def test_mask_head_scriptability(self): input_shape = ShapeSpec(channels=1024) mask_features = torch.randn(4, 1024, 14, 14) image_shapes = [(10, 10), (15, 15)] pred_instance0 = Instances(image_shapes[0]) pred_classes0 = torch.tensor([1, 2, 3], dtype=torch.int64) pred_instance0.pred_classes = pred_classes0 pred_instance1 = Instances(image_shapes[1]) pred_classes1 = torch.tensor([4], dtype=torch.int64) pred_instance1.pred_classes = pred_classes1 mask_head = MaskRCNNConvUpsampleHead( input_shape, num_classes=80, conv_dims=[256, 256] ).eval() # pred_instance will be in-place changed during the inference # process of `MaskRCNNConvUpsampleHead` origin_outputs = mask_head(mask_features, deepcopy([pred_instance0, pred_instance1])) fields = {"pred_masks": torch.Tensor, "pred_classes": torch.Tensor} with freeze_training_mode(mask_head), patch_instances(fields) as NewInstances: sciript_mask_head = torch.jit.script(mask_head) pred_instance0 = NewInstances.from_instances(pred_instance0) pred_instance1 = NewInstances.from_instances(pred_instance1) script_outputs = sciript_mask_head(mask_features, [pred_instance0, pred_instance1]) for origin_ins, script_ins in zip(origin_outputs, script_outputs): assert_instances_allclose(origin_ins, script_ins.to_instances(), rtol=0)
def test_keypoint_head_scriptability(self): input_shape = ShapeSpec(channels=1024, height=14, width=14) keypoint_features = torch.randn(4, 1024, 14, 14) image_shapes = [(10, 10), (15, 15)] pred_boxes0 = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6], [1, 5, 2, 8]], dtype=torch.float32) pred_instance0 = Instances(image_shapes[0]) pred_instance0.pred_boxes = Boxes(pred_boxes0) pred_boxes1 = torch.tensor([[7, 3, 10, 5]], dtype=torch.float32) pred_instance1 = Instances(image_shapes[1]) pred_instance1.pred_boxes = Boxes(pred_boxes1) keypoint_head = KRCNNConvDeconvUpsampleHead( input_shape, num_keypoints=17, conv_dims=[512, 512] ).eval() origin_outputs = keypoint_head( keypoint_features, deepcopy([pred_instance0, pred_instance1]) ) fields = { "pred_boxes": Boxes, "pred_keypoints": torch.Tensor, "pred_keypoint_heatmaps": torch.Tensor, } with freeze_training_mode(keypoint_head), patch_instances(fields) as NewInstances: sciript_keypoint_head = torch.jit.script(keypoint_head) pred_instance0 = NewInstances.from_instances(pred_instance0) pred_instance1 = NewInstances.from_instances(pred_instance1) script_outputs = sciript_keypoint_head( keypoint_features, [pred_instance0, pred_instance1] ) for origin_ins, script_ins in zip(origin_outputs, script_outputs): assert_instances_allclose(origin_ins, script_ins.to_instances(), rtol=0)
def test_StandardROIHeads_scriptability(self): cfg = get_cfg() cfg.MODEL.ROI_BOX_HEAD.NAME = "FastRCNNConvFCHead" cfg.MODEL.ROI_BOX_HEAD.NUM_FC = 2 cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignV2" cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10, 10, 5, 5) cfg.MODEL.MASK_ON = True cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST = 0.01 cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.01 num_images = 2 images_tensor = torch.rand(num_images, 20, 30) image_sizes = [(10, 10), (20, 30)] images = ImageList(images_tensor, image_sizes) num_channels = 1024 features = {"res4": torch.rand(num_images, num_channels, 1, 2)} feature_shape = {"res4": ShapeSpec(channels=num_channels, stride=16)} roi_heads = StandardROIHeads(cfg, feature_shape).eval() proposal0 = Instances(image_sizes[0]) proposal_boxes0 = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6]], dtype=torch.float32) proposal0.proposal_boxes = Boxes(proposal_boxes0) proposal0.objectness_logits = torch.tensor([0.5, 0.7], dtype=torch.float32) proposal1 = Instances(image_sizes[1]) proposal_boxes1 = torch.tensor([[1, 5, 2, 8], [7, 3, 10, 5]], dtype=torch.float32) proposal1.proposal_boxes = Boxes(proposal_boxes1) proposal1.objectness_logits = torch.tensor([0.1, 0.9], dtype=torch.float32) proposals = [proposal0, proposal1] pred_instances, _ = roi_heads(images, features, proposals) fields = { "objectness_logits": torch.Tensor, "proposal_boxes": Boxes, "pred_classes": torch.Tensor, "scores": torch.Tensor, "pred_masks": torch.Tensor, "pred_boxes": Boxes, "pred_keypoints": torch.Tensor, "pred_keypoint_heatmaps": torch.Tensor, } with freeze_training_mode(roi_heads), patch_instances( fields) as new_instances: proposal0 = new_instances.from_instances(proposal0) proposal1 = new_instances.from_instances(proposal1) proposals = [proposal0, proposal1] scripted_rot_heads = torch.jit.script(roi_heads) scripted_pred_instances, _ = scripted_rot_heads( images, features, proposals) for instance, scripted_instance in zip(pred_instances, scripted_pred_instances): assert_instances_allclose(instance, scripted_instance.to_instances(), rtol=0)