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from __future__ import print_function |
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import imageio |
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from PIL import Image |
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import numpy as np |
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import keras |
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from keras.layers import Input, Dense, Conv2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, Concatenate, Reshape, Activation |
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from keras.models import Model |
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from keras.regularizers import l2 |
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from keras.optimizers import SGD |
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from pool_helper import PoolHelper |
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from lrn import LRN |
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if keras.backend.backend() == 'tensorflow': |
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from keras import backend as K |
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import tensorflow as tf |
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from keras.utils.conv_utils import convert_kernel |
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def create_googlenet(weights_path=None): |
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# creates GoogLeNet a.k.a. Inception v1 (Szegedy, 2015) |
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input = Input(shape=(3, 224, 224)) |
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input_pad = ZeroPadding2D(padding=(3, 3))(input) |
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conv1_7x7_s2 = Conv2D(64, (7,7), strides=(2,2), padding='valid', activation='relu', name='conv1/7x7_s2', kernel_regularizer=l2(0.0002))(input_pad) |
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conv1_zero_pad = ZeroPadding2D(padding=(1, 1))(conv1_7x7_s2) |
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pool1_helper = PoolHelper()(conv1_zero_pad) |
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pool1_3x3_s2 = MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='valid', name='pool1/3x3_s2')(pool1_helper) |
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pool1_norm1 = LRN(name='pool1/norm1')(pool1_3x3_s2) |
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conv2_3x3_reduce = Conv2D(64, (1,1), padding='same', activation='relu', name='conv2/3x3_reduce', kernel_regularizer=l2(0.0002))(pool1_norm1) |
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conv2_3x3 = Conv2D(192, (3,3), padding='same', activation='relu', name='conv2/3x3', kernel_regularizer=l2(0.0002))(conv2_3x3_reduce) |
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conv2_norm2 = LRN(name='conv2/norm2')(conv2_3x3) |
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conv2_zero_pad = ZeroPadding2D(padding=(1, 1))(conv2_norm2) |
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pool2_helper = PoolHelper()(conv2_zero_pad) |
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pool2_3x3_s2 = MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='valid', name='pool2/3x3_s2')(pool2_helper) |
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inception_3a_1x1 = Conv2D(64, (1,1), padding='same', activation='relu', name='inception_3a/1x1', kernel_regularizer=l2(0.0002))(pool2_3x3_s2) |
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inception_3a_3x3_reduce = Conv2D(96, (1,1), padding='same', activation='relu', name='inception_3a/3x3_reduce', kernel_regularizer=l2(0.0002))(pool2_3x3_s2) |
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inception_3a_3x3_pad = ZeroPadding2D(padding=(1, 1))(inception_3a_3x3_reduce) |
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inception_3a_3x3 = Conv2D(128, (3,3), padding='valid', activation='relu', name='inception_3a/3x3', kernel_regularizer=l2(0.0002))(inception_3a_3x3_pad) |
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inception_3a_5x5_reduce = Conv2D(16, (1,1), padding='same', activation='relu', name='inception_3a/5x5_reduce', kernel_regularizer=l2(0.0002))(pool2_3x3_s2) |
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inception_3a_5x5_pad = ZeroPadding2D(padding=(2, 2))(inception_3a_5x5_reduce) |
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inception_3a_5x5 = Conv2D(32, (5,5), padding='valid', activation='relu', name='inception_3a/5x5', kernel_regularizer=l2(0.0002))(inception_3a_5x5_pad) |
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inception_3a_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_3a/pool')(pool2_3x3_s2) |
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inception_3a_pool_proj = Conv2D(32, (1,1), padding='same', activation='relu', name='inception_3a/pool_proj', kernel_regularizer=l2(0.0002))(inception_3a_pool) |
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inception_3a_output = Concatenate(axis=1, name='inception_3a/output')([inception_3a_1x1,inception_3a_3x3,inception_3a_5x5,inception_3a_pool_proj]) |
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inception_3b_1x1 = Conv2D(128, (1,1), padding='same', activation='relu', name='inception_3b/1x1', kernel_regularizer=l2(0.0002))(inception_3a_output) |
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inception_3b_3x3_reduce = Conv2D(128, (1,1), padding='same', activation='relu', name='inception_3b/3x3_reduce', kernel_regularizer=l2(0.0002))(inception_3a_output) |
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inception_3b_3x3_pad = ZeroPadding2D(padding=(1, 1))(inception_3b_3x3_reduce) |
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inception_3b_3x3 = Conv2D(192, (3,3), padding='valid', activation='relu', name='inception_3b/3x3', kernel_regularizer=l2(0.0002))(inception_3b_3x3_pad) |
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inception_3b_5x5_reduce = Conv2D(32, (1,1), padding='same', activation='relu', name='inception_3b/5x5_reduce', kernel_regularizer=l2(0.0002))(inception_3a_output) |
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inception_3b_5x5_pad = ZeroPadding2D(padding=(2, 2))(inception_3b_5x5_reduce) |
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inception_3b_5x5 = Conv2D(96, (5,5), padding='valid', activation='relu', name='inception_3b/5x5', kernel_regularizer=l2(0.0002))(inception_3b_5x5_pad) |
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inception_3b_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_3b/pool')(inception_3a_output) |
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inception_3b_pool_proj = Conv2D(64, (1,1), padding='same', activation='relu', name='inception_3b/pool_proj', kernel_regularizer=l2(0.0002))(inception_3b_pool) |
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inception_3b_output = Concatenate(axis=1, name='inception_3b/output')([inception_3b_1x1,inception_3b_3x3,inception_3b_5x5,inception_3b_pool_proj]) |
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inception_3b_output_zero_pad = ZeroPadding2D(padding=(1, 1))(inception_3b_output) |
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pool3_helper = PoolHelper()(inception_3b_output_zero_pad) |
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pool3_3x3_s2 = MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='valid', name='pool3/3x3_s2')(pool3_helper) |
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inception_4a_1x1 = Conv2D(192, (1,1), padding='same', activation='relu', name='inception_4a/1x1', kernel_regularizer=l2(0.0002))(pool3_3x3_s2) |
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inception_4a_3x3_reduce = Conv2D(96, (1,1), padding='same', activation='relu', name='inception_4a/3x3_reduce', kernel_regularizer=l2(0.0002))(pool3_3x3_s2) |
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inception_4a_3x3_pad = ZeroPadding2D(padding=(1, 1))(inception_4a_3x3_reduce) |
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inception_4a_3x3 = Conv2D(208, (3,3), padding='valid', activation='relu', name='inception_4a/3x3' ,kernel_regularizer=l2(0.0002))(inception_4a_3x3_pad) |
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inception_4a_5x5_reduce = Conv2D(16, (1,1), padding='same', activation='relu', name='inception_4a/5x5_reduce', kernel_regularizer=l2(0.0002))(pool3_3x3_s2) |
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inception_4a_5x5_pad = ZeroPadding2D(padding=(2, 2))(inception_4a_5x5_reduce) |
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inception_4a_5x5 = Conv2D(48, (5,5), padding='valid', activation='relu', name='inception_4a/5x5', kernel_regularizer=l2(0.0002))(inception_4a_5x5_pad) |
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inception_4a_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_4a/pool')(pool3_3x3_s2) |
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inception_4a_pool_proj = Conv2D(64, (1,1), padding='same', activation='relu', name='inception_4a/pool_proj', kernel_regularizer=l2(0.0002))(inception_4a_pool) |
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inception_4a_output = Concatenate(axis=1, name='inception_4a/output')([inception_4a_1x1,inception_4a_3x3,inception_4a_5x5,inception_4a_pool_proj]) |
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loss1_ave_pool = AveragePooling2D(pool_size=(5,5), strides=(3,3), name='loss1/ave_pool')(inception_4a_output) |
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loss1_conv = Conv2D(128, (1,1), padding='same', activation='relu', name='loss1/conv', kernel_regularizer=l2(0.0002))(loss1_ave_pool) |
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loss1_flat = Flatten()(loss1_conv) |
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loss1_fc = Dense(1024, activation='relu', name='loss1/fc', kernel_regularizer=l2(0.0002))(loss1_flat) |
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loss1_drop_fc = Dropout(rate=0.7)(loss1_fc) |
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loss1_classifier = Dense(1000, name='loss1/classifier', kernel_regularizer=l2(0.0002))(loss1_drop_fc) |
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loss1_classifier_act = Activation('softmax')(loss1_classifier) |
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inception_4b_1x1 = Conv2D(160, (1,1), padding='same', activation='relu', name='inception_4b/1x1', kernel_regularizer=l2(0.0002))(inception_4a_output) |
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inception_4b_3x3_reduce = Conv2D(112, (1,1), padding='same', activation='relu', name='inception_4b/3x3_reduce', kernel_regularizer=l2(0.0002))(inception_4a_output) |
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inception_4b_3x3_pad = ZeroPadding2D(padding=(1, 1))(inception_4b_3x3_reduce) |
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inception_4b_3x3 = Conv2D(224, (3,3), padding='valid', activation='relu', name='inception_4b/3x3', kernel_regularizer=l2(0.0002))(inception_4b_3x3_pad) |
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inception_4b_5x5_reduce = Conv2D(24, (1,1), padding='same', activation='relu', name='inception_4b/5x5_reduce', kernel_regularizer=l2(0.0002))(inception_4a_output) |
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inception_4b_5x5_pad = ZeroPadding2D(padding=(2, 2))(inception_4b_5x5_reduce) |
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inception_4b_5x5 = Conv2D(64, (5,5), padding='valid', activation='relu', name='inception_4b/5x5', kernel_regularizer=l2(0.0002))(inception_4b_5x5_pad) |
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inception_4b_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_4b/pool')(inception_4a_output) |
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inception_4b_pool_proj = Conv2D(64, (1,1), padding='same', activation='relu', name='inception_4b/pool_proj', kernel_regularizer=l2(0.0002))(inception_4b_pool) |
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inception_4b_output = Concatenate(axis=1, name='inception_4b/output')([inception_4b_1x1,inception_4b_3x3,inception_4b_5x5,inception_4b_pool_proj]) |
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inception_4c_1x1 = Conv2D(128, (1,1), padding='same', activation='relu', name='inception_4c/1x1', kernel_regularizer=l2(0.0002))(inception_4b_output) |
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inception_4c_3x3_reduce = Conv2D(128, (1,1), padding='same', activation='relu', name='inception_4c/3x3_reduce', kernel_regularizer=l2(0.0002))(inception_4b_output) |
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inception_4c_3x3_pad = ZeroPadding2D(padding=(1, 1))(inception_4c_3x3_reduce) |
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inception_4c_3x3 = Conv2D(256, (3,3), padding='valid', activation='relu', name='inception_4c/3x3', kernel_regularizer=l2(0.0002))(inception_4c_3x3_pad) |
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inception_4c_5x5_reduce = Conv2D(24, (1,1), padding='same', activation='relu', name='inception_4c/5x5_reduce', kernel_regularizer=l2(0.0002))(inception_4b_output) |
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inception_4c_5x5_pad = ZeroPadding2D(padding=(2, 2))(inception_4c_5x5_reduce) |
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inception_4c_5x5 = Conv2D(64, (5,5), padding='valid', activation='relu', name='inception_4c/5x5', kernel_regularizer=l2(0.0002))(inception_4c_5x5_pad) |
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inception_4c_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_4c/pool')(inception_4b_output) |
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inception_4c_pool_proj = Conv2D(64, (1,1), padding='same', activation='relu', name='inception_4c/pool_proj', kernel_regularizer=l2(0.0002))(inception_4c_pool) |
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inception_4c_output = Concatenate(axis=1, name='inception_4c/output')([inception_4c_1x1,inception_4c_3x3,inception_4c_5x5,inception_4c_pool_proj]) |
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inception_4d_1x1 = Conv2D(112, (1,1), padding='same', activation='relu', name='inception_4d/1x1', kernel_regularizer=l2(0.0002))(inception_4c_output) |
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inception_4d_3x3_reduce = Conv2D(144, (1,1), padding='same', activation='relu', name='inception_4d/3x3_reduce', kernel_regularizer=l2(0.0002))(inception_4c_output) |
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inception_4d_3x3_pad = ZeroPadding2D(padding=(1, 1))(inception_4d_3x3_reduce) |
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inception_4d_3x3 = Conv2D(288, (3,3), padding='valid', activation='relu', name='inception_4d/3x3', kernel_regularizer=l2(0.0002))(inception_4d_3x3_pad) |
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inception_4d_5x5_reduce = Conv2D(32, (1,1), padding='same', activation='relu', name='inception_4d/5x5_reduce', kernel_regularizer=l2(0.0002))(inception_4c_output) |
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inception_4d_5x5_pad = ZeroPadding2D(padding=(2, 2))(inception_4d_5x5_reduce) |
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inception_4d_5x5 = Conv2D(64, (5,5), padding='valid', activation='relu', name='inception_4d/5x5', kernel_regularizer=l2(0.0002))(inception_4d_5x5_pad) |
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inception_4d_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_4d/pool')(inception_4c_output) |
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inception_4d_pool_proj = Conv2D(64, (1,1), padding='same', activation='relu', name='inception_4d/pool_proj', kernel_regularizer=l2(0.0002))(inception_4d_pool) |
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inception_4d_output = Concatenate(axis=1, name='inception_4d/output')([inception_4d_1x1,inception_4d_3x3,inception_4d_5x5,inception_4d_pool_proj]) |
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loss2_ave_pool = AveragePooling2D(pool_size=(5,5), strides=(3,3), name='loss2/ave_pool')(inception_4d_output) |
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loss2_conv = Conv2D(128, (1,1), padding='same', activation='relu', name='loss2/conv', kernel_regularizer=l2(0.0002))(loss2_ave_pool) |
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loss2_flat = Flatten()(loss2_conv) |
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loss2_fc = Dense(1024, activation='relu', name='loss2/fc', kernel_regularizer=l2(0.0002))(loss2_flat) |
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loss2_drop_fc = Dropout(rate=0.7)(loss2_fc) |
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loss2_classifier = Dense(1000, name='loss2/classifier', kernel_regularizer=l2(0.0002))(loss2_drop_fc) |
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loss2_classifier_act = Activation('softmax')(loss2_classifier) |
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inception_4e_1x1 = Conv2D(256, (1,1), padding='same', activation='relu', name='inception_4e/1x1', kernel_regularizer=l2(0.0002))(inception_4d_output) |
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inception_4e_3x3_reduce = Conv2D(160, (1,1), padding='same', activation='relu', name='inception_4e/3x3_reduce', kernel_regularizer=l2(0.0002))(inception_4d_output) |
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inception_4e_3x3_pad = ZeroPadding2D(padding=(1, 1))(inception_4e_3x3_reduce) |
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inception_4e_3x3 = Conv2D(320, (3,3), padding='valid', activation='relu', name='inception_4e/3x3', kernel_regularizer=l2(0.0002))(inception_4e_3x3_pad) |
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inception_4e_5x5_reduce = Conv2D(32, (1,1), padding='same', activation='relu', name='inception_4e/5x5_reduce', kernel_regularizer=l2(0.0002))(inception_4d_output) |
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inception_4e_5x5_pad = ZeroPadding2D(padding=(2, 2))(inception_4e_5x5_reduce) |
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inception_4e_5x5 = Conv2D(128, (5,5), padding='valid', activation='relu', name='inception_4e/5x5', kernel_regularizer=l2(0.0002))(inception_4e_5x5_pad) |
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inception_4e_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_4e/pool')(inception_4d_output) |
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inception_4e_pool_proj = Conv2D(128, (1,1), padding='same', activation='relu', name='inception_4e/pool_proj', kernel_regularizer=l2(0.0002))(inception_4e_pool) |
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inception_4e_output = Concatenate(axis=1, name='inception_4e/output')([inception_4e_1x1,inception_4e_3x3,inception_4e_5x5,inception_4e_pool_proj]) |
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inception_4e_output_zero_pad = ZeroPadding2D(padding=(1, 1))(inception_4e_output) |
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pool4_helper = PoolHelper()(inception_4e_output_zero_pad) |
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pool4_3x3_s2 = MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='valid', name='pool4/3x3_s2')(pool4_helper) |
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inception_5a_1x1 = Conv2D(256, (1,1), padding='same', activation='relu', name='inception_5a/1x1', kernel_regularizer=l2(0.0002))(pool4_3x3_s2) |
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inception_5a_3x3_reduce = Conv2D(160, (1,1), padding='same', activation='relu', name='inception_5a/3x3_reduce', kernel_regularizer=l2(0.0002))(pool4_3x3_s2) |
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inception_5a_3x3_pad = ZeroPadding2D(padding=(1, 1))(inception_5a_3x3_reduce) |
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inception_5a_3x3 = Conv2D(320, (3,3), padding='valid', activation='relu', name='inception_5a/3x3', kernel_regularizer=l2(0.0002))(inception_5a_3x3_pad) |
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inception_5a_5x5_reduce = Conv2D(32, (1,1), padding='same', activation='relu', name='inception_5a/5x5_reduce', kernel_regularizer=l2(0.0002))(pool4_3x3_s2) |
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inception_5a_5x5_pad = ZeroPadding2D(padding=(2, 2))(inception_5a_5x5_reduce) |
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inception_5a_5x5 = Conv2D(128, (5,5), padding='valid', activation='relu', name='inception_5a/5x5', kernel_regularizer=l2(0.0002))(inception_5a_5x5_pad) |
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inception_5a_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_5a/pool')(pool4_3x3_s2) |
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inception_5a_pool_proj = Conv2D(128, (1,1), padding='same', activation='relu', name='inception_5a/pool_proj', kernel_regularizer=l2(0.0002))(inception_5a_pool) |
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inception_5a_output = Concatenate(axis=1, name='inception_5a/output')([inception_5a_1x1,inception_5a_3x3,inception_5a_5x5,inception_5a_pool_proj]) |
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inception_5b_1x1 = Conv2D(384, (1,1), padding='same', activation='relu', name='inception_5b/1x1', kernel_regularizer=l2(0.0002))(inception_5a_output) |
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inception_5b_3x3_reduce = Conv2D(192, (1,1), padding='same', activation='relu', name='inception_5b/3x3_reduce', kernel_regularizer=l2(0.0002))(inception_5a_output) |
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inception_5b_3x3_pad = ZeroPadding2D(padding=(1, 1))(inception_5b_3x3_reduce) |
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inception_5b_3x3 = Conv2D(384, (3,3), padding='valid', activation='relu', name='inception_5b/3x3', kernel_regularizer=l2(0.0002))(inception_5b_3x3_pad) |
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inception_5b_5x5_reduce = Conv2D(48, (1,1), padding='same', activation='relu', name='inception_5b/5x5_reduce', kernel_regularizer=l2(0.0002))(inception_5a_output) |
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inception_5b_5x5_pad = ZeroPadding2D(padding=(2, 2))(inception_5b_5x5_reduce) |
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inception_5b_5x5 = Conv2D(128, (5,5), padding='valid', activation='relu', name='inception_5b/5x5', kernel_regularizer=l2(0.0002))(inception_5b_5x5_pad) |
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inception_5b_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same', name='inception_5b/pool')(inception_5a_output) |
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inception_5b_pool_proj = Conv2D(128, (1,1), padding='same', activation='relu', name='inception_5b/pool_proj', kernel_regularizer=l2(0.0002))(inception_5b_pool) |
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inception_5b_output = Concatenate(axis=1, name='inception_5b/output')([inception_5b_1x1,inception_5b_3x3,inception_5b_5x5,inception_5b_pool_proj]) |
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pool5_7x7_s1 = AveragePooling2D(pool_size=(7,7), strides=(1,1), name='pool5/7x7_s2')(inception_5b_output) |
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loss3_flat = Flatten()(pool5_7x7_s1) |
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pool5_drop_7x7_s1 = Dropout(rate=0.4)(loss3_flat) |
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loss3_classifier = Dense(1000, name='loss3/classifier', kernel_regularizer=l2(0.0002))(pool5_drop_7x7_s1) |
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loss3_classifier_act = Activation('softmax', name='prob')(loss3_classifier) |
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googlenet = Model(inputs=input, outputs=[loss1_classifier_act,loss2_classifier_act,loss3_classifier_act]) |
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if weights_path: |
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googlenet.load_weights(weights_path) |
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if keras.backend.backend() == 'tensorflow': |
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# convert the convolutional kernels for tensorflow |
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ops = [] |
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for layer in googlenet.layers: |
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if layer.__class__.__name__ == 'Conv2D': |
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original_w = K.get_value(layer.kernel) |
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converted_w = convert_kernel(original_w) |
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ops.append(tf.assign(layer.kernel, converted_w).op) |
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K.get_session().run(ops) |
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return googlenet |
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if __name__ == "__main__": |
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img = imageio.imread('cat.jpg', pilmode='RGB') |
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img = np.array(Image.fromarray(img).resize((224, 224))).astype(np.float32) |
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img[:, :, 0] -= 123.68 |
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img[:, :, 1] -= 116.779 |
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img[:, :, 2] -= 103.939 |
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img[:,:,[0,1,2]] = img[:,:,[2,1,0]] |
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img = img.transpose((2, 0, 1)) |
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img = np.expand_dims(img, axis=0) |
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# Test pretrained model |
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model = create_googlenet('googlenet_weights.h5') |
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sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True) |
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model.compile(optimizer=sgd, loss='categorical_crossentropy') |
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out = model.predict(img) # note: the model has three outputs |
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labels = np.loadtxt('synset_words.txt', str, delimiter='\t') |
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predicted_label = np.argmax(out[2]) |
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predicted_class_name = labels[predicted_label] |
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print('Predicted Class: ', predicted_label, ', Class Name: ', predicted_class_name) |
@mikechen66
https://gist.github.com/joelouismarino/a2ede9ab3928f999575423b9887abd14#gistcomment-3451596