def get_loader(dataset, cfg, mode='train'):
assert mode in ('train', 'eval')
if mode == 'train':
sampler = RandomSampler(dataset,
batch_size=cfg.data.samples_per_gpu,
drop_last=True,
seed=0)
loader = DataLoader(dataset,
sampler,
num_workers=cfg.data.workers_per_gpu)
else:
samples_per_gpu = cfg.data.get('eval_samples_per_gpu',
cfg.data.samples_per_gpu)
workers_per_gpu = cfg.data.get('eval_workers_per_gpu',
cfg.data.workers_per_gpu)
if cfg.evaluation.multi_process is True:
sampler = SequentialSampler(dataset,
batch_size=samples_per_gpu,
drop_last=False)
else:
sampler = SequentialSampler(dataset,
batch_size=samples_per_gpu,
drop_last=False,
world_size=1,
rank=0)
loader = DataLoader(dataset, sampler, num_workers=workers_per_gpu)
return loader
def build_dataloader(rank, world_size, data_root, ann_file):
val_dataset = COCOJoints(data_root,
ann_file,
image_set="val2017",
order=("image", "boxes", "info"))
val_sampler = SequentialSampler(val_dataset,
1,
world_size=world_size,
rank=rank)
val_dataloader = DataLoader(
val_dataset,
sampler=val_sampler,
num_workers=4,
transform=T.Compose(
transforms=[
T.Normalize(mean=cfg.img_mean, std=cfg.img_std),
ExtendBoxes(
cfg.test_x_ext,
cfg.test_y_ext,
cfg.input_shape[1] / cfg.input_shape[0],
random_extend_prob=0,
),
RandomBoxAffine(
degrees=(0, 0),
scale=(1, 1),
output_shape=cfg.input_shape,
rotate_prob=0,
scale_prob=0,
),
T.ToMode(),
],
order=("image", "boxes", "info"),
),
)
return val_dataloader
def get_loader(dataset, cfg):
samples_per_gpu = cfg.data.test_samples_per_gpu
workers_per_gpu = cfg.data.test_workers_per_gpu
sampler = SequentialSampler(dataset,
batch_size=samples_per_gpu,
drop_last=False)
loader = DataLoader(dataset, sampler, num_workers=workers_per_gpu)
return loader
def build_dataloader(rank, world_size, data_dir):
val_dataset = COCODataset(
os.path.join(data_dir, "val2017"),
os.path.join(data_dir, "annotations/instances_val2017.json"),
order=["image", "info"],
)
val_sampler = SequentialSampler(val_dataset, 1, world_size=world_size, rank=rank)
val_dataloader = DataLoader(val_dataset, sampler=val_sampler, num_workers=2)
return val_dataloader
def build_dataset(root=Path('/home/zqh/data/omniglot-py'),
nway=5,
kshot=1,
kquery=1,
batch_size=32):
train_ds = OmniglotDataset(root, nway, kshot, kquery, mode='train')
train_smp = SequentialSampler(train_ds,
drop_last=True,
batch_size=batch_size)
train_loader = DataLoader(train_ds, sampler=train_smp, num_workers=4)
val_ds = OmniglotDataset(root, nway, kshot, kquery, mode='val')
val_smp = SequentialSampler(train_ds,
drop_last=True,
batch_size=batch_size)
val_loader = DataLoader(val_ds, sampler=val_smp, num_workers=4)
return train_loader, val_loader
def build_dataloader(rank, world_size, data_dir, cfg):
val_dataset = data_mapper[cfg.test_dataset["name"]](
os.path.join(data_dir, cfg.test_dataset["name"],
cfg.test_dataset["root"]),
os.path.join(data_dir, cfg.test_dataset["name"],
cfg.test_dataset["ann_file"]),
order=["image", "info"],
)
val_sampler = SequentialSampler(val_dataset,
1,
world_size=world_size,
rank=rank)
val_dataloader = DataLoader(val_dataset,
sampler=val_sampler,
num_workers=2)
return val_dataloader
from megengine.data.transform import RandomResizedCrop, Normalize, ToMode, Pad, Compose
import megengine.optimizer as optim
mge.set_log_file('log.txt')
logger = mge.get_logger(__name__)
#logdir = "logs/scalars/" + datetime.now().strftime("%Y%m%d-%H%M%S")
# dataset
root_dir = '/data/.cache/dataset/MNIST'
mnist_train_dataset = MNIST(root=root_dir, train=True, download=False)
mnist_test_dataset = MNIST(root=root_dir, train=False, download=False)
random_sampler = RandomSampler(dataset=mnist_train_dataset, batch_size=256)
sequential_sampler = SequentialSampler(dataset=mnist_test_dataset,
batch_size=256)
mnist_train_dataloader = DataLoader(
dataset=mnist_train_dataset,
sampler=random_sampler,
transform=Compose([
RandomResizedCrop(output_size=28),
# mean 和 std 分别是 MNIST 数据的均值和标准差，图片数值范围是 0~255
#Normalize(mean=0.1307*255, std=0.3081*255),
#Pad(2),
# 'CHW'表示把图片由 (height, width, channel) 格式转换成 (channel, height, width) 格式
#ToMode('CHW'),
]))
mnist_test_dataloader = DataLoader(
dataset=mnist_test_dataset,
sampler=sequential_sampler,
print(len(train_dataset), len(train_label))
print(len(test_dataset), len(test_label))
from typing import Tuple
from megengine.data.dataset import Dataset
class BostonTrainDataset(Dataset):
def __init__(self):
self.data = train_dataset
self.label = train_label
def __getitem__(self, index: int) -> Tuple:
return self.data[index], self.label[index]
def __len__(self) -> int:
return len(self.data)
boston_train_dataset = BostonTrainDataset()
print(len(boston_train_dataset))
from megengine.data import DataLoader
from megengine.data import SequentialSampler
sequential_sampler = SequentialSampler(dataset=boston_train_dataset, batch_size=100)
train_dataloader = DataLoader(dataset=boston_train_dataset, sampler=sequential_sampler)
for batch_data, batch_label in train_dataloader:
print(batch_data.shape, batch_label.shape, len(train_dataloader))
break
# 静态图比动态图快了很多
trace.enabled = True
if __name__ == '__main__':
np.random.seed(39)
train_dataset = XORDataset(30000)
test_dataset = XORDataset(10000)
# 这里为什么要传两次train_dataset
train_sampler = RandomSampler(dataset=train_dataset,
batch_size=32,
drop_last=True)
train_loader = DataLoader(train_dataset, sampler=train_sampler)
test_sampler = SequentialSampler(dataset=test_dataset,
batch_size=32,
drop_last=False)
test_loader = DataLoader(test_dataset, sampler=test_sampler)
# draw_dataset(train_dataset)
model = build_model()
optimizer = optim.SGD(
model.parameters(),
lr=0.01,
)
data = mge.tensor()
label = mge.tensor(dtype='int32')
total_epochs = 10
