Skip to content
Permalink
Branch: master
Find file Copy path
Find file Copy path
785 lines (674 sloc) 32.7 KB
# 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.
# ==============================================================================
"""Contains utility and supporting functions for ResNet.
This module contains ResNet code which does not directly build layers. This
includes dataset management, hyperparameter and optimizer code, and argument
parsing. Code for defining the ResNet layers can be found in resnet_model.py.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import math
import multiprocessing
import os
# pylint: disable=g-bad-import-order
from absl import flags
import tensorflow as tf
from official.resnet import resnet_model
from official.utils.flags import core as flags_core
from official.utils.export import export
from official.utils.logs import hooks_helper
from official.utils.logs import logger
from official.resnet import imagenet_preprocessing
from official.utils.misc import distribution_utils
from official.utils.misc import model_helpers
################################################################################
# Functions for input processing.
################################################################################
def process_record_dataset(dataset,
is_training,
batch_size,
shuffle_buffer,
parse_record_fn,
num_epochs=1,
dtype=tf.float32,
datasets_num_private_threads=None,
num_parallel_batches=1,
drop_remainder=False):
"""Given a Dataset with raw records, return an iterator over the records.
Args:
dataset: A Dataset representing raw records
is_training: A boolean denoting whether the input is for training.
batch_size: The number of samples per batch.
shuffle_buffer: The buffer size to use when shuffling records. A larger
value results in better randomness, but smaller values reduce startup
time and use less memory.
parse_record_fn: A function that takes a raw record and returns the
corresponding (image, label) pair.
num_epochs: The number of epochs to repeat the dataset.
dtype: Data type to use for images/features.
datasets_num_private_threads: Number of threads for a private
threadpool created for all datasets computation.
num_parallel_batches: Number of parallel batches for tf.data.
drop_remainder: A boolean indicates whether to drop the remainder of the
batches. If True, the batch dimension will be static.
Returns:
Dataset of (image, label) pairs ready for iteration.
"""
# Defines a specific size thread pool for tf.data operations.
if datasets_num_private_threads:
options = tf.data.Options()
options.experimental_threading.private_threadpool_size = (
datasets_num_private_threads)
dataset = dataset.with_options(options)
tf.compat.v1.logging.info('datasets_num_private_threads: %s',
datasets_num_private_threads)
# Disable intra-op parallelism to optimize for throughput instead of latency.
options = tf.data.Options()
options.experimental_threading.max_intra_op_parallelism = 1
dataset = dataset.with_options(options)
# Prefetches a batch at a time to smooth out the time taken to load input
# files for shuffling and processing.
dataset = dataset.prefetch(buffer_size=batch_size)
if is_training:
# Shuffles records before repeating to respect epoch boundaries.
dataset = dataset.shuffle(buffer_size=shuffle_buffer)
# Repeats the dataset for the number of epochs to train.
dataset = dataset.repeat(num_epochs)
# Parses the raw records into images and labels.
dataset = dataset.map(
lambda value: parse_record_fn(value, is_training, dtype),
num_parallel_calls=tf.data.experimental.AUTOTUNE)
dataset = dataset.batch(batch_size, drop_remainder=drop_remainder)
# Operations between the final prefetch and the get_next call to the iterator
# will happen synchronously during run time. We prefetch here again to
# background all of the above processing work and keep it out of the
# critical training path. Setting buffer_size to tf.contrib.data.AUTOTUNE
# allows DistributionStrategies to adjust how many batches to fetch based
# on how many devices are present.
dataset = dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
return dataset
def get_synth_input_fn(height, width, num_channels, num_classes,
dtype=tf.float32):
"""Returns an input function that returns a dataset with random data.
This input_fn returns a data set that iterates over a set of random data and
bypasses all preprocessing, e.g. jpeg decode and copy. The host to device
copy is still included. This used to find the upper throughput bound when
tunning the full input pipeline.
Args:
height: Integer height that will be used to create a fake image tensor.
width: Integer width that will be used to create a fake image tensor.
num_channels: Integer depth that will be used to create a fake image tensor.
num_classes: Number of classes that should be represented in the fake labels
tensor
dtype: Data type for features/images.
Returns:
An input_fn that can be used in place of a real one to return a dataset
that can be used for iteration.
"""
# pylint: disable=unused-argument
def input_fn(is_training, data_dir, batch_size, *args, **kwargs):
"""Returns dataset filled with random data."""
# Synthetic input should be within [0, 255].
inputs = tf.random.truncated_normal(
[batch_size] + [height, width, num_channels],
dtype=dtype,
mean=127,
stddev=60,
name='synthetic_inputs')
labels = tf.random.uniform(
[batch_size],
minval=0,
maxval=num_classes - 1,
dtype=tf.int32,
name='synthetic_labels')
data = tf.data.Dataset.from_tensors((inputs, labels)).repeat()
data = data.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
return data
return input_fn
def image_bytes_serving_input_fn(image_shape, dtype=tf.float32):
"""Serving input fn for raw jpeg images."""
def _preprocess_image(image_bytes):
"""Preprocess a single raw image."""
# Bounding box around the whole image.
bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=dtype, shape=[1, 1, 4])
height, width, num_channels = image_shape
image = imagenet_preprocessing.preprocess_image(
image_bytes, bbox, height, width, num_channels, is_training=False)
return image
image_bytes_list = tf.compat.v1.placeholder(
shape=[None], dtype=tf.string, name='input_tensor')
images = tf.map_fn(
_preprocess_image, image_bytes_list, back_prop=False, dtype=dtype)
return tf.estimator.export.TensorServingInputReceiver(
images, {'image_bytes': image_bytes_list})
def override_flags_and_set_envars_for_gpu_thread_pool(flags_obj):
"""Override flags and set env_vars for performance.
These settings exist to test the difference between using stock settings
and manual tuning. It also shows some of the ENV_VARS that can be tweaked to
squeeze a few extra examples per second. These settings are defaulted to the
current platform of interest, which changes over time.
On systems with small numbers of cpu cores, e.g. under 8 logical cores,
setting up a gpu thread pool with `tf_gpu_thread_mode=gpu_private` may perform
poorly.
Args:
flags_obj: Current flags, which will be adjusted possibly overriding
what has been set by the user on the command-line.
"""
cpu_count = multiprocessing.cpu_count()
tf.compat.v1.logging.info('Logical CPU cores: %s', cpu_count)
# Sets up thread pool for each GPU for op scheduling.
per_gpu_thread_count = 1
total_gpu_thread_count = per_gpu_thread_count * flags_obj.num_gpus
os.environ['TF_GPU_THREAD_MODE'] = flags_obj.tf_gpu_thread_mode
os.environ['TF_GPU_THREAD_COUNT'] = str(per_gpu_thread_count)
tf.compat.v1.logging.info('TF_GPU_THREAD_COUNT: %s',
os.environ['TF_GPU_THREAD_COUNT'])
tf.compat.v1.logging.info('TF_GPU_THREAD_MODE: %s',
os.environ['TF_GPU_THREAD_MODE'])
# Reduces general thread pool by number of threads used for GPU pool.
main_thread_count = cpu_count - total_gpu_thread_count
flags_obj.inter_op_parallelism_threads = main_thread_count
# Sets thread count for tf.data. Logical cores minus threads assign to the
# private GPU pool along with 2 thread per GPU for event monitoring and
# sending / receiving tensors.
num_monitoring_threads = 2 * flags_obj.num_gpus
flags_obj.datasets_num_private_threads = (cpu_count - total_gpu_thread_count
- num_monitoring_threads)
################################################################################
# Functions for running training/eval/validation loops for the model.
################################################################################
def learning_rate_with_decay(
batch_size, batch_denom, num_images, boundary_epochs, decay_rates,
base_lr=0.1, warmup=False):
"""Get a learning rate that decays step-wise as training progresses.
Args:
batch_size: the number of examples processed in each training batch.
batch_denom: this value will be used to scale the base learning rate.
`0.1 * batch size` is divided by this number, such that when
batch_denom == batch_size, the initial learning rate will be 0.1.
num_images: total number of images that will be used for training.
boundary_epochs: list of ints representing the epochs at which we
decay the learning rate.
decay_rates: list of floats representing the decay rates to be used
for scaling the learning rate. It should have one more element
than `boundary_epochs`, and all elements should have the same type.
base_lr: Initial learning rate scaled based on batch_denom.
warmup: Run a 5 epoch warmup to the initial lr.
Returns:
Returns a function that takes a single argument - the number of batches
trained so far (global_step)- and returns the learning rate to be used
for training the next batch.
"""
initial_learning_rate = base_lr * batch_size / batch_denom
batches_per_epoch = num_images / batch_size
# Reduce the learning rate at certain epochs.
# CIFAR-10: divide by 10 at epoch 100, 150, and 200
# ImageNet: divide by 10 at epoch 30, 60, 80, and 90
boundaries = [int(batches_per_epoch * epoch) for epoch in boundary_epochs]
vals = [initial_learning_rate * decay for decay in decay_rates]
def learning_rate_fn(global_step):
"""Builds scaled learning rate function with 5 epoch warm up."""
lr = tf.compat.v1.train.piecewise_constant(global_step, boundaries, vals)
if warmup:
warmup_steps = int(batches_per_epoch * 5)
warmup_lr = (
initial_learning_rate * tf.cast(global_step, tf.float32) / tf.cast(
warmup_steps, tf.float32))
return tf.cond(pred=global_step < warmup_steps,
true_fn=lambda: warmup_lr,
false_fn=lambda: lr)
return lr
def poly_rate_fn(global_step):
"""Handles linear scaling rule, gradual warmup, and LR decay.
The learning rate starts at 0, then it increases linearly per step. After
FLAGS.poly_warmup_epochs, we reach the base learning rate (scaled to account
for batch size). The learning rate is then decayed using a polynomial rate
decay schedule with power 2.0.
Args:
global_step: the current global_step
Returns:
returns the current learning rate
"""
# Learning rate schedule for LARS polynomial schedule
if flags.FLAGS.batch_size < 8192:
plr = 5.0
w_epochs = 5
elif flags.FLAGS.batch_size < 16384:
plr = 10.0
w_epochs = 5
elif flags.FLAGS.batch_size < 32768:
plr = 25.0
w_epochs = 5
else:
plr = 32.0
w_epochs = 14
w_steps = int(w_epochs * batches_per_epoch)
wrate = (plr * tf.cast(global_step, tf.float32) / tf.cast(
w_steps, tf.float32))
# TODO(pkanwar): use a flag to help calc num_epochs.
num_epochs = 90
train_steps = batches_per_epoch * num_epochs
min_step = tf.constant(1, dtype=tf.int64)
decay_steps = tf.maximum(min_step, tf.subtract(global_step, w_steps))
poly_rate = tf.train.polynomial_decay(
plr,
decay_steps,
train_steps - w_steps + 1,
power=2.0)
return tf.where(global_step <= w_steps, wrate, poly_rate)
# For LARS we have a new learning rate schedule
if flags.FLAGS.enable_lars:
return poly_rate_fn
return learning_rate_fn
def resnet_model_fn(features, labels, mode, model_class,
resnet_size, weight_decay, learning_rate_fn, momentum,
data_format, resnet_version, loss_scale,
loss_filter_fn=None, dtype=resnet_model.DEFAULT_DTYPE,
fine_tune=False, label_smoothing=0.0):
"""Shared functionality for different resnet model_fns.
Initializes the ResnetModel representing the model layers
and uses that model to build the necessary EstimatorSpecs for
the `mode` in question. For training, this means building losses,
the optimizer, and the train op that get passed into the EstimatorSpec.
For evaluation and prediction, the EstimatorSpec is returned without
a train op, but with the necessary parameters for the given mode.
Args:
features: tensor representing input images
labels: tensor representing class labels for all input images
mode: current estimator mode; should be one of
`tf.estimator.ModeKeys.TRAIN`, `EVALUATE`, `PREDICT`
model_class: a class representing a TensorFlow model that has a __call__
function. We assume here that this is a subclass of ResnetModel.
resnet_size: A single integer for the size of the ResNet model.
weight_decay: weight decay loss rate used to regularize learned variables.
learning_rate_fn: function that returns the current learning rate given
the current global_step
momentum: momentum term used for optimization
data_format: Input format ('channels_last', 'channels_first', or None).
If set to None, the format is dependent on whether a GPU is available.
resnet_version: Integer representing which version of the ResNet network to
use. See README for details. Valid values: [1, 2]
loss_scale: The factor to scale the loss for numerical stability. A detailed
summary is present in the arg parser help text.
loss_filter_fn: function that takes a string variable name and returns
True if the var should be included in loss calculation, and False
otherwise. If None, batch_normalization variables will be excluded
from the loss.
dtype: the TensorFlow dtype to use for calculations.
fine_tune: If True only train the dense layers(final layers).
label_smoothing: If greater than 0 then smooth the labels.
Returns:
EstimatorSpec parameterized according to the input params and the
current mode.
"""
# Generate a summary node for the images
tf.compat.v1.summary.image('images', features, max_outputs=6)
# Checks that features/images have same data type being used for calculations.
assert features.dtype == dtype
model = model_class(resnet_size, data_format, resnet_version=resnet_version,
dtype=dtype)
logits = model(features, mode == tf.estimator.ModeKeys.TRAIN)
# This acts as a no-op if the logits are already in fp32 (provided logits are
# not a SparseTensor). If dtype is is low precision, logits must be cast to
# fp32 for numerical stability.
logits = tf.cast(logits, tf.float32)
predictions = {
'classes': tf.argmax(input=logits, axis=1),
'probabilities': tf.nn.softmax(logits, name='softmax_tensor')
}
if mode == tf.estimator.ModeKeys.PREDICT:
# Return the predictions and the specification for serving a SavedModel
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
export_outputs={
'predict': tf.estimator.export.PredictOutput(predictions)
})
# Calculate loss, which includes softmax cross entropy and L2 regularization.
if label_smoothing != 0.0:
one_hot_labels = tf.one_hot(labels, 1001)
cross_entropy = tf.losses.softmax_cross_entropy(
logits=logits, onehot_labels=one_hot_labels,
label_smoothing=label_smoothing)
else:
cross_entropy = tf.compat.v1.losses.sparse_softmax_cross_entropy(
logits=logits, labels=labels)
# Create a tensor named cross_entropy for logging purposes.
tf.identity(cross_entropy, name='cross_entropy')
tf.compat.v1.summary.scalar('cross_entropy', cross_entropy)
# If no loss_filter_fn is passed, assume we want the default behavior,
# which is that batch_normalization variables are excluded from loss.
def exclude_batch_norm(name):
return 'batch_normalization' not in name
loss_filter_fn = loss_filter_fn or exclude_batch_norm
# Add weight decay to the loss.
l2_loss = weight_decay * tf.add_n(
# loss is computed using fp32 for numerical stability.
[
tf.nn.l2_loss(tf.cast(v, tf.float32))
for v in tf.compat.v1.trainable_variables()
if loss_filter_fn(v.name)
])
tf.compat.v1.summary.scalar('l2_loss', l2_loss)
loss = cross_entropy + l2_loss
if mode == tf.estimator.ModeKeys.TRAIN:
global_step = tf.compat.v1.train.get_or_create_global_step()
learning_rate = learning_rate_fn(global_step)
# Create a tensor named learning_rate for logging purposes
tf.identity(learning_rate, name='learning_rate')
tf.compat.v1.summary.scalar('learning_rate', learning_rate)
if flags.FLAGS.enable_lars:
optimizer = tf.contrib.opt.LARSOptimizer(
learning_rate,
momentum=momentum,
weight_decay=weight_decay,
skip_list=['batch_normalization', 'bias'])
else:
optimizer = tf.compat.v1.train.MomentumOptimizer(
learning_rate=learning_rate,
momentum=momentum
)
def _dense_grad_filter(gvs):
"""Only apply gradient updates to the final layer.
This function is used for fine tuning.
Args:
gvs: list of tuples with gradients and variable info
Returns:
filtered gradients so that only the dense layer remains
"""
return [(g, v) for g, v in gvs if 'dense' in v.name]
if loss_scale != 1:
# When computing fp16 gradients, often intermediate tensor values are
# so small, they underflow to 0. To avoid this, we multiply the loss by
# loss_scale to make these tensor values loss_scale times bigger.
scaled_grad_vars = optimizer.compute_gradients(loss * loss_scale)
if fine_tune:
scaled_grad_vars = _dense_grad_filter(scaled_grad_vars)
# Once the gradient computation is complete we can scale the gradients
# back to the correct scale before passing them to the optimizer.
unscaled_grad_vars = [(grad / loss_scale, var)
for grad, var in scaled_grad_vars]
minimize_op = optimizer.apply_gradients(unscaled_grad_vars, global_step)
else:
grad_vars = optimizer.compute_gradients(loss)
if fine_tune:
grad_vars = _dense_grad_filter(grad_vars)
minimize_op = optimizer.apply_gradients(grad_vars, global_step)
update_ops = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.UPDATE_OPS)
train_op = tf.group(minimize_op, update_ops)
else:
train_op = None
accuracy = tf.compat.v1.metrics.accuracy(labels, predictions['classes'])
accuracy_top_5 = tf.compat.v1.metrics.mean(
tf.nn.in_top_k(predictions=logits, targets=labels, k=5, name='top_5_op'))
metrics = {'accuracy': accuracy,
'accuracy_top_5': accuracy_top_5}
# Create a tensor named train_accuracy for logging purposes
tf.identity(accuracy[1], name='train_accuracy')
tf.identity(accuracy_top_5[1], name='train_accuracy_top_5')
tf.compat.v1.summary.scalar('train_accuracy', accuracy[1])
tf.compat.v1.summary.scalar('train_accuracy_top_5', accuracy_top_5[1])
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
loss=loss,
train_op=train_op,
eval_metric_ops=metrics)
def resnet_main(
flags_obj, model_function, input_function, dataset_name, shape=None):
"""Shared main loop for ResNet Models.
Args:
flags_obj: An object containing parsed flags. See define_resnet_flags()
for details.
model_function: the function that instantiates the Model and builds the
ops for train/eval. This will be passed directly into the estimator.
input_function: the function that processes the dataset and returns a
dataset that the estimator can train on. This will be wrapped with
all the relevant flags for running and passed to estimator.
dataset_name: the name of the dataset for training and evaluation. This is
used for logging purpose.
shape: list of ints representing the shape of the images used for training.
This is only used if flags_obj.export_dir is passed.
Returns:
Dict of results of the run.
"""
model_helpers.apply_clean(flags.FLAGS)
# Ensures flag override logic is only executed if explicitly triggered.
if flags_obj.tf_gpu_thread_mode:
override_flags_and_set_envars_for_gpu_thread_pool(flags_obj)
# Configures cluster spec for distribution strategy.
num_workers = distribution_utils.configure_cluster(flags_obj.worker_hosts,
flags_obj.task_index)
# Creates session config. allow_soft_placement = True, is required for
# multi-GPU and is not harmful for other modes.
session_config = tf.compat.v1.ConfigProto(
inter_op_parallelism_threads=flags_obj.inter_op_parallelism_threads,
intra_op_parallelism_threads=flags_obj.intra_op_parallelism_threads,
allow_soft_placement=True)
distribution_strategy = distribution_utils.get_distribution_strategy(
distribution_strategy=flags_obj.distribution_strategy,
num_gpus=flags_core.get_num_gpus(flags_obj),
num_workers=num_workers,
all_reduce_alg=flags_obj.all_reduce_alg)
# Creates a `RunConfig` that checkpoints every 24 hours which essentially
# results in checkpoints determined only by `epochs_between_evals`.
run_config = tf.estimator.RunConfig(
train_distribute=distribution_strategy,
session_config=session_config,
save_checkpoints_secs=60*60*24,
save_checkpoints_steps=None)
# Initializes model with all but the dense layer from pretrained ResNet.
if flags_obj.pretrained_model_checkpoint_path is not None:
warm_start_settings = tf.estimator.WarmStartSettings(
flags_obj.pretrained_model_checkpoint_path,
vars_to_warm_start='^(?!.*dense)')
else:
warm_start_settings = None
classifier = tf.estimator.Estimator(
model_fn=model_function, model_dir=flags_obj.model_dir, config=run_config,
warm_start_from=warm_start_settings, params={
'resnet_size': int(flags_obj.resnet_size),
'data_format': flags_obj.data_format,
'batch_size': flags_obj.batch_size,
'resnet_version': int(flags_obj.resnet_version),
'loss_scale': flags_core.get_loss_scale(flags_obj),
'dtype': flags_core.get_tf_dtype(flags_obj),
'fine_tune': flags_obj.fine_tune,
'num_workers': num_workers,
})
run_params = {
'batch_size': flags_obj.batch_size,
'dtype': flags_core.get_tf_dtype(flags_obj),
'resnet_size': flags_obj.resnet_size,
'resnet_version': flags_obj.resnet_version,
'synthetic_data': flags_obj.use_synthetic_data,
'train_epochs': flags_obj.train_epochs,
'num_workers': num_workers,
}
if flags_obj.use_synthetic_data:
dataset_name = dataset_name + '-synthetic'
benchmark_logger = logger.get_benchmark_logger()
benchmark_logger.log_run_info('resnet', dataset_name, run_params,
test_id=flags_obj.benchmark_test_id)
train_hooks = hooks_helper.get_train_hooks(
flags_obj.hooks,
model_dir=flags_obj.model_dir,
batch_size=flags_obj.batch_size)
def input_fn_train(num_epochs, input_context=None):
return input_function(
is_training=True,
data_dir=flags_obj.data_dir,
batch_size=distribution_utils.per_device_batch_size(
flags_obj.batch_size, flags_core.get_num_gpus(flags_obj)),
num_epochs=num_epochs,
dtype=flags_core.get_tf_dtype(flags_obj),
datasets_num_private_threads=flags_obj.datasets_num_private_threads,
num_parallel_batches=flags_obj.datasets_num_parallel_batches,
input_context=input_context)
def input_fn_eval():
return input_function(
is_training=False,
data_dir=flags_obj.data_dir,
batch_size=distribution_utils.per_device_batch_size(
flags_obj.batch_size, flags_core.get_num_gpus(flags_obj)),
num_epochs=1,
dtype=flags_core.get_tf_dtype(flags_obj))
train_epochs = (0 if flags_obj.eval_only or not flags_obj.train_epochs else
flags_obj.train_epochs)
use_train_and_evaluate = flags_obj.use_train_and_evaluate or num_workers > 1
if use_train_and_evaluate:
train_spec = tf.estimator.TrainSpec(
input_fn=lambda input_context=None: input_fn_train(
train_epochs, input_context=input_context),
hooks=train_hooks,
max_steps=flags_obj.max_train_steps)
eval_spec = tf.estimator.EvalSpec(input_fn=input_fn_eval)
tf.compat.v1.logging.info('Starting to train and evaluate.')
tf.estimator.train_and_evaluate(classifier, train_spec, eval_spec)
# tf.estimator.train_and_evalute doesn't return anything in multi-worker
# case.
return {}
else:
if train_epochs == 0:
# If --eval_only is set, perform a single loop with zero train epochs.
schedule, n_loops = [0], 1
else:
# Compute the number of times to loop while training. All but the last
# pass will train for `epochs_between_evals` epochs, while the last will
# train for the number needed to reach `training_epochs`. For instance if
# train_epochs = 25 and epochs_between_evals = 10
# schedule will be set to [10, 10, 5]. That is to say, the loop will:
# Train for 10 epochs and then evaluate.
# Train for another 10 epochs and then evaluate.
# Train for a final 5 epochs (to reach 25 epochs) and then evaluate.
n_loops = math.ceil(train_epochs / flags_obj.epochs_between_evals)
schedule = [flags_obj.epochs_between_evals for _ in range(int(n_loops))]
schedule[-1] = train_epochs - sum(schedule[:-1]) # over counting.
for cycle_index, num_train_epochs in enumerate(schedule):
tf.compat.v1.logging.info('Starting cycle: %d/%d', cycle_index,
int(n_loops))
if num_train_epochs:
# Since we are calling classifier.train immediately in each loop, the
# value of num_train_epochs in the lambda function will not be changed
# before it is used. So it is safe to ignore the pylint error here
# pylint: disable=cell-var-from-loop
classifier.train(
input_fn=lambda input_context=None: input_fn_train(
num_train_epochs, input_context=input_context),
hooks=train_hooks,
max_steps=flags_obj.max_train_steps)
# flags_obj.max_train_steps is generally associated with testing and
# profiling. As a result it is frequently called with synthetic data,
# which will iterate forever. Passing steps=flags_obj.max_train_steps
# allows the eval (which is generally unimportant in those circumstances)
# to terminate. Note that eval will run for max_train_steps each loop,
# regardless of the global_step count.
tf.compat.v1.logging.info('Starting to evaluate.')
eval_results = classifier.evaluate(input_fn=input_fn_eval,
steps=flags_obj.max_train_steps)
benchmark_logger.log_evaluation_result(eval_results)
if model_helpers.past_stop_threshold(
flags_obj.stop_threshold, eval_results['accuracy']):
break
if flags_obj.export_dir is not None:
# Exports a saved model for the given classifier.
export_dtype = flags_core.get_tf_dtype(flags_obj)
if flags_obj.image_bytes_as_serving_input:
input_receiver_fn = functools.partial(
image_bytes_serving_input_fn, shape, dtype=export_dtype)
else:
input_receiver_fn = export.build_tensor_serving_input_receiver_fn(
shape, batch_size=flags_obj.batch_size, dtype=export_dtype)
classifier.export_savedmodel(flags_obj.export_dir, input_receiver_fn,
strip_default_attrs=True)
stats = {}
stats['eval_results'] = eval_results
stats['train_hooks'] = train_hooks
return stats
def define_resnet_flags(resnet_size_choices=None, dynamic_loss_scale=False):
"""Add flags and validators for ResNet."""
flags_core.define_base()
flags_core.define_performance(num_parallel_calls=False,
tf_gpu_thread_mode=True,
datasets_num_private_threads=True,
datasets_num_parallel_batches=True,
dynamic_loss_scale=dynamic_loss_scale)
flags_core.define_image()
flags_core.define_benchmark()
flags.adopt_module_key_flags(flags_core)
flags.DEFINE_enum(
name='resnet_version', short_name='rv', default='1',
enum_values=['1', '2'],
help=flags_core.help_wrap(
'Version of ResNet. (1 or 2) See README.md for details.'))
flags.DEFINE_bool(
name='fine_tune', short_name='ft', default=False,
help=flags_core.help_wrap(
'If True do not train any parameters except for the final layer.'))
flags.DEFINE_string(
name='pretrained_model_checkpoint_path', short_name='pmcp', default=None,
help=flags_core.help_wrap(
'If not None initialize all the network except the final layer with '
'these values'))
flags.DEFINE_boolean(
name='eval_only', default=False,
help=flags_core.help_wrap('Skip training and only perform evaluation on '
'the latest checkpoint.'))
flags.DEFINE_boolean(
name='image_bytes_as_serving_input', default=False,
help=flags_core.help_wrap(
'If True exports savedmodel with serving signature that accepts '
'JPEG image bytes instead of a fixed size [HxWxC] tensor that '
'represents the image. The former is easier to use for serving at '
'the expense of image resize/cropping being done as part of model '
'inference. Note, this flag only applies to ImageNet and cannot '
'be used for CIFAR.'))
flags.DEFINE_boolean(
name='use_train_and_evaluate', default=False,
help=flags_core.help_wrap(
'If True, uses `tf.estimator.train_and_evaluate` for the training '
'and evaluation loop, instead of separate calls to `classifier.train '
'and `classifier.evaluate`, which is the default behavior.'))
flags.DEFINE_string(
name='worker_hosts', default=None,
help=flags_core.help_wrap(
'Comma-separated list of worker ip:port pairs for running '
'multi-worker models with DistributionStrategy. The user would '
'start the program on each host with identical value for this flag.'))
flags.DEFINE_integer(
name='task_index', default=-1,
help=flags_core.help_wrap('If multi-worker training, the task_index of '
'this worker.'))
flags.DEFINE_bool(
name='enable_lars', default=False,
help=flags_core.help_wrap(
'Enable LARS optimizer for large batch training.'))
flags.DEFINE_float(
name='label_smoothing', default=0.0,
help=flags_core.help_wrap(
'Label smoothing parameter used in the softmax_cross_entropy'))
flags.DEFINE_float(
name='weight_decay', default=1e-4,
help=flags_core.help_wrap(
'Weight decay coefficiant for l2 regularization.'))
choice_kwargs = dict(
name='resnet_size', short_name='rs', default='50',
help=flags_core.help_wrap('The size of the ResNet model to use.'))
if resnet_size_choices is None:
flags.DEFINE_string(**choice_kwargs)
else:
flags.DEFINE_enum(enum_values=resnet_size_choices, **choice_kwargs)
You can’t perform that action at this time.