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###############################################################################
# Copyright (C) 2020-2021 Habana Labs, Ltd. an Intel Company
###############################################################################
# List of changes:
# - Added evaluation from saved chekpoint via --evaluate_checkpoint_path flag
# - Added resuming from checkpoint via --resume_from_checkpoint_path and --resume_from_epoch flags
# - Added seed via --seed flag
# - Added densent general configuration to support densent121, densenet161, densenet169 via --model flag
# - Added warmup epochs via --warmup_epochs flag
# - Added support for HPU training with --run_on_hpu flag
# - Added HPUStrategy() distributed training with --use_hpu_strategy
# - Added steps-based training duration specification via --steps_per_epoch and --validation_steps flags
# - Added StepLearningRateScheduleWithWarmup learning rate scheduler for warmup
# - Added deterministic mode
import tensorflow as tf
from utils.dataset import get_dataset
from utils.arguments import DenseNetArgumentParser
from utils.misc import distribution_utils
from models.models import StepLearningRateScheduleWithWarmup
from models.models import get_optimizer
from config import config
from densenet import densenet_model
from TensorFlow.common.debug import dump_callback
from habana_frameworks.tensorflow import load_habana_module
from habana_frameworks.tensorflow.multinode_helpers import comm_size, comm_rank
from habana_frameworks.tensorflow.distribute import HPUStrategy
from TensorFlow.common.tb_utils import (
TensorBoardWithHParamsV2, ExamplesPerSecondKerasHookV2, TimeToTrainKerasHook)
import os
import random
import numpy as np
def set_deterministic():
os.environ['TF_DETERMINISTIC_OPS'] = '1'
np.random.seed(0)
random.seed(0)
tf.random.set_seed(0)
def main():
parser = DenseNetArgumentParser(
description=(
"train.py is the main training/evaluation script for DenseNet. "
"In order to run training on multiple Gaudi cards, run "
"train.py with mpirun."))
args, _ = parser.parse_known_args()
strategy = None
verbose = 2
if args.deterministic:
if args.inputs is None:
raise ValueError("Must provide inputs for deterministic mode")
if args.resume_from_checkpoint_path is None:
raise ValueError("Must provide checkpoint for deterministic mode")
if args.dtype == 'bf16':
os.environ['TF_BF16_CONVERSION'] = '1'
if args.run_on_hpu:
load_habana_module()
if args.use_hpu_strategy:
hls_addresses = str(os.environ.get(
"MULTI_HLS_IPS", "127.0.0.1")).split(",")
TF_BASE_PORT = 2410
mpi_rank = comm_rank()
mpi_size = comm_size()
if mpi_rank > 0:
verbose = 0
worker_hosts = ",".join([",".join([address + ':' + str(TF_BASE_PORT + rank)
for rank in range(mpi_size//len(hls_addresses))])
for address in hls_addresses])
task_index = mpi_rank
# Configures cluster spec for distribution strategy.
_ = distribution_utils.configure_cluster(worker_hosts, task_index)
strategy = HPUStrategy()
print('Number of devices: {}'.format(
strategy.num_replicas_in_sync))
else:
strategy = tf.distribute.MultiWorkerMirroredStrategy()
print('Number of devices: {}'.format(strategy.num_replicas_in_sync))
if args.seed is not None:
os.environ['TF_DETERMINISTIC_OPS'] = '1'
random.seed(args.seed)
np.random.seed(args.seed)
tf.random.set_seed(args.seed)
img_rows, img_cols = 224, 224 # Resolution of inputs
channel = 3
num_classes = 1000
batch_size = args.batch_size
nb_epoch = args.epochs
dataset_dir = args.dataset_dir
resume_from_checkpoint_path = args.resume_from_checkpoint_path
resume_from_epoch = args.resume_from_epoch
dropout_rate = args.dropout_rate
weight_decay = args.weight_decay
optim_name = args.optimizer
initial_lr = args.initial_lr
model_name = args.model
save_summary_steps = args.save_summary_steps
if model_name == "densenet121":
growth_rate = 32
nb_filter = 64
nb_layers = [6, 12, 24, 16]
elif model_name == "densenet161":
growth_rate = 48
nb_filter = 96
nb_layers = [6, 12, 36, 24]
elif model_name == "densenet169":
growth_rate = 32
nb_filter = 64
nb_layers = [6, 12, 32, 32]
else:
print("model is not supported")
exit(1)
# Load our model
if strategy:
with strategy.scope():
model = densenet_model(img_rows=img_rows, img_cols=img_cols, color_type=channel,
dropout_rate=dropout_rate, weight_decay=weight_decay, num_classes=num_classes,
growth_rate=growth_rate, nb_filter=nb_filter, nb_layers=nb_layers)
optimizer = get_optimizer(
model_name, optim_name, initial_lr, epsilon=1e-2)
model.compile(optimizer=optimizer,
loss='categorical_crossentropy', metrics=['accuracy'])
else:
model = densenet_model(img_rows=img_rows, img_cols=img_cols, color_type=channel,
dropout_rate=dropout_rate, weight_decay=weight_decay, num_classes=num_classes,
growth_rate=growth_rate, nb_filter=nb_filter, nb_layers=nb_layers)
optimizer = get_optimizer(
model_name, optim_name, initial_lr, epsilon=1e-2)
model.compile(optimizer=optimizer,
loss='categorical_crossentropy', metrics=['accuracy'])
# Start training
steps_per_epoch = 1281167 // batch_size
if args.steps_per_epoch is not None:
steps_per_epoch = args.steps_per_epoch
validation_steps = 50000 // batch_size
if args.validation_steps is not None:
validation_steps = args.validation_steps
warmup_steps = args.warmup_epochs * steps_per_epoch
lr_sched = {0: 1, 30: 0.1, 60: 0.01, 80: 0.001}
lr_sched_steps = {
epoch * steps_per_epoch: multiplier for (epoch, multiplier) in lr_sched.items()}
init_step=steps_per_epoch * resume_from_epoch if resume_from_epoch is not None else 0
lrate = StepLearningRateScheduleWithWarmup(initial_lr=initial_lr,
initial_global_step=init_step,
warmup_steps=warmup_steps,
decay_schedule=lr_sched_steps,
verbose=0)
save_name = model_name if not model_name.endswith('.h5') else \
os.path.split(model_name)[-1].split('.')[0].split('-')[0]
model_ckpt = tf.keras.callbacks.ModelCheckpoint(
os.path.join(args.model_dir, config.SAVE_DIR,
save_name) + '-ckpt-{epoch:03d}.h5',
monitor='train_loss')
log_dir = os.path.join(args.model_dir, config.LOG_DIR)
ttt = TimeToTrainKerasHook(output_dir=log_dir)
callbacks = [lrate, model_ckpt, ttt]
if save_summary_steps is not None and save_summary_steps > 0:
local_batch_size = batch_size
if args.use_hpu_strategy:
log_dir = os.path.join(log_dir, 'worker_' + str(comm_rank()))
local_batch_size = batch_size // strategy.num_replicas_in_sync
callbacks += [
TensorBoardWithHParamsV2(
args.__dict__, log_dir=log_dir,
update_freq=save_summary_steps, profile_batch=0),
ExamplesPerSecondKerasHookV2(
save_summary_steps, output_dir=log_dir,
batch_size=local_batch_size),
]
if (args.evaluate_checkpoint_path is not None):
model.load_weights(args.evaluate_checkpoint_path)
results = model.evaluate(x=ds_valid, steps=validation_steps)
print("Test loss, Test acc:", results)
exit()
if ((resume_from_epoch is not None) and (resume_from_checkpoint_path is not None)):
model.load_weights(resume_from_checkpoint_path)
if args.deterministic:
set_deterministic()
if not os.path.isfile(args.dump_config):
raise FileNotFoundError("wrong dump config path")
import pickle
x_path = os.path.join(args.inputs, "input")
y_path = os.path.join(args.inputs, "target")
x = pickle.load(open(x_path, 'rb'))
y = pickle.load(open(y_path, 'rb'))
with dump_callback(args.dump_config):
model.fit(x=x, y=y,
steps_per_epoch=steps_per_epoch,
callbacks=callbacks,
initial_epoch=resume_from_epoch,
epochs=nb_epoch,
shuffle=False,
verbose=verbose,
validation_data=None,
validation_steps=0,
)
else:
ds_train = get_dataset(dataset_dir, args.train_subset, batch_size, args.dataset_num_parallel_calls)
ds_valid = get_dataset(dataset_dir, args.val_subset, batch_size, args.dataset_num_parallel_calls)
model.fit(x=ds_train, y=None,
steps_per_epoch=steps_per_epoch,
callbacks=callbacks,
initial_epoch=resume_from_epoch,
epochs=nb_epoch,
shuffle=True,
verbose=verbose,
validation_data=(ds_valid, None),
validation_steps=validation_steps,
validation_freq=1,
)
if __name__ == '__main__':
main()
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