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import unittest |
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import torch |
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from parameterized import parameterized |
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from mmdet.registry import MODELS |
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from mmdet.structures import DetDataSample |
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from mmdet.testing._utils import demo_mm_inputs, get_detector_cfg |
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from mmdet.utils import register_all_modules |
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class TestMaskFormer(unittest.TestCase): |
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def setUp(self): |
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register_all_modules() |
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def _create_model_cfg(self): |
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cfg_path = 'maskformer/maskformer_r50_ms-16xb1-75e_coco.py' |
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model_cfg = get_detector_cfg(cfg_path) |
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base_channels = 32 |
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model_cfg.backbone.depth = 18 |
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model_cfg.backbone.init_cfg = None |
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model_cfg.backbone.base_channels = base_channels |
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model_cfg.panoptic_head.in_channels = [ |
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base_channels * 2**i for i in range(4) |
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] |
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model_cfg.panoptic_head.feat_channels = base_channels |
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model_cfg.panoptic_head.out_channels = base_channels |
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model_cfg.panoptic_head.pixel_decoder.encoder.\ |
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layer_cfg.self_attn_cfg.embed_dims = base_channels |
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model_cfg.panoptic_head.pixel_decoder.encoder.\ |
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layer_cfg.ffn_cfg.embed_dims = base_channels |
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model_cfg.panoptic_head.pixel_decoder.encoder.\ |
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layer_cfg.ffn_cfg.feedforward_channels = base_channels * 8 |
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model_cfg.panoptic_head.pixel_decoder.\ |
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positional_encoding.num_feats = base_channels // 2 |
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model_cfg.panoptic_head.positional_encoding.\ |
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num_feats = base_channels // 2 |
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model_cfg.panoptic_head.transformer_decoder.\ |
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layer_cfg.self_attn_cfg.embed_dims = base_channels |
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model_cfg.panoptic_head.transformer_decoder. \ |
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layer_cfg.cross_attn_cfg.embed_dims = base_channels |
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model_cfg.panoptic_head.transformer_decoder.\ |
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layer_cfg.ffn_cfg.embed_dims = base_channels |
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model_cfg.panoptic_head.transformer_decoder.\ |
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layer_cfg.ffn_cfg.feedforward_channels = base_channels * 8 |
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return model_cfg |
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def test_init(self): |
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model_cfg = self._create_model_cfg() |
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detector = MODELS.build(model_cfg) |
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detector.init_weights() |
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assert detector.backbone |
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assert detector.panoptic_head |
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@parameterized.expand([('cpu', ), ('cuda', )]) |
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def test_forward_loss_mode(self, device): |
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model_cfg = self._create_model_cfg() |
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detector = MODELS.build(model_cfg) |
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if device == 'cuda' and not torch.cuda.is_available(): |
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return unittest.skip('test requires GPU and torch+cuda') |
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detector = detector.to(device) |
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packed_inputs = demo_mm_inputs( |
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2, |
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image_shapes=[(3, 128, 127), (3, 91, 92)], |
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sem_seg_output_strides=1, |
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with_mask=True, |
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with_semantic=True) |
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data = detector.data_preprocessor(packed_inputs, True) |
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losses = detector.forward(**data, mode='loss') |
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self.assertIsInstance(losses, dict) |
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@parameterized.expand([('cpu', ), ('cuda', )]) |
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def test_forward_predict_mode(self, device): |
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model_cfg = self._create_model_cfg() |
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detector = MODELS.build(model_cfg) |
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if device == 'cuda' and not torch.cuda.is_available(): |
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return unittest.skip('test requires GPU and torch+cuda') |
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detector = detector.to(device) |
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packed_inputs = demo_mm_inputs( |
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2, |
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image_shapes=[(3, 128, 127), (3, 91, 92)], |
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sem_seg_output_strides=1, |
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with_mask=True, |
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with_semantic=True) |
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data = detector.data_preprocessor(packed_inputs, False) |
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detector.eval() |
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with torch.no_grad(): |
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batch_results = detector.forward(**data, mode='predict') |
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self.assertEqual(len(batch_results), 2) |
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self.assertIsInstance(batch_results[0], DetDataSample) |
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@parameterized.expand([('cpu', ), ('cuda', )]) |
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def test_forward_tensor_mode(self, device): |
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model_cfg = self._create_model_cfg() |
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detector = MODELS.build(model_cfg) |
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if device == 'cuda' and not torch.cuda.is_available(): |
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return unittest.skip('test requires GPU and torch+cuda') |
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detector = detector.to(device) |
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packed_inputs = demo_mm_inputs( |
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2, [[3, 128, 128], [3, 125, 130]], |
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sem_seg_output_strides=1, |
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with_mask=True, |
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with_semantic=True) |
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data = detector.data_preprocessor(packed_inputs, False) |
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out = detector.forward(**data, mode='tensor') |
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self.assertIsInstance(out, tuple) |
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class TestMask2Former(unittest.TestCase): |
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def setUp(self): |
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register_all_modules() |
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def _create_model_cfg(self, cfg_path): |
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model_cfg = get_detector_cfg(cfg_path) |
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base_channels = 32 |
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model_cfg.backbone.depth = 18 |
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model_cfg.backbone.init_cfg = None |
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model_cfg.backbone.base_channels = base_channels |
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model_cfg.panoptic_head.in_channels = [ |
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base_channels * 2**i for i in range(4) |
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] |
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model_cfg.panoptic_head.feat_channels = base_channels |
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model_cfg.panoptic_head.out_channels = base_channels |
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model_cfg.panoptic_head.pixel_decoder.encoder.\ |
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layer_cfg.self_attn_cfg.embed_dims = base_channels |
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model_cfg.panoptic_head.pixel_decoder.encoder.\ |
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layer_cfg.ffn_cfg.embed_dims = base_channels |
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model_cfg.panoptic_head.pixel_decoder.encoder.\ |
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layer_cfg.ffn_cfg.feedforward_channels = base_channels * 4 |
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model_cfg.panoptic_head.pixel_decoder.\ |
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positional_encoding.num_feats = base_channels // 2 |
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model_cfg.panoptic_head.positional_encoding.\ |
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num_feats = base_channels // 2 |
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model_cfg.panoptic_head.transformer_decoder.\ |
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layer_cfg.self_attn_cfg.embed_dims = base_channels |
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model_cfg.panoptic_head.transformer_decoder. \ |
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layer_cfg.cross_attn_cfg.embed_dims = base_channels |
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model_cfg.panoptic_head.transformer_decoder.\ |
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layer_cfg.ffn_cfg.embed_dims = base_channels |
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model_cfg.panoptic_head.transformer_decoder.\ |
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layer_cfg.ffn_cfg.feedforward_channels = base_channels * 8 |
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return model_cfg |
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def test_init(self): |
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model_cfg = self._create_model_cfg( |
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'mask2former/mask2former_r50_8xb2-lsj-50e_coco-panoptic.py') |
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detector = MODELS.build(model_cfg) |
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detector.init_weights() |
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assert detector.backbone |
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assert detector.panoptic_head |
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@parameterized.expand([ |
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('cpu', 'mask2former/mask2former_r50_8xb2-lsj-50e_coco-panoptic.py'), |
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('cpu', 'mask2former/mask2former_r50_8xb2-lsj-50e_coco.py'), |
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('cuda', 'mask2former/mask2former_r50_8xb2-lsj-50e_coco-panoptic.py'), |
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('cuda', 'mask2former/mask2former_r50_8xb2-lsj-50e_coco.py') |
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]) |
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def test_forward_loss_mode(self, device, cfg_path): |
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print(device, cfg_path) |
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with_semantic = 'panoptic' in cfg_path |
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model_cfg = self._create_model_cfg(cfg_path) |
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detector = MODELS.build(model_cfg) |
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if device == 'cuda' and not torch.cuda.is_available(): |
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return unittest.skip('test requires GPU and torch+cuda') |
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detector = detector.to(device) |
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packed_inputs = demo_mm_inputs( |
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2, |
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image_shapes=[(3, 128, 127), (3, 91, 92)], |
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sem_seg_output_strides=1, |
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with_mask=True, |
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with_semantic=with_semantic) |
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data = detector.data_preprocessor(packed_inputs, True) |
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losses = detector.forward(**data, mode='loss') |
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self.assertIsInstance(losses, dict) |
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@parameterized.expand([ |
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('cpu', 'mask2former/mask2former_r50_8xb2-lsj-50e_coco-panoptic.py'), |
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('cpu', 'mask2former/mask2former_r50_8xb2-lsj-50e_coco.py'), |
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('cuda', 'mask2former/mask2former_r50_8xb2-lsj-50e_coco-panoptic.py'), |
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('cuda', 'mask2former/mask2former_r50_8xb2-lsj-50e_coco.py') |
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]) |
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def test_forward_predict_mode(self, device, cfg_path): |
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with_semantic = 'panoptic' in cfg_path |
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model_cfg = self._create_model_cfg(cfg_path) |
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detector = MODELS.build(model_cfg) |
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if device == 'cuda' and not torch.cuda.is_available(): |
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return unittest.skip('test requires GPU and torch+cuda') |
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detector = detector.to(device) |
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packed_inputs = demo_mm_inputs( |
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2, |
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image_shapes=[(3, 128, 127), (3, 91, 92)], |
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sem_seg_output_strides=1, |
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with_mask=True, |
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with_semantic=with_semantic) |
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data = detector.data_preprocessor(packed_inputs, False) |
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detector.eval() |
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with torch.no_grad(): |
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batch_results = detector.forward(**data, mode='predict') |
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self.assertEqual(len(batch_results), 2) |
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self.assertIsInstance(batch_results[0], DetDataSample) |
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@parameterized.expand([ |
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('cpu', 'mask2former/mask2former_r50_8xb2-lsj-50e_coco-panoptic.py'), |
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('cpu', 'mask2former/mask2former_r50_8xb2-lsj-50e_coco.py'), |
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('cuda', 'mask2former/mask2former_r50_8xb2-lsj-50e_coco-panoptic.py'), |
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('cuda', 'mask2former/mask2former_r50_8xb2-lsj-50e_coco.py') |
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]) |
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def test_forward_tensor_mode(self, device, cfg_path): |
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with_semantic = 'panoptic' in cfg_path |
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model_cfg = self._create_model_cfg(cfg_path) |
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detector = MODELS.build(model_cfg) |
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if device == 'cuda' and not torch.cuda.is_available(): |
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return unittest.skip('test requires GPU and torch+cuda') |
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detector = detector.to(device) |
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packed_inputs = demo_mm_inputs( |
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2, [[3, 128, 128], [3, 125, 130]], |
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sem_seg_output_strides=1, |
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with_mask=True, |
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with_semantic=with_semantic) |
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data = detector.data_preprocessor(packed_inputs, False) |
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out = detector.forward(**data, mode='tensor') |
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self.assertIsInstance(out, tuple) |
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