机器学习课程大作业 - 基于深度神经网络的图像分类任务.zip

from __future__ import division import six from keras.models import Model from keras.layers import ( Input, Activation, Dense, Flatten ) from keras.layers.convolutional import ( Conv2D, MaxPooling2D, AveragePooling2D ) from keras.layers.merge import add from keras.layers import concatenate from keras.layers.normalization import BatchNormalization from keras.regularizers import l2 from keras import backend as K from layers.SpatialPyramidPooling import SpatialPyramidPooling regularizer_rate = 0.01 def _bn_relu(input): """Helper to build a BN -> relu block """ norm = BatchNormalization(axis = CHANNEL_AXIS)(input) return Activation("relu")(norm) def _conv_bn_relu(**conv_params): """Helper to build a conv -> BN -> relu block """ filters = conv_params["filters"] kernel_size = conv_params["kernel_size"] strides = conv_params.setdefault("strides", (1, 1)) kernel_initializer = conv_params.setdefault("kernel_initializer", "he_normal") padding = conv_params.setdefault("padding", "same") kernel_regularizer = conv_params.setdefault("kernel_regularizer", l2(regularizer_rate)) def f(input): conv = Conv2D(filters = filters, kernel_size = kernel_size, strides = strides, padding = padding, kernel_initializer = kernel_initializer, kernel_regularizer = kernel_regularizer)(input) return _bn_relu(conv) return f def _bn_relu_conv(**conv_params): """Helper to build a BN -> relu -> conv block. This is an improved scheme proposed in http://arxiv.org/pdf/1603.05027v2.pdf """ filters = conv_params["filters"] kernel_size = conv_params["kernel_size"] strides = conv_params.setdefault("strides", (1, 1)) kernel_initializer = conv_params.setdefault("kernel_initializer", "he_normal") padding = conv_params.setdefault("padding", "same") kernel_regularizer = conv_params.setdefault("kernel_regularizer", l2(regularizer_rate)) def f(input): activation = _bn_relu(input) return Conv2D(filters = filters, kernel_size = kernel_size, strides = strides, padding = padding, kernel_initializer = kernel_initializer, kernel_regularizer = kernel_regularizer)(activation) return f def _shortcut(input, residual): """Adds a shortcut between input and residual block and merges them with "sum" """ # Expand channels of shortcut to match residual. # Stride appropriately to match residual (width, height) # Should be int if network architecture is correctly configured. input_shape = K.int_shape(input) residual_shape = K.int_shape(residual) stride_width = int(round(input_shape[ROW_AXIS] / residual_shape[ROW_AXIS])) stride_height = int(round(input_shape[COL_AXIS] / residual_shape[COL_AXIS])) equal_channels = input_shape[CHANNEL_AXIS] == residual_shape[CHANNEL_AXIS] shortcut = input # 1 X 1 conv if shape is different. Else identity. if stride_width > 1 or stride_height > 1 or not equal_channels: shortcut = Conv2D(filters = residual_shape[CHANNEL_AXIS], kernel_size = (1, 1), strides = (stride_width, stride_height), padding = "valid", kernel_initializer = "he_normal", kernel_regularizer = l2(regularizer_rate))(input) return add([shortcut, residual]) def _residual_block(block_function, filters, repetitions, is_first_layer = False): """Builds a residual block with repeating bottleneck blocks. """ def f(input): for i in range(repetitions): init_strides = (1, 1) if i == 0 and not is_first_layer: init_strides = (2, 2) input = block_function(filters = filters, init_strides = init_strides, is_first_block_of_first_layer = (is_first_layer and i == 0))(input) return input return f def basic_block(filters, init_strides = (1, 1), is_first_block_of_first_layer = False): """Basic 3 X 3 convolution blocks for use on resnets with layers <= 34. Follows improved proposed scheme in http://arxiv.org/pdf/1603.05027v2.pdf """ def f(input): if is_first_block_of_first_layer: # don't repeat bn->relu since we just did bn->relu->maxpool conv1 = Conv2D(filters = filters, kernel_size = (3, 3), strides = init_strides, padding = "same", kernel_initializer = "he_normal", kernel_regularizer = l2(0.01))(input) else: conv1 = _bn_relu_conv(filters = filters, kernel_size = (3, 3), strides = init_strides)(input) residual = _bn_relu_conv(filters = filters, kernel_size = (3, 3))(conv1) return _shortcut(input, residual) return f def bottleneck(filters, init_strides = (1, 1), is_first_block_of_first_layer = False): """Bottleneck architecture for > 34 layer resnet. Follows improved proposed scheme in http://arxiv.org/pdf/1603.05027v2.pdf Returns: A final conv layer of filters * 4 """ def f(input): if is_first_block_of_first_layer: # don't repeat bn->relu since we just did bn->relu->maxpool conv_1_1 = Conv2D(filters = filters, kernel_size = (1, 1), strides = init_strides, padding = "same", kernel_initializer = "he_normal", kernel_regularizer = l2(regularizer_rate))(input) else: conv_1_1 = _bn_relu_conv(filters = filters, kernel_size = (3, 3), strides = init_strides)(input) conv_3_3 = _bn_relu_conv(filters = filters, kernel_size = (3, 3))(conv_1_1) residual = _bn_relu_conv(filters = filters * 4, kernel_size = (1, 1))(conv_3_3) return _shortcut(input, residual) return f def _handle_dim_ordering(): global ROW_AXIS global COL_AXIS global CHANNEL_AXIS if K.image_dim_ordering() == 'tf': ROW_AXIS = 1 COL_AXIS = 2 CHANNEL_AXIS = 3 else: CHANNEL_AXIS = 1 ROW_AXIS = 2 COL_AXIS = 3 def _get_block(identifier): if isinstance(identifier, six.string_types): res = globals().get(identifier) if not res: raise ValueError('Invalid {}'.format(identifier)) return res return identifier class ResnetBuilder(object): @staticmethod def build(input_shape, num_outputs, block_fn, repetitions, enhanced = False, withSift = False): """Builds a custom ResNet like architecture. Args: input_shape: The input shape in the form (nb_channels, nb_rows, nb_cols) num_outputs: The number of outputs at final softmax layer block_fn: The block function to use. This is either `basic_block` or `bottleneck`. The original paper used basic_block for layers < 50 repetitions: Number of repetitions of various block units. At each block unit, the number of filters are doubled and the input size is halved Returns: The keras `Model`. """ _handle_dim_ordering() if len(input_shape) != 3: raise Exception("Input shape should be a tuple (nb_channels, nb_rows, nb_cols)") # Permute dimension order if necessary if K.image_dim_ordering() == 'tf': input_shape = (input_shape[1], input_shape[2], input_shape[0]) # Load function from str if needed. block_fn = _get_block(block_fn) input = Input(shape = input_shape) conv1 = _conv_bn_relu(filters = 64, kernel_size = (7, 7), strides = (2