def get_dataloader(net, train_dataset, val_dataset, train_transform,
val_transform, batch_size, num_shards, args):
"""Get dataloader."""
train_bfn = FasterRCNNTrainBatchify(net, num_shards)
#train_sampler = gcv.nn.sampler.SplitSampler(train_dataset.get_im_aspect_ratio(),
# batch_size,
# num_parts=hvd.size() if args.horovod else 1,
# part_index=hvd.rank() if args.horovod else 0,
# shuffle=True)
train_sampler = gcv.nn.sampler.SplitSampler(
batch_size,
num_parts=hvd.size() if args.horovod else 1,
part_index=hvd.rank() if args.horovod else 0)
train_loader = mx.gluon.data.DataLoader(train_dataset.transform(
train_transform(net.short,
net.max_size,
net,
ashape=net.ashape,
multi_stage=args.use_fpn)),
batch_sampler=train_sampler,
batchify_fn=train_bfn,
num_workers=args.num_workers)
val_bfn = Tuple(*[Append() for _ in range(3)])
short = net.short[-1] if isinstance(net.short,
(tuple, list)) else net.short
# validation use 1 sample per device
val_loader = mx.gluon.data.DataLoader(val_dataset.transform(
val_transform(short, net.max_size)),
num_shards,
False,
batchify_fn=val_bfn,
last_batch='keep',
num_workers=args.num_workers)
return train_loader, val_loader
def get_faster_rcnn_dataloader(net, train_dataset, val_dataset, train_transform, val_transform,
batch_size, num_shards, args):
"""Get faster rcnn dataloader."""
if (not args.final_fit) and (not val_dataset):
train_dataset, val_dataset = _train_val_split(train_dataset, args.split_ratio)
train_bfn = FasterRCNNTrainBatchify(net, num_shards)
if hasattr(train_dataset, 'get_im_aspect_ratio'):
im_aspect_ratio = train_dataset.get_im_aspect_ratio()
else:
im_aspect_ratio = [1.] * len(train_dataset)
train_sampler = \
gcv.nn.sampler.SplitSortedBucketSampler(im_aspect_ratio, batch_size, num_parts=1,
part_index=0, shuffle=True)
train_loader = gluon.data.DataLoader(train_dataset.transform(
train_transform(net.short, net.max_size, net, ashape=net.ashape, multi_stage=True)),
batch_sampler=train_sampler, batchify_fn=train_bfn, num_workers=args.num_workers)
val_bfn = Tuple(*[Append() for _ in range(3)])
short = net.short[-1] if isinstance(net.short, (tuple, list)) else net.short
# validation use 1 sample per device
val_loader = None
if val_dataset:
val_loader = gluon.data.DataLoader(
val_dataset.transform(val_transform(short, net.max_size)), num_shards, False,
batchify_fn=val_bfn, last_batch='keep', num_workers=args.num_workers)
args.num_samples = len(train_dataset)
return train_loader, val_loader
def _get_dataloader(net, test_dataset, data_shape, batch_size,
num_workers, num_devices, args):
"""Get dataloader."""
if args.meta_arch == 'yolo3':
width, height = data_shape, data_shape
val_batchify_fn = Tuple(Stack(), Pad(pad_val=-1))
test_loader = gluon.data.DataLoader(
test_dataset.transform(
YOLO3DefaultValTransform(width, height)),
batch_size,
False,
batchify_fn=val_batchify_fn,
last_batch='keep',
num_workers=num_workers)
return test_loader
elif args.meta_arch == 'faster_rcnn':
"""Get faster rcnn dataloader."""
test_bfn = Tuple(*[Append() for _ in range(3)])
short = net.short[-1] if isinstance(net.short,
(tuple,
list)) else net.short
# validation use 1 sample per device
test_loader = gluon.data.DataLoader(
test_dataset.transform(
FasterRCNNDefaultValTransform(short, net.max_size)),
num_devices,
False,
batchify_fn=test_bfn,
last_batch='keep',
num_workers=args.num_workers)
return test_loader
else:
raise NotImplementedError('%s not implemented.' %
args.meta_arch)
def get_dataloader(net, train_dataset, val_dataset, train_transform,
val_transform, batch_size, num_shards, args):
"""Get dataloader."""
val_bfn = Tuple(*[Append() for _ in range(3)])
short = net.short[-1] if isinstance(net.short,
(tuple, list)) else net.short
val_loader = mx.gluon.data.DataLoader(val_dataset.transform(
val_transform(short, net.max_size)),
num_shards,
False,
batchify_fn=val_bfn,
last_batch='keep',
num_workers=args.num_workers)
return None, val_loader
def get_dataloader(net, train_dataset, val_dataset, train_transform,
val_transform, batch_size, num_shards, args):
"""Get dataloader."""
train_bfn = FasterRCNNTrainBatchify(net, num_shards)
if hasattr(train_dataset, 'get_im_aspect_ratio'):
im_aspect_ratio = train_dataset.get_im_aspect_ratio()
else:
im_aspect_ratio = [1.] * len(train_dataset)
train_sampler = \
gcv.nn.sampler.SplitSortedBucketSampler(im_aspect_ratio, batch_size,
num_parts=hvd.size() if args.horovod else 1,
part_index=hvd.rank() if args.horovod else 0,
shuffle=True)
# dataset: train_dataset.transform(train_transform(net.short, net.max_size, net, ashape=net.ashape, multi_stage=args.use_fpn))
# ashape: anchor 预先定义的大小
# multi_stage + ashape : 计算anchor
train_loader = mx.gluon.data.DataLoader(train_dataset.transform(
train_transform(net.short,
net.max_size,
net,
ashape=net.ashape,
multi_stage=args.use_fpn)),
batch_sampler=train_sampler,
batchify_fn=train_bfn,
num_workers=args.num_workers)
val_bfn = Tuple(*[Append() for _ in range(3)])
short = net.short[-1] if isinstance(net.short,
(tuple, list)) else net.short
# validation use 1 sample per device
# dataset: val_dataset.transform(val_transform(short, net.max_size))
# 每个item返回 img, bbox.astype('float32'), mx.nd.array([im_scale])
# bbox: x1, y1, x2, y2, class_id
# img最短边<= short，最长边<=net.max_size
# Tuple 不是python中的元组tuple
# Append(): 每个样本自成ndarray，所有样本数据的大小不必相同，返回的batch是列表
# val_bfn 有3个Append()，每个Append()处理dataset item的一个属性
val_loader = mx.gluon.data.DataLoader(val_dataset.transform(
val_transform(short, net.max_size)),
num_shards,
False,
batchify_fn=val_bfn,
last_batch='keep',
num_workers=args.num_workers)
return train_loader, val_loader
def get_dataloader(net, train_dataset, val_dataset, train_transform,
val_transform, batch_size, num_shards, args):
"""Get dataloader."""
train_bfn = FasterRCNNTrainBatchify(net, num_shards)
if hasattr(train_dataset, 'get_im_aspect_ratio'):
im_aspect_ratio = train_dataset.get_im_aspect_ratio()
else:
im_aspect_ratio = [1.] * len(train_dataset)
if args.horovod:
num_parts = hvd.size()
part_index = hvd.rank()
elif "perseus" in args.kv_store:
num_parts = kv.num_workers
part_index = kv.rank
else:
num_parts = 1
part_index = 0
train_sampler = \
gcv.nn.sampler.SplitSortedBucketSampler(im_aspect_ratio, batch_size,
num_parts=num_parts,
part_index=part_index,
shuffle=True)
train_loader = mx.gluon.data.DataLoader(train_dataset.transform(
train_transform(net.short,
net.max_size,
net,
ashape=net.ashape,
multi_stage=args.use_fpn)),
batch_sampler=train_sampler,
batchify_fn=train_bfn,
num_workers=args.num_workers)
val_bfn = Tuple(*[Append() for _ in range(3)])
short = net.short[-1] if isinstance(net.short,
(tuple, list)) else net.short
# validation use 1 sample per device
val_loader = mx.gluon.data.DataLoader(val_dataset.transform(
val_transform(short, net.max_size)),
num_shards,
False,
batchify_fn=val_bfn,
last_batch='keep',
num_workers=args.num_workers)
return train_loader, val_loader
# (mxnet uses BCHW format) before they are fed into neural networks.
#
# A handy DataLoader would be very convenient for us to apply different transforms and aggregate data into mini-batches.
#
# Because Faster-RCNN handles raw images with various aspect ratios and various shapes, we provide a
# :py:class:`gluoncv.data.batchify.Append`, which neither stack or pad images, but instead return lists.
# In such way, image tensors and labels returned have their own shapes, unaware of the rest in the same batch.
from gluoncv.data.batchify import Tuple, Append
from mxnet.gluon.data import DataLoader
batch_size = 2 # for tutorial, we use smaller batch-size
num_workers = 0 # you can make it larger(if your CPU has more cores) to accelerate data loading
# behavior of batchify_fn: stack images, and pad labels
batchify_fn = Tuple(Append(), Append())
train_loader = DataLoader(train_dataset.transform(train_transform),
batch_size,
shuffle=True,
batchify_fn=batchify_fn,
last_batch='rollover',
num_workers=num_workers)
val_loader = DataLoader(val_dataset.transform(val_transform),
batch_size,
shuffle=False,
batchify_fn=batchify_fn,
last_batch='keep',
num_workers=num_workers)
for ib, batch in enumerate(train_loader):
if ib > 3:
self._imglist = [file for file in glob.iglob(imgPath)]
self._csvPath = [file for file in glob.iglob(csvPath)]
self._len = len(self._imglist)
def __getitem__(self, idx):
img = image.imread(self._imglist[idx])
label = pd.read_csv(self._csvPath[idx])
return (img, label)
def __len__(self):
return self._len
if __name__ == "__main__":
batch_size = 2
batchify_fn = Tuple(Append(), Append())
train_dataset = DensemapDataset()
im = train_dataset[0]
def train_transform(*trans_data):
img = trans_data[0]
aug = gdata.vision.transforms.RandomFlipLeftRight()
return (aug(img), trans_data[1])
train_loader = DataLoader(train_dataset.transform(train_transform),
batch_size=2,
shuffle=True,
batchify_fn=batchify_fn)
for ib, batch in enumerate(train_loader):
# batch[0] 是 X ， batch[1] 是 y
# batch[0][0] 是第0个X
class_names=train_dataset.classes,
ax=ax)
plt.show()
##########################################################
# Data Loader
# -----------
# Data loader is identical to Faster R-CNN with the difference of mask input and output.
from gluoncv.data.batchify import Tuple, Append, MaskRCNNTrainBatchify
from mxnet.gluon.data import DataLoader
batch_size = 2 # for tutorial, we use smaller batch-size
num_workers = 0 # you can make it larger(if your CPU has more cores) to accelerate data loading
train_bfn = Tuple(*[Append() for _ in range(3)])
train_loader = DataLoader(train_dataset.transform(train_transform),
batch_size,
shuffle=True,
batchify_fn=train_bfn,
last_batch='rollover',
num_workers=num_workers)
val_bfn = Tuple(*[Append() for _ in range(2)])
val_loader = DataLoader(val_dataset.transform(val_transform),
batch_size,
shuffle=False,
batchify_fn=val_bfn,
last_batch='keep',
num_workers=num_workers)
for ib, batch in enumerate(train_loader):
def get_dataloader(net, train_dataset, val_dataset, train_transform, val_transform, batch_size,
num_shards, args):
"""Get dataloader."""
train_bfn = FasterRCNNTrainBatchify(net, num_shards)
train_sampler = \
gcv.nn.sampler.SplitSortedBucketSampler(train_dataset.get_im_aspect_ratio(),
batch_size,
num_parts=hvd.size() if args.horovod else 1,
part_index=hvd.rank() if args.horovod else 0,
shuffle=True)
train_loader = mx.gluon.data.DataLoader(train_dataset.transform(
train_transform(net.short, net.max_size, net, ashape=net.ashape, multi_stage=args.use_fpn)),
batch_sampler=train_sampler, batchify_fn=train_bfn, num_workers=args.num_workers)
val_bfn = Tuple(*[Append() for _ in range(3)])
short = net.short[-1] if isinstance(net.short, (tuple, list)) else net.short
# validation use 1 sample per device
val_loader = mx.gluon.data.DataLoader(
val_dataset.transform(val_transform(short, net.max_size)), num_shards, False,
batchify_fn=val_bfn, last_batch='keep', num_workers=args.num_workers)
return train_loader, val_loader
def save_params(net, logger, best_map, current_map, epoch, save_interval, prefix):
current_map = float(current_map)
if current_map > best_map[0]:
logger.info('[Epoch {}] mAP {} higher than current best {} saving to {}'.format(
epoch, current_map, best_map, '{:s}_best.params'.format(prefix)))
best_map[0] = current_map
net.save_parameters('{:s}_best.params'.format(prefix))
class_names=train_dataset.classes,
ax=ax)
plt.show()
##########################################################
# Data Loader
# -----------
# Data loader is identical to Faster R-CNN with the difference of mask input and output.
from gluoncv.data.batchify import Tuple, Append
from mxnet.gluon.data import DataLoader
batch_size = 2 # for tutorial, we use smaller batch-size
num_workers = 0 # you can make it larger(if your CPU has more cores) to accelerate data loading
train_bfn = Tuple(*[Append() for _ in range(3)])
train_loader = DataLoader(train_dataset.transform(train_transform),
batch_size,
shuffle=True,
batchify_fn=train_bfn,
last_batch='rollover',
num_workers=num_workers)
val_bfn = Tuple(*[Append() for _ in range(2)])
val_loader = DataLoader(val_dataset.transform(val_transform),
batch_size,
shuffle=False,
batchify_fn=val_bfn,
last_batch='keep',
num_workers=num_workers)
for ib, batch in enumerate(train_loader):
def get_voc_iterator(rank, num_workers, net, num_shards):
data_dir = "data-%d" % rank
try:
s3_client = boto3.client('s3')
for file in [
'VOCtrainval_06-Nov-2007.tar', 'VOCtest_06-Nov-2007.tar',
'VOCtrainval_11-May-2012.tar'
]:
s3_client.download_file(args.s3bucket, f'voc_tars/{file}',
f'/opt/ml/code/{file}')
with tarfile.open(filename) as tar:
tar.extractall(path=path)
except:
print('downloading from source')
download_voc(data_dir)
input_shape = (1, 256, 256, 3)
batch_size = args.batch_size
# might want to replace with mx.io.ImageDetRecordIter, this means you need data in RecordIO format
# train_iter = mx.io.MNISTIter(
# image="%s/train-images-idx3-ubyte" % data_dir,
# label="%s/train-labels-idx1-ubyte" % data_dir,
# input_shape=input_shape,
# batch_size=batch_size,
# shuffle=True,
# flat=False,
# num_parts=hvd.size(),
# part_index=hvd.rank()
# )
train_dataset = gdata.VOCDetection(
root=f'/opt/ml/code/data-{rank}/VOCdevkit/',
splits=[(2007, 'trainval'), (2012, 'trainval')])
val_dataset = gdata.VOCDetection(
root=f'/opt/ml/code/data-{rank}/VOCdevkit/',
splits=[(2007, 'test')])
val_metric = VOC07MApMetric(iou_thresh=0.5,
class_names=val_dataset.classes)
im_aspect_ratio = [1.] * len(train_dataset)
train_bfn = FasterRCNNTrainBatchify(net)
train_sampler = gluoncv.nn.sampler.SplitSortedBucketSampler(
im_aspect_ratio,
batch_size,
num_parts=hvd.size() if args.horovod else 1,
part_index=hvd.rank() if args.horovod else 0,
shuffle=True)
# had issue with multi_stage=True
train_iter = mx.gluon.data.DataLoader(train_dataset.transform(
FasterRCNNDefaultTrainTransform(net.short,
net.max_size,
net,
ashape=net.ashape,
multi_stage=False)),
batch_sampler=train_sampler,
batchify_fn=train_bfn,
num_workers=num_workers)
val_bfn = Tuple(*[Append() for _ in range(3)])
short = net.short[-1] if isinstance(net.short,
(tuple, list)) else net.short
# validation use 1 sample per device
val_iter = mx.gluon.data.DataLoader(val_dataset.transform(
FasterRCNNDefaultValTransform(short, net.max_size)),
num_shards,
False,
batchify_fn=val_bfn,
last_batch='keep',
num_workers=num_workers)
return train_iter, val_iter
