# encoding: utf-8
"""
@author: xingyu liao
@contact: sherlockliao01@gmail.com
"""
import logging
import os
import sys
from collections import OrderedDict
import torch
from torch.nn.parallel import DistributedDataParallel
sys.path.append('.')
from fastreid.config import get_cfg
from fastreid.data import build_reid_test_loader, build_reid_train_loader
from fastreid.evaluation.testing import flatten_results_dict
from fastreid.engine import default_argument_parser, default_setup, launch
from fastreid.modeling import build_model
from fastreid.solver import build_lr_scheduler, build_optimizer
from fastreid.evaluation import inference_on_dataset, print_csv_format, ReidEvaluator
from fastreid.utils.checkpoint import Checkpointer, PeriodicCheckpointer
from fastreid.utils import comm
from fastreid.utils.events import (
CommonMetricPrinter,
EventStorage,
JSONWriter,
TensorboardXWriter
)
logger = logging.getLogger("fastreid")
def get_evaluator(cfg, dataset_name, output_dir=None):
data_loader, num_query = build_reid_test_loader(cfg, dataset_name=dataset_name)
return data_loader, ReidEvaluator(cfg, num_query, output_dir)
def do_test(cfg, model):
results = OrderedDict()
for idx, dataset_name in enumerate(cfg.DATASETS.TESTS):
logger.info("Prepare testing set")
try:
data_loader, evaluator = get_evaluator(cfg, dataset_name)
except NotImplementedError:
logger.warn(
"No evaluator found. implement its `build_evaluator` method."
)
results[dataset_name] = {}
continue
results_i = inference_on_dataset(model, data_loader, evaluator, flip_test=cfg.TEST.FLIP.ENABLED)
results[dataset_name] = results_i
if comm.is_main_process():
assert isinstance(
results, dict
), "Evaluator must return a dict on the main process. Got {} instead.".format(
results
)
logger.info("Evaluation results for {} in csv format:".format(dataset_name))
results_i['dataset'] = dataset_name
print_csv_format(results_i)
if len(results) == 1:
results = list(results.values())[0]
return results
def do_train(cfg, model, resume=False):
data_loader = build_reid_train_loader(cfg)
data_loader_iter = iter(data_loader)
model.train()
optimizer = build_optimizer(cfg, model)
iters_per_epoch = len(data_loader.dataset) // cfg.SOLVER.IMS_PER_BATCH
scheduler = build_lr_scheduler(cfg, optimizer, iters_per_epoch)
checkpointer = Checkpointer(
model,
cfg.OUTPUT_DIR,
save_to_disk=comm.is_main_process(),
optimizer=optimizer,
**scheduler
)
start_epoch = (
checkpointer.resume_or_load(cfg.MODEL.WEIGHTS, resume=resume).get("epoch", -1) + 1
)
iteration = start_iter = start_epoch * iters_per_epoch
max_epoch = cfg.SOLVER.MAX_EPOCH
max_iter = max_epoch * iters_per_epoch
warmup_iters = cfg.SOLVER.WARMUP_ITERS
delay_epochs = cfg.SOLVER.DELAY_EPOCHS
periodic_checkpointer = PeriodicCheckpointer(checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD, max_epoch)
if len(cfg.DATASETS.TESTS) == 1:
metric_name = "metric"
else:
metric_name = cfg.DATASETS.TESTS[0] + "/metric"
writers = (
[
CommonMetricPrinter(max_iter),
JSONWriter(os.path.join(cfg.OUTPUT_DIR, "metrics.json")),
TensorboardXWriter(cfg.OUTPUT_DIR)
]
if comm.is_main_process()
else []
)
# compared to "train_net.py", we do not support some hooks, such as
# accurate timing, FP16 training and precise BN here,
# because they are not trivial to implement in a small training loop
logger.info("Start training from epoch {}".format(start_epoch))
with EventStorage(start_iter) as storage:
for epoch in range(start_epoch, max_epoch):
storage.epoch = epoch
for _ in range(iters_per_epoch):
data = next(data_loader_iter)
storage.iter = iteration
loss_dict = model(data)
losses = sum(loss_dict.values())
assert torch.isfinite(losses).all(), loss_dict
loss_dict_reduced = {k: v.item() for k, v in comm.reduce_dict(loss_dict).items()}
losses_reduced = sum(loss for loss in loss_dict_reduced.values())
if comm.is_main_process():
storage.put_scalars(total_loss=losses_reduced, **loss_dict_reduced)
optimizer.zero_grad()
losses.backward()
optimizer.step()
storage.put_scalar("lr", optimizer.param_groups[0]["lr"], smoothing_hint=False)
if iteration - start_iter > 5 and \
((iteration + 1) % 200 == 0 or iteration == max_iter - 1) and \
((iteration + 1) % iters_per_epoch != 0):
for writer in writers:
writer.write()
iteration += 1
if iteration <= warmup_iters:
scheduler["warmup_sched"].step()
# Write metrics after each epoch
for writer in writers:
writer.write()
if iteration > warmup_iters and (epoch + 1) > delay_epochs:
scheduler["lr_sched"].step()
if (
cfg.TEST.EVAL_PERIOD > 0
and (epoch + 1) % cfg.TEST.EVAL_PERIOD == 0
and iteration != max_iter - 1
):
results = do_test(cfg, model)
# Compared to "train_net.py", the test results are not dumped to EventStorage
else:
results = {}
flatten_results = flatten_results_dict(results)
metric_dict = dict(metric=flatten_results[metric_name] if metric_name in flatten_results else -1)
periodic_checkpointer.step(epoch, **metric_dict)
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(cfg, args)
return cfg
def main(args):
cfg = setup(args)
model = build_model(cfg)
logger.info("Model:\n{}".format(model))
if args.eval_only:
cfg.defrost()
cfg.MODEL.BACKBONE.PRETRAIN = False
Checkpointer(model).load(cfg.MODEL.WEIGHTS) # load trained model
return do_test(cfg, model)
distributed = comm.get_world_size() > 1
if distributed:
model = DistributedDataParallel(
model, device_ids=[comm.get_local_rank()], broadcast_buffers=False
)
do_train(cfg, model, resume=args.resume)
return do_test(cfg, model)
if __name__ == "__main__":
args = default_argument_parser().parse_args()
print("Command Line Args:", args)
launch(
main,
args.num_gpus,
num_machines=args.num_machines,
machine_rank=args.machine_rank,
dist_url=args.dist_url,
args=(args,),
)