| import torch.nn.functional as F |
|
|
|
|
| def compute_tensor_iu(seg, gt): |
| intersection = (seg & gt).float().sum() |
| union = (seg | gt).float().sum() |
|
|
| return intersection, union |
|
|
| def compute_tensor_iou(seg, gt): |
| intersection, union = compute_tensor_iu(seg, gt) |
| iou = (intersection + 1e-6) / (union + 1e-6) |
| |
| return iou |
|
|
| |
| def pad_divide_by(in_img, d): |
| h, w = in_img.shape[-2:] |
|
|
| if h % d > 0: |
| new_h = h + d - h % d |
| else: |
| new_h = h |
| if w % d > 0: |
| new_w = w + d - w % d |
| else: |
| new_w = w |
| lh, uh = int((new_h-h) / 2), int(new_h-h) - int((new_h-h) / 2) |
| lw, uw = int((new_w-w) / 2), int(new_w-w) - int((new_w-w) / 2) |
| pad_array = (int(lw), int(uw), int(lh), int(uh)) |
| out = F.pad(in_img, pad_array) |
| return out, pad_array |
|
|
| def unpad(img, pad): |
| if len(img.shape) == 4: |
| if pad[2]+pad[3] > 0: |
| img = img[:,:,pad[2]:-pad[3],:] |
| if pad[0]+pad[1] > 0: |
| img = img[:,:,:,pad[0]:-pad[1]] |
| elif len(img.shape) == 3: |
| if pad[2]+pad[3] > 0: |
| img = img[:,pad[2]:-pad[3],:] |
| if pad[0]+pad[1] > 0: |
| img = img[:,:,pad[0]:-pad[1]] |
| else: |
| raise NotImplementedError |
| return img |
