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ultralytics 8.0.188 fix .grad attribute leaf Tensor Warning (#5094)

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Glenn Jocher 2023-09-26 20:28:45 +02:00 • committed by GitHub
parent f2ed207571
commit 19c3314e68
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11 changed files with 78 additions and 41 deletions
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38
ultralytics/models/fastsam/prompt.py
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@ -87,7 +87,7 @@ class FastSAMPrompt:
pbar = TQDM(annotations, total=len(annotations))
for ann in pbar:
result_name = os.path.basename(ann.path)
image = ann.orig_img
image = ann.orig_img[..., ::-1] # BGR to RGB
original_h, original_w = ann.orig_shape
# for macOS only
# plt.switch_backend('TkAgg')
@ -108,17 +108,15 @@ class FastSAMPrompt:
mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8))
masks[i] = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8))
self.fast_show_mask(
masks,
plt.gca(),
random_color=mask_random_color,
bbox=bbox,
points=points,
pointlabel=point_label,
retinamask=retina,
target_height=original_h,
target_width=original_w,
)
self.fast_show_mask(masks,
plt.gca(),
random_color=mask_random_color,
bbox=bbox,
points=points,
pointlabel=point_label,
retinamask=retina,
target_height=original_h,
target_width=original_w)
if with_contours:
contour_all = []
@ -134,17 +132,11 @@ class FastSAMPrompt:
contour_mask = temp / 255 * color.reshape(1, 1, -1)
plt.imshow(contour_mask)
plt.axis('off')
fig = plt.gcf()
# Check if the canvas has been drawn
if fig.canvas.get_renderer() is None: # macOS requires this or tests fail
fig.canvas.draw()
# Save the figure
save_path = Path(output) / result_name
save_path.parent.mkdir(exist_ok=True, parents=True)
image = Image.frombytes('RGB', fig.canvas.get_width_height(), fig.canvas.tostring_rgb())
image.save(save_path)
plt.axis('off')
plt.savefig(save_path, bbox_inches='tight', pad_inches=0, transparent=True)
plt.close()
pbar.set_description(f'Saving {result_name} to {save_path}')
@ -263,8 +255,8 @@ class FastSAMPrompt:
orig_masks_area = torch.sum(masks, dim=(1, 2))
union = bbox_area + orig_masks_area - masks_area
IoUs = masks_area / union
max_iou_index = torch.argmax(IoUs)
iou = masks_area / union
max_iou_index = torch.argmax(iou)
self.results[0].masks.data = torch.tensor(np.array([masks[max_iou_index].cpu().numpy()]))
return self.results