A collection of inference modules for fastai2

Zachary Mueller

Last update: Oct 10, 2022

Related tags

Deep Learning fastinference

Overview

fastinference

A collection of inference modules for fastai including inference speedup and interpretability

Install

pip install fastinference

There are submodules available as well via:

pip install fastinference[interp] - Interpretability modules such as SHAP and Feature Importance
pip install fastinference[onnx-cpu] - ONNX for a CPU environment
pip install fastinference[onnx-gpu] - ONNX for a GPU environment

Wonderful Contributors:

(Using both their fastai handles as well as their GitHub handles if possible):

Pavel (Pak)

Comments

error in fully_decoded: ValueError: only one element tensors can be converted to Python scalars

This happens when enabling fully_decoded in get_preds. I don't know where this comes from, any ideas of where to start looking?

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-62-6fffcf6bb38e> in <module>
----> 1 foo = learn.get_preds(fully_decoded=True)

~/.local/lib/python3.6/site-packages/fastinference/inference/inference.py in get_preds(x, ds_idx, dl, raw_outs, decoded_loss, fully_decoded, **kwargs)
     70     else:
     71         outs.insert(0, raw)
---> 72     if fully_decoded: outs = _fully_decode(x.dls, inps, outs, dec_out, is_multi)
     73     if decoded_loss: outs = _decode_loss(x.dls.vocab, dec_out, outs)
     74     return outs

~/.local/lib/python3.6/site-packages/fastinference/inference/inference.py in _fully_decode(dl, inps, outs, dec_out, is_multi)
     14             inps[i] = torch.cat(inps[i], dim=0)
     15     else:
---> 16         inps = tensor(*inps[0])
     17     b = (*tuplify(inps), *tuplify(dec_out))
     18     try:

/usr/local/lib/python3.6/dist-packages/fastai2/torch_core.py in tensor(x, *rest, **kwargs)
    108     # if isinstance(x, (tuple,list)) and len(x)==0: return tensor(0)
    109     res = (x if isinstance(x, Tensor)
--> 110            else torch.tensor(x, **kwargs) if isinstance(x, (tuple,list))
    111            else _array2tensor(x) if isinstance(x, ndarray)
    112            else as_tensor(x.values, **kwargs) if isinstance(x, (pd.Series, pd.DataFrame))

ValueError: only one element tensors can be converted to Python scalars

opened by vrodriguezf 21

TypeError: requires_grad_() takes 1 positional argument but 2 were given

Here's the code I'm using

from fastai.text.all import *  
from fastinference.inference import *   
learn_class=load_learner('/content/gdrive/MyDrive/TuesdayChatbot/classifier_model-'+ver+'.pkl')   
test=["In the article there were several things left out."]   
learn_class.predict(test)   
learn_class.intrinsic_attention(test[0])

TypeError                                 Traceback (most recent call last)
<ipython-input-76-ba2c19ef8949> in <module>()
----> 1 learn_class.intrinsic_attention(test[0])

1 frames
/usr/local/lib/python3.6/dist-packages/fastinference/inference/text.py in _intrinsic_attention(learn, text, class_id)
    149     dl = learn.dls.test_dl([text])
    150     batch = next(iter(dl))[0]
--> 151     emb = learn.model[0].module.encoder(batch).detach().requires_grad_(True)
    152     lstm = learn.model[0].module(emb, True)
    153     learn.model.eval()

TypeError: requires_grad_() takes 1 positional argument but 2 were given

opened by randywreed 11

Fastinfernce has no tabular attirbute
Hi. The recent version of fastai 2.1.17 started throwing a lot of errors. One of them is related to fastinference. When I call Shap interpretation, it throws:

AttributeError: module 'fastinference' has no attribute 'tabular'
bug
opened by turgut090 8

"NameError: name '_ConstantFunc' is not defined"

from fastinference.inference import * (worked fine)

%%time for i in range(40): learn.predict(img)

This code returned:

~/miniconda3/envs/fastai2/lib/python3.6/site-packages/fastinference/inference/inference.py in predict(x, item, with_input, rm_type_tfms)
     79 def predict(x:Learner, item, with_input=False, rm_type_tfms=None):
     80         dl = x.dls.test_dl([item], rm_type_tfms=rm_type_tfms, num_workers=0)
---> 81         res = x.get_preds(dl=dl, with_input=with_input, with_decoded=True)
     82         return res

~/miniconda3/envs/fastai2/lib/python3.6/site-packages/fastinference/inference/inference.py in get_preds(self, ds_idx, dl, with_input, with_decoded, with_loss, raw, act, inner, reorder, cbs, **kwargs)
     55     if reorder and hasattr(dl, 'get_idxs'):
     56         idxs = dl.get_idxs()
---> 57         dl = dl.new(get_idxs = _ConstantFunc(idxs))
     58     cb = GatherPredsCallback(with_input=with_input, with_loss=with_loss, **kwargs)
     59     ctx_mgrs = self.validation_context(cbs=L(cbs)+[cb], inner=inner)

NameError: name '_ConstantFunc' is not defined

It seems that to run a predict with fastinference I need to have a databunch loaded with the learner itself.

The steps I followed: 1 - Import fastai 2 - Load a pre-trained model (this model was exported with.export()) with learn = load_learner('PATH_OF_LEARNER', cpu=False) 3 - Predict with a image loaded with opencv (cv2.imread(image_path)) 4 - Import fastinference with from fastinference.inference import * (no error returned) 5 - Repeat step 3 and got the error.

opened by lucasgabrielce 8

02_shap.interp.ipynb error?

Hi, I tried running this notebook and got an error at this line:

exp = ShapInterpretation(learn)
exp.decision_plot(class_id=0, row_idx=10)

TypeError                                 Traceback (most recent call last)
<ipython-input-12-907d69fd87c2> in <module>
----> 1 exp.decision_plot(class_id=0, row_idx=10)

<ipython-input-8-27630e1fb799> in decision_plot(self, class_id, row_idx, **kwargs)
     16     def decision_plot(self, class_id=0, row_idx=-1, **kwargs):
     17         "Visualize model decision using cumulative `SHAP` values."
---> 18         shap_vals, exp_val = _get_values(self, class_id)
     19         n_rows = shap_vals.shape[0]
     20         if row_idx == -1:

<ipython-input-10-91e394550211> in _get_values(interp, class_id)
      5     exp_vals = interp.explainer.expected_value
      6     if interp.is_multi_output:
----> 7         (class_name, class_idx) = _get_class_info(interp, class_id)
      8         print(f"Classification model detected, displaying score for the class {class_name}.")
      9         print("(use `class_id` to specify another class)")

<ipython-input-9-96fbf3aee7cf> in _get_class_info(interp, class_id)
      2 def _get_class_info(interp:ShapInterpretation, class_id):
      3     "Returns class name associated with index, or vice-versa"
----> 4     if isinstance(class_id, int): class_idx, class_name = class_id, interp.class_names[class_id]
      5     else: class_idx, class_name = interp.class_names.o2i[class_id], class_id
      6     return (class_name, class_idx)

TypeError: 'NoneType' object is not subscriptable

opened by ncduy0303 7

SHAP won't install "from fastinference.tabular import *" Fails

Trying to install, use SHAP.

!pip install fastinference fastai -q, this works

from fastinference.tabular import * produces:

ModuleNotFoundError Traceback (most recent call last) in () ----> 1 from fastinference.tabular import *

3 frames /usr/local/lib/python3.6/dist-packages/fastinference/tabular/init.py in () 3 raise ImportError("The interp module is not installed.") 4 ----> 5 from .shap import * 6 from .interpretation import *

/usr/local/lib/python3.6/dist-packages/fastinference/tabular/shap/init.py in () ----> 1 from .interp import *

/usr/local/lib/python3.6/dist-packages/fastinference/tabular/shap/interp.py in () 4 5 # Cell ----> 6 from .core import _prepare_data, _predict 7 import shap 8 from fastai.tabular.all import *

/usr/local/lib/python3.6/dist-packages/fastinference/tabular/shap/core.py in () 4 5 # Cell ----> 6 from fastai.tabular.all import * 7 8 # Cell

ModuleNotFoundError: No module named 'fastai.tabular.all'

NOTE: If your import is failing due to a missing package, you can manually install dependencies using either !pip or !apt.

To view examples of installing some common dependencies, click the "Open Examples" button below.

Have also tried: !pip install fastinference[interp] -q and no luck. Thanks!

opened by md598 5

can't import fastinference.onnx

Hi @muellerzr

I am running fastinference==0.0.32, fastai==2.1.9 and fastcore==1.3.12.

When I try to import from fastinference.onnx import * to use fastONNX class I get this error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-10-52ff53f82e02> in <module>
----> 1 from fastinference.onnx import *

~/.virtualenvs/challenge/lib/python3.6/site-packages/fastinference/onnx.py in <module>
     18 # Cell
     19 #export
---> 20 from .inference.inference import _decode_loss
     21 
     22 # Cell

~/.virtualenvs/challenge/lib/python3.6/site-packages/fastinference/inference/__init__.py in <module>
----> 1 from .inference import *
      2 from .text import *

~/.virtualenvs/challenge/lib/python3.6/site-packages/fastinference/inference/inference.py in <module>
     52 @patch
     53 def get_preds(self:Learner, ds_idx=1, dl=None, with_input=False, with_decoded=False, with_loss=False, raw=False, act=None,
---> 54                 inner=False, reorder=True, cbs=None, **kwargs):
     55     if dl is None: dl = self.dls[ds_idx].new(shuffled=False, drop_last=False)
     56     if reorder and hasattr(dl, 'get_idxs'):

~/.virtualenvs/challenge/lib/python3.6/site-packages/fastcore/meta.py in _f(f)
    114         to_f = getattr(to_f,'__func__',to_f)
    115         if hasattr(from_f,'__delwrap__'): return f
--> 116         sig = inspect.signature(from_f)
    117         sigd = dict(sig.parameters)
    118         k = sigd.pop('kwargs')

/usr/lib/python3.6/inspect.py in signature(obj, follow_wrapped)
   3063 def signature(obj, *, follow_wrapped=True):
   3064     """Get a signature object for the passed callable."""
-> 3065     return Signature.from_callable(obj, follow_wrapped=follow_wrapped)
   3066 
   3067 

/usr/lib/python3.6/inspect.py in from_callable(cls, obj, follow_wrapped)
   2813         """Constructs Signature for the given callable object."""
   2814         return _signature_from_callable(obj, sigcls=cls,
-> 2815                                         follow_wrapper_chains=follow_wrapped)
   2816 
   2817     @property

/usr/lib/python3.6/inspect.py in _signature_from_callable(obj, follow_wrapper_chains, skip_bound_arg, sigcls)
   2191 
   2192     if not callable(obj):
-> 2193         raise TypeError('{!r} is not a callable object'.format(obj))
   2194 
   2195     if isinstance(obj, types.MethodType):

TypeError: None is not a callable object

When I use fastai==2.1.5 and fastcore==1.3.2 this problem doesn't exist, but not using the updated fastai would cause other problems when running fastai interpretations eginterp.plot_top_losses.

opened by hududed 4

CUDA Out of Memory When Running Inference on a Large Test Set (And Proposed Fixes)
In inference.py's get_preds function, the inputs are being stored regardless of whether fully_decoded is True or False. These inputs are being stored on the GPU, and are only released from memory after the inference loop is finished.

As the size of the test set increases, the GPU will at some point run out of memory if the test set is large enough (In my test case, I ran out of memory with <50,000 items with an EfficientNet-B3A and image size of 224x224).

There's 2 ways to fix this:

1. store the inputs on CPU (I haven't tested this).

with torch.no_grad(): if is_multi: for i in range(x.dls.n_inp): #inps[i].append(batch[i]) inps[i].append(batch[i].cpu()) else: #inps.append(batch[:x.dls.n_inp]) inps.append(batch[:x.dls.n_inp].cpu()) # rest of the loop

2. Skip storing the inputs altogether if fully_decoded is False, as it's redundant. This should provide some speedup too. (Tested)

with torch.no_grad(): if fully_decoded: if is_multi: for i in range(x.dls.n_inp): inps[i].append(batch[i]) else: inps.append(batch[:x.dls.n_inp]) # rest of the loop

I've made this modification in my code and can vouch that it works. I can pass in arbitrarily long test sets and there's no issues.

I think (2) should be done regardless. I'm not sure about (1) as converting to .cpu() is an overhead, but perhaps we should leave this to the user (if they want to prioritise inference speed, they'd probably be skipping this anyways).
opened by rsomani95 4
New fastai inference API
Hey Zach, Let's work here prototyping the inference. What I would like to have (Santa's wishful letter).

Streamlined torchscript support on all fastai models, simple models should be compatible with jit.trace and more complex ones, with decisions with jit.script. The guys at facebook may be able to help here, they are super interested on this right now. The user should have simple image preprocessing/posprocessing to make inference work once the models is exported on plain pytorch. If jeremy splits the fastai lib on core/vision/etc... we could depende on the fastai core's.

ONNX: Exporting on all models, image encoders should work out of the box, some layers are missing for Unet's (PixelShuffle). Tabular should work also. Without being an expert, I would expect that torchscript replaces the ONNX pipeline in the future, one less layer.

RTTorch: We should start discussing with them probably, as the TensorRT frameworks is super fast for GPU inference. This could be done latter, once we have ONNX exports. I have a contact at NVIDIA that could help us export to TensorRT.

DeepStream? Stas Beckman is a guru on this topic, we could ask him what he thinks about it.

We should have tests that periodically verify that this functionality is not broken, and the performance is maintained. This is something fastai does not have right now and it needs, e.g., fastai's unet is slower than before, noted this the other day.

Another cool thing, would be to directly serve the model with torch.serve directly from fastai. Like,

learn.serve(port=5151)

and get a service running to make inference over HTTP.
opened by tcapelle 3
Returning N top predictions

Hi, I have a use case where I need multiple predictions and their probabilities - if the model is not confident enough in the prediction, the user gets a choice of N top predictions to choose the correct one themselves.

I've modified the fastinference code to implement this functionality (at the moment I just return a sorted list of all classes and their probabilities). Would you be interested in having it as a pull request? I haven't measured the speed and the code is a bit hacky at the moment, so I would need to clean it up first and integrate with the original functionality.

opened by andresti 2

pip install fastinference[interp] doesn't work

Running pip install fastinference[interp] is not working. I have the latest version of SHAP (0.39) already installed. Seems like it is crashing when trying to run setup.py for SHAP. fastinference seems to depend on SHAP 0.35.

(fastai) PS C:\work> pip install fastinference[interp]
Requirement already satisfied: fastinference[interp] in c:\users\pc\miniconda3\envs\fastai\lib\site-packages (0.0.36)
Requirement already satisfied: fastai>=2.0.0 in c:\work\ml\fastai (from fastinference[interp]) (2.3.1)
Collecting shap<0.36.0
  Using cached shap-0.35.0.tar.gz (273 kB)
Collecting plotly
  Using cached plotly-4.14.3-py2.py3-none-any.whl (13.2 MB)
Collecting plotnine
  Using cached plotnine-0.8.0-py3-none-any.whl (4.7 MB)
Requirement already satisfied: pip in c:\users\pc\miniconda3\envs\fastai\lib\site-packages (from fastai>=2.0.0->fastinference[interp]) (21.0.1)
Requirement already satisfied: packaging in c:\users\pc\appdata\roaming\python\python38\site-packages (from fastai>=2.0.0->fastinference[interp]) (20.4)
Requirement already satisfied: fastcore<1.4,>=1.3.8 in c:\users\pc\miniconda3\envs\fastai\lib\site-packages (from fastai>=2.0.0->fastinference[interp]) (1.3.20)
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Requirement already satisfied: MarkupSafe>=2.0 in c:\users\pc\miniconda3\envs\fastai\lib\site-packages (from jinja2->spacy<4->fastai>=2.0.0->fastinference[interp]) (2.0.1)
Requirement already satisfied: cycler>=0.10 in c:\users\pc\miniconda3\envs\fastai\lib\site-packages (from matplotlib->fastai>=2.0.0->fastinference[interp]) (0.10.0)
Requirement already satisfied: kiwisolver>=1.0.1 in c:\users\pc\miniconda3\envs\fastai\lib\site-packages (from matplotlib->fastai>=2.0.0->fastinference[interp]) (1.3.1)
Requirement already satisfied: pytz>=2017.3 in c:\users\pc\miniconda3\envs\fastai\lib\site-packages (from pandas->fastai>=2.0.0->fastinference[interp]) (2021.1)
Requirement already satisfied: retrying>=1.3.3 in c:\users\pc\miniconda3\envs\fastai\lib\site-packages (from plotly->fastinference[interp]) (1.3.3)
Requirement already satisfied: patsy>=0.5.1 in c:\users\pc\miniconda3\envs\fastai\lib\site-packages (from plotnine->fastinference[interp]) (0.5.1)
Requirement already satisfied: mizani>=0.7.3 in c:\users\pc\miniconda3\envs\fastai\lib\site-packages (from plotnine->fastinference[interp]) (0.7.3)
Requirement already satisfied: statsmodels>=0.12.1 in c:\users\pc\miniconda3\envs\fastai\lib\site-packages (from plotnine->fastinference[interp]) (0.12.2)
Requirement already satisfied: descartes>=1.1.0 in c:\users\pc\miniconda3\envs\fastai\lib\site-packages (from plotnine->fastinference[interp]) (1.1.0)
Requirement already satisfied: palettable in c:\users\pc\miniconda3\envs\fastai\lib\site-packages (from mizani>=0.7.3->plotnine->fastinference[interp]) (3.3.0)
Requirement already satisfied: joblib>=0.11 in c:\users\pc\miniconda3\envs\fastai\lib\site-packages (from scikit-learn->fastai>=2.0.0->fastinference[interp]) (1.0.1)
Requirement already satisfied: threadpoolctl>=2.0.0 in c:\users\pc\miniconda3\envs\fastai\lib\site-packages (from scikit-learn->fastai>=2.0.0->fastinference[interp]) (2.1.0)
Building wheels for collected packages: shap
  Building wheel for shap (setup.py) ... error
  ERROR: Command errored out with exit status 1:
   command: 'c:\users\pc\miniconda3\envs\fastai\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\pc\\AppData\\Local\\Temp\\pip-install-hejpe705\\shap_2eff441acfc04fe3bd0ac670c603bf74\\setup.py'"'"'; __file__='"'"'C:\\Users\\pc\\AppData\\Local\\Temp\\pip-install-hejpe705\\shap_2eff441acfc04fe3bd0ac670c603bf74\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'c:\users\pc\AppData\Local\Temp\pip-wheel-gcnda71l'
       cwd: c:\users\pc\AppData\Local\Temp\pip-install-hejpe705\shap_2eff441acfc04fe3bd0ac670c603bf74\
  Complete output (67 lines):
  running bdist_wheel
  running build
  running build_py
  creating build
  creating build\lib.win-amd64-3.8
  creating build\lib.win-amd64-3.8\shap
  copying shap\common.py -> build\lib.win-amd64-3.8\shap
  copying shap\datasets.py -> build\lib.win-amd64-3.8\shap
  copying shap\__init__.py -> build\lib.win-amd64-3.8\shap
  creating build\lib.win-amd64-3.8\shap\explainers
  copying shap\explainers\additive.py -> build\lib.win-amd64-3.8\shap\explainers
  copying shap\explainers\bruteforce.py -> build\lib.win-amd64-3.8\shap\explainers
  copying shap\explainers\explainer.py -> build\lib.win-amd64-3.8\shap\explainers
  copying shap\explainers\gradient.py -> build\lib.win-amd64-3.8\shap\explainers
  copying shap\explainers\kernel.py -> build\lib.win-amd64-3.8\shap\explainers
  copying shap\explainers\linear.py -> build\lib.win-amd64-3.8\shap\explainers
  copying shap\explainers\mimic.py -> build\lib.win-amd64-3.8\shap\explainers
  copying shap\explainers\partition.py -> build\lib.win-amd64-3.8\shap\explainers
  copying shap\explainers\permutation.py -> build\lib.win-amd64-3.8\shap\explainers
  copying shap\explainers\pytree.py -> build\lib.win-amd64-3.8\shap\explainers
  copying shap\explainers\sampling.py -> build\lib.win-amd64-3.8\shap\explainers
  copying shap\explainers\tf_utils.py -> build\lib.win-amd64-3.8\shap\explainers
  copying shap\explainers\tree.py -> build\lib.win-amd64-3.8\shap\explainers
  copying shap\explainers\__init__.py -> build\lib.win-amd64-3.8\shap\explainers
  creating build\lib.win-amd64-3.8\shap\explainers\other
  copying shap\explainers\other\coefficent.py -> build\lib.win-amd64-3.8\shap\explainers\other
  copying shap\explainers\other\lime.py -> build\lib.win-amd64-3.8\shap\explainers\other
  copying shap\explainers\other\maple.py -> build\lib.win-amd64-3.8\shap\explainers\other
  copying shap\explainers\other\random.py -> build\lib.win-amd64-3.8\shap\explainers\other
  copying shap\explainers\other\treegain.py -> build\lib.win-amd64-3.8\shap\explainers\other
  copying shap\explainers\other\__init__.py -> build\lib.win-amd64-3.8\shap\explainers\other
  creating build\lib.win-amd64-3.8\shap\explainers\deep
  copying shap\explainers\deep\deep_pytorch.py -> build\lib.win-amd64-3.8\shap\explainers\deep
  copying shap\explainers\deep\deep_tf.py -> build\lib.win-amd64-3.8\shap\explainers\deep
  copying shap\explainers\deep\__init__.py -> build\lib.win-amd64-3.8\shap\explainers\deep
  creating build\lib.win-amd64-3.8\shap\plots
  copying shap\plots\bar.py -> build\lib.win-amd64-3.8\shap\plots
  copying shap\plots\colorconv.py -> build\lib.win-amd64-3.8\shap\plots
  copying shap\plots\colors.py -> build\lib.win-amd64-3.8\shap\plots
  copying shap\plots\decision.py -> build\lib.win-amd64-3.8\shap\plots
  copying shap\plots\dependence.py -> build\lib.win-amd64-3.8\shap\plots
  copying shap\plots\embedding.py -> build\lib.win-amd64-3.8\shap\plots
  copying shap\plots\force.py -> build\lib.win-amd64-3.8\shap\plots
  copying shap\plots\force_matplotlib.py -> build\lib.win-amd64-3.8\shap\plots
  copying shap\plots\image.py -> build\lib.win-amd64-3.8\shap\plots
  copying shap\plots\monitoring.py -> build\lib.win-amd64-3.8\shap\plots
  copying shap\plots\partial_dependence.py -> build\lib.win-amd64-3.8\shap\plots
  copying shap\plots\summary.py -> build\lib.win-amd64-3.8\shap\plots
  copying shap\plots\text.py -> build\lib.win-amd64-3.8\shap\plots
  copying shap\plots\waterfall.py -> build\lib.win-amd64-3.8\shap\plots
  copying shap\plots\__init__.py -> build\lib.win-amd64-3.8\shap\plots
  creating build\lib.win-amd64-3.8\shap\benchmark
  copying shap\benchmark\experiments.py -> build\lib.win-amd64-3.8\shap\benchmark
  copying shap\benchmark\measures.py -> build\lib.win-amd64-3.8\shap\benchmark
  copying shap\benchmark\methods.py -> build\lib.win-amd64-3.8\shap\benchmark
  copying shap\benchmark\metrics.py -> build\lib.win-amd64-3.8\shap\benchmark
  copying shap\benchmark\models.py -> build\lib.win-amd64-3.8\shap\benchmark
  copying shap\benchmark\plots.py -> build\lib.win-amd64-3.8\shap\benchmark
  copying shap\benchmark\__init__.py -> build\lib.win-amd64-3.8\shap\benchmark
  creating build\lib.win-amd64-3.8\shap\plots\resources
  copying shap\plots\resources\bundle.js -> build\lib.win-amd64-3.8\shap\plots\resources
  copying shap\plots\resources\logoSmallGray.png -> build\lib.win-amd64-3.8\shap\plots\resources
  copying shap\tree_shap.h -> build\lib.win-amd64-3.8\shap
  running build_ext
  numpy.get_include() c:\users\pc\miniconda3\envs\fastai\lib\site-packages\numpy\core\include
  building 'shap._cext' extension
  error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/
  ----------------------------------------
  ERROR: Failed building wheel for shap
  Running setup.py clean for shap
Failed to build shap
Installing collected packages: shap, plotnine, plotly
  Attempting uninstall: shap
    Found existing installation: shap 0.39.0
    Uninstalling shap-0.39.0:
      Successfully uninstalled shap-0.39.0
    Running setup.py install for shap ... error
    ERROR: Command errored out with exit status 1:
     command: 'c:\users\pc\miniconda3\envs\fastai\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\pc\\AppData\\Local\\Temp\\pip-install-hejpe705\\shap_2eff441acfc04fe3bd0ac670c603bf74\\setup.py'"'"'; __file__='"'"'C:\\Users\\pc\\AppData\\Local\\Temp\\pip-install-hejpe705\\shap_2eff441acfc04fe3bd0ac670c603bf74\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'c:\users\pc\AppData\Local\Temp\pip-record-uor8hprs\install-record.txt' --single-version-externally-managed --compile --install-headers 'c:\users\pc\miniconda3\envs\fastai\Include\shap'
         cwd: c:\users\pc\AppData\Local\Temp\pip-install-hejpe705\shap_2eff441acfc04fe3bd0ac670c603bf74\
    Complete output (67 lines):
    running install
    running build
    running build_py
    creating build
    creating build\lib.win-amd64-3.8
    creating build\lib.win-amd64-3.8\shap
    copying shap\common.py -> build\lib.win-amd64-3.8\shap
    copying shap\datasets.py -> build\lib.win-amd64-3.8\shap
    copying shap\__init__.py -> build\lib.win-amd64-3.8\shap
    creating build\lib.win-amd64-3.8\shap\explainers
    copying shap\explainers\additive.py -> build\lib.win-amd64-3.8\shap\explainers
    copying shap\explainers\bruteforce.py -> build\lib.win-amd64-3.8\shap\explainers
    copying shap\explainers\explainer.py -> build\lib.win-amd64-3.8\shap\explainers
    copying shap\explainers\gradient.py -> build\lib.win-amd64-3.8\shap\explainers
    copying shap\explainers\kernel.py -> build\lib.win-amd64-3.8\shap\explainers
    copying shap\explainers\linear.py -> build\lib.win-amd64-3.8\shap\explainers
    copying shap\explainers\mimic.py -> build\lib.win-amd64-3.8\shap\explainers
    copying shap\explainers\partition.py -> build\lib.win-amd64-3.8\shap\explainers
    copying shap\explainers\permutation.py -> build\lib.win-amd64-3.8\shap\explainers
    copying shap\explainers\pytree.py -> build\lib.win-amd64-3.8\shap\explainers
    copying shap\explainers\sampling.py -> build\lib.win-amd64-3.8\shap\explainers
    copying shap\explainers\tf_utils.py -> build\lib.win-amd64-3.8\shap\explainers
    copying shap\explainers\tree.py -> build\lib.win-amd64-3.8\shap\explainers
    copying shap\explainers\__init__.py -> build\lib.win-amd64-3.8\shap\explainers
    creating build\lib.win-amd64-3.8\shap\explainers\other
    copying shap\explainers\other\coefficent.py -> build\lib.win-amd64-3.8\shap\explainers\other
    copying shap\explainers\other\lime.py -> build\lib.win-amd64-3.8\shap\explainers\other
    copying shap\explainers\other\maple.py -> build\lib.win-amd64-3.8\shap\explainers\other
    copying shap\explainers\other\random.py -> build\lib.win-amd64-3.8\shap\explainers\other
    copying shap\explainers\other\treegain.py -> build\lib.win-amd64-3.8\shap\explainers\other
    copying shap\explainers\other\__init__.py -> build\lib.win-amd64-3.8\shap\explainers\other
    creating build\lib.win-amd64-3.8\shap\explainers\deep
    copying shap\explainers\deep\deep_pytorch.py -> build\lib.win-amd64-3.8\shap\explainers\deep
    copying shap\explainers\deep\deep_tf.py -> build\lib.win-amd64-3.8\shap\explainers\deep
    copying shap\explainers\deep\__init__.py -> build\lib.win-amd64-3.8\shap\explainers\deep
    creating build\lib.win-amd64-3.8\shap\plots
    copying shap\plots\bar.py -> build\lib.win-amd64-3.8\shap\plots
    copying shap\plots\colorconv.py -> build\lib.win-amd64-3.8\shap\plots
    copying shap\plots\colors.py -> build\lib.win-amd64-3.8\shap\plots
    copying shap\plots\decision.py -> build\lib.win-amd64-3.8\shap\plots
    copying shap\plots\dependence.py -> build\lib.win-amd64-3.8\shap\plots
    copying shap\plots\embedding.py -> build\lib.win-amd64-3.8\shap\plots
    copying shap\plots\force.py -> build\lib.win-amd64-3.8\shap\plots
    copying shap\plots\force_matplotlib.py -> build\lib.win-amd64-3.8\shap\plots
    copying shap\plots\image.py -> build\lib.win-amd64-3.8\shap\plots
    copying shap\plots\monitoring.py -> build\lib.win-amd64-3.8\shap\plots
    copying shap\plots\partial_dependence.py -> build\lib.win-amd64-3.8\shap\plots
    copying shap\plots\summary.py -> build\lib.win-amd64-3.8\shap\plots
    copying shap\plots\text.py -> build\lib.win-amd64-3.8\shap\plots
    copying shap\plots\waterfall.py -> build\lib.win-amd64-3.8\shap\plots
    copying shap\plots\__init__.py -> build\lib.win-amd64-3.8\shap\plots
    creating build\lib.win-amd64-3.8\shap\benchmark
    copying shap\benchmark\experiments.py -> build\lib.win-amd64-3.8\shap\benchmark
    copying shap\benchmark\measures.py -> build\lib.win-amd64-3.8\shap\benchmark
    copying shap\benchmark\methods.py -> build\lib.win-amd64-3.8\shap\benchmark
    copying shap\benchmark\metrics.py -> build\lib.win-amd64-3.8\shap\benchmark
    copying shap\benchmark\models.py -> build\lib.win-amd64-3.8\shap\benchmark
    copying shap\benchmark\plots.py -> build\lib.win-amd64-3.8\shap\benchmark
    copying shap\benchmark\__init__.py -> build\lib.win-amd64-3.8\shap\benchmark
    creating build\lib.win-amd64-3.8\shap\plots\resources
    copying shap\plots\resources\bundle.js -> build\lib.win-amd64-3.8\shap\plots\resources
    copying shap\plots\resources\logoSmallGray.png -> build\lib.win-amd64-3.8\shap\plots\resources
    copying shap\tree_shap.h -> build\lib.win-amd64-3.8\shap
    running build_ext
    numpy.get_include() c:\users\pc\miniconda3\envs\fastai\lib\site-packages\numpy\core\include
    building 'shap._cext' extension
    error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/
    ----------------------------------------
  Rolling back uninstall of shap
  Moving to c:\users\pc\miniconda3\envs\fastai\lib\site-packages\shap-0.39.0.dist-info\
   from c:\users\pc\miniconda3\envs\fastai\Lib\site-packages\~hap-0.39.0.dist-info
  Moving to c:\users\pc\miniconda3\envs\fastai\lib\site-packages\shap\
   from c:\users\pc\miniconda3\envs\fastai\Lib\site-packages\~hap
ERROR: Command errored out with exit status 1: 'c:\users\pc\miniconda3\envs\fastai\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\pc\\AppData\\Local\\Temp\\pip-install-hejpe705\\shap_2eff441acfc04fe3bd0ac670c603bf74\\setup.py'"'"'; __file__='"'"'C:\\Users\\pc\\AppData\\Local\\Temp\\pip-install-hejpe705\\shap_2eff441acfc04fe3bd0ac670c603bf74\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'c:\users\pc\AppData\Local\Temp\pip-record-uor8hprs\install-record.txt' --single-version-externally-managed --compile --install-headers 'c:\users\pc\miniconda3\envs\fastai\Include\shap' Check the logs for full command output.

opened by EtienneT 2

Bump addressable from 2.7.0 to 2.8.1 in /docs
Bumps addressable from 2.7.0 to 2.8.1.

Changelog

Sourced from addressable's changelog.

Addressable 2.8.1

refactor Addressable::URI.normalize_path to address linter offenses (#430)

remove redundant colon in Addressable::URI::CharacterClasses::AUTHORITY regex (#438)

update gemspec to reflect supported Ruby versions (#466, #464, #463)

compatibility w/ public_suffix 5.x (#466, #465, #460)

fixes "invalid byte sequence in UTF-8" exception when unencoding URLs containing non UTF-8 characters (#459)

Ractor compatibility (#449)

use the whole string instead of a single line for template match (#431)

force UTF-8 encoding only if needed (#341)

#460: sporkmonger/addressable#460 #463: sporkmonger/addressable#463 #464: sporkmonger/addressable#464 #465: sporkmonger/addressable#465 #466: sporkmonger/addressable#466

Addressable 2.8.0

fixes ReDoS vulnerability in Addressable::Template#match

no longer replaces + with spaces in queries for non-http(s) schemes

fixed encoding ipv6 literals

the :compacted flag for normalized_query now dedupes parameters

fix broken escape_component alias

dropping support for Ruby 2.0 and 2.1

adding Ruby 3.0 compatibility for development tasks

drop support for rack-mount and remove Addressable::Template#generate

performance improvements

switch CI/CD to GitHub Actions

Commits

8657465 Update version, gemspec, and CHANGELOG for 2.8.1 (#474)

4fc5bb6 CI: remove Ubuntu 18.04 job (#473)

860fede Force UTF-8 encoding only if needed (#341)

99810af Merge pull request #431 from ojab/ct-_do_not_parse_multiline_strings

7ce0f48 Merge branch 'main' into ct-_do_not_parse_multiline_strings

7ecf751 Merge pull request #449 from okeeblow/freeze_concatenated_strings

41f12dd Merge branch 'main' into freeze_concatenated_strings

068f673 Merge pull request #459 from jarthod/iso-encoding-problem

b4c9882 Merge branch 'main' into iso-encoding-problem

08d27e8 Merge pull request #471 from sporkmonger/sporkmonger-enable-codeql

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dependencies
opened by dependabot[bot] 0
Bump tzinfo from 1.2.5 to 1.2.10 in /docs
Bumps tzinfo from 1.2.5 to 1.2.10.

Release notes

Sourced from tzinfo's releases.

v1.2.10

Fixed a relative path traversal bug that could cause arbitrary files to be loaded with require when used with RubyDataSource. Please refer to https://github.com/tzinfo/tzinfo/security/advisories/GHSA-5cm2-9h8c-rvfx for details. CVE-2022-31163.

Ignore the SECURITY file from Arch Linux's tzdata package. #134.

TZInfo v1.2.10 on RubyGems.org

v1.2.9

Fixed an incorrect InvalidTimezoneIdentifier exception raised when loading a zoneinfo file that includes rules specifying an additional transition to the final defined offset (for example, Africa/Casablanca in version 2018e of the Time Zone Database). #123.

TZInfo v1.2.9 on RubyGems.org

v1.2.8

Added support for handling "slim" format zoneinfo files that are produced by default by zic version 2020b and later. The POSIX-style TZ string is now used calculate DST transition times after the final defined transition in the file. The 64-bit section is now always used regardless of whether Time has support for 64-bit times. #120.

Rubinius is no longer supported.

TZInfo v1.2.8 on RubyGems.org

v1.2.7

Fixed 'wrong number of arguments' errors when running on JRuby 9.0. #114.

Fixed warnings when running on Ruby 2.8. #112.

TZInfo v1.2.7 on RubyGems.org

v1.2.6

Timezone#strftime('%s', time) will now return the correct number of seconds since the epoch. #91.

Removed the unused TZInfo::RubyDataSource::REQUIRE_PATH constant.

Fixed "SecurityError: Insecure operation - require" exceptions when loading data with recent Ruby releases in safe mode.

Fixed warnings when running on Ruby 2.7. #106 and #111.

TZInfo v1.2.6 on RubyGems.org

Changelog

Sourced from tzinfo's changelog.

Version 1.2.10 - 19-Jul-2022

Fixed a relative path traversal bug that could cause arbitrary files to be loaded with require when used with RubyDataSource. Please refer to https://github.com/tzinfo/tzinfo/security/advisories/GHSA-5cm2-9h8c-rvfx for details. CVE-2022-31163.

Ignore the SECURITY file from Arch Linux's tzdata package. #134.

Version 1.2.9 - 16-Dec-2020

Fixed an incorrect InvalidTimezoneIdentifier exception raised when loading a zoneinfo file that includes rules specifying an additional transition to the final defined offset (for example, Africa/Casablanca in version 2018e of the Time Zone Database). #123.

Version 1.2.8 - 8-Nov-2020

Added support for handling "slim" format zoneinfo files that are produced by default by zic version 2020b and later. The POSIX-style TZ string is now used calculate DST transition times after the final defined transition in the file. The 64-bit section is now always used regardless of whether Time has support for 64-bit times. #120.

Rubinius is no longer supported.

Version 1.2.7 - 2-Apr-2020

Fixed 'wrong number of arguments' errors when running on JRuby 9.0. #114.

Fixed warnings when running on Ruby 2.8. #112.

Version 1.2.6 - 24-Dec-2019

Timezone#strftime('%s', time) will now return the correct number of seconds since the epoch. #91.

Removed the unused TZInfo::RubyDataSource::REQUIRE_PATH constant.

Fixed "SecurityError: Insecure operation - require" exceptions when loading data with recent Ruby releases in safe mode.

Fixed warnings when running on Ruby 2.7. #106 and #111.

Commits

0814dcd Fix the release date.

fd05e2a Preparing v1.2.10.

b98c32e Merge branch 'fix-directory-traversal-1.2' into 1.2

ac3ee68 Remove unnecessary escaping of + within regex character classes.

9d49bf9 Fix relative path loading tests.

394c381 Remove private_constant for consistency and compatibility.

5e9f990 Exclude Arch Linux's SECURITY file from the time zone index.

17fc9e1 Workaround for 'Permission denied - NUL' errors with JRuby on Windows.

6bd7a51 Update copyright years.

9905ca9 Fix directory traversal in Timezone.get when using Ruby data source

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dependencies
opened by dependabot[bot] 0
Bump nokogiri from 1.12.5 to 1.13.6 in /docs
Bumps nokogiri from 1.12.5 to 1.13.6.

Release notes

Sourced from nokogiri's releases.

1.13.6 / 2022-05-08

Security

[CRuby] Address CVE-2022-29181, improper handling of unexpected data types, related to untrusted inputs to the SAX parsers. See GHSA-xh29-r2w5-wx8m for more information.

Improvements

{HTML4,XML}::SAX::{Parser,ParserContext} constructor methods now raise TypeError instead of segfaulting when an incorrect type is passed.

sha256:

58417c7c10f78cd1c0e1984f81538300d4ea98962cfd3f46f725efee48f9757a nokogiri-1.13.6-aarch64-linux.gem a2b04ec3b1b73ecc6fac619b41e9fdc70808b7a653b96ec97d04b7a23f158dbc nokogiri-1.13.6-arm64-darwin.gem 4437f2d03bc7da8854f4aaae89e24a98cf5c8b0212ae2bc003af7e65c7ee8e27 nokogiri-1.13.6-java.gem 99d3e212bbd5e80aa602a1f52d583e4f6e917ec594e6aa580f6aacc253eff984 nokogiri-1.13.6-x64-mingw-ucrt.gem a04f6154a75b6ed4fe2d0d0ff3ac02f094b54e150b50330448f834fa5726fbba nokogiri-1.13.6-x64-mingw32.gem a13f30c2863ef9e5e11240dd6d69ef114229d471018b44f2ff60bab28327de4d nokogiri-1.13.6-x86-linux.gem 63a2ca2f7a4f6bd9126e1695037f66c8eb72ed1e1740ef162b4480c57cc17dc6 nokogiri-1.13.6-x86-mingw32.gem 2b266e0eb18030763277b30dc3d64337f440191e2bd157027441ac56a59d9dfe nokogiri-1.13.6-x86_64-darwin.gem 3fa37b0c3b5744af45f9da3e4ae9cbd89480b35e12ae36b5e87a0452e0b38335 nokogiri-1.13.6-x86_64-linux.gem b1512fdc0aba446e1ee30de3e0671518eb363e75fab53486e99e8891d44b8587 nokogiri-1.13.6.gem

1.13.5 / 2022-05-04

Security

[CRuby] Vendored libxml2 is updated to address CVE-2022-29824. See GHSA-cgx6-hpwq-fhv5 for more information.

Dependencies

[CRuby] Vendored libxml2 is updated from v2.9.13 to v2.9.14.

Improvements

[CRuby] The libxml2 HTML4 parser no longer exhibits quadratic behavior when recovering some broken markup related to start-of-tag and bare < characters.

Changed

[CRuby] The libxml2 HTML4 parser in v2.9.14 recovers from some broken markup differently. Notably, the XML CDATA escape sequence <![CDATA[ and incorrectly-opened comments will result in HTML text nodes starting with <! instead of skipping the invalid tag. This behavior is a direct result of the quadratic-behavior fix noted above. The behavior of downstream sanitizers relying on this behavior will also change. Some tests describing the changed behavior are in test/html4/test_comments.rb.

... (truncated)

Changelog

Sourced from nokogiri's changelog.

1.13.6 / 2022-05-08

Security

[CRuby] Address CVE-2022-29181, improper handling of unexpected data types, related to untrusted inputs to the SAX parsers. See GHSA-xh29-r2w5-wx8m for more information.

Improvements

{HTML4,XML}::SAX::{Parser,ParserContext} constructor methods now raise TypeError instead of segfaulting when an incorrect type is passed.

1.13.5 / 2022-05-04

Security

[CRuby] Vendored libxml2 is updated to address CVE-2022-29824. See GHSA-cgx6-hpwq-fhv5 for more information.

Dependencies

[CRuby] Vendored libxml2 is updated from v2.9.13 to v2.9.14.

Improvements

[CRuby] The libxml2 HTML parser no longer exhibits quadratic behavior when recovering some broken markup related to start-of-tag and bare < characters.

Changed

[CRuby] The libxml2 HTML parser in v2.9.14 recovers from some broken markup differently. Notably, the XML CDATA escape sequence <![CDATA[ and incorrectly-opened comments will result in HTML text nodes starting with <! instead of skipping the invalid tag. This behavior is a direct result of the quadratic-behavior fix noted above. The behavior of downstream sanitizers relying on this behavior will also change. Some tests describing the changed behavior are in test/html4/test_comments.rb.

1.13.4 / 2022-04-11

Security

Address CVE-2022-24836, a regular expression denial-of-service vulnerability. See GHSA-crjr-9rc5-ghw8 for more information.

[CRuby] Vendored zlib is updated to address CVE-2018-25032. See GHSA-v6gp-9mmm-c6p5 for more information.

[JRuby] Vendored Xerces-J (xerces:xercesImpl) is updated to address CVE-2022-23437. See GHSA-xxx9-3xcr-gjj3 for more information.

[JRuby] Vendored nekohtml (org.cyberneko.html) is updated to address CVE-2022-24839. See GHSA-gx8x-g87m-h5q6 for more information.

Dependencies

[CRuby] Vendored zlib is updated from 1.2.11 to 1.2.12. (See LICENSE-DEPENDENCIES.md for details on which packages redistribute this library.)

[JRuby] Vendored Xerces-J (xerces:xercesImpl) is updated from 2.12.0 to 2.12.2.

[JRuby] Vendored nekohtml (org.cyberneko.html) is updated from a fork of 1.9.21 to 1.9.22.noko2. This fork is now publicly developed at https://github.com/sparklemotion/nekohtml

... (truncated)

Commits

b7817b6 version bump to v1.13.6

61b1a39 Merge pull request #2530 from sparklemotion/flavorjones-check-parse-memory-ty...

83cc451 fix: {HTML4,XML}::SAX::{Parser,ParserContext} check arg types

22c9e5b version bump to v1.13.5

6155881 doc: update CHANGELOG for v1.13.5

c519a47 Merge pull request #2527 from sparklemotion/2525-update-libxml-2_9_14-v1_13_x

66c2886 dep: update libxml2 to v2.9.14

b7c4cc3 test: unpend the LIBXML_LOADED_VERSION test on freebsd

eac7934 dev: require yaml

f3521ba style(rubocop): pend Style/FetchEnvVar for now

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dependencies
opened by dependabot[bot] 0

Remove package from results, alternative option

Here is a new option to use that is better:

    package_name:str, # The name of a python package
    depth_limit:int=1, # How deep to follow nested dependencies
) -> dict: # A dictionary of {package:version}
    "Recursively grabs dependencies of python package"
    pkgs = pipdeptree.get_installed_distributions(local_only=False, user_only=False)
    tree = pipdeptree.PackageDAG.from_pkgs(pkgs)
    tree = tree.filter([package_name], None)
    curr_depth=0
    def _get_deps(j, dep_dict={}, curr_depth=0):
        if curr_depth > depth_limit: return dep_dict
        if isinstance(j, list):
            for a in j:
                _get_deps(a, dep_dict, curr_depth)
        elif isinstance(j, dict):
            if 'package_name' in j.keys():
                if j['package_name'] not in dep_dict.keys() and j['package_name'] != package_name:
                    dep_dict[j['package_name']] = j['installed_version']
            if 'dependencies' in j.keys():
                curr_depth += 1
                return _get_deps(j['dependencies'], dep_dict, curr_depth)
        return dep_dict
    return _get_deps(ast.literal_eval(pipdeptree.render_json_tree(tree, 4)), {})

opened by muellerzr 0

Error importing interpretation module

It appears this is being triggered where get_features_core is being passed to the delegates decorator. I switched out get_features_core for TabularLearner.get_features_core, and this resolved the immediate error, but seemed to cause issues further downstream.

I don't know enough about the fastcore or fastinference libraries, to understand the desired behavior here

>>> from fastinference.tabular.interpretation import *
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/lib/python3.8/site-packages/fastinference/tabular/interpretation.py", line 138, in <module>
    def get_top_features_corr(x:TabularLearner, df:Optional[pd.DataFrame]=None, thresh:float=0.8, **kwargs):
  File "/lib/python3.8/site-packages/fastcore/meta.py", line 111, in _f
    if to is None: to_f,from_f = f.__base__.__init__,f.__init__
AttributeError: 'function' object has no attribute '__base__'

opened by ecatkins 0

A collection of inference modules for fastai2

Related tags

Overview

fastinference

Install

Wonderful Contributors:

Comments

from fastinference.tabular import * produces:

1. store the inputs on CPU (I haven't tested this).

2. Skip storing the inputs altogether if fully_decoded is False, as it's redundant. This should provide some speedup too. (Tested)

Addressable 2.8.1

Addressable 2.8.0

v1.2.10

v1.2.9

v1.2.8

v1.2.7

v1.2.6

Version 1.2.10 - 19-Jul-2022

Version 1.2.9 - 16-Dec-2020

Version 1.2.8 - 8-Nov-2020

Version 1.2.7 - 2-Apr-2020

Version 1.2.6 - 24-Dec-2019

1.13.6 / 2022-05-08

Security

Improvements

1.13.5 / 2022-05-04

Security

Dependencies

Improvements

Changed

1.13.6 / 2022-05-08

Security

Improvements

1.13.5 / 2022-05-04

Security

Dependencies

Improvements

Changed

1.13.4 / 2022-04-11

Security

Dependencies

Owner

Zachary Mueller

PyTorch-LIT is the Lite Inference Toolkit (LIT) for PyTorch which focuses on easy and fast inference of large models on end-devices.

Data-depth-inference - Data depth inference with python

A library of extension and helper modules for Python's data analysis and machine learning libraries.

Implementation of self-attention mechanisms for general purpose. Focused on computer vision modules. Ongoing repository.

Turning SymPy expressions into PyTorch modules.

Implementation of Bidirectional Recurrent Independent Mechanisms (Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules)

DI-HPC is an acceleration operator component for general algorithm modules in reinforcement learning algorithms

Implementation for our ICCV 2021 paper: Dual-Camera Super-Resolution with Aligned Attention Modules

Implementation for our ICCV 2021 paper: Dual-Camera Super-Resolution with Aligned Attention Modules

Weight initialization schemes for PyTorch nn.Modules

Nest - A flexible tool for building and sharing deep learning modules

This is a Machine Learning Based Hand Detector Project, It Uses Machine Learning Models and Modules Like Mediapipe, Developed By Google!

Pytorch modules for paralel models with same architecture. Ideal for multi agent-based systems

Stacs-ci - A set of modules to enable integration of STACS with commonly used CI / CD systems

Import Python modules from dicts and JSON formatted documents.

Snapchat-filters-app-opencv-python - Here we used opencv and other inbuilt python modules to create filter application like snapchat

Western-3DSlicer-Modules - Point-Set Registrations for Ultrasound Probe Calibrations

Torch-mutable-modules - Use in-place and assignment operations on PyTorch module parameters with support for autograd

Visyerres sgdf woob - Modules Woob pour l'intranet et autres sites Scouts et Guides de France

2. Skip storing the inputs altogether if `fully_decoded is False`, as it's redundant. This should provide some speedup too. (Tested)