV3.conv2d_bn(branch3x3dbl, 96, 3, 3) | |
| branch3x3dbl = InceptionV3.conv2d_bn(branch3x3dbl, 96, 3, 3, strides=(2, 2), padding='valid') | |
| branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x) | |
| x = layers.concatenate([branch3x3, branch3x3dbl, branch_pool], axis=channel_axis, name='mixed3') | |
| # mixed 4: 17 x 17 x 768 | |
| branch1x1 = InceptionV3.conv2d_bn(x, 192, 1, 1) | |
| branch7x7 = InceptionV3.conv2d_bn(x, 128, 1, 1) | |
| branch7x7 = InceptionV3.conv2d_bn(branch7x7, 128, 1, 7) | |
| branch7x7 = InceptionV3.conv2d_bn(branch7x7, 192, 7, 1) | |
| branch7x7dbl = InceptionV3.conv2d_bn(x, 128, 1, 1) | |
| branch7x7dbl = InceptionV3.conv2d_bn(branch7x7dbl, 128, 7, 1) | |
| branch7x7dbl = InceptionV3.conv2d_bn(branch7x7dbl, 128, 1, 7) | |
| branch7x7dbl = InceptionV3.conv2d_bn(branch7x7dbl, 128, 7, 1) | |
| branch7x7dbl = InceptionV3.conv2d_bn(branch7x7dbl, 192, 1, 7) | |
| branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) | |
| branch_pool = InceptionV3.conv2d_bn(branch_pool, 192, 1, 1) | |
| x = layers.concatenate( | |
| [branch1x1, branch7x7, branch7x7dbl, branch_pool], | |
| axis=channel_axis, | |
| name='mixed4') | |
| # mixed 5, 6: 17 x 17 x 768 | |
| for i in range(2): | |
| branch1x1 = InceptionV3.conv2d_bn(x, 192, 1, 1) | |
| branch7x7 = InceptionV3.conv2d_bn(x, 160, 1, 1) | |
| branch7x7 = InceptionV3.conv2d_bn(branch7x7, 160, 1, 7) | |
| branch7x7 = InceptionV3.conv2d_bn(branch7x7, 192, 7, 1) | |
| branch7x7dbl = InceptionV3.conv2d_bn(x, 160, 1, 1) | |
| branch7x7dbl = InceptionV3.conv2d_bn(branch7x7dbl, 160, 7, 1) | |
| branch7x7dbl = InceptionV3.conv2d_bn(branch7x7dbl, 160, 1, 7) | |
| branch7x7dbl = InceptionV3.conv2d_bn(branch7x7dbl, 160, 7, 1) | |
| branch7x7dbl = InceptionV3.conv2d_bn(branch7x7dbl, 192, 1, 7) | |
| branch_pool = AveragePooling2D( | |
| (3, 3), strides=(1, 1), padding='same')(x) | |
| branch_pool = InceptionV3.conv2d_bn(branch_pool, 192, 1, 1) | |
| x = layers.concatenate( | |
| [branch1x1, branch7x7, branch7x7dbl, branch_pool], | |
| axis=channel_axis, | |
| name='mixed' + str(5 + i)) | |
| # mixed 7: 17 x 17 x 768 | |
| branch1x1 = InceptionV3.conv2d_bn(x, 192, 1, 1) | |
| branch7x7 = InceptionV3.conv2d_bn(x, 192, 1, 1) | |
| branch7x7 = InceptionV3.conv2d_bn(branch7x7, 192, 1, 7) | |
| branch7x7 = InceptionV3.conv2d_bn(branch7x7, 192, 7, 1) | |
| branch7x7dbl = InceptionV3.conv2d_bn(x, 192, 1, 1) | |
| branch7x7dbl = InceptionV3.conv2d_bn(branch7x7dbl, 192, 7, 1) | |
| branch7x7dbl = InceptionV3.conv2d_bn(branch7x7dbl, 192, 1, 7) | |
| branch7x7dbl = InceptionV3.conv2d_bn(branch7x7dbl, 192, 7, 1) | |
| branch7x7dbl = InceptionV3.conv2d_bn(branch7x7dbl, 192, 1, 7) | |
| branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) | |
| branch_pool = InceptionV3.conv2d_bn(branch_pool, 192, 1, 1) | |
| C4 = x = layers.concatenate( | |
| [branch1x1, branch7x7, branch7x7dbl, branch_pool], | |
| axis=channel_axis, | |
| name='mixed7') | |
| # mixed 8: 8 x 8 x 1280 | |
| branch3x3 = InceptionV3.conv2d_bn(x, 192, 1, 1) | |
| branch3x3 = InceptionV3.conv2d_bn(branch3x3, 320, 3, 3, | |
| strides=(2, 2), padding='valid') | |
| branch7x7x3 = InceptionV3.conv2d_bn(x, 192, 1, 1) | |
| branch7x7x3 = InceptionV3.conv2d_bn(branch7x7x3, 192, 1, 7) | |
| branch7x7x3 = InceptionV3.conv2d_bn(branch7x7x3, 192, 7, 1) | |
| branch7x7x3 = InceptionV3.conv2d_bn( | |
| branch7x7x3, 192, 3, 3, strides=(2, 2), padding='valid') | |
| branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x) | |
| x = layers.concatenate( | |
| [branch3x3, branch7x7x3, branch_pool], axis=channel_axis, name='mixed8') | |
| # mixed 9, 10: 8 x 8 x 2048 | |
| for i in range(2): | |
| branch1x1 = InceptionV3.conv2d_bn(x, 320, 1, 1) | |
| branch3x3 = InceptionV3.conv2d_bn(x, 384, 1, 1) | |
| branch3x3_1 = InceptionV3.conv2d_bn(branch3x3, 384, 1, 3) | |
| branch3x3_2 = InceptionV3.conv2d_bn(branch3x3, 384, 3, 1) | |
| branch3x3 = layers.concatenate( | |
| [branch3x3_1, branch3x3_2], axis=channel_axis, name='mixed9_' + str(i)) | |
| branch3x3dbl = InceptionV3.conv2d_bn(x, 448, 1, 1) | |
| branch3x3dbl = InceptionV3.conv2d_bn(branch3x3dbl, 384, 3, 3) | |
| branch3x3dbl_1 = InceptionV3.conv2d_bn(branch3x3dbl, 384, 1, 3) | |
| branch3x3dbl_2 = InceptionV3.conv2d_bn(branch3x3dbl, 384, 3, 1) | |
| branch3x3dbl = layers.concatenate( | |
| [branch3x3dbl_1, branch3x3dbl_2], axis=channel_axis) | |
| branch_pool = AveragePooling2D( | |
| (3, 3), strides=(1, 1), padding='same')(x) | |
| branch_pool = InceptionV3.conv2d_bn(branch_pool, 192, 1, 1) | |
| x = layers.concatenate( | |
| [branch1x1, branch3x3, branch3x3dbl, branch_pool], | |
| axis=channel_axis, | |
| name='mixed' + str(9 + i)) | |
| C5 = x if stage5 else None | |
| return [None, C2, C3, C4, C5] |
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