# 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.
# ==============================================================================
"""Runs a ResNet model on the ImageNet dataset."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

from absl import app as absl_app
from absl import flags
from six.moves import range
import tensorflow as tf

from official.r1.resnet import imagenet_preprocessing
from official.r1.resnet import resnet_model
from official.r1.resnet import resnet_run_loop
from official.utils.flags import core as flags_core
from official.utils.logs import logger

############## npu modify begin #############
from npu_bridge.estimator import npu_ops
from hccl.manage.api import get_local_rank_id
from hccl.manage.api import get_rank_size
from hccl.manage.api import get_rank_id
from tensorflow.core.protobuf import rewriter_config_pb2

tf.compat.v1.logging.set_verbosity(tf.logging.INFO)
############## npu modify end ###############

DEFAULT_IMAGE_SIZE = 224
NUM_CHANNELS = 3
NUM_CLASSES = 1001

NUM_IMAGES = {
    'train': 1281167,
    'validation': 50000,
}

_NUM_TRAIN_FILES = 1024
_SHUFFLE_BUFFER = 10000

DATASET_NAME = 'ImageNet'

###############################################################################
# Data processing
###############################################################################
def get_filenames(is_training, data_dir):
  """Return filenames for dataset."""
  if is_training:
    return [
        os.path.join(data_dir, 'train-%05d-of-01024' % i)
        for i in range(_NUM_TRAIN_FILES)]
  else:
    return [
        os.path.join(data_dir, 'validation-%05d-of-00128' % i)
        for i in range(128)]


def _parse_example_proto(example_serialized):
  """Parses an Example proto containing a training example of an image.

  The output of the build_image_data.py image preprocessing script is a dataset
  containing serialized Example protocol buffers. Each Example proto contains
  the following fields (values are included as examples):

    image/height: 462
    image/width: 581
    image/colorspace: 'RGB'
    image/channels: 3
    image/class/label: 615
    image/class/synset: 'n03623198'
    image/class/text: 'knee pad'
    image/object/bbox/xmin: 0.1
    image/object/bbox/xmax: 0.9
    image/object/bbox/ymin: 0.2
    image/object/bbox/ymax: 0.6
    image/object/bbox/label: 615
    image/format: 'JPEG'
    image/filename: 'ILSVRC2012_val_00041207.JPEG'
    image/encoded: <JPEG encoded string>

  Args:
    example_serialized: scalar Tensor tf.string containing a serialized
      Example protocol buffer.

  Returns:
    image_buffer: Tensor tf.string containing the contents of a JPEG file.
    label: Tensor tf.int32 containing the label.
    bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
      where each coordinate is [0, 1) and the coordinates are arranged as
      [ymin, xmin, ymax, xmax].
  """
  # Dense features in Example proto.
  feature_map = {
      'image/encoded': tf.io.FixedLenFeature([], dtype=tf.string,
                                             default_value=''),
      'image/class/label': tf.io.FixedLenFeature([], dtype=tf.int64,
                                                 default_value=-1),
      'image/class/text': tf.io.FixedLenFeature([], dtype=tf.string,
                                                default_value=''),
  }
  sparse_float32 = tf.io.VarLenFeature(dtype=tf.float32)
  # Sparse features in Example proto.
  feature_map.update(
      {k: sparse_float32 for k in ['image/object/bbox/xmin',
                                   'image/object/bbox/ymin',
                                   'image/object/bbox/xmax',
                                   'image/object/bbox/ymax']})

  features = tf.io.parse_single_example(serialized=example_serialized,
                                        features=feature_map)
  label = tf.cast(features['image/class/label'], dtype=tf.int32)

  xmin = tf.expand_dims(features['image/object/bbox/xmin'].values, 0)
  ymin = tf.expand_dims(features['image/object/bbox/ymin'].values, 0)
  xmax = tf.expand_dims(features['image/object/bbox/xmax'].values, 0)
  ymax = tf.expand_dims(features['image/object/bbox/ymax'].values, 0)

  # Note that we impose an ordering of (y, x) just to make life difficult.
  bbox = tf.concat([ymin, xmin, ymax, xmax], 0)

  # Force the variable number of bounding boxes into the shape
  # [1, num_boxes, coords].
  bbox = tf.expand_dims(bbox, 0)
  bbox = tf.transpose(a=bbox, perm=[0, 2, 1])

  return features['image/encoded'], label, bbox


def parse_record(raw_record, is_training, dtype):
  """Parses a record containing a training example of an image.

  The input record is parsed into a label and image, and the image is passed
  through preprocessing steps (cropping, flipping, and so on).

  Args:
    raw_record: scalar Tensor tf.string containing a serialized
      Example protocol buffer.
    is_training: A boolean denoting whether the input is for training.
    dtype: data type to use for images/features.

  Returns:
    Tuple with processed image tensor and one-hot-encoded label tensor.
  """
  image_buffer, label, bbox = _parse_example_proto(raw_record)

  image = imagenet_preprocessing.preprocess_image(
      image_buffer=image_buffer,
      bbox=bbox,
      output_height=DEFAULT_IMAGE_SIZE,
      output_width=DEFAULT_IMAGE_SIZE,
      num_channels=NUM_CHANNELS,
      is_training=is_training)
  image = tf.cast(image, dtype)

  return image, label


def input_fn(is_training,
             data_dir,
             batch_size,
             num_epochs=1,
             dtype=tf.float32,
             datasets_num_private_threads=None,
             parse_record_fn=parse_record,
             input_context=None,
             drop_remainder=False,
             tf_data_experimental_slack=False):
  """Input function which provides batches for train or eval.

  Args:
    is_training: A boolean denoting whether the input is for training.
    data_dir: The directory containing the input data.
    batch_size: The number of samples per batch.
    num_epochs: The number of epochs to repeat the dataset.
    dtype: Data type to use for images/features
    datasets_num_private_threads: Number of private threads for tf.data.
    parse_record_fn: Function to use for parsing the records.
    input_context: A `tf.distribute.InputContext` object passed in by
      `tf.distribute.Strategy`.
    drop_remainder: A boolean indicates whether to drop the remainder of the
      batches. If True, the batch dimension will be static.
    tf_data_experimental_slack: Whether to enable tf.data's
      `experimental_slack` option.

  Returns:
    A dataset that can be used for iteration.
  """
  filenames = get_filenames(is_training, data_dir)
  dataset = tf.data.Dataset.from_tensor_slices(filenames)

  if input_context:
    ############## npu modify begin #############
    dataset = dataset.shard(get_rank_size(),
                              get_rank_id())
    ############## npu modify end ###############

  if is_training:
    # Shuffle the input files
    dataset = dataset.shuffle(buffer_size=_NUM_TRAIN_FILES)

  # Convert to individual records.
  # cycle_length = 10 means that up to 10 files will be read and deserialized in
  # parallel. You may want to increase this number if you have a large number of
  # CPU cores.
  dataset = dataset.interleave(
      tf.data.TFRecordDataset,
      cycle_length=10,
      num_parallel_calls=tf.data.experimental.AUTOTUNE)

  return resnet_run_loop.process_record_dataset(
      dataset=dataset,
      is_training=is_training,
      batch_size=batch_size,
      shuffle_buffer=_SHUFFLE_BUFFER,
      parse_record_fn=parse_record_fn,
      num_epochs=num_epochs,
      dtype=dtype,
      datasets_num_private_threads=datasets_num_private_threads,
      drop_remainder=drop_remainder,
      tf_data_experimental_slack=tf_data_experimental_slack,
  )


def get_synth_input_fn(dtype):
  return resnet_run_loop.get_synth_input_fn(
      DEFAULT_IMAGE_SIZE, DEFAULT_IMAGE_SIZE, NUM_CHANNELS, NUM_CLASSES,
      dtype=dtype)


###############################################################################
# Running the model
###############################################################################
class ImagenetModel(resnet_model.Model):
  """Model class with appropriate defaults for Imagenet data."""

  def __init__(self, resnet_size, data_format=None, num_classes=NUM_CLASSES,
               resnet_version=resnet_model.DEFAULT_VERSION,
               dtype=resnet_model.DEFAULT_DTYPE):
    """These are the parameters that work for Imagenet data.

    Args:
      resnet_size: The number of convolutional layers needed in the model.
      data_format: Either 'channels_first' or 'channels_last', specifying which
        data format to use when setting up the model.
      num_classes: The number of output classes needed from the model. This
        enables users to extend the same model to their own datasets.
      resnet_version: Integer representing which version of the ResNet network
        to use. See README for details. Valid values: [1, 2]
      dtype: The TensorFlow dtype to use for calculations.
    """

    # For bigger models, we want to use "bottleneck" layers
    if resnet_size < 50:
      bottleneck = False
    else:
      bottleneck = True

    super(ImagenetModel, self).__init__(
        resnet_size=resnet_size,
        bottleneck=bottleneck,
        num_classes=num_classes,
        num_filters=64,
        kernel_size=7,
        conv_stride=2,
        first_pool_size=3,
        first_pool_stride=2,
        block_sizes=_get_block_sizes(resnet_size),
        block_strides=[1, 2, 2, 2],
        resnet_version=resnet_version,
        data_format=data_format,
        dtype=dtype
    )


def _get_block_sizes(resnet_size):
  """Retrieve the size of each block_layer in the ResNet model.

  The number of block layers used for the Resnet model varies according
  to the size of the model. This helper grabs the layer set we want, throwing
  an error if a non-standard size has been selected.

  Args:
    resnet_size: The number of convolutional layers needed in the model.

  Returns:
    A list of block sizes to use in building the model.

  Raises:
    KeyError: if invalid resnet_size is received.
  """
  choices = {
      18: [2, 2, 2, 2],
      34: [3, 4, 6, 3],
      50: [3, 4, 6, 3],
      101: [3, 4, 23, 3],
      152: [3, 8, 36, 3],
      200: [3, 24, 36, 3]
  }

  try:
    return choices[resnet_size]
  except KeyError:
    err = ('Could not find layers for selected Resnet size.\n'
           'Size received: {}; sizes allowed: {}.'.format(
               resnet_size, list(choices.keys())))
    raise ValueError(err)


def imagenet_model_fn(features, labels, mode, params):
  """Our model_fn for ResNet to be used with our Estimator."""

  # Warmup and higher lr may not be valid for fine tuning with small batches
  # and smaller numbers of training images.
  if params['fine_tune']:
    warmup = False
    base_lr = .1
  else:
    warmup = True
    base_lr = .128

  learning_rate_fn = resnet_run_loop.learning_rate_with_decay(
      batch_size=params['num_gpus']*params['batch_size'],
      batch_denom=256, num_images=NUM_IMAGES['train'],
      boundary_epochs=[30, 60, 80, 90], decay_rates=[1, 0.1, 0.01, 0.001, 1e-4],
      warmup=warmup, base_lr=base_lr)

  return resnet_run_loop.resnet_model_fn(
      features=features,
      labels=labels,
      mode=mode,
      model_class=ImagenetModel,
      resnet_size=params['resnet_size'],
      weight_decay=flags.FLAGS.weight_decay,
      learning_rate_fn=learning_rate_fn,
      momentum=0.9,
      data_format=params['data_format'],
      resnet_version=params['resnet_version'],
      loss_scale=params['loss_scale'],
      loss_filter_fn=None,
      dtype=params['dtype'],
      fine_tune=params['fine_tune'],
      label_smoothing=flags.FLAGS.label_smoothing
  )


def define_imagenet_flags():
  resnet_run_loop.define_resnet_flags(
      resnet_size_choices=['18', '34', '50', '101', '152', '200'],
      dynamic_loss_scale=True,
      fp16_implementation=True)
  flags.adopt_module_key_flags(resnet_run_loop)
  flags_core.set_defaults(train_epochs=90)

  #Loss scale is defautt used because Davinci core supports mixed precision naturally
  flags_core.set_defaults(loss_scale='512')

def run_imagenet(flags_obj):
  """Run ResNet ImageNet training and eval loop.

  Args:
    flags_obj: An object containing parsed flag values.

  Returns:
    Dict of results of the run.  Contains the keys `eval_results` and
      `train_hooks`. `eval_results` contains accuracy (top_1) and
      accuracy_top_5. `train_hooks` is a list the instances of hooks used during
      training.
  """
  input_function = (flags_obj.use_synthetic_data and
                    get_synth_input_fn(flags_core.get_tf_dtype(flags_obj)) or
                    input_fn)

  result = resnet_run_loop.resnet_main(
      flags_obj, imagenet_model_fn, input_function, DATASET_NAME,NUM_IMAGES,
      shape=[DEFAULT_IMAGE_SIZE, DEFAULT_IMAGE_SIZE, NUM_CHANNELS],)

  return result
def main(_):
  ############## npu modify begin #############
  # Init NPU ,then can call HCCL Interface
  init_sess,npu_init=resnet_run_loop.init_npu()
  init_sess.run(npu_init)
  ############## npu modify end ###############

  with logger.benchmark_context(flags.FLAGS):
    run_imagenet(flags.FLAGS)


if __name__ == '__main__':
  tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
  define_imagenet_flags()
  absl_app.run(main)
