resnet18.py

# Copyright 2019 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
#
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# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# ==============================================================================
# pylint: disable=invalid-name
"""ResNet v2 models for Keras.
Reference:
  - [Identity Mappings in Deep Residual Networks]
    (https://arxiv.org/abs/1603.05027) (CVPR 2016)
"""
import os
from tensorflow.python.keras.applications import imagenet_utils
from tensorflow.python.keras.applications import resnet
from tensorflow.python.util.tf_export import keras_export

@keras_export('keras.applications.resnet_v2.ResNet18V2',
              'keras.applications.ResNet18V2')
def ResNet18V2(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation='softmax'):
  """Instantiates the ResNet50V2 architecture."""
  def stack_fn(x):
    x = resnet.stack2(x, 64, 2, name='conv2')
    x = resnet.stack2(x, 128, 2, name='conv3')
    x = resnet.stack2(x, 256, 2, name='conv4')
    return resnet.stack2(x, 512, 2, stride1=1, name='conv5')

  return resnet.ResNet(
      stack_fn,
      True,
      True,
      'resnet50v2',
      include_top,
      weights,
      input_tensor,
      input_shape,
      pooling,
      classes,
      classifier_activation=classifier_activation)

@keras_export('keras.applications.resnet_v2.ResNet50V2',
              'keras.applications.ResNet50V2')
def ResNet50V2(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation='softmax'):
  """Instantiates the ResNet50V2 architecture."""
  def stack_fn(x):
    x = resnet.stack2(x, 64, 3, name='conv2')
    x = resnet.stack2(x, 128, 4, name='conv3')
    x = resnet.stack2(x, 256, 6, name='conv4')
    return resnet.stack2(x, 512, 3, stride1=1, name='conv5')

  return resnet.ResNet(
      stack_fn,
      True,
      True,
      'resnet50v2',
      include_top,
      weights,
      input_tensor,
      input_shape,
      pooling,
      classes,
      classifier_activation=classifier_activation)


@keras_export('keras.applications.resnet_v2.ResNet101V2',
              'keras.applications.ResNet101V2')
def ResNet101V2(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation='softmax'):
  """Instantiates the ResNet101V2 architecture."""
  def stack_fn(x):
    x = resnet.stack2(x, 64, 3, name='conv2')
    x = resnet.stack2(x, 128, 4, name='conv3')
    x = resnet.stack2(x, 256, 23, name='conv4')
    return resnet.stack2(x, 512, 3, stride1=1, name='conv5')

  return resnet.ResNet(
      stack_fn,
      True,
      True,
      'resnet101v2',
      include_top,
      weights,
      input_tensor,
      input_shape,
      pooling,
      classes,
      classifier_activation=classifier_activation)


@keras_export('keras.applications.resnet_v2.ResNet152V2',
              'keras.applications.ResNet152V2')
def ResNet152V2(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation='softmax'):
  """Instantiates the ResNet152V2 architecture."""
  def stack_fn(x):
    x = resnet.stack2(x, 64, 3, name='conv2')
    x = resnet.stack2(x, 128, 8, name='conv3')
    x = resnet.stack2(x, 256, 36, name='conv4')
    return resnet.stack2(x, 512, 3, stride1=1, name='conv5')

  return resnet.ResNet(
      stack_fn,
      True,
      True,
      'resnet152v2',
      include_top,
      weights,
      input_tensor,
      input_shape,
      pooling,
      classes,
      classifier_activation=classifier_activation)


@keras_export('keras.applications.resnet_v2.preprocess_input')
def preprocess_input(x, data_format=None):
  return imagenet_utils.preprocess_input(
      x, data_format=data_format, mode='tf')


@keras_export('keras.applications.resnet_v2.decode_predictions')
def decode_predictions(preds, top=5):
  return imagenet_utils.decode_predictions(preds, top=top)


preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
    mode='',
    ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF,
    error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__

DOC = """
  Reference:
  - [Identity Mappings in Deep Residual Networks]
    (https://arxiv.org/abs/1603.05027) (CVPR 2016)
  For image classification use cases, see
  [this page for detailed examples](
    https://keras.io/api/applications/#usage-examples-for-image-classification-models).
  For transfer learning use cases, make sure to read the
  [guide to transfer learning & fine-tuning](
    https://keras.io/guides/transfer_learning/).
  Note: each Keras Application expects a specific kind of input preprocessing.
  For ResNetV2, call `tf.keras.applications.resnet_v2.preprocess_input` on your
  inputs before passing them to the model.
  `resnet_v2.preprocess_input` will scale input pixels between -1 and 1.
  Args:
    include_top: whether to include the fully-connected
      layer at the top of the network.
    weights: one of `None` (random initialization),
      'imagenet' (pre-training on ImageNet),
      or the path to the weights file to be loaded.
    input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
      to use as image input for the model.
    input_shape: optional shape tuple, only to be specified
      if `include_top` is False (otherwise the input shape
      has to be `(224, 224, 3)` (with `'channels_last'` data format)
      or `(3, 224, 224)` (with `'channels_first'` data format).
      It should have exactly 3 inputs channels,
      and width and height should be no smaller than 32.
      E.g. `(200, 200, 3)` would be one valid value.
    pooling: Optional pooling mode for feature extraction
      when `include_top` is `False`.
      - `None` means that the output of the model will be
          the 4D tensor output of the
          last convolutional block.
      - `avg` means that global average pooling
          will be applied to the output of the
          last convolutional block, and thus
          the output of the model will be a 2D tensor.
      - `max` means that global max pooling will
          be applied.
    classes: optional number of classes to classify images
      into, only to be specified if `include_top` is True, and
      if no `weights` argument is specified.
    classifier_activation: A `str` or callable. The activation function to use
      on the "top" layer. Ignored unless `include_top=True`. Set
      `classifier_activation=None` to return the logits of the "top" layer.
      When loading pretrained weights, `classifier_activation` can only
      be `None` or `"softmax"`.
  Returns:
    A `keras.Model` instance.
"""

setattr(ResNet50V2, '__doc__', ResNet50V2.__doc__ + DOC)
setattr(ResNet101V2, '__doc__', ResNet101V2.__doc__ + DOC)
setattr(ResNet152V2, '__doc__', ResNet152V2.__doc__ + DOC)


if __name__ == "__main__":
    import time
    import numpy as np
    print("begin ...")
    gpu_num = "1"
    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"  # see issue #152
    os.environ["CUDA_VISIBLE_DEVICES"] = gpu_num

    input_test = np.random.randn(1,256,256,3)
    num_classes = 1000
    net = ResNet18V2(include_top=True,
                     weights=None,
                     input_tensor=None,
                     input_shape=None,
                     pooling=None,
                     classes=num_classes,
                     classifier_activation='softmax')

    out = net(input_test)
    print('\ndummy.\n')
    n = 1000
    s_t = time.time()
    for i in range(n):
        net(input_test)
    e_t = time.time()
    t_t = (e_t - s_t) * 1000
    print('total time: {} ms / {}, average time: {} ms'.format(t_t, n, t_t/(n+1e-6)))

    print("\ndone!")