code stringlengths 17 6.64M |
|---|
@pytest.mark.parametrize('embed_dims', [False, 256])
def test_basetransformerlayer(embed_dims):
attn_cfgs = (dict(type='MultiheadAttention', embed_dims=256, num_heads=8),)
if embed_dims:
ffn_cfgs = dict(type='FFN', embed_dims=embed_dims, feedforward_channels=1024, num_fcs=2, ffn_drop=0.0, act_cfg=dict... |
def test_transformerlayersequence():
squeue = TransformerLayerSequence(num_layers=6, transformerlayers=dict(type='BaseTransformerLayer', attn_cfgs=[dict(type='MultiheadAttention', embed_dims=256, num_heads=8, dropout=0.1), dict(type='MultiheadAttention', embed_dims=256, num_heads=4)], feedforward_channels=1024, f... |
def test_drop_path():
drop_path = DropPath(drop_prob=0)
test_in = torch.rand(2, 3, 4, 5)
assert (test_in is drop_path(test_in))
drop_path = DropPath(drop_prob=0.1)
drop_path.training = False
test_in = torch.rand(2, 3, 4, 5)
assert (test_in is drop_path(test_in))
drop_path.training = Tr... |
def test_constant_init():
conv_module = nn.Conv2d(3, 16, 3)
constant_init(conv_module, 0.1)
assert conv_module.weight.allclose(torch.full_like(conv_module.weight, 0.1))
assert conv_module.bias.allclose(torch.zeros_like(conv_module.bias))
conv_module_no_bias = nn.Conv2d(3, 16, 3, bias=False)
co... |
def test_xavier_init():
conv_module = nn.Conv2d(3, 16, 3)
xavier_init(conv_module, bias=0.1)
assert conv_module.bias.allclose(torch.full_like(conv_module.bias, 0.1))
xavier_init(conv_module, distribution='uniform')
with pytest.raises(AssertionError):
xavier_init(conv_module, distribution='... |
def test_normal_init():
conv_module = nn.Conv2d(3, 16, 3)
normal_init(conv_module, bias=0.1)
assert conv_module.bias.allclose(torch.full_like(conv_module.bias, 0.1))
conv_module_no_bias = nn.Conv2d(3, 16, 3, bias=False)
normal_init(conv_module_no_bias)
|
def test_trunc_normal_init():
def _random_float(a, b):
return (((b - a) * random.random()) + a)
def _is_trunc_normal(tensor, mean, std, a, b):
z_samples = ((tensor.view((- 1)) - mean) / std)
z_samples = z_samples.tolist()
a0 = ((a - mean) / std)
b0 = ((b - mean) / std... |
def test_uniform_init():
conv_module = nn.Conv2d(3, 16, 3)
uniform_init(conv_module, bias=0.1)
assert conv_module.bias.allclose(torch.full_like(conv_module.bias, 0.1))
conv_module_no_bias = nn.Conv2d(3, 16, 3, bias=False)
uniform_init(conv_module_no_bias)
|
def test_kaiming_init():
conv_module = nn.Conv2d(3, 16, 3)
kaiming_init(conv_module, bias=0.1)
assert conv_module.bias.allclose(torch.full_like(conv_module.bias, 0.1))
kaiming_init(conv_module, distribution='uniform')
with pytest.raises(AssertionError):
kaiming_init(conv_module, distributi... |
def test_caffe_xavier_init():
conv_module = nn.Conv2d(3, 16, 3)
caffe2_xavier_init(conv_module)
|
def test_bias_init_with_prob():
conv_module = nn.Conv2d(3, 16, 3)
prior_prob = 0.1
normal_init(conv_module, bias=bias_init_with_prob(0.1))
bias = float((- np.log(((1 - prior_prob) / prior_prob))))
assert conv_module.bias.allclose(torch.full_like(conv_module.bias, bias))
|
def test_constaninit():
'test ConstantInit class.'
model = nn.Sequential(nn.Conv2d(3, 1, 3), nn.ReLU(), nn.Linear(1, 2))
func = ConstantInit(val=1, bias=2, layer='Conv2d')
func(model)
assert torch.equal(model[0].weight, torch.full(model[0].weight.shape, 1.0))
assert torch.equal(model[0].bias, ... |
def test_xavierinit():
'test XavierInit class.'
model = nn.Sequential(nn.Conv2d(3, 1, 3), nn.ReLU(), nn.Linear(1, 2))
func = XavierInit(bias=0.1, layer='Conv2d')
func(model)
assert model[0].bias.allclose(torch.full_like(model[2].bias, 0.1))
assert (not model[2].bias.allclose(torch.full_like(mo... |
def test_normalinit():
'test Normalinit class.'
model = nn.Sequential(nn.Conv2d(3, 1, 3), nn.ReLU(), nn.Linear(1, 2))
func = NormalInit(mean=100, std=1e-05, bias=200, layer=['Conv2d', 'Linear'])
func(model)
assert model[0].weight.allclose(torch.tensor(100.0))
assert model[2].weight.allclose(to... |
def test_truncnormalinit():
'test TruncNormalInit class.'
model = nn.Sequential(nn.Conv2d(3, 1, 3), nn.ReLU(), nn.Linear(1, 2))
func = TruncNormalInit(mean=100, std=1e-05, bias=200, a=0, b=200, layer=['Conv2d', 'Linear'])
func(model)
assert model[0].weight.allclose(torch.tensor(100.0))
assert ... |
def test_uniforminit():
'"test UniformInit class.'
model = nn.Sequential(nn.Conv2d(3, 1, 3), nn.ReLU(), nn.Linear(1, 2))
func = UniformInit(a=1, b=1, bias=2, layer=['Conv2d', 'Linear'])
func(model)
assert torch.equal(model[0].weight, torch.full(model[0].weight.shape, 1.0))
assert torch.equal(m... |
def test_kaiminginit():
'test KaimingInit class.'
model = nn.Sequential(nn.Conv2d(3, 1, 3), nn.ReLU(), nn.Linear(1, 2))
func = KaimingInit(bias=0.1, layer='Conv2d')
func(model)
assert torch.equal(model[0].bias, torch.full(model[0].bias.shape, 0.1))
assert (not torch.equal(model[2].bias, torch.... |
def test_caffe2xavierinit():
'test Caffe2XavierInit.'
model = nn.Sequential(nn.Conv2d(3, 1, 3), nn.ReLU(), nn.Linear(1, 2))
func = Caffe2XavierInit(bias=0.1, layer='Conv2d')
func(model)
assert torch.equal(model[0].bias, torch.full(model[0].bias.shape, 0.1))
assert (not torch.equal(model[2].bia... |
class FooModule(nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(1, 2)
self.conv2d = nn.Conv2d(3, 1, 3)
self.conv2d_2 = nn.Conv2d(3, 2, 3)
|
def test_pretrainedinit():
'test PretrainedInit class.'
modelA = FooModule()
constant_func = ConstantInit(val=1, bias=2, layer=['Conv2d', 'Linear'])
modelA.apply(constant_func)
modelB = FooModule()
funcB = PretrainedInit(checkpoint='modelA.pth')
modelC = nn.Linear(1, 2)
funcC = Pretrai... |
def test_initialize():
model = nn.Sequential(nn.Conv2d(3, 1, 3), nn.ReLU(), nn.Linear(1, 2))
foonet = FooModule()
init_cfg = dict(type='Constant', layer=['Conv2d', 'Linear'], val=1, bias=2)
initialize(model, init_cfg)
assert torch.equal(model[0].weight, torch.full(model[0].weight.shape, 1.0))
... |
@patch('torch.__version__', torch_version)
@pytest.mark.parametrize('in_w,in_h,in_channel,out_channel,kernel_size,stride,padding,dilation', [(10, 10, 1, 1, 3, 1, 0, 1), (20, 20, 3, 3, 5, 2, 1, 2)])
def test_conv2d(in_w, in_h, in_channel, out_channel, kernel_size, stride, padding, dilation):
'\n CommandLine:\n ... |
@patch('torch.__version__', torch_version)
@pytest.mark.parametrize('in_w,in_h,in_t,in_channel,out_channel,kernel_size,stride,padding,dilation', [(10, 10, 10, 1, 1, 3, 1, 0, 1), (20, 20, 20, 3, 3, 5, 2, 1, 2)])
def test_conv3d(in_w, in_h, in_t, in_channel, out_channel, kernel_size, stride, padding, dilation):
'\n... |
@patch('torch.__version__', torch_version)
@pytest.mark.parametrize('in_w,in_h,in_channel,out_channel,kernel_size,stride,padding,dilation', [(10, 10, 1, 1, 3, 1, 0, 1), (20, 20, 3, 3, 5, 2, 1, 2)])
def test_conv_transposed_2d(in_w, in_h, in_channel, out_channel, kernel_size, stride, padding, dilation):
x_empty = ... |
@patch('torch.__version__', torch_version)
@pytest.mark.parametrize('in_w,in_h,in_t,in_channel,out_channel,kernel_size,stride,padding,dilation', [(10, 10, 10, 1, 1, 3, 1, 0, 1), (20, 20, 20, 3, 3, 5, 2, 1, 2)])
def test_conv_transposed_3d(in_w, in_h, in_t, in_channel, out_channel, kernel_size, stride, padding, dilati... |
@patch('torch.__version__', torch_version)
@pytest.mark.parametrize('in_w,in_h,in_channel,out_channel,kernel_size,stride,padding,dilation', [(10, 10, 1, 1, 3, 1, 0, 1), (20, 20, 3, 3, 5, 2, 1, 2)])
def test_max_pool_2d(in_w, in_h, in_channel, out_channel, kernel_size, stride, padding, dilation):
x_empty = torch.r... |
@patch('torch.__version__', torch_version)
@pytest.mark.parametrize('in_w,in_h,in_t,in_channel,out_channel,kernel_size,stride,padding,dilation', [(10, 10, 10, 1, 1, 3, 1, 0, 1), (20, 20, 20, 3, 3, 5, 2, 1, 2)])
@pytest.mark.skipif(((torch.__version__ == 'parrots') and (not torch.cuda.is_available())), reason='parrots... |
@patch('torch.__version__', torch_version)
@pytest.mark.parametrize('in_w,in_h,in_feature,out_feature', [(10, 10, 1, 1), (20, 20, 3, 3)])
def test_linear(in_w, in_h, in_feature, out_feature):
x_empty = torch.randn(0, in_feature, requires_grad=True)
torch.manual_seed(0)
wrapper = Linear(in_feature, out_fea... |
@patch('mmcv.cnn.bricks.wrappers.TORCH_VERSION', (1, 10))
def test_nn_op_forward_called():
for m in ['Conv2d', 'ConvTranspose2d', 'MaxPool2d']:
with patch(f'torch.nn.{m}.forward') as nn_module_forward:
x_empty = torch.randn(0, 3, 10, 10)
wrapper = eval(m)(3, 2, 1)
wrapp... |
@contextmanager
def build_temporary_directory():
'Build a temporary directory containing many files to test\n ``FileClient.list_dir_or_file``.\n\n . \n\n | -- dir1 \n\n | -- | -- text3.txt \n\n | -- dir2 \n\n | -- | -- dir3 \n\n | -- | -- | -- text4.txt \n\n | -- | -- img.jpg \n\n | -- ... |
@contextmanager
def delete_and_reset_method(obj, method):
method_obj = deepcopy(getattr(type(obj), method))
try:
delattr(type(obj), method)
(yield)
finally:
setattr(type(obj), method, method_obj)
|
class MockS3Client():
def __init__(self, enable_mc=True):
self.enable_mc = enable_mc
def Get(self, filepath):
with open(filepath, 'rb') as f:
content = f.read()
return content
|
class MockPetrelClient():
def __init__(self, enable_mc=True, enable_multi_cluster=False):
self.enable_mc = enable_mc
self.enable_multi_cluster = enable_multi_cluster
def Get(self, filepath):
with open(filepath, 'rb') as f:
content = f.read()
return content
de... |
class MockMemcachedClient():
def __init__(self, server_list_cfg, client_cfg):
pass
def Get(self, filepath, buffer):
with open(filepath, 'rb') as f:
buffer.content = f.read()
|
class TestFileClient():
@classmethod
def setup_class(cls):
cls.test_data_dir = (Path(__file__).parent / 'data')
cls.img_path = (cls.test_data_dir / 'color.jpg')
cls.img_shape = (300, 400, 3)
cls.text_path = (cls.test_data_dir / 'filelist.txt')
def test_error(self):
... |
def _test_handler(file_format, test_obj, str_checker, mode='r+'):
dump_str = mmcv.dump(test_obj, file_format=file_format)
str_checker(dump_str)
tmp_filename = osp.join(tempfile.gettempdir(), 'mmcv_test_dump')
mmcv.dump(test_obj, tmp_filename, file_format=file_format)
assert osp.isfile(tmp_filename... |
def test_json():
def json_checker(dump_str):
assert (dump_str in ['[{"a": "abc", "b": 1}, 2, "c"]', '[{"b": 1, "a": "abc"}, 2, "c"]'])
_test_handler('json', obj_for_test, json_checker)
|
def test_yaml():
def yaml_checker(dump_str):
assert (dump_str in ['- {a: abc, b: 1}\n- 2\n- c\n', '- {b: 1, a: abc}\n- 2\n- c\n', '- a: abc\n b: 1\n- 2\n- c\n', '- b: 1\n a: abc\n- 2\n- c\n'])
_test_handler('yaml', obj_for_test, yaml_checker)
|
def test_pickle():
def pickle_checker(dump_str):
import pickle
assert (pickle.loads(dump_str) == obj_for_test)
_test_handler('pickle', obj_for_test, pickle_checker, mode='rb+')
|
def test_exception():
test_obj = [{'a': 'abc', 'b': 1}, 2, 'c']
with pytest.raises(ValueError):
mmcv.dump(test_obj)
with pytest.raises(TypeError):
mmcv.dump(test_obj, 'tmp.txt')
|
def test_register_handler():
@mmcv.register_handler('txt')
class TxtHandler1(mmcv.BaseFileHandler):
def load_from_fileobj(self, file):
return file.read()
def dump_to_fileobj(self, obj, file):
file.write(str(obj))
def dump_to_str(self, obj, **kwargs):
... |
def test_list_from_file():
filename = osp.join(osp.dirname(__file__), 'data/filelist.txt')
filelist = mmcv.list_from_file(filename)
assert (filelist == ['1.jpg', '2.jpg', '3.jpg', '4.jpg', '5.jpg'])
filelist = mmcv.list_from_file(filename, prefix='a/')
assert (filelist == ['a/1.jpg', 'a/2.jpg', 'a... |
def test_dict_from_file():
filename = osp.join(osp.dirname(__file__), 'data/mapping.txt')
mapping = mmcv.dict_from_file(filename)
assert (mapping == {'1': 'cat', '2': ['dog', 'cow'], '3': 'panda'})
mapping = mmcv.dict_from_file(filename, key_type=int)
assert (mapping == {1: 'cat', 2: ['dog', 'cow'... |
@pytest.mark.skipif((torch is None), reason='requires torch library')
def test_tensor2imgs():
with pytest.raises(AssertionError):
tensor = np.random.rand(2, 3, 3)
mmcv.tensor2imgs(tensor)
with pytest.raises(AssertionError):
tensor = torch.randn(2, 3, 3)
mmcv.tensor2imgs(tensor)... |
@patch('mmcv.__path__', [osp.join(osp.dirname(__file__), 'data/')])
def test_set_mmcv_home():
os.environ.pop(ENV_MMCV_HOME, None)
mmcv_home = osp.join(osp.dirname(__file__), 'data/model_zoo/mmcv_home/')
os.environ[ENV_MMCV_HOME] = mmcv_home
assert (_get_mmcv_home() == mmcv_home)
|
@patch('mmcv.__path__', [osp.join(osp.dirname(__file__), 'data/')])
def test_default_mmcv_home():
os.environ.pop(ENV_MMCV_HOME, None)
os.environ.pop(ENV_XDG_CACHE_HOME, None)
assert (_get_mmcv_home() == os.path.expanduser(os.path.join(DEFAULT_CACHE_DIR, 'mmcv')))
model_urls = get_external_models()
... |
@patch('mmcv.__path__', [osp.join(osp.dirname(__file__), 'data/')])
def test_get_external_models():
os.environ.pop(ENV_MMCV_HOME, None)
mmcv_home = osp.join(osp.dirname(__file__), 'data/model_zoo/mmcv_home/')
os.environ[ENV_MMCV_HOME] = mmcv_home
ext_urls = get_external_models()
assert (ext_urls =... |
@patch('mmcv.__path__', [osp.join(osp.dirname(__file__), 'data/')])
def test_get_deprecated_models():
os.environ.pop(ENV_MMCV_HOME, None)
mmcv_home = osp.join(osp.dirname(__file__), 'data/model_zoo/mmcv_home/')
os.environ[ENV_MMCV_HOME] = mmcv_home
dep_urls = get_deprecated_model_names()
assert (d... |
def load_from_http(url, map_location=None):
return ('url:' + url)
|
def load_url(url, map_location=None, model_dir=None):
return load_from_http(url)
|
def load(filepath, map_location=None):
return ('local:' + filepath)
|
@patch('mmcv.__path__', [osp.join(osp.dirname(__file__), 'data/')])
@patch('mmcv.runner.checkpoint.load_from_http', load_from_http)
@patch('mmcv.runner.checkpoint.load_url', load_url)
@patch('torch.load', load)
def test_load_external_url():
url = _load_checkpoint('modelzoo://resnet50')
if (TORCH_VERSION < '1.... |
@pytest.mark.parametrize('device', ['cpu', pytest.param('cuda', marks=pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support'))])
def test_active_rotated_filter(device):
feature = torch.tensor(np_feature, dtype=torch.float, device=device, requires_grad=True)
indices = torch.tensor(n... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_paconv_assign_scores():
scores = torch.tensor([[[[0.06947571, 0.6065746], [0.28462553, 0.8378516], [0.7595994, 0.97220325], [0.519155, 0.766185]], [[0.15348864, 0.6051019], [0.21510637, 0.31916398], [0.00236845, 0.584259... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_ball_query():
new_xyz = torch.tensor([[[(- 0.074), 1.3147, (- 1.3625)], [(- 2.2769), 2.7817, (- 0.2334)], [(- 0.4003), 2.4666, (- 0.5116)], [(- 0.074), 1.3147, (- 1.3625)], [(- 0.074), 1.3147, (- 1.3625)]], [[(- 2.0289),... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
class TestBBox(object):
def _test_bbox_overlaps(self, dtype=torch.float):
from mmcv.ops import bbox_overlaps
b1 = torch.tensor([[1.0, 1.0, 3.0, 4.0], [2.0, 2.0, 3.0, 4.0], [7.0, 7.0, 8.0, 8.0]]).cuda().type(dtype... |
class TestBilinearGridSample(object):
def _test_bilinear_grid_sample(self, dtype=torch.float, align_corners=False, multiplier=1, precision=0.001):
from mmcv.ops.point_sample import bilinear_grid_sample
input = torch.rand(1, 1, 20, 20, dtype=dtype)
grid = torch.Tensor([[[1, 0, 0], [0, 1, 0... |
def _test_border_align_allclose(device, dtype, pool_size):
if ((not torch.cuda.is_available()) and (device == 'cuda')):
pytest.skip('test requires GPU')
try:
from mmcv.ops import BorderAlign, border_align
except ModuleNotFoundError:
pytest.skip('BorderAlign op is not successfully c... |
@pytest.mark.parametrize('device', ['cuda'])
@pytest.mark.parametrize('dtype', [torch.float, torch.half, torch.double])
@pytest.mark.parametrize('pool_size', [1, 2])
def test_border_align(device, dtype, pool_size):
_test_border_align_allclose(device, dtype, pool_size)
|
class TestBoxIoURotated(object):
def test_box_iou_rotated_cpu(self):
from mmcv.ops import box_iou_rotated
np_boxes1 = np.asarray([[1.0, 1.0, 3.0, 4.0, 0.5], [2.0, 2.0, 3.0, 4.0, 0.6], [7.0, 7.0, 8.0, 8.0, 0.4]], dtype=np.float32)
np_boxes2 = np.asarray([[0.0, 2.0, 2.0, 5.0, 0.3], [2.0, 1.... |
class TestCarafe(object):
def test_carafe_naive_gradcheck(self):
if (not torch.cuda.is_available()):
return
from mmcv.ops import CARAFENaive
feat = torch.randn(2, 64, 3, 3, requires_grad=True, device='cuda').double()
mask = torch.randn(2, 100, 6, 6, requires_grad=True,... |
class Loss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input, target):
input = input.view((- 1))
target = target.view((- 1))
return torch.mean((input - target))
|
class TestCrissCrossAttention(object):
def test_cc_attention(self):
device = torch.device(('cuda:0' if torch.cuda.is_available() else 'cpu'))
from mmcv.ops import CrissCrossAttention
loss_func = Loss()
input = np.fromfile('tests/data/for_ccattention/ccattention_input.bin', dtype=n... |
def test_contour_expand():
from mmcv.ops import contour_expand
np_internal_kernel_label = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 0, 2, 0], [0, 0, 1, 1, 0, 0, 0, 0, 2, 0], [0, 0, 1, 1, 0, 0, 0, 0, 2, 0], [0, 0, 1, 1, 0, 0,... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_convex_iou():
pointsets = torch.from_numpy(np_pointsets).cuda().float()
polygons = torch.from_numpy(np_polygons).cuda().float()
expected_iou = torch.from_numpy(np_expected_iou).cuda().float()
assert torch.all... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_convex_giou():
pointsets = torch.from_numpy(np_pointsets).cuda().float()
polygons = torch.from_numpy(np_polygons).cuda().float()
expected_giou = torch.from_numpy(np_expected_giou).cuda().float()
expected_grad... |
def test_corner_pool_device_and_dtypes_cpu():
'\n CommandLine:\n xdoctest -m tests/test_corner_pool.py test_corner_pool_device_and_dtypes_cpu\n '
with pytest.raises(AssertionError):
pool = CornerPool('corner')
lr_tensor = torch.tensor([[[[0, 0, 0, 0, 0], [2, 1, 3, 0, 2], [... |
def assert_equal_tensor(tensor_a, tensor_b):
assert tensor_a.eq(tensor_b).all()
|
class TestCorrelation():
def _test_correlation(self, dtype=torch.float):
layer = Correlation(max_displacement=0)
input1 = torch.tensor(_input1, dtype=dtype).cuda()
input2 = torch.tensor(_input2, dtype=dtype).cuda()
input1.requires_grad = True
input2.requires_grad = True
... |
class TestDeformconv(object):
def _test_deformconv(self, dtype=torch.float, threshold=0.001, device='cuda', batch_size=10, im2col_step=2):
if ((not torch.cuda.is_available()) and (device == 'cuda')):
pytest.skip('test requires GPU')
from mmcv.ops import DeformConv2dPack
c_in =... |
class TestDeformRoIPool(object):
def test_deform_roi_pool_gradcheck(self):
if (not torch.cuda.is_available()):
return
from mmcv.ops import DeformRoIPoolPack
pool_h = 2
pool_w = 2
spatial_scale = 1.0
sampling_ratio = 2
for case in inputs:
... |
class Testfocalloss(object):
def _test_softmax(self, dtype=torch.float):
if (not torch.cuda.is_available()):
return
from mmcv.ops import softmax_focal_loss
alpha = 0.25
gamma = 2.0
for (case, output) in zip(inputs, softmax_outputs):
np_x = np.array(... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_fps():
xyz = torch.tensor([[[(- 0.2748), 1.002, (- 1.1674)], [0.1015, 1.3952, (- 1.2681)], [(- 0.807), 2.4137, (- 0.5845)], [(- 1.0001), 2.1982, (- 0.5859)], [0.3841, 1.8983, (- 0.7431)]], [[(- 1.0696), 3.0758, (- 0.1899... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_fps_with_dist():
xyz = torch.tensor([[[(- 0.2748), 1.002, (- 1.1674)], [0.1015, 1.3952, (- 1.2681)], [(- 0.807), 2.4137, (- 0.5845)], [(- 1.0001), 2.1982, (- 0.5859)], [0.3841, 1.8983, (- 0.7431)]], [[(- 1.0696), 3.0758,... |
class TestFusedBiasLeakyReLU(object):
@classmethod
def setup_class(cls):
if (not torch.cuda.is_available()):
return
cls.input_tensor = torch.randn((2, 2, 2, 2), requires_grad=True).cuda()
cls.bias = torch.zeros(2, requires_grad=True).cuda()
@pytest.mark.skipif((not to... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_gather_points():
features = torch.tensor([[[(- 1.6095), (- 0.1029), (- 0.8876), (- 1.2447), (- 2.4031), 0.3708, (- 1.1586), (- 1.4967), (- 0.48), 0.2252], [1.9138, 3.4979, 1.6854, 1.5631, 3.6776, 3.1154, 2.1705, 2.5221, ... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_grouping_points():
idx = torch.tensor([[[0, 0, 0], [3, 3, 3], [8, 8, 8], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [6, 6, 6], [9, 9, 9], [0, 0, 0], [0, 0, 0], [0, 0, 0]]]).int().cuda()
festures = torch.tensor([[[... |
class TestInfo(object):
def test_info(self):
if (not torch.cuda.is_available()):
return
from mmcv.ops import get_compiler_version, get_compiling_cuda_version
cv = get_compiler_version()
ccv = get_compiling_cuda_version()
assert (cv is not None)
assert (... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_boxes_iou_bev():
np_boxes1 = np.asarray([[1.0, 1.0, 3.0, 4.0, 0.5], [2.0, 2.0, 3.0, 4.0, 0.6], [7.0, 7.0, 8.0, 8.0, 0.4]], dtype=np.float32)
np_boxes2 = np.asarray([[0.0, 2.0, 2.0, 5.0, 0.3], [2.0, 1.0, 3.0, 3.0, 0.5... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_nms_bev():
np_boxes = np.array([[6.0, 3.0, 8.0, 7.0, 2.0], [3.0, 6.0, 9.0, 11.0, 1.0], [3.0, 7.0, 10.0, 12.0, 1.0], [1.0, 4.0, 13.0, 7.0, 3.0]], dtype=np.float32)
np_scores = np.array([0.6, 0.9, 0.7, 0.2], dtype=np.f... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_nms_normal_bev():
np_boxes = np.array([[6.0, 3.0, 8.0, 7.0, 2.0], [3.0, 6.0, 9.0, 11.0, 1.0], [3.0, 7.0, 10.0, 12.0, 1.0], [1.0, 4.0, 13.0, 7.0, 3.0]], dtype=np.float32)
np_scores = np.array([0.6, 0.9, 0.7, 0.2], dty... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_knn():
new_xyz = torch.tensor([[[(- 0.074), 1.3147, (- 1.3625)], [(- 2.2769), 2.7817, (- 0.2334)], [(- 0.4003), 2.4666, (- 0.5116)], [(- 0.074), 1.3147, (- 1.3625)], [(- 0.074), 1.3147, (- 1.3625)]], [[(- 2.0289), 2.4952... |
class TestMaskedConv2d(object):
def test_masked_conv2d(self):
if (not torch.cuda.is_available()):
return
from mmcv.ops import MaskedConv2d
input = torch.randn(1, 3, 16, 16, requires_grad=True, device='cuda')
mask = torch.randn(1, 16, 16, requires_grad=True, device='cud... |
def test_sum_cell():
inputs_x = torch.randn([2, 256, 32, 32])
inputs_y = torch.randn([2, 256, 16, 16])
sum_cell = SumCell(256, 256)
output = sum_cell(inputs_x, inputs_y, out_size=inputs_x.shape[(- 2):])
assert (output.size() == inputs_x.size())
output = sum_cell(inputs_x, inputs_y, out_size=in... |
def test_concat_cell():
inputs_x = torch.randn([2, 256, 32, 32])
inputs_y = torch.randn([2, 256, 16, 16])
concat_cell = ConcatCell(256, 256)
output = concat_cell(inputs_x, inputs_y, out_size=inputs_x.shape[(- 2):])
assert (output.size() == inputs_x.size())
output = concat_cell(inputs_x, inputs... |
def test_global_pool_cell():
inputs_x = torch.randn([2, 256, 32, 32])
inputs_y = torch.randn([2, 256, 32, 32])
gp_cell = GlobalPoolingCell(with_out_conv=False)
gp_cell_out = gp_cell(inputs_x, inputs_y, out_size=inputs_x.shape[(- 2):])
assert (gp_cell_out.size() == inputs_x.size())
gp_cell = Gl... |
def test_resize_methods():
inputs_x = torch.randn([2, 256, 128, 128])
target_resize_sizes = [(128, 128), (256, 256)]
resize_methods_list = ['nearest', 'bilinear']
for method in resize_methods_list:
merge_cell = BaseMergeCell(upsample_mode=method)
for target_size in target_resize_sizes:... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_min_area_polygons():
pointsets = torch.from_numpy(np_pointsets).cuda().float()
assert np.allclose(min_area_polygons(pointsets).cpu().numpy(), expected_polygons, atol=0.0001)
|
class TestMdconv(object):
def _test_mdconv(self, dtype=torch.float, device='cuda'):
if ((not torch.cuda.is_available()) and (device == 'cuda')):
pytest.skip('test requires GPU')
from mmcv.ops import ModulatedDeformConv2dPack
input = torch.tensor(input_t, dtype=dtype, device=de... |
@pytest.mark.parametrize('device_type', ['cpu', pytest.param('cuda:0', marks=pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support'))])
def test_multiscale_deformable_attention(device_type):
with pytest.raises(ValueError):
MultiScaleDeformableAttention(embed_dims=256, num_heads... |
def test_forward_multi_scale_deformable_attn_pytorch():
(N, M, D) = (1, 2, 2)
(Lq, L, P) = (2, 2, 2)
shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long)
S = sum([(H * W).item() for (H, W) in shapes])
torch.manual_seed(3)
value = (torch.rand(N, S, M, D) * 0.01)
sampling_locations =... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_forward_equal_with_pytorch_double():
(N, M, D) = (1, 2, 2)
(Lq, L, P) = (2, 2, 2)
shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long).cuda()
level_start_index = torch.cat((shapes.new_zeros((1,)), sha... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_forward_equal_with_pytorch_float():
(N, M, D) = (1, 2, 2)
(Lq, L, P) = (2, 2, 2)
shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long).cuda()
level_start_index = torch.cat((shapes.new_zeros((1,)), shap... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
@pytest.mark.parametrize('channels', [4, 30, 32, 64, 71, 1025])
def test_gradient_numerical(channels, grad_value=True, grad_sampling_loc=True, grad_attn_weight=True):
(N, M, _) = (1, 2, 2)
(Lq, L, P) = (2, 2, 2)
shapes = ... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_points_in_polygons():
points = np.array([[300.0, 300.0], [400.0, 400.0], [100.0, 100], [300, 250], [100, 0]])
polygons = np.array([[200.0, 200.0, 400.0, 400.0, 500.0, 200.0, 400.0, 100.0], [400.0, 400.0, 500.0, 500.0... |
class Loss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input, target):
input = input.view((- 1))
target = target.view((- 1))
return torch.mean((input - target))
|
class TestPSAMask(object):
def test_psa_mask_collect(self):
if (not torch.cuda.is_available()):
return
from mmcv.ops import PSAMask
test_loss = Loss()
input = np.fromfile('tests/data/for_psa_mask/psa_input.bin', dtype=np.float32)
output_collect = np.fromfile('t... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_roialign_rotated_gradcheck():
x = torch.tensor(np_feature, dtype=torch.float, device='cuda', requires_grad=True)
rois = torch.tensor(np_rois, dtype=torch.float, device='cuda')
froipool = RiRoIAlignRotated((pool_h... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_roialign_rotated_allclose():
x = torch.tensor(np_feature, dtype=torch.float, device='cuda', requires_grad=True)
rois = torch.tensor(np_rois, dtype=torch.float, device='cuda')
froipool = RiRoIAlignRotated((pool_h,... |
def _test_roialign_gradcheck(device, dtype):
if ((not torch.cuda.is_available()) and (device == 'cuda')):
pytest.skip('test requires GPU')
try:
from mmcv.ops import RoIAlign
except ModuleNotFoundError:
pytest.skip('RoIAlign op is not successfully compiled')
if (dtype is torch.h... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.