Source code for mlbench_core.models.tensorflow.resnet_model

# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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# ==============================================================================
"""Contains definitions for Residual Networks.
Residual networks ('v1' ResNets) were originally proposed in :cite:`he2016deep`.
The full preactivation 'v2' ResNet variant was introduced by :cite:`he2016identity`.
The key difference of the full preactivation 'v2' variant compared to the
'v1' variant in [1] is the use of batch normalization before every weight layer
rather than after.
"""

from __future__ import absolute_import, division, print_function

import tensorflow as tf

# _BATCH_NORM_DECAY = 1.0
_BATCH_NORM_DECAY = 0.9
_BATCH_NORM_EPSILON = 1e-5
DEFAULT_VERSION = 2
DEFAULT_DTYPE = tf.float32
CASTABLE_TYPES = (tf.float16,)
ALLOWED_TYPES = (DEFAULT_DTYPE,) + CASTABLE_TYPES

################################################################################
# Convenience functions for building the ResNet model.
################################################################################


[docs]def batch_norm(inputs, training, data_format): """Performs a batch normalization using a standard set of parameters.""" # We set fused=True for a significant performance boost. See # https://www.tensorflow.org/performance/performance_guide#common_fused_ops return tf.layers.batch_normalization( inputs=inputs, axis=1 if data_format == "channels_first" else 3, momentum=_BATCH_NORM_DECAY, epsilon=_BATCH_NORM_EPSILON, center=True, scale=True, trainable=True, training=training, fused=True,
)
[docs]def fixed_padding(inputs, kernel_size, data_format): """Pads the input along the spatial dimensions independently of input size. Args: inputs (:obj:`tf.Tensor`): A tensor of size [batch, channels, height_in, width_in] or [batch, height_in, width_in, channels] depending on data_format. kernel_size (int): The kernel to be used in the conv2d or max_pool2d operation. Should be a positive integer. data_format (str): The input format ('channels_last' or 'channels_first'). Returns: A tensor with the same format as the input with the data either intact (if kernel_size == 1) or padded (if kernel_size > 1). """ pad_total = kernel_size - 1 pad_beg = pad_total // 2 pad_end = pad_total - pad_beg if data_format == "channels_first": padded_inputs = tf.pad( inputs, [[0, 0], [0, 0], [pad_beg, pad_end], [pad_beg, pad_end]] ) else: padded_inputs = tf.pad( inputs, [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]] ) return padded_inputs
[docs]def conv2d_fixed_padding(inputs, filters, kernel_size, strides, data_format): """Strided 2-D convolution with explicit padding.""" # The padding is consistent and is based only on `kernel_size`, not on the # dimensions of `inputs` (as opposed to using `tf.layers.conv2d` alone). with tf.name_scope("conv2d_fixed_padding"): tf.identity(inputs, name="inputs_pre") if strides > 1: inputs = fixed_padding(inputs, kernel_size, data_format) tf.identity(inputs, name="inputs") layer = tf.layers.conv2d( inputs=inputs, filters=filters, kernel_size=kernel_size, strides=strides, padding=("SAME" if strides == 1 else "VALID"), use_bias=False, kernel_initializer=tf.variance_scaling_initializer(), data_format=data_format, # name="conv2d" ) return layer
################################################################################ # ResNet block definitions. ################################################################################ def _building_block_v1( inputs, filters, training, projection_shortcut, strides, data_format ): """A single block for ResNet v1, without a bottleneck. Convolution then batch normalization then ReLU as described by: Deep Residual Learning for Image Recognition https://arxiv.org/pdf/1512.03385.pdf by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Dec 2015. Args: inputs (:obj:`tf.Tensor`): A tensor of size [batch, channels, height_in, width_in] or [batch, height_in, width_in, channels] depending on data_format. filters (int): The number of filters for the convolutions. training (bool): A Boolean for whether the model is in training or inference mode. Needed for batch normalization. projection_shortcut (callable): The function to use for projection shortcuts (typically a 1x1 convolution when downsampling the input). strides (int): The block's stride. If greater than 1, this block will ultimately downsample the input. data_format (str): The input format ('channels_last' or 'channels_first'). Returns: The output tensor of the block; shape should match inputs. """ shortcut = inputs if projection_shortcut is not None: shortcut = projection_shortcut(inputs) shortcut = batch_norm( inputs=shortcut, training=training, data_format=data_format ) inputs = conv2d_fixed_padding( inputs=inputs, filters=filters, kernel_size=3, strides=strides, data_format=data_format, ) inputs = batch_norm(inputs, training, data_format) inputs = tf.nn.relu(inputs) inputs = conv2d_fixed_padding( inputs=inputs, filters=filters, kernel_size=3, strides=1, data_format=data_format, ) inputs = batch_norm(inputs, training, data_format) inputs += shortcut inputs = tf.nn.relu(inputs) return inputs def _building_block_v2( inputs, filters, training, projection_shortcut, strides, data_format ): """A single block for ResNet v2, without a bottleneck. Batch normalization then ReLu then convolution as described by: Identity Mappings in Deep Residual Networks https://arxiv.org/pdf/1603.05027.pdf by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Jul 2016. Args: inputs (:obj:`tf.Tensor`): A tensor of size [batch, channels, height_in, width_in] or [batch, height_in, width_in, channels] depending on data_format. filters (int): The number of filters for the convolutions. training (bool): A Boolean for whether the model is in training or inference mode. Needed for batch normalization. projection_shortcut (callable): The function to use for projection shortcuts (typically a 1x1 convolution when downsampling the input). strides (int): The block's stride. If greater than 1, this block will ultimately downsample the input. data_format (str): The input format ('channels_last' or 'channels_first'). Returns: The output tensor of the block; shape should match inputs. """ shortcut = inputs tf.identity(shortcut, name="inputs") with tf.name_scope("test_batch_norm"): inputs = batch_norm(inputs, training, data_format) tf.identity(inputs, name="inputs_batch_normed") inputs = tf.nn.relu(inputs) tf.identity(inputs, name="inputs_batch_normed_relu") # The projection shortcut should come after the first batch norm and ReLU # since it performs a 1x1 convolution. if projection_shortcut is not None: shortcut = projection_shortcut(inputs) tf.identity(shortcut, name="projected_shortcut") inputs = conv2d_fixed_padding( inputs=inputs, filters=filters, kernel_size=3, strides=strides, data_format=data_format, ) inputs = batch_norm(inputs, training, data_format) inputs = tf.nn.relu(inputs) inputs = conv2d_fixed_padding( inputs=inputs, filters=filters, kernel_size=3, strides=1, data_format=data_format, ) return inputs + shortcut def _bottleneck_block_v1( inputs, filters, training, projection_shortcut, strides, data_format ): """A single block for ResNet v1, with a bottleneck. Similar to _building_block_v1(), except using the "bottleneck" blocks described in: Convolution then batch normalization then ReLU as described by: Deep Residual Learning for Image Recognition https://arxiv.org/pdf/1512.03385.pdf by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Dec 2015. Args: inputs (:obj:`tf.Tensor`): A tensor of size [batch, channels, height_in, width_in] or [batch, height_in, width_in, channels] depending on data_format. filters (int): The number of filters for the convolutions. training (bool): A Boolean for whether the model is in training or inference mode. Needed for batch normalization. projection_shortcut (callable): The function to use for projection shortcuts (typically a 1x1 convolution when downsampling the input). strides (int): The block's stride. If greater than 1, this block will ultimately downsample the input. data_format (str): The input format ('channels_last' or 'channels_first'). Returns: The output tensor of the block; shape should match inputs. """ shortcut = inputs if projection_shortcut is not None: shortcut = projection_shortcut(inputs) shortcut = batch_norm( inputs=shortcut, training=training, data_format=data_format ) inputs = conv2d_fixed_padding( inputs=inputs, filters=filters, kernel_size=1, strides=1, data_format=data_format, ) inputs = batch_norm(inputs, training, data_format) inputs = tf.nn.relu(inputs) inputs = conv2d_fixed_padding( inputs=inputs, filters=filters, kernel_size=3, strides=strides, data_format=data_format, ) inputs = batch_norm(inputs, training, data_format) inputs = tf.nn.relu(inputs) inputs = conv2d_fixed_padding( inputs=inputs, filters=4 * filters, kernel_size=1, strides=1, data_format=data_format, ) inputs = batch_norm(inputs, training, data_format) inputs += shortcut inputs = tf.nn.relu(inputs) return inputs def _bottleneck_block_v2( inputs, filters, training, projection_shortcut, strides, data_format ): """A single block for ResNet v2, with a bottleneck. Similar to _building_block_v2(), except using the "bottleneck" blocks described in: Convolution then batch normalization then ReLU as described by: Deep Residual Learning for Image Recognition https://arxiv.org/pdf/1512.03385.pdf by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Dec 2015. Adapted to the ordering conventions of: Batch normalization then ReLu then convolution as described by: Identity Mappings in Deep Residual Networks https://arxiv.org/pdf/1603.05027.pdf by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Jul 2016. Args: inputs (:obj:`tf.Tensor`): A tensor of size [batch, channels, height_in, width_in] or [batch, height_in, width_in, channels] depending on data_format. filters (int): The number of filters for the convolutions. training (bool): A Boolean for whether the model is in training or inference mode. Needed for batch normalization. projection_shortcut (callable): The function to use for projection shortcuts (typically a 1x1 convolution when downsampling the input). strides (int): The block's stride. If greater than 1, this block will ultimately downsample the input. data_format (str): The input format ('channels_last' or 'channels_first'). Returns: The output tensor of the block; shape should match inputs. """ shortcut = inputs inputs = batch_norm(inputs, training, data_format) inputs = tf.nn.relu(inputs) # The projection shortcut should come after the first batch norm and ReLU # since it performs a 1x1 convolution. if projection_shortcut is not None: shortcut = projection_shortcut(inputs) inputs = conv2d_fixed_padding( inputs=inputs, filters=filters, kernel_size=1, strides=1, data_format=data_format, ) inputs = batch_norm(inputs, training, data_format) inputs = tf.nn.relu(inputs) inputs = conv2d_fixed_padding( inputs=inputs, filters=filters, kernel_size=3, strides=strides, data_format=data_format, ) inputs = batch_norm(inputs, training, data_format) inputs = tf.nn.relu(inputs) inputs = conv2d_fixed_padding( inputs=inputs, filters=4 * filters, kernel_size=1, strides=1, data_format=data_format, ) return inputs + shortcut
[docs]def block_layer( inputs, filters, bottleneck, block_fn, blocks, strides, training, name, data_format ): """Creates one layer of blocks for the ResNet model. Args: inputs (:obj:`tf.Tensor`): A tensor of size [batch, channels, height_in, width_in] or [batch, height_in, width_in, channels] depending on data_format. filters (int): The number of filters for the first convolution of the layer. bottleneck (bool): Is the block created a bottleneck block. block_fn (callable): The block to use within the model, either `building_block` or `bottleneck_block`. blocks (int): The number of blocks contained in the layer. strides (int): The stride to use for the first convolution of the layer. If greater than 1, this layer will ultimately downsample the input. training (bool): Either True or False, whether we are currently training the model. Needed for batch norm. name (str): A string name for the tensor output of the block layer. data_format (str): The input format ('channels_last' or 'channels_first'). Returns: The output tensor of the block layer. """ # Bottleneck blocks end with 4x the number of filters as they start with filters_out = filters * 4 if bottleneck else filters def projection_shortcut(inputs): with tf.name_scope("projection_shortcut"): layer = conv2d_fixed_padding( inputs=inputs, filters=filters_out, kernel_size=1, strides=strides, data_format=data_format, ) return layer # Only the first block per block_layer uses projection_shortcut and strides with tf.name_scope("block0"): inputs = block_fn( inputs, filters, training, projection_shortcut, strides, data_format ) for i in range(1, blocks): with tf.name_scope("block{}".format(i)): inputs = block_fn(inputs, filters, training, None, 1, data_format) return tf.identity(inputs, name)
[docs]class Model(object): """Base class for building the Resnet Model.""" def __init__( self, resnet_size, bottleneck, num_classes, num_filters, kernel_size, conv_stride, first_pool_size, first_pool_stride, block_sizes, block_strides, resnet_version=DEFAULT_VERSION, data_format=None, dtype=DEFAULT_DTYPE, ): """Creates a model for classifying an image. Args: resnet_size (int): A single integer for the size of the ResNet model. bottleneck (bool): Use regular blocks or bottleneck blocks. num_classes (int): The number of classes used as labels. num_filters (int): The number of filters to use for the first block layer of the model. This number is then doubled for each subsequent block layer. kernel_size (int): The kernel size to use for convolution. conv_stride (int): stride size for the initial convolutional layer first_pool_size (int or None): Pool size to be used for the first pooling layer. If none, the first pooling layer is skipped. first_pool_stride (int or None): stride size for the first pooling layer. Not used if first_pool_size is None. block_sizes (list): A list containing n values, where n is the number of sets of block layers desired. Each value should be the number of blocks in the i-th set. block_strides (list): List of integers representing the desired stride size for each of the sets of block layers. Should be same length as block_sizes. resnet_version (int): Integer representing which version of the ResNet network to use. See README for details. Valid values: [1, 2] data_format (str): Input format ('channels_last', 'channels_first', or None). If set to None, the format is dependent on whether a GPU is available. dtype (type): The TensorFlow dtype to use for calculations. If not specified tf.float32 is used. Raises: ValueError: if invalid version is selected. """ self.resnet_size = resnet_size if not data_format: data_format = ( "channels_first" if tf.test.is_built_with_cuda() else "channels_last" ) self.resnet_version = resnet_version if resnet_version not in (1, 2): raise ValueError( "Resnet version should be 1 or 2. See README for citations." ) self.bottleneck = bottleneck if bottleneck: if resnet_version == 1: self.block_fn = _bottleneck_block_v1 else: self.block_fn = _bottleneck_block_v2 else: if resnet_version == 1: self.block_fn = _building_block_v1 else: self.block_fn = _building_block_v2 if dtype not in ALLOWED_TYPES: raise ValueError("dtype must be one of: {}".format(ALLOWED_TYPES)) self.data_format = data_format self.num_classes = num_classes self.num_filters = num_filters self.kernel_size = kernel_size self.conv_stride = conv_stride self.first_pool_size = first_pool_size self.first_pool_stride = first_pool_stride self.block_sizes = block_sizes self.block_strides = block_strides self.dtype = dtype self.pre_activation = resnet_version == 2 def _custom_dtype_getter( self, getter, name, shape=None, dtype=DEFAULT_DTYPE, *args, **kwargs ): """Creates variables in fp32, then casts to fp16 if necessary. This function is a custom getter. A custom getter is a function with the same signature as tf.get_variable, except it has an additional getter parameter. Custom getters can be passed as the `custom_getter` parameter of tf.variable_scope. Then, tf.get_variable will call the custom getter, instead of directly getting a variable itself. This can be used to change the types of variables that are retrieved with tf.get_variable. The `getter` parameter is the underlying variable getter, that would have been called if no custom getter was used. Custom getters typically get a variable with `getter`, then modify it in some way. This custom getter will create an fp32 variable. If a low precision (e.g. float16) variable was requested it will then cast the variable to the requested dtype. The reason we do not directly create variables in low precision dtypes is that applying small gradients to such variables may cause the variable not to change. Args: getter: The underlying variable getter, that has the same signature as tf.get_variable and returns a variable. name: The name of the variable to get. shape: The shape of the variable to get. dtype (type): The dtype of the variable to get. Note that if this is a low precision dtype, the variable will be created as a tf.float32 variable, then cast to the appropriate dtype *args: Additional arguments to pass unmodified to getter. **kwargs: Additional keyword arguments to pass unmodified to getter. Returns: A variable which is cast to fp16 if necessary. """ if dtype in CASTABLE_TYPES: var = getter(name, shape, tf.float32, *args, **kwargs) return tf.cast(var, dtype=dtype, name=name + "_cast") else: return getter(name, shape, dtype, *args, **kwargs) def _model_variable_scope(self): """Returns a variable scope that the model should be created under. If self.dtype is a castable type, model variable will be created in fp32 then cast to self.dtype before being used. Returns: A variable scope for the model. """ return tf.variable_scope( "resnet_model", custom_getter=self._custom_dtype_getter ) def __call__(self, inputs, training): """Add operations to classify a batch of input images. Args: inputs (:obj:`tf.Tensor`): A Tensor representing a batch of input images. training (:obj:`tf.Tensor`): A boolean. Set to True to add operations required only when training the classifier. Returns: A logits Tensor with shape [<batch_size>, self.num_classes]. """ with self._model_variable_scope(): if self.data_format == "channels_first": # Convert the inputs from channels_last (NHWC) to channels_first (NCHW). # This provides a large performance boost on GPU. See # https://www.tensorflow.org/performance/performance_guide#data_formats inputs = tf.transpose(inputs, [0, 3, 1, 2]) inputs = conv2d_fixed_padding( inputs=inputs, filters=self.num_filters, kernel_size=self.kernel_size, strides=self.conv_stride, data_format=self.data_format, ) inputs = tf.identity(inputs, "initial_conv") # We do not include batch normalization or activation functions in V2 # for the initial conv1 because the first ResNet unit will perform these # for both the shortcut and non-shortcut paths as part of the first # block's projection. Cf. Appendix of [2]. if self.resnet_version == 1: inputs = batch_norm(inputs, training, self.data_format) inputs = tf.nn.relu(inputs) if self.first_pool_size: inputs = tf.layers.max_pooling2d( inputs=inputs, pool_size=self.first_pool_size, strides=self.first_pool_stride, padding="SAME", data_format=self.data_format, ) inputs = tf.identity(inputs, "initial_max_pool") for i, num_blocks in enumerate(self.block_sizes): num_filters = self.num_filters * (2 ** i) with tf.name_scope("block_layer_{}".format(i + 1)): inputs = block_layer( inputs=inputs, filters=num_filters, bottleneck=self.bottleneck, block_fn=self.block_fn, blocks=num_blocks, strides=self.block_strides[i], training=training, name="block_layer{}".format(i + 1), data_format=self.data_format, ) # Only apply the BN and ReLU for model that does pre_activation in each # building/bottleneck block, eg resnet V2. if self.pre_activation: inputs = batch_norm(inputs, training, self.data_format) inputs = tf.nn.relu(inputs) # The current top layer has shape # `batch_size x pool_size x pool_size x final_size`. # ResNet does an Average Pooling layer over pool_size, # but that is the same as doing a reduce_mean. We do a reduce_mean # here because it performs better than AveragePooling2D. axes = [2, 3] if self.data_format == "channels_first" else [1, 2] inputs = tf.reduce_mean(inputs, axes, keepdims=True) inputs = tf.identity(inputs, "final_reduce_mean") inputs = tf.squeeze(inputs, axes) inputs = tf.layers.dense(inputs=inputs, units=self.num_classes) inputs = tf.identity(inputs, "final_dense") return inputs
[docs]class Cifar10Model(Model): """Model class with appropriate defaults for CIFAR-10 data.""" def __init__( self, resnet_size, data_format=None, num_classes=10, resnet_version=2, dtype=tf.float32, ): """These are the parameters that work for CIFAR-10 data. Args: resnet_size (int): The number of convolutional layers needed in the model. data_format (str): Either 'channels_first' or 'channels_last', specifying which data format to use when setting up the model. num_classes (int): The number of output classes needed from the model. This enables users to extend the same model to their own datasets. resnet_version (int): Integer representing which version of the ResNet network to use. Valid values: [1, 2] dtype (tensorflow.python.framework.dtypes.DType): The TensorFlow dtype to use for calculations. Raises: ValueError: if invalid resnet_size is chosen """ if resnet_size % 6 != 2: raise ValueError("resnet_size must be 6n + 2:", resnet_size) num_blocks = (resnet_size - 2) // 6 super(Cifar10Model, self).__init__( resnet_size=resnet_size, bottleneck=False, num_classes=num_classes, num_filters=16, kernel_size=3, conv_stride=1, first_pool_size=None, first_pool_stride=None, block_sizes=[num_blocks] * 3, block_strides=[1, 2, 2], resnet_version=resnet_version, data_format=data_format, dtype=dtype,
)