Continuation after https://github.com/pytorch/pytorch/pull/90163.
Here is a script I used to find all the non-existing arguments in the docstrings (the script can give false positives in presence of *args/**kwargs or decorators):
_Edit:_
I've realized that the indentation is wrong for the last `break` in the script, so the script only gives output for a function if the first docstring argument is wrong. I'll create a separate PR if I find more issues with corrected script.
``` python
import ast
import os
import docstring_parser
for root, dirs, files in os.walk('.'):
for name in files:
if root.startswith("./.git/") or root.startswith("./third_party/"):
continue
if name.endswith(".py"):
full_name = os.path.join(root, name)
with open(full_name, "r") as source:
tree = ast.parse(source.read())
for node in ast.walk(tree):
if isinstance(node, ast.FunctionDef):
all_node_args = node.args.args
if node.args.vararg is not None:
all_node_args.append(node.args.vararg)
if node.args.kwarg is not None:
all_node_args.append(node.args.kwarg)
if node.args.posonlyargs is not None:
all_node_args.extend(node.args.posonlyargs)
if node.args.kwonlyargs is not None:
all_node_args.extend(node.args.kwonlyargs)
args = [a.arg for a in all_node_args]
docstring = docstring_parser.parse(ast.get_docstring(node))
doc_args = [a.arg_name for a in docstring.params]
clean_doc_args = []
for a in doc_args:
clean_a = ""
for c in a.split()[0]:
if c.isalnum() or c == '_':
clean_a += c
if clean_a:
clean_doc_args.append(clean_a)
doc_args = clean_doc_args
for a in doc_args:
if a not in args:
print(full_name, node.lineno, args, doc_args)
break
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90505
Approved by: https://github.com/malfet, https://github.com/ZainRizvi
Summary:
When calling the add_images() method on the tensorboard SummaryWriter with a uint8 NCHW tensor, the tensor is incorrectly scaled, resulting in overflow behavior. This leads to incorrect images being displayed in tensorboard.
Issue: https://github.com/pytorch/pytorch/issues/31459
Local Testing (ran this code with and without the PR changes and printed scale_factor):
import torch
import torchvision
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter()
x=torch.tensor([[[[1, 2, 3], [4, 5, 6]]]], dtype=torch.uint8)
writer.add_images("images", x)
Before- scale_factor: 255, After- scale_factor: 1
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31778
Differential Revision: D19289189
Pulled By: anjali411
fbshipit-source-id: 350a1650337244deae4fd8f8b7fb0e354ae6986b
Summary:
This PR adds TensorBoard logging support natively within PyTorch. It is based on the tensorboardX code developed by lanpa and relies on changes inside the tensorflow/tensorboard repo landing at https://github.com/tensorflow/tensorboard/pull/2065.
With these changes users can simply `pip install tensorboard; pip install torch` and then log PyTorch data directly to the TensorBoard protobuf format using
```
import torch
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter()
s1 = torch.rand(1)
writer.add_scalar('data/scalar1', s1[0], 0)
writer.close()
```
Design:
- `EventFileWriter` and `RecordWriter` from tensorboardX now live in tensorflow/tensorboard
- `SummaryWriter` and PyTorch-specific conversion from tensors, nn modules, etc. now live in pytorch/pytorch. We also support Caffe2 blobs and nets.
Action items:
- [x] `from torch.utils.tensorboard import SummaryWriter`
- [x] rename functions
- [x] unittests
- [x] move actual writing function to tensorflow/tensorboard in https://github.com/tensorflow/tensorboard/pull/2065
Review:
- Please review for PyTorch standard formatting, code usage, etc.
- Please verify unittest usage is correct and executing in CI
Any significant changes made here will likely be synced back to github.com/lanpa/tensorboardX/ in the future.
cc orionr, ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16196
Differential Revision: D15062901
Pulled By: orionr
fbshipit-source-id: 3812eb6aa07a2811979c5c7b70810261f9ea169e
