code stringlengths 17 6.64M |
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def _test_roialign_allclose(device, dtype):
if ((not torch.cuda.is_available()) and (device == 'cuda')):
pytest.skip('test requires GPU')
try:
from mmcv.ops import roi_align
except ModuleNotFoundError:
pytest.skip('test requires compilation')
pool_h = 2
pool_w = 2
spati... |
@pytest.mark.parametrize('device', ['cuda', 'cpu'])
@pytest.mark.parametrize('dtype', [torch.float, torch.double, torch.half])
def test_roialign(device, dtype):
if (dtype is torch.double):
_test_roialign_gradcheck(device=device, dtype=dtype)
_test_roialign_allclose(device=device, dtype=dtype)
|
def _test_roialign_rotated_gradcheck(device, dtype):
if ((not torch.cuda.is_available()) and (device == 'cuda')):
pytest.skip('unittest does not support GPU yet.')
try:
from mmcv.ops import RoIAlignRotated
except ModuleNotFoundError:
pytest.skip('RoIAlignRotated op is not successfu... |
def _test_roialign_rotated_allclose(device, dtype):
if ((not torch.cuda.is_available()) and (device == 'cuda')):
pytest.skip('unittest does not support GPU yet.')
try:
from mmcv.ops import RoIAlignRotated, roi_align_rotated
except ModuleNotFoundError:
pytest.skip('test requires com... |
@pytest.mark.parametrize('device', ['cuda', 'cpu'])
@pytest.mark.parametrize('dtype', [torch.float, torch.double, torch.half])
def test_roialign_rotated(device, dtype):
if (dtype is torch.double):
_test_roialign_rotated_gradcheck(device=device, dtype=dtype)
_test_roialign_rotated_allclose(device=devic... |
class TestRoiPool(object):
def test_roipool_gradcheck(self):
if (not torch.cuda.is_available()):
return
from mmcv.ops import RoIPool
pool_h = 2
pool_w = 2
spatial_scale = 1.0
for case in inputs:
np_input = np.array(case[0])
np_ro... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_RoIAwarePool3d():
roiaware_pool3d_max = RoIAwarePool3d(out_size=4, max_pts_per_voxel=128, mode='max')
roiaware_pool3d_avg = RoIAwarePool3d(out_size=4, max_pts_per_voxel=128, mode='avg')
rois = torch.tensor([[1.0,... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_points_in_boxes_part():
boxes = torch.tensor([[[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 0.3]], [[(- 10.0), 23.0, 16.0, 10, 20, 20, 0.5]]], dtype=torch.float32).cuda()
pts = torch.tensor([[[1, 2, 3.3], [1.2, 2.5, 3.0], [0.8, 2.... |
def test_points_in_boxes_cpu():
boxes = torch.tensor([[[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 0.3], [(- 10.0), 23.0, 16.0, 10, 20, 20, 0.5]]], dtype=torch.float32)
pts = torch.tensor([[[1, 2, 3.3], [1.2, 2.5, 3.0], [0.8, 2.1, 3.5], [1.6, 2.6, 3.6], [0.8, 1.2, 3.9], [(- 9.2), 21.0, 18.2], [3.8, 7.9, 6.3], [4.7, 3.5, (... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_points_in_boxes_all():
boxes = torch.tensor([[[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 0.3], [(- 10.0), 23.0, 16.0, 10, 20, 20, 0.5]]], dtype=torch.float32).cuda()
pts = torch.tensor([[[1, 2, 3.3], [1.2, 2.5, 3.0], [0.8, 2.1, ... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_gather_points():
feats = torch.tensor([[1, 2, 3.3], [1.2, 2.5, 3.0], [0.8, 2.1, 3.5], [1.6, 2.6, 3.6], [0.8, 1.2, 3.9], [(- 9.2), 21.0, 18.2], [3.8, 7.9, 6.3], [4.7, 3.5, (- 12.2)], [3.8, 7.6, (- 2)], [(- 10.6), (- 12.9)... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_rotated_feature_align():
feature = torch.tensor([[[[1.2924, (- 0.2172), (- 0.5222), 0.1172], [0.9144, 1.2248, 1.3115, (- 0.969)], [(- 0.8949), (- 1.1797), (- 0.9093), (- 0.3961)], [(- 0.4586), 0.5062, (- 0.7947), (- 0.73... |
def test_sacconv():
x = torch.rand(1, 3, 256, 256)
saconv = SAConv2d(3, 5, kernel_size=3, padding=1)
sac_out = saconv(x)
refer_conv = nn.Conv2d(3, 5, kernel_size=3, padding=1)
refer_out = refer_conv(x)
assert (sac_out.shape == refer_out.shape)
dalited_saconv = SAConv2d(3, 5, kernel_size=3,... |
def make_sparse_convmodule(in_channels, out_channels, kernel_size, indice_key, stride=1, padding=0, conv_type='SubMConv3d', norm_cfg=None, order=('conv', 'norm', 'act')):
'Make sparse convolution module.\n\n Args:\n in_channels (int): the number of input channels\n out_channels (int): the number ... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_make_sparse_convmodule():
voxel_features = torch.tensor([[6.56126, 0.9648336, (- 1.7339306), 0.315], [6.8162713, (- 2.480431), (- 1.3616394), 0.36], [11.643568, (- 4.744306), (- 1.3580885), 0.16], [23.482342, 6.5036807, ... |
class TestSyncBN(object):
def dist_init(self):
rank = int(os.environ['SLURM_PROCID'])
world_size = int(os.environ['SLURM_NTASKS'])
local_rank = int(os.environ['SLURM_LOCALID'])
node_list = str(os.environ['SLURM_NODELIST'])
node_parts = re.findall('[0-9]+', node_list)
... |
def remove_tmp_file(func):
@wraps(func)
def wrapper(*args, **kwargs):
onnx_file = 'tmp.onnx'
kwargs['onnx_file'] = onnx_file
try:
result = func(*args, **kwargs)
finally:
if os.path.exists(onnx_file):
os.remove(onnx_file)
return r... |
@remove_tmp_file
def export_nms_module_to_onnx(module, onnx_file):
torch_model = module()
torch_model.eval()
input = (torch.rand([100, 4], dtype=torch.float32), torch.rand([100], dtype=torch.float32))
torch.onnx.export(torch_model, input, onnx_file, opset_version=11, input_names=['boxes', 'scores'], o... |
def test_can_handle_nms_with_constant_maxnum():
class ModuleNMS(torch.nn.Module):
def forward(self, boxes, scores):
return nms(boxes, scores, iou_threshold=0.4, max_num=10)
onnx_model = export_nms_module_to_onnx(ModuleNMS)
preprocess_onnx_model = preprocess_onnx(onnx_model)
for n... |
def test_can_handle_nms_with_undefined_maxnum():
class ModuleNMS(torch.nn.Module):
def forward(self, boxes, scores):
return nms(boxes, scores, iou_threshold=0.4)
onnx_model = export_nms_module_to_onnx(ModuleNMS)
preprocess_onnx_model = preprocess_onnx(onnx_model)
for node in prep... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_three_interpolate():
features = torch.tensor([[[2.435, 4.7516, 4.4995, 2.435, 2.435, 2.435], [3.1236, 2.6278, 3.0447, 3.1236, 3.1236, 3.1236], [2.6732, 2.8677, 2.6436, 2.6732, 2.6732, 2.6732], [0.0124, 7.015, 7.0199, 0.0... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_three_nn():
known = torch.tensor([[[(- 1.8373), 3.5605, (- 0.7867)], [0.7615, 2.942, 0.2314], [(- 0.6503), 3.6637, (- 1.0622)], [(- 1.8373), 3.5605, (- 0.7867)], [(- 1.8373), 3.5605, (- 0.7867)]], [[(- 1.3399), 1.9991, (... |
def _test_tinshift_gradcheck(dtype):
try:
from mmcv.ops import tin_shift
except ModuleNotFoundError:
pytest.skip('TINShift op is not successfully compiled')
if (dtype == torch.half):
pytest.skip('"add_cpu/sub_cpu" not implemented for Half')
for shift in shifts:
np_input... |
def _test_tinshift_allclose(dtype):
try:
from mmcv.ops import tin_shift
except ModuleNotFoundError:
pytest.skip('TINShift op is not successfully compiled')
for (shift, output, grad) in zip(shifts, outputs, grads):
np_input = np.array(inputs)
np_shift = np.array(shift)
... |
def _test_tinshift_assert(dtype):
try:
from mmcv.ops import tin_shift
except ModuleNotFoundError:
pytest.skip('TINShift op is not successfully compiled')
inputs = [torch.rand(2, 3, 4, 2), torch.rand(2, 3, 4, 2)]
shifts = [torch.rand(2, 3), torch.rand(2, 5)]
for (x, shift) in zip(in... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
@pytest.mark.parametrize('dtype', [torch.float, torch.double, torch.half])
def test_tinshift(dtype):
_test_tinshift_allclose(dtype=dtype)
_test_tinshift_gradcheck(dtype=dtype)
_test_tinshift_assert(dtype=dtype)
|
def mock(*args, **kwargs):
pass
|
@patch('torch.distributed._broadcast_coalesced', mock)
@patch('torch.distributed.broadcast', mock)
@patch('torch.nn.parallel.DistributedDataParallel._ddp_init_helper', mock)
def test_is_module_wrapper():
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv = n... |
def test_get_input_device():
input = torch.zeros([1, 3, 3, 3])
assert (get_input_device(input) == (- 1))
inputs = [torch.zeros([1, 3, 3, 3]), torch.zeros([1, 4, 4, 4])]
assert (get_input_device(inputs) == (- 1))
if torch.cuda.is_available():
input = torch.zeros([1, 3, 3, 3]).cuda()
... |
def test_scatter():
input = torch.zeros([1, 3, 3, 3])
output = scatter(input=input, devices=[(- 1)])
assert torch.allclose(input, output)
inputs = [torch.zeros([1, 3, 3, 3]), torch.zeros([1, 4, 4, 4])]
outputs = scatter(input=inputs, devices=[(- 1)])
for (input, output) in zip(inputs, outputs)... |
def test_Scatter():
target_gpus = [(- 1)]
input = torch.zeros([1, 3, 3, 3])
outputs = Scatter.forward(target_gpus, input)
assert isinstance(outputs, tuple)
assert torch.allclose(input, outputs[0])
target_gpus = [(- 1)]
inputs = [torch.zeros([1, 3, 3, 3]), torch.zeros([1, 4, 4, 4])]
out... |
@COMPONENTS.register_module()
class FooConv1d(BaseModule):
def __init__(self, init_cfg=None):
super().__init__(init_cfg)
self.conv1d = nn.Conv1d(4, 1, 4)
def forward(self, x):
return self.conv1d(x)
|
@COMPONENTS.register_module()
class FooConv2d(BaseModule):
def __init__(self, init_cfg=None):
super().__init__(init_cfg)
self.conv2d = nn.Conv2d(3, 1, 3)
def forward(self, x):
return self.conv2d(x)
|
@COMPONENTS.register_module()
class FooLinear(BaseModule):
def __init__(self, init_cfg=None):
super().__init__(init_cfg)
self.linear = nn.Linear(3, 4)
def forward(self, x):
return self.linear(x)
|
@COMPONENTS.register_module()
class FooLinearConv1d(BaseModule):
def __init__(self, linear=None, conv1d=None, init_cfg=None):
super().__init__(init_cfg)
if (linear is not None):
self.linear = build_from_cfg(linear, COMPONENTS)
if (conv1d is not None):
self.conv1d =... |
@FOOMODELS.register_module()
class FooModel(BaseModule):
def __init__(self, component1=None, component2=None, component3=None, component4=None, init_cfg=None) -> None:
super().__init__(init_cfg)
if (component1 is not None):
self.component1 = build_from_cfg(component1, COMPONENTS)
... |
def test_initilization_info_logger():
import os
import torch.nn as nn
from mmcv.utils.logging import get_logger
class OverloadInitConv(nn.Conv2d, BaseModule):
def init_weights(self):
for p in self.parameters():
with torch.no_grad():
p.fill_(1)
... |
def test_update_init_info():
class DummyModel(BaseModule):
def __init__(self, init_cfg=None):
super().__init__(init_cfg)
self.conv1 = nn.Conv2d(1, 1, 1, 1)
self.conv3 = nn.Conv2d(1, 1, 1, 1)
self.fc1 = nn.Linear(1, 1)
model = DummyModel()
from coll... |
def test_model_weight_init():
'\n Config\n model (FooModel, Linear: weight=1, bias=2, Conv1d: weight=3, bias=4,\n Conv2d: weight=5, bias=6)\n ├──component1 (FooConv1d)\n ├──component2 (FooConv2d)\n ├──component3 (FooLinear)\n ├──component4 (FooLinearConv1d)\n ├──linear... |
def test_nest_components_weight_init():
'\n Config\n model (FooModel, Linear: weight=1, bias=2, Conv1d: weight=3, bias=4,\n Conv2d: weight=5, bias=6)\n ├──component1 (FooConv1d, Conv1d: weight=7, bias=8)\n ├──component2 (FooConv2d, Conv2d: weight=9, bias=10)\n ├──component3 (Foo... |
def test_without_layer_weight_init():
model_cfg = dict(type='FooModel', init_cfg=[dict(type='Constant', val=1, bias=2, layer='Linear'), dict(type='Constant', val=3, bias=4, layer='Conv1d'), dict(type='Constant', val=5, bias=6, layer='Conv2d')], component1=dict(type='FooConv1d', init_cfg=dict(type='Constant', val=... |
def test_override_weight_init():
model_cfg = dict(type='FooModel', init_cfg=[dict(type='Constant', val=10, bias=20, override=dict(name='reg'))], component1=dict(type='FooConv1d'), component3=dict(type='FooLinear'))
model = build_from_cfg(model_cfg, FOOMODELS)
model.init_weights()
assert torch.equal(mo... |
def test_sequential_model_weight_init():
seq_model_cfg = [dict(type='FooConv1d', init_cfg=dict(type='Constant', layer='Conv1d', val=0.0, bias=1.0)), dict(type='FooConv2d', init_cfg=dict(type='Constant', layer='Conv2d', val=2.0, bias=3.0))]
layers = [build_from_cfg(cfg, COMPONENTS) for cfg in seq_model_cfg]
... |
def test_modulelist_weight_init():
models_cfg = [dict(type='FooConv1d', init_cfg=dict(type='Constant', layer='Conv1d', val=0.0, bias=1.0)), dict(type='FooConv2d', init_cfg=dict(type='Constant', layer='Conv2d', val=2.0, bias=3.0))]
layers = [build_from_cfg(cfg, COMPONENTS) for cfg in models_cfg]
modellist ... |
def test_moduledict_weight_init():
models_cfg = dict(foo_conv_1d=dict(type='FooConv1d', init_cfg=dict(type='Constant', layer='Conv1d', val=0.0, bias=1.0)), foo_conv_2d=dict(type='FooConv2d', init_cfg=dict(type='Constant', layer='Conv2d', val=2.0, bias=3.0)))
layers = {name: build_from_cfg(cfg, COMPONENTS) for... |
@MODULE_WRAPPERS.register_module()
class DDPWrapper(object):
def __init__(self, module):
self.module = module
|
class Block(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(3, 3, 1)
self.norm = nn.BatchNorm2d(3)
|
class Model(nn.Module):
def __init__(self):
super().__init__()
self.block = Block()
self.conv = nn.Conv2d(3, 3, 1)
|
class Mockpavimodel(object):
def __init__(self, name='fakename'):
self.name = name
def download(self, file):
pass
|
def assert_tensor_equal(tensor_a, tensor_b):
assert tensor_a.eq(tensor_b).all()
|
def test_get_state_dict():
if (torch.__version__ == 'parrots'):
state_dict_keys = set(['block.conv.weight', 'block.conv.bias', 'block.norm.weight', 'block.norm.bias', 'block.norm.running_mean', 'block.norm.running_var', 'conv.weight', 'conv.bias'])
else:
state_dict_keys = set(['block.conv.weig... |
def test_load_pavimodel_dist():
sys.modules['pavi'] = MagicMock()
sys.modules['pavi.modelcloud'] = MagicMock()
pavimodel = Mockpavimodel()
import pavi
pavi.modelcloud.get = MagicMock(return_value=pavimodel)
with pytest.raises(AssertionError):
_ = load_from_pavi('MyPaviFolder/checkpoint... |
def test_load_checkpoint_with_prefix():
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)
model = FooModule()
nn.init.constant_(model... |
def test_load_checkpoint():
import os
import re
import tempfile
class PrefixModel(nn.Module):
def __init__(self):
super().__init__()
self.backbone = Model()
pmodel = PrefixModel()
model = Model()
checkpoint_path = os.path.join(tempfile.gettempdir(), 'check... |
def test_load_checkpoint_metadata():
import os
import tempfile
from mmcv.runner import load_checkpoint, save_checkpoint
class ModelV1(nn.Module):
def __init__(self):
super().__init__()
self.block = Block()
self.conv1 = nn.Conv2d(3, 3, 1)
self.c... |
def test_load_classes_name():
import os
import tempfile
from mmcv.runner import load_checkpoint, save_checkpoint
checkpoint_path = os.path.join(tempfile.gettempdir(), 'checkpoint.pth')
model = Model()
save_checkpoint(model, checkpoint_path)
checkpoint = load_checkpoint(model, checkpoint_pa... |
def test_checkpoint_loader():
import os
import tempfile
from mmcv.runner import CheckpointLoader, _load_checkpoint, save_checkpoint
checkpoint_path = os.path.join(tempfile.gettempdir(), 'checkpoint.pth')
model = Model()
save_checkpoint(model, checkpoint_path)
checkpoint = _load_checkpoint(... |
def test_save_checkpoint(tmp_path):
model = Model()
optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
with pytest.raises(TypeError):
save_checkpoint(model, '/path/of/your/filename', meta='invalid type')
filename = str((tmp_path / 'checkpoint1.pth'))
save_checkpoint(model, fil... |
def test_load_from_local():
import os
home_path = os.path.expanduser('~')
checkpoint_path = os.path.join(home_path, 'dummy_checkpoint_used_to_test_load_from_local.pth')
model = Model()
save_checkpoint(model, checkpoint_path)
checkpoint = load_from_local('~/dummy_checkpoint_used_to_test_load_fr... |
@patch('torch.cuda.device_count', return_value=1)
@patch('torch.cuda.set_device')
@patch('torch.distributed.init_process_group')
@patch('subprocess.getoutput', return_value='127.0.0.1')
def test_init_dist(mock_getoutput, mock_dist_init, mock_set_device, mock_device_count):
with pytest.raises(ValueError):
... |
class ExampleDataset(Dataset):
def __init__(self):
self.index = 0
self.eval_result = [1, 4, 3, 7, 2, (- 3), 4, 6]
def __getitem__(self, idx):
results = dict(x=torch.tensor([1]))
return results
def __len__(self):
return 1
@mock.create_autospec
def evaluat... |
class EvalDataset(ExampleDataset):
def evaluate(self, results, logger=None):
acc = self.eval_result[self.index]
output = OrderedDict(acc=acc, index=self.index, score=acc, loss_top=acc)
self.index += 1
return output
|
class Model(nn.Module):
def __init__(self):
super().__init__()
self.param = nn.Parameter(torch.tensor([1.0]))
def forward(self, x, **kwargs):
return (self.param * x)
def train_step(self, data_batch, optimizer, **kwargs):
return {'loss': torch.sum(self(data_batch['x']))}
... |
def _build_epoch_runner():
model = Model()
tmp_dir = tempfile.mkdtemp()
runner = EpochBasedRunner(model=model, work_dir=tmp_dir, logger=get_logger('demo'))
return runner
|
def _build_iter_runner():
model = Model()
tmp_dir = tempfile.mkdtemp()
runner = IterBasedRunner(model=model, work_dir=tmp_dir, logger=get_logger('demo'))
return runner
|
class EvalHook(BaseEvalHook):
_default_greater_keys = ['acc', 'top']
_default_less_keys = ['loss', 'loss_top']
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
|
class DistEvalHook(BaseDistEvalHook):
greater_keys = ['acc', 'top']
less_keys = ['loss', 'loss_top']
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
|
def test_eval_hook():
with pytest.raises(AssertionError):
test_dataset = Model()
data_loader = DataLoader(test_dataset)
EvalHook(data_loader, save_best=True)
with pytest.raises(TypeError):
test_dataset = Model()
data_loader = [DataLoader(test_dataset)]
EvalHook(... |
@patch('mmcv.engine.single_gpu_test', MagicMock)
@patch('mmcv.engine.multi_gpu_test', MagicMock)
@pytest.mark.parametrize('EvalHookParam', [EvalHook, DistEvalHook])
@pytest.mark.parametrize('_build_demo_runner,by_epoch', [(_build_epoch_runner, True), (_build_iter_runner, False)])
def test_start_param(EvalHookParam, _... |
@pytest.mark.parametrize('runner,by_epoch,eval_hook_priority', [(EpochBasedRunner, True, 'NORMAL'), (EpochBasedRunner, True, 'LOW'), (IterBasedRunner, False, 'LOW')])
def test_logger(runner, by_epoch, eval_hook_priority):
loader = DataLoader(EvalDataset())
model = Model()
data_loader = DataLoader(EvalData... |
def test_cast_tensor_type():
inputs = torch.FloatTensor([5.0])
src_type = torch.float32
dst_type = torch.int32
outputs = cast_tensor_type(inputs, src_type, dst_type)
assert isinstance(outputs, torch.Tensor)
assert (outputs.dtype == dst_type)
inputs = torch.FloatTensor([5.0])
src_type =... |
def test_auto_fp16():
with pytest.raises(TypeError):
class ExampleObject(object):
@auto_fp16()
def __call__(self, x):
return x
model = ExampleObject()
input_x = torch.ones(1, dtype=torch.float32)
model(input_x)
class ExampleModule(nn.M... |
def test_force_fp32():
with pytest.raises(TypeError):
class ExampleObject(object):
@force_fp32()
def __call__(self, x):
return x
model = ExampleObject()
input_x = torch.ones(1, dtype=torch.float32)
model(input_x)
class ExampleModule(nn... |
def test_optimizerhook():
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=2, kernel_size=3, stride=1, padding=1, dilation=1)
self.conv2 = nn.Conv2d(in_channels=2, out_channels=2, kernel_size=3, stride=1... |
def test_checkpoint_hook(tmp_path):
'xdoctest -m tests/test_runner/test_hooks.py test_checkpoint_hook.'
loader = DataLoader(torch.ones((5, 2)))
runner = _build_demo_runner('EpochBasedRunner', max_epochs=1)
runner.meta = dict()
checkpointhook = CheckpointHook(interval=1, by_epoch=True)
runner.r... |
def test_ema_hook():
'xdoctest -m tests/test_hooks.py test_ema_hook.'
class DemoModel(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(in_channels=1, out_channels=2, kernel_size=1, padding=1, bias=True)
self._init_weight()
def _ini... |
def test_custom_hook():
@HOOKS.register_module()
class ToyHook(Hook):
def __init__(self, info, *args, **kwargs):
super().__init__()
self.info = info
runner = _build_demo_runner_without_hook('EpochBasedRunner', max_epochs=1)
runner.register_custom_hooks(None)
asser... |
def test_pavi_hook():
sys.modules['pavi'] = MagicMock()
loader = DataLoader(torch.ones((5, 2)))
runner = _build_demo_runner()
runner.meta = dict(config_dict=dict(lr=0.02, gpu_ids=range(1)))
hook = PaviLoggerHook(add_graph=False, add_last_ckpt=True)
runner.register_hook(hook)
runner.run([lo... |
def test_sync_buffers_hook():
loader = DataLoader(torch.ones((5, 2)))
runner = _build_demo_runner()
runner.register_hook_from_cfg(dict(type='SyncBuffersHook'))
runner.run([loader, loader], [('train', 1), ('val', 1)])
shutil.rmtree(runner.work_dir)
|
@pytest.mark.parametrize('multi_optimizers, max_iters, gamma, cyclic_times', [(True, 8, 1, 1), (False, 8, 0.5, 2)])
def test_momentum_runner_hook(multi_optimizers, max_iters, gamma, cyclic_times):
'xdoctest -m tests/test_hooks.py test_momentum_runner_hook.'
sys.modules['pavi'] = MagicMock()
loader = DataL... |
@pytest.mark.parametrize('multi_optimizers', (True, False))
def test_cosine_runner_hook(multi_optimizers):
'xdoctest -m tests/test_hooks.py test_cosine_runner_hook.'
sys.modules['pavi'] = MagicMock()
loader = DataLoader(torch.ones((10, 2)))
runner = _build_demo_runner(multi_optimizers=multi_optimizers... |
@pytest.mark.parametrize('multi_optimizers, by_epoch', [(False, False), (True, False), (False, True), (True, True)])
def test_flat_cosine_runner_hook(multi_optimizers, by_epoch):
'xdoctest -m tests/test_hooks.py test_flat_cosine_runner_hook.'
sys.modules['pavi'] = MagicMock()
loader = DataLoader(torch.one... |
@pytest.mark.parametrize('multi_optimizers, max_iters', [(True, 10), (True, 2), (False, 10), (False, 2)])
def test_one_cycle_runner_hook(multi_optimizers, max_iters):
'Test OneCycleLrUpdaterHook and OneCycleMomentumUpdaterHook.'
with pytest.raises(AssertionError):
OneCycleLrUpdaterHook(max_lr=0.1, by_... |
@pytest.mark.parametrize('multi_optimizers', (True, False))
def test_cosine_restart_lr_update_hook(multi_optimizers):
'Test CosineRestartLrUpdaterHook.'
with pytest.raises(AssertionError):
CosineRestartLrUpdaterHook(by_epoch=False, periods=[2, 10], restart_weights=[0.5, 0.5], min_lr=0.1, min_lr_ratio=... |
@pytest.mark.parametrize('multi_optimizers', (True, False))
def test_step_runner_hook(multi_optimizers):
'Test StepLrUpdaterHook.'
with pytest.raises(TypeError):
StepLrUpdaterHook()
with pytest.raises(AssertionError):
StepLrUpdaterHook((- 10))
with pytest.raises(AssertionError):
... |
@pytest.mark.parametrize('multi_optimizers, max_iters, gamma, cyclic_times', [(True, 8, 1, 1), (False, 8, 0.5, 2)])
def test_cyclic_lr_update_hook(multi_optimizers, max_iters, gamma, cyclic_times):
'Test CyclicLrUpdateHook.'
with pytest.raises(AssertionError):
CyclicLrUpdaterHook(by_epoch=True)
wi... |
@pytest.mark.parametrize('log_model', (True, False))
def test_mlflow_hook(log_model):
sys.modules['mlflow'] = MagicMock()
sys.modules['mlflow.pytorch'] = MagicMock()
runner = _build_demo_runner()
loader = DataLoader(torch.ones((5, 2)))
hook = MlflowLoggerHook(exp_name='test', log_model=log_model)
... |
def test_segmind_hook():
sys.modules['segmind'] = MagicMock()
runner = _build_demo_runner()
hook = SegmindLoggerHook()
loader = DataLoader(torch.ones((5, 2)))
runner.register_hook(hook)
runner.run([loader, loader], [('train', 1), ('val', 1)])
shutil.rmtree(runner.work_dir)
hook.mlflow_... |
def test_wandb_hook():
sys.modules['wandb'] = MagicMock()
runner = _build_demo_runner()
hook = WandbLoggerHook(log_artifact=True)
loader = DataLoader(torch.ones((5, 2)))
runner.register_hook(hook)
runner.run([loader, loader], [('train', 1), ('val', 1)])
shutil.rmtree(runner.work_dir)
h... |
def test_neptune_hook():
sys.modules['neptune'] = MagicMock()
sys.modules['neptune.new'] = MagicMock()
runner = _build_demo_runner()
hook = NeptuneLoggerHook()
loader = DataLoader(torch.ones((5, 2)))
runner.register_hook(hook)
runner.run([loader, loader], [('train', 1), ('val', 1)])
sh... |
def test_dvclive_hook():
sys.modules['dvclive'] = MagicMock()
runner = _build_demo_runner()
hook = DvcliveLoggerHook()
dvclive_mock = hook.dvclive
loader = DataLoader(torch.ones((5, 2)))
runner.register_hook(hook)
runner.run([loader, loader], [('train', 1), ('val', 1)])
shutil.rmtree(r... |
def test_dvclive_hook_model_file(tmp_path):
sys.modules['dvclive'] = MagicMock()
runner = _build_demo_runner()
hook = DvcliveLoggerHook(model_file=osp.join(runner.work_dir, 'model.pth'))
runner.register_hook(hook)
loader = torch.utils.data.DataLoader(torch.ones((5, 2)))
loader = DataLoader(tor... |
def _build_demo_runner_without_hook(runner_type='EpochBasedRunner', max_epochs=1, max_iters=None, multi_optimizers=False):
class Model(nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(2, 1)
self.conv = nn.Conv2d(3, 3, 3)
def forward(... |
def _build_demo_runner(runner_type='EpochBasedRunner', max_epochs=1, max_iters=None, multi_optimizers=False):
log_config = dict(interval=1, hooks=[dict(type='TextLoggerHook')])
runner = _build_demo_runner_without_hook(runner_type, max_epochs, max_iters, multi_optimizers)
runner.register_checkpoint_hook(di... |
def test_runner_with_revise_keys():
import os
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(3, 3, 1)
class PrefixModel(nn.Module):
def __init__(self):
super().__init__()
self.backbone = Model()
p... |
def test_get_triggered_stages():
class ToyHook(Hook):
def before_run():
pass
def after_epoch():
pass
hook = ToyHook()
expected_stages = ['before_run', 'after_train_epoch', 'after_val_epoch']
assert (hook.get_triggered_stages() == expected_stages)
|
def test_gradient_cumulative_optimizer_hook():
class ToyModel(nn.Module):
def __init__(self, with_norm=False):
super().__init__()
self.fp16_enabled = False
self.fc = nn.Linear(3, 2)
nn.init.constant_(self.fc.weight, 1.0)
nn.init.constant_(self.... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_gradient_cumulative_fp16_optimizer_hook():
class ToyModel(nn.Module):
def __init__(self):
super().__init__()
self.fp16_enabled = False
self.fc = nn.Linear(3, 2)
n... |
class SubModel(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(2, 2, kernel_size=1, groups=2)
self.gn = nn.GroupNorm(2, 2)
self.param1 = nn.Parameter(torch.ones(1))
def forward(self, x):
return x
|
class ExampleModel(nn.Module):
def __init__(self):
super().__init__()
self.param1 = nn.Parameter(torch.ones(1))
self.conv1 = nn.Conv2d(3, 4, kernel_size=1, bias=False)
self.conv2 = nn.Conv2d(4, 2, kernel_size=1)
self.bn = nn.BatchNorm2d(2)
self.sub = SubModel()
... |
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