| Name | statictorch JSON |
| Version |
0.0.3
JSON |
| download |
| home_page | None |
| Summary | Provides type annotations for torch tensors. |
| upload_time | 2025-01-18 06:40:40 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | None |
| license | MIT License |
| keywords |
torch
pytorch
typing
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
Basic usages (only specify the count of dimensions):
```python
import torch
from statictorch import *
def f(x: Tensor2d):
pass
f(torch.zeros([2, 3])) # No runtime error, but static type checkers might say: "Tensor" is not assignable to "Tensor2d"
f(Tensor2d(torch.zeros([2, 3]))) # It's ok.
```
Note that `TensorNd()` directly return the given tensor. So at runtime you can't distinguish `x` and `TensorNd(x)`, and `isinstance(TensorNd(x), TensorNd)` will return `False`.
`TensorNd` is defined as generic classes, so that you could add a dimension descriptor for them:
```python
from statictorch import *
class Batch(TensorDimensionDescriptor):
pass
class Channel(TensorDimensionDescriptor):
pass
class Sample(TensorDimensionDescriptor):
pass
def train_on(x: Tensor3d[Batch, Channel, Sample], y: Tensor3d[Channel, Batch, Sample]):
...
def load_data() -> tuple[Tensor3d[Batch, Channel, Sample], Tensor3d[Batch, Channel, Sample]]:
...
data_x, data_y = load_data()
train_on(data_x, data_y) # Pylance: Argument of type "Tensor3d[Batch, Channel, Sample]" cannot be assigned to parameter "y" of type "Tensor3d[Channel, Batch, Sample]" in function "train_on"
# To solve the problem:
data_y_transposed = data_y.transpose(1, 0)
train_on(data_x, Tensor3d(data_y_transposed))
# Of course in some cases you want to force passing data_y, just simply cheating the type checker with:
train_on(data_x, Tensor3d(data_y))
```
`typing.cast` is also a good idea, especially when you want to call functions like `torch.stack` on a `list[TensorNd]`:
```python
import statictorch
import typing
from torch import Tensor
def work_on(tensors: list[Tensor]):
...
my_tensors: list[statictorch.Tensor0d] = []
work_on(my_tensors) # Pylance: Argument of type "list[Tensor0d]" cannot be assigned to parameter "tensors" of type "list[Tensor]" in function "work_on"
# To solve the problem:
work_on(typing.cast(list[Tensor], my_tensors))
# If you find typing.cast too long:
work_on(statictorch.anify(my_tensors))
```
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"description": "Basic usages (only specify the count of dimensions):\r\n\r\n```python\r\nimport torch\r\nfrom statictorch import *\r\n\r\ndef f(x: Tensor2d):\r\n pass\r\n\r\nf(torch.zeros([2, 3])) # No runtime error, but static type checkers might say: \"Tensor\" is not assignable to \"Tensor2d\"\r\nf(Tensor2d(torch.zeros([2, 3]))) # It's ok.\r\n```\r\n\r\nNote that `TensorNd()` directly return the given tensor. So at runtime you can't distinguish `x` and `TensorNd(x)`, and `isinstance(TensorNd(x), TensorNd)` will return `False`.\r\n\r\n`TensorNd` is defined as generic classes, so that you could add a dimension descriptor for them:\r\n\r\n```python\r\nfrom statictorch import *\r\n\r\nclass Batch(TensorDimensionDescriptor):\r\n pass\r\n\r\nclass Channel(TensorDimensionDescriptor):\r\n pass\r\n\r\nclass Sample(TensorDimensionDescriptor):\r\n pass\r\n\r\ndef train_on(x: Tensor3d[Batch, Channel, Sample], y: Tensor3d[Channel, Batch, Sample]):\r\n ...\r\n\r\ndef load_data() -> tuple[Tensor3d[Batch, Channel, Sample], Tensor3d[Batch, Channel, Sample]]:\r\n ...\r\n\r\ndata_x, data_y = load_data()\r\ntrain_on(data_x, data_y) # Pylance: Argument of type \"Tensor3d[Batch, Channel, Sample]\" cannot be assigned to parameter \"y\" of type \"Tensor3d[Channel, Batch, Sample]\" in function \"train_on\"\r\n\r\n# To solve the problem:\r\ndata_y_transposed = data_y.transpose(1, 0)\r\ntrain_on(data_x, Tensor3d(data_y_transposed))\r\n\r\n# Of course in some cases you want to force passing data_y, just simply cheating the type checker with:\r\ntrain_on(data_x, Tensor3d(data_y))\r\n```\r\n\r\n`typing.cast` is also a good idea, especially when you want to call functions like `torch.stack` on a `list[TensorNd]`:\r\n\r\n```python\r\nimport statictorch\r\nimport typing\r\nfrom torch import Tensor\r\n\r\ndef work_on(tensors: list[Tensor]):\r\n ...\r\n\r\nmy_tensors: list[statictorch.Tensor0d] = []\r\nwork_on(my_tensors) # Pylance: Argument of type \"list[Tensor0d]\" cannot be assigned to parameter \"tensors\" of type \"list[Tensor]\" in function \"work_on\"\r\n\r\n# To solve the problem:\r\nwork_on(typing.cast(list[Tensor], my_tensors))\r\n\r\n# If you find typing.cast too long:\r\nwork_on(statictorch.anify(my_tensors))\r\n```\r\n",
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