text stringlengths 5 22M | id stringlengths 12 177 | metadata dict | __index_level_0__ int64 0 1.37k |
|---|---|---|---|
# 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 tree.
import dynamicconv_cuda
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.incremental_decoding_utils import ... | COCO-LM/fairseq/fairseq/modules/dynamicconv_layer/dynamicconv_layer.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/modules/dynamicconv_layer/dynamicconv_layer.py",
"repo_id": "COCO-LM",
"token_count": 4118
} | 207 |
/**
* 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 tree.
*/
#include "lightconv_cuda.cuh"
#include "lightconv_cuda_forward.cu"
#include "lightconv_cuda_backward.cu"
#include "../cuda_utils.... | COCO-LM/fairseq/fairseq/modules/lightconv_layer/lightconv_cuda_kernel.cu/0 | {
"file_path": "COCO-LM/fairseq/fairseq/modules/lightconv_layer/lightconv_cuda_kernel.cu",
"repo_id": "COCO-LM",
"token_count": 4201
} | 208 |
# 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 tree.
import logging
import re
from operator import attrgetter, itemgetter
import numpy as np
import torch.distributed as dist
import torch.nn as n... | COCO-LM/fairseq/fairseq/modules/quantization/pq/utils.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/modules/quantization/pq/utils.py",
"repo_id": "COCO-LM",
"token_count": 5248
} | 209 |
# 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 tree.
from typing import Dict, List, Optional
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.modules import LayerNorm, M... | COCO-LM/fairseq/fairseq/modules/transformer_layer.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/modules/transformer_layer.py",
"repo_id": "COCO-LM",
"token_count": 8124
} | 210 |
# 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 tree.
import importlib
from collections.abc import Collection
from dataclasses import dataclass, field
from typing import List
import torch
from fa... | COCO-LM/fairseq/fairseq/optim/cpu_adam.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/optim/cpu_adam.py",
"repo_id": "COCO-LM",
"token_count": 3472
} | 211 |
# 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 tree.
import math
from dataclasses import dataclass, field
from typing import List
from omegaconf import II
from fairseq.dataclass import FairseqD... | COCO-LM/fairseq/fairseq/optim/lr_scheduler/triangular_lr_scheduler.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/optim/lr_scheduler/triangular_lr_scheduler.py",
"repo_id": "COCO-LM",
"token_count": 1166
} | 212 |
# 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 tree.
"""isort:skip_file"""
import argparse
import importlib
import os
from fairseq.dataclass import FairseqDataclass
from fairseq.dataclass.utils ... | COCO-LM/fairseq/fairseq/tasks/__init__.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/tasks/__init__.py",
"repo_id": "COCO-LM",
"token_count": 1856
} | 213 |
# 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 tree.
from dataclasses import dataclass, field
import itertools
import json
import logging
import os
from typing import Optional
from argparse impor... | COCO-LM/fairseq/fairseq/tasks/translation.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq/tasks/translation.py",
"repo_id": "COCO-LM",
"token_count": 8408
} | 214 |
#!/usr/bin/env python3
# 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 tree.
"""
Data pre-processing: build vocabularies and binarize training data.
"""
import logging
import os
import shutil
impo... | COCO-LM/fairseq/fairseq_cli/preprocess.py/0 | {
"file_path": "COCO-LM/fairseq/fairseq_cli/preprocess.py",
"repo_id": "COCO-LM",
"token_count": 7433
} | 215 |
try:
import torch
import fused_layernorm_cuda
from .fused_layer_norm import FusedLayerNorm
del torch
del fused_layernorm_cuda
del fused_layer_norm
except ImportError as err:
print("cannot import kernels, please install the package")
| COCO-LM/fairseq/fused_ops/fused_ops/layernorm/__init__.py/0 | {
"file_path": "COCO-LM/fairseq/fused_ops/fused_ops/layernorm/__init__.py",
"repo_id": "COCO-LM",
"token_count": 91
} | 216 |
# 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 tree.
"""
Use this script in order to build symmetric alignments for your translation
dataset.
This script depends on fast_align and mosesdecoder too... | COCO-LM/fairseq/scripts/build_sym_alignment.py/0 | {
"file_path": "COCO-LM/fairseq/scripts/build_sym_alignment.py",
"repo_id": "COCO-LM",
"token_count": 1626
} | 217 |
#!/usr/bin/env bash
rm -rf fsdp_dummy
mkdir -p fsdp_dummy
fairseq-train /private/home/sshleifer/data-bin/stories_mmap \
--ddp-backend fully_sharded --fp16 --fp16-init-scale 4 \
--cpu-offload --checkpoint-activations \
--task language_modeling --tokens-per-sample 256 --batch-size 8 \
--arch transformer_l... | COCO-LM/fairseq/scripts/test_fsdp.sh/0 | {
"file_path": "COCO-LM/fairseq/scripts/test_fsdp.sh",
"repo_id": "COCO-LM",
"token_count": 260
} | 218 |
#!/usr/bin/env python3
# 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 tree.
import unittest
import torch
from examples.speech_recognition.data import data_utils
class DataUtilsTest(unittest.Tes... | COCO-LM/fairseq/tests/speech_recognition/test_data_utils.py/0 | {
"file_path": "COCO-LM/fairseq/tests/speech_recognition/test_data_utils.py",
"repo_id": "COCO-LM",
"token_count": 1263
} | 219 |
# 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 tree.
import argparse
import copy
import logging
import unittest
import torch
from fairseq.optim.fp16_optimizer import FP16Optimizer, MemoryEfficie... | COCO-LM/fairseq/tests/test_fp16_optimizer.py/0 | {
"file_path": "COCO-LM/fairseq/tests/test_fp16_optimizer.py",
"repo_id": "COCO-LM",
"token_count": 1905
} | 220 |
# 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 tree.
import argparse
import tempfile
import unittest
import math
import numpy as np
import tests.utils as test_utils
import torch
from fairseq im... | COCO-LM/fairseq/tests/test_sequence_generator.py/0 | {
"file_path": "COCO-LM/fairseq/tests/test_sequence_generator.py",
"repo_id": "COCO-LM",
"token_count": 14600
} | 221 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
# Set pretrained model name, from ['cocolm-base', 'cocolm-large']
MODEL_NAME=$1
# GLUE task name, from ['MNLI', 'QQP', 'QNLI', 'SST-2', 'CoLA', 'RTE', 'MRPC', 'STS-B']
TASK=$2
# Path to GLUE dataset 'path/to/glue_data'
GLUE_PATH=$3
# Output p... | COCO-LM/huggingface/run_glue.sh/0 | {
"file_path": "COCO-LM/huggingface/run_glue.sh",
"repo_id": "COCO-LM",
"token_count": 758
} | 222 |
# ADE20k Semantic segmentation with CSWin
## Results and Models
| Backbone | Method | pretrain | Crop Size | Lr Schd | mIoU | mIoU (ms+flip) | #params | FLOPs | config | model | log |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| CSWin-T | UPerNet | ImageNet-1K ... | CSWin-Transformer/segmentation/README.md/0 | {
"file_path": "CSWin-Transformer/segmentation/README.md",
"repo_id": "CSWin-Transformer",
"token_count": 1359
} | 223 |
# tags: https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch/tags?quick-deploy=false
ARG BASE_IMAGE=openmpi4.1.0-cuda11.3-cudnn8-ubuntu20.04:latest
FROM mcr.microsoft.com/azureml/${BASE_IMAGE}
ARG DEBIAN_FRONTEND=noninteractive
RUN apt-get update && apt-get install -y --allow-downgrades --allow-change-held... | ClimaX/docker/Dockerfile/0 | {
"file_path": "ClimaX/docker/Dockerfile",
"repo_id": "ClimaX",
"token_count": 483
} | 224 |
[data-md-color-scheme="climax"] {
--md-primary-fg-color: #4C8D91;
--md-primary-fg-color--light: #91504c;
--md-primary-fg-color--dark: #16292a;
}
| ClimaX/docs/stylesheets/extra.css/0 | {
"file_path": "ClimaX/docs/stylesheets/extra.css",
"repo_id": "ClimaX",
"token_count": 86
} | 225 |
datadir: /data/CMIP6/CMCC
name: u_component_of_wind
cmip_name: ua
era_name: u
run: r1i1p1f1
res:
- 1.40625
# - 5.625
| ClimaX/snakemake_configs/CMCC/config_u_component_of_wind.yml/0 | {
"file_path": "ClimaX/snakemake_configs/CMCC/config_u_component_of_wind.yml",
"repo_id": "ClimaX",
"token_count": 66
} | 226 |
datadir: /data/CMIP6/MPI-ESM
server_prefix: http://esgf-data1.llnl.gov/thredds/fileServer/css03_data/CMIP6/CMIP
name: specific_humidity
cmip_name: hus
era_name: q
output_type: 6hrPlevPt
run: r1i1p1f1
version: v20190815
res:
- 1.40625
# - 5.625 | ClimaX/snakemake_configs/MPI-ESM/config_specific_humidity.yml/0 | {
"file_path": "ClimaX/snakemake_configs/MPI-ESM/config_specific_humidity.yml",
"repo_id": "ClimaX",
"token_count": 119
} | 227 |
### Adapted from https://github.com/duncanwp/ClimateBench/blob/main/prep_input_data.ipynb
import os
import numpy as np
import torch
import xarray as xr
from torch.utils.data import Dataset
from torchvision.transforms import transforms
def load_x_y(data_path, list_simu, out_var):
x_all, y_all = {}, {}
for si... | ClimaX/src/climax/climate_projection/dataset.py/0 | {
"file_path": "ClimaX/src/climax/climate_projection/dataset.py",
"repo_id": "ClimaX",
"token_count": 3190
} | 228 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
# credits: https://github.com/ashleve/lightning-hydra-template/blob/main/src/models/mnist_module.py
from typing import Any
import torch
from pytorch_lightning import LightningModule
from torchvision.transforms import transforms
from climax.regi... | ClimaX/src/climax/regional_forecast/module.py/0 | {
"file_path": "ClimaX/src/climax/regional_forecast/module.py",
"repo_id": "ClimaX",
"token_count": 4055
} | 229 |
import os
import skimage.util as util
from skimage import io
from skimage.transform import resize
with open('train.txt', 'r') as fd:
image_files = fd.readlines()
total = len(image_files)
cnt = 0
# path/to/deepfashion directory
root = '/path/to/deepfashion'
# path/to/save directory
save_root = 'path/to/save'
fo... | CoCosNet-v2/data/preprocess.py/0 | {
"file_path": "CoCosNet-v2/data/preprocess.py",
"repo_id": "CoCosNet-v2",
"token_count": 447
} | 230 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch
import torch.nn as nn
import torch.nn.functional as F
import random
from models.networks.convgru import BasicUpdateBlock
from models.networks.ops import *
"""patch match"""
class Evaluate(nn.Module):
def __init__(self, tempe... | CoCosNet-v2/models/networks/patch_match.py/0 | {
"file_path": "CoCosNet-v2/models/networks/patch_match.py",
"repo_id": "CoCosNet-v2",
"token_count": 3978
} | 231 |
"""
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import torch.utils.data as data
from PIL import Image
import torchvision.transforms as transforms
import numpy as np
import random
class BaseD... | CoCosNet/data/base_dataset.py/0 | {
"file_path": "CoCosNet/data/base_dataset.py",
"repo_id": "CoCosNet",
"token_count": 1932
} | 232 |
"""
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import torch.nn as nn
from torch.nn import init
class BaseNetwork(nn.Module):
def __init__(self):
super(BaseNetwork, self).__init_... | CoCosNet/models/networks/base_network.py/0 | {
"file_path": "CoCosNet/models/networks/base_network.py",
"repo_id": "CoCosNet",
"token_count": 1225
} | 233 |
# CodeExecutor
This repo provides the code for reproducing the experiments in [Code Execution with Pre-trained Language Models](https://arxiv.org/pdf/2305.05383.pdf). **CodeExecutor** is a pre-trained model that learns to predict the execution traces using a code execution pre-training task and curriculum learning.
T... | CodeBERT/CodeExecutor/README.md/0 | {
"file_path": "CodeBERT/CodeExecutor/README.md",
"repo_id": "CodeBERT",
"token_count": 2591
} | 234 |
# -*- coding: utf-8 -*-
# Natural Language Toolkit: BLEU Score
#
# Copyright (C) 2001-2020 NLTK Project
# Authors: Chin Yee Lee, Hengfeng Li, Ruxin Hou, Calvin Tanujaya Lim
# Contributors: Björn Mattsson, Dmitrijs Milajevs, Liling Tan
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
"""BLEU score i... | CodeBERT/CodeReviewer/code/evaluator/CodeBLEU/bleu.py/0 | {
"file_path": "CodeBERT/CodeReviewer/code/evaluator/CodeBLEU/bleu.py",
"repo_id": "CodeBERT",
"token_count": 11567
} | 235 |
echo -e "import nltk\nnltk.download('punkt')" > ttmp.py
python ttmp.py
rm ttmp.py | CodeBERT/CodeReviewer/code/sh/test_nltk.sh/0 | {
"file_path": "CodeBERT/CodeReviewer/code/sh/test_nltk.sh",
"repo_id": "CodeBERT",
"token_count": 36
} | 236 |
# Code Search
## Data Preprocess
Different from the setting of [CodeSearchNet](husain2019codesearchnet), the answer of each query is retrieved from the whole development and testing code corpus instead of 1,000 candidate codes. Besides, we observe that some queries contain content unrelated to the code, such as a l... | CodeBERT/GraphCodeBERT/codesearch/README.md/0 | {
"file_path": "CodeBERT/GraphCodeBERT/codesearch/README.md",
"repo_id": "CodeBERT",
"token_count": 1990
} | 237 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from tree_sitter import Language, Parser
Language.build_library(
# Store the library in the `build` directory
'my-languages.so',
# Include one or more languages
[
'tree-sitter-go',
'tree-sitter-javascript',
'tree-sitter-pyt... | CodeBERT/GraphCodeBERT/refinement/parser/build.py/0 | {
"file_path": "CodeBERT/GraphCodeBERT/refinement/parser/build.py",
"repo_id": "CodeBERT",
"token_count": 162
} | 238 |
# Clone Detection (BigCloneDetection)
## Data Download
```bash
mkdir dataset
cd dataset
wget https://github.com/microsoft/CodeXGLUE/raw/main/Code-Code/Clone-detection-BigCloneBench/dataset/data.jsonl
wget https://github.com/microsoft/CodeXGLUE/raw/main/Code-Code/Clone-detection-BigCloneBench/dataset/test.txt
wget htt... | CodeBERT/UniXcoder/downstream-tasks/clone-detection/BCB/README.md/0 | {
"file_path": "CodeBERT/UniXcoder/downstream-tasks/clone-detection/BCB/README.md",
"repo_id": "CodeBERT",
"token_count": 586
} | 239 |
pip install torch==1.6.0+cu92 torchvision==0.7.0+cu92 -f https://download.pytorch.org/whl/torch_stable.html > log.txt 2>&1
pip install sklearn scipy transformers tqdm > log.txt 2>&1
CUDA_VISIBLE_DEVICES=15,12,13,14
lang=java #programming language
lr=5e-5
batch_size=32
accm_steps=1
beam_size=3
source_length=512
target_l... | CodeBERT/UniXcoder/downstream-tasks/code-generation/run.sh/0 | {
"file_path": "CodeBERT/UniXcoder/downstream-tasks/code-generation/run.sh",
"repo_id": "CodeBERT",
"token_count": 609
} | 240 |
## CLUTRR
### Download the Code-Davinci-002 Inference Results
[Download Link](https://bdmbabel.blob.core.windows.net/public/clutrr.zip)
### Original Dataset
The dataset is synthesized from https://github.com/facebookresearch/clutrr, using the following Python script:
`python main.py --train_tasks 1.2,1.3 --test_ta... | CodeT/DIVERSE/data/clutrr.md/0 | {
"file_path": "CodeT/DIVERSE/data/clutrr.md",
"repo_id": "CodeT",
"token_count": 149
} | 241 |
import os
import time
from pathlib import Path
from prompt_file import *
def get_command_result(input, prompt_file):
"""
Checks if the input is a command and if so, executes it
Currently supported commands:
- start multi-turn
- stop multi-turn
- default context
- show context <n>
- vie... | Codex-CLI/src/commands.py/0 | {
"file_path": "Codex-CLI/src/commands.py",
"repo_id": "Codex-CLI",
"token_count": 2649
} | 242 |
Contributing to Microsoft Cognitive Services Client Libraries & Samples
===============================================
So, you want to contribute on a client library or sample for one of the Microsoft Cognitive Services.
Here's what you need to know.
1. Each SDK should include both a client library and a sample show... | Cognitive-Face-Python/CONTRIBUTING.md/0 | {
"file_path": "Cognitive-Face-Python/CONTRIBUTING.md",
"repo_id": "Cognitive-Face-Python",
"token_count": 577
} | 243 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
File: person_group.py
Description: Person Group section of the Cognitive Face API.
"""
from . import util
def create(person_group_id, name=None, user_data=None):
"""Create a new person group with specified `person_group_id`, `name` and
user-provided `user_data... | Cognitive-Face-Python/cognitive_face/person_group.py/0 | {
"file_path": "Cognitive-Face-Python/cognitive_face/person_group.py",
"repo_id": "Cognitive-Face-Python",
"token_count": 1523
} | 244 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
File: __init__.py
Description: Model components for Python SDK sample.
"""
from model.face import Face
| Cognitive-Face-Python/sample/model/__init__.py/0 | {
"file_path": "Cognitive-Face-Python/sample/model/__init__.py",
"repo_id": "Cognitive-Face-Python",
"token_count": 52
} | 245 |
export CUDA_VISIBLE_DEVICES=2
python t5_run_train.py \
--model_name_or_path t5-base \
--subtask Com \
--method ControlExp \
--train_file finetune \
--max_steps 50000 \
--save_steps 50000 \
--batch_size 8 \
--ebatch_size 16 \
--gas 1 \
--seed 1 \
--set set1 | ContextualSP/abstraction_probing/code/t5_code/Com_ControlExp_finetune.sh/0 | {
"file_path": "ContextualSP/abstraction_probing/code/t5_code/Com_ControlExp_finetune.sh",
"repo_id": "ContextualSP",
"token_count": 104
} | 246 |
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers.models.t5.modeling_t5 import (
T5PreTrainedModel,
T5Block,
T5LayerNorm,
T5Attention,
T5LayerCrossAttention,
T5LayerFF,
)
from transformers.modeling_outputs import (
Seq2Seq... | ContextualSP/abstraction_probing/code/t5_code/t5_model.py/0 | {
"file_path": "ContextualSP/abstraction_probing/code/t5_code/t5_model.py",
"repo_id": "ContextualSP",
"token_count": 24657
} | 247 |
theme: jekyll-theme-minimal | ContextualSP/adaptershare/_config.yml/0 | {
"file_path": "ContextualSP/adaptershare/_config.yml",
"repo_id": "ContextualSP",
"token_count": 10
} | 248 |
# coding=utf-8
# Copyright 2020 The HuggingFace Team All rights reserved.
# Copyright 2021 Microsoft All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apac... | ContextualSP/adaptershare/data_utils/utils_qa.py/0 | {
"file_path": "ContextualSP/adaptershare/data_utils/utils_qa.py",
"repo_id": "ContextualSP",
"token_count": 5608
} | 249 |
# Copyright (c) Microsoft. All rights reserved.
from random import shuffle
from data_utils.metrics import calc_metrics
def load_scitail(file):
"""Loading data of scitail"""
rows = []
cnt = 0
with open(file, encoding="utf8") as f:
for line in f:
blocks = line.strip().split("\t")
... | ContextualSP/adaptershare/experiments/glue/glue_utils.py/0 | {
"file_path": "ContextualSP/adaptershare/experiments/glue/glue_utils.py",
"repo_id": "ContextualSP",
"token_count": 6831
} | 250 |
## Quickstart
### Example of XNLI based on XLM-R
1. Download XNLI data </br>
2. Prepro </br>
> python experiments\xnli\xnli_prepro.py </br>
> python prepro_std.py --model xlm-roberta-base --task_def experiments\xnli\xnli_task_def.yml --rood_dir [XNLI-DIR]
3. Train
> python train.py --data_dir data\canonical_... | ContextualSP/adaptershare/experiments/xnli/README.md/0 | {
"file_path": "ContextualSP/adaptershare/experiments/xnli/README.md",
"repo_id": "ContextualSP",
"token_count": 198
} | 251 |
cola:
# PremiseOnly + Classification
data_format: PremiseOnly
dropout_p: 0.05
enable_san: false
metric_meta:
- ACC
- MCC
loss: CeCriterion
kd_loss: MseCriterion
n_class: 2
split_names:
- train
task_type: Classification
mnli:
# PremiseAndOneHypothesis + Classification
data_format: PremiseAn... | ContextualSP/adaptershare/int_test_data/glue/input/prepro_std/glue_task_def.yml/0 | {
"file_path": "ContextualSP/adaptershare/int_test_data/glue/input/prepro_std/glue_task_def.yml",
"repo_id": "ContextualSP",
"token_count": 305
} | 252 |
# coding=utf-8
# Copyright (c) Microsoft. All rights reserved.
import torch
from torch.nn.modules.loss import _Loss
import torch.nn.functional as F
import torch.nn as nn
from enum import IntEnum
def stable_kl(logit, target, epsilon=1e-6, reduce=True):
logit = logit.view(-1, logit.size(-1)).float()
target = t... | ContextualSP/adaptershare/mt_dnn/loss.py/0 | {
"file_path": "ContextualSP/adaptershare/mt_dnn/loss.py",
"repo_id": "ContextualSP",
"token_count": 5654
} | 253 |
import torch
import torch.nn as nn
from torch.optim import Optimizer, Adam
class WarmupPolynomialLRScheduler:
optimizer: Optimizer
num_warmup_steps: int
start_lr: float
end_lr: float
decay_steps: int
power: float
def __init__(self, optimizer: Optimizer, start_lr: float, num_warmup_step... | ContextualSP/awakening_latent_grounding/models/optmizers.py/0 | {
"file_path": "ContextualSP/awakening_latent_grounding/models/optmizers.py",
"repo_id": "ContextualSP",
"token_count": 1305
} | 254 |
from enum import Enum
import re
import json
from collections import defaultdict
from typing import List, Dict, Tuple
from dataclasses import dataclass, field
from transformers import BertTokenizer
"""
Constant values
"""
SOS_Token = '<sos>'
EOS_Token = '<eos>'
UNK_Token = '<unk>'
TBL_Token = '<tbl>'
VAL_Token = '<val>... | ContextualSP/awakening_latent_grounding/utils/data_types.py/0 | {
"file_path": "ContextualSP/awakening_latent_grounding/utils/data_types.py",
"repo_id": "ContextualSP",
"token_count": 10629
} | 255 |
import pdb
import random
import statistics
from itertools import chain
import math
import torch.nn.functional as F
from torch import nn
from masked_cross_entropy import *
from utils import Categorical
from modules.BinaryTreeBasedModule import BinaryTreeBasedModule
from utils import clamp_grad
import torch
USE_CUDA = ... | ContextualSP/compositional_generalization/model.py/0 | {
"file_path": "ContextualSP/compositional_generalization/model.py",
"repo_id": "ContextualSP",
"token_count": 33973
} | 256 |
#!/usr/bin/env bash
export model_file=../checkpoints/run_rewrite
export config_file=../configs/rewrite.jsonnet
export train_data_path=../dataset/Rewrite/train.txt
export validation_data_path=../dataset/Rewrite/dev.txt
export seed=2
allennlp train -s ${model_file} ${config_file} \
--include-package data_reader \
--inclu... | ContextualSP/incomplete_utterance_rewriting/src/train_rewrite.sh/0 | {
"file_path": "ContextualSP/incomplete_utterance_rewriting/src/train_rewrite.sh",
"repo_id": "ContextualSP",
"token_count": 186
} | 257 |
import re
from collections import Set, defaultdict
from typing import Dict, Tuple, List
from allennlp.data import Tokenizer, Token
from ordered_set import OrderedSet
from unidecode import unidecode
from .utils import TableColumn, read_dataset_schema, read_dataset_values
from allennlp.semparse.contexts.knowledge_graph... | ContextualSP/interactive_text_to_sql/src/context/db_context.py/0 | {
"file_path": "ContextualSP/interactive_text_to_sql/src/context/db_context.py",
"repo_id": "ContextualSP",
"token_count": 6280
} | 258 |
# coding: utf-8
question_template = "What do you mean by the word {0}? " \
"Is that an attribute name, an attribute value or others?" \
"Select a proper answer is you think we're have a misunderstanding of it."
| ContextualSP/interactive_text_to_sql/src/utils/templates.py/0 | {
"file_path": "ContextualSP/interactive_text_to_sql/src/utils/templates.py",
"repo_id": "ContextualSP",
"token_count": 98
} | 259 |
import git
def commit_diff(c):
"""Return the set of changed files.
Args:
c (git.Commit)
Returns:
set[str]: a set of file paths (relative to the git repo's root directory).
"""
changed = set()
def add_path(blob):
if blob is not None:
changed.add(blob.path)
... | ContextualSP/lemon/executor/gtd/git_utils.py/0 | {
"file_path": "ContextualSP/lemon/executor/gtd/git_utils.py",
"repo_id": "ContextualSP",
"token_count": 211
} | 260 |
# Copyright (C) 2006, 2008, 2009, 2010 by Canonical Ltd
# Written by John Arbash Meinel <john@arbash-meinel.com>
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License... | ContextualSP/lemon/executor/gtd/profile_imports.py/0 | {
"file_path": "ContextualSP/lemon/executor/gtd/profile_imports.py",
"repo_id": "ContextualSP",
"token_count": 2583
} | 261 |
from abc import ABCMeta, abstractmethod
class PredicatesComputer(object, metaclass=ABCMeta):
"""Compute the set of possible LF predicates for a context, along with
their alignments to the utterance tokens.
The resulting predicates are used as `choices` in ParseCase.
The alignments are used for soft c... | ContextualSP/lemon/executor/strongsup/predicates_computer.py/0 | {
"file_path": "ContextualSP/lemon/executor/strongsup/predicates_computer.py",
"repo_id": "ContextualSP",
"token_count": 256
} | 262 |
from abc import ABCMeta, abstractmethod
class RLongState(object, metaclass=ABCMeta):
"""Represents a row of objects, each of which has various properties.
Used in:
- RLongWorld as the initial state
- RLongDenotation as the current state during execution
- RLongValue as the final state
"""
... | ContextualSP/lemon/executor/strongsup/rlong/state.py/0 | {
"file_path": "ContextualSP/lemon/executor/strongsup/rlong/state.py",
"repo_id": "ContextualSP",
"token_count": 10345
} | 263 |
import numpy as np
from strongsup.predicate import Predicate
def softmax(stuff):
"""Quick and dirty way to compute softmax"""
return (np.exp(stuff) / np.sum(np.exp(stuff))).tolist()
class PredicateGenerator(object):
"""Generate predicates with the specified context."""
def __init__(self, context):
... | ContextualSP/lemon/executor/strongsup/tests/utils.py/0 | {
"file_path": "ContextualSP/lemon/executor/strongsup/tests/utils.py",
"repo_id": "ContextualSP",
"token_count": 202
} | 264 |
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
... | ContextualSP/lemon/propara_evaluator/aristo-leaderboard/LICENSE/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/LICENSE",
"repo_id": "ContextualSP",
"token_count": 3168
} | 265 |
name: grc
channels:
- defaults
dependencies:
- pip=20.2.2=py37_0
- python=3.7.5=h0371630_0
- pip:
- numpy==1.19.2
- overrides==3.1.0
- scikit-learn==0.23.2
- scipy==1.5.2
| ContextualSP/lemon/propara_evaluator/aristo-leaderboard/eqasc/code/environment.yml/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/eqasc/code/environment.yml",
"repo_id": "ContextualSP",
"token_count": 111
} | 266 |
from process.process import Process, Conversion, Move, Input, Output
from process.summary import ProcessSummary
from process.action_file import ActionFile
from process.sentence_file import sentences_from_sentences_file
| ContextualSP/lemon/propara_evaluator/aristo-leaderboard/propara/evaluator/process/__init__.py/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/propara/evaluator/process/__init__.py",
"repo_id": "ContextualSP",
"token_count": 50
} | 267 |
## SciTail Evaluator
This script evaluates predictions on the SciTail dataset and produces an accuracy score.
## Example
```bash
% python3 evaluator.py -a answers.jsonl -p predictions.csv -o metrics.json
% cat metrics.json
{"accuracy": 0.8}
```
## Usage
The script takes two input files and produces one output fil... | ContextualSP/lemon/propara_evaluator/aristo-leaderboard/scitail/evaluator/README.md/0 | {
"file_path": "ContextualSP/lemon/propara_evaluator/aristo-leaderboard/scitail/evaluator/README.md",
"repo_id": "ContextualSP",
"token_count": 413
} | 268 |
from transformers import Seq2SeqTrainer
from typing import Dict, List, Optional
import torch
import numpy as np
import logging
from torch.utils.data import Dataset
from typing import Any, Dict, List, Optional, Tuple, Union,NamedTuple
from transformers import Seq2SeqTrainer, is_torch_tpu_available
from transformers.trai... | ContextualSP/logigan/pre-training/GenTrainer.py/0 | {
"file_path": "ContextualSP/logigan/pre-training/GenTrainer.py",
"repo_id": "ContextualSP",
"token_count": 8488
} | 269 |
from z3 import *
import random
from random import shuffle
from itertools import combinations, product
from typing import List, Tuple
from functools import partial
from tqdm import tqdm
import os
solver = Solver()
vars_all_candidates = [chr(i) for i in list(range(97, 122))]
for symbol in vars_all_candidates:
# ini... | ContextualSP/poet/synthesize_logic_corpus.py/0 | {
"file_path": "ContextualSP/poet/synthesize_logic_corpus.py",
"repo_id": "ContextualSP",
"token_count": 2798
} | 270 |
import random
import torch
from torch import nn
from torch.nn.functional import softmax
from utils import Trie, Tree
MAX_LEN = 256
class Parser(nn.Module):
def __init__(self, src_dictionary, trg_dictionary, model, device):
super().__init__()
self.src_dictionary = src_dictionary
self.trg_d... | ContextualSP/poset_decoding/sketch_prediction/model.py/0 | {
"file_path": "ContextualSP/poset_decoding/sketch_prediction/model.py",
"repo_id": "ContextualSP",
"token_count": 3243
} | 271 |
## Build Documentation:
#### Install Requirements
```python
pip install -r requirements.txt
```
#### Build Documentation
```bash
# Enter docs folder.
cd docs
# Use sphinx autodoc to generate rst.
sphinx-apidoc -o source/ ../matchzoo/
# Generate html from rst
make clean
make html
```
This will install all the pa... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/docs/Readme.md/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/docs/Readme.md",
"repo_id": "ContextualSP",
"token_count": 261
} | 272 |
from .data_pack import DataPack, load_data_pack
from .pack import pack
| ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/data_pack/__init__.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/data_pack/__init__.py",
"repo_id": "ContextualSP",
"token_count": 21
} | 273 |
from pathlib import Path
from .load_glove_embedding import load_glove_embedding
from .load_fasttext_embedding import load_fasttext_embedding
DATA_ROOT = Path(__file__).parent
EMBED_RANK = DATA_ROOT.joinpath('embed_rank.txt')
EMBED_10 = DATA_ROOT.joinpath('embed_10_word2vec.txt')
EMBED_10_GLOVE = DATA_ROOT.joinpath('em... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/datasets/embeddings/__init__.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/datasets/embeddings/__init__.py",
"repo_id": "ContextualSP",
"token_count": 131
} | 274 |
"""The rank hinge loss."""
import torch
from torch import nn
import torch.nn.functional as F
class RankHingeLoss(nn.Module):
"""
Creates a criterion that measures rank hinge loss.
Given inputs :math:`x1`, :math:`x2`, two 1D mini-batch `Tensors`,
and a label 1D mini-batch tensor :math:`y` (containing ... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/losses/rank_hinge_loss.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/losses/rank_hinge_loss.py",
"repo_id": "ContextualSP",
"token_count": 1214
} | 275 |
"""An implementation of BiMPM Model."""
import typing
import torch
import torch.nn as nn
from torch.nn import functional as F
from matchzoo.engine import hyper_spaces
from matchzoo.engine.param_table import ParamTable
from matchzoo.engine.param import Param
from matchzoo.engine.base_model import BaseModel
class BiM... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/models/bimpm.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/models/bimpm.py",
"repo_id": "ContextualSP",
"token_count": 7150
} | 276 |
"""An implementation of MVLSTM Model."""
import typing
import torch
import torch.nn as nn
import torch.nn.functional as F
from matchzoo.engine.param_table import ParamTable
from matchzoo.engine.param import Param
from matchzoo.engine.base_model import BaseModel
from matchzoo.engine.base_callback import BaseCallback
f... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/models/mvlstm.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/models/mvlstm.py",
"repo_id": "ContextualSP",
"token_count": 2721
} | 277 |
"""Basic Preprocessor."""
from tqdm import tqdm
import typing
from . import units
from matchzoo import DataPack
from matchzoo.engine.base_preprocessor import BasePreprocessor
from .build_vocab_unit import build_vocab_unit
from .build_unit_from_data_pack import build_unit_from_data_pack
from .chain_transform import ch... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/preprocessors/basic_preprocessor.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/preprocessors/basic_preprocessor.py",
"repo_id": "ContextualSP",
"token_count": 3360
} | 278 |
import nltk
from .unit import Unit
class Stemming(Unit):
"""
Process unit for token stemming.
:param stemmer: stemmer to use, `porter` or `lancaster`.
"""
def __init__(self, stemmer='porter'):
"""Initialization."""
self.stemmer = stemmer
def transform(self, input_: list) ->... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/preprocessors/units/stemming.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/preprocessors/units/stemming.py",
"repo_id": "ContextualSP",
"token_count": 441
} | 279 |
"""Download file."""
import typing
from pathlib import Path
import os
import hashlib
import shutil
import sys
import tarfile
import time
import zipfile
import collections
import six
from six.moves.urllib.error import HTTPError
from six.moves.urllib.error import URLError
from six.moves.urllib.request import urlretrieve... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/utils/get_file.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/utils/get_file.py",
"repo_id": "ContextualSP",
"token_count": 5793
} | 280 |
import pytest
import hyperopt.pyll.base
from matchzoo.engine import hyper_spaces
@pytest.fixture(scope='module', params=[
lambda x: x + 2,
lambda x: x - 2,
lambda x: x * 2,
lambda x: x / 2,
lambda x: x // 2,
lambda x: x ** 2,
lambda x: 2 + x,
lambda x: 2 - x,
lambda x: 2 * x,
... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/tests/engine/test_hyper_spaces.py/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/tests/engine/test_hyper_spaces.py",
"repo_id": "ContextualSP",
"token_count": 386
} | 281 |
<jupyter_start><jupyter_code>%run init.ipynb
ranking_task = mz.tasks.Ranking(losses=mz.losses.RankCrossEntropyLoss(num_neg=1))
ranking_task.metrics = [
mz.metrics.NormalizedDiscountedCumulativeGain(k=3),
mz.metrics.NormalizedDiscountedCumulativeGain(k=5),
mz.metrics.MeanAveragePrecision()
]
preprocessor = m... | ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/tutorials/ranking/anmm.ipynb/0 | {
"file_path": "ContextualSP/poset_decoding/traversal_path_prediction/MatchZoo-py/tutorials/ranking/anmm.ipynb",
"repo_id": "ContextualSP",
"token_count": 814
} | 282 |
Coming soon. | ContextualSP/qaap/README.md/0 | {
"file_path": "ContextualSP/qaap/README.md",
"repo_id": "ContextualSP",
"token_count": 3
} | 283 |
set model_file=checkpoints_sparc/sparc_concat_none_model
set validation_file=dataset_sparc/dev.json
set validation_out_file=dataset_sparc/dev.jsonl
set prediction_out_file=predict.jsonl
python postprocess.py --valid_file %validation_file% --valid_out_file %validation_out_file%
allennlp predict ^
--include-package datas... | ContextualSP/semantic_parsing_in_context/bash_files/windows/predict.bat/0 | {
"file_path": "ContextualSP/semantic_parsing_in_context/bash_files/windows/predict.bat",
"repo_id": "ContextualSP",
"token_count": 225
} | 284 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from typing import Dict
from typing import List
from typing import Tuple
import editdistance
import numpy as np
from allennlp.common.checks import ConfigurationError
from allennlp.data import TokenIndexer, Tokenizer
from allennlp.data.fields.kno... | ContextualSP/semantic_parsing_in_context/dataset_reader/util.py/0 | {
"file_path": "ContextualSP/semantic_parsing_in_context/dataset_reader/util.py",
"repo_id": "ContextualSP",
"token_count": 4858
} | 285 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import torch
from typing import Tuple
def get_span_representation(forward_encoder_out, backward_encoder_out, span_start, span_end):
"""
Given a span start/end position, fetch the subtraction representation of the span from LSTM.
""... | ContextualSP/semantic_parsing_in_context/models/util.py/0 | {
"file_path": "ContextualSP/semantic_parsing_in_context/models/util.py",
"repo_id": "ContextualSP",
"token_count": 751
} | 286 |
{
"random_seed": 42,
"numpy_seed": 42,
"pytorch_seed": 42,
"dataset_reader": {
"type": "sparc",
"lazy": false,
"loading_limit": -1,
"context_mode": "turn",
"bert_mode": "v3",
"utterance_token_indexers": {
"bert": {
"type": "bert-pretrained",
"pretrained_model": "bert-base-uncased",
"do_lo... | ContextualSP/semantic_parsing_in_context/train_configs_bert/turn.none.jsonnet/0 | {
"file_path": "ContextualSP/semantic_parsing_in_context/train_configs_bert/turn.none.jsonnet",
"repo_id": "ContextualSP",
"token_count": 1110
} | 287 |
#!/bin/bash
#requirement:
#./data/spider
#./BART-large
# data/spider -> data/spider_schema_linking_tag
python step1_schema_linking.py --dataset=spider
# data/spider_schema_linking_tag -> dataset_post/spider_sl
python step2_serialization.py
###training
python train.py \
--dataset_path ./dataset_post/spider_sl/bin/... | ContextualSP/unified_parser_text_to_sql/running_pipeline.sh/0 | {
"file_path": "ContextualSP/unified_parser_text_to_sql/running_pipeline.sh",
"repo_id": "ContextualSP",
"token_count": 202
} | 288 |
## Data Preprocess
#### Get Parsed SQL Output
The SQL parsing script is `process_sql.py` in the main directory. Please refer to `parsed_sql_examples.sql` for the explanation of some parsed SQL output examples.
If you would like to use `process_sql.py` to parse SQL queries by yourself, `parse_sql_one.py` provides an ... | ContextualSP/unified_parser_text_to_sql/third_party/spider/preprocess/README.md/0 | {
"file_path": "ContextualSP/unified_parser_text_to_sql/third_party/spider/preprocess/README.md",
"repo_id": "ContextualSP",
"token_count": 238
} | 289 |
import math
import sys
from typing import Iterable, Optional
from timm.utils.model import unwrap_model
import torch
from timm.data import Mixup
from timm.utils import accuracy, ModelEma
from lib import utils
import random
import time
def sample_configs(choices):
config = {}
dimensions = ['mlp_ratio', 'num_he... | Cream/AutoFormer/supernet_engine.py/0 | {
"file_path": "Cream/AutoFormer/supernet_engine.py",
"repo_id": "Cream",
"token_count": 2771
} | 290 |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path as osp
import sys
def add_path(path):
if path not in sys.path:
sys.path.insert(0, path)
this_dir = osp.dirname(__file__)
lib_path = osp.join(this_dir, '..', 'lib')
add_path(lib_pa... | Cream/CDARTS/CDARTS/_init_paths.py/0 | {
"file_path": "Cream/CDARTS/CDARTS/_init_paths.py",
"repo_id": "Cream",
"token_count": 148
} | 291 |
import numpy as np
def quantize(arr, min_val, max_val, levels, dtype=np.int64):
"""Quantize an array of (-inf, inf) to [0, levels-1].
Args:
arr (ndarray): Input array.
min_val (scalar): Minimum value to be clipped.
max_val (scalar): Maximum value to be clipped.
levels (int): Q... | Cream/CDARTS/CDARTS_detection/mmcv/arraymisc/quantization.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmcv/arraymisc/quantization.py",
"repo_id": "Cream",
"token_count": 815
} | 292 |
from .colorspace import (bgr2gray, gray2bgr, bgr2rgb, rgb2bgr, bgr2hsv,
hsv2bgr, bgr2hls, hls2bgr, iminvert)
from .geometry import imflip, imrotate, imcrop, impad, impad_to_multiple
from .normalize import imnormalize, imdenormalize
from .resize import imresize, imresize_like, imrescale
__all__... | Cream/CDARTS/CDARTS_detection/mmcv/image/transforms/__init__.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmcv/image/transforms/__init__.py",
"repo_id": "Cream",
"token_count": 279
} | 293 |
from .hook import Hook
from .checkpoint import CheckpointHook
from .closure import ClosureHook
from .lr_updater import LrUpdaterHook
from .optimizer import OptimizerHook, OptimizerArchHook
from .iter_timer import IterTimerHook
from .sampler_seed import DistSamplerSeedHook
from .memory import EmptyCacheHook
from .logger... | Cream/CDARTS/CDARTS_detection/mmcv/runner/hooks/__init__.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmcv/runner/hooks/__init__.py",
"repo_id": "Cream",
"token_count": 257
} | 294 |
from enum import Enum
class Priority(Enum):
"""Hook priority levels.
+------------+------------+
| Level | Value |
+============+============+
| HIGHEST | 0 |
+------------+------------+
| VERY_HIGH | 10 |
+------------+------------+
| HIGH | 3... | Cream/CDARTS/CDARTS_detection/mmcv/runner/priority.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmcv/runner/priority.py",
"repo_id": "Cream",
"token_count": 562
} | 295 |
/* Generated by Cython 0.27.3 */
#define PY_SSIZE_T_CLEAN
#include "Python.h"
#ifndef Py_PYTHON_H
#error Python headers needed to compile C extensions, please install development version of Python.
#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000)
#error Cython ... | Cream/CDARTS/CDARTS_detection/mmcv/video/optflow_warp/flow_warp_module.cpp/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmcv/video/optflow_warp/flow_warp_module.cpp",
"repo_id": "Cream",
"token_count": 166554
} | 296 |
import warnings
import matplotlib.pyplot as plt
import mmcv
import numpy as np
import pycocotools.mask as maskUtils
import torch
from mmcv.parallel import collate, scatter
from mmcv.runner import load_checkpoint
from mmdet.core import get_classes
from mmdet.datasets.pipelines import Compose
from mmdet.models import b... | Cream/CDARTS/CDARTS_detection/mmdet/apis/inference.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/apis/inference.py",
"repo_id": "Cream",
"token_count": 2561
} | 297 |
from .base_sampler import BaseSampler
from .pseudo_sampler import PseudoSampler
from .random_sampler import RandomSampler
from .instance_balanced_pos_sampler import InstanceBalancedPosSampler
from .iou_balanced_neg_sampler import IoUBalancedNegSampler
from .combined_sampler import CombinedSampler
from .ohem_sampler imp... | Cream/CDARTS/CDARTS_detection/mmdet/core/bbox/samplers/__init__.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/core/bbox/samplers/__init__.py",
"repo_id": "Cream",
"token_count": 183
} | 298 |
from collections.abc import Sequence
import numpy as np
from terminaltables import AsciiTable
from mmdet.utils import print_log
from .bbox_overlaps import bbox_overlaps
def _recalls(all_ious, proposal_nums, thrs):
img_num = all_ious.shape[0]
total_gt_num = sum([ious.shape[0] for ious in all_ious])
_io... | Cream/CDARTS/CDARTS_detection/mmdet/core/evaluation/recall.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/core/evaluation/recall.py",
"repo_id": "Cream",
"token_count": 3195
} | 299 |
from .coco import CocoDataset
from .registry import DATASETS
@DATASETS.register_module
class CityscapesDataset(CocoDataset):
CLASSES = ('person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
'bicycle')
| Cream/CDARTS/CDARTS_detection/mmdet/datasets/cityscapes.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/datasets/cityscapes.py",
"repo_id": "Cream",
"token_count": 96
} | 300 |
from mmdet.core import eval_map, eval_recalls
from .registry import DATASETS
from .xml_style import XMLDataset
@DATASETS.register_module
class VOCDataset(XMLDataset):
CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
... | Cream/CDARTS/CDARTS_detection/mmdet/datasets/voc.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/datasets/voc.py",
"repo_id": "Cream",
"token_count": 1422
} | 301 |
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
class DropBlock2D(nn.Module):
r"""Randomly zeroes 2D spatial blocks of the input tensor.
As described in the paper
`DropBlock: A regularization method for convolutional networks`_ ,
dropping whole blocks of feature ma... | Cream/CDARTS/CDARTS_detection/mmdet/models/backbones/dropblock.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/models/backbones/dropblock.py",
"repo_id": "Cream",
"token_count": 2265
} | 302 |
import torch
import torch.nn.functional as F
from .cascade_rcnn import CascadeRCNN
from .. import builder
from ..registry import DETECTORS
from mmdet.core import (bbox2roi, bbox2result, build_assigner, build_sampler,
merge_aug_masks)
@DETECTORS.register_module
class HybridTaskCascade(CascadeR... | Cream/CDARTS/CDARTS_detection/mmdet/models/detectors/htc.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/models/detectors/htc.py",
"repo_id": "Cream",
"token_count": 10086
} | 303 |
import torch
import torch.nn as nn
from .utils import weighted_loss
from ..registry import LOSSES
@weighted_loss
def smooth_l1_loss(pred, target, beta=1.0):
assert beta > 0
assert pred.size() == target.size() and target.numel() > 0
diff = torch.abs(pred - target)
loss = torch.where(diff < beta, 0.5 *... | Cream/CDARTS/CDARTS_detection/mmdet/models/losses/smooth_l1_loss.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/models/losses/smooth_l1_loss.py",
"repo_id": "Cream",
"token_count": 625
} | 304 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.checkpoint import checkpoint
from mmcv.cnn.weight_init import caffe2_xavier_init
from ..utils import ConvModule
from ..registry import NECKS
@NECKS.register_module
class HRFPN(nn.Module):
"""HRFPN (High Resolution Feature Pyrmami... | Cream/CDARTS/CDARTS_detection/mmdet/models/necks/hrfpn.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/models/necks/hrfpn.py",
"repo_id": "Cream",
"token_count": 1639
} | 305 |
import torch
import torch.nn as nn
class Scale(nn.Module):
def __init__(self, scale=1.0):
super(Scale, self).__init__()
self.scale = nn.Parameter(torch.tensor(scale, dtype=torch.float))
def forward(self, x):
return x * self.scale
| Cream/CDARTS/CDARTS_detection/mmdet/models/utils/scale.py/0 | {
"file_path": "Cream/CDARTS/CDARTS_detection/mmdet/models/utils/scale.py",
"repo_id": "Cream",
"token_count": 113
} | 306 |
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