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