def regnety(name, pretrained=False, nc=1000):
"""Constructs a RegNetY model."""
is_valid = name in _REGNETY_URLS.keys() and name in _REGNETY_CFGS.keys()
assert is_valid, "RegNetY-{} not found in the model zoo.".format(name)
# Construct the model
cfg = _REGNETY_CFGS[name]
kwargs = {
"stem_type": "simple_stem_in",
"stem_w": 32,
"block_type": "res_bottleneck_block",
"ss": [2, 2, 2, 2],
"bms": [1.0, 1.0, 1.0, 1.0],
"se_r": 0.25,
"nc": nc,
"ds": cfg["ds"],
"ws": cfg["ws"],
"gws": [cfg["g"] for _ in range(4)],
}
model = AnyNet(**kwargs)
# Download and load the weights
if pretrained:
url = os.path.join(_URL_PREFIX, _REGNETY_URLS[name])
ws_path = cache_url(url, _DOWNLOAD_CACHE)
checkpoint.load_checkpoint(ws_path, model)
return model
def resnext(name, pretrained=False, nc=1000):
"""Constructs a ResNeXt model."""
is_valid = name in _RESNEXT_URLS.keys() and name in _RESNEXT_CFGS.keys()
assert is_valid, "ResNet-{} not found in the model zoo.".format(name)
# Construct the model
cfg = _RESNEXT_CFGS[name]
kwargs = {
"stem_type": "res_stem_in",
"stem_w": 64,
"block_type": "res_bottleneck_block",
"ss": [1, 2, 2, 2],
"bms": [0.5, 0.5, 0.5, 0.5],
"se_r": None,
"nc": nc,
"ds": cfg["ds"],
"ws": [256, 512, 1024, 2048],
"gws": [4, 8, 16, 32],
}
model = AnyNet(**kwargs)
# Download and load the weights
if pretrained:
url = os.path.join(_URL_PREFIX, _RESNEXT_URLS[name])
ws_path = cache_url(url, _DOWNLOAD_CACHE)
checkpoint.load_checkpoint(ws_path, model)
return model
def test_model():
"""Evaluates a trained model."""
# Setup training/testing environment
setup_env()
# Construct the model
model = setup_model()
# Load model weights
if cfg.TEST.WEIGHTS:
checkpoint.load_checkpoint(cfg.TEST.WEIGHTS, model)
logger.info("Loaded model weights from: {}".format(cfg.TEST.WEIGHTS))
elif checkpoint.has_checkpoint():
last_checkpoint = checkpoint.get_last_checkpoint()
checkpoint.load_checkpoint(last_checkpoint, model)
logger.info("Loaded checkpoint from: {}".format(last_checkpoint))
else:
print("ERROR: NO checkpoint! ")
os._exit()
# Create data loaders and meters
test_loader = loader.construct_test_loader()
test_meter = meters.TestMeter(len(test_loader))
# Evaluate the model
if cfg.TASK == 'psd' or cfg.TASK == 'fix':
result,ce_error=test_epoch_semi(test_loader, model, test_meter, 0)
else:
result,ce_error=test_epoch_semi(test_loader, model, test_meter, 0)
with open(cfg.OUT_DIR+'/result.txt','w') as f:
f.write(str(result["top1_err"])+'\n')
f.write(str(ce_error[0])+'\n')
f.write(str(ce_error[1])+'\n')
print(result["top1_err"],ce_error)
def train_kd_model():
"""Trains the model."""
# Setup training/testing environment
setup_env()
# Construct the model, loss_fun, and optimizer
model = setup_model()
loss_fun = builders.build_loss_fun().cuda()
optimizer = optim.construct_optimizer(model)
# Load checkpoint or initial weights
start_epoch = 0
if cfg.TRAIN.AUTO_RESUME and cp.has_checkpoint():
file = cp.get_last_checkpoint()
epoch = cp.load_checkpoint(file, model, optimizer)
logger.info("Loaded checkpoint from: {}".format(file))
start_epoch = epoch + 1
elif cfg.TRAIN.WEIGHTS:
cp.load_checkpoint(cfg.TRAIN.WEIGHTS, model, strict=False)
logger.info("Loaded initial weights from: {}".format(
cfg.TRAIN.WEIGHTS))
# Create data loaders and meters
train_loader = data_loader.construct_train_loader()
test_loader = data_loader.construct_test_loader()
train_meter = meters.TrainMeter(len(train_loader))
test_meter = meters.TestMeter(len(test_loader))
# Create a GradScaler for mixed precision training
scaler = amp.GradScaler(enabled=cfg.TRAIN.MIXED_PRECISION)
# Compute model and loader timings
if start_epoch == 0 and cfg.PREC_TIME.NUM_ITER > 0:
benchmark.compute_time_full(model, loss_fun, train_loader, test_loader)
# Perform the training loop
logger.info("Start epoch: {}".format(start_epoch + 1))
best_err = np.inf
# Create the teacher model
teacher = setup_teacher_model()
for cur_epoch in range(start_epoch, cfg.OPTIM.MAX_EPOCH):
# Train for one epoch
params = (train_loader, model, loss_fun, optimizer, scaler,
train_meter, teacher)
train_kd_epoch(*params, cur_epoch)
# Compute precise BN stats
if cfg.BN.USE_PRECISE_STATS:
net.compute_precise_bn_stats(model, train_loader)
# Evaluate the model
test_epoch(test_loader, model, test_meter, cur_epoch)
# Check if checkpoint is best so far (note: should checkpoint meters as well)
stats = test_meter.get_epoch_stats(cur_epoch)
best = stats["top1_err"] <= best_err
best_err = min(stats["top1_err"], best_err)
# Save a checkpoint
file = cp.save_checkpoint(model, optimizer, cur_epoch, best)
logger.info("Wrote checkpoint to: {}".format(file))
def train_model():
"""Trains the model."""
# Setup training/testing environment
setup_env()
# Construct the model, loss_fun, and optimizer
model = setup_model()
loss_fun = builders.build_loss_fun().cuda()
optimizer = optim.construct_optimizer(model)
# Load checkpoint or initial weights
start_epoch = 0
if cfg.TRAIN.AUTO_RESUME and checkpoint.has_checkpoint():
last_checkpoint = checkpoint.get_last_checkpoint()
checkpoint_epoch = checkpoint.load_checkpoint(last_checkpoint, model,
optimizer)
logger.info("Loaded checkpoint from: {}".format(last_checkpoint))
start_epoch = checkpoint_epoch + 1
elif cfg.TRAIN.WEIGHTS:
checkpoint.load_checkpoint(cfg.TRAIN.WEIGHTS, model)
logger.info("Loaded initial weights from: {}".format(
cfg.TRAIN.WEIGHTS))
# Create data loaders and meters
train_loader = loader.construct_train_loader()
test_loader = loader.construct_test_loader()
train_meter = meters.TrainMeter(len(train_loader))
test_meter = meters.TestMeter(len(test_loader))
# Compute model and loader timings
if start_epoch == 0 and cfg.PREC_TIME.NUM_ITER > 0:
benchmark.compute_time_full(model, loss_fun, train_loader, test_loader)
# Perform the training loop
logger.info("Start epoch: {}".format(start_epoch + 1))
for cur_epoch in range(start_epoch, cfg.OPTIM.MAX_EPOCH):
if hasattr(cfg, 'search_epoch'):
if cur_epoch >= cfg.search_epoch:
break
# Train for one epoch
train_epoch(train_loader, model, loss_fun, optimizer, train_meter,
cur_epoch)
# Compute precise BN stats
if cfg.BN.USE_PRECISE_STATS:
net.compute_precise_bn_stats(model, train_loader)
# Save a checkpoint
if (cur_epoch + 1) % cfg.TRAIN.CHECKPOINT_PERIOD == 0:
checkpoint_file = checkpoint.save_checkpoint(
model, optimizer, cur_epoch)
logger.info("Wrote checkpoint to: {}".format(checkpoint_file))
# Evaluate the model
next_epoch = cur_epoch + 1
if next_epoch % cfg.TRAIN.EVAL_PERIOD == 0 or next_epoch == cfg.OPTIM.MAX_EPOCH:
stats = test_epoch(test_loader, model, test_meter, cur_epoch)
nni.report_intermediate_result(stats['top1_err'])
nni.report_final_result(test_meter.min_top1_err)
def train_model():
"""Trains the model."""
# Setup training/testing environment
setup_env()
# Construct the model, ema, loss_fun, and optimizer
model = setup_model()
ema = deepcopy(model)
loss_fun = builders.build_loss_fun().cuda()
optimizer = optim.construct_optimizer(model)
# Load checkpoint or initial weights
start_epoch = 0
if cfg.TRAIN.AUTO_RESUME and cp.has_checkpoint():
file = cp.get_last_checkpoint()
epoch = cp.load_checkpoint(file, model, ema, optimizer)[0]
logger.info("Loaded checkpoint from: {}".format(file))
start_epoch = epoch + 1
elif cfg.TRAIN.WEIGHTS:
train_weights = get_weights_file(cfg.TRAIN.WEIGHTS)
cp.load_checkpoint(train_weights, model, ema)
logger.info("Loaded initial weights from: {}".format(train_weights))
# Create data loaders and meters
train_loader = data_loader.construct_train_loader()
test_loader = data_loader.construct_test_loader()
train_meter = meters.TrainMeter(len(train_loader))
test_meter = meters.TestMeter(len(test_loader))
ema_meter = meters.TestMeter(len(test_loader), "test_ema")
# Create a GradScaler for mixed precision training
scaler = amp.GradScaler(enabled=cfg.TRAIN.MIXED_PRECISION)
# Compute model and loader timings
if start_epoch == 0 and cfg.PREC_TIME.NUM_ITER > 0:
benchmark.compute_time_full(model, loss_fun, train_loader, test_loader)
# Perform the training loop
logger.info("Start epoch: {}".format(start_epoch + 1))
for cur_epoch in range(start_epoch, cfg.OPTIM.MAX_EPOCH):
# Train for one epoch
params = (train_loader, model, ema, loss_fun, optimizer, scaler,
train_meter)
train_epoch(*params, cur_epoch)
# Compute precise BN stats
if cfg.BN.USE_PRECISE_STATS:
net.compute_precise_bn_stats(model, train_loader)
net.compute_precise_bn_stats(ema, train_loader)
# Evaluate the model
test_epoch(test_loader, model, test_meter, cur_epoch)
test_epoch(test_loader, ema, ema_meter, cur_epoch)
test_err = test_meter.get_epoch_stats(cur_epoch)["top1_err"]
ema_err = ema_meter.get_epoch_stats(cur_epoch)["top1_err"]
# Save a checkpoint
file = cp.save_checkpoint(model, ema, optimizer, cur_epoch, test_err,
ema_err)
logger.info("Wrote checkpoint to: {}".format(file))
def build_model(name, pretrained=False, cfg_list=()):
"""Constructs a predefined model (note: loads global config as well)."""
# Load the config
reset_cfg()
config_file = get_config_file(name)
cfg.merge_from_file(config_file)
cfg.merge_from_list(cfg_list)
# Construct model
model = builders.build_model()
# Load pretrained weights
if pretrained:
weights_file = get_weights_file(name)
cp.load_checkpoint(weights_file, model)
return model
def test_model():
"""Evaluates a trained model."""
# Setup training/testing environment
setup_env()
# Construct the model
model = setup_model()
# Load model weights
checkpoint.load_checkpoint(cfg.TEST.WEIGHTS, model)
logger.info("Loaded model weights from: {}".format(cfg.TEST.WEIGHTS))
# Create data loaders and meters
test_loader = loader.construct_test_loader()
test_meter = meters.TestMeter(len(test_loader))
# Evaluate the model
test_epoch(test_loader, model, test_meter, 0)
def train_model():
"""Trains the model."""
# Setup training/testing environment
setup_env()
# Construct the model, loss_fun, and optimizer
model = setup_model()
loss_fun = builders.build_loss_fun().cuda()
optimizer = optim.construct_optimizer(model)
# Load checkpoint or initial weights
start_epoch = 0
if cfg.TRAIN.AUTO_RESUME and checkpoint.has_checkpoint():
last_checkpoint = checkpoint.get_last_checkpoint()
checkpoint_epoch = checkpoint.load_checkpoint(last_checkpoint, model,
optimizer)
logger.info("Loaded checkpoint from: {}".format(last_checkpoint))
start_epoch = checkpoint_epoch + 1
elif cfg.TRAIN.WEIGHTS:
checkpoint.load_checkpoint(cfg.TRAIN.WEIGHTS, model)
logger.info("Loaded initial weights from: {}".format(
cfg.TRAIN.WEIGHTS))
# Compute precise time
if start_epoch == 0 and cfg.PREC_TIME.ENABLED:
logger.info("Computing precise time...")
prec_time = net.compute_precise_time(model, loss_fun)
logger.info(logging.dump_json_stats(prec_time))
net.reset_bn_stats(model)
# Create data loaders and meters
train_loader = loader.construct_train_loader()
test_loader = loader.construct_test_loader()
train_meter = meters.TrainMeter(len(train_loader))
test_meter = meters.TestMeter(len(test_loader))
# Perform the training loop
logger.info("Start epoch: {}".format(start_epoch + 1))
for cur_epoch in range(start_epoch, cfg.OPTIM.MAX_EPOCH):
# Train for one epoch
train_epoch(train_loader, model, loss_fun, optimizer, train_meter,
cur_epoch)
# Compute precise BN stats
if cfg.BN.USE_PRECISE_STATS:
net.compute_precise_bn_stats(model, train_loader)
# Save a checkpoint
if (cur_epoch + 1) % cfg.TRAIN.CHECKPOINT_PERIOD == 0:
checkpoint_file = checkpoint.save_checkpoint(
model, optimizer, cur_epoch)
logger.info("Wrote checkpoint to: {}".format(checkpoint_file))
# Evaluate the model
next_epoch = cur_epoch + 1
if next_epoch % cfg.TRAIN.EVAL_PERIOD == 0 or next_epoch == cfg.OPTIM.MAX_EPOCH:
test_epoch(test_loader, model, test_meter, cur_epoch)
def main(weights=None, replace=None, kd=False, lr=BASE_LEARINING_RATE,
epochs=EPOCHS, cfg=None, batch=BATCH_SIZE, eval_only=False, out=None):
# run only evaluate if weights are provided
if eval_only:
print("Run Evaluation w/o Training")
dataloader_test = create_dataloader(DATA_PATH, "val", batch)
eval(weights, dataloader_test, replace, cfg)
return
# Build models
student_cfg = STUDENT if cfg is None else cfg
teacher = None
if 'EfficientNet' in student_cfg:
print("Build student model ({})".format(student_cfg))
student = effnet(student_cfg, pretrained=False).cuda()
if kd:
print("Build teacher model ({})".format(TEACHER))
teacher = effnet(TEACHER, pretrained=True).cuda()
else:
print("Build student model ({})".format(student_cfg))
student = regnety(student_cfg, pretrained=False).cuda()
if kd:
print("Build teacher model ({})".format(TEACHER))
teacher = regnety(TEACHER, pretrained=True).cuda()
# load students weights with the possible weights of the teacher
weights = weights if weights is not None else WEIGHTS_FILE
print("Load weights from: {}".format(weights))
cp.load_checkpoint(weights, student, replace=replace)
# Create data loaders
print("Create dataloaders")
dataloader_train = create_dataloader(DATA_PATH, "train", batch)
dataloader_test = create_dataloader(DATA_PATH, "val", batch)
# loss
if kd:
print("Create L2 Loss function")
loss_fn = lambda x, y: torch.sum(torch.pow(x - y, 2))
else:
print("Create Cross Entropy Loss function")
loss_fn = SoftCrossEntropyLoss().cuda()
# run training and evaluation after training
print("Start training")
kd_train(teacher, student, loss_fn, dataloader_train, dataloader_test,
kd=kd, lr=lr, epochs=epochs, batch=batch, out=out)
def test_model():
"""Evaluates the model."""
# Setup logging
logging.setup_logging()
# Show the config
logger.info("Config:\n{}".format(cfg))
# Fix the RNG seeds (see RNG comment in core/config.py for discussion)
np.random.seed(cfg.RNG_SEED)
torch.manual_seed(cfg.RNG_SEED)
# Configure the CUDNN backend
torch.backends.cudnn.benchmark = cfg.CUDNN.BENCHMARK
# Build the model (before the loaders to speed up debugging)
model = builders.build_model()
logger.info("Model:\n{}".format(model))
logger.info(logging.dump_json_stats(net.complexity(model)))
# Compute precise time
if cfg.PREC_TIME.ENABLED:
logger.info("Computing precise time...")
loss_fun = builders.build_loss_fun()
prec_time = net.compute_precise_time(model, loss_fun)
logger.info(logging.dump_json_stats(prec_time))
net.reset_bn_stats(model)
# Load model weights
checkpoint.load_checkpoint(cfg.TEST.WEIGHTS, model)
logger.info("Loaded model weights from: {}".format(cfg.TEST.WEIGHTS))
# Create data loaders
test_loader = loader.construct_test_loader()
# Create meters
test_meter = meters.TestMeter(len(test_loader))
# Evaluate the model
test_epoch(test_loader, model, test_meter, 0)
def eval(model_weights, loader, replace=None, cfg=None):
cfg_student = STUDENT if cfg is None else cfg
print("Start evaluation on {} with weights from {}...".format(cfg_student, model_weights))
meter = meters.TestMeter(len(loader))
if "EfficientNet" in cfg_student:
model = effnet(cfg_student, pretrained=False).cuda()
else:
model = regnety(cfg_student, pretrained=False).cuda()
cp.load_checkpoint(model_weights, model, replace=replace)
model.eval()
meter.reset()
start_time = time.time()
for cur_iter, (inputs, labels) in enumerate(loader):
inputs, labels = inputs.cuda(), labels.cuda(non_blocking=True)
preds = model(inputs)
top1_err, top5_err = meters.topk_errors(preds, labels, [1, 5])
top1_err, top5_err = top1_err.item(), top5_err.item()
meter.update_stats(top1_err, top5_err, inputs.size(0))
if (cur_iter + 1) % 100 == 0:
print("iter {}/{}".format(cur_iter + 1, len(loader)))
print("Total evaluation time: {}s".format(round(time.time() - start_time)))
print("**************************************")
print("Top1 accuracy: {:.2f}".format(100 - meter.get_epoch_stats(0)["min_top1_err"]))
print("**************************************")
def effnet(name, pretrained=False, nc=1000):
"""Constructs an EfficientNet model."""
is_valid = name in _EN_URLS.keys() and name in _EN_CFGS.keys()
assert is_valid, "EfficientNet-{} not found in the model zoo.".format(name)
# Construct the model
cfg = _EN_CFGS[name]
kwargs = {
"exp_rs": [1, 6, 6, 6, 6, 6, 6],
"se_r": 0.25,
"nc": nc,
"ss": [1, 2, 2, 2, 1, 2, 1],
"ks": [3, 3, 5, 3, 5, 5, 3],
"stem_w": cfg["sw"],
"ds": cfg["ds"],
"ws": cfg["ws"],
"head_w": cfg["hw"],
}
model = EffNet(**kwargs)
# Download and load the weights
if pretrained:
url = os.path.join(_URL_PREFIX, _EN_URLS[name])
ws_path = cache_url(url, _DOWNLOAD_CACHE)
checkpoint.load_checkpoint(ws_path, model)
return model
def train_model():
"""Trains the model."""
# Setup logging
logging.setup_logging()
# Show the config
logger.info("Config:\n{}".format(cfg))
# Fix the RNG seeds (see RNG comment in core/config.py for discussion)
np.random.seed(cfg.RNG_SEED)
torch.manual_seed(cfg.RNG_SEED)
# Configure the CUDNN backend
torch.backends.cudnn.benchmark = cfg.CUDNN.BENCHMARK
# Build the model (before the loaders to speed up debugging)
model = builders.build_model()
logger.info("Model:\n{}".format(model))
logger.info(logging.dump_json_stats(net.complexity(model)))
# Define the loss function
loss_fun = builders.build_loss_fun()
# Construct the optimizer
optimizer = optim.construct_optimizer(model)
# Load checkpoint or initial weights
start_epoch = 0
if cfg.TRAIN.AUTO_RESUME and checkpoint.has_checkpoint():
last_checkpoint = checkpoint.get_last_checkpoint()
checkpoint_epoch = checkpoint.load_checkpoint(last_checkpoint, model,
optimizer)
logger.info("Loaded checkpoint from: {}".format(last_checkpoint))
start_epoch = checkpoint_epoch + 1
elif cfg.TRAIN.WEIGHTS:
checkpoint.load_checkpoint(cfg.TRAIN.WEIGHTS, model)
logger.info("Loaded initial weights from: {}".format(
cfg.TRAIN.WEIGHTS))
# Compute precise time
if start_epoch == 0 and cfg.PREC_TIME.ENABLED:
logger.info("Computing precise time...")
prec_time = net.compute_precise_time(model, loss_fun)
logger.info(logging.dump_json_stats(prec_time))
net.reset_bn_stats(model)
# Create data loaders
train_loader = loader.construct_train_loader()
test_loader = loader.construct_test_loader()
# Create meters
train_meter = meters.TrainMeter(len(train_loader))
test_meter = meters.TestMeter(len(test_loader))
# Perform the training loop
logger.info("Start epoch: {}".format(start_epoch + 1))
for cur_epoch in range(start_epoch, cfg.OPTIM.MAX_EPOCH):
# Train for one epoch
train_epoch(train_loader, model, loss_fun, optimizer, train_meter,
cur_epoch)
# Compute precise BN stats
if cfg.BN.USE_PRECISE_STATS:
net.compute_precise_bn_stats(model, train_loader)
# Save a checkpoint
if checkpoint.is_checkpoint_epoch(cur_epoch):
checkpoint_file = checkpoint.save_checkpoint(
model, optimizer, cur_epoch)
logger.info("Wrote checkpoint to: {}".format(checkpoint_file))
# Evaluate the model
if is_eval_epoch(cur_epoch):
test_epoch(test_loader, model, test_meter, cur_epoch)
def test_ftta_model(corruptions, levels):
"""Use feed back to fine-tune some part of the model. (with all kind of corruptions)"""
all_results = []
for corruption_level in levels:
lvl_results = []
for corruption_type in corruptions:
cfg.TRAIN.CORRUPTION = corruption_type
cfg.TRAIN.LEVEL = corruption_level
cfg.TEST.CORRUPTION = corruption_type
cfg.TEST.LEVEL = corruption_level
# Setup training/testing environment
setup_env()
# Construct the model, loss_fun, and optimizer
model = setup_model()
loss_fun = builders.build_loss_fun().cuda()
optimizer = optim.construct_optimizer(model)
# Load checkpoint or initial weights
start_epoch = 0
checkpoint.load_checkpoint(cfg.TRAIN.WEIGHTS,
model,
strict=cfg.TRAIN.LOAD_STRICT)
logger.info("Loaded initial weights from: {}".format(
cfg.TRAIN.WEIGHTS))
# Create data loaders and meters
train_loader = loader.construct_train_loader()
test_loader = loader.construct_test_loader()
train_meter = meters.TrainMeter(len(train_loader))
test_meter = meters.TestMeter(len(test_loader))
# Compute model and loader timings
if start_epoch == 0 and cfg.PREC_TIME.NUM_ITER > 0:
benchmark.compute_time_full(model, loss_fun, train_loader,
test_loader)
# Perform the training loop
logger.info("Start epoch: {}".format(start_epoch + 1))
for cur_epoch in range(start_epoch, cfg.OPTIM.MAX_EPOCH):
if cfg.TRAIN.ADAPTATION != 'test_only':
if cfg.TRAIN.ADAPTATION == 'update_bn':
bn_update(model, train_loader)
elif cfg.TRAIN.ADAPTATION == 'min_entropy':
# Train for one epoch
train_epoch(train_loader, model, loss_fun, optimizer,
train_meter, cur_epoch)
bn_update(model, train_loader)
# Save a checkpoint
if (cur_epoch + 1) % cfg.TRAIN.CHECKPOINT_PERIOD == 0:
checkpoint_file = checkpoint.save_checkpoint(
model, optimizer, cur_epoch)
logger.info(
"Wrote checkpoint to: {}".format(checkpoint_file))
# Evaluate the model
next_epoch = cur_epoch + 1
if next_epoch % cfg.TRAIN.EVAL_PERIOD == 0 or next_epoch == cfg.OPTIM.MAX_EPOCH:
top1 = test_epoch(test_loader, model, test_meter,
cur_epoch)
lvl_results.append(top1)
all_results.append(lvl_results)
for lvl_idx in range(len(all_results)):
logger.info("corruption level: {}".format(levels[lvl_idx]))
logger.info("corruption types: {}".format(corruptions))
logger.info(all_results[lvl_idx])
# show_parameters(model)
return all_results
def train_model():
"""Trains the model."""
# Setup training/testing environment
setup_env()
# Construct the model, loss_fun, and optimizer
model = setup_model()
loss_fun = builders.build_loss_fun().cuda()
if "search" in cfg.MODEL.TYPE:
params_w = [v for k, v in model.named_parameters() if "alphas" not in k]
params_a = [v for k, v in model.named_parameters() if "alphas" in k]
optimizer_w = torch.optim.SGD(
params=params_w,
lr=cfg.OPTIM.BASE_LR,
momentum=cfg.OPTIM.MOMENTUM,
weight_decay=cfg.OPTIM.WEIGHT_DECAY,
dampening=cfg.OPTIM.DAMPENING,
nesterov=cfg.OPTIM.NESTEROV
)
if cfg.OPTIM.ARCH_OPTIM == "adam":
optimizer_a = torch.optim.Adam(
params=params_a,
lr=cfg.OPTIM.ARCH_BASE_LR,
betas=(0.5, 0.999),
weight_decay=cfg.OPTIM.ARCH_WEIGHT_DECAY
)
elif cfg.OPTIM.ARCH_OPTIM == "sgd":
optimizer_a = torch.optim.SGD(
params=params_a,
lr=cfg.OPTIM.ARCH_BASE_LR,
momentum=cfg.OPTIM.MOMENTUM,
weight_decay=cfg.OPTIM.ARCH_WEIGHT_DECAY,
dampening=cfg.OPTIM.DAMPENING,
nesterov=cfg.OPTIM.NESTEROV
)
optimizer = [optimizer_w, optimizer_a]
else:
optimizer = optim.construct_optimizer(model)
# Load checkpoint or initial weights
start_epoch = 0
if cfg.TRAIN.AUTO_RESUME and checkpoint.has_checkpoint():
last_checkpoint = checkpoint.get_last_checkpoint()
checkpoint_epoch = checkpoint.load_checkpoint(last_checkpoint, model, optimizer)
logger.info("Loaded checkpoint from: {}".format(last_checkpoint))
start_epoch = checkpoint_epoch + 1
elif cfg.TRAIN.WEIGHTS:
checkpoint.load_checkpoint(cfg.TRAIN.WEIGHTS, model)
logger.info("Loaded initial weights from: {}".format(cfg.TRAIN.WEIGHTS))
# Create data loaders and meters
if cfg.TRAIN.PORTION < 1:
if "search" in cfg.MODEL.TYPE:
train_loader = [loader._construct_loader(
dataset_name=cfg.TRAIN.DATASET,
split=cfg.TRAIN.SPLIT,
batch_size=int(cfg.TRAIN.BATCH_SIZE / cfg.NUM_GPUS),
shuffle=True,
drop_last=True,
portion=cfg.TRAIN.PORTION,
side="l"
),
loader._construct_loader(
dataset_name=cfg.TRAIN.DATASET,
split=cfg.TRAIN.SPLIT,
batch_size=int(cfg.TRAIN.BATCH_SIZE / cfg.NUM_GPUS),
shuffle=True,
drop_last=True,
portion=cfg.TRAIN.PORTION,
side="r"
)]
else:
train_loader = loader._construct_loader(
dataset_name=cfg.TRAIN.DATASET,
split=cfg.TRAIN.SPLIT,
batch_size=int(cfg.TRAIN.BATCH_SIZE / cfg.NUM_GPUS),
shuffle=True,
drop_last=True,
portion=cfg.TRAIN.PORTION,
side="l"
)
test_loader = loader._construct_loader(
dataset_name=cfg.TRAIN.DATASET,
split=cfg.TRAIN.SPLIT,
batch_size=int(cfg.TRAIN.BATCH_SIZE / cfg.NUM_GPUS),
shuffle=False,
drop_last=False,
portion=cfg.TRAIN.PORTION,
side="r"
)
else:
train_loader = loader.construct_train_loader()
test_loader = loader.construct_test_loader()
train_meter_type = meters.TrainMeterIoU if cfg.TASK == "seg" else meters.TrainMeter
test_meter_type = meters.TestMeterIoU if cfg.TASK == "seg" else meters.TestMeter
l = train_loader[0] if isinstance(train_loader, list) else train_loader
train_meter = train_meter_type(len(l))
test_meter = test_meter_type(len(test_loader))
# Compute model and loader timings
if start_epoch == 0 and cfg.PREC_TIME.NUM_ITER > 0:
l = train_loader[0] if isinstance(train_loader, list) else train_loader
benchmark.compute_time_full(model, loss_fun, l, test_loader)
# Perform the training loop
logger.info("Start epoch: {}".format(start_epoch + 1))
for cur_epoch in range(start_epoch, cfg.OPTIM.MAX_EPOCH):
# Train for one epoch
f = search_epoch if "search" in cfg.MODEL.TYPE else train_epoch
f(train_loader, model, loss_fun, optimizer, train_meter, cur_epoch)
# Compute precise BN stats
if cfg.BN.USE_PRECISE_STATS:
net.compute_precise_bn_stats(model, train_loader)
# Save a checkpoint
if (cur_epoch + 1) % cfg.TRAIN.CHECKPOINT_PERIOD == 0:
checkpoint_file = checkpoint.save_checkpoint(model, optimizer, cur_epoch)
logger.info("Wrote checkpoint to: {}".format(checkpoint_file))
# Evaluate the model
next_epoch = cur_epoch + 1
if next_epoch % cfg.TRAIN.EVAL_PERIOD == 0 or next_epoch == cfg.OPTIM.MAX_EPOCH:
test_epoch(test_loader, model, test_meter, cur_epoch)
def test():
"""Evaluates a trained model."""
# Setup training/testing environment
setup_env()
# Construct the model
model = setup_model()
# Load model weights
checkpoint.load_checkpoint(cfg.TEST.WEIGHTS, model)
logger.info("Loaded model weights from: {}".format(cfg.TEST.WEIGHTS))
# Create data loaders
test_loader = loader.construct_test_loader()
dataset = test_loader.dataset
# Enable eval mode
logs = []
model.eval()
for inputs, labels in test_loader:
# Transfer the data to the current GPU device
inputs, labels = inputs.cuda(), labels.cuda(non_blocking=True)
# Compute the predictions
preds = model(inputs)
if cfg.SOFTMAX:
preds = F.softmax(preds, dim=1)
else:
preds = torch.sigmoid(preds)
# Abnormal dataset format support
if cfg.TRAIN.DATASET == "abnormal":
labels = labels.argmax(dim=1)
# (batch_size, classes) -> (classes, batch_size)
for label, tail in zip(labels.tolist(), preds.tolist()):
logs.append([label, tail[0], tail])
imgs = [v["im_path"] for v in dataset._imdb]
class_ids = dataset._class_ids
assert len(imgs) == len(logs)
lines = []
outputs = []
lines.append(":".join(class_ids))
lines.append("{}".format(len(imgs)))
lines.append("im_path,label,score,score_1_n")
for im_path, (label, score, tail) in zip(imgs, logs):
tail = ",".join(["{:.3f}".format(v) for v in tail])
lines.append("{},{},{},{}".format(im_path, label, score, tail))
outputs.append([im_path, class_ids[label], score])
task_name = time.strftime("%m%d%H%M%S")
os.makedirs(os.path.join(cfg.OUT_DIR, task_name))
temp_file = "{}/threshold.png".format(task_name)
temp_file = os.path.join(cfg.OUT_DIR, temp_file)
score_thr = search_thr(logs, s1_thr=2, s2_thr=70, out_file=temp_file)
temp_file = "{}/results.csv".format(task_name)
temp_file = os.path.join(cfg.OUT_DIR, temp_file)
with open(temp_file, "w") as f:
f.write("\n".join(lines))
print(temp_file)
temp_file = "{}/results.pkl".format(task_name)
temp_file = os.path.join(cfg.OUT_DIR, temp_file)
with open(temp_file, "wb") as f:
pickle.dump(outputs, f)
print(temp_file)
hardmini(outputs, class_ids, task_name, score_thr)
return outputs
def train_model():
"""Trains the model."""
# Setup training/testing environment
setup_env()
# Construct the model, loss_fun, and optimizer
model = setup_model()
loss_fun = builders.build_loss_fun().cuda()
optimizer = optim.construct_optimizer(model)
# Load checkpoint or initial weights
start_epoch = 0
if cfg.TRAIN.AUTO_RESUME and checkpoint.has_checkpoint():
last_checkpoint = checkpoint.get_last_checkpoint()
checkpoint_epoch = checkpoint.load_checkpoint(last_checkpoint, model,
optimizer)
logger.info("Loaded checkpoint from: {}".format(last_checkpoint))
start_epoch = checkpoint_epoch + 1
elif cfg.TRAIN.WEIGHTS:
checkpoint.load_checkpoint(cfg.TRAIN.WEIGHTS, model)
logger.info("Loaded initial weights from: {}".format(
cfg.TRAIN.WEIGHTS))
# Create data loaders and meters
if cfg.TEST.DATASET == 'imagenet_dataset' or cfg.TRAIN.DATASET == 'imagenet_dataset':
dataset = loader.construct_train_loader()
train_loader = dataset.train_loader
test_loader = dataset.val_loader
else:
dataset = None
train_loader = loader.construct_train_loader()
test_loader = loader.construct_test_loader()
train_meter = meters.TrainMeter(len(train_loader))
test_meter = meters.TestMeter(len(test_loader))
# Compute model and loader timings
if start_epoch == 0 and cfg.PREC_TIME.NUM_ITER > 0:
benchmark.compute_time_full(model, loss_fun, train_loader, test_loader)
# Perform the training loop
logger.info("Start epoch: {}".format(start_epoch + 1))
for cur_epoch in range(start_epoch, cfg.OPTIM.MAX_EPOCH):
# Train for one epoch
train_epoch(train_loader, model, loss_fun, optimizer, train_meter,
cur_epoch)
# Compute precise BN stats
if cfg.BN.USE_PRECISE_STATS:
net.compute_precise_bn_stats(model, train_loader)
# Save a checkpoint
if (cur_epoch + 1) % cfg.TRAIN.CHECKPOINT_PERIOD == 0:
checkpoint_file = checkpoint.save_checkpoint(
model, optimizer, cur_epoch)
logger.info("Wrote checkpoint to: {}".format(checkpoint_file))
# Evaluate the model
next_epoch = cur_epoch + 1
if next_epoch % cfg.TRAIN.EVAL_PERIOD == 0 or next_epoch == cfg.OPTIM.MAX_EPOCH:
logger.info("Start testing")
test_epoch(test_loader, model, test_meter, cur_epoch)
if dataset is not None:
logger.info("Reset the dataset")
train_loader._dali_iterator.reset()
test_loader._dali_iterator.reset()
# clear memory
if torch.cuda.is_available():
torch.cuda.synchronize()
torch.cuda.empty_cache(
) # https://forums.fast.ai/t/clearing-gpu-memory-pytorch/14637
gc.collect()
