# -*- coding: utf-8 -*- """Inception-ResNet V1 model for tensorflow.keras. # Reference http://arxiv.org/abs/1602.07261 https://github.com/davidsandberg/facenet/blob/master/src/models/inception_resnet_v1.py https://github.com/myutwo150/keras-inception-resnet-v2/blob/master/inception_resnet_v2.py """ from functools import partial from tensorflow.keras.models import Model from tensorflow.keras.layers import Activation from tensorflow.keras.layers import BatchNormalization from tensorflow.keras.layers import Concatenate from tensorflow.keras.layers import Conv2D from tensorflow.keras.layers import Dense from tensorflow.keras.layers import Dropout from tensorflow.keras.layers import GlobalAveragePooling2D from tensorflow.keras.layers import Input from tensorflow.keras.layers import Lambda from tensorflow.keras.layers import MaxPooling2D from tensorflow.keras.layers import add from tensorflow.keras import backend as K def scaling(x, scale): return x * scale def conv2d_bn(x, filters, kernel_size, strides=1, padding='same', activation='relu', use_bias=False, name=None): x = Conv2D(filters, kernel_size, strides=strides, padding=padding, use_bias=use_bias, name=name)(x) if not use_bias: bn_axis = 1 if K.image_data_format() == 'channels_first' else 3 bn_name = _generate_layer_name('BatchNorm', prefix=name) x = BatchNormalization(axis=bn_axis, momentum=0.995, epsilon=0.001, scale=False, name=bn_name)(x) if activation is not None: ac_name = _generate_layer_name('Activation', prefix=name) x = Activation(activation, name=ac_name)(x) return x def _generate_layer_name(name, branch_idx=None, prefix=None): if prefix is None: return None if branch_idx is None: return '_'.join((prefix, name)) return '_'.join((prefix, 'Branch', str(branch_idx), name)) def _inception_resnet_block(x, scale, block_type, block_idx, activation='relu'): channel_axis = 1 if K.image_data_format() == 'channels_first' else 3 if block_idx is None: prefix = None else: prefix = '_'.join((block_type, str(block_idx))) name_fmt = partial(_generate_layer_name, prefix=prefix) if block_type == 'Block35': branch_0 = conv2d_bn(x, 32, 1, name=name_fmt('Conv2d_1x1', 0)) branch_1 = conv2d_bn(x, 32, 1, name=name_fmt('Conv2d_0a_1x1', 1)) branch_1 = conv2d_bn(branch_1, 32, 3, name=name_fmt('Conv2d_0b_3x3', 1)) branch_2 = conv2d_bn(x, 32, 1, name=name_fmt('Conv2d_0a_1x1', 2)) branch_2 = conv2d_bn(branch_2, 32, 3, name=name_fmt('Conv2d_0b_3x3', 2)) branch_2 = conv2d_bn(branch_2, 32, 3, name=name_fmt('Conv2d_0c_3x3', 2)) branches = [branch_0, branch_1, branch_2] elif block_type == 'Block17': branch_0 = conv2d_bn(x, 128, 1, name=name_fmt('Conv2d_1x1', 0)) branch_1 = conv2d_bn(x, 128, 1, name=name_fmt('Conv2d_0a_1x1', 1)) branch_1 = conv2d_bn(branch_1, 128, [1, 7], name=name_fmt('Conv2d_0b_1x7', 1)) branch_1 = conv2d_bn(branch_1, 128, [7, 1], name=name_fmt('Conv2d_0c_7x1', 1)) branches = [branch_0, branch_1] elif block_type == 'Block8': branch_0 = conv2d_bn(x, 192, 1, name=name_fmt('Conv2d_1x1', 0)) branch_1 = conv2d_bn(x, 192, 1, name=name_fmt('Conv2d_0a_1x1', 1)) branch_1 = conv2d_bn(branch_1, 192, [1, 3], name=name_fmt('Conv2d_0b_1x3', 1)) branch_1 = conv2d_bn(branch_1, 192, [3, 1], name=name_fmt('Conv2d_0c_3x1', 1)) branches = [branch_0, branch_1] else: raise ValueError('Unknown Inception-ResNet block type. ' 'Expects "Block35", "Block17" or "Block8", ' 'but got: ' + str(block_type)) mixed = Concatenate(axis=channel_axis, name=name_fmt('Concatenate'))(branches) up = conv2d_bn(mixed, K.int_shape(x)[channel_axis], 1, activation=None, use_bias=True, name=name_fmt('Conv2d_1x1')) up = Lambda(scaling, output_shape=K.int_shape(up)[1:], arguments={'scale': scale})(up) x = add([x, up]) if activation is not None: x = Activation(activation, name=name_fmt('Activation'))(x) return x def InceptionResNetV1Norm(input_shape=(160, 160, 3), classes=128, dropout_keep_prob=0.8, weights_path=None): inputs = Input(shape=input_shape) x = conv2d_bn(inputs, 32, 3, strides=2, padding='valid', name='Conv2d_1a_3x3') x = conv2d_bn(x, 32, 3, padding='valid', name='Conv2d_2a_3x3') x = conv2d_bn(x, 64, 3, name='Conv2d_2b_3x3') x = MaxPooling2D(3, strides=2, name='MaxPool_3a_3x3')(x) x = conv2d_bn(x, 80, 1, padding='valid', name='Conv2d_3b_1x1') x = conv2d_bn(x, 192, 3, padding='valid', name='Conv2d_4a_3x3') x = conv2d_bn(x, 256, 3, strides=2, padding='valid', name='Conv2d_4b_3x3') # 5x Block35 (Inception-ResNet-A block): for block_idx in range(1, 6): x = _inception_resnet_block(x, scale=0.17, block_type='Block35', block_idx=block_idx) # Mixed 6a (Reduction-A block): channel_axis = 1 if K.image_data_format() == 'channels_first' else 3 name_fmt = partial(_generate_layer_name, prefix='Mixed_6a') branch_0 = conv2d_bn(x, 384, 3, strides=2, padding='valid', name=name_fmt('Conv2d_1a_3x3', 0)) branch_1 = conv2d_bn(x, 192, 1, name=name_fmt('Conv2d_0a_1x1', 1)) branch_1 = conv2d_bn(branch_1, 192, 3, name=name_fmt('Conv2d_0b_3x3', 1)) branch_1 = conv2d_bn(branch_1, 256, 3, strides=2, padding='valid', name=name_fmt('Conv2d_1a_3x3', 1)) branch_pool = MaxPooling2D(3, strides=2, padding='valid', name=name_fmt('MaxPool_1a_3x3', 2))(x) branches = [branch_0, branch_1, branch_pool] x = Concatenate(axis=channel_axis, name='Mixed_6a')(branches) # 10x Block17 (Inception-ResNet-B block): for block_idx in range(1, 11): x = _inception_resnet_block(x, scale=0.1, block_type='Block17', block_idx=block_idx) # Mixed 7a (Reduction-B block): 8 x 8 x 2080 name_fmt = partial(_generate_layer_name, prefix='Mixed_7a') branch_0 = conv2d_bn(x, 256, 1, name=name_fmt('Conv2d_0a_1x1', 0)) branch_0 = conv2d_bn(branch_0, 384, 3, strides=2, padding='valid', name=name_fmt('Conv2d_1a_3x3', 0)) branch_1 = conv2d_bn(x, 256, 1, name=name_fmt('Conv2d_0a_1x1', 1)) branch_1 = conv2d_bn(branch_1, 256, 3, strides=2, padding='valid', name=name_fmt('Conv2d_1a_3x3', 1)) branch_2 = conv2d_bn(x, 256, 1, name=name_fmt('Conv2d_0a_1x1', 2)) branch_2 = conv2d_bn(branch_2, 256, 3, name=name_fmt('Conv2d_0b_3x3', 2)) branch_2 = conv2d_bn(branch_2, 256, 3, strides=2, padding='valid', name=name_fmt('Conv2d_1a_3x3', 2)) branch_pool = MaxPooling2D(3, strides=2, padding='valid', name=name_fmt('MaxPool_1a_3x3', 3))(x) branches = [branch_0, branch_1, branch_2, branch_pool] x = Concatenate(axis=channel_axis, name='Mixed_7a')(branches) # 5x Block8 (Inception-ResNet-C block): for block_idx in range(1, 6): x = _inception_resnet_block(x, scale=0.2, block_type='Block8', block_idx=block_idx) x = _inception_resnet_block(x, scale=1., activation=None, block_type='Block8', block_idx=6) # Classification block x = GlobalAveragePooling2D(name='AvgPool')(x) x = Dropout(1.0 - dropout_keep_prob, name='Dropout')(x) # Bottleneck x = Dense(classes, use_bias=False, name='Bottleneck')(x) bn_name = _generate_layer_name('BatchNorm', prefix='Bottleneck') x = BatchNormalization(momentum=0.995, epsilon=0.001, scale=False, name=bn_name)(x) x = Lambda(K.l2_normalize, arguments={'axis': 1}, name='normalize')(x) # Create model model = Model(inputs, x, name='inception_resnet_v1') if weights_path is not None: model.load_weights(weights_path) return model