optimize (22881a82) · Commits · IntelligentTEAM / ECCV 2020 Domain Adaption

detection/layers/init.py

+1 −1

Original line number	Diff line number	Diff line
		from .misc import FrozenBatchNorm2d
		from .misc import FrozenBatchNorm2d, cat
		from .losses import smooth_l1_loss

+10 −0

Original line number	Diff line number	Diff line
		import torch


		def cat(tensors, dim=0):
		"""
		Efficient version of torch.cat that avoids a copy if there is only a single element in a list
		"""
		assert isinstance(tensors, (list, tuple))
		if len(tensors) == 1:
		return tensors[0]
		return torch.cat(tensors, dim)


		class FrozenBatchNorm2d(torch.nn.Module):
		"""
		BatchNorm2d where the batch statistics and the affine parameters

+4 −3

Original line number	Diff line number	Diff line
		@@ -7,6 +7,7 @@ from torchvision import ops, models
		from torchvision.ops import boxes as box_ops

		from detection.layers import FrozenBatchNorm2d, smooth_l1_loss
		from detection.layers import cat
		from detection.modeling.utils import BalancedPositiveNegativeSampler, BoxCoder, Matcher


		@@ -71,8 +72,8 @@ BOX_PREDICTORS = {


		def fastrcnn_loss(class_logits, box_regression, labels, regression_targets):
		labels = torch.cat(labels, dim=0)
		regression_targets = torch.cat(regression_targets, dim=0)
		labels = cat(labels, dim=0)
		regression_targets = cat(regression_targets, dim=0)

		classification_loss = F.cross_entropy(class_logits, labels)

		@@ -153,7 +154,7 @@ class BoxHead(nn.Module):
		device = class_logits.device

		boxes_per_image = [box.shape[0] for box in proposals]
		proposals = torch.cat([box for box in proposals])
		proposals = cat([box for box in proposals])
		pred_boxes = self.box_coder.decode(
		box_regression.view(sum(boxes_per_image), -1), proposals
		)

+6 −6

Original line number	Diff line number	Diff line
		@@ -6,7 +6,7 @@ from torchvision import ops

		from torchvision.ops import boxes as box_ops

		from detection.layers import smooth_l1_loss
		from detection.layers import smooth_l1_loss, cat
		from .utils import BalancedPositiveNegativeSampler, Matcher, BoxCoder
		from .anchor_generator import AnchorGenerator

		@@ -90,7 +90,7 @@ class RPN(nn.Module):
		objectness = objectness.sigmoid()

		box_regression = box_regression.permute(0, 2, 3, 1).reshape(N, H * W * A, 4)
		concat_anchors = torch.cat(anchors, dim=0)
		concat_anchors = cat(anchors, dim=0)
		concat_anchors = concat_anchors.reshape(N, A * H * W, 4)

		num_anchors = A * H * W
		@@ -162,15 +162,15 @@ class RPN(nn.Module):

		sampled_pos_inds, sampled_neg_inds = self.sampler(labels)

		sampled_pos_inds = torch.nonzero(torch.cat(sampled_pos_inds, dim=0)).squeeze(1)
		sampled_neg_inds = torch.nonzero(torch.cat(sampled_neg_inds, dim=0)).squeeze(1)
		sampled_pos_inds = torch.nonzero(cat(sampled_pos_inds, dim=0)).squeeze(1)
		sampled_neg_inds = torch.nonzero(cat(sampled_neg_inds, dim=0)).squeeze(1)
		sampled_inds = torch.cat([sampled_pos_inds, sampled_neg_inds], dim=0)

		objectness = objectness.permute(0, 2, 3, 1).reshape(-1)
		box_regression = box_regression.permute(0, 2, 3, 1).reshape(-1, 4)

		labels = torch.cat(labels)
		regression_targets = torch.cat(regression_targets, dim=0)
		labels = cat(labels)
		regression_targets = cat(regression_targets, dim=0)

		box_loss = smooth_l1_loss(
		box_regression[sampled_pos_inds],