def test(args):
valid_dataset = SIDDValData(args.data)
valid_sampler = data.SequentialSampler(
valid_dataset, batch_size=1, drop_last=False
)
valid_dataloader = data.DataLoader(
valid_dataset,
sampler=valid_sampler,
num_workers=8,
)
model = UNetD(3)
with open(args.checkpoint, "rb") as f:
state = pickle.load(f)
model.load_state_dict(state["state_dict"])
model.eval()
def valid_step(image, label):
pred = model(image)
pred = image - pred
psnr_it = batch_PSNR(pred, label)
return psnr_it
def valid(func, data_queue):
psnr_v = 0.
for step, (image, label) in tqdm(enumerate(data_queue)):
image = megengine.tensor(image)
label = megengine.tensor(label)
psnr_it = func(image, label)
psnr_v += psnr_it
psnr_v /= step + 1
return psnr_v
psnr_v = valid(valid_step, valid_dataloader)
print("PSNR: {:.3f}".format(psnr_v.item()) )
def worker(world_size, args):
# pylint: disable=too-many-statements
rank = dist.get_rank()
if world_size > 1:
# Initialize distributed process group
logger.info("init distributed process group {} / {}".format(
rank, world_size))
model = models.__dict__[args.arch]()
if args.mode != "normal":
quantize_qat(model, qconfig=Q.ema_fakequant_qconfig)
if args.checkpoint:
logger.info("Load pretrained weights from %s", args.checkpoint)
ckpt = mge.load(args.checkpoint)
ckpt = ckpt["state_dict"] if "state_dict" in ckpt else ckpt
model.load_state_dict(ckpt, strict=False)
if args.mode == "quantized":
quantize(model)
# Define valid graph
def valid_func(image, label):
model.eval()
logits = model(image)
loss = F.loss.cross_entropy(logits, label, label_smooth=0.1)
acc1, acc5 = F.topk_accuracy(logits, label, (1, 5))
if dist.is_distributed(): # all_reduce_mean
loss = dist.functional.all_reduce_sum(loss) / dist.get_world_size()
acc1 = dist.functional.all_reduce_sum(acc1) / dist.get_world_size()
acc5 = dist.functional.all_reduce_sum(acc5) / dist.get_world_size()
return loss, acc1, acc5
# Build valid datasets
logger.info("preparing dataset..")
valid_dataset = data.dataset.ImageNet(args.data, train=False)
valid_sampler = data.SequentialSampler(valid_dataset,
batch_size=100,
drop_last=False)
valid_queue = data.DataLoader(
valid_dataset,
sampler=valid_sampler,
transform=T.Compose([
T.Resize(256),
T.CenterCrop(224),
T.Normalize(mean=128),
T.ToMode("CHW")
]),
num_workers=args.workers,
)
_, valid_acc, valid_acc5 = infer(valid_func, valid_queue, args)
if rank == 0:
logger.info("TEST %f, %f", valid_acc, valid_acc5)
def worker(rank, world_size, args):
if world_size > 1:
# Initialize distributed process group
logger.info("init distributed process group {} / {}".format(
rank, world_size))
dist.init_process_group(
master_ip="localhost",
master_port=23456,
world_size=world_size,
rank=rank,
dev=rank,
)
model = getattr(M, args.arch)(pretrained=(args.model is None))
if args.model:
logger.info("load weights from %s", args.model)
model.load_state_dict(mge.load(args.model), strict=False)
if args.quantized:
quantize(model)
@jit.trace(symbolic=True)
def valid_func(image, label):
model.eval()
logits = model(image)
loss = F.cross_entropy_with_softmax(logits, label)
acc1, acc5 = F.accuracy(logits, label, (1, 5))
if dist.is_distributed(): # all_reduce_mean
loss = dist.all_reduce_sum(loss,
"valid_loss") / dist.get_world_size()
acc1 = dist.all_reduce_sum(acc1,
"valid_acc1") / dist.get_world_size()
acc5 = dist.all_reduce_sum(acc5,
"valid_acc5") / dist.get_world_size()
return loss, acc1, acc5
logger.info("preparing dataset..")
valid_dataset = data.dataset.ImageNet(args.data, train=False)
valid_sampler = data.SequentialSampler(valid_dataset,
batch_size=args.batch_size,
drop_last=False)
valid_queue = data.DataLoader(
valid_dataset,
sampler=valid_sampler,
transform=T.Compose([
T.Resize(256),
T.CenterCrop(224),
T.Normalize(mean=[128.0, 128.0, 128.0], std=[1.0, 1.0,
1.0]), # BGR
T.ToMode("CHW"),
]),
num_workers=args.workers,
)
_, valid_acc, valid_acc5 = infer(valid_func, valid_queue, args)
logger.info("Valid %.3f / %.3f", valid_acc, valid_acc5)
def build_dataloader(dataset_dir):
val_dataset = dataset.PascalVOC(dataset_dir,
"val",
order=["image", "mask"])
val_sampler = data.SequentialSampler(val_dataset, cfg.VAL_BATCHES)
val_dataloader = data.DataLoader(
val_dataset,
sampler=val_sampler,
transform=T.Normalize(mean=cfg.IMG_MEAN,
std=cfg.IMG_STD,
order=["image", "mask"]),
num_workers=cfg.DATA_WORKERS,
)
return val_dataloader, val_dataset.__len__()
def prepare_dataset(name):
"""prepare dataset
Args:
name (str): name of the dataset, should be one of {facescrub, megaface}
Returns:
dataset (data.Dataset): required dataset
queue (data.DataLoader): corresponding dataloader
"""
preprocess = T.Compose([T.Normalize(mean=127.5, std=128), T.ToMode("CHW")])
dataset = get_eval_dataset(name, dataset_dir=configs["dataset_dir"])
sampler = data.SequentialSampler(dataset, batch_size=configs["batch_size"])
queue = data.DataLoader(dataset, sampler=sampler, transform=preprocess)
return dataset, queue
def build_dataset(args):
train_dataset = data.dataset.ImageNet(args.data, train=True)
train_sampler = data.Infinite(
data.RandomSampler(train_dataset,
batch_size=args.batch_size,
drop_last=True))
train_dataloader = data.DataLoader(
train_dataset,
sampler=train_sampler,
transform=T.Compose([ # Baseline Augmentation for small models
T.RandomResizedCrop(224),
T.RandomHorizontalFlip(),
T.Normalize(mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395]), # BGR
T.ToMode("CHW"),
]) if args.arch in ("resnet18", "resnet34") else T.Compose(
[ # Facebook Augmentation for large models
T.RandomResizedCrop(224),
T.RandomHorizontalFlip(),
T.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4),
T.Normalize(mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395]), # BGR
T.ToMode("CHW"),
]),
num_workers=args.workers,
)
valid_dataset = data.dataset.ImageNet(args.data, train=False)
valid_sampler = data.SequentialSampler(valid_dataset,
batch_size=100,
drop_last=False)
valid_dataloader = data.DataLoader(
valid_dataset,
sampler=valid_sampler,
transform=T.Compose([
T.Resize(256),
T.CenterCrop(224),
T.Normalize(mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395]), # BGR
T.ToMode("CHW"),
]),
num_workers=args.workers,
)
return train_dataloader, valid_dataloader
def build_dataset(args):
train_dataloader = None
valid_dataset = data.dataset.ImageNet(args.data, train=False)
valid_sampler = data.SequentialSampler(valid_dataset,
batch_size=100,
drop_last=False)
valid_dataloader = data.DataLoader(
valid_dataset,
sampler=valid_sampler,
transform=T.Compose([
T.Resize(256),
T.CenterCrop(224),
T.Normalize(mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395]), # BGR
T.ToMode("CHW"),
]),
num_workers=args.workers,
)
return train_dataloader, valid_dataloader
def build_dataset(args):
assert not args.batch_size//args.ngpus == 0 and not 4 // args.ngpus == 0
train_dataset = SIDDData(args.data, length=args.batch_size*args.steps_per_epoch)
train_sampler = data.Infinite(
data.RandomSampler(train_dataset, batch_size=args.batch_size//args.ngpus, drop_last=True)
)
train_dataloader = data.DataLoader(
train_dataset,
sampler=train_sampler,
num_workers=args.workers,
)
valid_dataset = SIDDValData(args.data)
valid_sampler = data.SequentialSampler(
valid_dataset, batch_size=4//args.ngpus, drop_last=False
)
valid_dataloader = data.DataLoader(
valid_dataset,
sampler=valid_sampler,
num_workers=args.workers,
)
return train_dataloader, valid_dataloader
def build_dataloader(dataset_dir, cfg):
if cfg.DATASET == "VOC2012":
val_dataset = EvalPascalVOC(dataset_dir,
"val",
order=["image", "mask", "info"])
elif cfg.DATASET == "Cityscapes":
val_dataset = dataset.Cityscapes(dataset_dir,
"val",
mode='gtFine',
order=["image", "mask", "info"])
else:
raise ValueError("Unsupported dataset {}".format(cfg.DATASET))
val_sampler = data.SequentialSampler(val_dataset, cfg.VAL_BATCHES)
val_dataloader = data.DataLoader(
val_dataset,
sampler=val_sampler,
transform=T.Normalize(mean=cfg.IMG_MEAN,
std=cfg.IMG_STD,
order=["image", "mask"]),
num_workers=cfg.DATA_WORKERS,
)
return val_dataloader, val_dataset.__len__()
def worker(rank, world_size, args):
if world_size > 1:
# Initialize distributed process group
logger.info("init distributed process group {} / {}".format(
rank, world_size))
dist.init_process_group(
master_ip="localhost",
master_port=23456,
world_size=world_size,
rank=rank,
dev=rank,
)
save_dir = os.path.join(args.save, args.arch)
model = getattr(M, args.arch)()
optimizer = optim.SGD(
get_parameters(model),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay,
)
# Define train and valid graph
@jit.trace(symbolic=True)
def train_func(image, label):
model.train()
logits = model(image)
loss = F.cross_entropy_with_softmax(logits, label, label_smooth=0.1)
acc1, acc5 = F.accuracy(logits, label, (1, 5))
optimizer.backward(loss) # compute gradients
if dist.is_distributed(): # all_reduce_mean
loss = dist.all_reduce_sum(loss,
"train_loss") / dist.get_world_size()
acc1 = dist.all_reduce_sum(acc1,
"train_acc1") / dist.get_world_size()
acc5 = dist.all_reduce_sum(acc5,
"train_acc5") / dist.get_world_size()
return loss, acc1, acc5
@jit.trace(symbolic=True)
def valid_func(image, label):
model.eval()
logits = model(image)
loss = F.cross_entropy_with_softmax(logits, label, label_smooth=0.1)
acc1, acc5 = F.accuracy(logits, label, (1, 5))
if dist.is_distributed(): # all_reduce_mean
loss = dist.all_reduce_sum(loss,
"valid_loss") / dist.get_world_size()
acc1 = dist.all_reduce_sum(acc1,
"valid_acc1") / dist.get_world_size()
acc5 = dist.all_reduce_sum(acc5,
"valid_acc5") / dist.get_world_size()
return loss, acc1, acc5
# Build train and valid datasets
logger.info("preparing dataset..")
train_dataset = data.dataset.ImageNet(args.data, train=True)
train_sampler = data.RandomSampler(train_dataset,
batch_size=args.batch_size,
drop_last=True)
train_queue = data.DataLoader(
train_dataset,
sampler=train_sampler,
transform=T.Compose([
T.RandomResizedCrop(224),
T.RandomHorizontalFlip(),
T.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4),
T.Normalize(mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395]), # BGR
T.ToMode("CHW"),
]),
num_workers=args.workers,
)
train_queue = infinite_iter(train_queue)
valid_dataset = data.dataset.ImageNet(args.data, train=False)
valid_sampler = data.SequentialSampler(valid_dataset,
batch_size=100,
drop_last=False)
valid_queue = data.DataLoader(
valid_dataset,
sampler=valid_sampler,
transform=T.Compose([
T.Resize(256),
T.CenterCrop(224),
T.Normalize(mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395]), # BGR
T.ToMode("CHW"),
]),
num_workers=args.workers,
)
# Start training
objs = AverageMeter("Loss")
top1 = AverageMeter("Acc@1")
top5 = AverageMeter("Acc@5")
total_time = AverageMeter("Time")
t = time.time()
for step in range(0, args.steps + 1250 + 1):
# Linear learning rate decay
decay = 1.0
decay = 1 - float(step) / args.steps if step < args.steps else 0.0
for param_group in optimizer.param_groups:
param_group["lr"] = args.learning_rate * decay
image, label = next(train_queue)
image = image.astype("float32")
label = label.astype("int32")
n = image.shape[0]
optimizer.zero_grad()
loss, acc1, acc5 = train_func(image, label)
optimizer.step()
top1.update(100 * acc1.numpy()[0], n)
top5.update(100 * acc5.numpy()[0], n)
objs.update(loss.numpy()[0], n)
total_time.update(time.time() - t)
t = time.time()
if step % args.report_freq == 0 and rank == 0:
logger.info(
"TRAIN %06d %f %s %s %s %s",
step,
args.learning_rate * decay,
objs,
top1,
top5,
total_time,
)
objs.reset()
top1.reset()
top5.reset()
total_time.reset()
if step % 10000 == 0 and rank == 0:
logger.info("SAVING %06d", step)
mge.save(
model.state_dict(),
os.path.join(save_dir, "checkpoint-{:06d}.pkl".format(step)),
)
if step % 10000 == 0 and step != 0:
_, valid_acc, valid_acc5 = infer(valid_func, valid_queue, args)
logger.info("TEST %06d %f, %f", step, valid_acc, valid_acc5)
mge.save(model.state_dict(),
os.path.join(save_dir, "checkpoint-{:06d}.pkl".format(step)))
_, valid_acc, valid_acc5 = infer(valid_func, valid_queue, args)
logger.info("TEST %06d %f, %f", step, valid_acc, valid_acc5)
def worker(rank, world_size, args):
# pylint: disable=too-many-statements
mge.set_log_file(os.path.join(args.save, args.arch, "log.txt"))
if world_size > 1:
# Initialize distributed process group
logger.info("init distributed process group {} / {}".format(
rank, world_size))
dist.init_process_group(
master_ip="localhost",
master_port=23456,
world_size=world_size,
rank=rank,
dev=rank,
)
save_dir = os.path.join(args.save, args.arch)
model = getattr(M, args.arch)()
step_start = 0
if args.model:
logger.info("load weights from %s", args.model)
model.load_state_dict(mge.load(args.model))
step_start = int(args.model.split("-")[1].split(".")[0])
optimizer = optim.SGD(
get_parameters(model),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay,
)
# Define train and valid graph
@jit.trace(symbolic=True)
def train_func(image, label):
model.train()
logits = model(image)
loss = F.cross_entropy_with_softmax(logits, label, label_smooth=0.)
acc1, acc5 = F.accuracy(logits, label, (1, 5))
optimizer.backward(loss) # compute gradients
if dist.is_distributed(): # all_reduce_mean
loss = dist.all_reduce_sum(loss,
"train_loss") / dist.get_world_size()
acc1 = dist.all_reduce_sum(acc1,
"train_acc1") / dist.get_world_size()
acc5 = dist.all_reduce_sum(acc5,
"train_acc5") / dist.get_world_size()
return loss, acc1, acc5
@jit.trace(symbolic=True)
def valid_func(image, label):
model.eval()
logits = model(image)
loss = F.cross_entropy_with_softmax(logits, label, label_smooth=0.)
acc1, acc5 = F.accuracy(logits, label, (1, 5))
if dist.is_distributed(): # all_reduce_mean
loss = dist.all_reduce_sum(loss,
"valid_loss") / dist.get_world_size()
acc1 = dist.all_reduce_sum(acc1,
"valid_acc1") / dist.get_world_size()
acc5 = dist.all_reduce_sum(acc5,
"valid_acc5") / dist.get_world_size()
return loss, acc1, acc5
# Build train and valid datasets
logger.info("preparing dataset..")
train_dataset = data.dataset.ImageNet(args.data, train=True)
train_sampler = data.Infinite(
data.RandomSampler(train_dataset,
batch_size=args.batch_size,
drop_last=True))
train_queue = data.DataLoader(
train_dataset,
sampler=train_sampler,
transform=T.Compose([
T.RandomResizedCrop(224),
T.RandomHorizontalFlip(),
T.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4),
T.ToMode("CHW"),
]),
num_workers=args.workers,
)
valid_dataset = data.dataset.ImageNet(args.data, train=False)
valid_sampler = data.SequentialSampler(valid_dataset,
batch_size=100,
drop_last=False)
valid_queue = data.DataLoader(
valid_dataset,
sampler=valid_sampler,
transform=T.Compose([
T.Resize(256),
T.CenterCrop(224),
T.ToMode("CHW"),
]),
num_workers=args.workers,
)
# Start training
objs = AverageMeter("Loss")
top1 = AverageMeter("Acc@1")
top5 = AverageMeter("Acc@5")
total_time = AverageMeter("Time")
t = time.time()
for step in range(step_start, args.steps + 1):
# Linear learning rate decay
decay = 1.0
decay = 1 - float(step) / args.steps if step < args.steps else 0
for param_group in optimizer.param_groups:
param_group["lr"] = args.learning_rate * decay
image, label = next(train_queue)
time_data = time.time() - t
image = image.astype("float32")
label = label.astype("int32")
n = image.shape[0]
optimizer.zero_grad()
loss, acc1, acc5 = train_func(image, label)
optimizer.step()
top1.update(100 * acc1.numpy()[0], n)
top5.update(100 * acc5.numpy()[0], n)
objs.update(loss.numpy()[0], n)
total_time.update(time.time() - t)
time_iter = time.time() - t
t = time.time()
if step % args.report_freq == 0 and rank == 0:
logger.info(
"TRAIN Iter %06d: lr = %f,\tloss = %f,\twc_loss = 1,\tTop-1 err = %f,\tTop-5 err = %f,\tdata_time = %f,\ttrain_time = %f,\tremain_hours=%f",
step,
args.learning_rate * decay,
float(objs.__str__().split()[1]),
1 - float(top1.__str__().split()[1]) / 100,
1 - float(top5.__str__().split()[1]) / 100,
time_data,
time_iter - time_data,
time_iter * (args.steps - step) / 3600,
)
objs.reset()
top1.reset()
top5.reset()
total_time.reset()
if step % 10000 == 0 and rank == 0 and step != 0:
logger.info("SAVING %06d", step)
mge.save(
model.state_dict(),
os.path.join(save_dir, "checkpoint-{:06d}.pkl".format(step)),
)
if step % 50000 == 0 and step != 0:
_, valid_acc, valid_acc5 = infer(valid_func, valid_queue, args)
logger.info(
"TEST Iter %06d: loss = %f,\tTop-1 err = %f,\tTop-5 err = %f",
step, _, 1 - valid_acc / 100, 1 - valid_acc5 / 100)
mge.save(model.state_dict(),
os.path.join(save_dir, "checkpoint-{:06d}.pkl".format(step)))
_, valid_acc, valid_acc5 = infer(valid_func, valid_queue, args)
logger.info("TEST Iter %06d: loss=%f,\tTop-1 err = %f,\tTop-5 err = %f",
step, _, 1 - valid_acc / 100, 1 - valid_acc5 / 100)
def worker(rank, world_size, args):
# pylint: disable=too-many-statements
if world_size > 1:
# Initialize distributed process group
logger.info("init distributed process group {} / {}".format(
rank, world_size))
dist.init_process_group(
master_ip="localhost",
master_port=23456,
world_size=world_size,
rank=rank,
dev=rank,
)
model = models.__dict__[args.arch]()
if args.mode != "normal":
Q.quantize_qat(model, Q.ema_fakequant_qconfig)
if args.checkpoint:
logger.info("Load pretrained weights from %s", args.checkpoint)
ckpt = mge.load(args.checkpoint)
ckpt = ckpt["state_dict"] if "state_dict" in ckpt else ckpt
model.load_state_dict(ckpt, strict=False)
if args.mode == "quantized":
Q.quantize(model)
# Define valid graph
@jit.trace(symbolic=True)
def valid_func(image, label):
model.eval()
logits = model(image)
loss = F.cross_entropy_with_softmax(logits, label, label_smooth=0.1)
acc1, acc5 = F.accuracy(logits, label, (1, 5))
if dist.is_distributed(): # all_reduce_mean
loss = dist.all_reduce_sum(loss,
"valid_loss") / dist.get_world_size()
acc1 = dist.all_reduce_sum(acc1,
"valid_acc1") / dist.get_world_size()
acc5 = dist.all_reduce_sum(acc5,
"valid_acc5") / dist.get_world_size()
return loss, acc1, acc5
# Build valid datasets
logger.info("preparing dataset..")
valid_dataset = data.dataset.ImageNet(args.data, train=False)
valid_sampler = data.SequentialSampler(valid_dataset,
batch_size=100,
drop_last=False)
valid_queue = data.DataLoader(
valid_dataset,
sampler=valid_sampler,
transform=T.Compose([
T.Resize(256),
T.CenterCrop(224),
T.Normalize(mean=128),
T.ToMode("CHW"),
]),
num_workers=args.workers,
)
_, valid_acc, valid_acc5 = infer(valid_func, valid_queue, args)
logger.info("TEST %f, %f", valid_acc, valid_acc5)
def worker(world_size, args):
# pylint: disable=too-many-statements
rank = dist.get_rank()
if world_size > 1:
# Initialize distributed process group
logger.info("init distributed process group {} / {}".format(rank, world_size))
save_dir = os.path.join(args.save, args.arch + "." + "calibration")
if not os.path.exists(save_dir):
os.makedirs(save_dir, exist_ok=True)
mge.set_log_file(os.path.join(save_dir, "log.txt"))
model = models.__dict__[args.arch]()
# load calibration model
assert args.checkpoint
logger.info("Load pretrained weights from %s", args.checkpoint)
ckpt = mge.load(args.checkpoint)
ckpt = ckpt["state_dict"] if "state_dict" in ckpt else ckpt
model.load_state_dict(ckpt, strict=False)
# Build valid datasets
valid_dataset = data.dataset.ImageNet(args.data, train=False)
valid_sampler = data.SequentialSampler(
valid_dataset, batch_size=100, drop_last=False
)
valid_queue = data.DataLoader(
valid_dataset,
sampler=valid_sampler,
transform=T.Compose(
[T.Resize(256), T.CenterCrop(224), T.Normalize(mean=128), T.ToMode("CHW")]
),
num_workers=args.workers,
)
# calibration
model.fc.disable_quantize()
model = quantize_qat(model, qconfig=Q.calibration_qconfig)
# calculate scale
def calculate_scale(image, label):
model.eval()
enable_observer(model)
logits = model(image)
loss = F.loss.cross_entropy(logits, label, label_smooth=0.1)
acc1, acc5 = F.topk_accuracy(logits, label, (1, 5))
if dist.is_distributed(): # all_reduce_mean
loss = dist.functional.all_reduce_sum(loss) / dist.get_world_size()
acc1 = dist.functional.all_reduce_sum(acc1) / dist.get_world_size()
acc5 = dist.functional.all_reduce_sum(acc5) / dist.get_world_size()
return loss, acc1, acc5
infer(calculate_scale, valid_queue, args)
# quantized
model = quantize(model)
# eval quantized model
def eval_func(image, label):
model.eval()
logits = model(image)
loss = F.loss.cross_entropy(logits, label, label_smooth=0.1)
acc1, acc5 = F.topk_accuracy(logits, label, (1, 5))
if dist.is_distributed(): # all_reduce_mean
loss = dist.functional.all_reduce_sum(loss) / dist.get_world_size()
acc1 = dist.functional.all_reduce_sum(acc1) / dist.get_world_size()
acc5 = dist.functional.all_reduce_sum(acc5) / dist.get_world_size()
return loss, acc1, acc5
_, valid_acc, valid_acc5 = infer(eval_func, valid_queue, args)
logger.info("TEST %f, %f", valid_acc, valid_acc5)
# save quantized model
mge.save(
{"step": -1, "state_dict": model.state_dict()},
os.path.join(save_dir, "checkpoint-calibration.pkl"),
)
logger.info(
"save in {}".format(os.path.join(save_dir, "checkpoint-calibration.pkl"))
)
def worker(rank, world_size, args):
# pylint: disable=too-many-statements
if world_size > 1:
# Initialize distributed process group
logger.info("init distributed process group {} / {}".format(
rank, world_size))
dist.init_process_group(
master_ip="localhost",
master_port=23456,
world_size=world_size,
rank=rank,
dev=rank,
)
save_dir = os.path.join(args.save, args.arch + "." + args.mode)
if not os.path.exists(save_dir):
os.makedirs(save_dir, exist_ok=True)
mge.set_log_file(os.path.join(save_dir, "log.txt"))
model = models.__dict__[args.arch]()
cfg = config.get_finetune_config(args.arch)
cfg.LEARNING_RATE *= world_size # scale learning rate in distributed training
total_batch_size = cfg.BATCH_SIZE * world_size
steps_per_epoch = 1280000 // total_batch_size
total_steps = steps_per_epoch * cfg.EPOCHS
if args.mode != "normal":
Q.quantize_qat(model, Q.ema_fakequant_qconfig)
if args.checkpoint:
logger.info("Load pretrained weights from %s", args.checkpoint)
ckpt = mge.load(args.checkpoint)
ckpt = ckpt["state_dict"] if "state_dict" in ckpt else ckpt
model.load_state_dict(ckpt, strict=False)
if args.mode == "quantized":
raise ValueError("mode = quantized only used during inference")
Q.quantize(model)
optimizer = optim.SGD(
get_parameters(model, cfg),
lr=cfg.LEARNING_RATE,
momentum=cfg.MOMENTUM,
)
# Define train and valid graph
@jit.trace(symbolic=True)
def train_func(image, label):
model.train()
logits = model(image)
loss = F.cross_entropy_with_softmax(logits, label, label_smooth=0.1)
acc1, acc5 = F.accuracy(logits, label, (1, 5))
optimizer.backward(loss) # compute gradients
if dist.is_distributed(): # all_reduce_mean
loss = dist.all_reduce_sum(loss,
"train_loss") / dist.get_world_size()
acc1 = dist.all_reduce_sum(acc1,
"train_acc1") / dist.get_world_size()
acc5 = dist.all_reduce_sum(acc5,
"train_acc5") / dist.get_world_size()
return loss, acc1, acc5
@jit.trace(symbolic=True)
def valid_func(image, label):
model.eval()
logits = model(image)
loss = F.cross_entropy_with_softmax(logits, label, label_smooth=0.1)
acc1, acc5 = F.accuracy(logits, label, (1, 5))
if dist.is_distributed(): # all_reduce_mean
loss = dist.all_reduce_sum(loss,
"valid_loss") / dist.get_world_size()
acc1 = dist.all_reduce_sum(acc1,
"valid_acc1") / dist.get_world_size()
acc5 = dist.all_reduce_sum(acc5,
"valid_acc5") / dist.get_world_size()
return loss, acc1, acc5
# Build train and valid datasets
logger.info("preparing dataset..")
train_dataset = data.dataset.ImageNet(args.data, train=True)
train_sampler = data.Infinite(
data.RandomSampler(train_dataset,
batch_size=cfg.BATCH_SIZE,
drop_last=True))
train_queue = data.DataLoader(
train_dataset,
sampler=train_sampler,
transform=T.Compose([
T.RandomResizedCrop(224),
T.RandomHorizontalFlip(),
cfg.COLOR_JITTOR,
T.Normalize(mean=128),
T.ToMode("CHW"),
]),
num_workers=args.workers,
)
train_queue = iter(train_queue)
valid_dataset = data.dataset.ImageNet(args.data, train=False)
valid_sampler = data.SequentialSampler(valid_dataset,
batch_size=100,
drop_last=False)
valid_queue = data.DataLoader(
valid_dataset,
sampler=valid_sampler,
transform=T.Compose([
T.Resize(256),
T.CenterCrop(224),
T.Normalize(mean=128),
T.ToMode("CHW"),
]),
num_workers=args.workers,
)
def adjust_learning_rate(step, epoch):
learning_rate = cfg.LEARNING_RATE
if cfg.SCHEDULER == "Linear":
learning_rate *= 1 - float(step) / total_steps
elif cfg.SCHEDULER == "Multistep":
learning_rate *= cfg.SCHEDULER_GAMMA**bisect.bisect_right(
cfg.SCHEDULER_STEPS, epoch)
else:
raise ValueError(cfg.SCHEDULER)
for param_group in optimizer.param_groups:
param_group["lr"] = learning_rate
return learning_rate
# Start training
objs = AverageMeter("Loss")
top1 = AverageMeter("Acc@1")
top5 = AverageMeter("Acc@5")
total_time = AverageMeter("Time")
t = time.time()
for step in range(0, total_steps):
# Linear learning rate decay
epoch = step // steps_per_epoch
learning_rate = adjust_learning_rate(step, epoch)
image, label = next(train_queue)
image = image.astype("float32")
label = label.astype("int32")
n = image.shape[0]
optimizer.zero_grad()
loss, acc1, acc5 = train_func(image, label)
optimizer.step()
top1.update(100 * acc1.numpy()[0], n)
top5.update(100 * acc5.numpy()[0], n)
objs.update(loss.numpy()[0], n)
total_time.update(time.time() - t)
t = time.time()
if step % args.report_freq == 0 and rank == 0:
logger.info("TRAIN e%d %06d %f %s %s %s %s", epoch, step,
learning_rate, objs, top1, top5, total_time)
objs.reset()
top1.reset()
top5.reset()
total_time.reset()
if step % 10000 == 0 and rank == 0:
logger.info("SAVING %06d", step)
mge.save(
{
"step": step,
"state_dict": model.state_dict()
},
os.path.join(save_dir, "checkpoint.pkl"),
)
if step % 10000 == 0 and step != 0:
_, valid_acc, valid_acc5 = infer(valid_func, valid_queue, args)
logger.info("TEST %06d %f, %f", step, valid_acc, valid_acc5)
mge.save({
"step": step,
"state_dict": model.state_dict()
}, os.path.join(save_dir, "checkpoint-final.pkl"))
_, valid_acc, valid_acc5 = infer(valid_func, valid_queue, args)
logger.info("TEST %06d %f, %f", step, valid_acc, valid_acc5)
def worker(rank, world_size, args):
if world_size > 1:
# Initialize distributed process group
logger.info("init distributed process group {} / {}".format(
rank, world_size))
dist.init_process_group(
master_ip="localhost",
master_port=23456,
world_size=world_size,
rank=rank,
dev=rank,
)
save_dir = os.path.join(args.save, args.arch)
model = getattr(M, args.arch)()
optimizer = optim.SGD(
model.parameters(requires_grad=True),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay,
)
scheduler = optim.MultiStepLR(optimizer, [30, 60, 80])
# Define train and valid graph
@jit.trace(symbolic=True)
def train_func(image, label):
model.train()
logits = model(image)
loss = F.cross_entropy_with_softmax(logits, label)
acc1, acc5 = F.accuracy(logits, label, (1, 5))
optimizer.backward(loss) # compute gradients
if dist.is_distributed(): # all_reduce_mean
loss = dist.all_reduce_sum(loss,
"train_loss") / dist.get_world_size()
acc1 = dist.all_reduce_sum(acc1,
"train_acc1") / dist.get_world_size()
acc5 = dist.all_reduce_sum(acc5,
"train_acc5") / dist.get_world_size()
return loss, acc1, acc5
@jit.trace(symbolic=True)
def valid_func(image, label):
model.eval()
logits = model(image)
loss = F.cross_entropy_with_softmax(logits, label)
acc1, acc5 = F.accuracy(logits, label, (1, 5))
if dist.is_distributed(): # all_reduce_mean
loss = dist.all_reduce_sum(loss,
"valid_loss") / dist.get_world_size()
acc1 = dist.all_reduce_sum(acc1,
"valid_acc1") / dist.get_world_size()
acc5 = dist.all_reduce_sum(acc5,
"valid_acc5") / dist.get_world_size()
return loss, acc1, acc5
# Build train and valid datasets
logger.info("preparing dataset..")
train_dataset = data.dataset.ImageNet(args.data, train=True)
train_sampler = data.RandomSampler(train_dataset,
batch_size=args.batch_size,
drop_last=True)
train_queue = data.DataLoader(
train_dataset,
sampler=train_sampler,
transform=T.Compose([ # Baseline Augmentation for small models
T.RandomResizedCrop(224),
T.RandomHorizontalFlip(),
T.Normalize(mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395]), # BGR
T.ToMode("CHW"),
]) if args.arch in ("resnet18", "resnet34") else T.Compose(
[ # Facebook Augmentation for large models
T.RandomResizedCrop(224),
T.RandomHorizontalFlip(),
T.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4),
T.Lighting(0.1),
T.Normalize(mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395]), # BGR
T.ToMode("CHW"),
]),
num_workers=args.workers,
)
valid_dataset = data.dataset.ImageNet(args.data, train=False)
valid_sampler = data.SequentialSampler(valid_dataset,
batch_size=100,
drop_last=False)
valid_queue = data.DataLoader(
valid_dataset,
sampler=valid_sampler,
transform=T.Compose([
T.Resize(256),
T.CenterCrop(224),
T.Normalize(mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395]), # BGR
T.ToMode("CHW"),
]),
num_workers=args.workers,
)
# Start training
top1_acc = 0
for epoch in range(0, args.epochs):
logger.info("Epoch %d LR %.3e", epoch, scheduler.get_lr()[0])
_, train_acc, train_acc5 = train(train_func,
train_queue,
optimizer,
args,
epoch=epoch)
logger.info("Epoch %d Train %.3f / %.3f", epoch, train_acc, train_acc5)
_, valid_acc, valid_acc5 = infer(valid_func,
valid_queue,
args,
epoch=epoch)
logger.info("Epoch %d Valid %.3f / %.3f", epoch, valid_acc, valid_acc5)
scheduler.step()
if rank == 0: # save checkpoint
mge.save(
{
"epoch": epoch + 1,
"state_dict": model.state_dict(),
"accuracy": valid_acc,
},
os.path.join(save_dir, "checkpoint.pkl"),
)
if valid_acc > top1_acc:
top1_acc = valid_acc
shutil.copy(
os.path.join(save_dir, "checkpoint.pkl"),
os.path.join(save_dir, "model_best.pkl"),
)
def worker(rank, world_size, args):
# pylint: disable=too-many-statements
if world_size > 1:
# Initialize distributed process group
logger.info("init distributed process group {} / {}".format(rank, world_size))
dist.init_process_group(
master_ip="localhost",
master_port=23456,
world_size=world_size,
rank=rank,
dev=rank,
)
save_dir = os.path.join(args.save, args.arch + "." + args.mode)
if not os.path.exists(save_dir):
os.makedirs(save_dir, exist_ok=True)
mge.set_log_file(os.path.join(save_dir, "log.txt"))
model = models.__dict__[args.arch]()
cfg = config.get_finetune_config(args.arch)
cfg.LEARNING_RATE *= world_size # scale learning rate in distributed training
total_batch_size = cfg.BATCH_SIZE * world_size
steps_per_epoch = 1280000 // total_batch_size
total_steps = steps_per_epoch * cfg.EPOCHS
# load calibration model
assert args.checkpoint
logger.info("Load pretrained weights from %s", args.checkpoint)
ckpt = mge.load(args.checkpoint)
ckpt = ckpt["state_dict"] if "state_dict" in ckpt else ckpt
model.load_state_dict(ckpt, strict=False)
# Build valid datasets
valid_dataset = data.dataset.ImageNet(args.data, train=False)
# valid_dataset = ImageNetNoriDataset(args.data)
valid_sampler = data.SequentialSampler(
valid_dataset, batch_size=100, drop_last=False
)
valid_queue = data.DataLoader(
valid_dataset,
sampler=valid_sampler,
transform=T.Compose(
[
T.Resize(256),
T.CenterCrop(224),
T.Normalize(mean=128),
T.ToMode("CHW"),
]
),
num_workers=args.workers,
)
# calibration
model.fc.disable_quantize()
model = quantize_qat(model, qconfig=Q.calibration_qconfig)
# calculate scale
@jit.trace(symbolic=True)
def calculate_scale(image, label):
model.eval()
enable_observer(model)
logits = model(image)
loss = F.cross_entropy_with_softmax(logits, label, label_smooth=0.1)
acc1, acc5 = F.accuracy(logits, label, (1, 5))
if dist.is_distributed(): # all_reduce_mean
loss = dist.all_reduce_sum(loss, "valid_loss") / dist.get_world_size()
acc1 = dist.all_reduce_sum(acc1, "valid_acc1") / dist.get_world_size()
acc5 = dist.all_reduce_sum(acc5, "valid_acc5") / dist.get_world_size()
return loss, acc1, acc5
# model.fc.disable_quantize()
infer(calculate_scale, valid_queue, args)
# quantized
model = quantize(model)
# eval quantized model
@jit.trace(symbolic=True)
def eval_func(image, label):
model.eval()
logits = model(image)
loss = F.cross_entropy_with_softmax(logits, label, label_smooth=0.1)
acc1, acc5 = F.accuracy(logits, label, (1, 5))
if dist.is_distributed(): # all_reduce_mean
loss = dist.all_reduce_sum(loss, "valid_loss") / dist.get_world_size()
acc1 = dist.all_reduce_sum(acc1, "valid_acc1") / dist.get_world_size()
acc5 = dist.all_reduce_sum(acc5, "valid_acc5") / dist.get_world_size()
return loss, acc1, acc5
_, valid_acc, valid_acc5 = infer(eval_func, valid_queue, args)
logger.info("TEST %f, %f", valid_acc, valid_acc5)
# save quantized model
mge.save(
{"step": -1, "state_dict": model.state_dict()},
os.path.join(save_dir, "checkpoint-calibration.pkl")
)
logger.info("save in {}".format(os.path.join(save_dir, "checkpoint-calibration.pkl")))
def worker(world_size, args):
# pylint: disable=too-many-statements
rank = dist.get_rank()
if world_size > 1:
logger.info("init distributed process group {} / {}".format(
rank, world_size))
save_dir = os.path.join(args.save, args.arch + "." + args.mode)
if not os.path.exists(save_dir):
os.makedirs(save_dir, exist_ok=True)
mge.set_log_file(os.path.join(save_dir, "log.txt"))
model = models.__dict__[args.arch]()
cfg = config.get_config(args.arch)
cfg.LEARNING_RATE *= world_size # scale learning rate in distributed training
total_batch_size = cfg.BATCH_SIZE * world_size
steps_per_epoch = 1280000 // total_batch_size
total_steps = steps_per_epoch * cfg.EPOCHS
if args.mode != "normal":
quantize_qat(model, qconfig=Q.ema_fakequant_qconfig)
if world_size > 1:
# Sync parameters
dist.bcast_list_(model.parameters(), dist.WORLD)
# Autodiff gradient manager
gm = autodiff.GradManager().attach(
model.parameters(),
callbacks=dist.make_allreduce_cb("MEAN") if world_size > 1 else None,
)
optimizer = optim.SGD(
get_parameters(model, cfg),
lr=cfg.LEARNING_RATE,
momentum=cfg.MOMENTUM,
)
# Define train and valid graph
def train_func(image, label):
with gm:
model.train()
logits = model(image)
loss = F.loss.cross_entropy(logits, label, label_smooth=0.1)
acc1, acc5 = F.topk_accuracy(logits, label, (1, 5))
gm.backward(loss)
optimizer.step().clear_grad()
return loss, acc1, acc5
def valid_func(image, label):
model.eval()
logits = model(image)
loss = F.loss.cross_entropy(logits, label, label_smooth=0.1)
acc1, acc5 = F.topk_accuracy(logits, label, (1, 5))
return loss, acc1, acc5
# Build train and valid datasets
logger.info("preparing dataset..")
train_dataset = data.dataset.ImageNet(args.data, train=True)
train_sampler = data.Infinite(
data.RandomSampler(train_dataset,
batch_size=cfg.BATCH_SIZE,
drop_last=True))
train_queue = data.DataLoader(
train_dataset,
sampler=train_sampler,
transform=T.Compose([
T.RandomResizedCrop(224),
T.RandomHorizontalFlip(),
cfg.COLOR_JITTOR,
T.Normalize(mean=128),
T.ToMode("CHW"),
]),
num_workers=args.workers,
)
train_queue = iter(train_queue)
valid_dataset = data.dataset.ImageNet(args.data, train=False)
valid_sampler = data.SequentialSampler(valid_dataset,
batch_size=100,
drop_last=False)
valid_queue = data.DataLoader(
valid_dataset,
sampler=valid_sampler,
transform=T.Compose([
T.Resize(256),
T.CenterCrop(224),
T.Normalize(mean=128),
T.ToMode("CHW")
]),
num_workers=args.workers,
)
def adjust_learning_rate(step, epoch):
learning_rate = cfg.LEARNING_RATE
if cfg.SCHEDULER == "Linear":
learning_rate *= 1 - float(step) / total_steps
elif cfg.SCHEDULER == "Multistep":
learning_rate *= cfg.SCHEDULER_GAMMA**bisect.bisect_right(
cfg.SCHEDULER_STEPS, epoch)
else:
raise ValueError(cfg.SCHEDULER)
for param_group in optimizer.param_groups:
param_group["lr"] = learning_rate
return learning_rate
# Start training
objs = AverageMeter("Loss")
top1 = AverageMeter("Acc@1")
top5 = AverageMeter("Acc@5")
total_time = AverageMeter("Time")
t = time.time()
for step in range(0, total_steps):
# Linear learning rate decay
epoch = step // steps_per_epoch
learning_rate = adjust_learning_rate(step, epoch)
image, label = next(train_queue)
image = mge.tensor(image, dtype="float32")
label = mge.tensor(label, dtype="int32")
n = image.shape[0]
loss, acc1, acc5 = train_func(image, label)
top1.update(100 * acc1.numpy()[0], n)
top5.update(100 * acc5.numpy()[0], n)
objs.update(loss.numpy()[0], n)
total_time.update(time.time() - t)
t = time.time()
if step % args.report_freq == 0 and rank == 0:
logger.info(
"TRAIN e%d %06d %f %s %s %s %s",
epoch,
step,
learning_rate,
objs,
top1,
top5,
total_time,
)
objs.reset()
top1.reset()
top5.reset()
total_time.reset()
if step != 0 and step % 10000 == 0 and rank == 0:
logger.info("SAVING %06d", step)
mge.save(
{
"step": step,
"state_dict": model.state_dict()
},
os.path.join(save_dir, "checkpoint.pkl"),
)
if step % 10000 == 0 and step != 0:
_, valid_acc, valid_acc5 = infer(valid_func, valid_queue, args)
logger.info("TEST %06d %f, %f", step, valid_acc, valid_acc5)
mge.save(
{
"step": step,
"state_dict": model.state_dict()
},
os.path.join(save_dir, "checkpoint-final.pkl"),
)
_, valid_acc, valid_acc5 = infer(valid_func, valid_queue, args)
logger.info("TEST %06d %f, %f", step, valid_acc, valid_acc5)
