Transfer learning on TensorFlow-Slim image classification model library
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.## 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.# ============================================================================="""MobileNet v1.MobileNet is a general architecture and can be used for multiple use cases.Depending on the use case, it can use different input layer size and differenthead (for example: embeddings, localization and classification).As described in https://arxiv.org/abs/1704.04861. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam100% Mobilenet V1 (base) with input size 224x224:See mobilenet_v1()Layer params macs--------------------------------------------------------------------------------MobilenetV1/Conv2d_0/Conv2D: 864 10,838,016MobilenetV1/Conv2d_1_depthwise/depthwise: 288 3,612,672MobilenetV1/Conv2d_1_pointwise/Conv2D: 2,048 25,690,112MobilenetV1/Conv2d_2_depthwise/depthwise: 576 1,806,336MobilenetV1/Conv2d_2_pointwise/Conv2D: 8,192 25,690,112MobilenetV1/Conv2d_3_depthwise/depthwise: 1,152 3,612,672MobilenetV1/Conv2d_3_pointwise/Conv2D: 16,384 51,380,224MobilenetV1/Conv2d_4_depthwise/depthwise: 1,152 903,168MobilenetV1/Conv2d_4_pointwise/Conv2D: 32,768 25,690,112MobilenetV1/Conv2d_5_depthwise/depthwise: 2,304 1,806,336MobilenetV1/Conv2d_5_pointwise/Conv2D: 65,536 51,380,224MobilenetV1/Conv2d_6_depthwise/depthwise: 2,304 451,584MobilenetV1/Conv2d_6_pointwise/Conv2D: 131,072 25,690,112MobilenetV1/Conv2d_7_depthwise/depthwise: 4,608 903,168MobilenetV1/Conv2d_7_pointwise/Conv2D: 262,144 51,380,224MobilenetV1/Conv2d_8_depthwise/depthwise: 4,608 903,168MobilenetV1/Conv2d_8_pointwise/Conv2D: 262,144 51,380,224MobilenetV1/Conv2d_9_depthwise/depthwise: 4,608 903,168MobilenetV1/Conv2d_9_pointwise/Conv2D: 262,144 51,380,224MobilenetV1/Conv2d_10_depthwise/depthwise: 4,608 903,168MobilenetV1/Conv2d_10_pointwise/Conv2D: 262,144 51,380,224MobilenetV1/Conv2d_11_depthwise/depthwise: 4,608 903,168MobilenetV1/Conv2d_11_pointwise/Conv2D: 262,144 51,380,224MobilenetV1/Conv2d_12_depthwise/depthwise: 4,608 225,792MobilenetV1/Conv2d_12_pointwise/Conv2D: 524,288 25,690,112MobilenetV1/Conv2d_13_depthwise/depthwise: 9,216 451,584MobilenetV1/Conv2d_13_pointwise/Conv2D: 1,048,576 51,380,224--------------------------------------------------------------------------------Total: 3,185,088 567,716,35275% Mobilenet V1 (base) with input size 128x128:See mobilenet_v1_075()Layer params macs--------------------------------------------------------------------------------MobilenetV1/Conv2d_0/Conv2D: 648 2,654,208MobilenetV1/Conv2d_1_depthwise/depthwise: 216 884,736MobilenetV1/Conv2d_1_pointwise/Conv2D: 1,152 4,718,592MobilenetV1/Conv2d_2_depthwise/depthwise: 432 442,368MobilenetV1/Conv2d_2_pointwise/Conv2D: 4,608 4,718,592MobilenetV1/Conv2d_3_depthwise/depthwise: 864 884,736MobilenetV1/Conv2d_3_pointwise/Conv2D: 9,216 9,437,184MobilenetV1/Conv2d_4_depthwise/depthwise: 864 221,184MobilenetV1/Conv2d_4_pointwise/Conv2D: 18,432 4,718,592MobilenetV1/Conv2d_5_depthwise/depthwise: 1,728 442,368MobilenetV1/Conv2d_5_pointwise/Conv2D: 36,864 9,437,184MobilenetV1/Conv2d_6_depthwise/depthwise: 1,728 110,592MobilenetV1/Conv2d_6_pointwise/Conv2D: 73,728 4,718,592MobilenetV1/Conv2d_7_depthwise/depthwise: 3,456 221,184MobilenetV1/Conv2d_7_pointwise/Conv2D: 147,456 9,437,184MobilenetV1/Conv2d_8_depthwise/depthwise: 3,456 221,184MobilenetV1/Conv2d_8_pointwise/Conv2D: 147,456 9,437,184MobilenetV1/Conv2d_9_depthwise/depthwise: 3,456 221,184MobilenetV1/Conv2d_9_pointwise/Conv2D: 147,456 9,437,184MobilenetV1/Conv2d_10_depthwise/depthwise: 3,456 221,184MobilenetV1/Conv2d_10_pointwise/Conv2D: 147,456 9,437,184MobilenetV1/Conv2d_11_depthwise/depthwise: 3,456 221,184MobilenetV1/Conv2d_11_pointwise/Conv2D: 147,456 9,437,184MobilenetV1/Conv2d_12_depthwise/depthwise: 3,456 55,296MobilenetV1/Conv2d_12_pointwise/Conv2D: 294,912 4,718,592MobilenetV1/Conv2d_13_depthwise/depthwise: 6,912 110,592MobilenetV1/Conv2d_13_pointwise/Conv2D: 589,824 9,437,184--------------------------------------------------------------------------------Total: 1,800,144 106,002,432"""# Tensorflow mandates these.from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionfrom collections import namedtupleimport functoolsimport tensorflow.compat.v1 as tfimport tf_slim as slim# Conv and DepthSepConv namedtuple define layers of the MobileNet architecture# Conv defines 3x3 convolution layers# DepthSepConv defines 3x3 depthwise convolution followed by 1x1 convolution.# stride is the stride of the convolution# depth is the number of channels or filters in a layerConv = namedtuple('Conv', ['kernel', 'stride', 'depth'])DepthSepConv = namedtuple('DepthSepConv', ['kernel', 'stride', 'depth'])# MOBILENETV1_CONV_DEFS specifies the MobileNet bodyMOBILENETV1_CONV_DEFS = [ Conv(kernel=[3, 3], stride=2, depth=32), DepthSepConv(kernel=[3, 3], stride=1, depth=64), DepthSepConv(kernel=[3, 3], stride=2, depth=128), DepthSepConv(kernel=[3, 3], stride=1, depth=128), DepthSepConv(kernel=[3, 3], stride=2, depth=256), DepthSepConv(kernel=[3, 3], stride=1, depth=256), DepthSepConv(kernel=[3, 3], stride=2, depth=512), DepthSepConv(kernel=[3, 3], stride=1, depth=512), DepthSepConv(kernel=[3, 3], stride=1, depth=512), DepthSepConv(kernel=[3, 3], stride=1, depth=512), DepthSepConv(kernel=[3, 3], stride=1, depth=512), DepthSepConv(kernel=[3, 3], stride=1, depth=512), DepthSepConv(kernel=[3, 3], stride=2, depth=1024), DepthSepConv(kernel=[3, 3], stride=1, depth=1024)]def _fixed_padding(inputs, kernel_size, rate=1): """Pads the input along the spatial dimensions independently of input size. Pads the input such that if it was used in a convolution with 'VALID' padding, the output would have the same dimensions as if the unpadded input was used in a convolution with 'SAME' padding. Args: inputs: A tensor of size [batch, height_in, width_in, channels]. kernel_size: The kernel to be used in the conv2d or max_pool2d operation. rate: An integer, rate for atrous convolution. Returns: output: A tensor of size [batch, height_out, width_out, channels] with the input, either intact (if kernel_size == 1) or padded (if kernel_size > 1). """ kernel_size_effective = [kernel_size[0] + (kernel_size[0] - 1) * (rate - 1), kernel_size[0] + (kernel_size[0] - 1) * (rate - 1)] pad_total = [kernel_size_effective[0] - 1, kernel_size_effective[1] - 1] pad_beg = [pad_total[0] // 2, pad_total[1] // 2] pad_end = [pad_total[0] - pad_beg[0], pad_total[1] - pad_beg[1]] padded_inputs = tf.pad( tensor=inputs, paddings=[[0, 0], [pad_beg[0], pad_end[0]], [pad_beg[1], pad_end[1]], [0, 0]]) return padded_inputsdef mobilenet_v1_base(inputs, final_endpoint='Conv2d_13_pointwise', min_depth=8, depth_multiplier=1.0, conv_defs=None, output_stride=None, use_explicit_padding=False, scope=None): """Mobilenet v1. Constructs a Mobilenet v1 network from inputs to the given final endpoint. Args: inputs: a tensor of shape [batch_size, height, width, channels]. final_endpoint: specifies the endpoint to construct the network up to. It can be one of ['Conv2d_0', 'Conv2d_1_pointwise', 'Conv2d_2_pointwise', 'Conv2d_3_pointwise', 'Conv2d_4_pointwise', 'Conv2d_5'_pointwise, 'Conv2d_6_pointwise', 'Conv2d_7_pointwise', 'Conv2d_8_pointwise', 'Conv2d_9_pointwise', 'Conv2d_10_pointwise', 'Conv2d_11_pointwise', 'Conv2d_12_pointwise', 'Conv2d_13_pointwise']. min_depth: Minimum depth value (number of channels) for all convolution ops. Enforced when depth_multiplier < 1, and not an active constraint when depth_multiplier >= 1. depth_multiplier: Float multiplier for the depth (number of channels) for all convolution ops. The value must be greater than zero. Typical usage will be to set this value in (0, 1) to reduce the number of parameters or computation cost of the model. conv_defs: A list of ConvDef namedtuples specifying the net architecture. output_stride: An integer that specifies the requested ratio of input to output spatial resolution. If not None, then we invoke atrous convolution if necessary to prevent the network from reducing the spatial resolution of the activation maps. Allowed values are 8 (accurate fully convolutional mode), 16 (fast fully convolutional mode), 32 (classification mode). use_explicit_padding: Use 'VALID' padding for convolutions, but prepad inputs so that the output dimensions are the same as if 'SAME' padding were used. scope: Optional variable_scope. Returns: tensor_out: output tensor corresponding to the final_endpoint. end_points: a set of activations for external use, for example summaries or losses. Raises: ValueError: if final_endpoint is not set to one of the predefined values, or depth_multiplier <= 0, or the target output_stride is not allowed. """ depth = lambda d: max(int(d * depth_multiplier), min_depth) end_points = {} # Used to find thinned depths for each layer. if depth_multiplier <= 0: raise ValueError('depth_multiplier is not greater than zero.') if conv_defs is None: conv_defs = MOBILENETV1_CONV_DEFS if output_stride is not None and output_stride not in [8, 16, 32]: raise ValueError('Only allowed output_stride values are 8, 16, 32.') padding = 'SAME' if use_explicit_padding: padding = 'VALID' with tf.variable_scope(scope, 'MobilenetV1', [inputs]): with slim.arg_scope([slim.conv2d, slim.separable_conv2d], padding=padding): # The current_stride variable keeps track of the output stride of the # activations, i.e., the running product of convolution strides up to the # current network layer. This allows us to invoke atrous convolution # whenever applying the next convolution would result in the activations # having output stride larger than the target output_stride. current_stride = 1 # The atrous convolution rate parameter. rate = 1 net = inputs for i, conv_def in enumerate(conv_defs): end_point_base = 'Conv2d_%d' % i if output_stride is not None and current_stride == output_stride: # If we have reached the target output_stride, then we need to employ # atrous convolution with stride=1 and multiply the atrous rate by the # current unit's stride for use in subsequent layers. layer_stride = 1 layer_rate = rate rate *= conv_def.stride else: layer_stride = conv_def.stride layer_rate = 1 current_stride *= conv_def.stride if isinstance(conv_def, Conv): end_point = end_point_base if use_explicit_padding: net = _fixed_padding(net, conv_def.kernel) net = slim.conv2d(net, depth(conv_def.depth), conv_def.kernel, stride=conv_def.stride, scope=end_point) end_points[end_point] = net if end_point == final_endpoint: return net, end_points elif isinstance(conv_def, DepthSepConv): end_point = end_point_base + '_depthwise' # By passing filters=None # separable_conv2d produces only a depthwise convolution layer if use_explicit_padding: net = _fixed_padding(net, conv_def.kernel, layer_rate) net = slim.separable_conv2d(net, None, conv_def.kernel, depth_multiplier=1, stride=layer_stride, rate=layer_rate, scope=end_point) end_points[end_point] = net if end_point == final_endpoint: return net, end_points end_point = end_point_base + '_pointwise' net = slim.conv2d(net, depth(conv_def.depth), [1, 1], stride=1, scope=end_point) end_points[end_point] = net if end_point == final_endpoint: return net, end_points else: raise ValueError('Unknown convolution type %s for layer %d' % (conv_def.ltype, i)) raise ValueError('Unknown final endpoint %s' % final_endpoint)def mobilenet_v1(inputs, num_classes=1000, dropout_keep_prob=0.999, is_training=True, min_depth=8, depth_multiplier=1.0, conv_defs=None, prediction_fn=slim.softmax, spatial_squeeze=True, reuse=None, scope='MobilenetV1', global_pool=False): """Mobilenet v1 model for classification. Args: inputs: a tensor of shape [batch_size, height, width, channels]. num_classes: number of predicted classes. If 0 or None, the logits layer is omitted and the input features to the logits layer (before dropout) are returned instead. dropout_keep_prob: the percentage of activation values that are retained. is_training: whether is training or not. min_depth: Minimum depth value (number of channels) for all convolution ops. Enforced when depth_multiplier < 1, and not an active constraint when depth_multiplier >= 1. depth_multiplier: Float multiplier for the depth (number of channels) for all convolution ops. The value must be greater than zero. Typical usage will be to set this value in (0, 1) to reduce the number of parameters or computation cost of the model. conv_defs: A list of ConvDef namedtuples specifying the net architecture. prediction_fn: a function to get predictions out of logits. spatial_squeeze: if True, logits is of shape is [B, C], if false logits is of shape [B, 1, 1, C], where B is batch_size and C is number of classes. reuse: whether or not the network and its variables should be reused. To be able to reuse 'scope' must be given. scope: Optional variable_scope. global_pool: Optional boolean flag to control the avgpooling before the logits layer. If false or unset, pooling is done with a fixed window that reduces default-sized inputs to 1x1, while larger inputs lead to larger outputs. If true, any input size is pooled down to 1x1. Returns: net: a 2D Tensor with the logits (pre-softmax activations) if num_classes is a non-zero integer, or the non-dropped-out input to the logits layer if num_classes is 0 or None. end_points: a dictionary from components of the network to the corresponding activation. Raises: ValueError: Input rank is invalid. """ input_shape = inputs.get_shape().as_list() if len(input_shape) != 4: raise ValueError('Invalid input tensor rank, expected 4, was: %d' % len(input_shape)) with tf.variable_scope( scope, 'MobilenetV1', [inputs], reuse=reuse) as scope: with slim.arg_scope([slim.batch_norm, slim.dropout], is_training=is_training): net, end_points = mobilenet_v1_base(inputs, scope=scope, min_depth=min_depth, depth_multiplier=depth_multiplier, conv_defs=conv_defs) with tf.variable_scope('Logits'): if global_pool: # Global average pooling. net = tf.reduce_mean( input_tensor=net, axis=[1, 2], keepdims=True, name='global_pool') end_points['global_pool'] = net else: # Pooling with a fixed kernel size. kernel_size = _reduced_kernel_size_for_small_input(net, [7, 7]) net = slim.avg_pool2d(net, kernel_size, padding='VALID', scope='AvgPool_1a') end_points['AvgPool_1a'] = net if not num_classes: return net, end_points # 1 x 1 x 1024 net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b') logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, normalizer_fn=None, scope='Conv2d_1c_1x1') if spatial_squeeze: logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze') end_points['Logits'] = logits if prediction_fn: end_points['Predictions'] = prediction_fn(logits, scope='Predictions') return logits, end_pointsmobilenet_v1.default_image_size = 224def wrapped_partial(func, *args, **kwargs): partial_func = functools.partial(func, *args, **kwargs) functools.update_wrapper(partial_func, func) return partial_funcmobilenet_v1_075 = wrapped_partial(mobilenet_v1, depth_multiplier=0.75)mobilenet_v1_050 = wrapped_partial(mobilenet_v1, depth_multiplier=0.50)mobilenet_v1_025 = wrapped_partial(mobilenet_v1, depth_multiplier=0.25)def _reduced_kernel_size_for_small_input(input_tensor, kernel_size): """Define kernel size which is automatically reduced for small input. If the shape of the input images is unknown at graph construction time this function assumes that the input images are large enough. Args: input_tensor: input tensor of size [batch_size, height, width, channels]. kernel_size: desired kernel size of length 2: [kernel_height, kernel_width] Returns: a tensor with the kernel size. """ shape = input_tensor.get_shape().as_list() if shape[1] is None or shape[2] is None: kernel_size_out = kernel_size else: kernel_size_out = [min(shape[1], kernel_size[0]), min(shape[2], kernel_size[1])] return kernel_size_outdef mobilenet_v1_arg_scope( is_training=True, weight_decay=0.00004, stddev=0.09, regularize_depthwise=False, batch_norm_decay=0.9997, batch_norm_epsilon=0.001, batch_norm_updates_collections=tf.GraphKeys.UPDATE_OPS, normalizer_fn=slim.batch_norm): """Defines the default MobilenetV1 arg scope. Args: is_training: Whether or not we're training the model. If this is set to None, the parameter is not added to the batch_norm arg_scope. weight_decay: The weight decay to use for regularizing the model. stddev: The standard deviation of the trunctated normal weight initializer. regularize_depthwise: Whether or not apply regularization on depthwise. batch_norm_decay: Decay for batch norm moving average. batch_norm_epsilon: Small float added to variance to avoid dividing by zero in batch norm. batch_norm_updates_collections: Collection for the update ops for batch norm. normalizer_fn: Normalization function to apply after convolution. Returns: An `arg_scope` to use for the mobilenet v1 model. """ batch_norm_params = { 'center': True, 'scale': True, 'decay': batch_norm_decay, 'epsilon': batch_norm_epsilon, 'updates_collections': batch_norm_updates_collections, } if is_training is not None: batch_norm_params['is_training'] = is_training # Set weight_decay for weights in Conv and DepthSepConv layers. weights_init = tf.truncated_normal_initializer(stddev=stddev) regularizer = slim.l2_regularizer(weight_decay) if regularize_depthwise: depthwise_regularizer = regularizer else: depthwise_regularizer = None with slim.arg_scope([slim.conv2d, slim.separable_conv2d], weights_initializer=weights_init, activation_fn=tf.nn.relu6, normalizer_fn=normalizer_fn): with slim.arg_scope([slim.batch_norm], **batch_norm_params): with slim.arg_scope([slim.conv2d], weights_regularizer=regularizer): with slim.arg_scope([slim.separable_conv2d], weights_regularizer=depthwise_regularizer) as sc: return sc