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Commit 424c2045 authored by Shining Sun's avatar Shining Sun
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Before all the data related change

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official/resnet/cifar10_main.py
View file @ 424c2045
......@@ -29,13 +29,13 @@ from official.utils.logs import logger
from official.resnet import resnet_model
from official.resnet import resnet_run_loop
_HEIGHT = 32
_WIDTH = 32
_NUM_CHANNELS = 3
_DEFAULT_IMAGE_BYTES = _HEIGHT * _WIDTH * _NUM_CHANNELS
HEIGHT = 32
WIDTH = 32
NUM_CHANNELS = 3
_DEFAULT_IMAGE_BYTES = HEIGHT * WIDTH * NUM_CHANNELS
# The record is the image plus a one-byte label
_RECORD_BYTES = _DEFAULT_IMAGE_BYTES + 1
_NUM_CLASSES = 10
NUM_CLASSES = 10
_NUM_DATA_FILES = 5
# TODO(tobyboyd): Change to best practice 45K(train)/5K(val)/10K(test) splits.
......@@ -79,7 +79,7 @@ def parse_record(raw_record, is_training, dtype):
# The remaining bytes after the label represent the image, which we reshape
# from [depth * height * width] to [depth, height, width].
depth_major = tf.reshape(record_vector[1:_RECORD_BYTES],
[_NUM_CHANNELS, _HEIGHT, _WIDTH])
[NUM_CHANNELS, HEIGHT, WIDTH])
# Convert from [depth, height, width] to [height, width, depth], and cast as
# float32.
......@@ -96,10 +96,10 @@ def preprocess_image(image, is_training):
if is_training:
# Resize the image to add four extra pixels on each side.
image = tf.image.resize_image_with_crop_or_pad(
image, _HEIGHT + 8, _WIDTH + 8)
image, HEIGHT + 8, WIDTH + 8)
# Randomly crop a [_HEIGHT, _WIDTH] section of the image.
image = tf.random_crop(image, [_HEIGHT, _WIDTH, _NUM_CHANNELS])
# Randomly crop a [HEIGHT, WIDTH] section of the image.
image = tf.random_crop(image, [HEIGHT, WIDTH, NUM_CHANNELS])
# Randomly flip the image horizontally.
image = tf.image.random_flip_left_right(image)
......@@ -145,7 +145,7 @@ def input_fn(is_training, data_dir, batch_size, num_epochs=1,
def get_synth_input_fn(dtype):
return resnet_run_loop.get_synth_input_fn(
_HEIGHT, _WIDTH, _NUM_CHANNELS, _NUM_CLASSES, dtype=dtype)
HEIGHT, WIDTH, NUM_CHANNELS, NUM_CLASSES, dtype=dtype)
###############################################################################
......@@ -154,7 +154,7 @@ def get_synth_input_fn(dtype):
class Cifar10Model(resnet_model.Model):
"""Model class with appropriate defaults for CIFAR-10 data."""
def __init__(self, resnet_size, data_format=None, num_classes=_NUM_CLASSES,
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 CIFAR-10 data.
......@@ -196,7 +196,7 @@ class Cifar10Model(resnet_model.Model):
def cifar10_model_fn(features, labels, mode, params):
"""Model function for CIFAR-10."""
features = tf.reshape(features, [-1, _HEIGHT, _WIDTH, _NUM_CHANNELS])
features = tf.reshape(features, [-1, HEIGHT, WIDTH, NUM_CHANNELS])
# Learning rate schedule follows arXiv:1512.03385 for ResNet-56 and under.
learning_rate_fn = resnet_run_loop.learning_rate_with_decay(
batch_size=params['batch_size'], batch_denom=128,
......@@ -261,7 +261,7 @@ def run_cifar(flags_obj):
input_fn)
resnet_run_loop.resnet_main(
flags_obj, cifar10_model_fn, input_function, DATASET_NAME,
shape=[_HEIGHT, _WIDTH, _NUM_CHANNELS])
shape=[HEIGHT, WIDTH, NUM_CHANNELS])
def main(_):
......
official/resnet/imagenet_main.py
View file @ 424c2045
......@@ -30,11 +30,11 @@ from official.resnet import imagenet_preprocessing
from official.resnet import resnet_model
from official.resnet import resnet_run_loop
_DEFAULT_IMAGE_SIZE = 224
_NUM_CHANNELS = 3
_NUM_CLASSES = 1001
DEFAULT_IMAGE_SIZE = 224
NUM_CHANNELS = 3
NUM_CLASSES = 1001
_NUM_IMAGES = {
NUM_IMAGES = {
'train': 1281167,
'validation': 50000,
}
......@@ -149,9 +149,9 @@ def parse_record(raw_record, is_training, dtype):
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,
output_height=DEFAULT_IMAGE_SIZE,
output_width=DEFAULT_IMAGE_SIZE,
num_channels=NUM_CHANNELS,
is_training=is_training)
image = tf.cast(image, dtype)
......@@ -206,7 +206,7 @@ def input_fn(is_training, data_dir, batch_size, num_epochs=1,
def get_synth_input_fn(dtype):
return resnet_run_loop.get_synth_input_fn(
_DEFAULT_IMAGE_SIZE, _DEFAULT_IMAGE_SIZE, _NUM_CHANNELS, _NUM_CLASSES,
DEFAULT_IMAGE_SIZE, DEFAULT_IMAGE_SIZE, NUM_CHANNELS, NUM_CLASSES,
dtype=dtype)
......@@ -216,7 +216,7 @@ def get_synth_input_fn(dtype):
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,
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.
......@@ -303,7 +303,7 @@ def imagenet_model_fn(features, labels, mode, params):
learning_rate_fn = resnet_run_loop.learning_rate_with_decay(
batch_size=params['batch_size'], batch_denom=256,
num_images=_NUM_IMAGES['train'], boundary_epochs=[30, 60, 80, 90],
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(
......@@ -343,7 +343,7 @@ def run_imagenet(flags_obj):
resnet_run_loop.resnet_main(
flags_obj, imagenet_model_fn, input_function, DATASET_NAME,
shape=[_DEFAULT_IMAGE_SIZE, _DEFAULT_IMAGE_SIZE, _NUM_CHANNELS])
shape=[DEFAULT_IMAGE_SIZE, DEFAULT_IMAGE_SIZE, NUM_CHANNELS])
def main(_):
......
official/resnet/keras/keras_cifar_main.py
View file @ 424c2045
......@@ -18,11 +18,8 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
from absl import app as absl_app
from absl import flags
import numpy as np
import tensorflow as tf # pylint: disable=g-bad-import-order
from official.resnet import cifar10_main as cifar_main
......@@ -68,6 +65,8 @@ def parse_record_keras(raw_record, is_training, dtype):
The input record is parsed into a label and image, and the image is passed
through preprocessing steps (cropping, flipping, and so on).
This method converts the label to onhot to fit the loss function.
Args:
raw_record: scalar Tensor tf.string containing a serialized
Example protocol buffer.
......@@ -78,7 +77,7 @@ def parse_record_keras(raw_record, is_training, dtype):
Tuple with processed image tensor and one-hot-encoded label tensor.
"""
image, label = cifar_main.parse_record(raw_record, is_training, dtype)
label = tf.sparse_to_dense(label, (cifar_main._NUM_CLASSES,), 1)
label = tf.sparse_to_dense(label, (cifar_main.NUM_CLASSES,), 1)
return image, label
......@@ -105,26 +104,26 @@ def run(flags_obj):
# pylint: disable=protected-access
if flags_obj.use_synthetic_data:
synth_input_fn = resnet_run_loop.get_synth_input_fn(
cifar_main._HEIGHT, cifar_main._WIDTH,
cifar_main._NUM_CHANNELS, cifar_main._NUM_CLASSES,
cifar_main.HEIGHT, cifar_main.WIDTH,
cifar_main.NUM_CHANNELS, cifar_main.NUM_CLASSES,
dtype=flags_core.get_tf_dtype(flags_obj))
train_input_dataset = synth_input_fn(
True,
flags_obj.data_dir,
batch_size=per_device_batch_size,
height=cifar_main._HEIGHT,
width=cifar_main._WIDTH,
num_channels=cifar_main._NUM_CHANNELS,
num_classes=cifar_main._NUM_CLASSES,
height=cifar_main.HEIGHT,
width=cifar_main.WIDTH,
num_channels=cifar_main.NUM_CHANNELS,
num_classes=cifar_main.NUM_CLASSES,
dtype=dtype)
eval_input_dataset = synth_input_fn(
False,
flags_obj.data_dir,
batch_size=per_device_batch_size,
height=cifar_main._HEIGHT,
width=cifar_main._WIDTH,
num_channels=cifar_main._NUM_CHANNELS,
num_classes=cifar_main._NUM_CLASSES,
height=cifar_main.HEIGHT,
width=cifar_main.WIDTH,
num_channels=cifar_main.NUM_CHANNELS,
num_classes=cifar_main.NUM_CLASSES,
dtype=dtype)
# pylint: enable=protected-access
......@@ -144,20 +143,22 @@ def run(flags_obj):
parse_record_fn=parse_record_keras)
optimizer = keras_common.get_optimizer()
strategy = keras_common.get_dist_strategy()
strategy = distribution_utils.get_distribution_strategy(
flags_obj.num_gpus, flags_obj.use_one_device_strategy)
model = resnet56.ResNet56(input_shape=(32, 32, 3),
classes=cifar_main._NUM_CLASSES)
classes=cifar_main.NUM_CLASSES)
model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['categorical_accuracy'],
strategy=strategy)
time_callback, tensorboard_callback, lr_callback = keras_common.get_fit_callbacks(
learning_rate_schedule)
time_callback, tensorboard_callback, lr_callback = keras_common.get_callbacks(
learning_rate_schedule, cifar_main.NUM_IMAGES['train'])
steps_per_epoch = cifar_main._NUM_IMAGES['train'] // flags_obj.batch_size
num_eval_steps = (cifar_main._NUM_IMAGES['validation'] //
steps_per_epoch = cifar_main.NUM_IMAGES['train'] // flags_obj.batch_size
num_eval_steps = (cifar_main.NUM_IMAGES['validation'] //
flags_obj.batch_size)
history = model.fit(train_input_dataset,
......@@ -176,7 +177,6 @@ def run(flags_obj):
steps=num_eval_steps,
verbose=1)
print('Test loss:', eval_output[0])
stats = keras_common.analyze_fit_and_eval_result(history, eval_output)
return stats
......@@ -188,6 +188,6 @@ def main(_):
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.DEBUG)
tf.logging.set_verbosity(tf.logging.INFO)
cifar_main.define_cifar_flags()
absl_app.run(main)
official/resnet/keras/keras_common.py
View file @ 424c2045
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
# 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.
......@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Common util functions an classes used by both keras cifar and imagenet."""
"""Common util functions and classes used by both keras cifar and imagenet."""
from __future__ import absolute_import
from __future__ import division
......@@ -20,13 +20,10 @@ from __future__ import print_function
import time
from absl import app as absl_app
from absl import flags
import numpy as np
import tensorflow as tf # pylint: disable=g-bad-import-order
from official.resnet import imagenet_main
from official.utils.misc import distribution_utils
from tensorflow.python.keras.optimizer_v2 import gradient_descent as gradient_descent_v2
......@@ -37,7 +34,7 @@ class TimeHistory(tf.keras.callbacks.Callback):
"""Callback for Keras models."""
def __init__(self, batch_size):
"""Callback for Keras models.
"""Callback for logging performance (# image/second).
Args:
batch_size: Total batch size.
......@@ -45,28 +42,22 @@ class TimeHistory(tf.keras.callbacks.Callback):
"""
self._batch_size = batch_size
super(TimeHistory, self).__init__()
self.log_batch_size = 100
def on_train_begin(self, logs=None):
self.epoch_times_secs = []
self.batch_times_secs = []
self.record_batch = True
def on_epoch_begin(self, epoch, logs=None):
self.epoch_time_start = time.time()
def on_epoch_end(self, epoch, logs=None):
self.epoch_times_secs.append(time.time() - self.epoch_time_start)
def on_batch_begin(self, batch, logs=None):
if self.record_batch:
self.batch_time_start = time.time()
self.record_batch = False
def on_batch_end(self, batch, logs=None):
n = 100
if batch % n == 0:
if batch % self.log_batch_size == 0:
last_n_batches = time.time() - self.batch_time_start
examples_per_second = (self._batch_size * n) / last_n_batches
examples_per_second =
(self._batch_size * self.log_batch_size) / last_n_batches
self.batch_times_secs.append(last_n_batches)
self.record_batch = True
# TODO(anjalisridhar): add timestamp as well.
......@@ -95,8 +86,8 @@ class LearningRateBatchScheduler(tf.keras.callbacks.Callback):
self.prev_lr = -1
def on_epoch_begin(self, epoch, logs=None):
#if not hasattr(self.model.optimizer, 'learning_rate'):
# raise ValueError('Optimizer must have a "learning_rate" attribute.')
if not hasattr(self.model.optimizer, 'learning_rate'):
raise ValueError('Optimizer must have a "learning_rate" attribute.')
self.epochs += 1
def on_batch_begin(self, batch, logs=None):
......@@ -120,31 +111,16 @@ def get_optimizer():
return optimizer
def get_dist_strategy():
if FLAGS.num_gpus == 1 and not FLAGS.use_one_device_strategy:
print('Not using distribution strategies.')
strategy = None
elif FLAGS.num_gpus > 1 and FLAGS.use_one_device_strategy:
rase ValueError("When %d GPUs are specified, use_one_device_strategy'
'flag cannot be set to True.")
else:
strategy = distribution_utils.get_distribution_strategy(
num_gpus=FLAGS.num_gpus)
return strategy
def get_fit_callbacks(learning_rate_schedule_fn):
def get_callbacks(learning_rate_schedule_fn, num_images):
time_callback = TimeHistory(FLAGS.batch_size)
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir=FLAGS.model_dir)
#update_freq="batch") # Add this if want per batch logging.
lr_callback = LearningRateBatchScheduler(
learning_rate_schedule_fn,
batch_size=FLAGS.batch_size,
num_images=imagenet_main._NUM_IMAGES['train'])
num_images=num_images)
return time_callback, tensorboard_callback, lr_callback
......@@ -155,6 +131,7 @@ def analyze_fit_and_eval_result(history, eval_output):
stats['training_loss'] = history.history['loss'][-1]
stats['training_accuracy_top_1'] = history.history['categorical_accuracy'][-1]
print('Test loss:{}'.format(stats['']))
print('top_1 accuracy:{}'.format(stats['accuracy_top_1']))
print('top_1_training_accuracy:{}'.format(stats['training_accuracy_top_1']))
......
official/resnet/keras/keras_imagenet_main.py
View file @ 424c2045
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
# 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.
......@@ -18,15 +18,11 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
from absl import app as absl_app
from absl import flags
import numpy as np
import tensorflow as tf # pylint: disable=g-bad-import-order
from official.resnet import imagenet_main
from official.resnet import imagenet_preprocessing
from official.resnet import resnet_run_loop
from official.resnet.keras import keras_common
from official.resnet.keras import resnet50
......@@ -104,22 +100,22 @@ def run_imagenet_with_keras(flags_obj):
# pylint: disable=protected-access
if flags_obj.use_synthetic_data:
synth_input_fn = resnet_run_loop.get_synth_input_fn(
imagenet_main._DEFAULT_IMAGE_SIZE, imagenet_main._DEFAULT_IMAGE_SIZE,
imagenet_main._NUM_CHANNELS, imagenet_main._NUM_CLASSES,
imagenet_main.DEFAULT_IMAGE_SIZE, imagenet_main.DEFAULT_IMAGE_SIZE,
imagenet_main.NUM_CHANNELS, imagenet_main.NUM_CLASSES,
dtype=flags_core.get_tf_dtype(flags_obj))
train_input_dataset = synth_input_fn(
batch_size=per_device_batch_size,
height=imagenet_main._DEFAULT_IMAGE_SIZE,
width=imagenet_main._DEFAULT_IMAGE_SIZE,
num_channels=imagenet_main._NUM_CHANNELS,
num_classes=imagenet_main._NUM_CLASSES,
height=imagenet_main.DEFAULT_IMAGE_SIZE,
width=imagenet_main.DEFAULT_IMAGE_SIZE,
num_channels=imagenet_main.NUM_CHANNELS,
num_classes=imagenet_main.NUM_CLASSES,
dtype=dtype)
eval_input_dataset = synth_input_fn(
batch_size=per_device_batch_size,
height=imagenet_main._DEFAULT_IMAGE_SIZE,
width=imagenet_main._DEFAULT_IMAGE_SIZE,
num_channels=imagenet_main._NUM_CHANNELS,
num_classes=imagenet_main._NUM_CLASSES,
height=imagenet_main.DEFAULT_IMAGE_SIZE,
width=imagenet_main.DEFAULT_IMAGE_SIZE,
num_channels=imagenet_main.NUM_CHANNELS,
num_classes=imagenet_main.NUM_CLASSES,
dtype=dtype)
# pylint: enable=protected-access
......@@ -140,20 +136,21 @@ def run_imagenet_with_keras(flags_obj):
optimizer = keras_common.get_optimizer()
strategy = keras_common.get_dist_strategy()
strategy = distribution_utils.get_distribution_strategy(
flags_obj.num_gpus, flags_obj.use_one_device_strategy)
model = resnet50.ResNet50(num_classes=imagenet_main._NUM_CLASSES)
model = resnet50.ResNet50(num_classes=imagenet_main.NUM_CLASSES)
model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['categorical_accuracy'],
distribute=strategy)
time_callback, tensorboard_callback, lr_callback = keras_common.get_fit_callbacks(
learning_rate_schedule)
time_callback, tensorboard_callback, lr_callback = keras_common.get_callbacks(
learning_rate_schedule, imagenet_main.NUM_IMAGES['train'])
steps_per_epoch = imagenet_main._NUM_IMAGES['train'] // flags_obj.batch_size
num_eval_steps = (imagenet_main._NUM_IMAGES['validation'] //
steps_per_epoch = imagenet_main.NUM_IMAGES['train'] // flags_obj.batch_size
num_eval_steps = (imagenet_main.NUM_IMAGES['validation'] //
flags_obj.batch_size)
history = model.fit(train_input_dataset,
......@@ -172,7 +169,6 @@ def run_imagenet_with_keras(flags_obj):
steps=num_eval_steps,
verbose=1)
print('Test loss:', eval_output[0])
stats = keras_common.analyze_fit_and_eval_result(history, eval_output)
return stats
......
official/resnet/keras/resnet56.py
View file @ 424c2045
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
# 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.
......
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