本文整理汇总了Python中torch.testing方法的典型用法代码示例。如果您正苦于以下问题：Python torch.testing方法的具体用法？Python torch.testing怎么用？Python torch.testing使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类torch的用法示例。
在下文中一共展示了torch.testing方法的8个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_to_numpy
# 需要导入模块: import torch [as 别名]
# 或者: from torch import testing [as 别名]
def test_to_numpy(np_torch_tuple, dim):
""" Test torch --> np conversion (right angles)"""
from_np, from_torch = np_torch_tuple
if dim == 0:
np_array = np.array(from_np)
torch_tensor = torch.tensor(from_torch)
elif dim == 1:
np_array = np.array([from_np])
torch_tensor = torch.tensor([from_torch])
elif dim == 2:
np_array = np.array([[from_np]])
torch_tensor = torch.tensor([[from_torch]])
else:
return
np_from_torch = transforms.to_numpy(torch_tensor, dim=dim)
np.testing.assert_allclose(np_array, np_from_torch) 示例2: test_from_numpy
# 需要导入模块: import torch [as 别名]
# 或者: from torch import testing [as 别名]
def test_from_numpy(np_torch_tuple, dim):
""" Test np --> torch conversion (right angles)"""
from_np, from_torch = np_torch_tuple
if dim == 0:
np_array = np.array(from_np)
torch_tensor = torch.tensor(from_torch)
elif dim == 1:
np_array = np.array([from_np])
torch_tensor = torch.tensor([from_torch])
elif dim == 2:
np_array = np.array([[from_np]])
torch_tensor = torch.tensor([[from_torch]])
else:
return
torch_from_np = transforms.from_numpy(np_array, dim=dim)
np.testing.assert_allclose(torch_tensor, torch_from_np) 示例3: test_last_pooling_with_mask
# 需要导入模块: import torch [as 别名]
# 或者: from torch import testing [as 别名]
def test_last_pooling_with_mask():
lp = pooling.LastPooling()
out = lp(TENSOR, padding_mask=MASK)
assert out.size() == torch.Size([2, 4])
expected = Tensor(
[
[5, 6, 7, 8],
[9, 10, 11, 12]
]
)
torch.testing.assert_allclose(out, expected) 示例4: test_first_pooling_with_mask
# 需要导入模块: import torch [as 别名]
# 或者: from torch import testing [as 别名]
def test_first_pooling_with_mask():
lp = pooling.FirstPooling()
out = lp(TENSOR, padding_mask=MASK)
assert out.size() == torch.Size([2, 4])
expected = Tensor(
[
[1, 2, 3, 4],
[1, 2, 3, 4]
]
)
torch.testing.assert_allclose(out, expected) 示例5: test_sum_pooling_with_mask
# 需要导入模块: import torch [as 别名]
# 或者: from torch import testing [as 别名]
def test_sum_pooling_with_mask():
lp = pooling.SumPooling()
out = lp(TENSOR, padding_mask=MASK)
assert out.size() == torch.Size([2, 4])
expected = Tensor(
[
[6, 8, 10, 12],
[15, 18, 21, 24]
]
)
torch.testing.assert_allclose(out, expected) 示例6: test_avg_pooling_with_mask
# 需要导入模块: import torch [as 别名]
# 或者: from torch import testing [as 别名]
def test_avg_pooling_with_mask():
lp = pooling.AvgPooling()
out = lp(TENSOR, padding_mask=MASK)
assert out.size() == torch.Size([2, 4])
expected = Tensor(
[
[3, 4, 5, 6],
[5, 6, 7, 8]
]
)
torch.testing.assert_allclose(out, expected) 示例7: test_model_updates
# 需要导入模块: import torch [as 别名]
# 或者: from torch import testing [as 别名]
def test_model_updates(inputs, model, target, whitelist=[]):
args = Args()
args.balanceloss = False
args.window_smooth = 0
criterion = default_criterion.DefaultCriterion(args)
optimizer = torch.optim.SGD(model.parameters(), lr=1.)
optimizer.zero_grad()
params = list(model.named_parameters())
initial_params = [(name, p.clone()) for (name, p) in params]
output = model(inputs)
meta = {}
_, loss, _ = criterion(output, target, meta)
loss.backward()
optimizer.step()
for (_, p0), (name, p1) in zip(initial_params, params):
if name in whitelist:
continue
try:
np.testing.assert_raises(AssertionError, torch.testing.assert_allclose, p0, p1)
except AssertionError:
if 'bias' in name:
print('Warning: {} not updating'.format(name))
continue
if p1.grad.norm() > 1e-6:
print('Warning: {} not significantly updating'.format(name))
continue
print('{} not updating'.format(name))
for (nn1, pp1) in params:
print('{} grad: {}'.format(nn1, pp1.grad.norm().item()))
import pdb
pdb.set_trace()
raise 示例8: test_return_ticket_np_torch
# 需要导入模块: import torch [as 别名]
# 或者: from torch import testing [as 别名]
def test_return_ticket_np_torch(dim):
""" Test torch --> np --> torch --> np conversion"""
max_tested_ndim = 4
# Random tensor shape
tensor_shape = [random.randint(1, 10) for _ in range(max_tested_ndim)]
# Make sure complex dimension has even shape
tensor_shape[dim] = 2 * tensor_shape[dim]
complex_tensor = torch.randn(tensor_shape)
np_array = transforms.to_numpy(complex_tensor, dim=dim)
tensor_back = transforms.from_numpy(np_array, dim=dim)
np_back = transforms.to_numpy(tensor_back, dim=dim)
# Check torch --> np --> torch
assert_allclose(complex_tensor, tensor_back)
# Check np --> torch --> np
np.testing.assert_allclose(np_array, np_back)