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Reshape the bbox tensor so that the last dimension has size 4. #144

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Reshape the bbox tensor so that the last dimension has size 4. #144

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34 changes: 14 additions & 20 deletions src/table_datasets.py
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Original file line number Diff line number Diff line change
Expand Up @@ -71,12 +71,12 @@ def crop_around_bbox_coco(image, crop_bbox, max_margin, target):
cropped_labels.append(label)

if len(cropped_bboxes) > 0:
target["boxes"] = torch.as_tensor(cropped_bboxes, dtype=torch.float32)
target["boxes"] = torch.as_tensor(cropped_bboxes, dtype=torch.float32).reshape(-1, 4)
target["labels"] = torch.as_tensor(cropped_labels, dtype=torch.int64)
w, h = img.size
target["size"] = torch.tensor([w, h])
return cropped_image, target

return image, target


Expand Down Expand Up @@ -162,7 +162,7 @@ def __call__(self, image, target):
cropped_labels.append(label)

if len(cropped_bboxes) > 0:
target["boxes"] = torch.as_tensor(cropped_bboxes, dtype=torch.float32)
target["boxes"] = torch.as_tensor(cropped_bboxes, dtype=torch.float32).reshape(-1, 4)
target["labels"] = torch.as_tensor(cropped_labels, dtype=torch.int64)
return cropped_image, target

Expand Down Expand Up @@ -202,9 +202,9 @@ def __call__(self, image, target):
if bbox[0] < bbox[2] - 1 and bbox[1] < bbox[3] - 1:
resized_bboxes.append(bbox)
resized_labels.append(label)

if len(resized_bboxes) > 0:
target["boxes"] = torch.as_tensor(resized_bboxes, dtype=torch.float32)
target["boxes"] = torch.as_tensor(resized_bboxes, dtype=torch.float32).reshape(-1, 4)
target["labels"] = torch.as_tensor(resized_labels, dtype=torch.int64)
return resized_image, target

Expand Down Expand Up @@ -290,9 +290,9 @@ def __call__(self, image, target):
if bbox[0] < bbox[2] and bbox[1] < bbox[3]:
cropped_bboxes.append(bbox)
cropped_labels.append(label)

if len(cropped_bboxes) > 0:
target["boxes"] = torch.as_tensor(cropped_bboxes, dtype=torch.float32)
target["boxes"] = torch.as_tensor(cropped_bboxes, dtype=torch.float32).reshape(-1, 4)
target["labels"] = torch.as_tensor(cropped_labels, dtype=torch.int64)
return cropped_image, target

Expand Down Expand Up @@ -324,9 +324,9 @@ def __call__(self, image, target):
if bbox[0] < bbox[2] and bbox[1] < bbox[3]:
cropped_bboxes.append(bbox)
cropped_labels.append(label)

if len(cropped_bboxes) > 0:
target["boxes"] = torch.as_tensor(cropped_bboxes, dtype=torch.float32)
target["boxes"] = torch.as_tensor(cropped_bboxes, dtype=torch.float32).reshape(-1, 4)
target["labels"] = torch.as_tensor(cropped_labels, dtype=torch.int64)
return cropped_image, target

Expand Down Expand Up @@ -413,7 +413,7 @@ def __call__(self, image, target):
bbox = [scale*elem for elem in bbox]
resized_bboxes.append(bbox)

target["boxes"] = torch.as_tensor(resized_bboxes, dtype=torch.float32)
target["boxes"] = torch.as_tensor(resized_bboxes, dtype=torch.float32).reshape(-1, 4)

return resized_image, target

Expand All @@ -433,7 +433,7 @@ def __call__(self, image, target):
bbox = [scale*elem for elem in bbox]
resized_bboxes.append(bbox)

target["boxes"] = torch.as_tensor(resized_bboxes, dtype=torch.float32)
target["boxes"] = torch.as_tensor(resized_bboxes, dtype=torch.float32).reshape(-1, 4)

return resized_image, target

Expand Down Expand Up @@ -619,22 +619,16 @@ def __getitem__(self, idx):
img = Image.open(img_path).convert("RGB")
w, h = img.size

if self.types[idx] == 1:
if self.types[idx] == 1:
bboxes, labels = read_pascal_voc(annot_path, class_map=self.class_map)

# Reduce class set
keep_indices = [idx for idx, label in enumerate(labels) if label in self.class_set]
bboxes = [bboxes[idx] for idx in keep_indices]
labels = [labels[idx] for idx in keep_indices]

# Convert to Torch Tensor
if len(labels) > 0:
bboxes = torch.as_tensor(bboxes, dtype=torch.float32)
labels = torch.as_tensor(labels, dtype=torch.int64)
else:
# Not clear if it's necessary to force the shape of bboxes to be (0, 4)
bboxes = torch.empty((0, 4), dtype=torch.float32)
labels = torch.empty((0,), dtype=torch.int64)
bboxes = torch.as_tensor(bboxes, dtype=torch.float32).reshape(-1, 4)
labels = torch.as_tensor(labels, dtype=torch.int64)
else:
bboxes = torch.empty((0, 4), dtype=torch.float32)
labels = torch.empty((0,), dtype=torch.int64)
Expand Down