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| # -*- coding: utf-8 -*- | ||
| '''Inception V3 model for Keras. | ||
| Note that the ImageNet weights provided are from a model that had not fully converged. | ||
| Inception v3 should be able to reach 6.9% top-5 error, but our model | ||
| only gets to 7.8% (same as a fully-converged ResNet 50). | ||
| For comparison, VGG16 only gets to 9.9%, quite a bit worse. | ||
| Also, do note that the input image format for this model is different than for | ||
| other models (299x299 instead of 224x224), and that the input preprocessing function | ||
| is also different. | ||
| # Reference: | ||
| - [Rethinking the Inception Architecture for Computer Vision](http://arxiv.org/abs/1512.00567) | ||
| ''' | ||
| from __future__ import print_function | ||
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| import numpy as np | ||
| import warnings | ||
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| from keras.models import Model | ||
| from keras.layers import Flatten, Dense, Input, BatchNormalization, merge | ||
| from keras.layers import Convolution2D, MaxPooling2D, AveragePooling2D | ||
| from keras.preprocessing import image | ||
| from keras.utils.layer_utils import convert_all_kernels_in_model | ||
| from keras.utils.data_utils import get_file | ||
| from keras import backend as K | ||
| from imagenet_utils import decode_predictions | ||
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| TH_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/inception_v3_weights_th_dim_ordering_th_kernels.h5' | ||
| TF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/inception_v3_weights_tf_dim_ordering_tf_kernels.h5' | ||
| TH_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/inception_v3_weights_th_dim_ordering_th_kernels_notop.h5' | ||
| TF_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5' | ||
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| def conv2d_bn(x, nb_filter, nb_row, nb_col, | ||
| border_mode='same', subsample=(1, 1), | ||
| name=None): | ||
| '''Utility function to apply conv + BN. | ||
| ''' | ||
| if name is not None: | ||
| bn_name = name + '_bn' | ||
| conv_name = name + '_conv' | ||
| else: | ||
| bn_name = None | ||
| conv_name = None | ||
| if K.image_dim_ordering() == 'th': | ||
| bn_axis = 1 | ||
| else: | ||
| bn_axis = 3 | ||
| x = Convolution2D(nb_filter, nb_row, nb_col, | ||
| subsample=subsample, | ||
| activation='relu', | ||
| border_mode=border_mode, | ||
| name=conv_name)(x) | ||
| x = BatchNormalization(axis=bn_axis, name=bn_name)(x) | ||
| return x | ||
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| def InceptionV3(include_top=True, weights='imagenet', | ||
| input_tensor=None): | ||
| '''Instantiate the Inception v3 architecture, | ||
| optionally loading weights pre-trained | ||
| on ImageNet. Note that when using TensorFlow, | ||
| for best performance you should set | ||
| `image_dim_ordering="tf"` in your Keras config | ||
| at ~/.keras/keras.json. | ||
| The model and the weights are compatible with both | ||
| TensorFlow and Theano. The dimension ordering | ||
| convention used by the model is the one | ||
| specified in your Keras config file. | ||
| Note that the default input image size for this model is 299x299. | ||
| # Arguments | ||
| include_top: whether to include the 3 fully-connected | ||
| layers at the top of the network. | ||
| weights: one of `None` (random initialization) | ||
| or "imagenet" (pre-training on ImageNet). | ||
| input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) | ||
| to use as image input for the model. | ||
| # Returns | ||
| A Keras model instance. | ||
| ''' | ||
| if weights not in {'imagenet', None}: | ||
| raise ValueError('The `weights` argument should be either ' | ||
| '`None` (random initialization) or `imagenet` ' | ||
| '(pre-training on ImageNet).') | ||
| # Determine proper input shape | ||
| if K.image_dim_ordering() == 'th': | ||
| if include_top: | ||
| input_shape = (3, 299, 299) | ||
| else: | ||
| input_shape = (3, None, None) | ||
| else: | ||
| if include_top: | ||
| input_shape = (299, 299, 3) | ||
| else: | ||
| input_shape = (None, None, 3) | ||
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| if input_tensor is None: | ||
| img_input = Input(shape=input_shape) | ||
| else: | ||
| if not K.is_keras_tensor(input_tensor): | ||
| img_input = Input(tensor=input_tensor) | ||
| else: | ||
| img_input = input_tensor | ||
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| if K.image_dim_ordering() == 'th': | ||
| channel_axis = 1 | ||
| else: | ||
| channel_axis = 3 | ||
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| x = conv2d_bn(img_input, 32, 3, 3, subsample=(2, 2), border_mode='valid') | ||
| x = conv2d_bn(x, 32, 3, 3, border_mode='valid') | ||
| x = conv2d_bn(x, 64, 3, 3) | ||
| x = MaxPooling2D((3, 3), strides=(2, 2))(x) | ||
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| x = conv2d_bn(x, 80, 1, 1, border_mode='valid') | ||
| x = conv2d_bn(x, 192, 3, 3, border_mode='valid') | ||
| x = MaxPooling2D((3, 3), strides=(2, 2))(x) | ||
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| # mixed: 35 x 35 x 256 | ||
| for i in range(3): | ||
| branch1x1 = conv2d_bn(x, 64, 1, 1) | ||
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| branch5x5 = conv2d_bn(x, 48, 1, 1) | ||
| branch5x5 = conv2d_bn(branch5x5, 64, 5, 5) | ||
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| branch3x3dbl = conv2d_bn(x, 64, 1, 1) | ||
| branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) | ||
| branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) | ||
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| branch_pool = AveragePooling2D( | ||
| (3, 3), strides=(1, 1), border_mode='same')(x) | ||
| branch_pool = conv2d_bn(branch_pool, 32, 1, 1) | ||
| x = merge([branch1x1, branch5x5, branch3x3dbl, branch_pool], | ||
| mode='concat', concat_axis=channel_axis) | ||
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| # mixed3: 17 x 17 x 768 | ||
| branch3x3 = conv2d_bn(x, 384, 3, 3, subsample=(2, 2), border_mode='valid') | ||
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| branch3x3dbl = conv2d_bn(x, 64, 1, 1) | ||
| branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) | ||
| branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3, | ||
| subsample=(2, 2), border_mode='valid') | ||
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| branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x) | ||
| x = merge([branch3x3, branch3x3dbl, branch_pool], | ||
| mode='concat', concat_axis=channel_axis) | ||
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| # mixed4: 17 x 17 x 768 | ||
| branch1x1 = conv2d_bn(x, 192, 1, 1) | ||
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| branch7x7 = conv2d_bn(x, 128, 1, 1) | ||
| branch7x7 = conv2d_bn(branch7x7, 128, 1, 7) | ||
| branch7x7 = conv2d_bn(branch7x7, 192, 7, 1) | ||
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| branch7x7dbl = conv2d_bn(x, 128, 1, 1) | ||
| branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1) | ||
| branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 1, 7) | ||
| branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1) | ||
| branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) | ||
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| branch_pool = AveragePooling2D((3, 3), strides=(1, 1), border_mode='same')(x) | ||
| branch_pool = conv2d_bn(branch_pool, 192, 1, 1) | ||
| x = merge([branch1x1, branch7x7, branch7x7dbl, branch_pool], | ||
| mode='concat', concat_axis=channel_axis) | ||
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| # mixed5: 17 x 17 x 768 | ||
| for _ in range(2): | ||
| branch1x1 = conv2d_bn(x, 192, 1, 1) | ||
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| branch7x7 = conv2d_bn(x, 160, 1, 1) | ||
| branch7x7 = conv2d_bn(branch7x7, 160, 1, 7) | ||
| branch7x7 = conv2d_bn(branch7x7, 192, 7, 1) | ||
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| branch7x7dbl = conv2d_bn(x, 160, 1, 1) | ||
| branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1) | ||
| branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 1, 7) | ||
| branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1) | ||
| branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) | ||
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| branch_pool = AveragePooling2D( | ||
| (3, 3), strides=(1, 1), border_mode='same')(x) | ||
| branch_pool = conv2d_bn(branch_pool, 192, 1, 1) | ||
| x = merge([branch1x1, branch7x7, branch7x7dbl, branch_pool], | ||
| mode='concat', concat_axis=channel_axis) | ||
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| # mixed7: 17 x 17 x 768 | ||
| branch1x1 = conv2d_bn(x, 192, 1, 1) | ||
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| branch7x7 = conv2d_bn(x, 192, 1, 1) | ||
| branch7x7 = conv2d_bn(branch7x7, 192, 1, 7) | ||
| branch7x7 = conv2d_bn(branch7x7, 192, 7, 1) | ||
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| branch7x7dbl = conv2d_bn(x, 160, 1, 1) | ||
| branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1) | ||
| branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) | ||
| branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1) | ||
| branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) | ||
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| branch_pool = AveragePooling2D((3, 3), strides=(1, 1), border_mode='same')(x) | ||
| branch_pool = conv2d_bn(branch_pool, 192, 1, 1) | ||
| x = merge([branch1x1, branch7x7, branch7x7dbl, branch_pool], | ||
| mode='concat', concat_axis=channel_axis) | ||
|
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| # mixed8: 8 x 8 x 1280 | ||
| branch3x3 = conv2d_bn(x, 192, 1, 1) | ||
| branch3x3 = conv2d_bn(branch3x3, 320, 3, 3, | ||
| subsample=(2, 2), border_mode='valid') | ||
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| branch7x7x3 = conv2d_bn(x, 192, 1, 1) | ||
| branch7x7x3 = conv2d_bn(branch7x7x3, 192, 1, 7) | ||
| branch7x7x3 = conv2d_bn(branch7x7x3, 192, 7, 1) | ||
| branch7x7x3 = conv2d_bn(branch7x7x3, 192, 3, 3, | ||
| subsample=(2, 2), border_mode='valid') | ||
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| branch_pool = AveragePooling2D((3, 3), strides=(2, 2))(x) | ||
| x = merge([branch3x3, branch7x7x3, branch_pool], | ||
| mode='concat', concat_axis=channel_axis) | ||
|
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| # mixed9: 8 x 8 x 2048 | ||
| for _ in range(2): | ||
| branch1x1 = conv2d_bn(x, 320, 1, 1) | ||
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| branch3x3 = conv2d_bn(x, 384, 1, 1) | ||
| branch3x3_1 = conv2d_bn(branch3x3, 384, 1, 3) | ||
| branch3x3_2 = conv2d_bn(branch3x3, 384, 3, 1) | ||
| branch3x3 = merge([branch3x3_1, branch3x3_2], | ||
| mode='concat', concat_axis=channel_axis) | ||
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| branch3x3dbl = conv2d_bn(x, 448, 1, 1) | ||
| branch3x3dbl = conv2d_bn(branch3x3dbl, 384, 3, 3) | ||
| branch3x3dbl_1 = conv2d_bn(branch3x3dbl, 384, 1, 3) | ||
| branch3x3dbl_2 = conv2d_bn(branch3x3dbl, 384, 3, 1) | ||
| branch3x3dbl = merge([branch3x3dbl_1, branch3x3dbl_2], | ||
| mode='concat', concat_axis=channel_axis) | ||
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| branch_pool = AveragePooling2D( | ||
| (3, 3), strides=(1, 1), border_mode='same')(x) | ||
| branch_pool = conv2d_bn(branch_pool, 192, 1, 1) | ||
| x = merge([branch1x1, branch3x3, branch3x3dbl, branch_pool], | ||
| mode='concat', concat_axis=channel_axis) | ||
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| if include_top: | ||
| # Classification block | ||
| x = AveragePooling2D((8, 8), strides=(8, 8), name='avg_pool')(x) | ||
| x = Flatten(name='flatten')(x) | ||
| x = Dense(1000, activation='softmax', name='predictions')(x) | ||
|
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| # Create model | ||
| model = Model(img_input, x) | ||
|
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| # load weights | ||
| if weights == 'imagenet': | ||
| print('K.image_dim_ordering:', K.image_dim_ordering()) | ||
| if K.image_dim_ordering() == 'th': | ||
| if include_top: | ||
| weights_path = get_file('inception_v3_weights_th_dim_ordering_th_kernels.h5', | ||
| TH_WEIGHTS_PATH, | ||
| cache_subdir='models', | ||
| md5_hash='b3baf3070cc4bf476d43a2ea61b0ca5f') | ||
| else: | ||
| weights_path = get_file('inception_v3_weights_th_dim_ordering_th_kernels_notop.h5', | ||
| TH_WEIGHTS_PATH_NO_TOP, | ||
| cache_subdir='models', | ||
| md5_hash='79aaa90ab4372b4593ba3df64e142f05') | ||
| model.load_weights(weights_path) | ||
| if K.backend() == 'tensorflow': | ||
| warnings.warn('You are using the TensorFlow backend, yet you ' | ||
| 'are using the Theano ' | ||
| 'image dimension ordering convention ' | ||
| '(`image_dim_ordering="th"`). ' | ||
| 'For best performance, set ' | ||
| '`image_dim_ordering="tf"` in ' | ||
| 'your Keras config ' | ||
| 'at ~/.keras/keras.json.') | ||
| convert_all_kernels_in_model(model) | ||
| else: | ||
| if include_top: | ||
| weights_path = get_file('inception_v3_weights_tf_dim_ordering_tf_kernels.h5', | ||
| TF_WEIGHTS_PATH, | ||
| cache_subdir='models', | ||
| md5_hash='fe114b3ff2ea4bf891e9353d1bbfb32f') | ||
| else: | ||
| weights_path = get_file('inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5', | ||
| TF_WEIGHTS_PATH_NO_TOP, | ||
| cache_subdir='models', | ||
| md5_hash='2f3609166de1d967d1a481094754f691') | ||
| model.load_weights(weights_path) | ||
| if K.backend() == 'theano': | ||
| convert_all_kernels_in_model(model) | ||
| return model | ||
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| def preprocess_input(x): | ||
| x /= 255. | ||
| x -= 0.5 | ||
| x *= 2. | ||
| return x | ||
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| if __name__ == '__main__': | ||
| model = InceptionV3(include_top=True, weights='imagenet') | ||
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| img_path = 'elephant.jpg' | ||
| img = image.load_img(img_path, target_size=(299, 299)) | ||
| x = image.img_to_array(img) | ||
| x = np.expand_dims(x, axis=0) | ||
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| x = preprocess_input(x) | ||
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| preds = model.predict(x) | ||
| print('Predicted:', decode_predictions(preds)) |
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