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def check_time(timer_id):
"Add check points in a single line.\n\n This method is suitable for running a task on a list of items. A timer will\n be registered when the method is called for the first time.\n\n Examples:\n >>> import time\n >>> import mmcv\n >>> for i in range(1, 6):\n ... |
def is_jit_tracing() -> bool:
if ((torch.__version__ != 'parrots') and (digit_version(torch.__version__) >= digit_version('1.6.0'))):
on_trace = torch.jit.is_tracing()
if isinstance(on_trace, bool):
return on_trace
else:
return torch._C._is_tracing()
else:
... |
def digit_version(version_str: str, length: int=4):
'Convert a version string into a tuple of integers.\n\n This method is usually used for comparing two versions. For pre-release\n versions: alpha < beta < rc.\n\n Args:\n version_str (str): The version string.\n length (int): The maximum n... |
def _minimal_ext_cmd(cmd):
env = {}
for k in ['SYSTEMROOT', 'PATH', 'HOME']:
v = os.environ.get(k)
if (v is not None):
env[k] = v
env['LANGUAGE'] = 'C'
env['LANG'] = 'C'
env['LC_ALL'] = 'C'
out = subprocess.Popen(cmd, stdout=subprocess.PIPE, env=env).communicate()[0... |
def get_git_hash(fallback='unknown', digits=None):
"Get the git hash of the current repo.\n\n Args:\n fallback (str, optional): The fallback string when git hash is\n unavailable. Defaults to 'unknown'.\n digits (int, optional): kept digits of the hash. Defaults to None,\n m... |
def parse_version_info(version_str: str, length: int=4) -> tuple:
'Parse a version string into a tuple.\n\n Args:\n version_str (str): The version string.\n length (int): The maximum number of version levels. Default: 4.\n\n Returns:\n tuple[int | str]: The version info, e.g., "1.3.0" i... |
class Cache():
def __init__(self, capacity):
self._cache = OrderedDict()
self._capacity = int(capacity)
if (capacity <= 0):
raise ValueError('capacity must be a positive integer')
@property
def capacity(self):
return self._capacity
@property
def size(... |
class VideoReader():
"Video class with similar usage to a list object.\n\n This video warpper class provides convenient apis to access frames.\n There exists an issue of OpenCV's VideoCapture class that jumping to a\n certain frame may be inaccurate. It is fixed in this class by checking\n the positio... |
def frames2video(frame_dir, video_file, fps=30, fourcc='XVID', filename_tmpl='{:06d}.jpg', start=0, end=0, show_progress=True):
'Read the frame images from a directory and join them as a video.\n\n Args:\n frame_dir (str): The directory containing video frames.\n video_file (str): Output filename... |
@requires_executable('ffmpeg')
def convert_video(in_file, out_file, print_cmd=False, pre_options='', **kwargs):
'Convert a video with ffmpeg.\n\n This provides a general api to ffmpeg, the executed command is::\n\n `ffmpeg -y <pre_options> -i <in_file> <options> <out_file>`\n\n Options(kwargs) are ma... |
@requires_executable('ffmpeg')
def resize_video(in_file, out_file, size=None, ratio=None, keep_ar=False, log_level='info', print_cmd=False):
'Resize a video.\n\n Args:\n in_file (str): Input video filename.\n out_file (str): Output video filename.\n size (tuple): Expected size (w, h), eg, ... |
@requires_executable('ffmpeg')
def cut_video(in_file, out_file, start=None, end=None, vcodec=None, acodec=None, log_level='info', print_cmd=False):
'Cut a clip from a video.\n\n Args:\n in_file (str): Input video filename.\n out_file (str): Output video filename.\n start (None or float): S... |
@requires_executable('ffmpeg')
def concat_video(video_list, out_file, vcodec=None, acodec=None, log_level='info', print_cmd=False):
'Concatenate multiple videos into a single one.\n\n Args:\n video_list (list): A list of video filenames\n out_file (str): Output video filename\n vcodec (Non... |
class Color(Enum):
'An enum that defines common colors.\n\n Contains red, green, blue, cyan, yellow, magenta, white and black.\n '
red = (0, 0, 255)
green = (0, 255, 0)
blue = (255, 0, 0)
cyan = (255, 255, 0)
yellow = (0, 255, 255)
magenta = (255, 0, 255)
white = (255, 255, 255)
... |
def color_val(color):
'Convert various input to color tuples.\n\n Args:\n color (:obj:`Color`/str/tuple/int/ndarray): Color inputs\n\n Returns:\n tuple[int]: A tuple of 3 integers indicating BGR channels.\n '
if is_str(color):
return Color[color].value
elif isinstance(color,... |
def choose_requirement(primary, secondary):
'If some version of primary requirement installed, return primary, else\n return secondary.'
try:
name = re.split('[!<>=]', primary)[0]
get_distribution(name)
except DistributionNotFound:
return secondary
return str(primary)
|
def get_version():
version_file = 'mmcv/version.py'
with open(version_file, 'r', encoding='utf-8') as f:
exec(compile(f.read(), version_file, 'exec'))
return locals()['__version__']
|
def parse_requirements(fname='requirements/runtime.txt', with_version=True):
'Parse the package dependencies listed in a requirements file but strips\n specific versioning information.\n\n Args:\n fname (str): path to requirements file\n with_version (bool, default=False): if True include vers... |
def get_extensions():
extensions = []
if (os.getenv('MMCV_WITH_TRT', '0') != '0'):
(bright_style, reset_style) = ('\x1b[1m', '\x1b[0m')
(red_text, blue_text) = ('\x1b[31m', '\x1b[34m')
white_background = '\x1b[107m'
msg = ((white_background + bright_style) + red_text)
m... |
def test_quantize():
arr = np.random.randn(10, 10)
levels = 20
qarr = mmcv.quantize(arr, (- 1), 1, levels)
assert (qarr.shape == arr.shape)
assert (qarr.dtype == np.dtype('int64'))
for i in range(arr.shape[0]):
for j in range(arr.shape[1]):
ref = min((levels - 1), int(np.fl... |
def test_dequantize():
levels = 20
qarr = np.random.randint(levels, size=(10, 10))
arr = mmcv.dequantize(qarr, (- 1), 1, levels)
assert (arr.shape == qarr.shape)
assert (arr.dtype == np.dtype('float64'))
for i in range(qarr.shape[0]):
for j in range(qarr.shape[1]):
assert (... |
def test_joint():
arr = np.random.randn(100, 100)
levels = 1000
qarr = mmcv.quantize(arr, (- 1), 1, levels)
recover = mmcv.dequantize(qarr, (- 1), 1, levels)
assert (np.abs((recover[(arr < (- 1))] + 0.999)).max() < 1e-06)
assert (np.abs((recover[(arr > 1)] - 0.999)).max() < 1e-06)
assert (... |
def test_build_conv_layer():
with pytest.raises(TypeError):
cfg = 'Conv2d'
build_conv_layer(cfg)
with pytest.raises(KeyError):
cfg = dict(kernel_size=3)
build_conv_layer(cfg)
with pytest.raises(KeyError):
cfg = dict(type='FancyConv')
build_conv_layer(cfg)
... |
def test_infer_norm_abbr():
with pytest.raises(TypeError):
infer_norm_abbr(0)
class MyNorm():
_abbr_ = 'mn'
assert (infer_norm_abbr(MyNorm) == 'mn')
class FancyBatchNorm():
pass
assert (infer_norm_abbr(FancyBatchNorm) == 'bn')
class FancyInstanceNorm():
pass
... |
def test_build_norm_layer():
with pytest.raises(TypeError):
cfg = 'BN'
build_norm_layer(cfg, 3)
with pytest.raises(KeyError):
cfg = dict()
build_norm_layer(cfg, 3)
with pytest.raises(KeyError):
cfg = dict(type='FancyNorm')
build_norm_layer(cfg, 3)
with p... |
def test_build_activation_layer():
with pytest.raises(TypeError):
cfg = 'ReLU'
build_activation_layer(cfg)
with pytest.raises(KeyError):
cfg = dict()
build_activation_layer(cfg)
with pytest.raises(KeyError):
cfg = dict(type='FancyReLU')
build_activation_laye... |
def test_build_padding_layer():
with pytest.raises(TypeError):
cfg = 'reflect'
build_padding_layer(cfg)
with pytest.raises(KeyError):
cfg = dict()
build_padding_layer(cfg)
with pytest.raises(KeyError):
cfg = dict(type='FancyPad')
build_padding_layer(cfg)
... |
def test_upsample_layer():
with pytest.raises(TypeError):
cfg = 'bilinear'
build_upsample_layer(cfg)
with pytest.raises(KeyError):
cfg = dict()
build_upsample_layer(cfg)
with pytest.raises(KeyError):
cfg = dict(type='FancyUpsample')
build_upsample_layer(cfg)... |
def test_pixel_shuffle_pack():
x_in = torch.rand(2, 3, 10, 10)
pixel_shuffle = PixelShufflePack(3, 3, scale_factor=2, upsample_kernel=3)
assert (pixel_shuffle.upsample_conv.kernel_size == (3, 3))
x_out = pixel_shuffle(x_in)
assert (x_out.shape == (2, 3, 20, 20))
|
def test_is_norm():
norm_set1 = [nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d, nn.InstanceNorm1d, nn.InstanceNorm2d, nn.InstanceNorm3d, nn.LayerNorm]
norm_set2 = [nn.GroupNorm]
for norm_type in norm_set1:
layer = norm_type(3)
assert is_norm(layer)
assert (not is_norm(layer, exclu... |
def test_infer_plugin_abbr():
with pytest.raises(TypeError):
infer_plugin_abbr(0)
class MyPlugin():
_abbr_ = 'mp'
assert (infer_plugin_abbr(MyPlugin) == 'mp')
class FancyPlugin():
pass
assert (infer_plugin_abbr(FancyPlugin) == 'fancy_plugin')
|
def test_build_plugin_layer():
with pytest.raises(TypeError):
cfg = 'Plugin'
build_plugin_layer(cfg)
with pytest.raises(KeyError):
cfg = dict()
build_plugin_layer(cfg)
with pytest.raises(KeyError):
cfg = dict(type='FancyPlugin')
build_plugin_layer(cfg)
w... |
def test_context_block():
with pytest.raises(AssertionError):
ContextBlock(16, (1.0 / 4), pooling_type='unsupport_type')
with pytest.raises(AssertionError):
ContextBlock(16, (1.0 / 4), fusion_types='unsupport_type')
with pytest.raises(AssertionError):
ContextBlock(16, (1.0 / 4), fu... |
def test_conv2d_samepadding():
inputs = torch.rand((1, 3, 28, 28))
conv = Conv2dAdaptivePadding(3, 3, kernel_size=3, stride=1)
output = conv(inputs)
assert (output.shape == inputs.shape)
inputs = torch.rand((1, 3, 13, 13))
conv = Conv2dAdaptivePadding(3, 3, kernel_size=3, stride=1)
output ... |
@CONV_LAYERS.register_module()
class ExampleConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, norm_cfg=None):
super(ExampleConv, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels... |
def test_conv_module():
with pytest.raises(AssertionError):
conv_cfg = 'conv'
ConvModule(3, 8, 2, conv_cfg=conv_cfg)
with pytest.raises(AssertionError):
norm_cfg = 'norm'
ConvModule(3, 8, 2, norm_cfg=norm_cfg)
with pytest.raises(KeyError):
act_cfg = dict(type='softm... |
def test_bias():
conv = ConvModule(3, 8, 2)
assert (conv.conv.bias is not None)
conv = ConvModule(3, 8, 2, norm_cfg=dict(type='BN'))
assert (conv.conv.bias is None)
conv = ConvModule(3, 8, 2, bias=False)
assert (conv.conv.bias is None)
with pytest.warns(UserWarning) as record:
Conv... |
def conv_forward(self, x):
return (x + '_conv')
|
def bn_forward(self, x):
return (x + '_bn')
|
def relu_forward(self, x):
return (x + '_relu')
|
@patch('torch.nn.ReLU.forward', relu_forward)
@patch('torch.nn.BatchNorm2d.forward', bn_forward)
@patch('torch.nn.Conv2d.forward', conv_forward)
def test_order():
with pytest.raises(AssertionError):
order = ['conv', 'norm', 'act']
ConvModule(3, 8, 2, order=order)
with pytest.raises(AssertionEr... |
def test_depthwise_separable_conv():
with pytest.raises(AssertionError):
DepthwiseSeparableConvModule(4, 8, 2, groups=2)
conv = DepthwiseSeparableConvModule(3, 8, 2)
assert (conv.depthwise_conv.conv.groups == 3)
assert (conv.pointwise_conv.conv.kernel_size == (1, 1))
assert (not conv.depth... |
class ExampleModel(nn.Module):
def __init__(self):
super().__init__()
self.conv2d = nn.Conv2d(3, 8, 3)
def forward(self, imgs):
x = torch.randn((1, *imgs))
return self.conv2d(x)
|
def input_constructor(x):
return dict(imgs=x)
|
def test_flops_counter():
with pytest.raises(AssertionError):
model = nn.Conv2d(3, 8, 3)
input_res = [1, 3, 16, 16]
get_model_complexity_info(model, input_res)
with pytest.raises(AssertionError):
model = nn.Conv2d(3, 8, 3)
input_res = tuple()
get_model_complexit... |
def test_flops_to_string():
flops = (6.54321 * (10.0 ** 9))
assert (flops_to_string(flops) == '6.54 GFLOPs')
assert (flops_to_string(flops, 'MFLOPs') == '6543.21 MFLOPs')
assert (flops_to_string(flops, 'KFLOPs') == '6543210.0 KFLOPs')
assert (flops_to_string(flops, 'FLOPs') == '6543210000.0 FLOPs'... |
def test_params_to_string():
num_params = (3.21 * (10.0 ** 7))
assert (params_to_string(num_params) == '32.1 M')
num_params = (4.56 * (10.0 ** 5))
assert (params_to_string(num_params) == '456.0 k')
num_params = (7.89 * (10.0 ** 2))
assert (params_to_string(num_params) == '789.0')
num_param... |
def test_fuse_conv_bn():
inputs = torch.rand((1, 3, 5, 5))
modules = nn.ModuleList()
modules.append(nn.BatchNorm2d(3))
modules.append(ConvModule(3, 5, 3, norm_cfg=dict(type='BN')))
modules.append(ConvModule(5, 5, 3, norm_cfg=dict(type='BN')))
modules = nn.Sequential(*modules)
fused_modules... |
def test_context_block():
imgs = torch.randn(2, 16, 20, 20)
gen_attention_block = GeneralizedAttention(16, attention_type='1000')
assert (gen_attention_block.query_conv.in_channels == 16)
assert (gen_attention_block.key_conv.in_channels == 16)
assert (gen_attention_block.key_conv.in_channels == 16... |
def test_hsigmoid():
with pytest.raises(AssertionError):
HSigmoid(divisor=0)
act = HSigmoid()
input_shape = torch.Size([1, 3, 64, 64])
input = torch.randn(input_shape)
output = act(input)
expected_output = torch.min(torch.max(((input + 3) / 6), torch.zeros(input_shape)), torch.ones(inp... |
def test_hswish():
act = HSwish(inplace=True)
assert act.act.inplace
act = HSwish()
assert (not act.act.inplace)
input = torch.randn(1, 3, 64, 64)
expected_output = ((input * relu6((input + 3))) / 6)
output = act(input)
assert (output.shape == expected_output.shape)
assert torch.eq... |
def test_build_model_from_cfg():
BACKBONES = mmcv.Registry('backbone', build_func=build_model_from_cfg)
@BACKBONES.register_module()
class ResNet(nn.Module):
def __init__(self, depth, stages=4):
super().__init__()
self.depth = depth
self.stages = stages
... |
def test_nonlocal():
with pytest.raises(ValueError):
_NonLocalNd(3, mode='unsupport_mode')
_NonLocalNd(3)
_NonLocalNd(3, norm_cfg=dict(type='BN'))
_NonLocalNd(3, zeros_init=False)
_NonLocalNd(3, norm_cfg=dict(type='BN'), zeros_init=False)
|
def test_nonlocal3d():
imgs = torch.randn(2, 3, 10, 20, 20)
nonlocal_3d = NonLocal3d(3)
if (torch.__version__ == 'parrots'):
if torch.cuda.is_available():
imgs = imgs.cuda()
nonlocal_3d.cuda()
out = nonlocal_3d(imgs)
assert (out.shape == imgs.shape)
nonlocal_3d ... |
def test_nonlocal2d():
imgs = torch.randn(2, 3, 20, 20)
nonlocal_2d = NonLocal2d(3)
if (torch.__version__ == 'parrots'):
if torch.cuda.is_available():
imgs = imgs.cuda()
nonlocal_2d.cuda()
out = nonlocal_2d(imgs)
assert (out.shape == imgs.shape)
imgs = torch.ran... |
def test_nonlocal1d():
imgs = torch.randn(2, 3, 20)
nonlocal_1d = NonLocal1d(3)
if (torch.__version__ == 'parrots'):
if torch.cuda.is_available():
imgs = imgs.cuda()
nonlocal_1d.cuda()
out = nonlocal_1d(imgs)
assert (out.shape == imgs.shape)
imgs = torch.randn(2... |
def test_revert_syncbn():
conv = ConvModule(3, 8, 2, norm_cfg=dict(type='SyncBN'))
x = torch.randn(1, 3, 10, 10)
with pytest.raises(ValueError):
y = conv(x)
conv = revert_sync_batchnorm(conv)
y = conv(x)
assert (y.shape == (1, 8, 9, 9))
|
def test_revert_mmsyncbn():
if (('SLURM_NTASKS' not in os.environ) or (int(os.environ['SLURM_NTASKS']) < 2)):
print('Must run on slurm with more than 1 process!\nsrun -p test --gres=gpu:2 -n2')
return
rank = int(os.environ['SLURM_PROCID'])
world_size = int(os.environ['SLURM_NTASKS'])
l... |
def test_scale():
scale = Scale()
assert (scale.scale.data == 1.0)
assert (scale.scale.dtype == torch.float)
x = torch.rand(1, 3, 64, 64)
output = scale(x)
assert (output.shape == (1, 3, 64, 64))
scale = Scale(10.0)
assert (scale.scale.data == 10.0)
assert (scale.scale.dtype == tor... |
def test_swish():
act = Swish()
input = torch.randn(1, 3, 64, 64)
expected_output = (input * F.sigmoid(input))
output = act(input)
assert (output.shape == expected_output.shape)
assert torch.equal(output, expected_output)
|
def test_adaptive_padding():
for padding in ('same', 'corner'):
kernel_size = 16
stride = 16
dilation = 1
input = torch.rand(1, 1, 15, 17)
adap_pad = AdaptivePadding(kernel_size=kernel_size, stride=stride, dilation=dilation, padding=padding)
out = adap_pad(input)
... |
def test_patch_embed():
B = 2
H = 3
W = 4
C = 3
embed_dims = 10
kernel_size = 3
stride = 1
dummy_input = torch.rand(B, C, H, W)
patch_merge_1 = PatchEmbed(in_channels=C, embed_dims=embed_dims, kernel_size=kernel_size, stride=stride, padding=0, dilation=1, norm_cfg=None)
(x1, sh... |
def test_patch_merging():
in_c = 3
out_c = 4
kernel_size = 3
stride = 3
padding = 1
dilation = 1
bias = False
patch_merge = PatchMerging(in_channels=in_c, out_channels=out_c, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias)
(B, L, C) = (1, 100,... |
def test_multiheadattention():
MultiheadAttention(embed_dims=5, num_heads=5, attn_drop=0, proj_drop=0, dropout_layer=dict(type='Dropout', drop_prob=0.0), batch_first=True)
batch_dim = 2
embed_dim = 5
num_query = 100
attn_batch_first = MultiheadAttention(embed_dims=5, num_heads=5, attn_drop=0, proj... |
def test_ffn():
with pytest.raises(AssertionError):
FFN(num_fcs=1)
FFN(dropout=0, add_residual=True)
ffn = FFN(dropout=0, add_identity=True)
input_tensor = torch.rand(2, 20, 256)
input_tensor_nbc = input_tensor.transpose(0, 1)
assert torch.allclose(ffn(input_tensor).sum(), ffn(input_te... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='Cuda not available')
def test_basetransformerlayer_cuda():
operation_order = ('self_attn', 'ffn')
baselayer = BaseTransformerLayer(operation_order=operation_order, batch_first=True, attn_cfgs=dict(type='MultiheadAttention', embed_dims=256, num_heads... |
@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()
|
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