def infer_per_pixel_scores_single_image(model,
raw_input,
out_shape,
apply_softmax=True):
"""
Performs inference with PyTorch model and resize predictions to a given size.
Args:
model: PyTorch model inherited from torch.Module class.
raw_input: PyTorch Tensor
out_shape: Output size (height, width).
apply_softmax: Whether to apply softmax function after inference or not.
Returns:
Inference resulting numpy array resized to a given size.
"""
model_input = torch.stack([raw_input], 0) # add dim #0 (batch size 1)
model_input = cuda_variable(model_input, volatile=True)
output = model(model_input)
if apply_softmax:
output = torch_functional.softmax(output, dim=1)
output = output.data.cpu().numpy()[0] # from batch to 3d
pred = np.transpose(output, (1, 2, 0))
return sly_image.resize(pred, out_shape)
def scale(img: np.ndarray, ann: Annotation, frow: float = None, fcol: float = None, f: float = None) \
-> (np.ndarray, Annotation):
"""
Resize the input image array and annotation to the given size.
Args:
img: Input image array.
ann: Input annotation.
frow: Desired height scale height value
frow: Desired width scale width value
f: Desired height and width scale values in one
Returns:
A tuple containing resized image array and annotation.
"""
_validate_image_annotation_shape(img, ann)
new_size = sly_image.restore_proportional_size(in_size=ann.img_size, frow=frow, fcol=fcol, f=f)
res_img = sly_image.resize(img, new_size)
res_ann = ann.resize(new_size)
return res_img, res_ann
def resize(img: np.ndarray, ann: Annotation, size: tuple) -> (np.ndarray, Annotation):
"""
Resize the input image array and annotation to the given size.
Args:
img: Input image array.
ann: Input annotation.
size: Desired size (height, width) in pixels or -1. If one of values is -1 and "keep": true then for
specific width height will be automatically computed to keep aspect ratio.
Returns:
A tuple containing resized image array and annotation.
"""
_validate_image_annotation_shape(img, ann)
height = take_with_default(size[0], -1) # For backward capability
width = take_with_default(size[1], -1)
size = (height, width)
new_size = sly_image.restore_proportional_size(in_size=ann.img_size, out_size=size)
res_img = sly_image.resize(img, new_size)
res_ann = ann.resize(new_size)
return res_img, res_ann
def _get_sample_impl(self, img_fpath, ann_fpath):
img = sly_image.read(img_fpath)
ann = self.load_annotation(ann_fpath)
gt = self.make_gt(img.shape, ann)
img = sly_image.resize(img, self._out_size)
return img, gt
