def get_transform(train):
transforms = []
transforms.append(T.ToTensor())
if train:
# Augmentations go here... Won't do any for now
pass
# transforms.append(T.RandomHorizontalFlip(0.5))
return T.Compose(transforms)
def get_transform(train):
transforms = []
transforms.append(T.ToTensor())
if train:
transforms.append(T.RandomHorizontalFlip(0.5))
# 注意：这里将`.ToTensor()`放到`.RandomHorizontalFlip()`前
# 会导致报错 `TypeError: img should be PIL Image. Got <class `torch.Tensor`>`
return T.Compose(transforms)
def get_transform(train):
transforms = []
# converts the image, a PIL image, into a PyTorch Tensor
transforms.append(T.ToTensor())
if train:
# during training, randomly flip the training images
# and ground-truth for data augmentation
transforms.append(T.RandomHorizontalFlip(0.5))
return T.Compose(transforms)
boxes = np.zeros([mask.shape[-1], 4], dtype=np.int32)
for i in range(mask.shape[-1]):
m = mask[:, :, i]
# Bounding box.
horizontal_indicies = np.where(np.any(m, axis=0))[0]
vertical_indicies = np.where(np.any(m, axis=1))[0]
if horizontal_indicies.shape[0]:
x1, x2 = horizontal_indicies[[0, -1]]
y1, y2 = vertical_indicies[[0, -1]]
# x2 and y2 should not be part of the box. Increment by 1.
x2 += 1
y2 += 1
else:
# No mask for this instance. Might happen due to
# resizing or cropping. Set bbox to zeros
x1, x2, y1, y2 = 0, 0, 0, 0
# boxes[i] = np.array([y1, x1, y2, x2])
boxes[i] = np.array([x1, y1, x2, y2])
return boxes.astype(np.int32, copy=False)
if __name__ == "__main__":
dataset = PennFudanDataset(root='./data-ins/train/',
transforms=T.Compose([T.ToTensor()]))
loader = DataLoader(dataset, batch_size=16, shuffle=False, num_workers=0)
for i, image, label in enumerate(loader):
print(image.shape)
def get_transform(train):
transforms = []
transforms.append(T.ToTensor())
if train:
transforms.append(T.RandomHorizontalFlip(0.5))
return T.Compose(transforms)
def get_transform(train):
transforms = []
transforms.append(T.ToTensor())
# transforms.append(T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)))
return T.Compose(transforms)