update some TF file

TensorFlow/ComputeVision/Classification/benchmarks-master/scripts/tf_cnn_benchmarks/models/lenet_model.py

+# 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.
+# ==============================================================================
+
+"""Lenet model configuration.
+
+References:
+  LeCun, Yann, Leon Bottou, Yoshua Bengio, and Patrick Haffner
+  Gradient-based learning applied to document recognition
+  Proceedings of the IEEE (1998)
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from models import model
+
+
+class Lenet5Model(model.CNNModel):
+  """Lenet5."""
+
+  def __init__(self, params=None):
+    super(Lenet5Model, self).__init__('lenet5', 28, 32, 0.005, params=params)
+
+  def add_inference(self, cnn):
+    # Note: This matches TF's MNIST tutorial model
+    cnn.conv(32, 5, 5)
+    cnn.mpool(2, 2)
+    cnn.conv(64, 5, 5)
+    cnn.mpool(2, 2)
+    cnn.reshape([-1, 64 * 7 * 7])
+    cnn.affine(512)

TensorFlow/ComputeVision/Classification/benchmarks-master/scripts/tf_cnn_benchmarks/models/model.py

+# 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.
+# ==============================================================================
+"""Base model configuration for CNN benchmarks."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from collections import namedtuple
+
+import tensorflow.compat.v1 as tf
+
+import convnet_builder
+import mlperf
+from tensorflow.python.ops import variables as variables_module  # pylint: disable=g-direct-tensorflow-import
+
+# BuildNetworkResult encapsulate the result (e.g. logits) of a
+# Model.build_network() call.
+BuildNetworkResult = namedtuple(
+    'BuildNetworkResult',
+    [
+        'logits',  # logits of the network
+        'extra_info',  # Model specific extra information
+    ])
+
+
+class Model(object):
+  """Base model config for DNN benchmarks."""
+
+  def __init__(self,
+               model_name,
+               batch_size,
+               learning_rate,
+               fp16_loss_scale,
+               params=None):
+    self.model_name = model_name
+    self.batch_size = batch_size
+    self.default_batch_size = batch_size
+    self.learning_rate = learning_rate
+    # TODO(reedwm) Set custom loss scales for each model instead of using the
+    # default of 128.
+    self.fp16_loss_scale = fp16_loss_scale
+
+    # use_tf_layers specifies whether to build the model using tf.layers.
+    # fp16_vars specifies whether to create the variables in float16.
+    if params:
+      self.use_tf_layers = params.use_tf_layers
+      self.fp16_vars = params.fp16_vars
+      self.data_type = tf.float16 if params.use_fp16 else tf.float32
+    else:
+      self.use_tf_layers = True
+      self.fp16_vars = False
+      self.data_type = tf.float32
+
+  def get_model_name(self):
+    return self.model_name
+
+  def get_batch_size(self):
+    return self.batch_size
+
+  def set_batch_size(self, batch_size):
+    self.batch_size = batch_size
+
+  def get_default_batch_size(self):
+    return self.default_batch_size
+
+  def get_fp16_loss_scale(self):
+    return self.fp16_loss_scale
+
+  def filter_l2_loss_vars(self, variables):
+    """Filters out variables that the L2 loss should not be computed for.
+
+    By default, this filters out batch normalization variables and keeps all
+    other variables. This behavior can be overridden by subclasses.
+
+    Args:
+      variables: A list of the trainable variables.
+
+    Returns:
+      A list of variables that the L2 loss should be computed for.
+    """
+    mlperf.logger.log(key=mlperf.tags.MODEL_EXCLUDE_BN_FROM_L2,
+                      value=True)
+    return [v for v in variables if 'batchnorm' not in v.name]
+
+  def get_learning_rate(self, global_step, batch_size):
+    del global_step
+    del batch_size
+    return self.learning_rate
+
+  def get_input_shapes(self, subset):
+    """Returns the list of expected shapes of all the inputs to this model."""
+    del subset
+    raise NotImplementedError('Must be implemented in derived classes')
+
+  def get_input_data_types(self, subset):
+    """Returns the list of data types of all the inputs to this model."""
+    del subset
+    raise NotImplementedError('Must be implemented in derived classes')
+
+  def get_synthetic_inputs(self, input_name, nclass):
+    """Returns the ops to generate synthetic inputs."""
+    raise NotImplementedError('Must be implemented in derived classes')
+
+  def build_network(self, inputs, phase_train, nclass):
+    """Builds the forward pass of the model.
+
+    Args:
+      inputs: The list of inputs, including labels
+      phase_train: True during training. False during evaluation.
+      nclass: Number of classes that the inputs can belong to.
+
+    Returns:
+      A BuildNetworkResult which contains the logits and model-specific extra
+        information.
+    """
+    raise NotImplementedError('Must be implemented in derived classes')
+
+  def loss_function(self, inputs, build_network_result):
+    """Returns the op to measure the loss of the model.
+
+    Args:
+      inputs: the input list of the model.
+      build_network_result: a BuildNetworkResult returned by build_network().
+
+    Returns:
+      The loss tensor of the model.
+    """
+    raise NotImplementedError('Must be implemented in derived classes')
+
+  # TODO(laigd): have accuracy_function() take build_network_result instead.
+  def accuracy_function(self, inputs, logits):
+    """Returns the ops to measure the accuracy of the model."""
+    raise NotImplementedError('Must be implemented in derived classes')
+
+  def postprocess(self, results):
+    """Postprocess results returned from model in Python."""
+    return results
+
+  def reached_target(self):
+    """Define custom methods to stop training when model's target is reached."""
+    return False
+
+
+class CNNModel(Model):
+  """Base model configuration for CNN benchmarks."""
+
+  # TODO(laigd): reduce the number of parameters and read everything from
+  # params.
+  def __init__(self,
+               model,
+               image_size,
+               batch_size,
+               learning_rate,
+               layer_counts=None,
+               fp16_loss_scale=128,
+               params=None):
+    super(CNNModel, self).__init__(
+        model, batch_size, learning_rate, fp16_loss_scale,
+        params=params)
+    self.image_size = image_size
+    self.layer_counts = layer_counts
+    self.depth = 3
+    self.params = params
+    self.data_format = params.data_format if params else 'NCHW'
+
+  def get_layer_counts(self):
+    return self.layer_counts
+
+  def skip_final_affine_layer(self):
+    """Returns if the caller of this class should skip the final affine layer.
+
+    Normally, this class adds a final affine layer to the model after calling
+    self.add_inference(), to generate the logits. If a subclass override this
+    method to return True, the caller should not add the final affine layer.
+
+    This is useful for tests.
+    """
+    return False
+
+  def add_backbone_saver(self):
+    """Creates a tf.train.Saver as self.backbone_saver for loading backbone.
+
+    A tf.train.Saver must be created and saved in self.backbone_saver before
+    calling load_backbone_model, with correct variable name mapping to load
+    variables from checkpoint correctly into the current model.
+    """
+    raise NotImplementedError(self.getName() + ' does not have backbone model.')
+
+  def load_backbone_model(self, sess, backbone_model_path):
+    """Loads variable values from a pre-trained backbone model.
+
+    This should be used at the beginning of the training process for transfer
+    learning models using checkpoints of base models.
+
+    Args:
+      sess: session to train the model.
+      backbone_model_path: path to backbone model checkpoint file.
+    """
+    del sess, backbone_model_path
+    raise NotImplementedError(self.getName() + ' does not have backbone model.')
+
+  def add_inference(self, cnn):
+    """Adds the core layers of the CNN's forward pass.
+
+    This should build the forward pass layers, except for the initial transpose
+    of the images and the final Dense layer producing the logits. The layers
+    should be build with the ConvNetBuilder `cnn`, so that when this function
+    returns, `cnn.top_layer` and `cnn.top_size` refer to the last layer and the
+    number of units of the layer layer, respectively.
+
+    Args:
+      cnn: A ConvNetBuilder to build the forward pass layers with.
+    """
+    del cnn
+    raise NotImplementedError('Must be implemented in derived classes')
+
+  def get_input_data_types(self, subset):
+    """Return data types of inputs for the specified subset."""
+    del subset  # Same types for both 'train' and 'validation' subsets.
+    return [self.data_type, tf.int32]
+
+  def get_input_shapes(self, subset):
+    """Return data shapes of inputs for the specified subset."""
+    del subset  # Same shapes for both 'train' and 'validation' subsets.
+    # Each input is of shape [batch_size, height, width, depth]
+    # Each label is of shape [batch_size]
+    return [[self.batch_size, self.image_size, self.image_size, self.depth],
+            [self.batch_size]]
+
+  def get_synthetic_inputs(self, input_name, nclass):
+    # Synthetic input should be within [0, 255].
+    image_shape, label_shape = self.get_input_shapes('train')
+    inputs = tf.truncated_normal(
+        image_shape,
+        dtype=self.data_type,
+        mean=127,
+        stddev=60,
+        name=self.model_name + '_synthetic_inputs')
+    inputs = variables_module.VariableV1(
+        inputs, trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES],
+        name=input_name)
+    labels = tf.random_uniform(
+        label_shape,
+        minval=0,
+        maxval=nclass - 1,
+        dtype=tf.int32,
+        name=self.model_name + '_synthetic_labels')
+    return (inputs, labels)
+
+  def gpu_preprocess_nhwc(self, images, phase_train=True):
+    del phase_train
+    return images
+
+  def build_network(self,
+                    inputs,
+                    phase_train=True,
+                    nclass=1001):
+    """Returns logits from input images.
+
+    Args:
+      inputs: The input images and labels
+      phase_train: True during training. False during evaluation.
+      nclass: Number of classes that the images can belong to.
+
+    Returns:
+      A BuildNetworkResult which contains the logits and model-specific extra
+        information.
+    """
+    images = inputs[0]
+    images = self.gpu_preprocess_nhwc(images, phase_train)
+    if self.data_format == 'NCHW':
+      images = tf.transpose(images, [0, 3, 1, 2])
+    var_type = tf.float32
+    if self.data_type == tf.float16 and self.fp16_vars:
+      var_type = tf.float16
+    network = convnet_builder.ConvNetBuilder(
+        images, self.depth, phase_train, self.use_tf_layers, self.data_format,
+        self.data_type, var_type)
+    with tf.variable_scope('cg', custom_getter=network.get_custom_getter()):
+      self.add_inference(network)
+      # Add the final fully-connected class layer
+      logits = (
+          network.affine(nclass, activation='linear')
+          if not self.skip_final_affine_layer() else network.top_layer)
+      mlperf.logger.log(key=mlperf.tags.MODEL_HP_FINAL_SHAPE,
+                        value=logits.shape.as_list()[1:])
+      aux_logits = None
+      if network.aux_top_layer is not None:
+        with network.switch_to_aux_top_layer():
+          aux_logits = network.affine(nclass, activation='linear', stddev=0.001)
+    if self.data_type == tf.float16:
+      # TODO(reedwm): Determine if we should do this cast here.
+      logits = tf.cast(logits, tf.float32)
+      if aux_logits is not None:
+        aux_logits = tf.cast(aux_logits, tf.float32)
+    return BuildNetworkResult(
+        logits=logits, extra_info=None if aux_logits is None else aux_logits)
+
+  def loss_function(self, inputs, build_network_result):
+    """Returns the op to measure the loss of the model."""
+    logits = build_network_result.logits
+    _, labels = inputs
+    # TODO(laigd): consider putting the aux logit in the Inception model,
+    # which could call super.loss_function twice, once with the normal logits
+    # and once with the aux logits.
+    aux_logits = build_network_result.extra_info
+    with tf.name_scope('xentropy'):
+      mlperf.logger.log(key=mlperf.tags.MODEL_HP_LOSS_FN, value=mlperf.tags.CCE)
+      cross_entropy = tf.losses.sparse_softmax_cross_entropy(
+          logits=logits, labels=labels)
+      loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')
+    if aux_logits is not None:
+      with tf.name_scope('aux_xentropy'):
+        aux_cross_entropy = tf.losses.sparse_softmax_cross_entropy(
+            logits=aux_logits, labels=labels)
+        aux_loss = 0.4 * tf.reduce_mean(aux_cross_entropy, name='aux_loss')
+        loss = tf.add_n([loss, aux_loss])
+    return loss
+
+  def accuracy_function(self, inputs, logits):
+    """Returns the ops to measure the accuracy of the model."""
+    _, labels = inputs
+    top_1_op = tf.reduce_sum(
+        tf.cast(tf.nn.in_top_k(logits, labels, 1), self.data_type))
+    top_5_op = tf.reduce_sum(
+        tf.cast(tf.nn.in_top_k(logits, labels, 5), self.data_type))
+    return {'top_1_accuracy': top_1_op, 'top_5_accuracy': top_5_op}

TensorFlow/ComputeVision/Classification/benchmarks-master/scripts/tf_cnn_benchmarks/models/model_config.py

+# 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.
+# ==============================================================================
+
+"""Model configurations for CNN benchmarks.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from functools import partial
+
+from models import alexnet_model
+from models import densenet_model
+from models import googlenet_model
+from models import inception_model
+from models import lenet_model
+from models import official_resnet_model
+from models import overfeat_model
+from models import resnet_model
+from models import trivial_model
+from models import vgg_model
+from models.experimental import deepspeech
+from models.experimental import official_ncf_model
+
+
+_model_name_to_imagenet_model = {
+    'vgg11': vgg_model.Vgg11Model,
+    'vgg16': vgg_model.Vgg16Model,
+    'vgg19': vgg_model.Vgg19Model,
+    'lenet': lenet_model.Lenet5Model,
+    'googlenet': googlenet_model.GooglenetModel,
+    'overfeat': overfeat_model.OverfeatModel,
+    'alexnet': alexnet_model.AlexnetModel,
+    'trivial': trivial_model.TrivialModel,
+    'inception3': inception_model.Inceptionv3Model,
+    'inception4': inception_model.Inceptionv4Model,
+    'official_resnet18_v2':
+        partial(official_resnet_model.ImagenetResnetModel, 18),
+    'official_resnet34_v2':
+        partial(official_resnet_model.ImagenetResnetModel, 34),
+    'official_resnet50_v2':
+        partial(official_resnet_model.ImagenetResnetModel, 50),
+    'official_resnet101_v2':
+        partial(official_resnet_model.ImagenetResnetModel, 101),
+    'official_resnet152_v2':
+        partial(official_resnet_model.ImagenetResnetModel, 152),
+    'official_resnet200_v2':
+        partial(official_resnet_model.ImagenetResnetModel, 200),
+    'official_resnet18':
+        partial(official_resnet_model.ImagenetResnetModel, 18, version=1),
+    'official_resnet34':
+        partial(official_resnet_model.ImagenetResnetModel, 34, version=1),
+    'official_resnet50':
+        partial(official_resnet_model.ImagenetResnetModel, 50, version=1),
+    'official_resnet101':
+        partial(official_resnet_model.ImagenetResnetModel, 101, version=1),
+    'official_resnet152':
+        partial(official_resnet_model.ImagenetResnetModel, 152, version=1),
+    'official_resnet200':
+        partial(official_resnet_model.ImagenetResnetModel, 200, version=1),
+    'resnet50': resnet_model.create_resnet50_model,
+    'resnet50_v1.5': resnet_model.create_resnet50_v1_5_model,
+    'resnet50_v2': resnet_model.create_resnet50_v2_model,
+    'resnet101': resnet_model.create_resnet101_model,
+    'resnet101_v2': resnet_model.create_resnet101_v2_model,
+    'resnet152': resnet_model.create_resnet152_model,
+    'resnet152_v2': resnet_model.create_resnet152_v2_model,
+    'ncf': official_ncf_model.NcfModel,
+}
+
+
+_model_name_to_cifar_model = {
+    'alexnet': alexnet_model.AlexnetCifar10Model,
+    'resnet20': resnet_model.create_resnet20_cifar_model,
+    'resnet20_v2': resnet_model.create_resnet20_v2_cifar_model,
+    'resnet32': resnet_model.create_resnet32_cifar_model,
+    'resnet32_v2': resnet_model.create_resnet32_v2_cifar_model,
+    'resnet44': resnet_model.create_resnet44_cifar_model,
+    'resnet44_v2': resnet_model.create_resnet44_v2_cifar_model,
+    'resnet56': resnet_model.create_resnet56_cifar_model,
+    'resnet56_v2': resnet_model.create_resnet56_v2_cifar_model,
+    'resnet110': resnet_model.create_resnet110_cifar_model,
+    'resnet110_v2': resnet_model.create_resnet110_v2_cifar_model,
+    'trivial': trivial_model.TrivialCifar10Model,
+    'densenet40_k12': densenet_model.create_densenet40_k12_model,
+    'densenet100_k12': densenet_model.create_densenet100_k12_model,
+    'densenet100_k24': densenet_model.create_densenet100_k24_model,
+}
+
+
+_model_name_to_object_detection_model = {
+    'trivial': trivial_model.TrivialSSD300Model,
+}
+
+
+def _get_model_map(dataset_name):
+  """Get name to model map for specified dataset."""
+  if dataset_name == 'cifar10':
+    return _model_name_to_cifar_model
+  elif dataset_name in ('imagenet', 'synthetic'):
+    return _model_name_to_imagenet_model
+  elif dataset_name == 'librispeech':
+    return {'deepspeech2': deepspeech.DeepSpeech2Model}
+  elif dataset_name == 'coco':
+    return _model_name_to_object_detection_model
+  else:
+    raise ValueError('Invalid dataset name: %s' % dataset_name)
+
+
+# A model map dict can have this string as a value when TF2 is used, to indicate
+# the model is only available in TF1.
+_TF1_ONLY_STRING = 'TF1_ONLY'
+
+
+def get_model_config(model_name, dataset, params):
+  """Map model name to model network configuration."""
+  model_map = _get_model_map(dataset.name)
+  if model_name not in model_map:
+    raise ValueError('Invalid model name \'%s\' for dataset \'%s\'' %
+                     (model_name, dataset.name))
+  model = model_map[model_name](params=params)
+  if model == 'TF1_ONLY':
+    raise ValueError('Model \'%s\' can only be used with TensorFlow 1'
+                     % (model_name,))
+  return model
+
+
+def register_model(model_name, dataset_name, model_func):
+  """Register a new model that can be obtained with `get_model_config`."""
+  model_map = _get_model_map(dataset_name)
+  if model_name in model_map:
+    raise ValueError('Model "%s" is already registered for dataset "%s"' %
+                     (model_name, dataset_name))
+  model_map[model_name] = model_func
+
+
+# pylint: disable=g-import-not-at-top
+try:
+  from tensorflow.contrib import slim  # pylint: disable=unused-import
+  can_import_contrib = True
+except ImportError:
+  can_import_contrib = False
+
+
+def register_tf1_models():
+  """Registers all the TensorFlow 1-only models.
+
+  TF 1-only models use contrib, which was removed in TF 2. If contrib can be
+  imported, the TF 1-only models are registered normally. If contrib cannot be
+  imported, the models are registered with the 'TF1_ONLY' string instead, which
+  will cause an error to be thrown if these models are used.
+  """
+  if can_import_contrib:
+    from models.tf1_only import mobilenet_v2
+    from models.tf1_only import nasnet_model
+    from models.tf1_only import ssd_model
+    register_model('mobilenet', 'imagenet', mobilenet_v2.MobilenetModel)
+    register_model('nasnet', 'imagenet', nasnet_model.NasnetModel)
+    register_model('nasnetlarge', 'imagenet', nasnet_model.NasnetLargeModel)
+    register_model('nasnet', 'cifar10', nasnet_model.NasnetCifarModel)
+    register_model('ssd300', 'coco', ssd_model.SSD300Model)
+  else:
+    register_model('mobilenet', 'imagenet', 'TF1_ONLY')
+    register_model('nasnet', 'imagenet', 'TF1_ONLY')
+    register_model('nasnetlarge', 'imagenet', 'TF1_ONLY')
+    register_model('nasnet', 'cifar10', 'TF1_ONLY')
+    register_model('ssd300', 'coco', 'TF1_ONLY')
+

TensorFlow/ComputeVision/Classification/benchmarks-master/scripts/tf_cnn_benchmarks/models/official_resnet_model.py

+# 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.
+# ==============================================================================
+"""Import official resnet models."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow.compat.v1 as tf
+import datasets
+from models import model as model_lib
+
+
+class ImagenetResnetModel(model_lib.CNNModel):
+  """Official resnet models."""
+
+  def __init__(self, resnet_size, version=2, params=None):
+    """These are the parameters that work for Imagenet data.
+
+    Args:
+      resnet_size: The number of convolutional layers needed in the model.
+      version: 1 or 2 for v1 or v2, respectively.
+      params: params passed by BenchmarkCNN.
+    """
+    default_batch_sizes = {
+        50: 128,
+        101: 32,
+        152: 32
+    }
+    batch_size = default_batch_sizes.get(resnet_size, 32)
+    default_learning_rate = 0.0125 * batch_size / 32
+    model_name = 'official_resnet_{}_v{}'.format(resnet_size, version)
+    super(ImagenetResnetModel, self).__init__(
+        model_name, 224, batch_size, default_learning_rate, params=params)
+    self.resnet_size = resnet_size
+    self.version = version
+
+  def get_learning_rate(self, global_step, batch_size):
+    num_batches_per_epoch = (
+        float(datasets.IMAGENET_NUM_TRAIN_IMAGES) / batch_size)
+    boundaries = [int(num_batches_per_epoch * x) for x in [30, 60, 80, 90]]
+    values = [1, 0.1, 0.01, 0.001, 0.0001]
+    adjusted_learning_rate = (
+        self.learning_rate / self.default_batch_size * batch_size)
+    values = [v * adjusted_learning_rate for v in values]
+    return tf.train.piecewise_constant(global_step, boundaries, values)
+
+  def build_network(self, images, phase_train=True, nclass=1001,
+                    data_type=tf.float32):
+    # pylint: disable=g-import-not-at-top
+    try:
+      from official.r1.resnet.imagenet_main import ImagenetModel
+    except ImportError:
+      tf.logging.fatal('Please include tensorflow/models to the PYTHONPATH.')
+      raise
+    images = tf.cast(images, data_type)
+    model_class = ImagenetModel(resnet_size=self.resnet_size,
+                                resnet_version=self.version,
+                                # The official model dtype seems to be ignored,
+                                # as the dtype it uses is the dtype of the input
+                                # images. Doesn't hurt to set it though.
+                                dtype=data_type)
+    logits = model_class(images, phase_train)
+    logits = tf.cast(logits, tf.float32)
+    return model_lib.BuildNetworkResult(logits=logits, extra_info=None)

TensorFlow/ComputeVision/Classification/benchmarks-master/scripts/tf_cnn_benchmarks/models/overfeat_model.py

+# 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.
+# ==============================================================================
+"""Overfeat model configuration.
+
+References:
+  OverFeat: Integrated Recognition, Localization and Detection using
+  Convolutional Networks
+  Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus,
+  Yann LeCun, 2014
+  http://arxiv.org/abs/1312.6229
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from models import model
+
+
+class OverfeatModel(model.CNNModel):
+  """OverfeatModel."""
+
+  def __init__(self, params=None):
+    super(OverfeatModel, self).__init__(
+        'overfeat', 231, 32, 0.005, params=params)
+
+  def add_inference(self, cnn):
+    # Note: VALID requires padding the images by 3 in width and height
+    cnn.conv(96, 11, 11, 4, 4, mode='VALID')
+    cnn.mpool(2, 2)
+    cnn.conv(256, 5, 5, 1, 1, mode='VALID')
+    cnn.mpool(2, 2)
+    cnn.conv(512, 3, 3)
+    cnn.conv(1024, 3, 3)
+    cnn.conv(1024, 3, 3)
+    cnn.mpool(2, 2)
+    cnn.reshape([-1, 1024 * 6 * 6])
+    cnn.affine(3072)
+    cnn.dropout()
+    cnn.affine(4096)
+    cnn.dropout()

TensorFlow/ComputeVision/Classification/benchmarks-master/scripts/tf_cnn_benchmarks/models/resnet_model.py

+# 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.
+# ==============================================================================
+
+"""Resnet model configuration.
+
+References:
+  Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
+  Deep Residual Learning for Image Recognition
+  arXiv:1512.03385 (2015)
+
+  Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
+  Identity Mappings in Deep Residual Networks
+  arXiv:1603.05027 (2016)
+
+  Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy,
+  Alan L. Yuille
+  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets,
+  Atrous Convolution, and Fully Connected CRFs
+  arXiv:1606.00915 (2016)
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+from six.moves import xrange  # pylint: disable=redefined-builtin
+import tensorflow.compat.v1 as tf
+import datasets
+import mlperf
+from models import model as model_lib
+
+
+def bottleneck_block_v1(cnn, depth, depth_bottleneck, stride):
+  """Bottleneck block with identity short-cut for ResNet v1.
+
+  Args:
+    cnn: the network to append bottleneck blocks.
+    depth: the number of output filters for this bottleneck block.
+    depth_bottleneck: the number of bottleneck filters for this block.
+    stride: Stride used in the first layer of the bottleneck block.
+  """
+  input_layer = cnn.top_layer
+  in_size = cnn.top_size
+  name_key = 'resnet_v1'
+  name = name_key + str(cnn.counts[name_key])
+  cnn.counts[name_key] += 1
+
+  with tf.variable_scope(name):
+    if depth == in_size:
+      if stride == 1:
+        shortcut = input_layer
+      else:
+        shortcut = cnn.apool(
+            1, 1, stride, stride, input_layer=input_layer,
+            num_channels_in=in_size)
+        mlperf.logger.log_projection(input_tensor=input_layer,
+                                     output_tensor=shortcut)
+    else:
+      shortcut = cnn.conv(
+          depth, 1, 1, stride, stride, activation=None,
+          use_batch_norm=True, input_layer=input_layer,
+          num_channels_in=in_size, bias=None)
+    cnn.conv(depth_bottleneck, 1, 1, stride, stride,
+             input_layer=input_layer, num_channels_in=in_size,
+             use_batch_norm=True, bias=None)
+    cnn.conv(depth_bottleneck, 3, 3, 1, 1, mode='SAME_RESNET',
+             use_batch_norm=True, bias=None)
+    res = cnn.conv(depth, 1, 1, 1, 1, activation=None,
+                   use_batch_norm=True, bias=None)
+    mlperf.logger.log(key=mlperf.tags.MODEL_HP_SHORTCUT_ADD)
+    mlperf.logger.log(key=mlperf.tags.MODEL_HP_RELU)
+    output = tf.nn.relu(shortcut + res)
+    cnn.top_layer = output
+    cnn.top_size = depth
+
+
+def bottleneck_block_v1_5(cnn, depth, depth_bottleneck, stride):
+  """Bottleneck block with identity short-cut for ResNet v1.5.
+
+  ResNet v1.5 is the informal name for ResNet v1 where stride 2 is used in the
+  first 3x3 convolution of each block instead of the first 1x1 convolution.
+
+  First seen at https://github.com/facebook/fb.resnet.torch. Used in the paper
+  "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour"
+  (arXiv:1706.02677v2) and by fast.ai to train to accuracy in 45 epochs using
+  multiple image sizes.
+
+  Args:
+    cnn: the network to append bottleneck blocks.
+    depth: the number of output filters for this bottleneck block.
+    depth_bottleneck: the number of bottleneck filters for this block.
+    stride: Stride used in the first layer of the bottleneck block.
+  """
+  input_layer = cnn.top_layer
+  in_size = cnn.top_size
+  name_key = 'resnet_v1.5'
+  name = name_key + str(cnn.counts[name_key])
+  cnn.counts[name_key] += 1
+
+  with tf.variable_scope(name):
+    if depth == in_size:
+      if stride == 1:
+        shortcut = input_layer
+      else:
+        shortcut = cnn.apool(
+            1, 1, stride, stride, input_layer=input_layer,
+            num_channels_in=in_size)
+        mlperf.logger.log_projection(input_tensor=input_layer,
+                                     output_tensor=shortcut)
+    else:
+      shortcut = cnn.conv(
+          depth, 1, 1, stride, stride, activation=None,
+          use_batch_norm=True, input_layer=input_layer,
+          num_channels_in=in_size, bias=None)
+      mlperf.logger.log_projection(input_tensor=input_layer,
+                                   output_tensor=shortcut)
+    cnn.conv(depth_bottleneck, 1, 1, 1, 1,
+             input_layer=input_layer, num_channels_in=in_size,
+             use_batch_norm=True, bias=None)
+    cnn.conv(depth_bottleneck, 3, 3, stride, stride, mode='SAME_RESNET',
+             use_batch_norm=True, bias=None)
+    res = cnn.conv(depth, 1, 1, 1, 1, activation=None,
+                   use_batch_norm=True, bias=None)
+    mlperf.logger.log(key=mlperf.tags.MODEL_HP_SHORTCUT_ADD)
+    mlperf.logger.log(key=mlperf.tags.MODEL_HP_RELU)
+    output = tf.nn.relu(shortcut + res)
+    cnn.top_layer = output
+    cnn.top_size = depth
+
+
+def bottleneck_block_v2(cnn, depth, depth_bottleneck, stride):
+  """Bottleneck block with identity short-cut for ResNet v2.
+
+  The main difference from v1 is that a batch norm and relu are done at the
+  start of the block, instead of the end. This initial batch norm and relu is
+  collectively called a pre-activation.
+
+  Args:
+    cnn: the network to append bottleneck blocks.
+    depth: the number of output filters for this bottleneck block.
+    depth_bottleneck: the number of bottleneck filters for this block.
+    stride: Stride used in the first layer of the bottleneck block.
+  """
+  input_layer = cnn.top_layer
+  in_size = cnn.top_size
+  name_key = 'resnet_v2'
+  name = name_key + str(cnn.counts[name_key])
+  cnn.counts[name_key] += 1
+
+  preact = cnn.batch_norm()
+  mlperf.logger.log(key=mlperf.tags.MODEL_HP_RELU)
+  preact = tf.nn.relu(preact)
+  with tf.variable_scope(name):
+    if depth == in_size:
+      if stride == 1:
+        shortcut = input_layer
+      else:
+        shortcut = cnn.apool(
+            1, 1, stride, stride, input_layer=input_layer,
+            num_channels_in=in_size)
+        mlperf.logger.log_projection(input_tensor=input_layer,
+                                     output_tensor=shortcut)
+    else:
+      shortcut = cnn.conv(
+          depth, 1, 1, stride, stride, activation=None, use_batch_norm=False,
+          input_layer=preact, num_channels_in=in_size, bias=None)
+    cnn.conv(depth_bottleneck, 1, 1, stride, stride,
+             input_layer=preact, num_channels_in=in_size,
+             use_batch_norm=True, bias=None)
+    cnn.conv(depth_bottleneck, 3, 3, 1, 1, mode='SAME_RESNET',
+             use_batch_norm=True, bias=None)
+    res = cnn.conv(depth, 1, 1, 1, 1, activation=None,
+                   use_batch_norm=False, bias=None)
+    mlperf.logger.log(key=mlperf.tags.MODEL_HP_SHORTCUT_ADD)
+    output = shortcut + res
+    cnn.top_layer = output
+    cnn.top_size = depth
+
+
+def bottleneck_block(cnn, depth, depth_bottleneck, stride, version):
+  """Bottleneck block with identity short-cut.
+
+  Args:
+    cnn: the network to append bottleneck blocks.
+    depth: the number of output filters for this bottleneck block.
+    depth_bottleneck: the number of bottleneck filters for this block.
+    stride: Stride used in the first layer of the bottleneck block.
+    version: version of ResNet to build.
+  """
+  mlperf.logger.log(key=mlperf.tags.MODEL_HP_BLOCK_TYPE,
+                    value=mlperf.tags.BOTTLENECK_BLOCK)
+  mlperf.logger.log_begin_block(
+      input_tensor=cnn.top_layer, block_type=mlperf.tags.BOTTLENECK_BLOCK)
+  if version == 'v2':
+    bottleneck_block_v2(cnn, depth, depth_bottleneck, stride)
+  elif version == 'v1.5':
+    bottleneck_block_v1_5(cnn, depth, depth_bottleneck, stride)
+  else:
+    bottleneck_block_v1(cnn, depth, depth_bottleneck, stride)
+  mlperf.logger.log_end_block(output_tensor=cnn.top_layer)
+
+
+def residual_block(cnn, depth, stride, version, projection_shortcut=False):
+  """Residual block with identity short-cut.
+
+  Args:
+    cnn: the network to append residual blocks.
+    depth: the number of output filters for this residual block.
+    stride: Stride used in the first layer of the residual block.
+    version: version of ResNet to build.
+    projection_shortcut: indicator of using projection shortcut, even if top
+      size and depth are equal
+  """
+  pre_activation = True if version == 'v2' else False
+  input_layer = cnn.top_layer
+  in_size = cnn.top_size
+
+  if projection_shortcut:
+    shortcut = cnn.conv(
+        depth, 1, 1, stride, stride, activation=None,
+        use_batch_norm=True, input_layer=input_layer,
+        num_channels_in=in_size, bias=None)
+  elif in_size != depth:
+    # Plan A of shortcut.
+    shortcut = cnn.apool(1, 1, stride, stride,
+                         input_layer=input_layer,
+                         num_channels_in=in_size)
+    padding = (depth - in_size) // 2
+    if cnn.channel_pos == 'channels_last':
+      shortcut = tf.pad(
+          shortcut, [[0, 0], [0, 0], [0, 0], [padding, padding]])
+    else:
+      shortcut = tf.pad(
+          shortcut, [[0, 0], [padding, padding], [0, 0], [0, 0]])
+  else:
+    shortcut = input_layer
+  if pre_activation:
+    res = cnn.batch_norm(input_layer)
+    res = tf.nn.relu(res)
+  else:
+    res = input_layer
+  cnn.conv(depth, 3, 3, stride, stride,
+           input_layer=res, num_channels_in=in_size,
+           use_batch_norm=True, bias=None)
+  if pre_activation:
+    res = cnn.conv(depth, 3, 3, 1, 1, activation=None,
+                   use_batch_norm=False, bias=None)
+    output = shortcut + res
+  else:
+    res = cnn.conv(depth, 3, 3, 1, 1, activation=None,
+                   use_batch_norm=True, bias=None)
+    output = tf.nn.relu(shortcut + res)
+  cnn.top_layer = output
+  cnn.top_size = depth
+
+
+class ResnetModel(model_lib.CNNModel):
+  """Resnet cnn network configuration."""
+
+  def __init__(self, model, layer_counts, params=None):
+    default_batch_sizes = {
+        'resnet50': 64,
+        'resnet101': 32,
+        'resnet152': 32,
+        'resnet50_v1.5': 64,
+        'resnet101_v1.5': 32,
+        'resnet152_v1.5': 32,
+        'resnet50_v2': 64,
+        'resnet101_v2': 32,
+        'resnet152_v2': 32,
+    }
+    batch_size = default_batch_sizes.get(model, 32)
+    # The ResNet paper uses a starting lr of .1 at bs=256.
+    self.base_lr_batch_size = 256
+    base_lr = 0.128
+    if params and params.resnet_base_lr:
+      base_lr = params.resnet_base_lr
+
+    super(ResnetModel, self).__init__(model, 224, batch_size, base_lr,
+                                      layer_counts, params=params)
+    if 'v2' in model:
+      self.version = 'v2'
+    elif 'v1.5' in model:
+      self.version = 'v1.5'
+    else:
+      self.version = 'v1'
+
+  def add_inference(self, cnn):
+    if self.layer_counts is None:
+      raise ValueError('Layer counts not specified for %s' % self.get_model())
+    # Drop batch size from shape logging.
+    mlperf.logger.log(key=mlperf.tags.MODEL_HP_INITIAL_SHAPE,
+                      value=cnn.top_layer.shape.as_list()[1:])
+    cnn.use_batch_norm = True
+    cnn.batch_norm_config = {'decay': 0.9, 'epsilon': 1e-5, 'scale': True}
+    cnn.conv(64, 7, 7, 2, 2, mode='SAME_RESNET', use_batch_norm=True)
+    cnn.mpool(3, 3, 2, 2, mode='SAME')
+    for _ in xrange(self.layer_counts[0]):
+      bottleneck_block(cnn, 256, 64, 1, self.version)
+    for i in xrange(self.layer_counts[1]):
+      stride = 2 if i == 0 else 1
+      bottleneck_block(cnn, 512, 128, stride, self.version)
+    for i in xrange(self.layer_counts[2]):
+      stride = 2 if i == 0 else 1
+      bottleneck_block(cnn, 1024, 256, stride, self.version)
+    for i in xrange(self.layer_counts[3]):
+      stride = 2 if i == 0 else 1
+      bottleneck_block(cnn, 2048, 512, stride, self.version)
+    if self.version == 'v2':
+      cnn.batch_norm()
+      cnn.top_layer = tf.nn.relu(cnn.top_layer)
+    cnn.spatial_mean()
+
+  def get_learning_rate(self, global_step, batch_size):
+    rescaled_lr = self.get_scaled_base_learning_rate(batch_size)
+    num_batches_per_epoch = (
+        datasets.IMAGENET_NUM_TRAIN_IMAGES / batch_size)
+    boundaries = [int(num_batches_per_epoch * x) for x in [30, 60, 80, 90]]
+    values = [1, 0.1, 0.01, 0.001, 0.0001]
+    values = [rescaled_lr * v for v in values]
+    lr = tf.train.piecewise_constant(global_step, boundaries, values)
+    warmup_steps = int(num_batches_per_epoch * 5)
+    mlperf.logger.log(key=mlperf.tags.OPT_LR_WARMUP_STEPS, value=warmup_steps)
+    warmup_lr = (
+        rescaled_lr * tf.cast(global_step, tf.float32) / tf.cast(
+            warmup_steps, tf.float32))
+    return tf.cond(global_step < warmup_steps, lambda: warmup_lr, lambda: lr)
+
+  def get_scaled_base_learning_rate(self, batch_size):
+    """Calculates base learning rate for creating lr schedule.
+
+    In replicated mode, gradients are summed rather than averaged which, with
+    the sgd and momentum optimizers, increases the effective learning rate by
+    lr * num_gpus. Dividing the base lr by num_gpus negates the increase.
+
+    Args:
+      batch_size: Total batch-size.
+
+    Returns:
+      Base learning rate to use to create lr schedule.
+    """
+    base_lr = self.learning_rate
+    if self.params.variable_update == 'replicated':
+      base_lr = self.learning_rate / self.params.num_gpus
+    scaled_lr = base_lr * (batch_size / self.base_lr_batch_size)
+    return scaled_lr
+
+
+def create_resnet50_model(params):
+  return ResnetModel('resnet50', (3, 4, 6, 3), params=params)
+
+
+def create_resnet50_v1_5_model(params):
+  return ResnetModel('resnet50_v1.5', (3, 4, 6, 3), params=params)
+
+
+def create_resnet50_v2_model(params):
+  return ResnetModel('resnet50_v2', (3, 4, 6, 3), params=params)
+
+
+def create_resnet101_model(params):
+  return ResnetModel('resnet101', (3, 4, 23, 3), params=params)
+
+
+def create_resnet101_v2_model(params):
+  return ResnetModel('resnet101_v2', (3, 4, 23, 3), params=params)
+
+
+def create_resnet152_model(params):
+  return ResnetModel('resnet152', (3, 8, 36, 3), params=params)
+
+
+def create_resnet152_v2_model(params):
+  return ResnetModel('resnet152_v2', (3, 8, 36, 3), params=params)
+
+
+class ResnetCifar10Model(model_lib.CNNModel):
+  """Resnet cnn network configuration for Cifar 10 dataset.
+
+  V1 model architecture follows the one defined in the paper:
+  https://arxiv.org/pdf/1512.03385.pdf.
+
+  V2 model architecture follows the one defined in the paper:
+  https://arxiv.org/pdf/1603.05027.pdf.
+  """
+
+  def __init__(self, model, layer_counts, params=None):
+    if 'v2' in model:
+      self.version = 'v2'
+    else:
+      self.version = 'v1'
+    super(ResnetCifar10Model, self).__init__(
+        model, 32, 128, 0.1, layer_counts, params=params)
+
+  def add_inference(self, cnn):
+    if self.layer_counts is None:
+      raise ValueError('Layer counts not specified for %s' % self.get_model())
+
+    cnn.use_batch_norm = True
+    cnn.batch_norm_config = {'decay': 0.9, 'epsilon': 1e-5, 'scale': True}
+    if self.version == 'v2':
+      cnn.conv(16, 3, 3, 1, 1, use_batch_norm=True)
+    else:
+      cnn.conv(16, 3, 3, 1, 1, activation=None, use_batch_norm=True)
+    for i in xrange(self.layer_counts[0]):
+      # reshape to batch_size x 16 x 32 x 32
+      residual_block(cnn, 16, 1, self.version)
+    for i in xrange(self.layer_counts[1]):
+      # Subsampling is performed at the first convolution with a stride of 2
+      stride = 2 if i == 0 else 1
+      # reshape to batch_size x 32 x 16 x 16
+      residual_block(cnn, 32, stride, self.version)
+    for i in xrange(self.layer_counts[2]):
+      stride = 2 if i == 0 else 1
+      # reshape to batch_size x 64 x 8 x 8
+      residual_block(cnn, 64, stride, self.version)
+    if self.version == 'v2':
+      cnn.batch_norm()
+      cnn.top_layer = tf.nn.relu(cnn.top_layer)
+    cnn.spatial_mean()
+
+  def get_learning_rate(self, global_step, batch_size):
+    num_batches_per_epoch = int(50000 / batch_size)
+    boundaries = num_batches_per_epoch * np.array([82, 123, 300],
+                                                  dtype=np.int64)
+    boundaries = [x for x in boundaries]
+    values = [0.1, 0.01, 0.001, 0.0002]
+    return tf.train.piecewise_constant(global_step, boundaries, values)
+
+
+def create_resnet20_cifar_model(params):
+  return ResnetCifar10Model('resnet20', (3, 3, 3), params=params)
+
+
+def create_resnet20_v2_cifar_model(params):
+  return ResnetCifar10Model('resnet20_v2', (3, 3, 3), params=params)
+
+
+def create_resnet32_cifar_model(params):
+  return ResnetCifar10Model('resnet32', (5, 5, 5), params=params)
+
+
+def create_resnet32_v2_cifar_model(params):
+  return ResnetCifar10Model('resnet32_v2', (5, 5, 5), params=params)
+
+
+def create_resnet44_cifar_model(params):
+  return ResnetCifar10Model('resnet44', (7, 7, 7), params=params)
+
+
+def create_resnet44_v2_cifar_model(params):
+  return ResnetCifar10Model('resnet44_v2', (7, 7, 7), params=params)
+
+
+def create_resnet56_cifar_model(params):
+  return ResnetCifar10Model('resnet56', (9, 9, 9), params=params)
+
+
+def create_resnet56_v2_cifar_model(params):
+  return ResnetCifar10Model('resnet56_v2', (9, 9, 9), params=params)
+
+
+def create_resnet110_cifar_model(params):
+  return ResnetCifar10Model('resnet110', (18, 18, 18), params=params)
+
+
+def create_resnet110_v2_cifar_model(params):
+  return ResnetCifar10Model('resnet110_v2', (18, 18, 18), params=params)

TensorFlow/ComputeVision/Classification/benchmarks-master/scripts/tf_cnn_benchmarks/models/resnet_model_test.py

+# Copyright 2018 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.
+# ==============================================================================
+"""Tests for resnet_model."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import mock
+import tensorflow.compat.v1 as tf
+
+from models import resnet_model
+
+
+class ResNetModelTest(tf.test.TestCase):
+
+  def testGetScaledBaseLearningRateOneGpuLrFromParams(self):
+    """Verifies setting params.resnet_base_lr pipes through."""
+    lr = self._get_scaled_base_learning_rate(1,
+                                             'parameter_server',
+                                             256,
+                                             base_lr=.050)
+    self.assertEqual(lr, .050)
+
+  def testGetScaledBaseLearningRateOneGpu(self):
+    lr = self._get_scaled_base_learning_rate(1, 'parameter_server', 128)
+    self.assertEqual(lr, .064)
+
+  def testGetScaledBaseLearningRateEightGpuReplicated(self):
+    lr = self._get_scaled_base_learning_rate(8, 'replicated', 256 * 8)
+    self.assertEqual(lr, .128)
+
+  def testGetScaledBaseLearningRateTwoGpuParameter(self):
+    lr = self._get_scaled_base_learning_rate(2, 'parameter_server', 256 * 2)
+    self.assertEqual(lr, .256)
+
+  def testGetScaledBaseLearningRateTwoGpuUneven(self):
+    lr = self._get_scaled_base_learning_rate(2, 'replicated', 13)
+    self.assertEqual(lr, 0.0032500000000000003)
+
+  def _get_scaled_base_learning_rate(self,
+                                     num_gpus,
+                                     variable_update,
+                                     batch_size,
+                                     base_lr=None):
+    """Simplifies testing different learning rate calculations.
+
+    Args:
+      num_gpus: Number of GPUs to be used.
+      variable_update: Type of variable update used.
+      batch_size: Total batch size.
+      base_lr: Base learning rate before scaling.
+
+    Returns:
+      Base learning rate that would be used to create lr schedule.
+    """
+    params = mock.Mock()
+    params.num_gpus = num_gpus
+    params.variable_update = variable_update
+    if base_lr:
+      params.resnet_base_lr = base_lr
+    resnet50_model = resnet_model.ResnetModel('resnet50', 50, params=params)
+    return resnet50_model.get_scaled_base_learning_rate(batch_size)
+
+
+if __name__ == '__main__':
+  tf.disable_v2_behavior()
+  tf.test.main()

TensorFlow/ComputeVision/Classification/benchmarks-master/scripts/tf_cnn_benchmarks/models/tf1_only/mobilenet.py

+# Copyright 2018 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 Base Class, branched from slim for fp16 performance study."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import collections
+import contextlib
+import copy
+import os
+
+import tensorflow.compat.v1 as tf
+from tensorflow.contrib import slim as contrib_slim
+
+slim = contrib_slim
+
+
+@slim.add_arg_scope
+def apply_activation(x, name=None, activation_fn=None):
+  return activation_fn(x, name=name) if activation_fn else x
+
+
+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(inputs, [[0, 0], [pad_beg[0], pad_end[0]],
+                                  [pad_beg[1], pad_end[1]], [0, 0]])
+  return padded_inputs
+
+
+def _make_divisible(v, divisor, min_value=None):
+  if min_value is None:
+    min_value = divisor
+  new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+  # Make sure that round down does not go down by more than 10%.
+  if new_v < 0.9 * v:
+    new_v += divisor
+  return new_v
+
+
+@contextlib.contextmanager
+def _set_arg_scope_defaults(defaults):
+  """Sets arg scope defaults for all items present in defaults.
+
+  Args:
+    defaults: dictionary/list of pairs, containing a mapping from
+    function to a dictionary of default args.
+
+  Yields:
+    context manager where all defaults are set.
+  """
+  if hasattr(defaults, 'items'):
+    items = list(defaults.items())
+  else:
+    items = defaults
+  if not items:
+    yield
+  else:
+    func, default_arg = items[0]
+    with slim.arg_scope(func, **default_arg):
+      with _set_arg_scope_defaults(items[1:]):
+        yield
+
+
+@slim.add_arg_scope
+def depth_multiplier(output_params,
+                     multiplier,
+                     divisible_by=8,
+                     min_depth=8,
+                     **unused_kwargs):
+  if 'num_outputs' not in output_params:
+    return
+  d = output_params['num_outputs']
+  output_params['num_outputs'] = _make_divisible(d * multiplier, divisible_by,
+                                                 min_depth)
+
+
+_Op = collections.namedtuple('Op', ['op', 'params', 'multiplier_func'])
+
+
+def op(opfunc, **params):
+  multiplier = params.pop('multiplier_transorm', depth_multiplier)
+  return _Op(opfunc, params=params, multiplier_func=multiplier)
+
+
+class NoOpScope(object):
+  """No-op context manager."""
+
+  def __enter__(self):
+    return
+
+  def __exit__(self, exc_type, exc_value, traceback):
+    return False
+
+
+def safe_arg_scope(funcs, **kwargs):
+  """Returns `slim.arg_scope` with all None arguments removed.
+
+  Args:
+    funcs: Functions to pass to `arg_scope`.
+    **kwargs: Arguments to pass to `arg_scope`.
+
+  Returns:
+    arg_scope or No-op context manager.
+
+  Note: can be useful if None value should be interpreted as "do not overwrite
+    this parameter value".
+  """
+  filtered_args = {name: value for name, value in kwargs.items()
+                   if value is not None}
+  if filtered_args:
+    return slim.arg_scope(funcs, **filtered_args)
+  else:
+    return NoOpScope()
+
+
+@slim.add_arg_scope
+def mobilenet_base(  # pylint: disable=invalid-name
+    inputs,
+    conv_defs,
+    multiplier=1.0,
+    final_endpoint=None,
+    output_stride=None,
+    use_explicit_padding=False,
+    scope=None,
+    is_training=False):
+  """Mobilenet base network.
+
+  Constructs a network from inputs to the given final endpoint. By default
+  the network is constructed in inference mode. To create network
+  in training mode use:
+
+  with slim.arg_scope(mobilenet.training_scope()):
+     logits, endpoints = mobilenet_base(...)
+
+  Args:
+    inputs: a tensor of shape [batch_size, height, width, channels].
+    conv_defs: A list of op(...) layers specifying the net architecture.
+    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.
+    final_endpoint: The name of last layer, for early termination for
+    for V1-based networks: last layer is "layer_14", for V2: "layer_20"
+    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 1 or any even number, excluding
+      zero. Typical values are 8 (accurate fully convolutional mode), 16
+      (fast fully convolutional mode), and 32 (classification mode).
+
+      NOTE- output_stride relies on all consequent operators to support dilated
+      operators via "rate" parameter. This might require wrapping non-conv
+      operators to operate properly.
+
+    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.
+    is_training: How to setup batch_norm and other ops. Note: most of the time
+      this does not need be set directly. Use mobilenet.training_scope() to set
+      up training instead. This parameter is here for backward compatibility
+      only. It is safe to set it to the value matching
+      training_scope(is_training=...). It is also safe to explicitly set
+      it to False, even if there is outer training_scope set to to training.
+      (The network will be built in inference mode). If this is set to None,
+      no arg_scope is added for slim.batch_norm's is_training parameter.
+
+  Returns:
+    tensor_out: output tensor.
+    end_points: a set of activations for external use, for example summaries or
+                losses.
+
+  Raises:
+    ValueError: depth_multiplier <= 0, or the target output_stride is not
+                allowed.
+  """
+  if multiplier <= 0:
+    raise ValueError('multiplier is not greater than zero.')
+
+  # Set conv defs defaults and overrides.
+  conv_defs_defaults = conv_defs.get('defaults', {})
+  conv_defs_overrides = conv_defs.get('overrides', {})
+  if use_explicit_padding:
+    conv_defs_overrides = copy.deepcopy(conv_defs_overrides)
+    conv_defs_overrides[
+        (slim.conv2d, slim.separable_conv2d)] = {'padding': 'VALID'}
+
+  if output_stride is not None:
+    if output_stride == 0 or (output_stride > 1 and output_stride % 2):
+      raise ValueError('Output stride must be None, 1 or a multiple of 2.')
+
+  # a) Set the tensorflow scope
+  # b) set padding to default: note we might consider removing this
+  # since it is also set by mobilenet_scope
+  # c) set all defaults
+  # d) set all extra overrides.
+  with _scope_all(scope, default_scope='Mobilenet'), \
+      safe_arg_scope([slim.batch_norm], is_training=is_training), \
+      _set_arg_scope_defaults(conv_defs_defaults), \
+      _set_arg_scope_defaults(conv_defs_overrides):
+    # 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
+    # Insert default parameters before the base scope which includes
+    # any custom overrides set in mobilenet.
+    end_points = {}
+    scopes = {}
+    for i, opdef in enumerate(conv_defs['spec']):
+      params = dict(opdef.params)
+      opdef.multiplier_func(params, multiplier)
+      stride = params.get('stride', 1)
+      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 *= stride
+      else:
+        layer_stride = stride
+        layer_rate = 1
+        current_stride *= stride
+      # Update params.
+      params['stride'] = layer_stride
+      # Only insert rate to params if rate > 1.
+      if layer_rate > 1:
+        params['rate'] = layer_rate
+      # Set padding
+      if use_explicit_padding:
+        if 'kernel_size' in params:
+          net = _fixed_padding(net, params['kernel_size'], layer_rate)
+        else:
+          params['use_explicit_padding'] = True
+
+      end_point = 'layer_%d' % (i + 1)
+      try:
+        net = opdef.op(net, **params)
+      except Exception:
+        print('Failed to create op %i: %r params: %r' % (i, opdef, params))
+        raise
+      end_points[end_point] = net
+      scope = os.path.dirname(net.name)
+      scopes[scope] = end_point
+      if final_endpoint is not None and end_point == final_endpoint:
+        break
+
+    # Add all tensors that end with 'output' to
+    # endpoints
+    for t in net.graph.get_operations():
+      scope = os.path.dirname(t.name)
+      bn = os.path.basename(t.name)
+      if scope in scopes and t.name.endswith('output'):
+        end_points[scopes[scope] + '/' + bn] = t.outputs[0]
+    return net, end_points
+
+
+@contextlib.contextmanager
+def _scope_all(scope, default_scope=None):
+  with tf.variable_scope(scope, default_name=default_scope) as s,\
+       tf.name_scope(s.original_name_scope):
+    yield s
+
+
+@slim.add_arg_scope
+def mobilenet(inputs,
+              num_classes=1001,
+              prediction_fn=slim.softmax,
+              reuse=None,
+              scope='Mobilenet',
+              base_only=False,
+              **mobilenet_args):
+  """Mobilenet model for classification, supports both V1 and V2.
+
+  Note: default mode is inference, use mobilenet.training_scope to create
+  training network.
+
+
+  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.
+    prediction_fn: a function to get predictions out of logits
+      (default softmax).
+    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.
+    base_only: if True will only create the base of the network (no pooling
+    and no logits).
+    **mobilenet_args: passed to mobilenet_base verbatim.
+      - conv_defs: list of conv defs
+      - 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.
+      - output_stride: will ensure that the last layer has at most total stride.
+      If the architecture calls for more stride than that provided
+      (e.g. output_stride=16, but the architecture has 5 stride=2 operators),
+      it will replace output_stride with fractional convolutions using Atrous
+      Convolutions.
+
+  Returns:
+    logits: the pre-softmax activations, a tensor of size
+      [batch_size, num_classes]
+    end_points: a dictionary from components of the network to the corresponding
+      activation tensor.
+
+  Raises:
+    ValueError: Input rank is invalid.
+  """
+  is_training = mobilenet_args.get('is_training', False)
+  input_shape = inputs.get_shape().as_list()
+  if len(input_shape) != 4:
+    raise ValueError('Expected rank 4 input, was: %d' % len(input_shape))
+
+  with tf.variable_scope(scope, 'Mobilenet', reuse=reuse) as scope:
+    inputs = tf.identity(inputs, 'input')
+    net, end_points = mobilenet_base(inputs, scope=scope, **mobilenet_args)
+    if base_only:
+      return net, end_points
+
+    net = tf.identity(net, name='embedding')
+
+    with tf.variable_scope('Logits'):
+      net = global_pool(net)
+      end_points['global_pool'] = net
+      if not num_classes:
+        return net, end_points
+      net = slim.dropout(net, scope='Dropout', is_training=is_training)
+      # 1 x 1 x num_classes
+      # Note: legacy scope name.
+      logits = slim.conv2d(
+          net,
+          num_classes, [1, 1],
+          activation_fn=None,
+          normalizer_fn=None,
+          biases_initializer=tf.zeros_initializer(),
+          scope='Conv2d_1c_1x1')
+
+      logits = tf.squeeze(logits, [1, 2])
+
+      logits = tf.identity(logits, name='output')
+    end_points['Logits'] = logits
+    if prediction_fn:
+      end_points['Predictions'] = prediction_fn(logits, 'Predictions')
+  return logits, end_points
+
+
+def global_pool(input_tensor, pool_op=tf.nn.avg_pool):
+  """Applies avg pool to produce 1x1 output.
+
+  NOTE: This function is funcitonally equivalenet to reduce_mean, but it has
+  baked in average pool which has better support across hardware.
+
+  Args:
+    input_tensor: input tensor
+    pool_op: pooling op (avg pool is default)
+  Returns:
+    a tensor batch_size x 1 x 1 x depth.
+  """
+  shape = input_tensor.get_shape().as_list()
+  if shape[1] is None or shape[2] is None:
+    kernel_size = tf.convert_to_tensor(
+        [1, tf.shape(input_tensor)[1],
+         tf.shape(input_tensor)[2], 1])
+  else:
+    kernel_size = [1, shape[1], shape[2], 1]
+  output = pool_op(
+      input_tensor, ksize=kernel_size, strides=[1, 1, 1, 1], padding='VALID')
+  # Recover output shape, for unknown shape.
+  output.set_shape([None, 1, 1, None])
+  return output
+
+
+def training_scope(is_training=True,
+                   weight_decay=0.00004,
+                   stddev=0.09,
+                   dropout_keep_prob=0.8,
+                   bn_decay=0.997):
+  """Defines Mobilenet training scope.
+
+  Usage:
+     with tf.contrib.slim.arg_scope(mobilenet.training_scope()):
+       logits, endpoints = mobilenet_v2.mobilenet(input_tensor)
+
+     # the network created will be trainble with dropout/batch norm
+     # initialized appropriately.
+  Args:
+    is_training: if set to False this will ensure that all customizations are
+      set to non-training mode. This might be helpful for code that is reused
+      across both training/evaluation, but most of the time training_scope with
+      value False is not needed. If this is set to None, the parameters is not
+      added to the batch_norm arg_scope.
+
+    weight_decay: The weight decay to use for regularizing the model.
+    stddev: Standard deviation for initialization, if negative uses xavier.
+    dropout_keep_prob: dropout keep probability (not set if equals to None).
+    bn_decay: decay for the batch norm moving averages (not set if equals to
+      None).
+
+  Returns:
+    An argument scope to use via arg_scope.
+  """
+  # Note: do not introduce parameters that would change the inference
+  # model here (for example whether to use bias), modify conv_def instead.
+  batch_norm_params = {
+      'decay': bn_decay,
+      'is_training': is_training
+  }
+  if stddev < 0:
+    weight_intitializer = slim.initializers.xavier_initializer()
+  else:
+    weight_intitializer = tf.truncated_normal_initializer(stddev=stddev)
+
+  # Set weight_decay for weights in Conv and FC layers.
+  with slim.arg_scope(
+      [slim.conv2d, slim.fully_connected, slim.separable_conv2d],
+      weights_initializer=weight_intitializer,
+      normalizer_fn=slim.batch_norm), \
+      slim.arg_scope([mobilenet_base, mobilenet], is_training=is_training),\
+      safe_arg_scope([slim.batch_norm], **batch_norm_params), \
+      safe_arg_scope([slim.dropout], is_training=is_training,
+                     keep_prob=dropout_keep_prob), \
+      slim.arg_scope([slim.conv2d], \
+                     weights_regularizer=slim.l2_regularizer(weight_decay)), \
+      slim.arg_scope([slim.separable_conv2d], weights_regularizer=None) as s:
+    return s

TensorFlow/ComputeVision/Classification/benchmarks-master/scripts/tf_cnn_benchmarks/models/tf1_only/mobilenet_conv_blocks.py

+# Copyright 2018 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.
+# ==============================================================================
+"""Convolution blocks for mobilenet."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import contextlib
+import functools
+
+import tensorflow.compat.v1 as tf
+from tensorflow.contrib import slim
+
+
+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(inputs, [[0, 0], [pad_beg[0], pad_end[0]],
+                                  [pad_beg[1], pad_end[1]], [0, 0]])
+  return padded_inputs
+
+
+def _make_divisible(v, divisor, min_value=None):
+  if min_value is None:
+    min_value = divisor
+  new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+  # Make sure that round down does not go down by more than 10%.
+  if new_v < 0.9 * v:
+    new_v += divisor
+  return new_v
+
+
+def _split_divisible(num, num_ways, divisible_by=8):
+  """Evenly splits num, num_ways so each piece is a multiple of divisible_by."""
+  assert num % divisible_by == 0
+  assert num // num_ways >= divisible_by
+  # Note: want to round down, we adjust each split to match the total.
+  base = num // num_ways // divisible_by * divisible_by
+  result = []
+  accumulated = 0
+  for i in range(num_ways):
+    r = base
+    while accumulated + r < num * (i + 1) // num_ways:
+      r += divisible_by
+    result.append(r)
+    accumulated += r
+  assert accumulated == num
+  return result
+
+
+@contextlib.contextmanager
+def _v1_compatible_scope_naming(scope):  # pylint: disable=g-missing-docstring
+  if scope is None:  # Create uniqified separable blocks.
+    with tf.variable_scope(None, default_name='separable') as s, \
+         tf.name_scope(s.original_name_scope):
+      yield ''
+  else:
+    # We use scope_depthwise, scope_pointwise for compatibility with V1 ckpts.
+    # which provide numbered scopes.
+    scope += '_'
+    yield scope
+
+
+@slim.add_arg_scope
+def split_separable_conv2d(input_tensor,
+                           num_outputs,
+                           scope=None,
+                           normalizer_fn=None,
+                           stride=1,
+                           rate=1,
+                           endpoints=None,
+                           use_explicit_padding=False):
+  """Separable mobilenet V1 style convolution.
+
+  Depthwise convolution, with default non-linearity,
+  followed by 1x1 depthwise convolution.  This is similar to
+  slim.separable_conv2d, but differs in tha it applies batch
+  normalization and non-linearity to depthwise. This  matches
+  the basic building of Mobilenet Paper
+  (https://arxiv.org/abs/1704.04861)
+
+  Args:
+    input_tensor: input
+    num_outputs: number of outputs
+    scope: optional name of the scope. Note if provided it will use
+    scope_depthwise for deptwhise, and scope_pointwise for pointwise.
+    normalizer_fn: which normalizer function to use for depthwise/pointwise
+    stride: stride
+    rate: output rate (also known as dilation rate)
+    endpoints: optional, if provided, will export additional tensors to it.
+    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.
+
+  Returns:
+    output tesnor
+  """
+
+  with _v1_compatible_scope_naming(scope) as scope:
+    dw_scope = scope + 'depthwise'
+    endpoints = endpoints if endpoints is not None else {}
+    kernel_size = [3, 3]
+    padding = 'SAME'
+    if use_explicit_padding:
+      padding = 'VALID'
+      input_tensor = _fixed_padding(input_tensor, kernel_size, rate)
+    net = slim.separable_conv2d(
+        input_tensor,
+        None,
+        kernel_size,
+        depth_multiplier=1,
+        stride=stride,
+        rate=rate,
+        normalizer_fn=normalizer_fn,
+        padding=padding,
+        scope=dw_scope)
+
+    endpoints[dw_scope] = net
+
+    pw_scope = scope + 'pointwise'
+    net = slim.conv2d(
+        net,
+        num_outputs, [1, 1],
+        stride=1,
+        normalizer_fn=normalizer_fn,
+        scope=pw_scope)
+    endpoints[pw_scope] = net
+  return net
+
+
+def expand_input_by_factor(n, divisible_by=8):
+  return lambda num_inputs, **_: _make_divisible(num_inputs * n, divisible_by)
+
+
+@slim.add_arg_scope
+def expanded_conv(input_tensor,
+                  num_outputs,
+                  expansion_size=expand_input_by_factor(6),
+                  stride=1,
+                  rate=1,
+                  kernel_size=(3, 3),
+                  residual=True,
+                  normalizer_fn=None,
+                  split_projection=1,
+                  split_expansion=1,
+                  expansion_transform=None,
+                  depthwise_location='expansion',
+                  depthwise_channel_multiplier=1,
+                  endpoints=None,
+                  use_explicit_padding=False,
+                  padding='SAME',
+                  scope=None):
+  """Depthwise Convolution Block with expansion.
+
+  Builds a composite convolution that has the following structure
+  expansion (1x1) -> depthwise (kernel_size) -> projection (1x1)
+
+  Args:
+    input_tensor: input
+    num_outputs: number of outputs in the final layer.
+    expansion_size: the size of expansion, could be a constant or a callable.
+      If latter it will be provided 'num_inputs' as an input. For forward
+      compatibility it should accept arbitrary keyword arguments.
+      Default will expand the input by factor of 6.
+    stride: depthwise stride
+    rate: depthwise rate
+    kernel_size: depthwise kernel
+    residual: whether to include residual connection between input
+      and output.
+    normalizer_fn: batchnorm or otherwise
+    split_projection: how many ways to split projection operator
+      (that is conv expansion->bottleneck)
+    split_expansion: how many ways to split expansion op
+      (that is conv bottleneck->expansion) ops will keep depth divisible
+      by this value.
+    expansion_transform: Optional function that takes expansion
+      as a single input and returns output.
+    depthwise_location: where to put depthwise covnvolutions supported
+      values None, 'input', 'output', 'expansion'
+    depthwise_channel_multiplier: depthwise channel multiplier:
+    each input will replicated (with different filters)
+    that many times. So if input had c channels,
+    output will have c x depthwise_channel_multpilier.
+    endpoints: An optional dictionary into which intermediate endpoints are
+      placed. The keys "expansion_output", "depthwise_output",
+      "projection_output" and "expansion_transform" are always populated, even
+      if the corresponding functions are not invoked.
+    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.
+    padding: Padding type to use if `use_explicit_padding` is not set.
+    scope: optional scope.
+
+  Returns:
+    Tensor of depth num_outputs
+
+  Raises:
+    TypeError: on inval
+  """
+  with tf.variable_scope(scope, default_name='expanded_conv') as s, \
+       tf.name_scope(s.original_name_scope):
+    prev_depth = input_tensor.get_shape().as_list()[3]
+    if  depthwise_location not in [None, 'input', 'output', 'expansion']:
+      raise TypeError('%r is unknown value for depthwise_location' %
+                      depthwise_location)
+    if use_explicit_padding:
+      if padding != 'SAME':
+        raise TypeError('`use_explicit_padding` should only be used with '
+                        '"SAME" padding.')
+      padding = 'VALID'
+    depthwise_func = functools.partial(
+        slim.separable_conv2d,
+        num_outputs=None,
+        kernel_size=kernel_size,
+        depth_multiplier=depthwise_channel_multiplier,
+        stride=stride,
+        rate=rate,
+        normalizer_fn=normalizer_fn,
+        padding=padding,
+        scope='depthwise')
+    # b1 -> b2 * r -> b2
+    #   i -> (o * r) (bottleneck) -> o
+    input_tensor = tf.identity(input_tensor, 'input')
+    net = input_tensor
+
+    if depthwise_location == 'input':
+      if use_explicit_padding:
+        net = _fixed_padding(net, kernel_size, rate)
+      net = depthwise_func(net, activation_fn=None)
+
+    if callable(expansion_size):
+      inner_size = expansion_size(num_inputs=prev_depth)
+    else:
+      inner_size = expansion_size
+
+    if inner_size > net.shape[3]:
+      net = split_conv(
+          net,
+          inner_size,
+          num_ways=split_expansion,
+          scope='expand',
+          stride=1,
+          normalizer_fn=normalizer_fn)
+      net = tf.identity(net, 'expansion_output')
+    if endpoints is not None:
+      endpoints['expansion_output'] = net
+
+    if depthwise_location == 'expansion':
+      if use_explicit_padding:
+        net = _fixed_padding(net, kernel_size, rate)
+      net = depthwise_func(net)
+
+    net = tf.identity(net, name='depthwise_output')
+    if endpoints is not None:
+      endpoints['depthwise_output'] = net
+    if expansion_transform:
+      net = expansion_transform(expansion_tensor=net, input_tensor=input_tensor)
+    # Note in contrast with expansion, we always have
+    # projection to produce the desired output size.
+    net = split_conv(
+        net,
+        num_outputs,
+        num_ways=split_projection,
+        stride=1,
+        scope='project',
+        normalizer_fn=normalizer_fn,
+        activation_fn=tf.identity)
+    if endpoints is not None:
+      endpoints['projection_output'] = net
+    if depthwise_location == 'output':
+      if use_explicit_padding:
+        net = _fixed_padding(net, kernel_size, rate)
+      net = depthwise_func(net, activation_fn=None)
+
+    if callable(residual):  # custom residual
+      net = residual(input_tensor=input_tensor, output_tensor=net)
+    elif (residual and
+          # stride check enforces that we don't add residuals when spatial
+          # dimensions are None
+          stride == 1 and
+          # Depth matches
+          net.get_shape().as_list()[3] ==
+          input_tensor.get_shape().as_list()[3]):
+      net += input_tensor
+    return tf.identity(net, name='output')
+
+
+def split_conv(input_tensor,
+               num_outputs,
+               num_ways,
+               scope,
+               divisible_by=8,
+               **kwargs):
+  """Creates a split convolution.
+
+  Split convolution splits the input and output into
+  'num_blocks' blocks of approximately the same size each,
+  and only connects $i$-th input to $i$ output.
+
+  Args:
+    input_tensor: input tensor
+    num_outputs: number of output filters
+    num_ways: num blocks to split by.
+    scope: scope for all the operators.
+    divisible_by: make sure that every part is divisiable by this.
+    **kwargs: will be passed directly into conv2d operator
+  Returns:
+    tensor
+  """
+  b = input_tensor.get_shape().as_list()[3]
+
+  if num_ways == 1 or min(b // num_ways,
+                          num_outputs // num_ways) < divisible_by:
+    # Don't do any splitting if we end up with less than 8 filters
+    # on either side.
+    return slim.conv2d(input_tensor, num_outputs, [1, 1], scope=scope, **kwargs)
+
+  outs = []
+  input_splits = _split_divisible(b, num_ways, divisible_by=divisible_by)
+  output_splits = _split_divisible(
+      num_outputs, num_ways, divisible_by=divisible_by)
+  inputs = tf.split(input_tensor, input_splits, axis=3, name='split_' + scope)
+  base = scope
+  for i, (input_tensor, out_size) in enumerate(zip(inputs, output_splits)):
+    scope = base + '_part_%d' % (i,)
+    n = slim.conv2d(input_tensor, out_size, [1, 1], scope=scope, **kwargs)
+    n = tf.identity(n, scope + '_output')
+    outs.append(n)
+  return tf.concat(outs, 3, name=scope + '_concat')

TensorFlow/ComputeVision/Classification/benchmarks-master/scripts/tf_cnn_benchmarks/models/tf1_only/mobilenet_test.py

+# Copyright 2018 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.
+# ==============================================================================
+"""Tests for mobilenet_v2, branched from slim for fp16 performance study."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import copy
+
+import tensorflow.compat.v1 as tf
+
+from models.tf1_only import mobilenet
+from models.tf1_only import mobilenet_conv_blocks as ops
+from models.tf1_only import mobilenet_v2
+from tensorflow.contrib import slim
+
+
+def find_ops(optype):
+  """Find ops of a given type in graphdef or a graph.
+
+  Args:
+    optype: operation type (e.g. Conv2D)
+  Returns:
+     List of operations.
+  """
+  gd = tf.get_default_graph()
+  return [var for var in gd.get_operations() if var.type == optype]
+
+
+class MobilenetV2Test(tf.test.TestCase):
+
+  def setUp(self):  # pylint: disable=g-missing-super-call
+    tf.reset_default_graph()
+
+  def testCreation(self):
+    spec = dict(mobilenet_v2.V2_DEF)
+    _, ep = mobilenet.mobilenet(
+        tf.placeholder(tf.float32, (10, 224, 224, 16)), conv_defs=spec)
+    num_convs = len(find_ops('Conv2D'))
+
+    # This is mostly a sanity test. No deep reason for these particular
+    # constants.
+    #
+    # All but first 2 and last one have  two convolutions, and there is one
+    # extra conv that is not in the spec. (logits)
+    self.assertEqual(num_convs, len(spec['spec']) * 2 - 2)
+    # Check that depthwise are exposed.
+    for i in range(2, 17):
+      self.assertIn('layer_%d/depthwise_output' % i, ep)
+
+  def testCreationNoClasses(self):
+    spec = copy.deepcopy(mobilenet_v2.V2_DEF)
+    net, ep = mobilenet.mobilenet(
+        tf.placeholder(tf.float32, (10, 224, 224, 16)), conv_defs=spec,
+        num_classes=None)
+    self.assertIs(net, ep['global_pool'])
+
+  def testImageSizes(self):
+    for input_size, output_size in [(224, 7), (192, 6), (160, 5),
+                                    (128, 4), (96, 3)]:
+      tf.reset_default_graph()
+      _, ep = mobilenet_v2.mobilenet(
+          tf.placeholder(tf.float32, (10, input_size, input_size, 3)))
+
+      self.assertEqual(ep['layer_18/output'].get_shape().as_list()[1:3],
+                       [output_size] * 2)
+
+  def testWithSplits(self):
+    spec = copy.deepcopy(mobilenet_v2.V2_DEF)
+    spec['overrides'] = {
+        (ops.expanded_conv,): dict(split_expansion=2),
+    }
+    _, _ = mobilenet.mobilenet(
+        tf.placeholder(tf.float32, (10, 224, 224, 16)), conv_defs=spec)
+    num_convs = len(find_ops('Conv2D'))
+    # All but 3 op has 3 conv operatore, the remainign 3 have one
+    # and there is one unaccounted.
+    self.assertEqual(num_convs, len(spec['spec']) * 3 - 5)
+
+  def testWithOutputStride8(self):
+    out, _ = mobilenet.mobilenet_base(
+        tf.placeholder(tf.float32, (10, 224, 224, 16)),
+        conv_defs=mobilenet_v2.V2_DEF,
+        output_stride=8,
+        scope='MobilenetV2')
+    self.assertEqual(out.get_shape().as_list()[1:3], [28, 28])
+
+  def testDivisibleBy(self):
+    tf.reset_default_graph()
+    mobilenet_v2.mobilenet(
+        tf.placeholder(tf.float32, (10, 224, 224, 16)),
+        conv_defs=mobilenet_v2.V2_DEF,
+        divisible_by=16,
+        min_depth=32)
+    s = [op.outputs[0].get_shape().as_list()[-1] for op in find_ops('Conv2D')]
+    s = set(s)
+    self.assertSameElements([32, 64, 96, 160, 192, 320, 384, 576, 960, 1280,
+                             1001], s)
+
+  def testDivisibleByWithArgScope(self):
+    tf.reset_default_graph()
+    # Verifies that depth_multiplier arg scope actually works
+    # if no default min_depth is provided.
+    with slim.arg_scope((mobilenet.depth_multiplier,), min_depth=32):
+      mobilenet_v2.mobilenet(
+          tf.placeholder(tf.float32, (10, 224, 224, 2)),
+          conv_defs=mobilenet_v2.V2_DEF, depth_multiplier=0.1)
+      s = [op.outputs[0].get_shape().as_list()[-1] for op in find_ops('Conv2D')]
+      s = set(s)
+      self.assertSameElements(s, [32, 192, 128, 1001])
+
+  def testFineGrained(self):
+    tf.reset_default_graph()
+    # Verifies that depth_multiplier arg scope actually works
+    # if no default min_depth is provided.
+
+    mobilenet_v2.mobilenet(
+        tf.placeholder(tf.float32, (10, 224, 224, 2)),
+        conv_defs=mobilenet_v2.V2_DEF, depth_multiplier=0.01,
+        finegrain_classification_mode=True)
+    s = [op.outputs[0].get_shape().as_list()[-1] for op in find_ops('Conv2D')]
+    s = set(s)
+    # All convolutions will be 8->48, except for the last one.
+    self.assertSameElements(s, [8, 48, 1001, 1280])
+
+  def testMobilenetBase(self):
+    tf.reset_default_graph()
+    # Verifies that mobilenet_base returns pre-pooling layer.
+    with slim.arg_scope((mobilenet.depth_multiplier,), min_depth=32):
+      net, _ = mobilenet_v2.mobilenet_base(
+          tf.placeholder(tf.float32, (10, 224, 224, 16)),
+          conv_defs=mobilenet_v2.V2_DEF, depth_multiplier=0.1)
+      self.assertEqual(net.get_shape().as_list(), [10, 7, 7, 128])
+
+  def testWithOutputStride16(self):
+    tf.reset_default_graph()
+    out, _ = mobilenet.mobilenet_base(
+        tf.placeholder(tf.float32, (10, 224, 224, 16)),
+        conv_defs=mobilenet_v2.V2_DEF,
+        output_stride=16)
+    self.assertEqual(out.get_shape().as_list()[1:3], [14, 14])
+
+  def testWithOutputStride8AndExplicitPadding(self):
+    tf.reset_default_graph()
+    out, _ = mobilenet.mobilenet_base(
+        tf.placeholder(tf.float32, (10, 224, 224, 16)),
+        conv_defs=mobilenet_v2.V2_DEF,
+        output_stride=8,
+        use_explicit_padding=True,
+        scope='MobilenetV2')
+    self.assertEqual(out.get_shape().as_list()[1:3], [28, 28])
+
+  def testWithOutputStride16AndExplicitPadding(self):
+    tf.reset_default_graph()
+    out, _ = mobilenet.mobilenet_base(
+        tf.placeholder(tf.float32, (10, 224, 224, 16)),
+        conv_defs=mobilenet_v2.V2_DEF,
+        output_stride=16,
+        use_explicit_padding=True)
+    self.assertEqual(out.get_shape().as_list()[1:3], [14, 14])
+
+  def testBatchNormScopeDoesNotHaveIsTrainingWhenItsSetToNone(self):
+    sc = mobilenet.training_scope(is_training=None)
+    self.assertNotIn('is_training', sc[slim.arg_scope_func_key(
+        slim.batch_norm)])
+
+  def testBatchNormScopeDoesHasIsTrainingWhenItsNotNone(self):
+    sc = mobilenet.training_scope(is_training=False)
+    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
+    sc = mobilenet.training_scope(is_training=True)
+    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
+    sc = mobilenet.training_scope()
+    self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])
+
+
+if __name__ == '__main__':
+  tf.disable_v2_behavior()
+  tf.test.main()

TensorFlow/ComputeVision/Classification/benchmarks-master/scripts/tf_cnn_benchmarks/models/tf1_only/mobilenet_v2.py

+# Copyright 2018 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 V2 model, branched from slim models for fp16 performance study.
+
+Architecture: https://arxiv.org/abs/1801.04381
+
+The base model gives 72.2% accuracy on ImageNet, with 300MMadds,
+3.4 M parameters.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import copy
+
+import tensorflow.compat.v1 as tf
+
+from models import model
+from models.tf1_only import mobilenet as lib
+from models.tf1_only import mobilenet_conv_blocks as ops
+from tensorflow.contrib import slim
+
+op = lib.op
+
+expand_input = ops.expand_input_by_factor
+
+# pyformat: disable
+# Architecture: https://arxiv.org/abs/1801.04381
+V2_DEF = dict(
+    defaults={
+        # Note: these parameters of batch norm affect the architecture
+        # that's why they are here and not in training_scope.
+        (slim.batch_norm,): {'center': True, 'scale': True},
+        (slim.conv2d, slim.fully_connected, slim.separable_conv2d): {
+            'normalizer_fn': slim.batch_norm, 'activation_fn': tf.nn.relu6
+        },
+        (ops.expanded_conv,): {
+            'expansion_size': expand_input(6),
+            'split_expansion': 1,
+            'normalizer_fn': slim.batch_norm,
+            'residual': True
+        },
+        (slim.conv2d, slim.separable_conv2d): {'padding': 'SAME'}
+    },
+    spec=[
+        op(slim.conv2d, stride=2, num_outputs=32, kernel_size=[3, 3]),
+        op(ops.expanded_conv,
+           expansion_size=expand_input(1, divisible_by=1),
+           num_outputs=16),
+        op(ops.expanded_conv, stride=2, num_outputs=24),
+        op(ops.expanded_conv, stride=1, num_outputs=24),
+        op(ops.expanded_conv, stride=2, num_outputs=32),
+        op(ops.expanded_conv, stride=1, num_outputs=32),
+        op(ops.expanded_conv, stride=1, num_outputs=32),
+        op(ops.expanded_conv, stride=2, num_outputs=64),
+        op(ops.expanded_conv, stride=1, num_outputs=64),
+        op(ops.expanded_conv, stride=1, num_outputs=64),
+        op(ops.expanded_conv, stride=1, num_outputs=64),
+        op(ops.expanded_conv, stride=1, num_outputs=96),
+        op(ops.expanded_conv, stride=1, num_outputs=96),
+        op(ops.expanded_conv, stride=1, num_outputs=96),
+        op(ops.expanded_conv, stride=2, num_outputs=160),
+        op(ops.expanded_conv, stride=1, num_outputs=160),
+        op(ops.expanded_conv, stride=1, num_outputs=160),
+        op(ops.expanded_conv, stride=1, num_outputs=320),
+        op(slim.conv2d, stride=1, kernel_size=[1, 1], num_outputs=1280)
+    ],
+)
+# pyformat: enable
+
+
+@slim.add_arg_scope
+def mobilenet(input_tensor,
+              num_classes=1001,
+              depth_multiplier=1.0,
+              scope='MobilenetV2',
+              conv_defs=None,
+              finegrain_classification_mode=False,
+              min_depth=None,
+              divisible_by=None,
+              **kwargs):
+  """Creates mobilenet V2 network.
+
+  Inference mode is created by default. To create training use training_scope
+  below.
+
+  with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
+     logits, endpoints = mobilenet_v2.mobilenet(input_tensor)
+
+  Args:
+    input_tensor: The input tensor
+    num_classes: number of classes
+    depth_multiplier: The multiplier applied to scale number of
+    channels in each layer. Note: this is called depth multiplier in the
+    paper but the name is kept for consistency with slim's model builder.
+    scope: Scope of the operator
+    conv_defs: Allows to override default conv def.
+    finegrain_classification_mode: When set to True, the model
+    will keep the last layer large even for small multipliers. Following
+    https://arxiv.org/abs/1801.04381
+    suggests that it improves performance for ImageNet-type of problems.
+      *Note* ignored if final_endpoint makes the builder exit earlier.
+    min_depth: If provided, will ensure that all layers will have that
+    many channels after application of depth multiplier.
+    divisible_by: If provided will ensure that all layers # channels
+    will be divisible by this number.
+    **kwargs: passed directly to mobilenet.mobilenet:
+      prediction_fn- what prediction function to use.
+      reuse-: whether to reuse variables (if reuse set to true, scope
+      must be given).
+  Returns:
+    logits/endpoints pair
+
+  Raises:
+    ValueError: On invalid arguments
+  """
+  if conv_defs is None:
+    conv_defs = V2_DEF
+  if 'multiplier' in kwargs:
+    raise ValueError('mobilenetv2 doesn\'t support generic '
+                     'multiplier parameter use "depth_multiplier" instead.')
+  if finegrain_classification_mode:
+    conv_defs = copy.deepcopy(conv_defs)
+    if depth_multiplier < 1:
+      conv_defs['spec'][-1].params['num_outputs'] /= depth_multiplier
+
+  depth_args = {}
+  # NB: do not set depth_args unless they are provided to avoid overriding
+  # whatever default depth_multiplier might have thanks to arg_scope.
+  if min_depth is not None:
+    depth_args['min_depth'] = min_depth
+  if divisible_by is not None:
+    depth_args['divisible_by'] = divisible_by
+
+  with slim.arg_scope((lib.depth_multiplier,), **depth_args):
+    return lib.mobilenet(
+        input_tensor,
+        num_classes=num_classes,
+        conv_defs=conv_defs,
+        scope=scope,
+        multiplier=depth_multiplier,
+        **kwargs)
+
+
+@slim.add_arg_scope
+def mobilenet_base(input_tensor, depth_multiplier=1.0, **kwargs):
+  """Creates base of the mobilenet (no pooling and no logits) ."""
+  return mobilenet(
+      input_tensor, depth_multiplier=depth_multiplier, base_only=True, **kwargs)
+
+
+def training_scope(**kwargs):
+  """Defines MobilenetV2 training scope.
+
+  Usage:
+     with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
+       logits, endpoints = mobilenet_v2.mobilenet(input_tensor)
+
+  with slim.
+
+  Args:
+    **kwargs: Passed to mobilenet.training_scope. The following parameters
+    are supported:
+      weight_decay- The weight decay to use for regularizing the model.
+      stddev-  Standard deviation for initialization, if negative uses xavier.
+      dropout_keep_prob- dropout keep probability
+      bn_decay- decay for the batch norm moving averages.
+
+  Returns:
+    An `arg_scope` to use for the mobilenet v2 model.
+  """
+  return lib.training_scope(**kwargs)
+
+
+class MobilenetModel(model.CNNModel):
+  """Mobilenet model configuration."""
+
+  def __init__(self, params=None):
+    super(MobilenetModel, self).__init__(
+        'mobilenet', 224, 32, 0.005, params=params)
+
+  def add_inference(self, cnn):
+    with slim.arg_scope(training_scope(is_training=cnn.phase_train)):
+      cnn.top_layer, _ = mobilenet(cnn.top_layer, is_training=cnn.phase_train)
+      cnn.top_size = cnn.top_layer.shape[-1].value

TensorFlow/ComputeVision/Classification/benchmarks-master/scripts/tf_cnn_benchmarks/models/tf1_only/nasnet_model.py

+# Copyright 2018 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.
+# ==============================================================================
+"""Model configurations for nasnet.
+
+Paper: https://arxiv.org/abs/1707.07012
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow.compat.v1 as tf
+
+from models import model
+from models.tf1_only import nasnet_utils
+from tensorflow.contrib import framework as contrib_framework
+from tensorflow.contrib import layers as contrib_layers
+from tensorflow.contrib import slim
+from tensorflow.contrib import training as contrib_training
+
+arg_scope = contrib_framework.arg_scope
+
+
+# Notes for training NASNet Cifar Model
+# -------------------------------------
+# batch_size: 32
+# learning rate: 0.025
+# cosine (single period) learning rate decay
+# auxiliary head loss weighting: 0.4
+# clip global norm of all gradients by 5
+def _cifar_config(is_training=True, data_format=None, total_steps=None):
+  drop_path_keep_prob = 1.0 if not is_training else 0.6
+  return contrib_training.HParams(
+      stem_multiplier=3.0,
+      drop_path_keep_prob=drop_path_keep_prob,
+      num_cells=18,
+      use_aux_head=1,
+      num_conv_filters=32,
+      dense_dropout_keep_prob=1.0,
+      filter_scaling_rate=2.0,
+      num_reduction_layers=2,
+      skip_reduction_layer_input=0,
+      data_format=data_format or 'NHWC',
+      # 600 epochs with a batch size of 32
+      # This is used for the drop path probabilities since it needs to increase
+      # the drop out probability over the course of training.
+      total_training_steps=total_steps or 937500,
+  )
+
+
+# Notes for training large NASNet model on ImageNet
+# -------------------------------------
+# batch size (per replica): 16
+# learning rate: 0.015 * 100
+# learning rate decay factor: 0.97
+# num epochs per decay: 2.4
+# sync sgd with 100 replicas
+# auxiliary head loss weighting: 0.4
+# label smoothing: 0.1
+# clip global norm of all gradients by 10
+def _large_imagenet_config(is_training=True, data_format=None,
+                           total_steps=None):
+  drop_path_keep_prob = 1.0 if not is_training else 0.7
+  return contrib_training.HParams(
+      stem_multiplier=3.0,
+      dense_dropout_keep_prob=0.5,
+      num_cells=18,
+      filter_scaling_rate=2.0,
+      num_conv_filters=168,
+      drop_path_keep_prob=drop_path_keep_prob,
+      use_aux_head=1,
+      num_reduction_layers=2,
+      skip_reduction_layer_input=1,
+      data_format=data_format or 'NHWC',
+      total_training_steps=total_steps or 250000,
+  )
+
+
+# Notes for training the mobile NASNet ImageNet model
+# -------------------------------------
+# batch size (per replica): 32
+# learning rate: 0.04 * 50
+# learning rate scaling factor: 0.97
+# num epochs per decay: 2.4
+# sync sgd with 50 replicas
+# auxiliary head weighting: 0.4
+# label smoothing: 0.1
+# clip global norm of all gradients by 10
+def _mobile_imagenet_config(data_format=None, total_steps=None):
+  return contrib_training.HParams(
+      stem_multiplier=1.0,
+      dense_dropout_keep_prob=0.5,
+      num_cells=12,
+      filter_scaling_rate=2.0,
+      drop_path_keep_prob=1.0,
+      num_conv_filters=44,
+      use_aux_head=1,
+      num_reduction_layers=2,
+      skip_reduction_layer_input=0,
+      data_format=data_format or 'NHWC',
+      total_training_steps=total_steps or 250000,
+  )
+
+
+def nasnet_cifar_arg_scope(weight_decay=5e-4,
+                           batch_norm_decay=0.9,
+                           batch_norm_epsilon=1e-5):
+  """Defines the default arg scope for the NASNet-A Cifar model.
+
+  Args:
+    weight_decay: The weight decay to use for regularizing the model.
+    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.
+  Returns:
+    An `arg_scope` to use for the NASNet Cifar Model.
+  """
+  batch_norm_params = {
+      # Decay for the moving averages.
+      'decay': batch_norm_decay,
+      # epsilon to prevent 0s in variance.
+      'epsilon': batch_norm_epsilon,
+      'scale': True,
+      'fused': True,
+  }
+  weights_regularizer = contrib_layers.l2_regularizer(weight_decay)
+  weights_initializer = contrib_layers.variance_scaling_initializer(
+      mode='FAN_OUT')
+  with arg_scope(
+      [slim.fully_connected, slim.conv2d, slim.separable_conv2d],
+      weights_regularizer=weights_regularizer,
+      weights_initializer=weights_initializer):
+    with arg_scope([slim.fully_connected], activation_fn=None, scope='FC'):
+      with arg_scope(
+          [slim.conv2d, slim.separable_conv2d],
+          activation_fn=None,
+          biases_initializer=None):
+        with arg_scope([slim.batch_norm], **batch_norm_params) as sc:
+          return sc
+
+
+def nasnet_mobile_arg_scope(weight_decay=4e-5,
+                            batch_norm_decay=0.9997,
+                            batch_norm_epsilon=1e-3):
+  """Defines the default arg scope for the NASNet-A Mobile ImageNet model.
+
+  Args:
+    weight_decay: The weight decay to use for regularizing the model.
+    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.
+  Returns:
+    An `arg_scope` to use for the NASNet Mobile Model.
+  """
+  batch_norm_params = {
+      # Decay for the moving averages.
+      'decay': batch_norm_decay,
+      # epsilon to prevent 0s in variance.
+      'epsilon': batch_norm_epsilon,
+      'scale': True,
+      'fused': True,
+  }
+  weights_regularizer = contrib_layers.l2_regularizer(weight_decay)
+  weights_initializer = contrib_layers.variance_scaling_initializer(
+      mode='FAN_OUT')
+  with arg_scope(
+      [slim.fully_connected, slim.conv2d, slim.separable_conv2d],
+      weights_regularizer=weights_regularizer,
+      weights_initializer=weights_initializer):
+    with arg_scope([slim.fully_connected], activation_fn=None, scope='FC'):
+      with arg_scope(
+          [slim.conv2d, slim.separable_conv2d],
+          activation_fn=None,
+          biases_initializer=None):
+        with arg_scope([slim.batch_norm], **batch_norm_params) as sc:
+          return sc
+
+
+def nasnet_large_arg_scope(weight_decay=5e-5,
+                           batch_norm_decay=0.9997,
+                           batch_norm_epsilon=1e-3):
+  """Defines the default arg scope for the NASNet-A Large ImageNet model.
+
+  Args:
+    weight_decay: The weight decay to use for regularizing the model.
+    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.
+  Returns:
+    An `arg_scope` to use for the NASNet Large Model.
+  """
+  batch_norm_params = {
+      # Decay for the moving averages.
+      'decay': batch_norm_decay,
+      # epsilon to prevent 0s in variance.
+      'epsilon': batch_norm_epsilon,
+      'scale': True,
+      'fused': True,
+  }
+  weights_regularizer = contrib_layers.l2_regularizer(weight_decay)
+  weights_initializer = contrib_layers.variance_scaling_initializer(
+      mode='FAN_OUT')
+  with arg_scope(
+      [slim.fully_connected, slim.conv2d, slim.separable_conv2d],
+      weights_regularizer=weights_regularizer,
+      weights_initializer=weights_initializer):
+    with arg_scope([slim.fully_connected], activation_fn=None, scope='FC'):
+      with arg_scope(
+          [slim.conv2d, slim.separable_conv2d],
+          activation_fn=None,
+          biases_initializer=None):
+        with arg_scope([slim.batch_norm], **batch_norm_params) as sc:
+          return sc
+
+
+def _build_aux_head(net, end_points, num_classes, hparams, scope):
+  """Auxiliary head used for all models across all datasets."""
+  with tf.variable_scope(scope):
+    aux_logits = tf.identity(net)
+    with tf.variable_scope('aux_logits'):
+      aux_logits = slim.avg_pool2d(
+          aux_logits, [5, 5], stride=3, padding='VALID')
+      aux_logits = slim.conv2d(aux_logits, 128, [1, 1], scope='proj')
+      aux_logits = slim.batch_norm(aux_logits, scope='aux_bn0')
+      aux_logits = tf.nn.relu(aux_logits)
+      # Shape of feature map before the final layer.
+      shape = aux_logits.shape
+      if hparams.data_format == 'NHWC':
+        shape = shape[1:3]
+      else:
+        shape = shape[2:4]
+      aux_logits = slim.conv2d(aux_logits, 768, shape, padding='VALID')
+      aux_logits = slim.batch_norm(aux_logits, scope='aux_bn1')
+      aux_logits = tf.nn.relu(aux_logits)
+      aux_logits = contrib_layers.flatten(aux_logits)
+      aux_logits = slim.fully_connected(aux_logits, num_classes)
+      end_points['AuxLogits'] = aux_logits
+
+
+def _imagenet_stem(inputs, hparams, stem_cell):
+  """Stem used for models trained on ImageNet."""
+  num_stem_cells = 2
+
+  # 149 x 149 x 32
+  num_stem_filters = int(32 * hparams.stem_multiplier)
+  net = slim.conv2d(
+      inputs,
+      num_stem_filters, [3, 3],
+      stride=2,
+      scope='conv0',
+      padding='VALID')
+  net = slim.batch_norm(net, scope='conv0_bn')
+
+  # Run the reduction cells
+  cell_outputs = [None, net]
+  filter_scaling = 1.0 / (hparams.filter_scaling_rate**num_stem_cells)
+  for cell_num in range(num_stem_cells):
+    net = stem_cell(
+        net,
+        scope='cell_stem_{}'.format(cell_num),
+        filter_scaling=filter_scaling,
+        stride=2,
+        prev_layer=cell_outputs[-2],
+        cell_num=cell_num)
+    cell_outputs.append(net)
+    filter_scaling *= hparams.filter_scaling_rate
+  return net, cell_outputs
+
+
+def _cifar_stem(inputs, hparams):
+  """Stem used for models trained on Cifar."""
+  num_stem_filters = int(hparams.num_conv_filters * hparams.stem_multiplier)
+  net = slim.conv2d(inputs, num_stem_filters, 3, scope='l1_stem_3x3')
+  net = slim.batch_norm(net, scope='l1_stem_bn')
+  return net, [None, net]
+
+
+def build_nasnet_cifar(images,
+                       num_classes=None,
+                       is_training=True,
+                       data_format=None,
+                       total_steps=None):
+  """Build NASNet model for the Cifar Dataset."""
+  hparams = _cifar_config(
+      is_training=is_training, data_format=data_format, total_steps=total_steps)
+
+  if tf.test.is_gpu_available() and hparams.data_format == 'NHWC':
+    tf.logging.info('A GPU is available on the machine, consider using NCHW '
+                    'data format for increased speed on GPU.')
+
+  # Calculate the total number of cells in the network
+  # Add 2 for the reduction cells
+  total_num_cells = hparams.num_cells + 2
+
+  normal_cell = nasnet_utils.NasNetANormalCell(
+      hparams.num_conv_filters, hparams.drop_path_keep_prob, total_num_cells,
+      hparams.total_training_steps)
+  reduction_cell = nasnet_utils.NasNetAReductionCell(
+      hparams.num_conv_filters, hparams.drop_path_keep_prob, total_num_cells,
+      hparams.total_training_steps)
+  with arg_scope(
+      [slim.dropout, nasnet_utils.drop_path, slim.batch_norm],
+      is_training=is_training):
+    with arg_scope(
+        [
+            slim.avg_pool2d, slim.max_pool2d, slim.conv2d, slim.batch_norm,
+            slim.separable_conv2d, nasnet_utils.factorized_reduction,
+            nasnet_utils.global_avg_pool, nasnet_utils.get_channel_index,
+            nasnet_utils.get_channel_dim
+        ],
+        data_format=hparams.data_format):
+      return _build_nasnet_base(
+          images,
+          normal_cell=normal_cell,
+          reduction_cell=reduction_cell,
+          num_classes=num_classes,
+          hparams=hparams,
+          is_training=is_training,
+          stem_type='cifar')
+
+
+build_nasnet_cifar.default_image_size = 32
+
+
+def build_nasnet_mobile(images,
+                        num_classes=None,
+                        is_training=True,
+                        data_format=None,
+                        total_steps=None,
+                        final_endpoint=None):
+  """Build NASNet Mobile model for the ImageNet Dataset."""
+  hparams = _mobile_imagenet_config(
+      data_format=data_format, total_steps=total_steps)
+
+  if tf.test.is_gpu_available() and hparams.data_format == 'NHWC':
+    tf.logging.info('A GPU is available on the machine, consider using NCHW '
+                    'data format for increased speed on GPU.')
+
+  # Calculate the total number of cells in the network
+  # Add 2 for the reduction cells
+  total_num_cells = hparams.num_cells + 2
+  # If ImageNet, then add an additional two for the stem cells
+  total_num_cells += 2
+
+  normal_cell = nasnet_utils.NasNetANormalCell(
+      hparams.num_conv_filters, hparams.drop_path_keep_prob, total_num_cells,
+      hparams.total_training_steps)
+  reduction_cell = nasnet_utils.NasNetAReductionCell(
+      hparams.num_conv_filters, hparams.drop_path_keep_prob, total_num_cells,
+      hparams.total_training_steps)
+  with arg_scope(
+      [slim.dropout, nasnet_utils.drop_path, slim.batch_norm],
+      is_training=is_training):
+    with arg_scope(
+        [
+            slim.avg_pool2d, slim.max_pool2d, slim.conv2d, slim.batch_norm,
+            slim.separable_conv2d, nasnet_utils.factorized_reduction,
+            nasnet_utils.global_avg_pool, nasnet_utils.get_channel_index,
+            nasnet_utils.get_channel_dim
+        ],
+        data_format=hparams.data_format):
+      return _build_nasnet_base(
+          images,
+          normal_cell=normal_cell,
+          reduction_cell=reduction_cell,
+          num_classes=num_classes,
+          hparams=hparams,
+          is_training=is_training,
+          stem_type='imagenet',
+          final_endpoint=final_endpoint)
+
+
+build_nasnet_mobile.default_image_size = 224
+
+
+def build_nasnet_large(images,
+                       num_classes=None,
+                       is_training=True,
+                       data_format=None,
+                       total_steps=None,
+                       final_endpoint=None):
+  """Build NASNet Large model for the ImageNet Dataset."""
+  hparams = _large_imagenet_config(
+      is_training=is_training, data_format=data_format, total_steps=total_steps)
+
+  if tf.test.is_gpu_available() and hparams.data_format == 'NHWC':
+    tf.logging.info('A GPU is available on the machine, consider using NCHW '
+                    'data format for increased speed on GPU.')
+
+  # Calculate the total number of cells in the network
+  # Add 2 for the reduction cells
+  total_num_cells = hparams.num_cells + 2
+  # If ImageNet, then add an additional two for the stem cells
+  total_num_cells += 2
+
+  normal_cell = nasnet_utils.NasNetANormalCell(
+      hparams.num_conv_filters, hparams.drop_path_keep_prob, total_num_cells,
+      hparams.total_training_steps)
+  reduction_cell = nasnet_utils.NasNetAReductionCell(
+      hparams.num_conv_filters, hparams.drop_path_keep_prob, total_num_cells,
+      hparams.total_training_steps)
+  with arg_scope(
+      [slim.dropout, nasnet_utils.drop_path, slim.batch_norm],
+      is_training=is_training):
+    with arg_scope(
+        [
+            slim.avg_pool2d, slim.max_pool2d, slim.conv2d, slim.batch_norm,
+            slim.separable_conv2d, nasnet_utils.factorized_reduction,
+            nasnet_utils.global_avg_pool, nasnet_utils.get_channel_index,
+            nasnet_utils.get_channel_dim
+        ],
+        data_format=hparams.data_format):
+      return _build_nasnet_base(
+          images,
+          normal_cell=normal_cell,
+          reduction_cell=reduction_cell,
+          num_classes=num_classes,
+          hparams=hparams,
+          is_training=is_training,
+          stem_type='imagenet',
+          final_endpoint=final_endpoint)
+
+
+build_nasnet_large.default_image_size = 331
+
+
+def _build_nasnet_base(images,
+                       normal_cell,
+                       reduction_cell,
+                       num_classes,
+                       hparams,
+                       is_training,
+                       stem_type,
+                       final_endpoint=None):
+  """Constructs a NASNet image model."""
+
+  end_points = {}
+
+  def add_and_check_endpoint(endpoint_name, net):
+    end_points[endpoint_name] = net
+    return final_endpoint and (endpoint_name == final_endpoint)
+
+  # Find where to place the reduction cells or stride normal cells
+  reduction_indices = nasnet_utils.calc_reduction_layers(
+      hparams.num_cells, hparams.num_reduction_layers)
+  stem_cell = reduction_cell
+
+  if stem_type == 'imagenet':
+    stem = lambda: _imagenet_stem(images, hparams, stem_cell)
+  elif stem_type == 'cifar':
+    stem = lambda: _cifar_stem(images, hparams)
+  else:
+    raise ValueError('Unknown stem_type: ', stem_type)
+  net, cell_outputs = stem()
+  if add_and_check_endpoint('Stem', net):
+    return net, end_points
+
+  # Setup for building in the auxiliary head.
+  aux_head_cell_idxes = []
+  if len(reduction_indices) >= 2:
+    aux_head_cell_idxes.append(reduction_indices[1] - 1)
+
+  # Run the cells
+  filter_scaling = 1.0
+  # true_cell_num accounts for the stem cells
+  true_cell_num = 2 if stem_type == 'imagenet' else 0
+  for cell_num in range(hparams.num_cells):
+    stride = 1
+    if hparams.skip_reduction_layer_input:
+      prev_layer = cell_outputs[-2]
+    if cell_num in reduction_indices:
+      filter_scaling *= hparams.filter_scaling_rate
+      net = reduction_cell(
+          net,
+          scope='reduction_cell_{}'.format(reduction_indices.index(cell_num)),
+          filter_scaling=filter_scaling,
+          stride=2,
+          prev_layer=cell_outputs[-2],
+          cell_num=true_cell_num)
+      if add_and_check_endpoint(
+          'Reduction_Cell_{}'.format(reduction_indices.index(cell_num)), net):
+        return net, end_points
+      true_cell_num += 1
+      cell_outputs.append(net)
+    if not hparams.skip_reduction_layer_input:
+      prev_layer = cell_outputs[-2]
+    net = normal_cell(
+        net,
+        scope='cell_{}'.format(cell_num),
+        filter_scaling=filter_scaling,
+        stride=stride,
+        prev_layer=prev_layer,
+        cell_num=true_cell_num)
+
+    if add_and_check_endpoint('Cell_{}'.format(cell_num), net):
+      return net, end_points
+    true_cell_num += 1
+    if (hparams.use_aux_head and cell_num in aux_head_cell_idxes and
+        num_classes and is_training):
+      aux_net = tf.nn.relu(net)
+      _build_aux_head(
+          aux_net,
+          end_points,
+          num_classes,
+          hparams,
+          scope='aux_{}'.format(cell_num))
+    cell_outputs.append(net)
+
+  # Final softmax layer
+  with tf.variable_scope('final_layer'):
+    net = tf.nn.relu(net)
+    net = nasnet_utils.global_avg_pool(net)
+    if add_and_check_endpoint('global_pool', net) or num_classes is None:
+      return net, end_points
+    net = slim.dropout(net, hparams.dense_dropout_keep_prob, scope='dropout')
+    logits = slim.fully_connected(net, num_classes)
+
+    if add_and_check_endpoint('Logits', logits):
+      return net, end_points
+
+    predictions = tf.nn.softmax(logits, name='predictions')
+    if add_and_check_endpoint('Predictions', predictions):
+      return net, end_points
+  return logits, end_points
+
+
+class NasnetModel(model.CNNModel):
+  """Nasnet model configuration."""
+
+  def __init__(self, params=None):
+    super(NasnetModel, self).__init__('nasnet', 224, 32, 0.005, params=params)
+
+  def add_inference(self, cnn):
+    tf.logging.info('input_image_shape: {}'.format(cnn.top_layer.shape))
+    cnn.top_layer, _ = build_nasnet_mobile(
+        images=cnn.top_layer,
+        is_training=cnn.phase_train,
+        data_format=cnn.data_format)
+    cnn.top_size = cnn.top_layer.shape[-1].value
+
+
+class NasnetLargeModel(model.CNNModel):
+  """Nasnet model configuration."""
+
+  def __init__(self, params=None):
+    super(NasnetLargeModel, self).__init__(
+        'nasnet', 331, 16, 0.005, params=params)
+
+  def add_inference(self, cnn):
+    tf.logging.info('input_image_shape: {}'.format(cnn.top_layer.shape))
+    cnn.top_layer, _ = build_nasnet_large(
+        images=cnn.top_layer,
+        is_training=cnn.phase_train,
+        data_format=cnn.data_format)
+    cnn.top_size = cnn.top_layer.shape[-1].value
+
+
+class NasnetCifarModel(model.CNNModel):
+  """Nasnet cifar model configuration."""
+
+  def __init__(self, params=None):
+    super(NasnetCifarModel, self).__init__(
+        'nasnet', 32, 32, 0.025, params=params)
+
+  def add_inference(self, cnn):
+    tf.logging.info('input_image_shape: {}'.format(cnn.top_layer.shape))
+    cnn.top_layer, _ = build_nasnet_cifar(
+        images=cnn.top_layer,
+        is_training=cnn.phase_train,
+        data_format=cnn.data_format)
+    cnn.top_size = cnn.top_layer.shape[-1].value

TensorFlow/ComputeVision/Classification/benchmarks-master/scripts/tf_cnn_benchmarks/models/tf1_only/nasnet_test.py

+# Copyright 2018 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.
+# ==============================================================================
+"""Tests for nasnet."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow.compat.v1 as tf
+
+from models.tf1_only import nasnet_model as nasnet
+from tensorflow.contrib import slim
+
+
+class NASNetTest(tf.test.TestCase):
+
+  def testBuildLogitsCifarModel(self):
+    batch_size = 5
+    height, width = 32, 32
+    num_classes = 10
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(nasnet.nasnet_cifar_arg_scope()):
+      logits, end_points = nasnet.build_nasnet_cifar(inputs, num_classes)
+    auxlogits = end_points['AuxLogits']
+    predictions = end_points['Predictions']
+    self.assertListEqual(auxlogits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertListEqual(predictions.get_shape().as_list(),
+                         [batch_size, num_classes])
+
+  def testBuildLogitsMobileModel(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()):
+      logits, end_points = nasnet.build_nasnet_mobile(inputs, num_classes)
+    auxlogits = end_points['AuxLogits']
+    predictions = end_points['Predictions']
+    self.assertListEqual(auxlogits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertListEqual(predictions.get_shape().as_list(),
+                         [batch_size, num_classes])
+
+  def testBuildLogitsLargeModel(self):
+    batch_size = 5
+    height, width = 331, 331
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(nasnet.nasnet_large_arg_scope()):
+      logits, end_points = nasnet.build_nasnet_large(inputs, num_classes)
+    auxlogits = end_points['AuxLogits']
+    predictions = end_points['Predictions']
+    self.assertListEqual(auxlogits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertListEqual(logits.get_shape().as_list(),
+                         [batch_size, num_classes])
+    self.assertListEqual(predictions.get_shape().as_list(),
+                         [batch_size, num_classes])
+
+  def testBuildPreLogitsCifarModel(self):
+    batch_size = 5
+    height, width = 32, 32
+    num_classes = None
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(nasnet.nasnet_cifar_arg_scope()):
+      net, end_points = nasnet.build_nasnet_cifar(inputs, num_classes)
+    self.assertNotIn('AuxLogits', end_points)
+    self.assertNotIn('Predictions', end_points)
+    self.assertTrue(net.op.name.startswith('final_layer/Mean'))
+    self.assertListEqual(net.get_shape().as_list(), [batch_size, 768])
+
+  def testBuildPreLogitsMobileModel(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = None
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()):
+      net, end_points = nasnet.build_nasnet_mobile(inputs, num_classes)
+    self.assertNotIn('AuxLogits', end_points)
+    self.assertNotIn('Predictions', end_points)
+    self.assertTrue(net.op.name.startswith('final_layer/Mean'))
+    self.assertListEqual(net.get_shape().as_list(), [batch_size, 1056])
+
+  def testBuildPreLogitsLargeModel(self):
+    batch_size = 5
+    height, width = 331, 331
+    num_classes = None
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(nasnet.nasnet_large_arg_scope()):
+      net, end_points = nasnet.build_nasnet_large(inputs, num_classes)
+    self.assertNotIn('AuxLogits', end_points)
+    self.assertNotIn('Predictions', end_points)
+    self.assertTrue(net.op.name.startswith('final_layer/Mean'))
+    self.assertListEqual(net.get_shape().as_list(), [batch_size, 4032])
+
+  def testAllEndPointsShapesCifarModel(self):
+    batch_size = 5
+    height, width = 32, 32
+    num_classes = 10
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(nasnet.nasnet_cifar_arg_scope()):
+      _, end_points = nasnet.build_nasnet_cifar(inputs, num_classes)
+    endpoints_shapes = {'Stem': [batch_size, 32, 32, 96],
+                        'Cell_0': [batch_size, 32, 32, 192],
+                        'Cell_1': [batch_size, 32, 32, 192],
+                        'Cell_2': [batch_size, 32, 32, 192],
+                        'Cell_3': [batch_size, 32, 32, 192],
+                        'Cell_4': [batch_size, 32, 32, 192],
+                        'Cell_5': [batch_size, 32, 32, 192],
+                        'Cell_6': [batch_size, 16, 16, 384],
+                        'Cell_7': [batch_size, 16, 16, 384],
+                        'Cell_8': [batch_size, 16, 16, 384],
+                        'Cell_9': [batch_size, 16, 16, 384],
+                        'Cell_10': [batch_size, 16, 16, 384],
+                        'Cell_11': [batch_size, 16, 16, 384],
+                        'Cell_12': [batch_size, 8, 8, 768],
+                        'Cell_13': [batch_size, 8, 8, 768],
+                        'Cell_14': [batch_size, 8, 8, 768],
+                        'Cell_15': [batch_size, 8, 8, 768],
+                        'Cell_16': [batch_size, 8, 8, 768],
+                        'Cell_17': [batch_size, 8, 8, 768],
+                        'Reduction_Cell_0': [batch_size, 16, 16, 256],
+                        'Reduction_Cell_1': [batch_size, 8, 8, 512],
+                        'global_pool': [batch_size, 768],
+                        # Logits and predictions
+                        'AuxLogits': [batch_size, num_classes],
+                        'Logits': [batch_size, num_classes],
+                        'Predictions': [batch_size, num_classes]}
+    self.assertCountEqual(endpoints_shapes.keys(), end_points.keys())
+    for endpoint_name in endpoints_shapes:
+      tf.logging.info('Endpoint name: {}'.format(endpoint_name))
+      expected_shape = endpoints_shapes[endpoint_name]
+      self.assertIn(endpoint_name, end_points)
+      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+                           expected_shape)
+
+  def testAllEndPointsShapesMobileModel(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()):
+      _, end_points = nasnet.build_nasnet_mobile(inputs, num_classes)
+    endpoints_shapes = {'Stem': [batch_size, 28, 28, 88],
+                        'Cell_0': [batch_size, 28, 28, 264],
+                        'Cell_1': [batch_size, 28, 28, 264],
+                        'Cell_2': [batch_size, 28, 28, 264],
+                        'Cell_3': [batch_size, 28, 28, 264],
+                        'Cell_4': [batch_size, 14, 14, 528],
+                        'Cell_5': [batch_size, 14, 14, 528],
+                        'Cell_6': [batch_size, 14, 14, 528],
+                        'Cell_7': [batch_size, 14, 14, 528],
+                        'Cell_8': [batch_size, 7, 7, 1056],
+                        'Cell_9': [batch_size, 7, 7, 1056],
+                        'Cell_10': [batch_size, 7, 7, 1056],
+                        'Cell_11': [batch_size, 7, 7, 1056],
+                        'Reduction_Cell_0': [batch_size, 14, 14, 352],
+                        'Reduction_Cell_1': [batch_size, 7, 7, 704],
+                        'global_pool': [batch_size, 1056],
+                        # Logits and predictions
+                        'AuxLogits': [batch_size, num_classes],
+                        'Logits': [batch_size, num_classes],
+                        'Predictions': [batch_size, num_classes]}
+    self.assertCountEqual(endpoints_shapes.keys(), end_points.keys())
+    for endpoint_name in endpoints_shapes:
+      tf.logging.info('Endpoint name: {}'.format(endpoint_name))
+      expected_shape = endpoints_shapes[endpoint_name]
+      self.assertIn(endpoint_name, end_points)
+      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+                           expected_shape)
+
+  def testAllEndPointsShapesLargeModel(self):
+    batch_size = 5
+    height, width = 331, 331
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    with slim.arg_scope(nasnet.nasnet_large_arg_scope()):
+      _, end_points = nasnet.build_nasnet_large(inputs, num_classes)
+    endpoints_shapes = {'Stem': [batch_size, 42, 42, 336],
+                        'Cell_0': [batch_size, 42, 42, 1008],
+                        'Cell_1': [batch_size, 42, 42, 1008],
+                        'Cell_2': [batch_size, 42, 42, 1008],
+                        'Cell_3': [batch_size, 42, 42, 1008],
+                        'Cell_4': [batch_size, 42, 42, 1008],
+                        'Cell_5': [batch_size, 42, 42, 1008],
+                        'Cell_6': [batch_size, 21, 21, 2016],
+                        'Cell_7': [batch_size, 21, 21, 2016],
+                        'Cell_8': [batch_size, 21, 21, 2016],
+                        'Cell_9': [batch_size, 21, 21, 2016],
+                        'Cell_10': [batch_size, 21, 21, 2016],
+                        'Cell_11': [batch_size, 21, 21, 2016],
+                        'Cell_12': [batch_size, 11, 11, 4032],
+                        'Cell_13': [batch_size, 11, 11, 4032],
+                        'Cell_14': [batch_size, 11, 11, 4032],
+                        'Cell_15': [batch_size, 11, 11, 4032],
+                        'Cell_16': [batch_size, 11, 11, 4032],
+                        'Cell_17': [batch_size, 11, 11, 4032],
+                        'Reduction_Cell_0': [batch_size, 21, 21, 1344],
+                        'Reduction_Cell_1': [batch_size, 11, 11, 2688],
+                        'global_pool': [batch_size, 4032],
+                        # Logits and predictions
+                        'AuxLogits': [batch_size, num_classes],
+                        'Logits': [batch_size, num_classes],
+                        'Predictions': [batch_size, num_classes]}
+    self.assertCountEqual(endpoints_shapes.keys(), end_points.keys())
+    for endpoint_name in endpoints_shapes:
+      tf.logging.info('Endpoint name: {}'.format(endpoint_name))
+      expected_shape = endpoints_shapes[endpoint_name]
+      self.assertIn(endpoint_name, end_points)
+      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
+                           expected_shape)
+
+  def testVariablesSetDeviceMobileModel(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    inputs = tf.random_uniform((batch_size, height, width, 3))
+    tf.train.create_global_step()
+    # Force all Variables to reside on the device.
+    with tf.variable_scope('on_cpu'), tf.device('/cpu:0'):
+      with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()):
+        nasnet.build_nasnet_mobile(inputs, num_classes)
+    with tf.variable_scope('on_gpu'), tf.device('/gpu:0'):
+      with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()):
+        nasnet.build_nasnet_mobile(inputs, num_classes)
+    for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_cpu'):
+      self.assertDeviceEqual(v.device, '/cpu:0')
+    for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_gpu'):
+      self.assertDeviceEqual(v.device, '/gpu:0')
+
+  def testUnknownBatchSizeMobileModel(self):
+    batch_size = 1
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session() as sess:
+      inputs = tf.placeholder(tf.float32, (None, height, width, 3))
+      with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()):
+        logits, _ = nasnet.build_nasnet_mobile(inputs, num_classes)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [None, num_classes])
+      images = tf.random_uniform((batch_size, height, width, 3))
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(logits, {inputs: images.eval()})
+      self.assertEqual(output.shape, (batch_size, num_classes))
+
+  def testEvaluationMobileModel(self):
+    batch_size = 2
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session() as sess:
+      eval_inputs = tf.random_uniform((batch_size, height, width, 3))
+      with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()):
+        logits, _ = nasnet.build_nasnet_mobile(eval_inputs,
+                                               num_classes,
+                                               is_training=False)
+      predictions = tf.argmax(logits, 1)
+      sess.run(tf.global_variables_initializer())
+      output = sess.run(predictions)
+      self.assertEqual(output.shape, (batch_size,))
+
+
+if __name__ == '__main__':
+  tf.disable_v2_behavior()
+  tf.test.main()

TensorFlow/ComputeVision/Classification/benchmarks-master/scripts/tf_cnn_benchmarks/models/tf1_only/nasnet_utils.py

+# Copyright 2018 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.
+# ==============================================================================
+"""A custom module for some common operations used by NASNet.
+
+Functions exposed in this file:
+- calc_reduction_layers
+- get_channel_index
+- get_channel_dim
+- global_avg_pool
+- factorized_reduction
+- drop_path
+
+Classes exposed in this file:
+- NasNetABaseCell
+- NasNetANormalCell
+- NasNetAReductionCell
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow.compat.v1 as tf
+from tensorflow.contrib import framework as contrib_framework
+from tensorflow.contrib import slim
+
+arg_scope = contrib_framework.arg_scope
+
+DATA_FORMAT_NCHW = 'NCHW'
+DATA_FORMAT_NHWC = 'NHWC'
+INVALID = 'null'
+
+
+def calc_reduction_layers(num_cells, num_reduction_layers):
+  """Figure out what layers should have reductions."""
+  reduction_layers = []
+  for pool_num in range(1, num_reduction_layers + 1):
+    layer_num = (float(pool_num) / (num_reduction_layers + 1)) * num_cells
+    layer_num = int(layer_num)
+    reduction_layers.append(layer_num)
+  return reduction_layers
+
+
+@contrib_framework.add_arg_scope
+def get_channel_index(data_format=INVALID):
+  assert data_format != INVALID
+  axis = 3 if data_format == 'NHWC' else 1
+  return axis
+
+
+@contrib_framework.add_arg_scope
+def get_channel_dim(shape, data_format=INVALID):
+  assert data_format != INVALID
+  assert len(shape) == 4
+  if data_format == 'NHWC':
+    return int(shape[3])
+  elif data_format == 'NCHW':
+    return int(shape[1])
+  else:
+    raise ValueError('Not a valid data_format', data_format)
+
+
+@contrib_framework.add_arg_scope
+def global_avg_pool(x, data_format=INVALID):
+  """Average pool away the height and width spatial dimensions of x."""
+  assert data_format != INVALID
+  assert data_format in ['NHWC', 'NCHW']
+  assert x.shape.ndims == 4
+  if data_format == 'NHWC':
+    return tf.reduce_mean(x, [1, 2])
+  else:
+    return tf.reduce_mean(x, [2, 3])
+
+
+@contrib_framework.add_arg_scope
+def factorized_reduction(net, output_filters, stride, data_format=INVALID):
+  """Reduces the shape of net without information loss due to striding."""
+  assert output_filters % 2 == 0, (
+      'Need even number of filters when using this factorized reduction.')
+  assert data_format != INVALID
+  if stride == 1:
+    net = slim.conv2d(net, output_filters, 1, scope='path_conv')
+    net = slim.batch_norm(net, scope='path_bn')
+    return net
+  if data_format == 'NHWC':
+    stride_spec = [1, stride, stride, 1]
+  else:
+    stride_spec = [1, 1, stride, stride]
+
+  # Skip path 1
+  path1 = tf.nn.avg_pool(
+      net, [1, 1, 1, 1], stride_spec, 'VALID', data_format=data_format)
+  path1 = slim.conv2d(path1, int(output_filters / 2), 1, scope='path1_conv')
+
+  # Skip path 2
+  # First pad with 0's on the right and bottom, then shift the filter to
+  # include those 0's that were added.
+  if data_format == 'NHWC':
+    pad_arr = [[0, 0], [0, 1], [0, 1], [0, 0]]
+    path2 = tf.pad(net, pad_arr)[:, 1:, 1:, :]
+    concat_axis = 3
+  else:
+    pad_arr = [[0, 0], [0, 0], [0, 1], [0, 1]]
+    path2 = tf.pad(net, pad_arr)[:, :, 1:, 1:]
+    concat_axis = 1
+
+  path2 = tf.nn.avg_pool(
+      path2, [1, 1, 1, 1], stride_spec, 'VALID', data_format=data_format)
+  path2 = slim.conv2d(path2, int(output_filters / 2), 1, scope='path2_conv')
+
+  # Concat and apply BN
+  final_path = tf.concat(values=[path1, path2], axis=concat_axis)
+  final_path = slim.batch_norm(final_path, scope='final_path_bn')
+  return final_path
+
+
+@contrib_framework.add_arg_scope
+def drop_path(net, keep_prob, is_training=True):
+  """Drops out a whole example hiddenstate with the specified probability."""
+  if is_training:
+    batch_size = tf.shape(net)[0]
+    noise_shape = [batch_size, 1, 1, 1]
+    keep_prob = tf.cast(keep_prob, dtype=net.dtype)
+    random_tensor = keep_prob
+    random_tensor += tf.random_uniform(noise_shape, dtype=net.dtype)
+    binary_tensor = tf.floor(random_tensor)
+    net = tf.div(net, keep_prob) * binary_tensor
+  return net
+
+
+def _operation_to_filter_shape(operation):
+  splitted_operation = operation.split('x')
+  filter_shape = int(splitted_operation[0][-1])
+  assert filter_shape == int(
+      splitted_operation[1][0]), 'Rectangular filters not supported.'
+  return filter_shape
+
+
+def _operation_to_num_layers(operation):
+  splitted_operation = operation.split('_')
+  if 'x' in splitted_operation[-1]:
+    return 1
+  return int(splitted_operation[-1])
+
+
+def _operation_to_info(operation):
+  """Takes in operation name and returns meta information.
+
+  An example would be 'separable_3x3_4' -> (3, 4).
+
+  Args:
+    operation: String that corresponds to convolution operation.
+
+  Returns:
+    Tuple of (filter shape, num layers).
+  """
+  num_layers = _operation_to_num_layers(operation)
+  filter_shape = _operation_to_filter_shape(operation)
+  return num_layers, filter_shape
+
+
+def _stacked_separable_conv(net, stride, operation, filter_size):
+  """Takes in an operations and parses it to the correct sep operation."""
+  num_layers, kernel_size = _operation_to_info(operation)
+  net_type = net.dtype
+  net = tf.cast(net, tf.float32) if net_type == tf.float16 else net
+
+  for layer_num in range(num_layers - 1):
+    net = tf.nn.relu(net)
+    net = slim.separable_conv2d(
+        net,
+        filter_size,
+        kernel_size,
+        depth_multiplier=1,
+        scope='separable_{0}x{0}_{1}'.format(kernel_size, layer_num + 1),
+        stride=stride)
+    net = slim.batch_norm(
+        net, scope='bn_sep_{0}x{0}_{1}'.format(kernel_size, layer_num + 1))
+    stride = 1
+  net = tf.nn.relu(net)
+  net = slim.separable_conv2d(
+      net,
+      filter_size,
+      kernel_size,
+      depth_multiplier=1,
+      scope='separable_{0}x{0}_{1}'.format(kernel_size, num_layers),
+      stride=stride)
+  net = slim.batch_norm(
+      net, scope='bn_sep_{0}x{0}_{1}'.format(kernel_size, num_layers))
+  net = tf.cast(net, net_type)
+  return net
+
+
+def _operation_to_pooling_type(operation):
+  """Takes in the operation string and returns the pooling type."""
+  splitted_operation = operation.split('_')
+  return splitted_operation[0]
+
+
+def _operation_to_pooling_shape(operation):
+  """Takes in the operation string and returns the pooling kernel shape."""
+  splitted_operation = operation.split('_')
+  shape = splitted_operation[-1]
+  assert 'x' in shape
+  filter_height, filter_width = shape.split('x')
+  assert filter_height == filter_width
+  return int(filter_height)
+
+
+def _operation_to_pooling_info(operation):
+  """Parses the pooling operation string to return its type and shape."""
+  pooling_type = _operation_to_pooling_type(operation)
+  pooling_shape = _operation_to_pooling_shape(operation)
+  return pooling_type, pooling_shape
+
+
+def _pooling(net, stride, operation):
+  """Parses operation and performs the correct pooling operation on net."""
+  padding = 'SAME'
+  pooling_type, pooling_shape = _operation_to_pooling_info(operation)
+  if pooling_type == 'avg':
+    net = slim.avg_pool2d(net, pooling_shape, stride=stride, padding=padding)
+  elif pooling_type == 'max':
+    net = slim.max_pool2d(net, pooling_shape, stride=stride, padding=padding)
+  else:
+    raise NotImplementedError('Unimplemented pooling type: ', pooling_type)
+  return net
+
+
+class NasNetABaseCell(object):  # pylint: disable=g-classes-have-attributes
+  """NASNet Cell class that is used as a 'layer' in image architectures.
+
+  Args:
+    num_conv_filters: The number of filters for each convolution operation.
+    operations: List of operations that are performed in the NASNet Cell in
+      order.
+    used_hiddenstates: Binary array that signals if the hiddenstate was used
+      within the cell. This is used to determine what outputs of the cell
+      should be concatenated together.
+    hiddenstate_indices: Determines what hiddenstates should be combined
+      together with the specified operations to create the NASNet cell.
+  """
+
+  def __init__(self, num_conv_filters, operations, used_hiddenstates,
+               hiddenstate_indices, drop_path_keep_prob, total_num_cells,
+               total_training_steps):
+    self._num_conv_filters = num_conv_filters
+    self._operations = operations
+    self._used_hiddenstates = used_hiddenstates
+    self._hiddenstate_indices = hiddenstate_indices
+    self._drop_path_keep_prob = drop_path_keep_prob
+    self._total_num_cells = total_num_cells
+    self._total_training_steps = total_training_steps
+
+  def _reduce_prev_layer(self, prev_layer, curr_layer):
+    """Matches dimension of prev_layer to the curr_layer."""
+    # Set the prev layer to the current layer if it is none
+    if prev_layer is None:
+      return curr_layer
+    curr_num_filters = self._filter_size
+    prev_num_filters = get_channel_dim(prev_layer.shape)
+    curr_filter_shape = int(curr_layer.shape[2])
+    prev_filter_shape = int(prev_layer.shape[2])
+    if curr_filter_shape != prev_filter_shape:
+      prev_layer = tf.nn.relu(prev_layer)
+      prev_layer = factorized_reduction(prev_layer, curr_num_filters, stride=2)
+    elif curr_num_filters != prev_num_filters:
+      prev_layer = tf.nn.relu(prev_layer)
+      prev_layer = slim.conv2d(
+          prev_layer, curr_num_filters, 1, scope='prev_1x1')
+      prev_layer = slim.batch_norm(prev_layer, scope='prev_bn')
+    return prev_layer
+
+  def _cell_base(self, net, prev_layer):
+    """Runs the beginning of the conv cell before the predicted ops are run."""
+    num_filters = self._filter_size
+
+    # Check to be sure prev layer stuff is setup correctly
+    prev_layer = self._reduce_prev_layer(prev_layer, net)
+
+    net = tf.nn.relu(net)
+    net = slim.conv2d(net, num_filters, 1, scope='1x1')
+    net = slim.batch_norm(net, scope='beginning_bn')
+    split_axis = get_channel_index()
+    net = tf.split(axis=split_axis, num_or_size_splits=1, value=net)
+    for split in net:
+      assert int(split.shape[split_axis] == int(
+          self._num_conv_filters * self._filter_scaling))
+    net.append(prev_layer)
+    return net
+
+  def __call__(self,
+               net,
+               scope=None,
+               filter_scaling=1,
+               stride=1,
+               prev_layer=None,
+               cell_num=-1):
+    """Runs the conv cell."""
+    self._cell_num = cell_num
+    self._filter_scaling = filter_scaling
+    self._filter_size = int(self._num_conv_filters * filter_scaling)
+
+    i = 0
+    with tf.variable_scope(scope):
+      net = self._cell_base(net, prev_layer)
+      for iteration in range(5):
+        with tf.variable_scope('comb_iter_{}'.format(iteration)):
+          left_hiddenstate_idx, right_hiddenstate_idx = (
+              self._hiddenstate_indices[i], self._hiddenstate_indices[i + 1])
+          original_input_left = left_hiddenstate_idx < 2
+          original_input_right = right_hiddenstate_idx < 2
+          h1 = net[left_hiddenstate_idx]
+          h2 = net[right_hiddenstate_idx]
+
+          operation_left = self._operations[i]
+          operation_right = self._operations[i + 1]
+          i += 2
+          # Apply conv operations
+          with tf.variable_scope('left'):
+            h1 = self._apply_conv_operation(h1, operation_left, stride,
+                                            original_input_left)
+          with tf.variable_scope('right'):
+            h2 = self._apply_conv_operation(h2, operation_right, stride,
+                                            original_input_right)
+
+          # Combine hidden states using 'add'.
+          with tf.variable_scope('combine'):
+            h = h1 + h2
+
+          # Add hiddenstate to the list of hiddenstates we can choose from
+          net.append(h)
+
+      with tf.variable_scope('cell_output'):
+        net = self._combine_unused_states(net)
+
+      return net
+
+  def _apply_conv_operation(self, net, operation, stride,
+                            is_from_original_input):
+    """Applies the predicted conv operation to net."""
+    # Dont stride if this is not one of the original hiddenstates
+    if stride > 1 and not is_from_original_input:
+      stride = 1
+    input_filters = get_channel_dim(net.shape)
+    filter_size = self._filter_size
+    if 'separable' in operation:
+      net = _stacked_separable_conv(net, stride, operation, filter_size)
+    elif operation in ['none']:
+      # Check if a stride is needed, then use a strided 1x1 here
+      if stride > 1 or (input_filters != filter_size):
+        net = tf.nn.relu(net)
+        net = slim.conv2d(net, filter_size, 1, stride=stride, scope='1x1')
+        net = slim.batch_norm(net, scope='bn_1')
+    elif 'pool' in operation:
+      net = _pooling(net, stride, operation)
+      if input_filters != filter_size:
+        net = slim.conv2d(net, filter_size, 1, stride=1, scope='1x1')
+        net = slim.batch_norm(net, scope='bn_1')
+    else:
+      raise ValueError('Unimplemented operation', operation)
+
+    if operation != 'none':
+      net = self._apply_drop_path(net)
+    return net
+
+  def _combine_unused_states(self, net):
+    """Concatenate the unused hidden states of the cell."""
+    used_hiddenstates = self._used_hiddenstates
+
+    final_height = int(net[-1].shape[2])
+    final_num_filters = get_channel_dim(net[-1].shape)
+    assert len(used_hiddenstates) == len(net)
+    for idx, used_h in enumerate(used_hiddenstates):
+      curr_height = int(net[idx].shape[2])
+      curr_num_filters = get_channel_dim(net[idx].shape)
+
+      # Determine if a reduction should be applied to make the number of
+      # filters match.
+      should_reduce = final_num_filters != curr_num_filters
+      should_reduce = (final_height != curr_height) or should_reduce
+      should_reduce = should_reduce and not used_h
+      if should_reduce:
+        stride = 2 if final_height != curr_height else 1
+        with tf.variable_scope('reduction_{}'.format(idx)):
+          net[idx] = factorized_reduction(net[idx], final_num_filters, stride)
+
+    states_to_combine = ([
+        h for h, is_used in zip(net, used_hiddenstates) if not is_used
+    ])
+
+    # Return the concat of all the states
+    concat_axis = get_channel_index()
+    net = tf.concat(values=states_to_combine, axis=concat_axis)
+    return net
+
+  @contrib_framework.add_arg_scope  # No public API. For internal use only.
+  def _apply_drop_path(self,
+                       net,
+                       current_step=None,
+                       use_summaries=True,
+                       drop_connect_version='v3'):
+    """Apply drop_path regularization.
+
+    Args:
+      net: the Tensor that gets drop_path regularization applied.
+      current_step: a float32 Tensor with the current global_step value,
+        to be divided by hparams.total_training_steps. Usually None, which
+        defaults to tf.train.get_or_create_global_step() properly casted.
+      use_summaries: a Python boolean. If set to False, no summaries are output.
+      drop_connect_version: one of 'v1', 'v2', 'v3', controlling whether
+        the dropout rate is scaled by current_step (v1), layer (v2), or
+        both (v3, the default).
+
+    Returns:
+      The dropped-out value of `net`.
+    """
+    drop_path_keep_prob = self._drop_path_keep_prob
+    if drop_path_keep_prob < 1.0:
+      assert drop_connect_version in ['v1', 'v2', 'v3']
+      if drop_connect_version in ['v2', 'v3']:
+        # Scale keep prob by layer number
+        assert self._cell_num != -1
+        # The added 2 is for the reduction cells
+        num_cells = self._total_num_cells
+        layer_ratio = (self._cell_num + 1) / float(num_cells)
+        if use_summaries:
+          with tf.device('/cpu:0'):
+            tf.summary.scalar('layer_ratio', layer_ratio)
+        drop_path_keep_prob = 1 - layer_ratio * (1 - drop_path_keep_prob)
+      if drop_connect_version in ['v1', 'v3']:
+        # Decrease the keep probability over time
+        if not current_step:
+          current_step = tf.cast(tf.train.get_or_create_global_step(),
+                                 tf.float32)
+        drop_path_burn_in_steps = self._total_training_steps
+        current_ratio = current_step / drop_path_burn_in_steps
+        current_ratio = tf.minimum(1.0, current_ratio)
+        if use_summaries:
+          with tf.device('/cpu:0'):
+            tf.summary.scalar('current_ratio', current_ratio)
+        drop_path_keep_prob = (1 - current_ratio * (1 - drop_path_keep_prob))
+      if use_summaries:
+        with tf.device('/cpu:0'):
+          tf.summary.scalar('drop_path_keep_prob', drop_path_keep_prob)
+      net = drop_path(net, drop_path_keep_prob)
+    return net
+
+
+class NasNetANormalCell(NasNetABaseCell):
+  """NASNetA Normal Cell."""
+
+  def __init__(self, num_conv_filters, drop_path_keep_prob, total_num_cells,
+               total_training_steps):
+    operations = [
+        'separable_5x5_2', 'separable_3x3_2', 'separable_5x5_2',
+        'separable_3x3_2', 'avg_pool_3x3', 'none', 'avg_pool_3x3',
+        'avg_pool_3x3', 'separable_3x3_2', 'none'
+    ]
+    used_hiddenstates = [1, 0, 0, 0, 0, 0, 0]
+    hiddenstate_indices = [0, 1, 1, 1, 0, 1, 1, 1, 0, 0]
+    super(NasNetANormalCell, self).__init__(
+        num_conv_filters, operations, used_hiddenstates, hiddenstate_indices,
+        drop_path_keep_prob, total_num_cells, total_training_steps)
+
+
+class NasNetAReductionCell(NasNetABaseCell):
+  """NASNetA Reduction Cell."""
+
+  def __init__(self, num_conv_filters, drop_path_keep_prob, total_num_cells,
+               total_training_steps):
+    operations = [
+        'separable_5x5_2', 'separable_7x7_2', 'max_pool_3x3', 'separable_7x7_2',
+        'avg_pool_3x3', 'separable_5x5_2', 'none', 'avg_pool_3x3',
+        'separable_3x3_2', 'max_pool_3x3'
+    ]
+    used_hiddenstates = [1, 1, 1, 0, 0, 0, 0]
+    hiddenstate_indices = [0, 1, 0, 1, 0, 1, 3, 2, 2, 0]
+    super(NasNetAReductionCell, self).__init__(
+        num_conv_filters, operations, used_hiddenstates, hiddenstate_indices,
+        drop_path_keep_prob, total_num_cells, total_training_steps)

TensorFlow/ComputeVision/Classification/benchmarks-master/scripts/tf_cnn_benchmarks/models/tf1_only/ssd_model.py

+# Copyright 2018 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.
+# ==============================================================================
+
+
+"""SSD300 Model Configuration.
+
+References:
+  Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed,
+  Cheng-Yang Fu, Alexander C. Berg
+  SSD: Single Shot MultiBox Detector
+  arXiv:1512.02325
+
+Ported from MLPerf reference implementation:
+  https://github.com/mlperf/reference/tree/ssd/single_stage_detector/ssd
+
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import multiprocessing
+import os
+import re
+import threading
+import tensorflow.compat.v1 as tf
+
+# pylint: disable=g-direct-tensorflow-import
+import constants
+import mlperf
+import ssd_constants
+from cnn_util import log_fn
+from models import model as model_lib
+from models import resnet_model
+from tensorflow.contrib import layers as contrib_layers
+from tensorflow.python.ops import variables
+
+BACKBONE_MODEL_SCOPE_NAME = 'resnet34_backbone'
+
+
+class SSD300Model(model_lib.CNNModel):
+  """Single Shot Multibox Detection (SSD) model for 300x300 image datasets."""
+
+  def __init__(self, label_num=ssd_constants.NUM_CLASSES, batch_size=32,
+               learning_rate=1e-3, backbone='resnet34', params=None):
+    super(SSD300Model, self).__init__('ssd300', 300, batch_size, learning_rate,
+                                      params=params)
+    # For COCO dataset, 80 categories + 1 background = 81 labels
+    self.label_num = label_num
+
+    # Currently only support ResNet-34 as backbone model
+    if backbone != 'resnet34':
+      raise ValueError('Invalid backbone model %s for SSD.' % backbone)
+    mlperf.logger.log(key=mlperf.tags.BACKBONE, value=backbone)
+
+    # Number of channels and default boxes associated with the following layers:
+    #   ResNet34 layer, Conv7, Conv8_2, Conv9_2, Conv10_2, Conv11_2
+    self.out_chan = [256, 512, 512, 256, 256, 256]
+    mlperf.logger.log(key=mlperf.tags.LOC_CONF_OUT_CHANNELS,
+                      value=self.out_chan)
+
+    # Number of default boxes from layers of different scales
+    #   38x38x4, 19x19x6, 10x10x6, 5x5x6, 3x3x4, 1x1x4
+    self.num_dboxes = [4, 6, 6, 6, 4, 4]
+    mlperf.logger.log(key=mlperf.tags.NUM_DEFAULTS_PER_CELL,
+                      value=self.num_dboxes)
+
+    # TODO(haoyuzhang): in order to correctly restore in replicated mode, need
+    # to create a saver for each tower before graph is finalized. Use variable
+    # manager for better efficiency.
+    self.backbone_savers = []
+
+    # Collected predictions for eval stage. It maps each image id in eval
+    # dataset to a dict containing the following information:
+    #   source_id: raw ID of image
+    #   raw_shape: raw shape of image
+    #   pred_box: encoded box coordinates of prediction
+    #   pred_scores: scores of classes in prediction
+    self.predictions = {}
+
+    # Global step when predictions are collected.
+    self.eval_global_step = 0
+
+    # Average precision. In asynchronous eval mode, this is the latest AP we
+    # get so far and may not be the results at current eval step.
+    self.eval_coco_ap = 0
+
+    # Process, queues, and thread for asynchronous evaluation. When enabled,
+    # create a separate process (async_eval_process) that continuously pull
+    # intermediate results from the predictions queue (a multiprocessing queue),
+    # process them, and push final results into results queue (another
+    # multiprocessing queue). The main thread is responsible to push messages
+    # into predictions queue, and start a separate thread to continuously pull
+    # messages from results queue to update final results.
+    # Message in predictions queue should be a tuple of two elements:
+    #    (evaluation step, predictions)
+    # Message in results queue should be a tuple of two elements:
+    #    (evaluation step, final results)
+    self.async_eval_process = None
+    self.async_eval_predictions_queue = None
+    self.async_eval_results_queue = None
+    self.async_eval_results_getter_thread = None
+
+    # The MLPerf reference uses a starting lr of 1e-3 at bs=32.
+    self.base_lr_batch_size = 32
+
+  def skip_final_affine_layer(self):
+    return True
+
+  def gpu_preprocess_nhwc(self, images, phase_train=True):
+    try:
+      import ssd_dataloader  # pylint: disable=g-import-not-at-top
+    except ImportError:
+      raise ImportError('To use the COCO dataset, you must clone the '
+                        'repo https://github.com/tensorflow/models and add '
+                        'tensorflow/models and tensorflow/models/research to '
+                        'the PYTHONPATH, and compile the protobufs by '
+                        'following https://github.com/tensorflow/models/blob/'
+                        'master/research/object_detection/g3doc/installation.md'
+                        '#protobuf-compilation ; To evaluate using COCO'
+                        'metric, download and install Python COCO API from'
+                        'https://github.com/cocodataset/cocoapi')
+
+    if phase_train:
+      images = ssd_dataloader.color_jitter(
+          images, brightness=0.125, contrast=0.5, saturation=0.5, hue=0.05)
+      images = ssd_dataloader.normalize_image(images)
+    return images
+
+  def add_backbone_model(self, cnn):
+    # --------------------------------------------------------------------------
+    # Resnet-34 backbone model -- modified for SSD
+    # --------------------------------------------------------------------------
+
+    # Input 300x300, output 150x150
+    cnn.conv(64, 7, 7, 2, 2, mode='SAME_RESNET', use_batch_norm=True)
+    cnn.mpool(3, 3, 2, 2, mode='SAME')
+
+    resnet34_layers = [3, 4, 6, 3]
+    version = 'v1'
+
+    # ResNet-34 block group 1
+    # Input 150x150, output 75x75
+    for i in range(resnet34_layers[0]):
+      # Last argument forces residual_block to use projection shortcut, even
+      # though the numbers of input and output channels are equal
+      resnet_model.residual_block(cnn, 64, 1, version)
+
+    # ResNet-34 block group 2
+    # Input 75x75, output 38x38
+    for i in range(resnet34_layers[1]):
+      stride = 2 if i == 0 else 1
+      resnet_model.residual_block(cnn, 128, stride, version, i == 0)
+
+    # ResNet-34 block group 3
+    # This block group is modified: first layer uses stride=1 so that the image
+    # size does not change in group of layers
+    # Input 38x38, output 38x38
+    for i in range(resnet34_layers[2]):
+      # The following line is intentionally commented out to differentiate from
+      # the original ResNet-34 model
+      # stride = 2 if i == 0 else 1
+      resnet_model.residual_block(cnn, 256, stride, version, i == 0)
+
+    # ResNet-34 block group 4: removed final block group
+    # The following 3 lines are intentionally commented out to differentiate
+    # from the original ResNet-34 model
+    # for i in range(resnet34_layers[3]):
+    #   stride = 2 if i == 0 else 1
+    #   resnet_model.residual_block(cnn, 512, stride, version, i == 0)
+
+  def add_inference(self, cnn):
+    cnn.use_batch_norm = True
+    cnn.batch_norm_config = {'decay': ssd_constants.BATCH_NORM_DECAY,
+                             'epsilon': ssd_constants.BATCH_NORM_EPSILON,
+                             'scale': True}
+
+    with tf.variable_scope(BACKBONE_MODEL_SCOPE_NAME):
+      self.add_backbone_model(cnn)
+
+    # --------------------------------------------------------------------------
+    # SSD additional layers
+    # --------------------------------------------------------------------------
+
+    def add_ssd_layer(cnn, depth, k_size, stride, mode):
+      return cnn.conv(
+          depth,
+          k_size,
+          k_size,
+          stride,
+          stride,
+          mode=mode,
+          use_batch_norm=False,
+          kernel_initializer=contrib_layers.xavier_initializer())
+
+    # Activations for feature maps of different layers
+    self.activations = [cnn.top_layer]
+    # Conv7_1, Conv7_2
+    # Input 38x38, output 19x19
+    add_ssd_layer(cnn, 256, 1, 1, 'valid')
+    self.activations.append(add_ssd_layer(cnn, 512, 3, 2, 'same'))
+
+    # Conv8_1, Conv8_2
+    # Input 19x19, output 10x10
+    add_ssd_layer(cnn, 256, 1, 1, 'valid')
+    self.activations.append(add_ssd_layer(cnn, 512, 3, 2, 'same'))
+
+    # Conv9_1, Conv9_2
+    # Input 10x10, output 5x5
+    add_ssd_layer(cnn, 128, 1, 1, 'valid')
+    self.activations.append(add_ssd_layer(cnn, 256, 3, 2, 'same'))
+
+    # Conv10_1, Conv10_2
+    # Input 5x5, output 3x3
+    add_ssd_layer(cnn, 128, 1, 1, 'valid')
+    self.activations.append(add_ssd_layer(cnn, 256, 3, 1, 'valid'))
+
+    # Conv11_1, Conv11_2
+    # Input 3x3, output 1x1
+    add_ssd_layer(cnn, 128, 1, 1, 'valid')
+    self.activations.append(add_ssd_layer(cnn, 256, 3, 1, 'valid'))
+
+    self.loc = []
+    self.conf = []
+
+    for nd, ac, oc in zip(self.num_dboxes, self.activations, self.out_chan):
+      l = cnn.conv(
+          nd * 4,
+          3,
+          3,
+          1,
+          1,
+          input_layer=ac,
+          num_channels_in=oc,
+          activation=None,
+          use_batch_norm=False,
+          kernel_initializer=contrib_layers.xavier_initializer())
+      scale = l.get_shape()[-1]
+      # shape = [batch_size, nd * 4, scale, scale]
+      l = tf.reshape(l, [self.batch_size, nd, 4, scale, scale])
+      # shape = [batch_size, nd, 4, scale, scale]
+      l = tf.transpose(l, [0, 1, 3, 4, 2])
+      # shape = [batch_size, nd, scale, scale, 4]
+      self.loc.append(tf.reshape(l, [self.batch_size, -1, 4]))
+      # shape = [batch_size, nd * scale * scale, 4]
+
+      c = cnn.conv(
+          nd * self.label_num,
+          3,
+          3,
+          1,
+          1,
+          input_layer=ac,
+          num_channels_in=oc,
+          activation=None,
+          use_batch_norm=False,
+          kernel_initializer=contrib_layers.xavier_initializer())
+      # shape = [batch_size, nd * label_num, scale, scale]
+      c = tf.reshape(c, [self.batch_size, nd, self.label_num, scale, scale])
+      # shape = [batch_size, nd, label_num, scale, scale]
+      c = tf.transpose(c, [0, 1, 3, 4, 2])
+      # shape = [batch_size, nd, scale, scale, label_num]
+      self.conf.append(tf.reshape(c, [self.batch_size, -1, self.label_num]))
+      # shape = [batch_size, nd * scale * scale, label_num]
+
+    # Shape of locs: [batch_size, NUM_SSD_BOXES, 4]
+    # Shape of confs: [batch_size, NUM_SSD_BOXES, label_num]
+    locs, confs = tf.concat(self.loc, 1), tf.concat(self.conf, 1)
+
+    # Pack location and confidence outputs into a single output layer
+    # Shape of logits: [batch_size, NUM_SSD_BOXES, 4+label_num]
+    logits = tf.concat([locs, confs], 2)
+
+    cnn.top_layer = logits
+    cnn.top_size = 4 + self.label_num
+
+    return cnn.top_layer
+
+  def get_learning_rate(self, global_step, batch_size):
+    rescaled_lr = self.get_scaled_base_learning_rate(batch_size)
+    # Defined in MLPerf reference model
+    boundaries = [160000, 200000]
+    boundaries = [b * self.base_lr_batch_size // batch_size for b in boundaries]
+    decays = [1, 0.1, 0.01]
+    learning_rates = [rescaled_lr * d for d in decays]
+    lr = tf.train.piecewise_constant(global_step, boundaries, learning_rates)
+    warmup_steps = int(118287 / batch_size * 5)
+    warmup_lr = (
+        rescaled_lr * tf.cast(global_step, tf.float32) / tf.cast(
+            warmup_steps, tf.float32))
+    return tf.cond(global_step < warmup_steps, lambda: warmup_lr, lambda: lr)
+
+  def get_scaled_base_learning_rate(self, batch_size):
+    """Calculates base learning rate for creating lr schedule.
+
+    In replicated mode, gradients are summed rather than averaged which, with
+    the sgd and momentum optimizers, increases the effective learning rate by
+    lr * num_gpus. Dividing the base lr by num_gpus negates the increase.
+
+    Args:
+      batch_size: Total batch-size.
+
+    Returns:
+      Base learning rate to use to create lr schedule.
+    """
+    base_lr = self.learning_rate
+    if self.params.variable_update == 'replicated':
+      base_lr = self.learning_rate / self.params.num_gpus
+    scaled_lr = base_lr * (batch_size / self.base_lr_batch_size)
+    return scaled_lr
+
+  def _collect_backbone_vars(self):
+    backbone_vars = tf.get_collection(
+        tf.GraphKeys.GLOBAL_VARIABLES, scope='.*'+ BACKBONE_MODEL_SCOPE_NAME)
+    var_list = {}
+
+    # Assume variables in the checkpoint are following the naming convention of
+    # a model checkpoint trained with TF official model
+    # TODO(haoyuzhang): the following variable name parsing is hacky and easy
+    # to break if there is change in naming convention of either benchmarks or
+    # official models.
+    for v in backbone_vars:
+      # conv2d variable example (model <-- checkpoint):
+      #   v/cg/conv24/conv2d/kernel:0 <-- conv2d_24/kernel
+      if 'conv2d' in v.name:
+        re_match = re.search(r'conv(\d+)/conv2d/(.+):', v.name)
+        if re_match:
+          layer_id = int(re_match.group(1))
+          param_name = re_match.group(2)
+          vname_in_ckpt = self._var_name_in_official_model_ckpt(
+              'conv2d', layer_id, param_name)
+          var_list[vname_in_ckpt] = v
+
+      # batchnorm varariable example:
+      #   v/cg/conv24/batchnorm25/gamma:0 <-- batch_normalization_25/gamma
+      elif 'batchnorm' in v.name:
+        re_match = re.search(r'batchnorm(\d+)/(.+):', v.name)
+        if re_match:
+          layer_id = int(re_match.group(1))
+          param_name = re_match.group(2)
+          vname_in_ckpt = self._var_name_in_official_model_ckpt(
+              'batch_normalization', layer_id, param_name)
+          var_list[vname_in_ckpt] = v
+
+    return var_list
+
+  def _var_name_in_official_model_ckpt(self, layer_name, layer_id, param_name):
+    """Return variable names according to convention in TF official models."""
+    vname_in_ckpt = layer_name
+    if layer_id > 0:
+      vname_in_ckpt += '_' + str(layer_id)
+    vname_in_ckpt += '/' + param_name
+    return vname_in_ckpt
+
+  def loss_function(self, inputs, build_network_result):
+    logits = build_network_result.logits
+
+    # Unpack model output back to locations and confidence scores of predictions
+    # Shape of pred_loc: [batch_size, NUM_SSD_BOXES, 4]
+    # Shape of pred_label: [batch_size, NUM_SSD_BOXES, label_num]
+    pred_loc, pred_label = tf.split(logits, [4, self.label_num], 2)
+
+    # Shape of gt_loc: [batch_size, NUM_SSD_BOXES, 4]
+    # Shape of gt_label: [batch_size, NUM_SSD_BOXES, 1]
+    # Shape of num_gt: [batch_size]
+    _, gt_loc, gt_label, num_gt = inputs
+    gt_label = tf.cast(gt_label, tf.int32)
+
+    box_loss = self._localization_loss(pred_loc, gt_loc, gt_label, num_gt)
+    class_loss = self._classification_loss(pred_label, gt_label, num_gt)
+
+    tf.summary.scalar('box_loss', tf.reduce_mean(box_loss))
+    tf.summary.scalar('class_loss', tf.reduce_mean(class_loss))
+    return class_loss + box_loss
+
+  def _localization_loss(self, pred_loc, gt_loc, gt_label, num_matched_boxes):
+    """Computes the localization loss.
+
+    Computes the localization loss using smooth l1 loss.
+    Args:
+      pred_loc: a flatten tensor that includes all predicted locations. The
+        shape is [batch_size, num_anchors, 4].
+      gt_loc: a tensor representing box regression targets in
+        [batch_size, num_anchors, 4].
+      gt_label: a tensor that represents the classification groundtruth targets.
+        The shape is [batch_size, num_anchors, 1].
+      num_matched_boxes: the number of anchors that are matched to a groundtruth
+        targets, used as the loss normalizater. The shape is [batch_size].
+    Returns:
+      box_loss: a float32 representing total box regression loss.
+    """
+    mask = tf.greater(tf.squeeze(gt_label), 0)
+    float_mask = tf.cast(mask, tf.float32)
+
+    smooth_l1 = tf.reduce_sum(tf.losses.huber_loss(
+        gt_loc, pred_loc,
+        reduction=tf.losses.Reduction.NONE
+    ), axis=2)
+    smooth_l1 = tf.multiply(smooth_l1, float_mask)
+    box_loss = tf.reduce_sum(smooth_l1, axis=1)
+
+    return tf.reduce_mean(box_loss / num_matched_boxes)
+
+  def _classification_loss(self, pred_label, gt_label, num_matched_boxes):
+    """Computes the classification loss.
+
+    Computes the classification loss with hard negative mining.
+    Args:
+      pred_label: a flatten tensor that includes all predicted class. The shape
+        is [batch_size, num_anchors, num_classes].
+      gt_label: a tensor that represents the classification groundtruth targets.
+        The shape is [batch_size, num_anchors, 1].
+      num_matched_boxes: the number of anchors that are matched to a groundtruth
+        targets. This is used as the loss normalizater.
+
+    Returns:
+      box_loss: a float32 representing total box regression loss.
+    """
+    cross_entropy = tf.losses.sparse_softmax_cross_entropy(
+        gt_label, pred_label, reduction=tf.losses.Reduction.NONE)
+
+    mask = tf.greater(tf.squeeze(gt_label), 0)
+    float_mask = tf.cast(mask, tf.float32)
+
+    # Hard example mining
+    neg_masked_cross_entropy = cross_entropy * (1 - float_mask)
+    relative_position = tf.argsort(
+        tf.argsort(
+            neg_masked_cross_entropy, direction='DESCENDING'))
+    num_neg_boxes = tf.minimum(
+        tf.to_int32(num_matched_boxes) * ssd_constants.NEGS_PER_POSITIVE,
+        ssd_constants.NUM_SSD_BOXES)
+    top_k_neg_mask = tf.cast(tf.less(
+        relative_position,
+        tf.tile(num_neg_boxes[:, tf.newaxis], (1, ssd_constants.NUM_SSD_BOXES))
+    ), tf.float32)
+
+    class_loss = tf.reduce_sum(
+        tf.multiply(cross_entropy, float_mask + top_k_neg_mask), axis=1)
+
+    return tf.reduce_mean(class_loss / num_matched_boxes)
+
+  def add_backbone_saver(self):
+    # Create saver with mapping from variable names in checkpoint of backbone
+    # model to variables in SSD model
+    backbone_var_list = self._collect_backbone_vars()
+    self.backbone_savers.append(tf.train.Saver(backbone_var_list))
+
+  def load_backbone_model(self, sess, backbone_model_path):
+    for saver in self.backbone_savers:
+      saver.restore(sess, backbone_model_path)
+
+  def get_input_data_types(self, subset):
+    if subset == 'validation':
+      return [self.data_type, tf.float32, tf.float32, tf.float32, tf.int32]
+    return [self.data_type, tf.float32, tf.float32, tf.float32]
+
+  def get_input_shapes(self, subset):
+    """Return encoded tensor shapes for train and eval data respectively."""
+    if subset == 'validation':
+      # Validation data shapes:
+      # 1. images
+      # 2. ground truth locations of boxes
+      # 3. ground truth classes of objects in boxes
+      # 4. source image IDs
+      # 5. raw image shapes
+      return [
+          [self.batch_size, self.image_size, self.image_size, self.depth],
+          [self.batch_size, ssd_constants.MAX_NUM_EVAL_BOXES, 4],
+          [self.batch_size, ssd_constants.MAX_NUM_EVAL_BOXES, 1],
+          [self.batch_size],
+          [self.batch_size, 3],
+      ]
+
+    # Training data shapes:
+    # 1. images
+    # 2. ground truth locations of boxes
+    # 3. ground truth classes of objects in boxes
+    # 4. numbers of objects in images
+    return [
+        [self.batch_size, self.image_size, self.image_size, self.depth],
+        [self.batch_size, ssd_constants.NUM_SSD_BOXES, 4],
+        [self.batch_size, ssd_constants.NUM_SSD_BOXES, 1],
+        [self.batch_size]
+    ]
+
+  def accuracy_function(self, inputs, logits):
+    """Returns the ops to measure the mean precision of the model."""
+    try:
+      import ssd_dataloader  # pylint: disable=g-import-not-at-top
+      from object_detection.box_coders import faster_rcnn_box_coder  # pylint: disable=g-import-not-at-top
+      from object_detection.core import box_coder  # pylint: disable=g-import-not-at-top
+      from object_detection.core import box_list  # pylint: disable=g-import-not-at-top
+    except ImportError:
+      raise ImportError('To use the COCO dataset, you must clone the '
+                        'repo https://github.com/tensorflow/models and add '
+                        'tensorflow/models and tensorflow/models/research to '
+                        'the PYTHONPATH, and compile the protobufs by '
+                        'following https://github.com/tensorflow/models/blob/'
+                        'master/research/object_detection/g3doc/installation.md'
+                        '#protobuf-compilation ; To evaluate using COCO'
+                        'metric, download and install Python COCO API from'
+                        'https://github.com/cocodataset/cocoapi')
+
+    # Unpack model output back to locations and confidence scores of predictions
+    # pred_locs: relative locations (coordinates) of objects in all SSD boxes
+    # shape: [batch_size, NUM_SSD_BOXES, 4]
+    # pred_labels: confidence scores of objects being of all categories
+    # shape: [batch_size, NUM_SSD_BOXES, label_num]
+    pred_locs, pred_labels = tf.split(logits, [4, self.label_num], 2)
+
+    ssd_box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder(
+        scale_factors=ssd_constants.BOX_CODER_SCALES)
+    anchors = box_list.BoxList(
+        tf.convert_to_tensor(ssd_dataloader.DefaultBoxes()('ltrb')))
+    pred_boxes = box_coder.batch_decode(
+        encoded_boxes=pred_locs, box_coder=ssd_box_coder, anchors=anchors)
+
+    pred_scores = tf.nn.softmax(pred_labels, axis=2)
+
+    # TODO(haoyuzhang): maybe use `gt_boxes` and `gt_classes` for visualization.
+    _, gt_boxes, gt_classes, source_id, raw_shape = inputs  # pylint: disable=unused-variable
+
+    return {
+        (constants.UNREDUCED_ACCURACY_OP_PREFIX +
+         ssd_constants.PRED_BOXES): pred_boxes,
+        (constants.UNREDUCED_ACCURACY_OP_PREFIX +
+         ssd_constants.PRED_SCORES): pred_scores,
+        # TODO(haoyuzhang): maybe use these values for visualization.
+        # constants.UNREDUCED_ACCURACY_OP_PREFIX+'gt_boxes': gt_boxes,
+        # constants.UNREDUCED_ACCURACY_OP_PREFIX+'gt_classes': gt_classes,
+        (constants.UNREDUCED_ACCURACY_OP_PREFIX +
+         ssd_constants.SOURCE_ID): source_id,
+        (constants.UNREDUCED_ACCURACY_OP_PREFIX +
+         ssd_constants.RAW_SHAPE): raw_shape
+    }
+
+  def postprocess(self, results):
+    """Postprocess results returned from model."""
+    try:
+      import coco_metric  # pylint: disable=g-import-not-at-top
+    except ImportError:
+      raise ImportError('To use the COCO dataset, you must clone the '
+                        'repo https://github.com/tensorflow/models and add '
+                        'tensorflow/models and tensorflow/models/research to '
+                        'the PYTHONPATH, and compile the protobufs by '
+                        'following https://github.com/tensorflow/models/blob/'
+                        'master/research/object_detection/g3doc/installation.md'
+                        '#protobuf-compilation ; To evaluate using COCO'
+                        'metric, download and install Python COCO API from'
+                        'https://github.com/cocodataset/cocoapi')
+
+    pred_boxes = results[ssd_constants.PRED_BOXES]
+    pred_scores = results[ssd_constants.PRED_SCORES]
+    # TODO(haoyuzhang): maybe use these values for visualization.
+    # gt_boxes = results['gt_boxes']
+    # gt_classes = results['gt_classes']
+    source_id = results[ssd_constants.SOURCE_ID]
+    raw_shape = results[ssd_constants.RAW_SHAPE]
+
+    # COCO evaluation requires processing COCO_NUM_VAL_IMAGES exactly once. Due
+    # to rounding errors (i.e., COCO_NUM_VAL_IMAGES % batch_size != 0), setting
+    # `num_eval_epochs` to 1 is not enough and will often miss some images. We
+    # expect user to set `num_eval_epochs` to >1, which will leave some unused
+    # images from previous steps in `predictions`. Here we check if we are doing
+    # eval at a new global step.
+    if results['global_step'] > self.eval_global_step:
+      self.eval_global_step = results['global_step']
+      self.predictions.clear()
+
+    for i, sid in enumerate(source_id):
+      self.predictions[int(sid)] = {
+          ssd_constants.PRED_BOXES: pred_boxes[i],
+          ssd_constants.PRED_SCORES: pred_scores[i],
+          ssd_constants.SOURCE_ID: source_id[i],
+          ssd_constants.RAW_SHAPE: raw_shape[i]
+      }
+
+    # COCO metric calculates mAP only after a full epoch of evaluation. Return
+    # dummy results for top_N_accuracy to be compatible with benchmar_cnn.py.
+    if len(self.predictions) >= ssd_constants.COCO_NUM_VAL_IMAGES:
+      log_fn('Got results for all {:d} eval examples. Calculate mAP...'.format(
+          ssd_constants.COCO_NUM_VAL_IMAGES))
+
+      annotation_file = os.path.join(self.params.data_dir,
+                                     ssd_constants.ANNOTATION_FILE)
+      # Size of predictions before decoding about 15--30GB, while size after
+      # decoding is 100--200MB. When using async eval mode, decoding takes
+      # 20--30 seconds of main thread time but is necessary to avoid OOM during
+      # inter-process communication.
+      decoded_preds = coco_metric.decode_predictions(self.predictions.values())
+      self.predictions.clear()
+
+      if self.params.collect_eval_results_async:
+        def _eval_results_getter():
+          """Iteratively get eval results from async eval process."""
+          while True:
+            step, eval_results = self.async_eval_results_queue.get()
+            self.eval_coco_ap = eval_results['COCO/AP']
+            mlperf.logger.log_eval_accuracy(
+                self.eval_coco_ap, step, self.batch_size * self.params.num_gpus,
+                ssd_constants.COCO_NUM_TRAIN_IMAGES)
+            if self.reached_target():
+              # Reached target, clear all pending messages in predictions queue
+              # and insert poison pill to stop the async eval process.
+              while not self.async_eval_predictions_queue.empty():
+                self.async_eval_predictions_queue.get()
+              self.async_eval_predictions_queue.put('STOP')
+              break
+
+        if not self.async_eval_process:
+          # Limiting the number of messages in predictions queue to prevent OOM.
+          # Each message (predictions data) can potentially consume a lot of
+          # memory, and normally there should only be few messages in the queue.
+          # If often blocked on this, consider reducing eval frequency.
+          self.async_eval_predictions_queue = multiprocessing.Queue(2)
+          self.async_eval_results_queue = multiprocessing.Queue()
+
+          # Reason to use a Process as opposed to Thread is mainly the
+          # computationally intensive eval runner. Python multithreading is not
+          # truly running in parallel, a runner thread would get significantly
+          # delayed (or alternatively delay the main thread).
+          self.async_eval_process = multiprocessing.Process(
+              target=coco_metric.async_eval_runner,
+              args=(self.async_eval_predictions_queue,
+                    self.async_eval_results_queue,
+                    annotation_file))
+          self.async_eval_process.daemon = True
+          self.async_eval_process.start()
+
+          self.async_eval_results_getter_thread = threading.Thread(
+              target=_eval_results_getter, args=())
+          self.async_eval_results_getter_thread.daemon = True
+          self.async_eval_results_getter_thread.start()
+
+        self.async_eval_predictions_queue.put(
+            (self.eval_global_step, decoded_preds))
+        return {'top_1_accuracy': 0, 'top_5_accuracy': 0.}
+
+      eval_results = coco_metric.compute_map(decoded_preds, annotation_file)
+      self.eval_coco_ap = eval_results['COCO/AP']
+      ret = {'top_1_accuracy': self.eval_coco_ap, 'top_5_accuracy': 0.}
+      for metric_key, metric_value in eval_results.items():
+        ret[constants.SIMPLE_VALUE_RESULT_PREFIX + metric_key] = metric_value
+      mlperf.logger.log_eval_accuracy(self.eval_coco_ap, self.eval_global_step,
+                                      self.batch_size * self.params.num_gpus,
+                                      ssd_constants.COCO_NUM_TRAIN_IMAGES)
+      return ret
+    log_fn('Got {:d} out of {:d} eval examples.'
+           ' Waiting for the remaining to calculate mAP...'.format(
+               len(self.predictions), ssd_constants.COCO_NUM_VAL_IMAGES))
+    return {'top_1_accuracy': self.eval_coco_ap, 'top_5_accuracy': 0.}
+
+  def get_synthetic_inputs(self, input_name, nclass):
+    """Generating synthetic data matching real data shape and type."""
+    inputs = tf.random_uniform(
+        self.get_input_shapes('train')[0], dtype=self.data_type)
+    inputs = variables.VariableV1(inputs, trainable=False,
+                                  collections=[tf.GraphKeys.LOCAL_VARIABLES],
+                                  name=input_name)
+    boxes = tf.random_uniform(
+        [self.batch_size, ssd_constants.NUM_SSD_BOXES, 4], dtype=tf.float32)
+    classes = tf.random_uniform(
+        [self.batch_size, ssd_constants.NUM_SSD_BOXES, 1], dtype=tf.float32)
+    nboxes = tf.random_uniform(
+        [self.batch_size], minval=1, maxval=10, dtype=tf.float32)
+    return (inputs, boxes, classes, nboxes)
+
+  def reached_target(self):
+    return (self.params.stop_at_top_1_accuracy and
+            self.eval_coco_ap >= self.params.stop_at_top_1_accuracy)

TensorFlow/ComputeVision/Classification/benchmarks-master/scripts/tf_cnn_benchmarks/models/trivial_model.py

+# 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.
+# ==============================================================================
+"""Trivial model configuration."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow.compat.v1 as tf
+from models import model
+
+
+class TrivialModel(model.CNNModel):
+  """Trivial model configuration."""
+
+  def __init__(self, params=None):
+    super(TrivialModel, self).__init__(
+        'trivial', 224 + 3, 32, 0.005, params=params)
+
+  def add_inference(self, cnn):
+    cnn.reshape([-1, 227 * 227 * 3])
+    cnn.affine(1)
+    cnn.affine(4096)
+
+
+class TrivialCifar10Model(model.CNNModel):
+  """Trivial cifar10 model configuration."""
+
+  def __init__(self, params=None):
+    super(TrivialCifar10Model, self).__init__(
+        'trivial', 32, 32, 0.005, params=params)
+
+  def add_inference(self, cnn):
+    cnn.reshape([-1, 32 * 32 * 3])
+    cnn.affine(1)
+    cnn.affine(4096)
+
+
+class TrivialSSD300Model(model.CNNModel):
+  """Trivial SSD300 model configuration."""
+
+  def __init__(self, params=None):
+    super(TrivialSSD300Model, self).__init__(
+        'trivial', 300, params.batch_size, 0.005, params=params)
+
+  def add_inference(self, cnn):
+    cnn.reshape([-1, 300 * 300 * 3])
+    cnn.affine(1)
+    cnn.affine(4096)
+
+  def get_input_shapes(self, subset):
+    return [[self.batch_size, 300, 300, 3],
+            [self.batch_size, 8732, 4],
+            [self.batch_size, 8732, 1],
+            [self.batch_size]]
+
+  def loss_function(self, inputs, build_network_result):
+    images, _, _, labels = inputs
+    labels = tf.cast(labels, tf.int32)
+    return super(TrivialSSD300Model, self).loss_function(
+        (images, labels), build_network_result)

TensorFlow/ComputeVision/Classification/benchmarks-master/scripts/tf_cnn_benchmarks/models/vgg_model.py

+# 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.
+# ==============================================================================
+"""Vgg model configuration.
+
+Includes multiple models: vgg11, vgg16, vgg19, corresponding to
+  model A, D, and E in Table 1 of [1].
+
+References:
+[1]  Simonyan, Karen, Andrew Zisserman
+     Very Deep Convolutional Networks for Large-Scale Image Recognition
+     arXiv:1409.1556 (2014)
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from six.moves import xrange  # pylint: disable=redefined-builtin
+from models import model
+
+
+def _construct_vgg(cnn, num_conv_layers):
+  """Build vgg architecture from blocks."""
+  assert len(num_conv_layers) == 5
+  for _ in xrange(num_conv_layers[0]):
+    cnn.conv(64, 3, 3)
+  cnn.mpool(2, 2)
+  for _ in xrange(num_conv_layers[1]):
+    cnn.conv(128, 3, 3)
+  cnn.mpool(2, 2)
+  for _ in xrange(num_conv_layers[2]):
+    cnn.conv(256, 3, 3)
+  cnn.mpool(2, 2)
+  for _ in xrange(num_conv_layers[3]):
+    cnn.conv(512, 3, 3)
+  cnn.mpool(2, 2)
+  for _ in xrange(num_conv_layers[4]):
+    cnn.conv(512, 3, 3)
+  cnn.mpool(2, 2)
+  cnn.reshape([-1, 512 * 7 * 7])
+  cnn.affine(4096)
+  cnn.dropout()
+  cnn.affine(4096)
+  cnn.dropout()
+
+
+class Vgg11Model(model.CNNModel):
+
+  def __init__(self, params=None):
+    super(Vgg11Model, self).__init__('vgg11', 224, 64, 0.005, params=params)
+
+  def add_inference(self, cnn):
+    _construct_vgg(cnn, [1, 1, 2, 2, 2])
+
+
+class Vgg16Model(model.CNNModel):
+
+  def __init__(self, params=None):
+    super(Vgg16Model, self).__init__('vgg16', 224, 64, 0.005, params=params)
+
+  def add_inference(self, cnn):
+    _construct_vgg(cnn, [2, 2, 3, 3, 3])
+
+
+class Vgg19Model(model.CNNModel):
+
+  def __init__(self, params=None):
+    super(Vgg19Model, self).__init__('vgg19', 224, 64, 0.005, params=params)
+
+  def add_inference(self, cnn):
+    _construct_vgg(cnn, [2, 2, 4, 4, 4])