repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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diffmimic | diffmimic-main/diffmimic/utils/io.py | import brax
import jax.numpy as jnp
from brax import QP
def deserialize_qp(nparray) -> brax.QP:
"""
Get QP from a trajectory numpy array
"""
num_bodies = nparray.shape[-1] // 13 # pos (,3) rot (,4) vel (,3) ang (,3)
batch_dims = nparray.shape[:-1]
slices = [num_bodies * x for x in [0, 3, 7,... | 1,191 | 33.057143 | 86 | py |
diffmimic | diffmimic-main/data/tools/amass_converter.py | import os
import numpy as np
from diffmimic.mimic_envs.system_configs import *
from brax import math
from brax.physics import bodies
from brax.physics.base import QP, vec_to_arr
from data.tools.rotation_utils.conversions import *
from data.tools.joint_utils import *
from data.tools.rotation_utils.quaternion import *
f... | 12,299 | 38.423077 | 120 | py |
diffmimic | diffmimic-main/data/tools/aist_converter.py | import os
import numpy as np
from diffmimic.mimic_envs.system_configs import *
from brax import math
from brax.physics import bodies
from brax.physics.base import QP, vec_to_arr
from data.tools.rotation_utils.conversions import *
from data.tools.joint_utils import *
from data.tools.rotation_utils.quaternion import *
f... | 12,007 | 37.860841 | 120 | py |
diffmimic | diffmimic-main/data/tools/rotation_utils/conversions.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
from typing import Optional
import torch
import torch.nn.functional as F
def quaternion_to_euler(x, axis='XYZ... | 20,109 | 31.699187 | 88 | py |
anime-face-detector | anime-face-detector-main/demo_gradio.py | import argparse
import functools
import pathlib
import cv2
import gradio as gr
import numpy as np
import PIL.Image
import torch
import anime_face_detector
def detect(img, face_score_threshold: float, landmark_score_threshold: float,
detector: anime_face_detector.LandmarkDetector) -> PIL.Image.Image:
... | 3,910 | 33.610619 | 165 | py |
anime-face-detector | anime-face-detector-main/anime_face_detector/detector.py | from __future__ import annotations
import pathlib
import warnings
from typing import Optional, Union
import cv2
import mmcv
import numpy as np
import torch.nn as nn
from mmdet.apis import inference_detector, init_detector
from mmpose.apis import inference_top_down_pose_model, init_pose_model
from mmpose.datasets impo... | 5,849 | 38.527027 | 79 | py |
anime-face-detector | anime-face-detector-main/anime_face_detector/__init__.py | import pathlib
import torch
from .detector import LandmarkDetector
def get_config_path(model_name: str) -> pathlib.Path:
assert model_name in ['faster-rcnn', 'yolov3', 'hrnetv2']
package_path = pathlib.Path(__file__).parent.resolve()
if model_name in ['faster-rcnn', 'yolov3']:
config_dir = pack... | 2,163 | 38.345455 | 98 | py |
anime-face-detector | anime-face-detector-main/anime_face_detector/configs/mmdet/faster-rcnn.py | model = dict(type='FasterRCNN',
backbone=dict(type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True)... | 3,338 | 48.835821 | 78 | py |
VectorQuantizedVAE | VectorQuantizedVAE-master/model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Categorical, RelaxedOneHotCategorical
import math
class VQEmbeddingEMA(nn.Module):
def __init__(self, latent_dim, num_embeddings, embedding_dim, commitment_cost=0.25, decay=0.999, epsilon=1e-5):
super(VQEmbe... | 7,126 | 36.909574 | 115 | py |
VectorQuantizedVAE | VectorQuantizedVAE-master/train.py | import argparse
from pathlib import Path
import numpy as np
from tqdm import tqdm
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torchvision import datasets, transforms, utils
from model import VQVAE, GSSOFT
def save_checkpoin... | 11,846 | 41.310714 | 118 | py |
psal | psal-master/test.py | import torch
from PIL import Image
from time import time
from torchvision import transforms
from torchvision.utils import save_image
from psal import PSAttention
PSIZE = 7
HPSIZE = PSIZE//2
tf = transforms.ToTensor()
imgb = "data/img1_512.png"
imga = "data/img2_512.png"
s = 512
a = tf(Image.open(imga).resize((s,... | 874 | 24 | 93 | py |
psal | psal-master/setup.py | from setuptools import setup, find_packages
from torch.utils import cpp_extension
setup(name='psal',
package_dir={"psal": "src"},
py_modules=["psal.psal_attention"],
ext_modules=[
cpp_extension.CUDAExtension('psal.patchmatch', ['src/patchmatch.cu']),
cpp_extension.CUDAExtension('p... | 462 | 29.866667 | 93 | py |
psal | psal-master/src/psal_attention.py | import torch
from torch.autograd import Function
from torch.nn.functional import pad, unfold, conv2d
from .patchmatch import backward, patchmatch
from .patchmatch_masked import backward_masked, patchmatch_masked
class PatchMatch(Function):
@staticmethod
def forward(ctx, a, b, patch_size=3, n_iters=10):
... | 6,987 | 43.227848 | 115 | py |
swissbert | swissbert-master/evaluation/swissner/run_ner.py | # Adapted from https://github.com/huggingface/transformers/blob/6f79d264422245d88c7a34032c1a8254a0c65752/examples/pytorch/token-classification/run_ner.py
#!/usr/bin/env python
# coding=utf-8
# Copyright 2020 The HuggingFace Team All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
#... | 29,543 | 40.32028 | 153 | py |
swissbert | swissbert-master/evaluation/romansh_alignment/utils.py | from typing import List, Union, Tuple
import numpy as np
import torch
from nltk import Alignment
from transformers import BatchEncoding
class AlignmentLevel(str):
WORD = "word"
TOKEN = "token"
class WordToTokenStrategy(str):
ALL_TOKENS = "all-tokens"
FIRST_TOKEN = "first-token"
class TokenToWordS... | 6,082 | 36.549383 | 145 | py |
swissbert | swissbert-master/evaluation/romansh_alignment/word_aligners/simalign_aligner.py | from collections import Counter
from typing import Tuple, List, Union
import torch
from networkx.algorithms.bipartite import from_biadjacency_matrix
from scipy.sparse import csr_matrix
from tqdm import tqdm
from evaluation.romansh_alignment.utils import LayerAggregation, subword_to_word_map, AlignmentLevel, WordAlign... | 7,157 | 39.902857 | 115 | py |
swissbert | swissbert-master/evaluation/romansh_alignment/encoders/hf.py | import math
from typing import Union
import numpy as np
import torch
from transformers import PreTrainedModel
from evaluation.romansh_alignment.encoders import SentenceEncoder
from evaluation.romansh_alignment.utils import LayerAggregation
class HuggingfaceEncoder(SentenceEncoder):
def __init__(self,
... | 4,411 | 42.683168 | 132 | py |
swissbert | swissbert-master/pretraining/fairseq_additions/models/swissbert/hub_interface.py | from fairseq import utils
from fairseq.models.xmod import XMODHubInterface
class SwissBERTHubInterface(XMODHubInterface):
def fill_mask(self, masked_input: str, topk: int = 5, **kwargs):
"""
Source: https://github.com/facebookresearch/fairseq/blob/58cc6cca18f15e6d56e3f60c959fe4f878960a60/fairseq/... | 2,844 | 37.445946 | 150 | py |
swissbert | swissbert-master/pretraining/fairseq_additions/models/swissbert/model.py | import logging
from argparse import Namespace
from pathlib import Path
from typing import Optional
import torch
from fairseq.models import register_model_architecture, register_model
from fairseq.models.roberta import base_architecture
from fairseq.models.xmod import XMODModel
from omegaconf import DictConfig
from fa... | 9,556 | 36.332031 | 142 | py |
swissbert | swissbert-master/pretraining/fairseq_additions/tasks/multilingual_masked_lm_xmod.py | # Adapted from https://github.com/facebookresearch/fairseq/blob/a48f235636557b8d3bc4922a6fa90f3a0fa57955/fairseq/tasks/multilingual_masked_lm.py
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tre... | 14,651 | 35.721805 | 144 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/setup.py | # This Python file uses the following encoding: utf-8
# !/usr/bin/env python
# Welcome to the Intel Extension for PyTorch setup.py.
#
# Environment variables you are probably interested in:
#
# DEBUG
# build with -O0 and -g (debug symbols)
#
# RELEASE
# build with optimization level -O2
#
# REL_WITH_DEB_... | 35,565 | 32.521206 | 97 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tools/linter/clang_format_all.py | #!/usr/bin/env python3
"""
A script that runs clang-format on all C/C++ files in CLANG_FORMAT_ALLOWLIST. There is
also a diff mode which simply checks if clang-format would make any changes, which is useful for
CI purposes.
If clang-format is not available, the script also downloads a platform-appropriate binary from
... | 5,337 | 31.54878 | 128 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tools/linter/translate_annotations.py | #!/usr/bin/env python3
import argparse
import json
import re
import subprocess
from bisect import bisect_right
from collections import defaultdict
from typing import (Callable, DefaultDict, Generic, List, Optional, Pattern,
Sequence, TypeVar, cast)
from typing_extensions import TypedDict
class H... | 5,447 | 29.099448 | 119 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tools/linter/mypy_wrapper.py | #!/usr/bin/env python3
"""
This module is meant to be run as a script (see the docstring of main
below) and passed the filename of any Python file in this repo, to
typecheck that file using only the subset of our mypy configs that apply
to it.
Since editors (e.g. VS Code) can be configured to use this wrapper
script ... | 7,511 | 32.99095 | 86 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tools/linter/clang_tidy/__main__.py | import argparse
import pathlib
import os
import shutil
import subprocess
import re
import sys
from typing import List
from tools.linter.clang_tidy.run import run
from tools.linter.clang_tidy.generate_build_files import generate_build_files
from tools.linter.install.clang_tidy import INSTALLATION_PATH, PLATFORM_TO_URL... | 7,789 | 33.622222 | 108 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/features/graph_capture.py | import torch
import torchvision.models as models
model = models.resnet50(weights='ResNet50_Weights.DEFAULT')
model.eval()
data = torch.rand(1, 3, 224, 224)
#################### code changes ####################
import intel_extension_for_pytorch as ipex
model = ipex.optimize(model, graph_mode=True)
##################... | 394 | 25.333333 | 59 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/features/int8_recipe_tuning/imagenet_autotune.py | import os
import torch
import torchvision.models as models
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import intel_extension_for_pytorch as ipex
model_names = sorted(name for name in models.__dict__
if name.islower() and not name.startswith("__")
and callable(models.__dict_... | 6,397 | 34.743017 | 105 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/features/int8_recipe_tuning/int8_autotune.py | import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor
import intel_extension_for_pytorch as ipex
########################################################################
# Reference for training portion:
# https://pytorch... | 3,634 | 26.961538 | 101 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/features/graph_optimization/folding.py | import torch
import torchvision.models as models
model = models.resnet50(weights='ResNet50_Weights.DEFAULT')
model.eval()
x = torch.randn(4, 3, 224, 224)
with torch.no_grad():
model = torch.jit.trace(model, x, check_trace=False).eval()
# Fold the BatchNormalization and propagate constant
torch.jit.freeze(model)... | 369 | 25.428571 | 61 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/features/graph_optimization/int8.py | import torch
import torchvision.models as models
import intel_extension_for_pytorch as ipex
from intel_extension_for_pytorch.quantization import prepare, convert
# construct the model
model = models.resnet50(weights='ResNet50_Weights.DEFAULT')
qconfig = ipex.quantization.default_static_qconfig
model.eval()
example_inp... | 1,636 | 29.314815 | 86 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/features/graph_optimization/fp32_bf16.py | import torch
import torchvision.models as models
# Import the Intel Extension for PyTorch
import intel_extension_for_pytorch as ipex
model = models.resnet50(weights='ResNet50_Weights.DEFAULT')
model.eval()
# Apply some fusions at the front end
model = ipex.optimize(model, dtype=torch.float32)
x = torch.randn(4, 3, ... | 543 | 26.2 | 61 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/features/hypertune/resnet50.py | import torch
import torchvision.models as models
def inference(model, data):
with torch.no_grad():
# warm up
for _ in range(100):
model(data)
# measure
import time
measure_iter = 100
start = time.time()
for _ in range(measure_iter):
output = model(data)
end = time.time()
... | 1,904 | 28.307692 | 162 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/training/single_instance_training_bf16.py | import torch
import torchvision
import intel_extension_for_pytorch as ipex
LR = 0.001
DOWNLOAD = True
DATA = 'datasets/cifar10/'
transform = torchvision.transforms.Compose([
torchvision.transforms.Resize((224, 224)),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5,... | 1,148 | 25.72093 | 82 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/training/single_instance_training_fp32.py | import torch
import torchvision
import intel_extension_for_pytorch as ipex
LR = 0.001
DOWNLOAD = True
DATA = 'datasets/cifar10/'
transform = torchvision.transforms.Compose([
torchvision.transforms.Resize((224, 224)),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5,... | 1,090 | 24.97619 | 70 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/inference/cpp/model_gen.py | #!/usr/bin/env python
# encoding: utf-8
import torch
import torchvision
model = torchvision.models.resnet50(pretrained=True)
model.eval()
input = torch.rand(1, 3, 224, 224)
model = torch.jit.trace(model, input, check_trace=False)
model.save('resnet50.pt')
print("save mode to: resnet50.pt")
| 296 | 18.8 | 56 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/inference/python/bert_fast_inference_bf16.py | import torch
from transformers import BertModel
model = BertModel.from_pretrained("bert-base-uncased")
model.eval()
vocab_size = model.config.vocab_size
batch_size = 1
seq_length = 512
data = torch.randint(vocab_size, size=[batch_size, seq_length])
torch.manual_seed(43)
#################### code changes ############... | 516 | 24.85 | 63 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/inference/python/resnet50_imperative_mode_inference_fp32.py | import torch
import torchvision.models as models
model = models.resnet50(weights='ResNet50_Weights.DEFAULT')
model.eval()
data = torch.rand(1, 3, 224, 224)
#################### code changes ####################
import intel_extension_for_pytorch as ipex
model = ipex.optimize(model)
###################################... | 376 | 25.928571 | 59 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/inference/python/bert_torchdynamo_mode_inference_fp32.py | import torch
from transformers import BertModel
model = BertModel.from_pretrained("bert-base-uncased")
model.eval()
vocab_size = model.config.vocab_size
batch_size = 1
seq_length = 512
data = torch.randint(vocab_size, size=[batch_size, seq_length])
# Experimental Feature
#################### code changes ###########... | 539 | 24.714286 | 63 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/inference/python/bert_torchscript_mode_inference_fp32.py | import torch
from transformers import BertModel
model = BertModel.from_pretrained("bert-base-uncased")
model.eval()
vocab_size = model.config.vocab_size
batch_size = 1
seq_length = 512
data = torch.randint(vocab_size, size=[batch_size, seq_length])
#################### code changes ####################
import intel_... | 640 | 28.136364 | 71 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/inference/python/resnet50_torchscript_mode_inference_bf16.py | import torch
import torchvision.models as models
model = models.resnet50(weights='ResNet50_Weights.DEFAULT')
model.eval()
data = torch.rand(1, 3, 224, 224)
#################### code changes ####################
import intel_extension_for_pytorch as ipex
model = ipex.optimize(model, dtype=torch.bfloat16)
#############... | 521 | 28 | 60 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/inference/python/bert_general_inference_script.py | import torch
from transformers import BertModel
def inference(model, data):
with torch.no_grad():
# warm up
for _ in range(100):
model(data)
# measure
import time
start = time.time()
for _ in range(100):
model(data)
end = time.time()
print('Inference took {:.2f} ms in ave... | 2,049 | 29.147059 | 92 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/inference/python/resnet50_general_inference_script.py | import torch
import torchvision.models as models
def inference(model, data):
with torch.no_grad():
# warm up
for _ in range(100):
model(data)
# measure
import time
start = time.time()
for _ in range(100):
output = model(data)
end = time.time()
print('Inference took {:.2f}... | 1,644 | 25.967213 | 92 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/inference/python/int8_deployment.py | import torch
#################### code changes ####################
import intel_extension_for_pytorch as ipex
######################################################
model = torch.jit.load('quantized_model.pt')
model.eval()
model = torch.jit.freeze(model)
data = torch.rand(1, 3, 224, 224)
with torch.no_grad():
mode... | 327 | 26.333333 | 54 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/inference/python/int8_calibration_dynamic.py | import os
import torch
#################### code changes ####################
import intel_extension_for_pytorch as ipex
from intel_extension_for_pytorch.quantization import prepare, convert
######################################################
##### Example Model #####
from transformers import BertModel
model = Ber... | 1,348 | 37.542857 | 109 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/inference/python/bert_torchscript_mode_inference_bf16.py | import torch
from transformers import BertModel
model = BertModel.from_pretrained("bert-base-uncased")
model.eval()
vocab_size = model.config.vocab_size
batch_size = 1
seq_length = 512
data = torch.randint(vocab_size, size=[batch_size, seq_length])
#################### code changes ####################
import intel_... | 689 | 29 | 71 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/inference/python/int8_calibration_static.py | import os
import torch
#################### code changes ####################
import intel_extension_for_pytorch as ipex
from intel_extension_for_pytorch.quantization import prepare, convert
######################################################
##### Example Model #####
import torchvision.models as models
model = mod... | 1,982 | 33.789474 | 107 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/inference/python/resnet50_torchdynamo_mode_inference_fp32.py | import torch
import torchvision.models as models
model = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
model.eval()
data = torch.rand(1, 3, 224, 224)
# Experimental Feature
#################### code changes ####################
import intel_extension_for_pytorch as ipex
model = ipex.optimize(model)
model =... | 450 | 25.529412 | 64 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/inference/python/resnet50_torchscript_mode_inference_fp32.py | import torch
import torchvision.models as models
model = models.resnet50(weights='ResNet50_Weights.DEFAULT')
model.eval()
data = torch.rand(1, 3, 224, 224)
#################### code changes ####################
import intel_extension_for_pytorch as ipex
model = ipex.optimize(model)
###################################... | 480 | 25.722222 | 59 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/inference/python/resnet50_imperative_mode_inference_bf16.py | import torch
import torchvision.models as models
model = models.resnet50(weights='ResNet50_Weights.DEFAULT')
model.eval()
data = torch.rand(1, 3, 224, 224)
#################### code changes ####################
import intel_extension_for_pytorch as ipex
model = ipex.optimize(model, dtype=torch.bfloat16)
#############... | 425 | 27.4 | 59 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/inference/python/bert_imperative_mode_inference_fp32.py | import torch
from transformers import BertModel
model = BertModel.from_pretrained("bert-base-uncased")
model.eval()
vocab_size = model.config.vocab_size
batch_size = 1
seq_length = 512
data = torch.randint(vocab_size, size=[batch_size, seq_length])
#################### code changes ####################
import intel_... | 470 | 25.166667 | 63 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/examples/cpu/inference/python/bert_imperative_mode_inference_bf16.py | import torch
from transformers import BertModel
model = BertModel.from_pretrained("bert-base-uncased")
model.eval()
vocab_size = model.config.vocab_size
batch_size = 1
seq_length = 512
data = torch.randint(vocab_size, size=[batch_size, seq_length])
#################### code changes ####################
import intel_... | 519 | 26.368421 | 63 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/scripts/collect_env.py | # Referenced from https://github.com/pytorch/pytorch/blob/master/torch/utils/collect_env.py
# Run it with `python collect_env.py`.
import locale
import re
import subprocess
import sys
import os
from collections import namedtuple
try:
import torch
TORCH_AVAILABLE = True
except (ImportError, NameError, Attribut... | 14,855 | 27.790698 | 91 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/scripts/tools/setup/flake8.py | import os
import shutil
import subprocess
import sys
def check_flake8_errors(base_dir, filepath):
if shutil.which("flake8") is None:
print(
"WARNING: Please install flake8 by pip install -r requirements-flake8.txt to check format!"
)
flak8_cmd = ["flake8"] # '--quiet'
if shut... | 1,700 | 30.5 | 103 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_inductor.py | import torch
import intel_extension_for_pytorch as ipex
import unittest
from torch.utils._pytree import tree_flatten, tree_unflatten
from torch.testing._internal.common_utils import TestCase
# TODO(jgong5): import and pass all inductor tests from stock pytorch
def check_model(
self: TestCase,
model,
exam... | 9,345 | 32.259786 | 100 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_emb.py | import torch
import torch.nn as nn
import unittest
import itertools
import copy
from torch.testing._internal.common_utils import TestCase
import intel_extension_for_pytorch as ipex
ipex_emb_fn = ipex.nn.functional._embeddingbag._embeddingbag
aten_emb_fn = ipex.nn.functional._embeddingbag.torch_embedding_bag
class Em... | 6,570 | 35.505556 | 88 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_tensor_method.py | import torch
import unittest
from common_utils import TestCase
class TestTesorMethod(TestCase):
def test_numpy(self):
# float tensor, numpy array will share memory with torch tensor.
x = torch.randn(2, 3)
y = torch.from_numpy(x.numpy())
self.assertEqual(x, y)
self.assertEqu... | 666 | 29.318182 | 79 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_shared_param.py | import unittest
import copy
import torch
import intel_extension_for_pytorch as ipex
from torch.testing._internal.common_utils import TestCase
from torch.optim import (
Adadelta,
Adagrad,
Adam,
AdamW,
Adamax,
ASGD,
RMSprop,
Rprop,
SGD,
)
from intel_extension_for_pytorch.optim._lamb ... | 7,295 | 35.48 | 88 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_linear_fuse_eltwise.py | import unittest
import torch
import intel_extension_for_pytorch as ipex
from torch.testing._internal.common_utils import TestCase
import copy
class MLP(torch.nn.Module):
def __init__(self):
super(MLP, self).__init__()
self.mlp = torch.nn.ModuleList()
self.mlp.append(torch.nn.Linear(10, 10)... | 1,838 | 32.436364 | 77 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_ao_jit_ipex_quantization.py | import sys
import os
import itertools
import tempfile
import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from torch.testing import FileCheck
import copy
import json
from test_autocast import get_rand_seed
import intel_extension_for_pytorch as ipex
from test_ao_j... | 34,156 | 35.609861 | 131 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_interaction.py | import unittest
import torch
import intel_extension_for_pytorch as ipex
from common_utils import TestCase
import itertools
class TestInteractionCases(TestCase):
def test_interaction(self):
def interact_fusion(x, ly):
A = [x] + ly
R = ipex.nn.functional.interaction(*A)
r... | 2,405 | 33.869565 | 92 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_check.py | import unittest
import torch
import torch.nn as nn
import intel_extension_for_pytorch as ipex
from common_utils import TestCase
class Conv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, **kwargs):
super(Conv, self).__init__()
self.conv = nn.Conv2d(
in_channels,... | 5,248 | 31.80625 | 103 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_cpu_ops.py | import unittest
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import random
import itertools
import intel_extension_for_pytorch as ipex
from common_utils import TestCase
import torch.autograd.functional as autogradF
from copy import deepcopy
try:
import torchvision
HAS_TORCHVI... | 57,816 | 40.386543 | 105 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_graph_capture.py | import unittest
import copy
import os
import tempfile
import torch
import torch.nn as nn
import torch.nn.functional as F
import intel_extension_for_pytorch as ipex
from common_utils import TestCase
from common_ipex_conf import runtime_thread_affinity_test_env
from torch.utils import ThroughputBenchmark
try:
import... | 25,582 | 35.599428 | 88 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_dropout.py | import unittest
import torch
import torch.nn as nn
import intel_extension_for_pytorch as ipex
from common_utils import TestCase
from torch.testing import FileCheck
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.dropout = nn.Dropout(0.5)
def forward(self, x):
... | 1,352 | 28.413043 | 83 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/linear_prepack.py | import torch
import intel_extension_for_pytorch as ipex
from common_utils import int8_calibration
ipex.core.enable_auto_dnnl()
ic = 1024
oc = 1024
bs = 16
LL = torch.nn.Linear(ic, oc).to(ipex.DEVICE)
def get_input():
return torch.rand(bs, ic).to(ipex.DEVICE)
def run_linear(auto_mix_conf=None):
for i in r... | 1,179 | 23.081633 | 74 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_runtime_api_jit.py | import unittest
import torch
import intel_extension_for_pytorch as ipex
from torch.testing._internal.jit_utils import JitTestCase
from test_ao_jit_llga_utils import JitLlgaTestCase
from test_runtime_api import TestInputOutputModule
from common_ipex_conf import runtime_thread_affinity_test_env
class SimpleNet(torch.nn... | 34,536 | 34.752588 | 92 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_optimizer.py | # This Python file uses the following encoding: utf-8
# !/usr/bin/env python
import torch
import intel_extension_for_pytorch as ipex # flake8: noqa
import itertools
import unittest
from torch.testing._internal.common_utils import TestCase
from common_utils import TestModule, _empty_weight_bias_parameter_names
import b... | 32,432 | 29.74218 | 112 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_rnnt_custom_kernel.py | import unittest
import copy
from itertools import product
import torch
from common_utils import TestCase
class TestRNNTUpdateBatch(TestCase):
def _test_org(
self, hidden, hidden_prime, x, batch_size, max_symbol, blank_id, loop_cnt
):
f = x[:, 0, :]
max_lens = torch.tensor(
... | 8,395 | 31.92549 | 85 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/common_nn.py | """
From PyTorch:
Copyright (c) 2016- Facebook, Inc (Adam Paszke)
Copyright (c) 2014- Facebook, Inc (Soumith Chintala)
Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu)
Copyright (c) 2011-2012 NEC Labora... | 200,725 | 35.986549 | 119 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_ipex_custom_op.py | import torch
import intel_extension_for_pytorch as ipex
import unittest
from common_utils import TestCase
class TestCustomOp(TestCase):
# Port from test_torch
def test_add_softmax(self):
# smaller input which can't can in AVX512
a = torch.randn(2, 3)
b = torch.randn(2, 3)
orig_... | 1,732 | 30.509091 | 61 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_launcher.py | import unittest
from common_utils import TestCase
from utils.cpuinfo import construct_numa_config
from intel_extension_for_pytorch.cpu.launch import (
CPUPoolList,
Launcher,
DistributedTrainingLauncher,
)
import os
from os.path import expanduser
import glob
import subprocess
class TestLauncher(TestCase):
... | 28,110 | 36.682306 | 121 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_fake_tensor.py | import unittest
import itertools
import copy
import torch
from torch._subclasses.fake_tensor import (
FakeTensor,
FakeTensorMode,
)
import intel_extension_for_pytorch as ipex
from common_utils import TestCase
conv_module = {1: torch.nn.Conv1d, 2: torch.nn.Conv2d, 3: torch.nn.Conv3d}
convtranspose_module = {... | 14,096 | 35.615584 | 131 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/common_device_type.py | """
From PyTorch:
Copyright (c) 2016- Facebook, Inc (Adam Paszke)
Copyright (c) 2014- Facebook, Inc (Soumith Chintala)
Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu)
Copyright (c) 2011-2012 NEC Labora... | 23,264 | 35.238318 | 106 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_rmsnorm.py | import unittest
import torch
from torch import nn
from common_utils import TestCase
class RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):... | 1,514 | 32.666667 | 85 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_cumsum.py | import torch
import unittest
from common_utils import TestCase
class TestCumSum(TestCase):
# Port from test_torch
def test_cumsum(self):
for dtype in [torch.float, torch.double, torch.long]:
x = torch.randn(17, 4097).to(dtype)
res1 = torch.ops.torch_ipex.cumsum(x, 1)
... | 2,346 | 39.465517 | 92 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_autocast.py | import unittest
import copy
import torch
import torch.nn as nn
import intel_extension_for_pytorch as ipex
import intel_extension_for_pytorch._C as core
from common_utils import TestCase
from torch.testing._internal.common_utils import TestCase as TorchTestCase
import time
import sys
import itertools
import collections
... | 44,348 | 38.597321 | 131 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_import.py | import unittest
import subprocess
class TestImport(unittest.TestCase):
def test_import_ipex_without_warning(self):
command = 'python -c "import intel_extension_for_pytorch" '
with subprocess.Popen(
command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT
) as p:
... | 466 | 24.944444 | 81 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_ipex_optimize.py | import torch
import torch.fx.experimental.optimization as optimization
import intel_extension_for_pytorch as ipex
import intel_extension_for_pytorch._C as core
from intel_extension_for_pytorch.nn.utils._weight_prepack import (
_IPEXLinear as _IPEXLinear,
_IPEXConv2d as _IPEXConv2d,
)
from torch.testing._interna... | 33,897 | 39.021251 | 108 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_nms.py | import unittest
import torch
import torch.nn as nn
from common_utils import TestCase
import time
import torch.nn.functional as F
import os
def nms(dets, scores, threshold, sorted=False):
return torch.ops.torch_ipex.nms(dets, scores, threshold, sorted)
batch_score_nms = torch.ops.torch_ipex.batch_score_nms
paral... | 22,971 | 38.134583 | 112 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_add_layernorm.py | import unittest
import torch
from common_utils import TestCase
class add_layernorm(torch.nn.Module):
def __init__(self, size):
super(add_layernorm, self).__init__()
self.layer_norm = torch.nn.LayerNorm(size)
def forward(self, a, b):
x = torch.add(a, b)
x = self.layer_norm(x)
... | 2,070 | 36.654545 | 117 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_roialign.py | import unittest
import itertools
import torch
import intel_extension_for_pytorch as ipex
from common_utils import TestCase
import numpy as np
import math
import copy
try:
import torchvision
HAS_TORCHVISION = True
except ImportError:
HAS_TORCHVISION = False
skipIfNoTorchVision = unittest.skipIf(not HAS_TO... | 14,399 | 35.180905 | 88 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_jit.py | from __future__ import division
from __future__ import print_function
import logging
"""
From PyTorch:
Copyright (c) 2016- Facebook, Inc (Adam Paszke)
Copyright (c) 2014- Facebook, Inc (Soumith Chintala)
Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
Copyright (c) 201... | 201,624 | 36.002202 | 123 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_ao_jit_llga_quantization_fuser.py | # This Python file uses the following encoding: utf-8
# !/usr/bin/env python
import unittest
import itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
from test_ao_jit_llga_utils import (
JitLlgaTestCase,
LLGA_FUSION_GROUP,
get_eltwise_fn,
)
from torch.quantization.quantize_fx imp... | 83,103 | 35.385289 | 120 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_toolkit.py | import torch
import intel_extension_for_pytorch as ipex
from common_utils import TestCase
import sklearn.metrics
import numpy as np
class ToolkitTester(TestCase):
def test_multi_thread_sklearn_metric_eval_roc_auc_score(self):
targets = np.random.randint(0, 2, size=10)
scores = torch.rand(10)
... | 892 | 36.208333 | 81 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/code_free_optimization.py | import argparse
import torch
import torch.nn as nn
class ConvBatchNorm(torch.nn.Module):
def __init__(
self,
):
super(ConvBatchNorm, self).__init__()
self.conv = torch.nn.Conv2d(
3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3)
)
self.bn = torch.nn.B... | 1,700 | 27.35 | 82 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_quantization_default_recipe.py | import itertools
import tempfile
import torch
import torch.nn as nn
from torch.testing import FileCheck
from torch.ao.quantization import (
MinMaxObserver,
PerChannelMinMaxObserver,
QConfig,
QConfigMapping,
)
import copy
import intel_extension_for_pytorch as ipex
from test_ao_jit_llga_utils import JitL... | 22,852 | 38.198971 | 100 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_layer_norm.py | import unittest
import torch
from common_utils import TestCase
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.layer_norm = torch.nn.LayerNorm(10)
def forward(self, x):
x = self.layer_norm(x)
return x
class LayerNormTester(TestCase):
def tes... | 1,866 | 34.903846 | 70 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/common_utils.py | """
From PyTorch:
Copyright (c) 2016- Facebook, Inc (Adam Paszke)
Copyright (c) 2014- Facebook, Inc (Soumith Chintala)
Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu)
Copyright (c) 2011-2012 NEC Labora... | 61,950 | 34.000565 | 119 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_deepspeed.py | import sys
import os
import unittest
import torch
import torch.nn as nn
from torch.testing._internal.common_utils import TestCase
import intel_extension_for_pytorch as ipex
from intel_extension_for_pytorch.nn.utils._weight_prepack import (
may_import_deepspeed_modules,
_IPEXLinear,
_IPEXLinearAllreduce,
)
... | 4,913 | 32.202703 | 110 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_weight_cast.py | import unittest
import copy
import torch
from intel_extension_for_pytorch.nn.utils._weight_cast import (
weight_dtype_convert_with_ipex as cast,
)
from intel_extension_for_pytorch.nn.utils._parameter_wrapper import (
IPEX_WEIGHT_CONVERT_MODULE_CPU as IPEX_WEIGHT_CONVERT_MODULE_CPU,
)
from intel_extension_for_p... | 7,898 | 40.793651 | 88 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_weight_prepack.py | import unittest
import itertools
import copy
import os
import time
import sys
from intel_extension_for_pytorch.utils.channels_last_1d import (
to_channels_last_1d,
is_contiguous_channels_last_1d,
)
try:
import torchvision
HAS_TORCHVISION = True
except ImportError:
HAS_TORCHVISION = False
skipIfNoT... | 85,990 | 38.19371 | 131 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_instance_norm.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import unittest
from common_utils import TestCase
from torch.nn import InstanceNorm2d, InstanceNorm3d, BatchNorm2d, BatchNorm3d
bn_m = {2: BatchNorm2d, 3: BatchNorm3d}
inst_m = {2: InstanceNorm2d, 3: InstanceNorm3d}
class InstanceNo... | 4,197 | 33.130081 | 88 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/common_methods_invocations.py | """
From PyTorch:
Copyright (c) 2016- Facebook, Inc (Adam Paszke)
Copyright (c) 2014- Facebook, Inc (Soumith Chintala)
Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu)
Copyright (c) 2011-2012 NEC Labora... | 84,592 | 29.330943 | 119 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/runtime.py | import argparse
import intel_extension_for_pytorch as ipex
def create_cpu_pool(args):
core_ids = [1, 2]
cpu_pool = ipex.cpu.runtime.CPUPool(core_ids)
print("The created CPUPool has core is: {}".format(cpu_pool.core_ids), flush=True)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
p... | 496 | 28.235294 | 86 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/common_ipex_conf.py | import torch
import intel_extension_for_pytorch as ipex
from functools import wraps
class AutoMixPrecision(object):
def __init__(self, enable_or_not=False, train=False):
self.old_value = ipex.get_auto_mix_precision()
self.train_old_value = ipex.get_train()
self.enable_or_not = enable_or_no... | 1,776 | 29.637931 | 98 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_ao_jit_llga_throughput_benchmark.py | import torch
from torch.utils import ThroughputBenchmark
from torch.testing import assert_allclose
import intel_extension_for_pytorch as ipex
from test_ao_jit_llga_utils import JitLlgaTestCase
class LinearEltwise(torch.nn.Module):
def __init__(self, D_in, H, D_out):
super(LinearEltwise, self).__init__()
... | 1,600 | 25.245902 | 78 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/linear_reorder.py | import torch
import intel_extension_for_pytorch as ipex
import torch.nn as nn
import itertools
class Model(nn.Module):
def __init__(self, ic, oc, bias):
super(Model, self).__init__()
self.linear = nn.Linear(ic, oc, bias=bias)
def forward(self, input):
return self.linear(input)
def r... | 1,829 | 32.272727 | 76 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/test_compile.py | import unittest
import itertools
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import intel_extension_for_pytorch as ipex
from common_utils import TestCase
class Conv_Bn_Relu(nn.Module):
def __init__(self):
super(Conv_Bn_Relu, self).__init__()
self.conv = nn.Con... | 1,493 | 28.88 | 76 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/fpmath_mode.py | import argparse
import torch
import torch.nn as nn
from torch.optim import SGD
import intel_extension_for_pytorch as ipex
class TestModel(torch.nn.Module):
def __init__(self, ic, oc, bias):
super(TestModel, self).__init__()
self.conv = torch.nn.Conv2d(
3, 64, kernel_size=(7, 7), stride... | 2,511 | 30.012346 | 83 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/tests/cpu/override.py | import torch
import intel_extension_for_pytorch as ipex
torch_function = [
"rand",
"randint",
"arange",
"bartlett_window",
"blackman_window",
"empty",
"_empty_affine_quantized",
"_empty_per_channel_affine_quantized",
"empty_strided",
"eye",
"full",
"from_file",
"from... | 1,016 | 19.34 | 62 | py |
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