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Include contrib folder and pytorch stringifier
This PR introduces a new contrib folder under pudb for community customization. A stringifier for pytorch is included. Detailed changelog: * A module was added under pudb/contrib/stringifiers as place to store custom stringifiers. * A stringifier for pytorch tensors and modules is included. * An option was added in the settings menu for the user to enable or disable the cotrib content for users that want to stick to the core pudb installation. * Changes were made in var_view.py and settings.py to allow for inclusion of the contrib/stringifiers in the configuration menu. Signed-off-by: Giorgos Paraskevopoulos <[email protected]>
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# Community contributed extensions for pudb | ||
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Here the community can extend pudb with custom stringifiers, themes and shells. | ||
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## How to contribute your stringifiers | ||
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Simply add a new python module inside `contrib/stringifiers` that contains your custom stringifier. | ||
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Then add your stringifier to the `CONTRIB_STRINGIFIERS` dict inside | ||
`contrib/stringifiers/__init__.py`. | ||
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The new options should appear in the pudb settings pane after setting the `Enable community contributed content` option. |
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from pudb.contrib.stringifiers import CONTRIB_STRINGIFIERS |
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from pudb.contrib.stringifiers.torch_stringifier import torch_stringifier_fn | ||
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CONTRIB_STRINGIFIERS = { | ||
# User contributed stringifiers | ||
# Use the contrib prefix for all keys to avoid clashes with the core stringifiers | ||
# and make known to the user that this is community contributed code | ||
"contrib.pytorch": torch_stringifier_fn, | ||
} |
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from typing import Any | ||
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try: | ||
import torch | ||
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HAVE_TORCH = 1 | ||
except: | ||
HAVE_TORCH = 0 | ||
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from pudb.var_view import default_stringifier | ||
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def torch_stringifier_fn(value: Any) -> str: | ||
if not HAVE_TORCH: | ||
# Fall back to default stringifier | ||
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return default_stringifier(value) | ||
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if isinstance(value, torch.nn.Module): | ||
device: str = str(next(value.parameters()).device) | ||
params: int = sum([p.numel() for p in value.parameters() if p.requires_grad]) | ||
rep: str = value.__repr__() if len(value.__repr__()) < 55 else type( | ||
value | ||
).__name__ | ||
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return "{}[{}] Params: {}".format(rep, device, params) | ||
elif isinstance(value, torch.Tensor): | ||
return "{}[{}][{}] {}".format( | ||
type(value).__name__, | ||
str(value.dtype).replace("torch.", ""), | ||
str(value.device), | ||
str(list(value.shape)), | ||
) | ||
else: | ||
# Fall back to default stringifier | ||
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return default_stringifier(value) |
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try: | ||
import torch | ||
HAVE_TORCH = True | ||
except ImportError: | ||
HAVE_TORCH = False | ||
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from pudb.var_view import default_stringifier | ||
from pudb.contrib.stringifiers.torch_stringifier import torch_stringifier_fn | ||
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def test_tensor(): | ||
if HAVE_TORCH: | ||
x = torch.randn(10, 5, 4) | ||
assert torch_stringifier_fn(x) == "Tensor[float32][cpu] [10, 5, 4]" | ||
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def test_conv_module(): | ||
if HAVE_TORCH: | ||
x = torch.nn.Conv2d(20, 10, 3) | ||
assert torch_stringifier_fn(x) == "Conv2d(20, 10, kernel_size=(3, 3), stride=(1, 1))[cpu] Params: 1810" | ||
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def test_linear_module(): | ||
if HAVE_TORCH: | ||
x = torch.nn.Linear(5, 2, bias=False) | ||
assert torch_stringifier_fn(x) == "Linear(in_features=5, out_features=2, bias=False)[cpu] Params: 10" | ||
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def test_long_module_repr_should_revert_to_type(): | ||
if HAVE_TORCH: | ||
x = torch.nn.Transformer() | ||
assert torch_stringifier_fn(x) == "Transformer[cpu] Params: 44140544" | ||
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def test_reverts_to_default_for_str(): | ||
x = "Everyone has his day, and some days last longer than others." | ||
assert torch_stringifier_fn(x) == default_stringifier(x) | ||
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def test_reverts_to_default_for_dict(): | ||
x = {"a": 1, "b": 2, "c": 3} | ||
assert torch_stringifier_fn(x) == default_stringifier(x) | ||
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def test_reverts_to_default_for_list(): | ||
x = list(range(1000)) | ||
assert torch_stringifier_fn(x) == default_stringifier(x) |