Spaces:
Running
Running
File size: 8,572 Bytes
0b8359d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 |
# 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
#
# 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 using custom training loops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from official.modeling import performance
from official.staging.training import grad_utils
from official.staging.training import standard_runnable
from official.staging.training import utils
from official.utils.flags import core as flags_core
from official.vision.image_classification.resnet import common
from official.vision.image_classification.resnet import imagenet_preprocessing
from official.vision.image_classification.resnet import resnet_model
class ResnetRunnable(standard_runnable.StandardTrainable,
standard_runnable.StandardEvaluable):
"""Implements the training and evaluation APIs for Resnet model."""
def __init__(self, flags_obj, time_callback, epoch_steps):
standard_runnable.StandardTrainable.__init__(self,
flags_obj.use_tf_while_loop,
flags_obj.use_tf_function)
standard_runnable.StandardEvaluable.__init__(self,
flags_obj.use_tf_function)
self.strategy = tf.distribute.get_strategy()
self.flags_obj = flags_obj
self.dtype = flags_core.get_tf_dtype(flags_obj)
self.time_callback = time_callback
# Input pipeline related
batch_size = flags_obj.batch_size
if batch_size % self.strategy.num_replicas_in_sync != 0:
raise ValueError(
'Batch size must be divisible by number of replicas : {}'.format(
self.strategy.num_replicas_in_sync))
# As auto rebatching is not supported in
# `experimental_distribute_datasets_from_function()` API, which is
# required when cloning dataset to multiple workers in eager mode,
# we use per-replica batch size.
self.batch_size = int(batch_size / self.strategy.num_replicas_in_sync)
if self.flags_obj.use_synthetic_data:
self.input_fn = common.get_synth_input_fn(
height=imagenet_preprocessing.DEFAULT_IMAGE_SIZE,
width=imagenet_preprocessing.DEFAULT_IMAGE_SIZE,
num_channels=imagenet_preprocessing.NUM_CHANNELS,
num_classes=imagenet_preprocessing.NUM_CLASSES,
dtype=self.dtype,
drop_remainder=True)
else:
self.input_fn = imagenet_preprocessing.input_fn
self.model = resnet_model.resnet50(
num_classes=imagenet_preprocessing.NUM_CLASSES,
use_l2_regularizer=not flags_obj.single_l2_loss_op)
lr_schedule = common.PiecewiseConstantDecayWithWarmup(
batch_size=flags_obj.batch_size,
epoch_size=imagenet_preprocessing.NUM_IMAGES['train'],
warmup_epochs=common.LR_SCHEDULE[0][1],
boundaries=list(p[1] for p in common.LR_SCHEDULE[1:]),
multipliers=list(p[0] for p in common.LR_SCHEDULE),
compute_lr_on_cpu=True)
self.optimizer = common.get_optimizer(lr_schedule)
# Make sure iterations variable is created inside scope.
self.global_step = self.optimizer.iterations
use_graph_rewrite = flags_obj.fp16_implementation == 'graph_rewrite'
if use_graph_rewrite and not flags_obj.use_tf_function:
raise ValueError('--fp16_implementation=graph_rewrite requires '
'--use_tf_function to be true')
self.optimizer = performance.configure_optimizer(
self.optimizer,
use_float16=self.dtype == tf.float16,
use_graph_rewrite=use_graph_rewrite,
loss_scale=flags_core.get_loss_scale(flags_obj, default_for_fp16=128))
self.train_loss = tf.keras.metrics.Mean('train_loss', dtype=tf.float32)
self.train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(
'train_accuracy', dtype=tf.float32)
self.test_loss = tf.keras.metrics.Mean('test_loss', dtype=tf.float32)
self.test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(
'test_accuracy', dtype=tf.float32)
self.checkpoint = tf.train.Checkpoint(
model=self.model, optimizer=self.optimizer)
# Handling epochs.
self.epoch_steps = epoch_steps
self.epoch_helper = utils.EpochHelper(epoch_steps, self.global_step)
def build_train_dataset(self):
"""See base class."""
return utils.make_distributed_dataset(
self.strategy,
self.input_fn,
is_training=True,
data_dir=self.flags_obj.data_dir,
batch_size=self.batch_size,
parse_record_fn=imagenet_preprocessing.parse_record,
datasets_num_private_threads=self.flags_obj
.datasets_num_private_threads,
dtype=self.dtype,
drop_remainder=True)
def build_eval_dataset(self):
"""See base class."""
return utils.make_distributed_dataset(
self.strategy,
self.input_fn,
is_training=False,
data_dir=self.flags_obj.data_dir,
batch_size=self.batch_size,
parse_record_fn=imagenet_preprocessing.parse_record,
dtype=self.dtype)
def train_loop_begin(self):
"""See base class."""
# Reset all metrics
self.train_loss.reset_states()
self.train_accuracy.reset_states()
self._epoch_begin()
self.time_callback.on_batch_begin(self.epoch_helper.batch_index)
def train_step(self, iterator):
"""See base class."""
def step_fn(inputs):
"""Function to run on the device."""
images, labels = inputs
with tf.GradientTape() as tape:
logits = self.model(images, training=True)
prediction_loss = tf.keras.losses.sparse_categorical_crossentropy(
labels, logits)
loss = tf.reduce_sum(prediction_loss) * (1.0 /
self.flags_obj.batch_size)
num_replicas = self.strategy.num_replicas_in_sync
l2_weight_decay = 1e-4
if self.flags_obj.single_l2_loss_op:
l2_loss = l2_weight_decay * 2 * tf.add_n([
tf.nn.l2_loss(v)
for v in self.model.trainable_variables
if 'bn' not in v.name
])
loss += (l2_loss / num_replicas)
else:
loss += (tf.reduce_sum(self.model.losses) / num_replicas)
grad_utils.minimize_using_explicit_allreduce(
tape, self.optimizer, loss, self.model.trainable_variables)
self.train_loss.update_state(loss)
self.train_accuracy.update_state(labels, logits)
self.strategy.run(step_fn, args=(next(iterator),))
def train_loop_end(self):
"""See base class."""
metrics = {
'train_loss': self.train_loss.result(),
'train_accuracy': self.train_accuracy.result(),
}
self.time_callback.on_batch_end(self.epoch_helper.batch_index - 1)
self._epoch_end()
return metrics
def eval_begin(self):
"""See base class."""
self.test_loss.reset_states()
self.test_accuracy.reset_states()
def eval_step(self, iterator):
"""See base class."""
def step_fn(inputs):
"""Function to run on the device."""
images, labels = inputs
logits = self.model(images, training=False)
loss = tf.keras.losses.sparse_categorical_crossentropy(labels, logits)
loss = tf.reduce_sum(loss) * (1.0 / self.flags_obj.batch_size)
self.test_loss.update_state(loss)
self.test_accuracy.update_state(labels, logits)
self.strategy.run(step_fn, args=(next(iterator),))
def eval_end(self):
"""See base class."""
return {
'test_loss': self.test_loss.result(),
'test_accuracy': self.test_accuracy.result()
}
def _epoch_begin(self):
if self.epoch_helper.epoch_begin():
self.time_callback.on_epoch_begin(self.epoch_helper.current_epoch)
def _epoch_end(self):
if self.epoch_helper.epoch_end():
self.time_callback.on_epoch_end(self.epoch_helper.current_epoch)
|