mirrors
/
yolov7
mirror of https://github.com/WongKinYiu/yolov7.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

utils/loss.py minor bug fix (#1344) 

* utils/loss.py L.NO 742

* Changed np.int to np.int32 due to deprecation of np.int
pull/1229/head^2
Rohan Patankar 2023-01-02 01:59:01 +05:30 committed by GitHub
parent 48052c42c4
commit 2fdc7f1439
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 4 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
utils/datasets.py
View File

@ -1200,7 +1200,7 @@ def pastein(image, labels, sample_labels, sample_images, sample_masks):
r_image = cv2.resize(sample_images[sel_ind], (r_w, r_h)) r_image = cv2.resize(sample_images[sel_ind], (r_w, r_h))
temp_crop = image[ymin:ymin+r_h, xmin:xmin+r_w] temp_crop = image[ymin:ymin+r_h, xmin:xmin+r_w]
m_ind = r_mask > 0 m_ind = r_mask > 0
if m_ind.astype(np.int).sum() > 60: if m_ind.astype(np.int32).sum() > 60:
temp_crop[m_ind] = r_image[m_ind] temp_crop[m_ind] = r_image[m_ind]
#print(sample_labels[sel_ind]) #print(sample_labels[sel_ind])
#print(sample_images[sel_ind].shape) #print(sample_images[sel_ind].shape)

4
utils/general.py
View File

@ -219,7 +219,7 @@ def labels_to_class_weights(labels, nc=80):
return torch.Tensor() return torch.Tensor()
labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO
classes = labels[:, 0].astype(np.int) # labels = [class xywh] classes = labels[:, 0].astype(np.int32) # labels = [class xywh]
weights = np.bincount(classes, minlength=nc) # occurrences per class weights = np.bincount(classes, minlength=nc) # occurrences per class
# Prepend gridpoint count (for uCE training) # Prepend gridpoint count (for uCE training)
@ -234,7 +234,7 @@ def labels_to_class_weights(labels, nc=80):
def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)): def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
# Produces image weights based on class_weights and image contents # Produces image weights based on class_weights and image contents
class_counts = np.array([np.bincount(x[:, 0].astype(np.int), minlength=nc) for x in labels]) class_counts = np.array([np.bincount(x[:, 0].astype(np.int32), minlength=nc) for x in labels])
image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1) image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1)
# index = random.choices(range(n), weights=image_weights, k=1) # weight image sample # index = random.choices(range(n), weights=image_weights, k=1) # weight image sample
return image_weights return image_weights

2
utils/loss.py
View File

@ -739,7 +739,7 @@ class ComputeLossOTA:
+ 3.0 * pair_wise_iou_loss + 3.0 * pair_wise_iou_loss
) )
matching_matrix = torch.zeros_like(cost, device="cpu") matching_matrix = torch.zeros_like(cost, device=device)
for gt_idx in range(num_gt): for gt_idx in range(num_gt):
_, pos_idx = torch.topk( _, pos_idx = torch.topk(