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# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet.structures import DetDataSample
from mmdet.testing import demo_mm_inputs, get_detector_cfg
from mmdet.utils import register_all_modules
class TestRPN(TestCase):
def setUp(self):
register_all_modules()
@parameterized.expand(['rpn/rpn_r50_fpn_1x_coco.py'])
def test_init(self, cfg_file):
model = get_detector_cfg(cfg_file)
# backbone convert to ResNet18
model.backbone.depth = 18
model.neck.in_channels = [64, 128, 256, 512]
model.backbone.init_cfg = None
from mmdet.registry import MODELS
detector = MODELS.build(model)
self.assertTrue(detector.backbone)
self.assertTrue(detector.neck)
self.assertTrue(detector.bbox_head)
# if rpn.num_classes > 1, force set rpn.num_classes = 1
model.rpn_head.num_classes = 2
detector = MODELS.build(model)
self.assertEqual(detector.bbox_head.num_classes, 1)
@parameterized.expand([('rpn/rpn_r50_fpn_1x_coco.py', ('cpu', 'cuda'))])
def test_rpn_forward_loss_mode(self, cfg_file, devices):
model = get_detector_cfg(cfg_file)
# backbone convert to ResNet18
model.backbone.depth = 18
model.neck.in_channels = [64, 128, 256, 512]
model.backbone.init_cfg = None
from mmdet.registry import MODELS
assert all([device in ['cpu', 'cuda'] for device in devices])
for device in devices:
detector = MODELS.build(model)
if device == 'cuda':
if not torch.cuda.is_available():
return unittest.skip('test requires GPU and torch+cuda')
detector = detector.cuda()
packed_inputs = demo_mm_inputs(2, [[3, 128, 128], [3, 125, 130]])
data = detector.data_preprocessor(packed_inputs, True)
# Test forward train
losses = detector.forward(**data, mode='loss')
self.assertIsInstance(losses, dict)
@parameterized.expand([('rpn/rpn_r50_fpn_1x_coco.py', ('cpu', 'cuda'))])
def test_rpn_forward_predict_mode(self, cfg_file, devices):
model = get_detector_cfg(cfg_file)
# backbone convert to ResNet18
model.backbone.depth = 18
model.neck.in_channels = [64, 128, 256, 512]
model.backbone.init_cfg = None
from mmdet.registry import MODELS
assert all([device in ['cpu', 'cuda'] for device in devices])
for device in devices:
detector = MODELS.build(model)
if device == 'cuda':
if not torch.cuda.is_available():
return unittest.skip('test requires GPU and torch+cuda')
detector = detector.cuda()
packed_inputs = demo_mm_inputs(2, [[3, 128, 128], [3, 125, 130]])
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([('rpn/rpn_r50_fpn_1x_coco.py', ('cpu', 'cuda'))])
def test_rpn_forward_tensor_mode(self, cfg_file, devices):
model = get_detector_cfg(cfg_file)
# backbone convert to ResNet18
model.backbone.depth = 18
model.neck.in_channels = [64, 128, 256, 512]
model.backbone.init_cfg = None
from mmdet.registry import MODELS
assert all([device in ['cpu', 'cuda'] for device in devices])
for device in devices:
detector = MODELS.build(model)
if device == 'cuda':
if not torch.cuda.is_available():
return unittest.skip('test requires GPU and torch+cuda')
detector = detector.cuda()
packed_inputs = demo_mm_inputs(2, [[3, 128, 128], [3, 125, 130]])
data = detector.data_preprocessor(packed_inputs, False)
batch_results = detector.forward(**data, mode='tensor')
self.assertIsInstance(batch_results, tuple)
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