10 examples of 'pytorch tensor to numpy' in Python

Every line of 'pytorch tensor to numpy' code snippets is scanned for vulnerabilities by our powerful machine learning engine that combs millions of open source libraries, ensuring your Python code is secure.

All examples are scanned by Snyk Code

By copying the Snyk Code Snippets you agree to
this disclaimer
26def torch2numpy(X):
27 """Convert a torch float32 tensor to a numpy array, sharing the same memory.
28 """
29 return X.detach().numpy()
Important

Use secure code every time

Secure your code as it's written. Use Snyk Code to scan source code in minutes – no build needed – and fix issues immediately. Enable Snyk Code

29@staticmethod
30def to_numpy(tensor):
31 if isinstance(tensor, cp.ndarray):
32 return cp.asnumpy(tensor)
33 return tensor
396def tensor_to_ndarray(tensor):
397 """
398 Convert float tensor into numpy image
399
400 :param tensor: input tensor
401 :type tensor: torch.Tensor
402 :return: numpy image
403 :rtype: np.ndarray
404 """
405 tensor_np = tensor.permute(1, 2, 0).cpu().numpy()
406 tensor_np = tensor_np.astype(np.float32)
407 tensor_np = (tensor_np * 255).astype(np.uint8)
408 return tensor_np
142@staticmethod
143def videos_to_numpy(tensor):
144 generated = tensor.transpose(0, 1, 2, 3, 4)
145 generated[generated < -1] = -1
146 generated[generated > 1] = 1
147 generated = (generated + 1) / 2 * 255
148 return generated.astype('uint8')
20def to_torch(ndarray):
21 if type(ndarray).__module__ == 'numpy':
22 return torch.from_numpy(ndarray)
23 elif not torch.is_tensor(ndarray):
24 raise ValueError("Cannot convert {} to torch tensor".format(type(ndarray)))
25 return ndarray
45def as_numpy(tensor):
46 if isinstance(tensor, list):
47 return [as_numpy(t) for t in tensor]
48 assert isinstance(tensor, core.LoDTensor)
49 lod = tensor.lod()
50 tensor_data = np.array(tensor)
51 if len(lod) == 0:
52 ans = tensor_data
53 else:
54 #raise RuntimeError("LoD Calculate lacks unit tests and buggy")
55 ans = tensor_data
56 # elif len(lod) == 1:
57 # ans = []
58 # idx = 0
59 # while idx < len(lod) - 1:
60 # ans.append(tensor_data[lod[idx]:lod[idx + 1]])
61 # idx += 1
62 # else:
63 # for l in reversed(lod):
64 # ans = []
65 # idx = 0
66 # while idx < len(l) - 1:
67 # ans.append(tensor_data[l[idx]:l[idx + 1]])
68 # idx += 1
69 # tensor_data = ans
70 # ans = tensor_data
71 return ans
32def to_torch(array):
33 if len(array.shape) == 4:
34 array = np.transpose(array, (0, 3, 1, 2))
35 return torch.from_numpy(array).to(device).float()
49def transform_to_numpy_image(tensor_image):
50 image = tensor_image.cpu().detach().numpy()
51 image = np.transpose(image, (0, 2, 3, 1))
52 if image.shape[3] != 3:
53 image = np.repeat(image, 3, axis=3)
54 else:
55 image = (image / 2 + 0.5)
56 return image
40def from_numpy(np_data):
41 return th.from_numpy(np_data)
36def to_torch(nparray):
37 tensor = torch.from_numpy(nparray).float().cuda()
38 return torch.autograd.Variable(tensor, requires_grad=False)

Related snippets