Commit ⋅ pytorch/vision

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torchvision/models/detection/retinanet.py changed.

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test/test_models_detection_negative_samples.py has changed.

@@ -107,21 +107,16 @@

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            # determine only the foreground
            foreground_idxs_per_image = matched_idxs_per_image >= 0
            num_foreground = foreground_idxs_per_image.sum()
            # no matched_idxs means there were no annotations in this image
            # TODO: enable support for images without annotations that works on distributed
            if False:  # matched_idxs_per_image.numel() == 0:
                gt_classes_target = torch.zeros_like(cls_logits_per_image)
                valid_idxs_per_image = torch.arange(cls_logits_per_image.shape[0])
            else:
                # create the target classification
                gt_classes_target = torch.zeros_like(cls_logits_per_image)
                gt_classes_target[
                    foreground_idxs_per_image,
                    targets_per_image['labels'][matched_idxs_per_image[foreground_idxs_per_image]]
                ] = 1.0

                # find indices for which anchors should be ignored
                valid_idxs_per_image = matched_idxs_per_image != self.BETWEEN_THRESHOLDS

            # create the target classification
            gt_classes_target = torch.zeros_like(cls_logits_per_image)
            gt_classes_target[
                foreground_idxs_per_image,
                targets_per_image['labels'][matched_idxs_per_image[foreground_idxs_per_image]]
            ] = 1.0

            # find indices for which anchors should be ignored
            valid_idxs_per_image = matched_idxs_per_image != self.BETWEEN_THRESHOLDS

            # compute the classification loss
            losses.append(sigmoid_focal_loss(

@@ -191,23 +186,12 @@

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        for targets_per_image, bbox_regression_per_image, anchors_per_image, matched_idxs_per_image in \
                zip(targets, bbox_regression, anchors, matched_idxs):
            # no matched_idxs means there were no annotations in this image
            # TODO enable support for images without annotations with distributed support
            # if matched_idxs_per_image.numel() == 0:
            #     continue

            # get the targets corresponding GT for each proposal
            # NB: need to clamp the indices because we can have a single
            # GT in the image, and matched_idxs can be -2, which goes
            # out of bounds
            matched_gt_boxes_per_image = targets_per_image['boxes'][matched_idxs_per_image.clamp(min=0)]

            # determine only the foreground indices, ignore the rest
            foreground_idxs_per_image = matched_idxs_per_image >= 0
            num_foreground = foreground_idxs_per_image.sum()
            foreground_idxs_per_image = torch.where(matched_idxs_per_image >= 0)[0]
            num_foreground = foreground_idxs_per_image.numel()

            # select only the foreground boxes
            matched_gt_boxes_per_image = matched_gt_boxes_per_image[foreground_idxs_per_image, :]
            matched_gt_boxes_per_image = targets_per_image['boxes'][matched_idxs_per_image[foreground_idxs_per_image]]
            bbox_regression_per_image = bbox_regression_per_image[foreground_idxs_per_image, :]
            anchors_per_image = anchors_per_image[foreground_idxs_per_image, :]


@@ -403,7 +387,7 @@

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        matched_idxs = []
        for anchors_per_image, targets_per_image in zip(anchors, targets):
            if targets_per_image['boxes'].numel() == 0:
                matched_idxs.append(torch.empty((0,), dtype=torch.int32))
                matched_idxs.append(torch.full((anchors_per_image.size(0),), -1, dtype=torch.int64))
                continue

            match_quality_matrix = box_ops.box_iou(targets_per_image['boxes'], anchors_per_image)

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torchvision	8,899	6,483	445	1,971	72.85%
Project Totals (99 files)	8,899	6,483	445	1,971	72.85%

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107	107		# determine only the foreground
108	108		foreground_idxs_per_image = matched_idxs_per_image >= 0
109	109		num_foreground = foreground_idxs_per_image.sum()
110		-	# no matched_idxs means there were no annotations in this image
111		-	# TODO: enable support for images without annotations that works on distributed
112		-	if False: # matched_idxs_per_image.numel() == 0:
113		-	gt_classes_target = torch.zeros_like(cls_logits_per_image)
114		-	valid_idxs_per_image = torch.arange(cls_logits_per_image.shape[0])
115		-	else:
116		-	# create the target classification
117		-	gt_classes_target = torch.zeros_like(cls_logits_per_image)
118		-	gt_classes_target[
119		-	foreground_idxs_per_image,
120		-	targets_per_image['labels'][matched_idxs_per_image[foreground_idxs_per_image]]
121		-	] = 1.0
122		-
123		-	# find indices for which anchors should be ignored
124		-	valid_idxs_per_image = matched_idxs_per_image != self.BETWEEN_THRESHOLDS
	110	+
	111	+	# create the target classification
	112	+	gt_classes_target = torch.zeros_like(cls_logits_per_image)
	113	+	gt_classes_target[
	114	+	foreground_idxs_per_image,
	115	+	targets_per_image['labels'][matched_idxs_per_image[foreground_idxs_per_image]]
	116	+	] = 1.0
	117	+
	118	+	# find indices for which anchors should be ignored
	119	+	valid_idxs_per_image = matched_idxs_per_image != self.BETWEEN_THRESHOLDS
125	120
126	121		# compute the classification loss
127	122		losses.append(sigmoid_focal_loss(

191	186
192	187		for targets_per_image, bbox_regression_per_image, anchors_per_image, matched_idxs_per_image in \
193	188		zip(targets, bbox_regression, anchors, matched_idxs):
194		-	# no matched_idxs means there were no annotations in this image
195		-	# TODO enable support for images without annotations with distributed support
196		-	# if matched_idxs_per_image.numel() == 0:
197		-	# continue
198		-
199		-	# get the targets corresponding GT for each proposal
200		-	# NB: need to clamp the indices because we can have a single
201		-	# GT in the image, and matched_idxs can be -2, which goes
202		-	# out of bounds
203		-	matched_gt_boxes_per_image = targets_per_image['boxes'][matched_idxs_per_image.clamp(min=0)]
204		-
205	189		# determine only the foreground indices, ignore the rest
206		-	foreground_idxs_per_image = matched_idxs_per_image >= 0
207		-	num_foreground = foreground_idxs_per_image.sum()
	190	+	foreground_idxs_per_image = torch.where(matched_idxs_per_image >= 0)[0]
	191	+	num_foreground = foreground_idxs_per_image.numel()
208	192
209	193		# select only the foreground boxes
210		-	matched_gt_boxes_per_image = matched_gt_boxes_per_image[foreground_idxs_per_image, :]
	194	+	matched_gt_boxes_per_image = targets_per_image['boxes'][matched_idxs_per_image[foreground_idxs_per_image]]
211	195		bbox_regression_per_image = bbox_regression_per_image[foreground_idxs_per_image, :]
212	196		anchors_per_image = anchors_per_image[foreground_idxs_per_image, :]
213	197

403	387		matched_idxs = []
404	388		for anchors_per_image, targets_per_image in zip(anchors, targets):
405	389		if targets_per_image['boxes'].numel() == 0:
406		-	matched_idxs.append(torch.empty((0,), dtype=torch.int32))
	390	+	matched_idxs.append(torch.full((anchors_per_image.size(0),), -1, dtype=torch.int64))
407	391		continue
408	392
409	393		match_quality_matrix = box_ops.box_iou(targets_per_image['boxes'], anchors_per_image)

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