本文整理汇总了Python中preprocess.get_transform方法的典型用法代码示例。如果您正苦于以下问题：Python preprocess.get_transform方法的具体用法？Python preprocess.get_transform怎么用？Python preprocess.get_transform使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类preprocess的用法示例。
在下文中一共展示了preprocess.get_transform方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: get_loader
# 需要导入模块: import preprocess [as 别名]
# 或者: from preprocess import get_transform [as 别名]
def get_loader(self, force_update=False, override_settings=None, subset_indices=None):
if force_update or self.regime.update(self.epoch, self.steps):
setting = self.get_setting()
if override_settings is not None:
setting.update(override_settings)
self._transform = get_transform(**setting['transform'])
setting['data'].setdefault('transform', self._transform)
self._data = get_dataset(**setting['data'])
if subset_indices is not None:
self._data = Subset(self._data, subset_indices)
if setting['other'].get('distributed', False):
setting['loader']['sampler'] = DistributedSampler(self._data)
setting['loader']['shuffle'] = None
# pin-memory currently broken for distributed
setting['loader']['pin_memory'] = False
self._sampler = setting['loader'].get('sampler', None)
self._loader = torch.utils.data.DataLoader(
self._data, **setting['loader'])
return self._loader 示例2: __getitem__
# 需要导入模块: import preprocess [as 别名]
# 或者: from preprocess import get_transform [as 别名]
def __getitem__(self, index):
left = self.left[index]
normal = self.normal[index]
gt = self.gts[index]
left_img = self.loader(left)
w,h = left_img.size
input1,mask1 = self.inloader(gt)
sparse,mask = self.sloader(normal)
th, tw = 256, 512
x1 = random.randint(0, w - tw)
y1 = random.randint(0, h - th)
left_img = left_img.crop((x1, y1, x1 + tw, y1 + th))
data_in1 = input1[y1:y1 + th, x1:x1 + tw,:]
sparse_n = sparse[y1:y1 + th, x1:x1 + tw,:]
mask = mask[y1:y1 + th, x1:x1 + tw,:]
mask1 = mask1[y1:y1 + th, x1:x1 + tw, :]
processed = preprocess.get_transform(augment=False)
# processed = scale_crop2()
left_img = processed(left_img)
sparse_n = processed(sparse_n)
return left_img,sparse_n,mask,mask1,data_in1 示例3: __getitem__
# 需要导入模块: import preprocess [as 别名]
# 或者: from preprocess import get_transform [as 别名]
def __getitem__(self, index):
up = self.up[index]
down = self.down[index]
disp_name= self.disp_name[index]
equi_info = self.equi_infos
up_img = self.loader(up)
down_img = self.loader(down)
disp = self.dploader(disp_name)
up_img = np.concatenate([np.array(up_img), equi_info],2)
down_img = np.concatenate([np.array(down_img), equi_info],2)
if self.training:
h, w = up_img.shape[0], up_img.shape[1]
th, tw = 512, 256
# vertical remaining cropping
x1 = random.randint(0, w - tw)
y1 = random.randint(0, h - th)
up_img = up_img[y1:y1+th, x1:x1+tw, :]
down_img = down_img[y1:y1+th, x1:x1+tw, :]
disp = np.ascontiguousarray(disp,dtype=np.float32)
disp = disp[y1:y1 + th, x1:x1 + tw]
# preprocessing
processed = preprocess.get_transform(augment=False)
up_img = processed(up_img)
down_img = processed(down_img)
return up_img, down_img, disp
else:
disp = np.ascontiguousarray(disp,dtype=np.float32)
processed = preprocess.get_transform(augment=False)
up_img = processed(up_img)
down_img = processed(down_img)
return up_img, down_img, disp 示例4: __getitem__
# 需要导入模块: import preprocess [as 别名]
# 或者: from preprocess import get_transform [as 别名]
def __getitem__(self, index):
left = self.left[index]
input = self.input[index]
sparse = self.sparse[index]
left_img = self.loader(left)
index_str = self.left[index].split('/')[-4][0:10]
params_t = INSTICS[index_str]
params = np.ones((256,512,3),dtype=np.float32)
params[:, :, 0] = params[:,:,0] * params_t[0]
params[:, :, 1] = params[:, :, 1] * params_t[1]
params[:, :, 2] = params[:, :, 2] * params_t[2]
h,w,c= left_img.shape
input1 = self.inloader(input)
sparse,mask = self.sloader(sparse)
th, tw = 256,512
x1 = random.randint(0, w - tw)
y1 = random.randint(0, h - th)
mask = np.reshape(mask, [sparse.shape[0], sparse.shape[1], 1]).astype(np.float32)
params = np.reshape(params, [256, 512, 3]).astype(np.float32)
left_img = left_img[y1:y1 + th, x1:x1 + tw, :]
data_in1 = input1[y1:y1 + th, x1:x1 + tw,:]
sparse = sparse[y1:y1 + th, x1:x1 + tw, :]
mask = mask[y1:y1 + th, x1:x1 + tw,:]
processed = preprocess.get_transform(augment=False)
left_img = processed(left_img)
sparse = processed(sparse)
mask = processed(mask)
return left_img,data_in1,sparse,mask,params 示例5: __getitem__
# 需要导入模块: import preprocess [as 别名]
# 或者: from preprocess import get_transform [as 别名]
def __getitem__(self, index):
left = self.left[index]
right = self.right[index]
disp_L= self.disp_L[index]
left_img = self.loader(left)
right_img = self.loader(right)
dataL = self.dploader(disp_L)
if self.training:
w, h = left_img.size
th, tw = 256, 512
x1 = random.randint(0, w - tw)
y1 = random.randint(0, h - th)
left_img = left_img.crop((x1, y1, x1 + tw, y1 + th))
right_img = right_img.crop((x1, y1, x1 + tw, y1 + th))
dataL = np.ascontiguousarray(dataL,dtype=np.float32)/256
dataL = dataL[y1:y1 + th, x1:x1 + tw]
processed = preprocess.get_transform(augment=False)
left_img = processed(left_img)
right_img = processed(right_img)
return left_img, right_img, dataL
else:
w, h = left_img.size
left_img = left_img.crop((w-1232, h-368, w, h))
right_img = right_img.crop((w-1232, h-368, w, h))
w1, h1 = left_img.size
dataL = dataL.crop((w-1232, h-368, w, h))
dataL = np.ascontiguousarray(dataL,dtype=np.float32)/256
processed = preprocess.get_transform(augment=False)
left_img = processed(left_img)
right_img = processed(right_img)
return left_img, right_img, dataL