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Add check for target_sizes is None in post_process_image_guided_detection for owlv2 #31934

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merged 3 commits into from
Jul 26, 2024
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Add check for target_sizes is None in post_process_image_guided_detection for owlv2 #31934

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e8c6043
Add check for target_sizes is None in post_process_image_guided_detec…
catalys1 Jul 12, 2024
26af6fc
Make sure Owlvit and Owlv2 in sync
catalys1 Jul 15, 2024
66e3f06
Fix incorrect indentation; add check for correct size of target_sizes
catalys1 Jul 22, 2024
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15 changes: 10 additions & 5 deletions src/transformers/models/owlv2/image_processing_owlv2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -565,9 +565,9 @@ def post_process_image_guided_detection(self, outputs, threshold=0.0, nms_thresh
"""
logits, target_boxes = outputs.logits, outputs.target_pred_boxes

if len(logits) != len(target_sizes):
if target_sizes is not None and len(logits) != len(target_sizes):
raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
if target_sizes.shape[1] != 2:
if target_sizes is not None and target_sizes.shape[1] != 2:
raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")

probs = torch.max(logits, dim=-1)
Expand All @@ -588,9 +588,14 @@ def post_process_image_guided_detection(self, outputs, threshold=0.0, nms_thresh
scores[idx][ious > nms_threshold] = 0.0

# Convert from relative [0, 1] to absolute [0, height] coordinates
img_h, img_w = target_sizes.unbind(1)
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(target_boxes.device)
target_boxes = target_boxes * scale_fct[:, None, :]
if target_sizes is not None:
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if isinstance(target_sizes, List):
img_h = torch.tensor([i[0] for i in target_sizes])
img_w = torch.tensor([i[1] for i in target_sizes])
else:
img_h, img_w = target_sizes.unbind(1)
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(target_boxes.device)
target_boxes = target_boxes * scale_fct[:, None, :]

# Compute box display alphas based on prediction scores
results = []
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15 changes: 10 additions & 5 deletions src/transformers/models/owlvit/image_processing_owlvit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -556,9 +556,9 @@ def post_process_image_guided_detection(self, outputs, threshold=0.0, nms_thresh
"""
logits, target_boxes = outputs.logits, outputs.target_pred_boxes

if len(logits) != len(target_sizes):
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if target_sizes is not None and len(logits) != len(target_sizes):
raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
if target_sizes.shape[1] != 2:
if target_sizes is not None and target_sizes.shape[1] != 2:
raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")

probs = torch.max(logits, dim=-1)
Expand All @@ -579,9 +579,14 @@ def post_process_image_guided_detection(self, outputs, threshold=0.0, nms_thresh
scores[idx][ious > nms_threshold] = 0.0

# Convert from relative [0, 1] to absolute [0, height] coordinates
img_h, img_w = target_sizes.unbind(1)
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(target_boxes.device)
target_boxes = target_boxes * scale_fct[:, None, :]
if target_sizes is not None:
if isinstance(target_sizes, List):
img_h = torch.tensor([i[0] for i in target_sizes])
img_w = torch.tensor([i[1] for i in target_sizes])
else:
img_h, img_w = target_sizes.unbind(1)
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(target_boxes.device)
target_boxes = target_boxes * scale_fct[:, None, :]

# Compute box display alphas based on prediction scores
results = []
Expand Down