🐛 Bug

This is a documentation-related bug. In the TransfoXL documentation, the tokenization example is wrong. The snippet goes:

tokenizer = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103')
...
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1

This code output

With the latest version of scipy.misc, scipy.misc.toimage is no longer available. To load and save an image as png we now have to use PIL, breaking tensorboard image summary.

Here is how I fixed the bug:
1./ At the end of main.py, log a uint8 image
logger.image_summary(tag, (images * 255).astype(np.uint8), step+1)
2./ In Logger class, package image as bytes with the PIL library (mode="L

Traceback (most recent call last):
File "/home/ubuntu/Real-Time-Voice-Cloning-master/toolbox/init.py", line 59, in
self.ui.browser_load_button.clicked.connect(lambda: self.load_from_browser())
File "/home/ubuntu/Real-Time-Voice-Cloning-master/toolbox/init.py", line 122, in load_from_browser
self.add_real_utterance(wav, name, speaker_name)
File "/home/ubuntu/Real

I had download pretrained model by 'bash scripts/download_cyclegan_model.sh cityscapes_label2photo',
and run 'test.py' to generate photos,
and run 'scripts/eval_cityscapes/evaluate.py',
finally the result as following is far less than the paper result

the paper result is following:

Judging by the logic in https://github.com/horovod/horovod/blob/38e91bee84efbb5b563a4928027a75dc3974633b/setup.py#L1369 it is clear, that before installing Horovod one needs to install the underlying framework(s) (TensorFlow, PyTorch, ...).

This is not mentioned in the installation instructions which made me think, I can install Horovod and then any framework I like (or switch between them) and

I trained RetinaNet with Resnet 34 backbone on COCO.

I would like to add it to the model Zoo.

It looks like weights for other networks are located at https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/

How can I upload weights there?

Train a simple NER tagger for Swedish trained for instance over this dataset.

For this task, we need to adapt the NLPTaskDataFetcher for the appropriate Swedish dataset and train a simple model using Swedish word embeddings. How to train a model is [illustrated here](https://github.com/zalandoresearch/flair/blob/master/resources/docs/TUTORIAL_TRAI

#3602 switched our docs from restructured text to markdown, which is a big improvement. However, there are some left over traces of rst formatting in the docstrings. It would be great if we could comb through these and update them.

• commands
• common
• data
• interpret
• models
• modules
• nn
• predictors
• tools
• training

How to help

Support for storing large tensor values in external files was introduced in #678, but AFAICT is undocumented.

This is a pretty important feature, functionally, but it's also important for end users who may not realise that they need to move around more than just the *.onnx file.

I would suggest it should be documented in IR.md, and perhaps there are other locations from which it could be s

Hi! I tried running generate to evaluate transformer.wmt14.en-fr on the WMT'14 test set but was only able to get a BLEU score of 35.42. I ran prepare-wmt14en2fr.sh and fairseq-preprocess on the data beforehand as well. Could you share the command for evaluating the Transformer ENFR WMT'14 model?

Here is what I'm using:

fairseq-generate data-bin/wmt14_en_fr/ \
    --path checkpoin

Traceback (most recent call last):
File "main.py", line 234, in
fire.Fire()
File "/home/zhangqilong/anaconda3/lib/python3.6/site-packages/fire/core.py", line 127, in Fire
component_trace = _Fire(component, args, context, name)
File "/home/zhangqilong/anaconda3/lib/python3.6/site-packages/fire/core.py", line 366, in _Fire
component, remaining_args)
File "/home/zh

❓ Questions & Help

when i run the following example:
examples/mnist_voxel_grid.py

it stoped and warning :

python3.7/site-packages/torch_geometric/nn/conv/spline_conv.py:104: UserWarning: We do not recommend using the non-optimized CPU version of SplineConv. If possible, please convert your data to the GPU.
warnings.warn('We do not recommend using the non-optimized CPU '

how can i fi

The last pooling layer of SENet should be change from self.avg_pool = nn.AvgPool2d(7, stride=1) to self.avg_pool = nn.AdaptiveAvgPool2d((1, 1)) so that the model can take different input size.

Describe the bug
I try to run tensorboardX/examples/demo_graph.py for jupyter notebook (launched by anaconda navigator) and I get the error seen at Additional context.

I just copy paste the code to notebook from Github.

Minimal runnable code to reproduce the behavior
class SimpleModel(nn.Module):
def init(self):
super(SimpleModel, self).init()

Originated as a forum post

Let's add a tutorial demonstrating how to serve trained models in C++ using torch.jit.save() and torch::jit::load(). I believe there is some subtlety requiring a torch.jit.trace() to wrap poutine.trace and poutine.replay logic for SVI mod

I'm building an edited version of the tensorflow-py36-cuda90 dockerfile where I pip install some more packages

# ==================================================================
# module list
# ------------------------------------------------------------------
# python        3.6    (apt)
# tensorflow    latest (pip)
# ================================================================

is it Grid Search can solve CASH problems with NNI , it seems that it is usually used for hyper-parameters optimization, have you guys have finished some revision for Grid Search for solving CASH problems.

about Cash problems can refer to ：microsoft/nni#1178

anaconda3/envs/py27t04/include/python2.7 -c _roi_crop.c -o ./_roi_crop.o
In file included from /home/wangzhonghao/anaconda3/envs/py27t04/lib/python2.7/site-packages/torch/utils/ffi/../../lib/include/THC/THC.h:4:0,
from _roi_crop.c:493:
/home/wangzhonghao/anaconda3/envs/py27t04/lib/python2.7/site-packages/torch/utils/ffi/../../lib/include/THC/THCGeneral.h:12:18: fatal error: cud

Describe the bug
This whole file seems to be largely un-tested by our unit testing suite.
https://github.com/OpenMined/PySyft/blob/master/syft/frameworks/torch/nn/rnn.py

Expected behavior
This file should have 100% test coverage.

Make sure to uncomment the codecov omit flag for this file OpenMined/PySyft#2896 (review)