keras ResNet50源码 作者：马育民 • 2020-05-12 11:39 • 阅读：10249 # 源码网址 [点击](https://github.com/keras-team/keras-applications/blob/master/keras_applications/resnet_common.py "点击") # 分析代码 参见 https://www.malaoshi.top/show_1EF5LiLIgrnW.html # 简化代码 ``` import os import numpy as np import tensorflow as tf from tensorflow.keras import layers,models BASE_WEIGHTS_PATH = ( 'https://github.com/keras-team/keras-applications/' 'releases/download/resnet/') WEIGHTS_HASHES = { 'resnet50': ('2cb95161c43110f7111970584f804107', '4d473c1dd8becc155b73f8504c6f6626'), 'resnet101': ('f1aeb4b969a6efcfb50fad2f0c20cfc5', '88cf7a10940856eca736dc7b7e228a21'), 'resnet152': ('100835be76be38e30d865e96f2aaae62', 'ee4c566cf9a93f14d82f913c2dc6dd0c'), 'resnet50v2': ('3ef43a0b657b3be2300d5770ece849e0', 'fac2f116257151a9d068a22e544a4917'), 'resnet101v2': ('6343647c601c52e1368623803854d971', 'c0ed64b8031c3730f411d2eb4eea35b5'), 'resnet152v2': ('a49b44d1979771252814e80f8ec446f9', 'ed17cf2e0169df9d443503ef94b23b33'), 'resnext50': ('67a5b30d522ed92f75a1f16eef299d1a', '62527c363bdd9ec598bed41947b379fc'), 'resnext101': ('34fb605428fcc7aa4d62f44404c11509', '0f678c91647380debd923963594981b3') } def block1(x, filters, kernel_size=3, stride=1, conv_shortcut=True, name=None): """A residual block. # Arguments x: input tensor. filters: integer, filters of the bottleneck layer. kernel_size: default 3, kernel size of the bottleneck layer. stride: default 1, stride of the first layer. conv_shortcut: default True, use convolution shortcut if True, otherwise identity shortcut. name: string, block label. # Returns Output tensor for the residual block. """ if conv_shortcut is True: shortcut = layers.Conv2D(4 * filters, 1, strides=stride, name=name + '_0_conv')(x) shortcut = layers.BatchNormalization( epsilon=1.001e-5, name=name + '_0_bn')(shortcut) else: shortcut = x x = layers.Conv2D(filters, 1, strides=stride, name=name + '_1_conv')(x) x = layers.BatchNormalization( epsilon=1.001e-5, name=name + '_1_bn')(x) x = layers.Activation('relu', name=name + '_1_relu')(x) x = layers.Conv2D(filters, kernel_size, padding='SAME', name=name + '_2_conv')(x) x = layers.BatchNormalization( epsilon=1.001e-5, name=name + '_2_bn')(x) x = layers.Activation('relu', name=name + '_2_relu')(x) x = layers.Conv2D(4 * filters, 1, name=name + '_3_conv')(x) x = layers.BatchNormalization( epsilon=1.001e-5, name=name + '_3_bn')(x) x = layers.Add(name=name + '_add')([shortcut, x]) x = layers.Activation('relu', name=name + '_out')(x) return x def stack1(x, filters, blocks, stride1=2, name=None): """A set of stacked residual blocks. # Arguments x: input tensor. filters: integer, filters of the bottleneck layer in a block. blocks: integer, blocks in the stacked blocks. stride1: default 2, stride of the first layer in the first block. name: string, stack label. # Returns Output tensor for the stacked blocks. """ x = block1(x, filters, stride=stride1, name=name + '_block1') for i in range(2, blocks+1): x = block1(x, filters, conv_shortcut=False, name=name + '_block' + str(i)) return x def ResNet(stack_fn, model_name='resnet', include_top=True, weights='imagenet', input_shape=None, pooling=None, classes=1000): """Instantiates the ResNet, ResNetV2, and ResNeXt architecture. Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your Keras config at `~/.keras/keras.json`. # Arguments stack_fn: a function that returns output tensor for the stacked residual blocks. model_name: string, model name. include_top: whether to include the fully-connected layer at the top of the network. weights: one of `None` (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. input_shape: optional shape tuple, only to be specified if `include_top` is False (otherwise the input shape has to be `(224, 224, 3)` (with `channels_last` data format) or `(3, 224, 224)` (with `channels_first` data format). It should have exactly 3 inputs channels. pooling: optional pooling mode for feature extraction when `include_top` is `False`. - `None` means that the output of the model will be the 4D tensor output of the last convolutional layer. - `avg` means that global average pooling will be applied to the output of the last convolutional layer, and thus the output of the model will be a 2D tensor. - `max` means that global max pooling will be applied. classes: optional number of classes to classify images into, only to be specified if `include_top` is True, and if no `weights` argument is specified. # Returns A Keras model instance. # Raises ValueError: in case of invalid argument for `weights`, or invalid input shape. """ img_input = layers.Input(shape=input_shape) print("img_input:",img_input.shape) # (None, 224, 224, 3) x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)), name='conv1_pad')(img_input) temp = x print("x:",x.shape) # (None, 230, 230, 3) x = layers.Conv2D(64, 7, strides=2, name='conv1_conv')(x) print("x:",x.shape) # (None, 112, 112, 64) x = layers.BatchNormalization( epsilon=1.001e-5, name='conv1_bn')(x) x = layers.Activation('relu', name='conv1_relu')(x) x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name='pool1_pad')(x) print("x:",x.shape) # (None, 114, 114, 64) x = layers.MaxPooling2D(3, strides=2, name='pool1_pool')(x) print("x:",x.shape) x = stack_fn(x) print("x:",x.shape) if include_top: x = layers.GlobalAveragePooling2D(name='avg_pool')(x) x = layers.Dense(classes, activation='softmax', name='probs')(x) else: if pooling == 'avg': x = layers.GlobalAveragePooling2D(name='avg_pool')(x) elif pooling == 'max': x = layers.GlobalMaxPooling2D(name='max_pool')(x) # Create model. model = models.Model(img_input, [temp,x], name=model_name) # Load weights. if (weights == 'imagenet') and (model_name in WEIGHTS_HASHES): if include_top: file_name = model_name + '_weights_tf_dim_ordering_tf_kernels.h5' file_hash = WEIGHTS_HASHES[model_name][0] else: file_name = model_name + '_weights_tf_dim_ordering_tf_kernels_notop.h5' file_hash = WEIGHTS_HASHES[model_name][1] weights_path = tf.keras.utils.get_file(file_name, BASE_WEIGHTS_PATH + file_name, cache_subdir='models', file_hash=file_hash) by_name = True if 'resnext' in model_name else False model.load_weights(weights_path, by_name=by_name) elif weights is not None: model.load_weights(weights) return model def ResNet50(include_top=True, weights='imagenet', input_shape=None, pooling=None, classes=1000): def stack_fn(x): x = stack1(x, 64, 3, stride1=1, name='conv2') x = stack1(x, 128, 4, name='conv3') x = stack1(x, 256, 6, name='conv4') x = stack1(x, 512, 3, name='conv5') return x return ResNet(stack_fn, 'resnet50', include_top, weights, input_shape, pooling, classes) def ResNet101(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): def stack_fn(x): x = stack1(x, 64, 3, stride1=1, name='conv2') x = stack1(x, 128, 4, name='conv3') x = stack1(x, 256, 23, name='conv4') x = stack1(x, 512, 3, name='conv5') return x return ResNet(stack_fn, 'resnet101', include_top, weights, input_shape, pooling, classes) def ResNet152(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): def stack_fn(x): x = stack1(x, 64, 3, stride1=1, name='conv2') x = stack1(x, 128, 8, name='conv3') x = stack1(x, 256, 36, name='conv4') x = stack1(x, 512, 3, name='conv5') return x return ResNet(stack_fn, 'resnet152', include_top, weights, input_shape, pooling, classes) ``` 原文出处：http://malaoshi.top/show_1EF5VZ5GbCLo.html