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conversions.yaml
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conversions.yaml
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types:
- numpy: np.ndarray
pytorch: torch.Tensor
- numpy: np.float32
pytorch: torch.float32; torch.float
- numpy: np.float64
pytorch: torch.float64; torch.double
- numpy: np.float16
pytorch: torch.float16; torch.half
- numpy: np.int8
pytorch: torch.int8
- numpy: np.uint8
pytorch: torch.uint8
- numpy: np.int16
pytorch: torch.int16; torch.short
- numpy: np.int32
pytorch: torch.int32; torch.int
- numpy: np.int64
pytorch: torch.int64; torch.long
ones and zeros:
- numpy: np.empty((2, 3))
pytorch: torch.empty(2, 3)
- numpy: np.empty_like(x)
pytorch: torch.empty_like(x)
- numpy: np.eye
pytorch: torch.eye
- numpy: np.identity
pytorch: torch.eye
- numpy: np.ones
pytorch: torch.ones
- numpy: np.ones_like
pytorch: torch.ones_like
- numpy: np.zeros
pytorch: torch.zeros
- numpy: np.zeros_like
pytorch: torch.zeros_like
from existing data:
- numpy: np.array([[1, 2], [3, 4]])
pytorch: torch.tensor([[1, 2], [3, 4]])
- numpy: |
np.array([3.2, 4.3], dtype=np.float16)
np.float16([3.2, 4.3])
pytorch: torch.tensor([3.2, 4.3], dtype=torch.float16)
- numpy: x.copy()
pytorch: x.clone()
- numpy: x.astype(np.float32)
pytorch: x.type(torch.float32); x.float()
- numpy:
content: np.fromfile(file)
skip_test: true
pytorch:
content: torch.tensor(torch.Storage(file))
skip_test: true
- numpy: np.frombuffer
pytorch:
- numpy: np.fromfunction
pytorch:
- numpy: np.fromiter
pytorch:
- numpy: np.fromstring
pytorch:
- numpy: np.load
pytorch: torch.load
- numpy: np.loadtxt
pytorch:
- numpy: np.concatenate
pytorch: torch.cat
numerical ranges:
- numpy: np.arange(10)
pytorch: torch.arange(10)
- numpy: np.arange(2, 3, 0.1)
pytorch: torch.arange(2, 3, 0.1)
- numpy: np.linspace
pytorch: torch.linspace
- numpy: np.logspace
pytorch: torch.logspace
linear algebra:
- numpy: np.dot
pytorch: |
torch.dot # 1D arrays only
torch.mm # 2D arrays only
torch.mv # matrix-vector (2D x 1D)
- numpy: np.matmul
pytorch: torch.matmul
- numpy: np.tensordot
pytorch: torch.tensordot
- numpy: np.einsum
pytorch: torch.einsum
building matrices:
- numpy: np.diag
pytorch: torch.diag
- numpy: np.tril
pytorch: torch.tril
- numpy: np.triu
pytorch: torch.triu
attributes:
- numpy: x.shape
pytorch: x.shape; x.size()
- numpy: x.strides
pytorch: x.stride()
- numpy: x.ndim
pytorch: x.dim()
- numpy: x.data
pytorch: x.data
- numpy: x.size
pytorch: x.nelement()
- numpy: x.dtype
pytorch: x.dtype
indexing:
- numpy: x[0]
pytorch: x[0]
- numpy: x[:, 0]
pytorch: x[:, 0]
- numpy:
content: x[indices]
skip_test: true
pytorch:
content: x[indices]
skip_test: true
- numpy:
content: np.take(x, indices)
skip_test: true
pytorch:
content: torch.take(x, torch.LongTensor(indices))
skip_test: true
- numpy: x[x != 0]
pytorch: x[x != 0]
shape manipulation:
- numpy: x.reshape
pytorch: x.reshape; x.view
- numpy: x.resize()
pytorch: x.resize_
- numpy:
pytorch: x.resize_as_
- numpy: |
x = np.arange(6).reshape(3, 2, 1)
x.transpose(2, 0, 1) # 012 -> 201
pytorch: |
x = torch.arange(6).reshape(3, 2, 1)
x.permute(2, 0, 1); x.transpose(1, 2).transpose(0, 1) # 012 -> 021 -> 201
- numpy: x.flatten
pytorch: x.view(-1)
- numpy: x.squeeze()
pytorch: x.squeeze()
- numpy: x[:, None]; np.expand_dims(x, 1)
pytorch: x[:, None]; x.unsqueeze(1)
item selection and manipulation:
- numpy: np.put
pytorch:
- numpy: x.put
pytorch: x.put_
- numpy: |
x = np.array([1, 2, 3])
x.repeat(2) # [1, 1, 2, 2, 3, 3]
pytorch: |
x = torch.tensor([1, 2, 3])
x.repeat_interleave(2) # [1, 1, 2, 2, 3, 3]
x.repeat(2) # [1, 2, 3, 1, 2, 3]
x.repeat(2).reshape(2, -1).transpose(1, 0).reshape(-1)
# [1, 1, 2, 2, 3, 3]
- numpy: np.tile(x, (3, 2))
pytorch: x.repeat(3, 2)
- numpy: |
x = np.array([[0, 1], [2, 3], [4, 5]])
idxs = np.array([0, 2])
np.choose(idxs, x) # [0, 5]
pytorch: |
x = torch.tensor([[0, 1], [2, 3], [4, 5]])
idxs = torch.tensor([0, 2])
x[idxs, torch.arange(x.shape[1])] # [0, 5]
torch.gather(x, 0, idxs[None, :])[0] # [0, 5]
- numpy: np.sort
pytorch:
content: sorted, indices = torch.sort(x, [dim])
skip_test: true
- numpy: np.argsort
pytorch:
content: sorted, indices = torch.sort(x, [dim])
skip_test: true
- numpy: np.nonzero
pytorch: torch.nonzero
- numpy: np.where
pytorch: torch.where
- numpy: x[::-1]
pytorch: torch.flip(x, [0])
- numpy: np.unique(x)
pytorch: torch.unique(x)
calculation:
- numpy: x.min
pytorch: x.min
- numpy: x.argmin
pytorch: x.argmin
- numpy: x.max
pytorch: x.max
- numpy: x.argmax
pytorch: x.argmax
- numpy: x.clip
pytorch: x.clamp
- numpy: x.round
pytorch: x.round
- numpy: np.floor(x)
pytorch:
content: torch.floor(x); x.floor()
skip_test: true
- numpy: np.ceil(x)
pytorch:
content: torch.ceil(x); x.ceil()
skip_test: true
- numpy: x.trace
pytorch: x.trace
- numpy: x.sum
pytorch: x.sum
- numpy: x.sum(axis=0)
pytorch: x.sum(0)
- numpy: x.cumsum
pytorch: x.cumsum
- numpy: x.mean
pytorch: x.mean
- numpy: x.std
pytorch: x.std
- numpy: x.prod
pytorch: x.prod
- numpy: x.cumprod
pytorch: x.cumprod
- numpy: x.all
pytorch: x.all
- numpy: x.any
pytorch: x.any
arithmetic and comparison operations:
- numpy: np.less
pytorch: x.lt
- numpy: np.less_equal
pytorch: x.le
- numpy: np.greater
pytorch: x.gt
- numpy: np.greater_equal
pytorch: x.ge
- numpy: np.equal
pytorch: x.eq
- numpy: np.not_equal
pytorch: x.ne
random numbers:
- numpy: np.random.seed
pytorch: torch.manual_seed
- numpy: np.random.permutation(5)
pytorch: torch.randperm(5)
numerical operations:
- numpy: np.sign
pytorch: torch.sign
- numpy: np.sqrt
pytorch: torch.sqrt