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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import datetime
import logging
import time
import torch
import torch.distributed as dist
from maskrcnn_benchmark.utils.comm import get_world_size
from maskrcnn_benchmark.utils.metric_logger import MetricLogger
def reduce_loss_dict(all_loss_dict):
"""
Reduce the loss dictionary from all processes so that process with rank
0 has the averaged results. Returns a dict with the same fields as
loss_dict, after reduction.
"""
world_size = get_world_size()
with torch.no_grad():
loss_names = []
all_losses = []
for loss_dict in all_loss_dict:
for k in sorted(loss_dict.keys()):
loss_names.append(k)
all_losses.append(loss_dict[k])
all_losses = torch.stack(all_losses, dim=0)
if world_size > 1:
dist.reduce(all_losses, dst=0)
if dist.get_rank() == 0:
# only main process gets accumulated, so only divide by
# world_size in this case
all_losses /= world_size
reduced_losses = {}
for k, v in zip(loss_names, all_losses):
if k not in reduced_losses:
reduced_losses[k] = v / len(all_loss_dict)
reduced_losses[k] += v / len(all_loss_dict)
return reduced_losses
def do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
):
logger = logging.getLogger("maskrcnn_benchmark.trainer")
logger.info("Start training")
meters = MetricLogger(delimiter=" ")
epoch_per_stage = arguments["epoch_per_stage"]
max_iter = sum(len(stage_loader) * epoch_per_stage[si] for si, stage_loader in enumerate(data_loader))
max_iter += epoch_per_stage[-1] * min(len(stage_loader) for stage_loader in data_loader)
model.train()
start_training_time = time.time()
end = time.time()
for stage_i, stage_loader in enumerate(data_loader):
for ep in range(epoch_per_stage[stage_i]):
start_iter = arguments["iteration"]
for iteration, (images, targets, _) in enumerate(stage_loader, start_iter):
data_time = time.time() - end
iteration = iteration + 1
arguments["iteration"] = iteration
scheduler[stage_i].step()
all_stage_loss_dict = []
images = images.to(device)
targets = [target.to(device) for target in targets]
loss_dict = model(images, targets, stage_i)
all_stage_loss_dict.append(loss_dict)
losses = sum(loss for loss_dict in all_stage_loss_dict for loss in loss_dict.values())
# reduce losses over all GPUs for logging purposes
loss_dict_reduced = reduce_loss_dict(all_stage_loss_dict)
losses_reduced = sum(loss for loss in loss_dict_reduced.values())
meters.update(loss=losses_reduced, **loss_dict_reduced)
optimizer.zero_grad()
losses.backward()
optimizer.step()
batch_time = time.time() - end
end = time.time()
meters.update(time=batch_time, data=data_time)
eta_seconds = meters.time.global_avg * (max_iter - iteration)
eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
if iteration % 20 == 0 or iteration == max_iter:
logger.info(
meters.delimiter.join(
[
"eta: {eta}",
"iter: {iter}",
"{meters}",
"lr: {lr:.6f}",
"max mem: {memory:.0f}",
]
).format(
eta=eta_string,
iter=iteration,
meters=str(meters),
lr=optimizer.param_groups[0]["lr"],
memory=torch.cuda.max_memory_allocated() / 1024.0 / 1024.0,
)
)
if iteration % checkpoint_period == 0:
checkpointer.save("model_{:07d}".format(iteration), **arguments)
if iteration == max_iter:
checkpointer.save("model_final", **arguments)
for ep in range(epoch_per_stage[-1]):
start_iter = arguments["iteration"]
for iteration, stage_loader in enumerate(zip(*data_loader), start_iter):
data_time = time.time() - end
iteration = iteration + 1
arguments["iteration"] = iteration
scheduler[-1].step()
all_task_loss_dict = []
for stage_i, (images, targets, _) in enumerate(stage_loader):
images = images.to(device)
targets = [target.to(device) for target in targets]
loss_dict = model(images, targets, stage_i)
all_task_loss_dict.append(loss_dict)
losses = sum(loss for loss_dict in all_task_loss_dict for loss in loss_dict.values())
# reduce losses over all GPUs for logging purposes
loss_dict_reduced = reduce_loss_dict(all_task_loss_dict)
losses_reduced = sum(loss for loss in loss_dict_reduced.values())
meters.update(loss=losses_reduced, **loss_dict_reduced)
optimizer.zero_grad()
losses.backward()
optimizer.step()
batch_time = time.time() - end
end = time.time()
meters.update(time=batch_time, data=data_time)
eta_seconds = meters.time.global_avg * (max_iter - iteration)
eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
if iteration % 20 == 0 or iteration == max_iter:
logger.info(
meters.delimiter.join(
[
"eta: {eta}",
"iter: {iter}",
"{meters}",
"lr: {lr:.6f}",
"max mem: {memory:.0f}",
]
).format(
eta=eta_string,
iter=iteration,
meters=str(meters),
lr=optimizer.param_groups[0]["lr"],
memory=torch.cuda.max_memory_allocated() / 1024.0 / 1024.0,
)
)
if iteration % checkpoint_period == 0:
checkpointer.save("model_{:07d}".format(iteration), **arguments)
if iteration == max_iter:
checkpointer.save("model_final", **arguments)
total_training_time = time.time() - start_training_time
total_time_str = str(datetime.timedelta(seconds=total_training_time))
logger.info("Total training time: {} ({:.4f} s / it)".format(total_time_str, total_training_time / (max_iter)))
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