Summary:
Resolves issue https://github.com/pytorch/pytorch/issues/31603
- A minor spelling typo is corrected: "suitible" --> "suitable"
- A minor quality of life improvement is added: the data format strings are better rendered as fixed width to indicate that they are string constants. "CHW" --> "`CHW`"
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31604
Differential Revision: D19697293
Pulled By: ezyang
fbshipit-source-id: ee38b0d4c9ca8a233ac9243c310d9a3b42ad6f32
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30430
When a module isn't a TracedModule, attempt to get name information with `original_name` property on module and default to 'Module' when no such property exists.
Test Plan:
### Change child module to scripted module:
```
model = torchvision.models.alexnet()
model.classifier = torch.jit.script(model.classifier)
```
### Add graph
```
w = SummaryWriter()
w.add_graph(model, torch.rand((2, 3, 224, 224)))
w.close()
```
### No errors
However, graph is disconnected at parts and hard to understand.
{F223327878}
Reviewed By: sanekmelnikov
Differential Revision: D18690836
fbshipit-source-id: 42295d06b7c1d48d5401776dca1e0d12cd64b49d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30286
add_hparams() in torch.utils.tensorboard.writer produced the following error
python3.7/site-packages/torch/utils/tensorboard/writer.py", line 294, in add_hparams
with SummaryWriter(log_dir=os.path.join(self.file_writer.get_logdir(), str(time.time()))) as w_hp:
AttributeError: 'NoneType' object has no attribute 'get_logdir'
Other methods such as add_scalar() and add_histogram() use self._get_file_writer() instead of self.file_writer directly.
Test Plan:
```
writer = summary_writer()
writer.add_hparams({"a": 0, "b": 0}, {"hparam/test_accuracy": 0.5}))
writer.flush()
writer.close()
```
Reviewed By: J0Nreynolds, sanekmelnikov
Differential Revision: D18650610
fbshipit-source-id: 1039dd2067d37913a8a131c8b372491a63154899
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30244
This makes several small changes to the tensorboard graph parsing methods to address the recent changes to the PyTorch JIT trace/graph.
- Inline graph to get information for all nodes
- Assign and propagate scope names to GetAttr nodes
- Prune all useless GetAttr nodes (any with a ClassType output type - tensors and primitives are kept)
- Create output nodes so output tensor shape can be examined
Reviewed By: sanekmelnikov
Differential Revision: D18556323
fbshipit-source-id: b73a809bacfa554c3fe9c4ae3563525f57539874
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27716
This uses the gfile filesystem abstraction that allows for writing to any filesystem that satisfies the interface (including S3).
Test Plan: Tested with local files and using internal S3 equivalent.
Reviewed By: natalialunova
Differential Revision: D17530694
fbshipit-source-id: c1f88c035fc03d91186b39092e42489f1c03d2cd
Summary:
The mesh plugin is now supported by default TensorBoard install, so removing this comment.
cc sanekmelnikov lanpa natalialunova
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27146
Differential Revision: D17717610
Pulled By: orionr
fbshipit-source-id: 516efad5b800f7261b1dc6728e798c830d88b6ca
Summary:
This fixes the empty graph problem since pytorch 1.2
To prevent such things happen, we have to make the test harder.
There 3 levels of verification.
lv 1. make sure that the graph is saved to some event file. <--currently here
lv 2. make sure the file can be read by tensorboard.
lv 3. make sure the graph in tensorboard is human-friendly.
I think (3) must be involved by a human.
(2) is possible, but it will be useless if we want to use lv 3 directly.
cc orionr
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25599
Reviewed By: sanekmelnikov
Differential Revision: D17229276
Pulled By: orionr
fbshipit-source-id: b39f2f1805ee0b3a456b2c69d97e6e3622f5220e
Summary:
Fixes https://github.com/pytorch/pytorch/issues/22389
In most cases we only import `PIL` methods when we need them, but we missed a spot.
cc lanpa natalialunova sanekmelnikov
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23023
Reviewed By: sanekmelnikov
Differential Revision: D16373492
Pulled By: orionr
fbshipit-source-id: b08bf8a9b5a861390eadf62eda21ac055777180f
Summary:
This cleans up the `torch.utils.tensorboard` API to remove all kwargs usage (which isn't clear to the user) and removes the "experimental" warning in prep for our 1.2 release.
We also don't need the additional PyTorch version checks now that we are in the codebase itself.
cc ezyang lanpa natalialunova
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21786
Reviewed By: natalialunova
Differential Revision: D15854892
Pulled By: orionr
fbshipit-source-id: 06b8498826946e578824d4b15c910edb3c2c20c6
Summary:
The new implementation of tracing supports more module. So many error-handling code can be removed by placing the old one (LegacyTracedModule).
cc orionr
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21339
Reviewed By: natalialunova
Differential Revision: D15695154
Pulled By: orionr
fbshipit-source-id: af7d35754e9f34bd1a0ad7b72a9ebe276ff8ab98
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21100
Added multifile flag to write scalar data into separate files. This can slow down dashboard loading.
Reviewed By: orionr
Differential Revision: D15548913
fbshipit-source-id: dd39a7f76f93025d28f14babbf933e39860e6910
Summary:
I accidentally added a TF dependency in #20413 by using the from tensorboard.plugins.mesh.summary import _get_json_config import.
I'm removing it at the cost of code duplication.
orionr, Please review.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21066
Reviewed By: natalialunova
Differential Revision: D15538746
Pulled By: orionr
fbshipit-source-id: 8a822719a4a9f5d67f1badb474e3a73cefce507f
Summary:
As a part of https://github.com/pytorch/pytorch/pull/20580 I noticed that we had some unusual variable naming in `summary.py`. This cleans it up and also removes some variables that weren't being used.
I'll wait until we have an `add_custom_scalars` test to land this.
cc lanpa natalialunova
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20861
Differential Revision: D15503420
Pulled By: orionr
fbshipit-source-id: 86d105a346198a1ca543d1c5d297804402ab5a0c
Summary:
I started adding support for the new **[mesh/point cloud](https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/g3doc/tensorboard.md)** data type introduced to TensorBoard recently.
I created the functions to add the data, created the appropriate summaries.
This new data type however requires a **Merged** summary containing the data for the vertices, colors and faces.
I got stuck at this stage. Maybe someone can help. lanpa?
I converted the example code by Google to PyTorch:
```python
import numpy as np
import trimesh
import torch
from torch.utils.tensorboard import SummaryWriter
sample_mesh = 'https://storage.googleapis.com/tensorflow-graphics/tensorboard/test_data/ShortDance07_a175_00001.ply'
log_dir = 'runs/torch'
batch_size = 1
# Camera and scene configuration.
config_dict = {
'camera': {'cls': 'PerspectiveCamera', 'fov': 75},
'lights': [
{
'cls': 'AmbientLight',
'color': '#ffffff',
'intensity': 0.75,
}, {
'cls': 'DirectionalLight',
'color': '#ffffff',
'intensity': 0.75,
'position': [0, -1, 2],
}],
'material': {
'cls': 'MeshStandardMaterial',
'roughness': 1,
'metalness': 0
}
}
# Read all sample PLY files.
mesh = trimesh.load_remote(sample_mesh)
vertices = np.array(mesh.vertices)
# Currently only supports RGB colors.
colors = np.array(mesh.visual.vertex_colors[:, :3])
faces = np.array(mesh.faces)
# Add batch dimension, so our data will be of shape BxNxC.
vertices = np.expand_dims(vertices, 0)
colors = np.expand_dims(colors, 0)
faces = np.expand_dims(faces, 0)
# Create data placeholders of the same shape as data itself.
vertices_tensor = torch.as_tensor(vertices)
faces_tensor = torch.as_tensor(faces)
colors_tensor = torch.as_tensor(colors)
writer = SummaryWriter(log_dir)
writer.add_mesh('mesh_color_tensor', vertices=vertices_tensor, faces=faces_tensor,
colors=colors_tensor, config_dict=config_dict)
writer.close()
```
I tried adding only the vertex summary, hence the others are supposed to be optional.
I got the following error from TensorBoard and it also didn't display the points:
```
Traceback (most recent call last):
File "/home/dawars/workspace/pytorch/venv/lib/python3.6/site-packages/werkzeug/serving.py", line 302, in run_wsgi
execute(self.server.app)
File "/home/dawars/workspace/pytorch/venv/lib/python3.6/site-packages/werkzeug/serving.py", line 290, in execute
application_iter = app(environ, start_response)
File "/home/dawars/workspace/pytorch/venv/lib/python3.6/site-packages/tensorboard/backend/application.py", line 309, in __call__
return self.data_applications[clean_path](environ, start_response)
File "/home/dawars/workspace/pytorch/venv/lib/python3.6/site-packages/werkzeug/wrappers/base_request.py", line 235, in application
resp = f(*args[:-2] + (request,))
File "/home/dawars/workspace/pytorch/venv/lib/python3.6/site-packages/tensorboard/plugins/mesh/mesh_plugin.py", line 252, in _serve_mesh_metadata
tensor_events = self._collect_tensor_events(request)
File "/home/dawars/workspace/pytorch/venv/lib/python3.6/site-packages/tensorboard/plugins/mesh/mesh_plugin.py", line 188, in _collect_tensor_events
tensors = self._multiplexer.Tensors(run, instance_tag)
File "/home/dawars/workspace/pytorch/venv/lib/python3.6/site-packages/tensorboard/backend/event_processing/plugin_event_multiplexer.py", line 400, in Tensors
return accumulator.Tensors(tag)
File "/home/dawars/workspace/pytorch/venv/lib/python3.6/site-packages/tensorboard/backend/event_processing/plugin_event_accumulator.py", line 437, in Tensors
return self.tensors_by_tag[tag].Items(_TENSOR_RESERVOIR_KEY)
KeyError: 'mesh_color_tensor_COLOR'
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20413
Differential Revision: D15500737
Pulled By: orionr
fbshipit-source-id: 426e8b966037d08c065bce5198fd485fd80a2b67
Summary:
When adding custom scalars like this
```python
from torch.utils.tensorboard import SummaryWriter
with SummaryWriter() as writer:
writer.add_custom_scalars({'Stuff': {
'Losses': ['MultiLine', ['loss/(one|two)']],
'Metrics': ['MultiLine', ['metric/(three|four)']],
}})
```
This error is raised:
```
TypeError: Parameter to MergeFrom() must be instance of same class: expected tensorboard.SummaryMetadata.PluginData got list.
```
Removing the square brackets around `SummaryMetadata.PluginData(plugin_name='custom_scalars')` should be enough to fix it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20580
Differential Revision: D15469700
Pulled By: orionr
fbshipit-source-id: 7ce58034bc2a74ab149fee6419319db68d8abafe
Summary:
The duplicated code of `_optimize_trace` in _pytorch_graph.py is used to bypass some optimization step which causes missing scope.
It seems that most of the problematic steps have been fixed recently. Standard models implemented in torchvision are visually inspected before the commit. However, the `+=` in 50d54a82d1/torchvision/models/resnet.py (L63) will let f4d9bfaa4d/torch/onnx/utils.py (L159) produce a bad result. It can be fixed by replacing it with `out += identity`. This also implies that `+=` has non-intuitive behavior.
cc orionr ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20394
Reviewed By: NarineK
Differential Revision: D15452204
Pulled By: orionr
fbshipit-source-id: eaa4c13f16551c78dc6419f1e22eb2c560af4cc5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20607
Add a new method SummaryWriter.flush() that iterates through all of the FileWriters and flushes them
Reviewed By: orionr
Differential Revision: D15380124
fbshipit-source-id: 1975f3f61c5ae3754552bfdb23f2cd78f687d19f
Summary:
Given that tensorboardX and our PyTorch 1.1 release had `log_dir` as the argument for SummaryWriter initialization and member variable (which some users access), we need to preserve this name. However, we might deprecate this in the future and I've added a `get_logdir` method that can be used in the future.
cc natalialunova, lanpa
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20382
Reviewed By: NarineK
Differential Revision: D15300941
Pulled By: orionr
fbshipit-source-id: a29a70fcbc614a32ebfa6c655962fdff081af1af
Summary:
As a part of supporting writing data into TensorBoard readable format, we show more example on how to use the function in addition to the API docs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20008
Reviewed By: natalialunova
Differential Revision: D15261502
Pulled By: orionr
fbshipit-source-id: 16611695a27e74bfcdf311e7cad40196e0947038
Summary:
Add logging import and a failed MLP model that confirms that we don't fail `add_graph` when graph optimization fails.
This addresses part of https://github.com/pytorch/pytorch/issues/18903
cc lanpa ezyang natalialunova
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20115
Reviewed By: natalialunova
Differential Revision: D15206765
Pulled By: orionr
fbshipit-source-id: c40b7e2671ef845a1529a2910ba030159f53f393
Summary:
This takes care of some outstanding review comments for https://github.com/pytorch/pytorch/pull/16196/
Specifically:
1. Add comment about kind
2. Add comment about GraphPy
3. Remove ONNX version comment
4. Remove scalar_dict from SummaryWriter and all history functions
cc lanpa ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20038
Reviewed By: natalialunova
Differential Revision: D15177257
Pulled By: orionr
fbshipit-source-id: 218aa799d8b7dbb58f422a331236bba4959347de
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
