def __getitem__(self, index):
img = ImageHelper.read_image(
self.item_list[index][0],
tool=self.configer.get('data', 'image_tool'),
mode=self.configer.get('data', 'input_mode'))
ori_img_size = ImageHelper.get_size(img)
if self.aug_transform is not None:
img = self.aug_transform(img)
border_hw = ImageHelper.get_size(img)[::-1]
if self.img_transform is not None:
img = self.img_transform(img)
meta = dict(ori_img_size=ori_img_size,
border_hw=border_hw,
img_path=self.item_list[index][0],
filename=self.item_list[index][1])
return dict(img=DataContainer(img,
stack=True,
return_dc=True,
samples_per_gpu=True),
meta=DataContainer(meta,
stack=False,
cpu_only=True,
return_dc=True,
samples_per_gpu=True))
def todc(data_list,
samples_per_gpu=True,
stack=False,
cpu_only=False,
device_ids=None,
concat=False):
if not samples_per_gpu:
if not stack:
return DataContainer(data_list,
stack=stack,
samples_per_gpu=samples_per_gpu,
cpu_only=cpu_only)
else:
return DataContainer(torch.stack(data_list, 0),
stack=stack,
samples_per_gpu=samples_per_gpu,
cpu_only=cpu_only)
device_ids = list(range(
torch.cuda.device_count())) if device_ids is None else device_ids
samples = (len(data_list) - 1 + len(device_ids)) // len(device_ids)
stacked = []
for i in range(0, len(data_list), samples):
if not stack and not concat:
stacked.append(data_list[i:i + samples])
elif stack:
stacked.append(torch.stack(data_list[i:i + samples], 0))
else:
stacked.append(torch.cat(data_list[i:i + samples], 0))
return DataContainer(stacked,
stack=stack,
samples_per_gpu=samples_per_gpu,
cpu_only=cpu_only)
def __getitem__(self, index):
imgA = ImageHelper.read_image(
self.imgA_list[index],
tool=self.configer.get('data', 'image_tool'),
mode=self.configer.get('data', 'input_mode'))
indexB = random.randint(0,
len(self.imgB_list) - 1) % len(self.imgB_list)
imgB = ImageHelper.read_image(
self.imgB_list[indexB],
tool=self.configer.get('data', 'image_tool'),
mode=self.configer.get('data', 'input_mode'))
if self.aug_transform is not None:
imgA = self.aug_transform(imgA)
imgB = self.aug_transform(imgB)
if self.img_transform is not None:
imgA = self.img_transform(imgA)
imgB = self.img_transform(imgB)
return dict(imgA=DataContainer(imgA, stack=True),
imgB=DataContainer(imgB, stack=True),
labelA=DataContainer(self.labelA_list[index], stack=True),
labelB=DataContainer(self.labelB_list[indexB], stack=True))
def stack(batch, data_key=None, device_ids=None):
if isinstance(batch[0][data_key], DataContainer):
if batch[0][data_key].stack:
assert isinstance(batch[0][data_key].data, torch.Tensor) or \
isinstance(batch[0][data_key].data, int_classes) or \
isinstance(batch[0][data_key].data, float) or \
isinstance(batch[0][data_key].data, string_classes) or \
isinstance(batch[0][data_key].data, collections.Mapping) or \
isinstance(batch[0][data_key].data, collections.Sequence)
stacked = []
if batch[0][data_key].samples_per_gpu and len(device_ids) > 1:
samples_per_gpu = (len(batch) - 1 +
len(device_ids)) // len(device_ids)
for i in range(0, len(batch), samples_per_gpu):
stacked.append(
default_collate([
sample[data_key].data
for sample in batch[i:i + samples_per_gpu]
]))
else:
stacked = default_collate(
[sample[data_key].data for sample in batch])
if batch[0][data_key].return_dc and len(device_ids) > 1:
return DataContainer(
stacked,
stack=batch[0][data_key].stack,
samples_per_gpu=batch[0][data_key].samples_per_gpu,
cpu_only=batch[0][data_key].cpu_only)
else:
return stacked
else:
stacked = []
if batch[0][data_key].samples_per_gpu and len(device_ids) > 1:
samples_per_gpu = (len(batch) - 1 +
len(device_ids)) // len(device_ids)
for i in range(0, len(batch), samples_per_gpu):
stacked.append([
sample[data_key].data
for sample in batch[i:i + samples_per_gpu]
])
else:
stacked = [sample[data_key].data for sample in batch]
if batch[0][data_key].return_dc and len(device_ids) > 1:
return DataContainer(
stacked,
stack=batch[0][data_key].stack,
samples_per_gpu=batch[0][data_key].samples_per_gpu,
cpu_only=batch[0][data_key].cpu_only)
else:
return stacked
else:
return default_collate([sample[data_key] for sample in batch])
def __getitem__(self, index):
img = ImageHelper.read_image(
self.img_list[index],
tool=self.configer.get('data', 'image_tool'),
mode=self.configer.get('data', 'input_mode'))
label = self.label_list[index]
if self.aug_transform is not None:
img = self.aug_transform(img)
if self.img_transform is not None:
img = self.img_transform(img)
return dict(
img=DataContainer(img, stack=True),
label=DataContainer(label, stack=True),
)
def __getitem__(self, index):
img = ImageHelper.read_image(self.img_list[index],
tool=self.configer.get('data', 'image_tool'),
mode=self.configer.get('data', 'input_mode'))
labels, bboxes, polygons = self.__read_json_file(self.json_list[index])
if self.aug_transform is not None:
img, bboxes, labels, polygons = self.aug_transform(img, bboxes=bboxes,
labels=labels, polygons=polygons)
if self.img_transform is not None:
img = self.img_transform(img)
return dict(
img=DataContainer(img, stack=True),
bboxes=DataContainer(bboxes, stack=False),
labels=DataContainer(labels, stack=False),
polygons=DataContainer(polygons, stack=False, cpu_only=True)
)
def __getitem__(self, index):
img = ImageHelper.read_image(self.img_list[index],
tool=self.configer.get('data', 'image_tool'),
mode=self.configer.get('data', 'input_mode'))
if os.path.exists(self.mask_list[index]):
maskmap = ImageHelper.read_image(self.mask_list[index],
tool=self.configer.get('data', 'image_tool'), mode='P')
else:
maskmap = np.ones((img.size[1], img.size[0]), dtype=np.uint8)
if self.configer.get('data', 'image_tool') == 'pil':
maskmap = ImageHelper.to_img(maskmap)
kpts, bboxes = self.__read_json_file(self.json_list[index])
if self.aug_transform is not None and len(bboxes) > 0:
img, maskmap, kpts, bboxes = self.aug_transform(img, maskmap=maskmap, kpts=kpts, bboxes=bboxes)
elif self.aug_transform is not None:
img, maskmap, kpts = self.aug_transform(img, maskmap=maskmap, kpts=kpts)
width, height = ImageHelper.get_size(maskmap)
maskmap = ImageHelper.resize(maskmap,
(width // self.configer.get('network', 'stride'),
height // self.configer.get('network', 'stride')),
interpolation='nearest')
maskmap = torch.from_numpy(np.array(maskmap, dtype=np.float32))
maskmap = maskmap.unsqueeze(0)
heatmap = self.heatmap_generator(kpts, [width, height], maskmap)
vecmap = self.paf_generator(kpts, [width, height], maskmap)
if self.img_transform is not None:
img = self.img_transform(img)
meta = dict(
kpts=kpts,
)
return dict(
img=DataContainer(img, stack=True),
heatmap=DataContainer(heatmap, stack=True),
maskmap=DataContainer(maskmap, stack=True),
vecmap=DataContainer(vecmap, stack=True),
meta=DataContainer(meta, stack=False, cpu_only=True),
)
def __getitem__(self, index):
img = ImageHelper.read_image(
self.img_list[index],
tool=self.configer.get('data', 'image_tool'),
mode=self.configer.get('data', 'input_mode'))
img_size = ImageHelper.get_size(img)
bboxes, labels = self.__read_json_file(self.json_list[index])
ori_bboxes, ori_labels = bboxes.copy(), labels.copy()
if self.aug_transform is not None:
img, bboxes, labels = self.aug_transform(img,
bboxes=bboxes,
labels=labels)
labels = torch.from_numpy(labels).long()
bboxes = torch.from_numpy(bboxes).float()
meta = dict(ori_img_size=img_size,
border_wh=ImageHelper.get_size(img),
ori_bboxes=torch.from_numpy(ori_bboxes).float(),
ori_labels=torch.from_numpy(ori_labels).long())
if self.img_transform is not None:
img = self.img_transform(img)
return dict(img=DataContainer(img,
stack=True,
return_dc=True,
samples_per_gpu=True),
bboxes=DataContainer(bboxes,
stack=False,
return_dc=True,
samples_per_gpu=True),
labels=DataContainer(labels,
stack=False,
return_dc=True,
samples_per_gpu=True),
meta=DataContainer(meta,
stack=False,
cpu_only=True,
return_dc=True,
samples_per_gpu=True))
def __getitem__(self, index):
img = ImageHelper.read_image(
self.img_list[index],
tool=self.configer.get('data', 'image_tool'),
mode=self.configer.get('data', 'input_mode'))
kpts, bboxes = self.__read_json_file(self.json_list[index])
if self.aug_transform is not None:
img, kpts, bboxes = self.aug_transform(img,
kpts=kpts,
bboxes=bboxes)
heatmap = self.heatmap_generator(kpts, ImageHelper.get_size(img))
if self.img_transform is not None:
img = self.img_transform(img)
return dict(
img=DataContainer(img, stack=True),
heatmap=DataContainer(heatmap, stack=True),
)
def __getitem__(self, index):
imgA = ImageHelper.read_image(
self.imgA_list[index],
tool=self.configer.get('data', 'image_tool'),
mode=self.configer.get('data', 'input_mode'))
imgB = ImageHelper.read_image(
self.imgB_list[index],
tool=self.configer.get('data', 'image_tool'),
mode=self.configer.get('data', 'input_mode'))
if self.aug_transform is not None:
imgA, imgB = self.aug_transform([imgA, imgB])
if self.img_transform is not None:
imgA = self.img_transform(imgA)
imgB = self.img_transform(imgB)
return dict(
imgA=DataContainer(imgA, stack=True),
imgB=DataContainer(imgB, stack=True),
)
def __getitem__(self, index):
img = ImageHelper.read_image(
self.img_list[index],
tool=self.configer.get('data', 'image_tool'),
mode=self.configer.get('data', 'input_mode'))
img_size = ImageHelper.get_size(img)
labelmap = ImageHelper.read_image(self.label_list[index],
tool=self.configer.get(
'data', 'image_tool'),
mode='P')
if self.configer.get('data.label_list', default=None):
labelmap = self._encode_label(labelmap)
if self.configer.get('data.reduce_zero_label', default=None):
labelmap = self._reduce_zero_label(labelmap)
ori_target = ImageHelper.to_np(labelmap)
if self.aug_transform is not None:
img, labelmap = self.aug_transform(img, labelmap=labelmap)
border_size = ImageHelper.get_size(img)
if self.img_transform is not None:
img = self.img_transform(img)
if self.label_transform is not None:
labelmap = self.label_transform(labelmap)
meta = dict(ori_img_wh=img_size,
border_wh=border_size,
ori_target=ori_target)
return dict(
img=DataContainer(img, stack=True),
labelmap=DataContainer(labelmap, stack=True),
meta=DataContainer(meta, stack=False, cpu_only=True),
)
