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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AISFormer | AISFormer-master/tests/test_checkpoint.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
from collections import OrderedDict
import torch
from torch import nn
from detectron2.checkpoint.c2_model_loading import align_and_update_state_dicts
from detectron2.utils.logger import setup_logger
class TestCheckpointer(unittest.TestCase):
def ... | 1,705 | 33.12 | 79 | py |
AISFormer | AISFormer-master/tests/test_model_analysis.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import torch
from torch import nn
from detectron2.utils.analysis import find_unused_parameters, flop_count_operators, parameter_count
from detectron2.utils.testing import get_model_no_weights
class RetinaNetTest(unittest.TestCase):
def setUp(se... | 2,890 | 34.691358 | 99 | py |
AISFormer | AISFormer-master/tests/test_export_torchscript.py | # Copyright (c) Facebook, Inc. and its affiliates.
import json
import os
import random
import tempfile
import unittest
import torch
from torch import Tensor, nn
from detectron2 import model_zoo
from detectron2.config import get_cfg
from detectron2.config.instantiate import dump_dataclass, instantiate
from detectron2.... | 11,457 | 37.579125 | 100 | py |
AISFormer | AISFormer-master/tests/test_registry.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import torch
from detectron2.modeling.meta_arch import GeneralizedRCNN
from detectron2.utils.registry import _convert_target_to_string, locate
class A:
class B:
pass
class TestLocate(unittest.TestCase):
def _test_obj(self, obj):
... | 1,243 | 26.043478 | 71 | py |
AISFormer | AISFormer-master/tests/test_export_caffe2.py | # Copyright (c) Facebook, Inc. and its affiliates.
# -*- coding: utf-8 -*-
import copy
import os
import tempfile
import unittest
import torch
from detectron2 import model_zoo
from detectron2.export import Caffe2Model, Caffe2Tracer
from detectron2.utils.logger import setup_logger
from detectron2.utils.testing import g... | 1,960 | 36 | 95 | py |
AISFormer | AISFormer-master/tests/test_engine.py | # Copyright (c) Facebook, Inc. and its affiliates.
import json
import math
import os
import tempfile
import time
import unittest
from unittest import mock
import torch
from fvcore.common.checkpoint import Checkpointer
from torch import nn
from detectron2 import model_zoo
from detectron2.config import configurable, ge... | 7,464 | 38.919786 | 98 | py |
AISFormer | AISFormer-master/tests/test_visualizer.py | # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
import os
import tempfile
import unittest
import cv2
import torch
from detectron2.data import MetadataCatalog
from detectron2.structures import BoxMode, Instances, RotatedBoxes
from detectron2.utils.visualizer import ColorMo... | 10,457 | 36.483871 | 94 | py |
AISFormer | AISFormer-master/tests/test_scheduler.py | # Copyright (c) Facebook, Inc. and its affiliates.
import math
import numpy as np
from unittest import TestCase
import torch
from fvcore.common.param_scheduler import CosineParamScheduler, MultiStepParamScheduler
from torch import nn
from detectron2.solver import LRMultiplier, WarmupParamScheduler, build_lr_scheduler... | 3,751 | 30.266667 | 88 | py |
AISFormer | AISFormer-master/tests/config/test_yacs_config.py | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
import os
import tempfile
import unittest
import torch
from omegaconf import OmegaConf
from detectron2 import model_zoo
from detectron2.config import configurable, downgrade_config, get_cfg, upgrade_config
from detectron2.layers import ShapeSpe... | 8,458 | 30.214022 | 95 | py |
AISFormer | AISFormer-master/tests/layers/test_mask_ops.py | # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
import contextlib
import io
import numpy as np
import unittest
from collections import defaultdict
import torch
import tqdm
from fvcore.common.benchmark import benchmark
from pycocotools.coco import COCO
from tabulate import tabulate
from torch... | 7,269 | 34.812808 | 100 | py |
AISFormer | AISFormer-master/tests/layers/test_roi_align_rotated.py | # Copyright (c) Facebook, Inc. and its affiliates.
import logging
import unittest
import cv2
import torch
from torch.autograd import Variable, gradcheck
from detectron2.layers.roi_align import ROIAlign
from detectron2.layers.roi_align_rotated import ROIAlignRotated
logger = logging.getLogger(__name__)
class ROIAlig... | 6,716 | 36.949153 | 100 | py |
AISFormer | AISFormer-master/tests/layers/test_blocks.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import torch
from torch import nn
from detectron2.layers import ASPP, DepthwiseSeparableConv2d, FrozenBatchNorm2d
from detectron2.modeling.backbone.resnet import BasicStem, ResNet
"""
Test for misc layers.
"""
class TestBlocks(unittest.TestCase):
... | 1,807 | 33.769231 | 99 | py |
AISFormer | AISFormer-master/tests/layers/test_deformable.py | # Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
import unittest
import torch
from detectron2.layers import DeformConv, ModulatedDeformConv
from detectron2.utils.env import TORCH_VERSION
@unittest.skipIf(
TORCH_VERSION == (1, 8) and torch.cuda.is_available(),
"This test fails under cuda1... | 7,636 | 42.392045 | 97 | py |
AISFormer | AISFormer-master/tests/layers/test_nms.py | # Copyright (c) Facebook, Inc. and its affiliates.
from __future__ import absolute_import, division, print_function, unicode_literals
import unittest
import torch
from detectron2.layers import batched_nms
from detectron2.utils.testing import random_boxes
class TestNMS(unittest.TestCase):
def _create_tensors(self... | 1,176 | 33.617647 | 94 | py |
AISFormer | AISFormer-master/tests/layers/test_nms_rotated.py | # Copyright (c) Facebook, Inc. and its affiliates.
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
import unittest
from copy import deepcopy
import torch
from torchvision import ops
from detectron2.layers import batched_nms, batched_nms_rotated, nms_rotated
from de... | 7,367 | 41.589595 | 96 | py |
AISFormer | AISFormer-master/tests/layers/test_losses.py | # Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
import unittest
import torch
from detectron2.layers import ciou_loss, diou_loss
class TestLosses(unittest.TestCase):
def test_diou_loss(self):
"""
loss = 1 - iou + d/c
where,
d = (distance between centers of the... | 3,045 | 35.698795 | 82 | py |
AISFormer | AISFormer-master/tests/layers/test_roi_align.py | # Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
import unittest
from copy import copy
import cv2
import torch
from fvcore.common.benchmark import benchmark
from torch.nn import functional as F
from detectron2.layers.roi_align import ROIAlign, roi_align
class ROIAlignTest(unittest.TestCase):
... | 7,766 | 35.810427 | 99 | py |
AISFormer | AISFormer-master/tests/data/test_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
import os
import pickle
import sys
import unittest
from functools import partial
import torch
from iopath.common.file_io import LazyPath
from detectron2 import model_zoo
from detectron2.config import instantiate
from detectron2.data import (
DatasetFromList,
... | 5,188 | 32.915033 | 88 | py |
AISFormer | AISFormer-master/tests/data/test_sampler.py | # Copyright (c) Facebook, Inc. and its affiliates.
import itertools
import math
import operator
import unittest
import torch
from torch.utils import data
from torch.utils.data.sampler import SequentialSampler
from detectron2.data.build import worker_init_reset_seed
from detectron2.data.common import DatasetFromList, T... | 3,907 | 33.892857 | 96 | py |
AISFormer | AISFormer-master/tests/data/test_coco_evaluation.py | # Copyright (c) Facebook, Inc. and its affiliates.
import contextlib
import copy
import io
import json
import numpy as np
import os
import tempfile
import unittest
import torch
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
from detectron2.data import DatasetCatalog
from detectron2.evaluat... | 8,798 | 62.302158 | 2,000 | py |
AISFormer | AISFormer-master/tests/data/test_transforms.py | # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
import logging
import numpy as np
import unittest
from unittest import mock
import torch
from PIL import Image, ImageOps
from torch.nn import functional as F
from detectron2.config import get_cfg
from detectron2.data import detection_utils
fro... | 11,260 | 40.862454 | 98 | py |
AISFormer | AISFormer-master/tests/modeling/test_matcher.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
from typing import List
import torch
from detectron2.config import get_cfg
from detectron2.modeling.matcher import Matcher
class TestMatcher(unittest.TestCase):
def test_scriptability(self):
cfg = get_cfg()
anchor_matcher = Matche... | 1,663 | 37.697674 | 98 | py |
AISFormer | AISFormer-master/tests/modeling/test_roi_pooler.py | # Copyright (c) Facebook, Inc. and its affiliates.
import logging
import unittest
import torch
from detectron2.modeling.poolers import ROIPooler
from detectron2.structures import Boxes, RotatedBoxes
from detectron2.utils.testing import random_boxes
logger = logging.getLogger(__name__)
class TestROIPooler(unittest.T... | 5,694 | 33.307229 | 87 | py |
AISFormer | AISFormer-master/tests/modeling/test_fast_rcnn.py | # Copyright (c) Facebook, Inc. and its affiliates.
import logging
import unittest
import torch
from detectron2.layers import ShapeSpec
from detectron2.modeling.box_regression import Box2BoxTransform, Box2BoxTransformRotated
from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers
from detectron2.modeli... | 7,038 | 39.924419 | 100 | py |
AISFormer | AISFormer-master/tests/modeling/test_model_e2e.py | # Copyright (c) Facebook, Inc. and its affiliates.
import itertools
import unittest
from contextlib import contextmanager
from copy import deepcopy
import torch
from detectron2.structures import BitMasks, Boxes, ImageList, Instances
from detectron2.utils.events import EventStorage
from detectron2.utils.testing impor... | 8,650 | 36.942982 | 99 | py |
AISFormer | AISFormer-master/tests/modeling/test_box2box_transform.py | # Copyright (c) Facebook, Inc. and its affiliates.
import logging
import unittest
import torch
from detectron2.modeling.box_regression import (
Box2BoxTransform,
Box2BoxTransformLinear,
Box2BoxTransformRotated,
)
from detectron2.utils.testing import random_boxes
logger = logging.getLogger(__name__)
clas... | 3,647 | 37.4 | 99 | py |
AISFormer | AISFormer-master/tests/modeling/test_rpn.py | # Copyright (c) Facebook, Inc. and its affiliates.
import logging
import unittest
import torch
from detectron2.config import get_cfg
from detectron2.export import scripting_with_instances
from detectron2.layers import ShapeSpec
from detectron2.modeling.backbone import build_backbone
from detectron2.modeling.proposal_g... | 11,270 | 41.855513 | 99 | py |
AISFormer | AISFormer-master/tests/modeling/test_mmdet.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
from detectron2.layers import ShapeSpec
from detectron2.modeling.mmdet_wrapper import MMDetBackbone, MMDetDetector
try:
import mmdet.models # noqa
HAS_MMDET = True
except ImportError:
HAS_MMDET = False
@unittest.skipIf(not HAS_MMDET, "... | 7,426 | 38.716578 | 99 | py |
AISFormer | AISFormer-master/tests/modeling/test_backbone.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import unittest
import torch
import detectron2.export.torchscript # apply patch # noqa
from detectron2 import model_zoo
from detectron2.config import get_cfg
from detectron2.layers import ShapeSpec
from detectron2.modeling.backbone import build_r... | 1,181 | 32.771429 | 80 | py |
AISFormer | AISFormer-master/tests/modeling/test_anchor_generator.py | # Copyright (c) Facebook, Inc. and its affiliates.
import logging
import unittest
import torch
from detectron2.config import get_cfg
from detectron2.layers import ShapeSpec
from detectron2.modeling.anchor_generator import DefaultAnchorGenerator, RotatedAnchorGenerator
logger = logging.getLogger(__name__)
class Test... | 4,723 | 38.041322 | 95 | py |
AISFormer | AISFormer-master/tests/modeling/test_roi_heads.py | # Copyright (c) Facebook, Inc. and its affiliates.
import logging
import unittest
from copy import deepcopy
import torch
from torch import nn
from detectron2 import model_zoo
from detectron2.config import get_cfg
from detectron2.export.torchscript_patch import (
freeze_training_mode,
patch_builtin_len,
pat... | 13,989 | 42.179012 | 99 | py |
AISFormer | AISFormer-master/tests/tracking/test_vanilla_hungarian_bbox_iou_tracker.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
from typing import Dict
import numpy as np
import torch
from detectron2.config import CfgNode as CfgNode_, instantiate
from detectron2.structures import Boxes, Instances
from detectron2.tracking.base_tracker import build_tracker_head
from detectron2.tr... | 10,443 | 44.017241 | 119 | py |
AISFormer | AISFormer-master/tests/tracking/test_bbox_iou_tracker.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import torch
import numpy as np
from typing import Dict
from detectron2.structures import Boxes, Instances
from detectron2.config import instantiate, CfgNode as CfgNode_
from detectron2.tracking.base_tracker import build_tracker_head
from detectron2.tr... | 6,738 | 41.11875 | 90 | py |
AISFormer | AISFormer-master/tests/tracking/test_iou_weighted_hungarian_bbox_iou_tracker.py | # Copyright (c) Facebook, Inc. and its affiliates.
import copy
import unittest
from typing import Dict
import numpy as np
import torch
from detectron2.config import CfgNode as CfgNode_, instantiate
from detectron2.structures import Boxes, Instances
from detectron2.tracking.base_tracker import build_tracker_head
from d... | 10,575 | 44.196581 | 128 | py |
AISFormer | AISFormer-master/tests/tracking/test_hungarian_tracker.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
from typing import Dict
import numpy as np
import torch
from detectron2.config import instantiate
from detectron2.structures import Boxes, Instances
class TestBaseHungarianTracker(unittest.TestCase):
def setUp(self):
self._img_size = np.a... | 3,953 | 37.38835 | 90 | py |
AISFormer | AISFormer-master/tests/structures/test_rotated_boxes.py | # Copyright (c) Facebook, Inc. and its affiliates.
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import math
import random
import unittest
import torch
from fvcore.common.benchmark import benchmark
from detectron2.layers.rotated_boxes import pairwise_iou_rotated
from... | 18,603 | 41.474886 | 100 | py |
AISFormer | AISFormer-master/tests/structures/test_boxes.py | # Copyright (c) Facebook, Inc. and its affiliates.
import json
import math
import numpy as np
import unittest
import torch
from detectron2.structures import Boxes, BoxMode, pairwise_ioa, pairwise_iou
from detectron2.utils.testing import reload_script_model
class TestBoxMode(unittest.TestCase):
def _convert_xy_to... | 8,354 | 36.299107 | 100 | py |
AISFormer | AISFormer-master/tests/structures/test_imagelist.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
from typing import List, Sequence, Tuple
import torch
from detectron2.structures import ImageList
class TestImageList(unittest.TestCase):
def test_imagelist_padding_tracing(self):
# test that the trace does not contain hard-coded constan... | 2,976 | 38.171053 | 86 | py |
AISFormer | AISFormer-master/tests/structures/test_masks.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import torch
from detectron2.structures.masks import BitMasks, PolygonMasks, polygons_to_bitmask
class TestBitMask(unittest.TestCase):
def test_get_bounding_box(self):
masks = torch.tensor(
[
[
... | 2,003 | 36.111111 | 96 | py |
AISFormer | AISFormer-master/tests/structures/test_keypoints.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import torch
from detectron2.structures.keypoints import Keypoints
class TestKeypoints(unittest.TestCase):
def test_cat_keypoints(self):
keypoints1 = Keypoints(torch.rand(2, 21, 3))
keypoints2 = Keypoints(torch.rand(4, 21, 3))
... | 610 | 29.55 | 88 | py |
AISFormer | AISFormer-master/tests/structures/test_instances.py | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import torch
from torch import Tensor
from detectron2.export.torchscript import patch_instances
from detectron2.structures import Boxes, Instances
from detectron2.utils.testing import convert_scripted_instances
class TestInstances(unittest.TestCase):... | 8,466 | 37.486364 | 98 | py |
AISFormer | AISFormer-master/demo/predictor.py | # Copyright (c) Facebook, Inc. and its affiliates.
import atexit
import bisect
import multiprocessing as mp
from collections import deque
import cv2
import torch
from detectron2.data import MetadataCatalog
from detectron2.engine.defaults import DefaultPredictor
from detectron2.utils.video_visualizer import VideoVisual... | 7,844 | 34.497738 | 96 | py |
AISFormer | AISFormer-master/demo/dino_attn_vis.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# 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.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 2,976 | 30.336842 | 102 | py |
AISFormer | AISFormer-master/docs/conf.py | # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
# flake8: noqa
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path ... | 12,778 | 32.365535 | 140 | py |
AISFormer | AISFormer-master/dev/packaging/gen_install_table.py | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
# -*- coding: utf-8 -*-
import argparse
template = """<details><summary> install </summary><pre><code>\
python -m pip install detectron2{d2_version} -f \\
https://dl.fbaipublicfiles.com/detectron2/wheels/{cuda}/torch{torch}/index.html
</code><... | 2,014 | 30.484375 | 95 | py |
fooling-partial-dependence | fooling-partial-dependence-main/xor.py | # ---------------------------------
# test the algorithm implementation
# on a multidimensional XOR problem
# > python xor.py
# ---------------------------------
import tensorflow as tf
import argparse
parser = argparse.ArgumentParser(description='main')
parser.add_argument('--algorithm', default="gradient", type=str... | 2,013 | 32.016393 | 87 | py |
fooling-partial-dependence | fooling-partial-dependence-main/heart-gradient.py | # ---------------------------------
# example: heart / NN / variable
# > python heart-gradient.py
# ---------------------------------
import tensorflow as tf
import argparse
parser = argparse.ArgumentParser(description='main')
parser.add_argument('--variable', default="age", type=str, help='variable')
parser.add_argu... | 1,921 | 28.569231 | 83 | py |
fooling-partial-dependence | fooling-partial-dependence-main/code/explainer.py | import numpy as np
import pandas as pd
import warnings
class Explainer:
def __init__(self, model, data, predict_function=None):
self.model = model
if isinstance(data, pd.DataFrame):
self.data = data
elif isinstance(data, np.ndarray):
warnings.warn("`data` is a numpy... | 4,800 | 39.686441 | 109 | py |
autovc | autovc-master/main.py | import os
import argparse
from solver_encoder import Solver
from data_loader import get_loader
from torch.backends import cudnn
def str2bool(v):
return v.lower() in ('true')
def main(config):
# For fast training.
cudnn.benchmark = True
# Data loader.
vcc_loader = get_loader(config.data_dir, conf... | 1,414 | 29.76087 | 102 | py |
autovc | autovc-master/model_bl.py | import torch
import torch.nn as nn
class D_VECTOR(nn.Module):
"""d vector speaker embedding."""
def __init__(self, num_layers=3, dim_input=40, dim_cell=256, dim_emb=64):
super(D_VECTOR, self).__init__()
self.lstm = nn.LSTM(input_size=dim_input, hidden_size=dim_cell,
... | 737 | 32.545455 | 77 | py |
autovc | autovc-master/model_vc.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class LinearNorm(torch.nn.Module):
def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
super(LinearNorm, self).__init__()
self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
... | 6,621 | 31.302439 | 99 | py |
autovc | autovc-master/solver_encoder.py | from model_vc import Generator
import torch
import torch.nn.functional as F
import time
import datetime
class Solver(object):
def __init__(self, vcc_loader, config):
"""Initialize configurations."""
# Data loader.
self.vcc_loader = vcc_loader
# Model configurations.
self... | 4,394 | 33.606299 | 139 | py |
autovc | autovc-master/data_loader.py | from torch.utils import data
import torch
import numpy as np
import pickle
import os
from multiprocessing import Process, Manager
class Utterances(data.Dataset):
"""Dataset class for the Utterances dataset."""
def __init__(self, root_dir, len_crop):
"""Initialize and preprocess the Ut... | 3,077 | 30.090909 | 79 | py |
autovc | autovc-master/synthesis.py | # coding: utf-8
"""
Synthesis waveform from trained WaveNet.
Modified from https://github.com/r9y9/wavenet_vocoder
"""
import torch
from tqdm import tqdm
import librosa
from hparams import hparams
from wavenet_vocoder import builder
torch.set_num_threads(4)
use_cuda = torch.cuda.is_available()
device = torch.device(... | 2,006 | 26.493151 | 89 | py |
autovc | autovc-master/make_metadata.py | """
Generate speaker embeddings and metadata for training
"""
import os
import pickle
from model_bl import D_VECTOR
from collections import OrderedDict
import numpy as np
import torch
C = D_VECTOR(dim_input=80, dim_cell=768, dim_emb=256).eval().cuda()
c_checkpoint = torch.load('3000000-BL.ckpt')
new_state_dict = Order... | 2,046 | 33.116667 | 80 | py |
DS3L | DS3L-master/load_dataset.py | from torchvision import datasets
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
from torch.utils.data import Sampler
from torchvision import datasets
import numpy as np
import torch
import torchvision.transforms as tv_transforms
COUNTS = {
"svhn": {"train": 73257, "test": 26032, "vali... | 9,159 | 39.352423 | 129 | py |
DS3L | DS3L-master/transform.py | import torch
import torch.nn.functional as F
import random
class transform:
def __init__(self, flip=True, r_crop=True, g_noise=True):
self.flip = flip
self.r_crop = r_crop
self.g_noise = g_noise
def __call__(self, x):
if self.flip and random.random() > 0.5:
x = x.fl... | 637 | 28 | 61 | py |
DS3L | DS3L-master/train.py | import numpy as np
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
from load_dataset import *
import transform
from wideresnet import WideResNet, CNN, WNet
import argparse
import math
import time
import os
parser = argparse.ArgumentParser(description='manual to ... | 7,838 | 34.310811 | 130 | py |
DS3L | DS3L-master/wideresnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from torch.autograd import Variable
import random
def to_var(x, requires_grad=True):
if torch.cuda.is_available():
x = x.cuda()
return Variable(x, requires_grad=requires_grad)
class MetaModule(nn.Module):
# adopted fr... | 13,337 | 36.571831 | 117 | py |
GP-VAE | GP-VAE-master/lib/healing_mnist.py | """
Data loader for the Healing MNIST data set (c.f. https://arxiv.org/abs/1511.05121)
Adapted from https://github.com/Nikita6000/deep_kalman_filter_for_BM/blob/master/healing_mnist.py
"""
import numpy as np
import scipy.ndimage
from tensorflow.keras.datasets import mnist
def apply_square(img, square_size):
im... | 2,966 | 33.103448 | 116 | py |
GP-VAE | GP-VAE-master/lib/models.py | """
TensorFlow models for use in this project.
"""
from .utils import *
from .nn_utils import *
from .gp_kernel import *
from tensorflow_probability import distributions as tfd
import tensorflow as tf
# Encoders
class DiagonalEncoder(tf.keras.Model):
def __init__(self, z_size, hidden_sizes=(64, 64), **kwargs)... | 19,816 | 44.14123 | 116 | py |
GP-VAE | GP-VAE-master/lib/nn_utils.py | import tensorflow as tf
''' NN utils '''
def make_nn(output_size, hidden_sizes):
""" Creates fully connected neural network
:param output_size: output dimensionality
:param hidden_sizes: tuple of hidden layer sizes.
The tuple length sets the number of hid... | 2,133 | 42.55102 | 83 | py |
multixrank | multixrank-master/doc/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/stable/config
# -- Path setup ------------------------------------------------------------... | 5,484 | 29.988701 | 81 | py |
private-pgm | private-pgm-master/examples/torch_example.py | from mbi import Dataset, FactoredInference, Domain
import numpy as np
from scipy import sparse
"""
This file is essentially the same as adult_example with two key differences:
(1) it is run with the torch backend
(2) scipy.sparse.eye(n) is replaced with Identity(n), which improves speed by a factor of 2
Note... | 2,192 | 28.635135 | 97 | py |
private-pgm | private-pgm-master/src/mbi/inference.py | import numpy as np
from mbi import Domain, GraphicalModel, callbacks, CliqueVector
from scipy.sparse.linalg import LinearOperator, eigsh, lsmr, aslinearoperator
from scipy import optimize, sparse
from functools import partial
from collections import defaultdict
class FactoredInference:
def __init__(self, domain, b... | 17,110 | 45.497283 | 144 | py |
private-pgm | private-pgm-master/src/mbi/torch_factor.py | import numpy as np
import torch
class Factor:
device = "cuda" if torch.cuda.is_available() else "cpu"
def __init__(self, domain, values):
""" Initialize a factor over the given domain
:param domain: the domain of the factor
:param values: the ndarray or tensor of factor values (f... | 7,375 | 35.696517 | 101 | py |
private-pgm | private-pgm-master/src/mbi/local_inference.py | import numpy as np
from mbi import Domain, GraphicalModel, callbacks, FactorGraph, RegionGraph, CliqueVector
from scipy.sparse.linalg import LinearOperator, eigsh, lsmr, aslinearoperator
from scipy import optimize, sparse
from functools import partial
from collections import defaultdict
from copy import deepcopy
"""
T... | 11,476 | 44.908 | 168 | py |
private-pgm | private-pgm-master/src/mbi/mixture_inference.py | from mbi import Dataset, Factor, CliqueVector
from scipy.optimize import minimize
from collections import defaultdict
import numpy as np
import jax.numpy as jnp
from jax import vjp
from jax.nn import softmax as jax_softmax
from scipy.special import softmax
from functools import reduce
from scipy.sparse.linalg import ls... | 7,339 | 36.835052 | 120 | py |
private-pgm | private-pgm-master/src/mbi/__init__.py | from mbi.domain import Domain
from mbi.dataset import Dataset
from mbi.factor import Factor
from mbi.clique_vector import CliqueVector
from mbi.graphical_model import GraphicalModel
from mbi.factor_graph import FactorGraph
from mbi.region_graph import RegionGraph
from mbi.inference import FactoredInference
from mbi.loc... | 570 | 34.6875 | 77 | py |
private-pgm | private-pgm-master/test/test_torch.py | import unittest
from mbi import Domain, FactoredInference
import numpy as np
import test_inference
try:
import torch
from mbi.torch_factor import Factor
skip = False
except:
skip = True
class TestFactor(unittest.TestCase):
def setUp(self):
if skip: raise unittest.SkipTest('PyTorch not inst... | 3,026 | 31.902174 | 86 | py |
P2IL | P2IL-main/linear_PPIL/run.py | import torch
import gym
import random
import argparse
import numpy as np
import sys
import pickle
sys.path.insert(0,"../")
from lpimi.envs.wrappers import TimeLimit
parser = argparse.ArgumentParser(description='IM Learning')
parser.add_argument('--env-name', default="Hopper-v2", metavar='G',
help='... | 38,499 | 41.920847 | 158 | py |
P2IL | P2IL-main/linear_PPIL/run_iq_learn.py | import torch
import gym
import random
import argparse
import numpy as np
import sys
import pickle
sys.path.insert(0,"../")
from lpimi.envs.wrappers import TimeLimit
parser = argparse.ArgumentParser(description='IM Learning')
parser.add_argument('--env-name', default="Hopper-v2", metavar='G',
help='... | 9,104 | 37.256303 | 130 | py |
P2IL | P2IL-main/linear_PPIL/lpimi/envs/scuric/two_state.py | """Python Script Template."""
from collections import defaultdict
from rllib.environment.mdp import MDP
class TwoState(MDP):
"""Implementation of a Two State Problem with stochastic transitions."""
def __init__(self, reward_0=1.0):
num_states = 2
num_actions = 2
transitions = self._b... | 1,963 | 34.709091 | 88 | py |
P2IL | P2IL-main/linear_PPIL/lpimi/algorithm/baselines/GAIL/core/a2c.py | import torch
def a2c_step(policy_net, value_net, optimizer_policy, optimizer_value, states, actions, returns, advantages, l2_reg):
"""update critic"""
values_pred = value_net(states)
value_loss = (values_pred - returns).pow(2).mean()
# weight decay
for param in value_net.parameters():
val... | 729 | 30.73913 | 117 | py |
P2IL | P2IL-main/linear_PPIL/lpimi/algorithm/baselines/GAIL/core/ppo.py | import torch
def ppo_step(policy_net, value_net, optimizer_policy, optimizer_value, optim_value_iternum, states, actions,
returns, advantages, fixed_log_probs, clip_epsilon, l2_reg):
"""update critic"""
for _ in range(optim_value_iternum):
values_pred = value_net(states)
value_lo... | 1,032 | 35.892857 | 108 | py |
P2IL | P2IL-main/linear_PPIL/lpimi/algorithm/baselines/GAIL/core/agent.py | import multiprocessing
from utils.replay_memory import Memory
from utils.torch import *
import math
import time
import os
os.environ["OMP_NUM_THREADS"] = "1"
def collect_samples(pid, queue, env, policy, custom_reward,
mean_action, render, running_state, min_batch_size):
if pid > 0:
tor... | 5,677 | 36.111111 | 102 | py |
P2IL | P2IL-main/linear_PPIL/lpimi/algorithm/baselines/GAIL/core/common.py | import torch
from utils import to_device
def estimate_advantages(rewards, masks, values, gamma, tau, device):
rewards, masks, values = to_device(torch.device('cpu'), rewards, masks, values)
tensor_type = type(rewards)
deltas = tensor_type(rewards.size(0), 1)
advantages = tensor_type(rewards.size(0), 1... | 841 | 32.68 | 83 | py |
P2IL | P2IL-main/linear_PPIL/lpimi/algorithm/baselines/GAIL/core/trpo.py | import numpy as np
import scipy.optimize
from utils import *
def conjugate_gradients(Avp_f, b, nsteps, rdotr_tol=1e-10):
x = zeros(b.size(), device=b.device)
r = b.clone()
p = b.clone()
rdotr = torch.dot(r, r)
for i in range(nsteps):
Avp = Avp_f(p)
alpha = rdotr / torch.dot(p, Avp)... | 4,672 | 36.384 | 115 | py |
P2IL | P2IL-main/linear_PPIL/lpimi/algorithm/baselines/GAIL/examples/trpo_gym.py | import argparse
import gym
import os
import sys
import pickle
import time
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from utils import *
from models.mlp_policy import Policy
from models.mlp_critic import Value
from models.mlp_policy_disc import DiscretePolicy
from core.trpo import ... | 5,833 | 44.224806 | 158 | py |
P2IL | P2IL-main/linear_PPIL/lpimi/algorithm/baselines/GAIL/examples/a2c_gym.py | import argparse
import gym
import os
import sys
import pickle
import time
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from utils import *
from models.mlp_policy import Policy
from models.mlp_critic import Value
from models.mlp_policy_disc import DiscretePolicy
from core.a2c import a... | 5,721 | 43.703125 | 158 | py |
P2IL | P2IL-main/linear_PPIL/lpimi/algorithm/baselines/GAIL/examples/ppo_gym.py | import argparse
import gym
import os
import sys
import pickle
import time
import lpimi.envs.scuric
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../../../../../external/scuri-rllib/')))
from utils import *
from mo... | 8,369 | 46.288136 | 197 | py |
P2IL | P2IL-main/linear_PPIL/lpimi/algorithm/baselines/GAIL/models/mlp_policy_disc.py | import torch.nn as nn
import torch
from utils.math import *
class DiscretePolicy(nn.Module):
def __init__(self, state_dim, action_num, hidden_size=(128, 128), activation='tanh'):
super().__init__()
self.is_disc_action = True
if activation == 'tanh':
self.activation = torch.tanh... | 1,702 | 30.537037 | 89 | py |
P2IL | P2IL-main/linear_PPIL/lpimi/algorithm/baselines/GAIL/models/mlp_policy.py | import torch.nn as nn
import torch
from utils.math import *
class Policy(nn.Module):
def __init__(self, state_dim, action_dim, hidden_size=(128, 128), activation='tanh', log_std=0):
super().__init__()
self.is_disc_action = False
if activation == 'tanh':
self.activation = torch.... | 2,426 | 32.708333 | 101 | py |
P2IL | P2IL-main/linear_PPIL/lpimi/algorithm/baselines/GAIL/models/mlp_critic.py | import torch.nn as nn
import torch
class Value(nn.Module):
def __init__(self, state_dim, hidden_size=(128, 128), activation='tanh'):
super().__init__()
if activation == 'tanh':
self.activation = torch.tanh
elif activation == 'relu':
self.activation = torch.relu
... | 902 | 28.129032 | 77 | py |
P2IL | P2IL-main/linear_PPIL/lpimi/algorithm/baselines/GAIL/models/mlp_discriminator.py | import torch.nn as nn
import torch
class Discriminator(nn.Module):
def __init__(self, num_inputs, hidden_size=(128, 128), activation='tanh'):
super().__init__()
if activation == 'tanh':
self.activation = torch.tanh
elif activation == 'relu':
self.activation = torch.... | 905 | 28.225806 | 78 | py |
P2IL | P2IL-main/linear_PPIL/lpimi/algorithm/baselines/GAIL/gail/evaluate_expert.py | import argparse
import gym
import os
import sys
import pickle
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../../../../../external/scuri-rllib/')))
from itertools import count
from utils import *
from rllib.envir... | 4,182 | 39.61165 | 208 | py |
P2IL | P2IL-main/linear_PPIL/lpimi/algorithm/baselines/GAIL/gail/gail_gym.py | import argparse
import gym
import os
import sys
import pickle
import time
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../../../../../external/scuri-rllib/')))
sys.path.append(os.path.abspath(os.path.join(os.path... | 12,342 | 48.372 | 189 | py |
P2IL | P2IL-main/linear_PPIL/lpimi/algorithm/baselines/GAIL/gail/save_expert_traj.py | import argparse
import gym
import os
import sys
import pickle
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../../../../../external/scuri-rllib/')))
from itertools import count
from utils import *
from rllib.envir... | 4,710 | 42.62037 | 208 | py |
P2IL | P2IL-main/linear_PPIL/lpimi/algorithm/baselines/GAIL/utils/replay_memory.py | from collections import namedtuple
import random
# Taken from
# https://github.com/pytorch/tutorials/blob/master/Reinforcement%20(Q-)Learning%20with%20PyTorch.ipynb
Transition = namedtuple('Transition', ('state', 'action', 'mask', 'next_state',
'reward'))
class Memory(object):... | 862 | 26.83871 | 102 | py |
P2IL | P2IL-main/linear_PPIL/lpimi/algorithm/baselines/GAIL/utils/torch.py | import torch
import numpy as np
tensor = torch.tensor
DoubleTensor = torch.DoubleTensor
FloatTensor = torch.FloatTensor
LongTensor = torch.LongTensor
ByteTensor = torch.ByteTensor
ones = torch.ones
zeros = torch.zeros
def to_categorical(y, num_classes):
""" 1-hot encodes a tensor """
return np.eye(num_classes... | 2,069 | 25.538462 | 102 | py |
P2IL | P2IL-main/linear_PPIL/lpimi/algorithm/baselines/GAIL/utils/math.py | import torch
import math
def normal_entropy(std):
var = std.pow(2)
entropy = 0.5 + 0.5 * torch.log(2 * var * math.pi)
return entropy.sum(1, keepdim=True)
def normal_log_density(x, mean, log_std, std):
var = std.pow(2)
log_density = -(x - mean).pow(2) / (2 * var) - 0.5 * math.log(2 * math.pi) - l... | 371 | 23.8 | 88 | py |
P2IL | P2IL-main/linear_PPIL/lpimi/algorithm/baselines/GAIL/utils/__init__.py | from utils.replay_memory import *
from utils.zfilter import *
from utils.torch import *
from utils.math import *
from utils.tools import *
| 139 | 22.333333 | 33 | py |
P2IL | P2IL-main/offline_PPIL/memory.py | from collections import deque
import numpy as np
import random
import torch
from wrappers.atari_wrapper import LazyFrames
from expert.expert_dataset import ExpertDataset
class Memory(object):
def __init__(self, memory_size: int, seed: int = 0) -> None:
random.seed(seed)
self.memory_size = memory_... | 2,623 | 35.957746 | 99 | py |
P2IL | P2IL-main/offline_PPIL/utils.py | import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision.utils import make_grid, save_image
class eval_mode(object):
def __init__(self, *models):
self.models = models
def __enter__(self):
self.prev_states = []
... | 4,650 | 32.702899 | 123 | py |
P2IL | P2IL-main/offline_PPIL/logger.py | from torch.utils.tensorboard import SummaryWriter
from collections import defaultdict
import json
import os
import csv
import shutil
import torch
import numpy as np
from termcolor import colored
COMMON_TRAIN_FORMAT = [
('episode', 'E', 'int'),
('step', 'S', 'int'),
('episode_reward', 'R', 'float'),
('d... | 7,909 | 33.541485 | 78 | py |
P2IL | P2IL-main/offline_PPIL/train.py | import datetime
import os
import random
import time
from collections import deque
from itertools import count
import types
import pickle
import hydra
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import wandb
from omegaconf import DictConfig, OmegaConf
from tensorboardX import S... | 22,664 | 38.417391 | 121 | py |
P2IL | P2IL-main/offline_PPIL/expert/expert_dataset.py | from typing import Any, Dict, IO, List, Tuple
import numpy as np
import pickle
import torch
from torch.utils.data import Dataset
import os
class ExpertDataset(Dataset):
"""Dataset for expert trajectories.
Assumes expert dataset is a dict with keys {states, actions, rewards, lengths} with values
of given... | 5,479 | 35.052632 | 100 | py |
P2IL | P2IL-main/offline_PPIL/scripts/run_bc.py | """
Copyright 2022 Div Garg. All rights reserved.
Example training code for IQ-Learn which minimially modifies `train_rl.py`.
"""
import datetime
import os
import random
import time
from collections import deque
from itertools import count
import types
import hydra
import pickle
import numpy as np
import torch
impor... | 5,178 | 34.472603 | 112 | py |
P2IL | P2IL-main/offline_PPIL/scripts/inspect_experts.py | import datetime
import os
import random
import time
from collections import deque
from itertools import count
import types
import pickle
import hydra
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import wandb
from omegaconf import DictConfig, OmegaConf
from tensorboardX import S... | 955 | 27.969697 | 87 | py |
P2IL | P2IL-main/offline_PPIL/wrappers/atari_wrapper.py | import gym
import numpy as np
import torch
from collections import deque
from gym import spaces
class FrameStack(gym.Wrapper):
def __init__(self, env, k):
"""Stack k last frames.
Returns lazy array, which is much more memory efficient.
Expects inputs to be of shape num_channels x height x ... | 2,968 | 30.252632 | 96 | py |
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