''' Copyright 2017 TensorFlow Authors and Kent Sommer Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ''' import numpy as np # Sys import warnings # Keras Core from keras.layers.convolutional import MaxPooling2D, Convolution2D, AveragePooling2D from keras.layers import Input, Dropout, Dense, Flatten, Activation from keras.layers.normalization import BatchNormalization from keras.layers.merge import concatenate from keras import regularizers from keras import initializers from keras.models import Model # Backend from keras import backend as K # Utils from keras.utils.layer_utils import convert_all_kernels_in_model from keras.utils.data_utils import get_file ######################################################################################### # Implements the Inception Network v4 (http://arxiv.org/pdf/1602.07261v1.pdf) in Keras. # ######################################################################################### WEIGHTS_PATH = 'https://github.com/kentsommer/keras-inceptionV4/releases/download/2.1/inception-v4_weights_tf_dim_ordering_tf_kernels.h5' WEIGHTS_PATH_NO_TOP = 'https://github.com/kentsommer/keras-inceptionV4/releases/download/2.1/inception-v4_weights_tf_dim_ordering_tf_kernels_notop.h5' def preprocess_input(x): x = np.divide(x, 255.0) x = np.subtract(x, 0.5) x = np.multiply(x, 2.0) return x def conv2d_bn(x, nb_filter, num_row, num_col, padding='same', strides=(1, 1), use_bias=False): """ Utility function to apply conv + BN. (Slightly modified from https://github.com/fchollet/keras/blob/master/keras/applications/inception_v3.py) """ if K.image_data_format() == 'channels_first': channel_axis = 1 else: channel_axis = -1 x = Convolution2D(nb_filter, (num_row, num_col), strides=strides, padding=padding, use_bias=use_bias, kernel_regularizer=regularizers.l2(0.00004), kernel_initializer=initializers.VarianceScaling(scale=2.0, mode='fan_in', distribution='normal', seed=None))(x) x = BatchNormalization(axis=channel_axis, momentum=0.9997, scale=False)(x) x = Activation('relu')(x) return x def block_inception_a(input): if K.image_data_format() == 'channels_first': channel_axis = 1 else: channel_axis = -1 branch_0 = conv2d_bn(input, 96, 1, 1) branch_1 = conv2d_bn(input, 64, 1, 1) branch_1 = conv2d_bn(branch_1, 96, 3, 3) branch_2 = conv2d_bn(input, 64, 1, 1) branch_2 = conv2d_bn(branch_2, 96, 3, 3) branch_2 = conv2d_bn(branch_2, 96, 3, 3) branch_3 = AveragePooling2D((3,3), strides=(1,1), padding='same')(input) branch_3 = conv2d_bn(branch_3, 96, 1, 1) x = concatenate([branch_0, branch_1, branch_2, branch_3], axis=channel_axis) return x def block_reduction_a(input): if K.image_data_format() == 'channels_first': channel_axis = 1 else: channel_axis = -1 branch_0 = conv2d_bn(input, 384, 3, 3, strides=(2,2), padding='valid') branch_1 = conv2d_bn(input, 192, 1, 1) branch_1 = conv2d_bn(branch_1, 224, 3, 3) branch_1 = conv2d_bn(branch_1, 256, 3, 3, strides=(2,2), padding='valid') branch_2 = MaxPooling2D((3,3), strides=(2,2), padding='valid')(input) x = concatenate([branch_0, branch_1, branch_2], axis=channel_axis) return x def block_inception_b(input): if K.image_data_format() == 'channels_first': channel_axis = 1 else: channel_axis = -1 branch_0 = conv2d_bn(input, 384, 1, 1) branch_1 = conv2d_bn(input, 192, 1, 1) branch_1 = conv2d_bn(branch_1, 224, 1, 7) branch_1 = conv2d_bn(branch_1, 256, 7, 1) branch_2 = conv2d_bn(input, 192, 1, 1) branch_2 = conv2d_bn(branch_2, 192, 7, 1) branch_2 = conv2d_bn(branch_2, 224, 1, 7) branch_2 = conv2d_bn(branch_2, 224, 7, 1) branch_2 = conv2d_bn(branch_2, 256, 1, 7) branch_3 = AveragePooling2D((3,3), strides=(1,1), padding='same')(input) branch_3 = conv2d_bn(branch_3, 128, 1, 1) x = concatenate([branch_0, branch_1, branch_2, branch_3], axis=channel_axis) return x def block_reduction_b(input): if K.image_data_format() == 'channels_first': channel_axis = 1 else: channel_axis = -1 branch_0 = conv2d_bn(input, 192, 1, 1) branch_0 = conv2d_bn(branch_0, 192, 3, 3, strides=(2, 2), padding='valid') branch_1 = conv2d_bn(input, 256, 1, 1) branch_1 = conv2d_bn(branch_1, 256, 1, 7) branch_1 = conv2d_bn(branch_1, 320, 7, 1) branch_1 = conv2d_bn(branch_1, 320, 3, 3, strides=(2,2), padding='valid') branch_2 = MaxPooling2D((3, 3), strides=(2, 2), padding='valid')(input) x = concatenate([branch_0, branch_1, branch_2], axis=channel_axis) return x def block_inception_c(input): if K.image_data_format() == 'channels_first': channel_axis = 1 else: channel_axis = -1 branch_0 = conv2d_bn(input, 256, 1, 1) branch_1 = conv2d_bn(input, 384, 1, 1) branch_10 = conv2d_bn(branch_1, 256, 1, 3) branch_11 = conv2d_bn(branch_1, 256, 3, 1) branch_1 = concatenate([branch_10, branch_11], axis=channel_axis) branch_2 = conv2d_bn(input, 384, 1, 1) branch_2 = conv2d_bn(branch_2, 448, 3, 1) branch_2 = conv2d_bn(branch_2, 512, 1, 3) branch_20 = conv2d_bn(branch_2, 256, 1, 3) branch_21 = conv2d_bn(branch_2, 256, 3, 1) branch_2 = concatenate([branch_20, branch_21], axis=channel_axis) branch_3 = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(input) branch_3 = conv2d_bn(branch_3, 256, 1, 1) x = concatenate([branch_0, branch_1, branch_2, branch_3], axis=channel_axis) return x def inception_v4_base(input): if K.image_data_format() == 'channels_first': channel_axis = 1 else: channel_axis = -1 # Input Shape is 299 x 299 x 3 (th) or 3 x 299 x 299 (th) net = conv2d_bn(input, 32, 3, 3, strides=(2,2), padding='valid') net = conv2d_bn(net, 32, 3, 3, padding='valid') net = conv2d_bn(net, 64, 3, 3) branch_0 = MaxPooling2D((3,3), strides=(2,2), padding='valid')(net) branch_1 = conv2d_bn(net, 96, 3, 3, strides=(2,2), padding='valid') net = concatenate([branch_0, branch_1], axis=channel_axis) branch_0 = conv2d_bn(net, 64, 1, 1) branch_0 = conv2d_bn(branch_0, 96, 3, 3, padding='valid') branch_1 = conv2d_bn(net, 64, 1, 1) branch_1 = conv2d_bn(branch_1, 64, 1, 7) branch_1 = conv2d_bn(branch_1, 64, 7, 1) branch_1 = conv2d_bn(branch_1, 96, 3, 3, padding='valid') net = concatenate([branch_0, branch_1], axis=channel_axis) branch_0 = conv2d_bn(net, 192, 3, 3, strides=(2,2), padding='valid') branch_1 = MaxPooling2D((3,3), strides=(2,2), padding='valid')(net) net = concatenate([branch_0, branch_1], axis=channel_axis) # 35 x 35 x 384 # 4 x Inception-A blocks for idx in range(4): net = block_inception_a(net) # 35 x 35 x 384 # Reduction-A block net = block_reduction_a(net) # 17 x 17 x 1024 # 7 x Inception-B blocks for idx in range(7): net = block_inception_b(net) # 17 x 17 x 1024 # Reduction-B block net = block_reduction_b(net) # 8 x 8 x 1536 # 3 x Inception-C blocks for idx in range(3): net = block_inception_c(net) return net def inception_v4(num_classes, dropout_keep_prob, weights, include_top): ''' Creates the inception v4 network Args: num_classes: number of classes dropout_keep_prob: float, the fraction to keep before final layer. Returns: logits: the logits outputs of the model. ''' # Input Shape is 299 x 299 x 3 (tf) or 3 x 299 x 299 (th) if K.image_data_format() == 'channels_first': inputs = Input((3, 299, 299)) else: inputs = Input((299, 299, 3)) # Make inception base x = inception_v4_base(inputs) # Final pooling and prediction if include_top: # 1 x 1 x 1536 x = AveragePooling2D((8,8), padding='valid')(x) x = Dropout(dropout_keep_prob)(x) x = Flatten()(x) # 1536 x = Dense(units=num_classes, activation='softmax')(x) model = Model(inputs, x, name='inception_v4') # load weights if weights == 'imagenet': if K.image_data_format() == 'channels_first': if K.backend() == 'tensorflow': warnings.warn('You are using the TensorFlow backend, yet you ' 'are using the Theano ' 'image data format convention ' '(`image_data_format="channels_first"`). ' 'For best performance, set ' '`image_data_format="channels_last"` in ' 'your Keras config ' 'at ~/.keras/keras.json.') if include_top: weights_path = get_file( 'inception-v4_weights_tf_dim_ordering_tf_kernels.h5', WEIGHTS_PATH, cache_subdir='models', md5_hash='9fe79d77f793fe874470d84ca6ba4a3b') else: weights_path = get_file( 'inception-v4_weights_tf_dim_ordering_tf_kernels_notop.h5', WEIGHTS_PATH_NO_TOP, cache_subdir='models', md5_hash='9296b46b5971573064d12e4669110969') model.load_weights(weights_path, by_name=True) return model def create_model(num_classes=1001, dropout_prob=0.2, weights=None, include_top=True): return inception_v4(num_classes, dropout_prob, weights, include_top)