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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qlib | qlib-main/qlib/contrib/model/tcn.py | # MIT License
# Copyright (c) 2018 CMU Locus Lab
import torch.nn as nn
from torch.nn.utils import weight_norm
class Chomp1d(nn.Module):
def __init__(self, chomp_size):
super(Chomp1d, self).__init__()
self.chomp_size = chomp_size
def forward(self, x):
return x[:, :, : -self.chomp_size]... | 2,606 | 32.857143 | 114 | py |
qlib | qlib-main/qlib/contrib/model/pytorch_tcts.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from __future__ import division
from __future__ import print_function
import numpy as np
import pandas as pd
import copy
import random
from ...utils import get_or_create_path
from ...log import get_module_logger
import torch
import torch.nn as... | 14,289 | 32.623529 | 116 | py |
qlib | qlib-main/qlib/rl/trainer/callbacks.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
"""Callbacks to insert customized recipes during the training.
Mimicks the hooks of Keras / PyTorch-Lightning, but tailored for the context of RL.
"""
from __future__ import annotations
import copy
import os
import shutil
import time
from datet... | 11,576 | 38.64726 | 130 | py |
qlib | qlib-main/qlib/rl/trainer/trainer.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from __future__ import annotations
import collections
import copy
from contextlib import AbstractContextManager, contextmanager
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, Iterable, List, OrderedDict, Seq... | 13,476 | 36.856742 | 127 | py |
qlib | qlib-main/qlib/rl/order_execution/network.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from __future__ import annotations
from typing import List, Tuple, cast
import torch
import torch.nn as nn
from tianshou.data import Batch
from qlib.typehint import Literal
from .interpreter import FullHistoryObs
__all__ = ["Recurrent"]
cl... | 4,830 | 33.262411 | 119 | py |
qlib | qlib-main/qlib/rl/order_execution/strategy.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from __future__ import annotations
import collections
from types import GeneratorType
from typing import Any, Callable, cast, Dict, Generator, List, Optional, Tuple, Union
import warnings
import numpy as np
import pandas as pd
import torch
from... | 21,177 | 37.365942 | 117 | py |
qlib | qlib-main/qlib/rl/order_execution/policy.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from __future__ import annotations
from pathlib import Path
from typing import Any, Dict, Generator, Iterable, Optional, OrderedDict, Tuple, cast
import gym
import numpy as np
import torch
import torch.nn as nn
from gym.spaces import Discrete
f... | 7,008 | 28.44958 | 114 | py |
qlib | qlib-main/qlib/rl/utils/data_queue.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from __future__ import annotations
import multiprocessing
from multiprocessing.sharedctypes import Synchronized
import os
import threading
import time
import warnings
from queue import Empty
from typing import Any, Generator, Generic, Sequence, ... | 6,597 | 33.910053 | 112 | py |
qlib | qlib-main/qlib/rl/contrib/train_onpolicy.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from __future__ import annotations
import argparse
import os
import random
import sys
import warnings
from pathlib import Path
from typing import cast, List, Optional
import numpy as np
import pandas as pd
import torch
import yaml
from qlib.back... | 10,105 | 36.568773 | 115 | py |
qlib | qlib-main/qlib/rl/contrib/backtest.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from __future__ import annotations
import argparse
import copy
import os
import pickle
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Optional, Tuple, Union, cast
import numpy as np
import pandas as p... | 13,230 | 33.366234 | 120 | py |
qlib | qlib-main/tests/rl/test_trainer.py | import os
import random
import sys
from pathlib import Path
import pytest
import torch
import torch.nn as nn
from gym import spaces
from tianshou.policy import PPOPolicy
from qlib.config import C
from qlib.log import set_log_with_config
from qlib.rl.interpreter import StateInterpreter, ActionInterpreter
from qlib.rl... | 6,245 | 29.768473 | 115 | py |
qlib | qlib-main/tests/rl/test_saoe_simple.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import sys
from functools import partial
from pathlib import Path
from typing import NamedTuple
import numpy as np
import pandas as pd
import pytest
import torch
from tianshou.data import Batch
from qlib.backtest import Order
from qlib.config i... | 13,012 | 38.433333 | 118 | py |
qlib | qlib-main/tests/rl/test_finite_env.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from collections import Counter
import gym
import numpy as np
from tianshou.data import Batch, Collector
from tianshou.policy import BasePolicy
from torch.utils.data import DataLoader, Dataset, DistributedSampler
from qlib.rl.utils.finite_env im... | 7,864 | 30.46 | 120 | py |
qlib | qlib-main/tests/rl/test_data_queue.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import multiprocessing
import time
import numpy as np
import pandas as pd
from torch.utils.data import Dataset, DataLoader
from qlib.rl.utils.data_queue import DataQueue
class DummyDataset(Dataset):
def __init__(self, length):
sel... | 2,395 | 25.921348 | 124 | py |
qlib | qlib-main/tests/rl/test_logger.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from random import randint, choice
from pathlib import Path
import re
from typing import Any, Tuple
import gym
import numpy as np
import pandas as pd
from gym import spaces
from tianshou.data import Collector, Batch
from tianshou.policy import ... | 5,278 | 31.386503 | 98 | py |
qlib | qlib-main/tests/data_mid_layer_tests/test_dataset.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import unittest
import pytest
import sys
from qlib.tests import TestAutoData
from qlib.data.dataset import TSDatasetH
import numpy as np
import time
from qlib.data.dataset.handler import DataHandlerLP
class TestDataset(TestAutoData):
@pytes... | 3,726 | 33.509259 | 117 | py |
qlib | qlib-main/docs/conf.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# QLib documentation build configuration file, created by
# sphinx-quickstart on Wed Sep 27 15:16:05 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values a... | 6,728 | 27.75641 | 79 | py |
iffDetector | iffDetector-master/configs/fovea_r50_fpn_4gpu_1x.py | # model settings
model = dict(
type='FOVEA',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 51... | 3,730 | 29.333333 | 78 | py |
iffDetector | iffDetector-master/iff-heads/FoveaBox/fovea_head.py | import torch
import torch.nn as nn
from mmcv.cnn import normal_init
from mmdet.core import multi_apply, multiclass_nms
from mmdet.ops import DeformConv
from ..builder import build_loss
from ..registry import HEADS
from ..utils import ConvModule, bias_init_with_prob
INF = 1e8
class FeatureAlign(nn.Module):
def ... | 17,249 | 40.566265 | 91 | py |
iffDetector | iffDetector-master/iff-heads/YOLOv2/darknet.py | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import utils.network as net_utils
import cfgs.config as cfg
from layers.reorg.reorg_layer import ReorgLayer
from utils.cython_bbox import bbox_ious, anchor_intersections
from utils.cython_yolo import yolo_to_bbox
from functools impor... | 12,617 | 39.184713 | 125 | py |
iffDetector | iffDetector-master/iff-heads/FreeAnchor-ResNeXt101/retinanet.py | import numpy as np
import torch
from torch import nn
from maskrcnn_benchmark.modeling.box_coder import BoxCoder
from .retinanet_loss import make_retinanet_loss_evaluator
from .free_anchor_loss import make_free_anchor_loss_evaluator
from .anchor_generator import make_anchor_generator_retinanet
from .retinanet_infer imp... | 6,248 | 35.331395 | 156 | py |
fcaf3d | fcaf3d-master/setup.py | from setuptools import find_packages, setup
import os
import torch
from torch.utils.cpp_extension import (BuildExtension, CppExtension,
CUDAExtension)
def readme():
with open('README.md', encoding='utf-8') as f:
content = f.read()
return content
version_file =... | 10,179 | 36.843866 | 125 | py |
fcaf3d | fcaf3d-master/tools/test.py | import argparse
import mmcv
import os
import torch
import warnings
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
wrap_fp16_model)
from... | 8,412 | 38.130233 | 98 | py |
fcaf3d | fcaf3d-master/tools/test5x5.py | import os
import torch
import argparse
import numpy as np
from collections import defaultdict
from mmcv import Config
from mmcv.runner import load_checkpoint, init_dist, get_dist_info
from mmcv.parallel import MMDistributedDataParallel
from mmdet.apis import set_random_seed, multi_gpu_test
from mmdet3d.models import bu... | 2,390 | 35.227273 | 100 | py |
fcaf3d | fcaf3d-master/tools/train.py | from __future__ import division
import argparse
import copy
import mmcv
import os
import time
import torch
import warnings
from mmcv import Config, DictAction
from mmcv.runner import get_dist_info, init_dist
from os import path as osp
from mmdet import __version__ as mmdet_version
from mmdet3d import __version__ as m... | 8,411 | 36.553571 | 125 | py |
fcaf3d | fcaf3d-master/tools/data_converter/create_gt_database.py | import mmcv
import numpy as np
import pickle
from mmcv import track_iter_progress
from mmcv.ops import roi_align
from os import path as osp
from pycocotools import mask as maskUtils
from pycocotools.coco import COCO
from mmdet3d.core.bbox import box_np_ops as box_np_ops
from mmdet3d.datasets import build_dataset
from ... | 12,606 | 36.298817 | 79 | py |
fcaf3d | fcaf3d-master/tools/misc/fuse_conv_bn.py | import argparse
import torch
from mmcv.runner import save_checkpoint
from torch import nn as nn
from mmdet.apis import init_model
def fuse_conv_bn(conv, bn):
"""During inference, the functionary of batch norm layers is turned off but
only the mean and var alone channels are used, which exposes the chance to
... | 2,192 | 31.731343 | 79 | py |
fcaf3d | fcaf3d-master/tools/model_converters/publish_model.py | import argparse
import subprocess
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename')
parser.add_argument('out_file', help='output checkpoint filename')
args = pars... | 1,027 | 28.371429 | 77 | py |
fcaf3d | fcaf3d-master/tools/model_converters/regnet2mmdet.py | import argparse
import torch
from collections import OrderedDict
def convert_stem(model_key, model_weight, state_dict, converted_names):
new_key = model_key.replace('stem.conv', 'conv1')
new_key = new_key.replace('stem.bn', 'bn1')
state_dict[new_key] = model_weight
converted_names.add(model_key)
p... | 3,014 | 32.876404 | 77 | py |
fcaf3d | fcaf3d-master/tools/model_converters/convert_votenet_checkpoints.py | import argparse
import tempfile
import torch
from mmcv import Config
from mmcv.runner import load_state_dict
from mmdet3d.models import build_detector
def parse_args():
parser = argparse.ArgumentParser(
description='MMDet3D upgrade model version(before v0.6.0) of VoteNet')
parser.add_argument('checkp... | 5,042 | 32.177632 | 79 | py |
fcaf3d | fcaf3d-master/tools/analysis_tools/benchmark.py | import argparse
import time
import torch
from mmcv import Config
from mmcv.parallel import MMDataParallel
from mmcv.runner import load_checkpoint
from mmdet3d.datasets import build_dataloader, build_dataset
from mmdet3d.models import build_detector
from mmdet.core import wrap_fp16_model
from tools.misc.fuse_conv_bn im... | 2,961 | 29.854167 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_runtime/test_config.py | from os.path import dirname, exists, join, relpath
def _get_config_directory():
"""Find the predefined detector config directory."""
try:
# Assume we are running in the source mmdetection3d repo
repo_dpath = dirname(dirname(dirname(__file__)))
except NameError:
# For IPython develo... | 10,523 | 37.269091 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_runtime/test_apis.py | import numpy as np
import os
import pytest
import tempfile
import torch
from mmcv.parallel import MMDataParallel
from os.path import dirname, exists, join
from mmdet3d.apis import (convert_SyncBN, inference_detector,
inference_mono_3d_detector,
inference_multi_modali... | 15,708 | 42.515235 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_detectors.py | import copy
import numpy as np
import pytest
import random
import torch
from os.path import dirname, exists, join
from mmdet3d.core.bbox import (CameraInstance3DBoxes, DepthInstance3DBoxes,
LiDARInstance3DBoxes)
from mmdet3d.models.builder import build_detector
def _setup_seed(seed):
... | 17,900 | 37.006369 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_forward.py | """Test model forward process.
CommandLine:
pytest tests/test_models/test_forward.py
xdoctest tests/test_models/test_forward.py zero
"""
import copy
import numpy as np
import torch
from os.path import dirname, exists, join
def _get_config_directory():
"""Find the predefined detector config directory."""
... | 6,164 | 28.639423 | 78 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_backbones.py | import numpy as np
import pytest
import torch
from mmdet3d.models import build_backbone
def test_pointnet2_sa_ssg():
if not torch.cuda.is_available():
pytest.skip()
cfg = dict(
type='PointNet2SASSG',
in_channels=6,
num_points=(32, 16),
radius=(0.8, 1.2),
num_s... | 12,119 | 39.535117 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_segmentors.py | import copy
import numpy as np
import pytest
import torch
from os.path import dirname, exists, join
from mmdet3d.models.builder import build_segmentor
from mmdet.apis import set_random_seed
def _get_config_directory():
"""Find the predefined detector config directory."""
try:
# Assume we are running ... | 6,083 | 36.555556 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_voxel_encoder/test_voxelize.py | import numpy as np
import pytest
import torch
from mmdet3d.core.voxel.voxel_generator import VoxelGenerator
from mmdet3d.datasets.pipelines import LoadPointsFromFile
from mmdet3d.ops.voxel.voxelize import Voxelization
def _get_voxel_points_indices(points, coors, voxel):
result_form = np.equal(coors, voxel)
r... | 3,612 | 42.011905 | 78 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_voxel_encoder/test_dynamic_scatter.py | import pytest
import torch
from torch.autograd import gradcheck
from mmdet3d.ops import DynamicScatter
def test_dynamic_scatter():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
feats = torch.rand(
size=(200000, 3), dtype=torch.float32, device='cuda') * 100... | 3,809 | 39.531915 | 78 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_voxel_encoder/test_voxel_encoders.py | import torch
from mmdet3d.models.builder import build_voxel_encoder
def test_pillar_feature_net():
pillar_feature_net_cfg = dict(
type='PillarFeatureNet',
in_channels=5,
feat_channels=[64],
with_distance=False,
voxel_size=(0.2, 0.2, 8),
point_cloud_range=(-51.2, -5... | 1,098 | 31.323529 | 68 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_fusion/test_point_fusion.py | """Tests the core function of point fusion.
CommandLine:
pytest tests/test_models/test_fusion/test_point_fusion.py
"""
import torch
from mmdet3d.models.fusion_layers import PointFusion
def test_sample_single():
# this function makes sure the rewriting of 3d coords transformation
# in point fusion does ... | 2,075 | 33.032787 | 75 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_fusion/test_vote_fusion.py | """Tests the core function of vote fusion.
CommandLine:
pytest tests/test_models/test_fusion/test_vote_fusion.py
"""
import torch
from mmdet3d.models.fusion_layers import VoteFusion
def test_vote_fusion():
img_meta = {
'ori_shape': (530, 730, 3),
'img_shape': (600, 826, 3),
'pad_sha... | 14,784 | 44.916149 | 78 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_fusion/test_fusion_coord_trans.py | """Tests coords transformation in fusion modules.
CommandLine:
pytest tests/test_models/test_fusion/test_fusion_coord_trans.py
"""
import torch
from mmdet3d.models.fusion_layers import apply_3d_transformation
def test_coords_transformation():
"""Test the transformation of 3d coords."""
# H+R+S+T, not ... | 4,910 | 34.846715 | 76 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_common_modules/test_roiaware_pool3d.py | import pytest
import torch
from mmdet3d.ops.roiaware_pool3d import (RoIAwarePool3d, points_in_boxes_batch,
points_in_boxes_cpu,
points_in_boxes_gpu)
def test_RoIAwarePool3d():
# RoIAwarePool3d only support gpu version currently.
... | 5,750 | 43.581395 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_common_modules/test_vote_module.py | import torch
def test_vote_module():
from mmdet3d.models.model_utils import VoteModule
vote_loss = dict(
type='ChamferDistance',
mode='l1',
reduction='none',
loss_dst_weight=10.0)
self = VoteModule(vote_per_seed=3, in_channels=8, vote_loss=vote_loss)
seed_xyz = torch.... | 1,332 | 33.179487 | 77 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_common_modules/test_paconv_modules.py | import numpy as np
import pytest
import torch
def test_paconv_sa_module_msg():
if not torch.cuda.is_available():
pytest.skip()
from mmdet3d.ops import PAConvSAModuleMSG
# paconv_num_kernels should have same length as mlp_channels
with pytest.raises(AssertionError):
self = PAConvSAModu... | 10,333 | 34.14966 | 74 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_common_modules/test_pointnet_ops.py | import pytest
import torch
from mmdet3d.ops import (ball_query, furthest_point_sample,
furthest_point_sample_with_dist, gather_points,
grouping_operation, knn, three_interpolate, three_nn)
def test_fps():
if not torch.cuda.is_available():
pytest.skip()
... | 20,601 | 49.869136 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_common_modules/test_middle_encoders.py | import pytest
import torch
from mmdet3d.models.builder import build_middle_encoder
def test_sparse_encoder():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
sparse_encoder_cfg = dict(
type='SparseEncoder',
in_channels=5,
sparse_shape=[40, 102... | 988 | 35.62963 | 76 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_common_modules/test_paconv_ops.py | import pytest
import torch
from mmdet3d.ops import PAConv, PAConvCUDA, assign_score_withk
def test_paconv_assign_scores():
if not torch.cuda.is_available():
pytest.skip()
scores = torch.tensor([[[[0.06947571, 0.6065746], [0.28462553, 0.8378516],
[0.7595994, 0.97220325], [... | 12,625 | 54.867257 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_common_modules/test_pointnet_modules.py | import numpy as np
import pytest
import torch
def test_pointnet_sa_module_msg():
if not torch.cuda.is_available():
pytest.skip()
from mmdet3d.ops import PointSAModuleMSG
self = PointSAModuleMSG(
num_point=16,
radii=[0.2, 0.4],
sample_nums=[4, 8],
mlp_channels=[[12,... | 7,077 | 31.172727 | 74 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_common_modules/test_sparse_unet.py | import torch
from mmdet3d.ops import SparseBasicBlock
from mmdet3d.ops import spconv as spconv
def test_SparseUNet():
from mmdet3d.models.middle_encoders.sparse_unet import SparseUNet
self = SparseUNet(in_channels=4, sparse_shape=[41, 1600, 1408])
# test encoder layers
assert len(self.encoder_layers... | 5,796 | 41.313869 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_heads/test_roi_extractors.py | import pytest
import torch
from mmdet3d.models.roi_heads.roi_extractors import Single3DRoIAwareExtractor
def test_single_roiaware_extractor():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
roi_layer_cfg = dict(
type='RoIAwarePool3d', out_size=4, max_pts_pe... | 1,265 | 39.83871 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_heads/test_semantic_heads.py | import pytest
import torch
from mmdet3d.core.bbox import LiDARInstance3DBoxes
def test_PointwiseSemanticHead():
# PointwiseSemanticHead only support gpu version currently.
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
from mmdet3d.models.builder import build_he... | 2,989 | 35.463415 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_heads/test_heads.py | import copy
import numpy as np
import pytest
import random
import torch
from os.path import dirname, exists, join
from mmdet3d.core.bbox import (Box3DMode, CameraInstance3DBoxes,
DepthInstance3DBoxes, LiDARInstance3DBoxes)
from mmdet3d.models.builder import build_head
from mmdet.apis imp... | 47,229 | 37.586601 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_heads/test_pointnet2_decode_head.py | import numpy as np
import pytest
import torch
from mmcv.cnn.bricks import ConvModule
from mmdet3d.models.builder import build_head
def test_pn2_decode_head_loss():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
pn2_decode_head_cfg = dict(
type='PointNet2Head... | 2,954 | 34.60241 | 71 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_heads/test_parta2_bbox_head.py | import pytest
import torch
from mmcv import Config
from torch.nn import BatchNorm1d, ReLU
from mmdet3d.core.bbox import Box3DMode, LiDARInstance3DBoxes
from mmdet3d.core.bbox.samplers import IoUNegPiecewiseSampler
from mmdet3d.models import PartA2BboxHead
from mmdet3d.ops import make_sparse_convmodule
from mmdet3d.ops... | 20,566 | 40.633603 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_models/test_necks/test_necks.py | import pytest
import torch
from mmdet3d.models.builder import build_backbone, build_neck
def test_centerpoint_fpn():
second_cfg = dict(
type='SECOND',
in_channels=64,
out_channels=[64, 128, 256],
layer_nums=[3, 5, 5],
layer_strides=[2, 2, 2],
norm_cfg=dict(type='B... | 1,786 | 29.288136 | 72 | py |
fcaf3d | fcaf3d-master/tests/test_data/test_datasets/test_dataset_wrappers.py | import numpy as np
import torch
from mmdet3d.datasets.builder import build_dataset
def test_getitem():
np.random.seed(1)
torch.manual_seed(1)
point_cloud_range = [-50, -50, -5, 50, 50, 3]
file_client_args = dict(backend='disk')
class_names = [
'car', 'truck', 'trailer', 'bus', 'constructi... | 2,843 | 35 | 78 | py |
fcaf3d | fcaf3d-master/tests/test_data/test_datasets/test_kitti_mono_dataset.py | import mmcv
import numpy as np
import pytest
import torch
from mmdet3d.datasets import KittiMonoDataset
def test_getitem():
np.random.seed(0)
class_names = ['Pedestrian', 'Cyclist', 'Car']
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
pipeline = [
... | 8,686 | 39.404651 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_data/test_datasets/test_kitti_dataset.py | import numpy as np
import os
import pytest
import tempfile
import torch
from mmdet3d.core.bbox import LiDARInstance3DBoxes
from mmdet3d.datasets import KittiDataset
def _generate_kitti_dataset_config():
data_root = 'tests/data/kitti'
ann_file = 'tests/data/kitti/kitti_infos_train.pkl'
classes = ['Pedestr... | 18,631 | 40.775785 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_data/test_datasets/test_scannet_dataset.py | import copy
import numpy as np
import pytest
import torch
from mmdet3d.datasets import ScanNetDataset, ScanNetSegDataset
def test_getitem():
np.random.seed(0)
root_path = './tests/data/scannet/'
ann_file = './tests/data/scannet/scannet_infos.pkl'
class_names = ('cabinet', 'bed', 'chair', 'sofa', 'tab... | 27,013 | 38.551977 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_data/test_datasets/test_nuscenes_mono_dataset.py | import mmcv
import numpy as np
import pytest
import tempfile
import torch
from os import path as osp
from mmdet3d.datasets import NuScenesMonoDataset
def test_getitem():
np.random.seed(0)
class_names = [
'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
'motorcycle', 'pedes... | 7,552 | 38.134715 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_data/test_datasets/test_waymo_dataset.py | import numpy as np
import pytest
import tempfile
import torch
from mmdet3d.datasets import WaymoDataset
def _generate_waymo_train_dataset_config():
data_root = 'tests/data/waymo/kitti_format/'
ann_file = 'tests/data/waymo/kitti_format/waymo_infos_train.pkl'
classes = ['Car', 'Pedestrian', 'Cyclist']
... | 10,777 | 40.137405 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_data/test_datasets/test_s3dis_dataset.py | import numpy as np
import pytest
import torch
from mmdet3d.datasets import S3DISSegDataset
def test_seg_getitem():
np.random.seed(0)
root_path = './tests/data/s3dis/'
ann_file = './tests/data/s3dis/s3dis_infos.pkl'
class_names = ('ceiling', 'floor', 'wall', 'beam', 'column', 'window',
... | 10,942 | 37.12892 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_data/test_datasets/test_sunrgbd_dataset.py | import numpy as np
import pytest
import torch
from mmdet3d.datasets import SUNRGBDDataset
def _generate_sunrgbd_dataset_config():
root_path = './tests/data/sunrgbd'
ann_file = './tests/data/sunrgbd/sunrgbd_infos.pkl'
class_names = ('bed', 'table', 'sofa', 'chair', 'toilet', 'desk',
'dr... | 13,254 | 40.551724 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_data/test_datasets/test_nuscene_dataset.py | import numpy as np
import tempfile
import torch
from mmdet3d.datasets import NuScenesDataset
def test_getitem():
np.random.seed(0)
point_cloud_range = [-50, -50, -5, 50, 50, 3]
file_client_args = dict(backend='disk')
class_names = [
'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'b... | 4,260 | 35.732759 | 78 | py |
fcaf3d | fcaf3d-master/tests/test_data/test_datasets/test_lyft_dataset.py | import mmcv
import numpy as np
import tempfile
import torch
from mmdet3d.datasets import LyftDataset
def test_getitem():
np.random.seed(0)
torch.manual_seed(0)
root_path = './tests/data/lyft'
ann_file = './tests/data/lyft/lyft_infos.pkl'
class_names = ('car', 'truck', 'bus', 'emergency_vehicle', ... | 7,542 | 43.633136 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_data/test_pipelines/test_outdoor_pipeline.py | import numpy as np
import torch
from mmdet3d.core.bbox import LiDARInstance3DBoxes
from mmdet3d.datasets.pipelines import Compose
def test_outdoor_aug_pipeline():
point_cloud_range = [0, -40, -3, 70.4, 40, 1]
class_names = ['Car']
np.random.seed(0)
train_pipeline = [
dict(
type='... | 10,195 | 39.621514 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_data/test_pipelines/test_indoor_pipeline.py | import mmcv
import numpy as np
import torch
from os import path as osp
from mmdet3d.core.bbox import DepthInstance3DBoxes
from mmdet3d.datasets.pipelines import Compose
def test_scannet_pipeline():
class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'pictu... | 12,886 | 38.530675 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_data/test_pipelines/test_augmentations/test_transforms_3d.py | import mmcv
import numpy as np
import pytest
import torch
from mmdet3d.core import (Box3DMode, CameraInstance3DBoxes,
DepthInstance3DBoxes, LiDARInstance3DBoxes)
from mmdet3d.core.points import DepthPoints, LiDARPoints
from mmdet3d.datasets import (BackgroundPointsFilter, GlobalAlignment,
... | 29,545 | 40.731638 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_data/test_pipelines/test_augmentations/test_test_augment_utils.py | import numpy as np
import torch
from mmdet3d.core.points import DepthPoints
from mmdet3d.datasets.pipelines import MultiScaleFlipAug3D
def test_multi_scale_flip_aug_3D():
np.random.seed(0)
transforms = [{
'type': 'GlobalRotScaleTrans',
'rot_range': [-0.1, 0.1],
'scale_ratio_range': [0... | 2,227 | 34.935484 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_data/test_pipelines/test_loadings/test_load_images_from_multi_views.py | import numpy as np
import torch
from mmcv.parallel import DataContainer
from mmdet3d.datasets.pipelines import (DefaultFormatBundle,
LoadMultiViewImageFromFiles)
def test_load_multi_view_image_from_files():
multi_view_img_loader = LoadMultiViewImageFromFiles(to_float32=Tru... | 1,720 | 36.413043 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_utils/test_assigners.py | """Tests the Assigner objects.
CommandLine:
pytest tests/test_utils/test_assigner.py
xdoctest tests/test_utils/test_assigner.py zero
"""
import torch
from mmdet3d.core.bbox.assigners import MaxIoUAssigner
def test_max_iou_assigner():
self = MaxIoUAssigner(
pos_iou_thr=0.5,
neg_iou_thr=0.... | 4,253 | 27.36 | 77 | py |
fcaf3d | fcaf3d-master/tests/test_utils/test_samplers.py | import pytest
import torch
from mmdet3d.core.bbox.assigners import MaxIoUAssigner
from mmdet3d.core.bbox.samplers import IoUNegPiecewiseSampler
def test_iou_piecewise_sampler():
if not torch.cuda.is_available():
pytest.skip()
assigner = MaxIoUAssigner(
pos_iou_thr=0.55,
neg_iou_thr=0.... | 1,684 | 37.295455 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_utils/test_nms.py | import numpy as np
import torch
def test_aligned_3d_nms():
from mmdet3d.core.post_processing import aligned_3d_nms
boxes = torch.tensor([[1.2261, 0.6679, -1.2678, 2.6547, 1.0428, 0.1000],
[5.0919, 0.6512, 0.7238, 5.4821, 1.2451, 2.1095],
[6.8392, -1.2205, 0... | 4,029 | 52.733333 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_utils/test_bbox_coders.py | import torch
from mmdet3d.core.bbox import DepthInstance3DBoxes, LiDARInstance3DBoxes
from mmdet.core import build_bbox_coder
def test_partial_bin_based_box_coder():
box_coder_cfg = dict(
type='PartialBinBasedBBoxCoder',
num_sizes=10,
num_dir_bins=12,
with_rot=True,
mean_s... | 17,284 | 47.827684 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_utils/test_points.py | import numpy as np
import pytest
import torch
from mmdet3d.core.points import (BasePoints, CameraPoints, DepthPoints,
LiDARPoints)
def test_base_points():
# test empty initialization
empty_boxes = []
points = BasePoints(empty_boxes)
assert points.tensor.shape[0] == 0
... | 50,298 | 45.230699 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_utils/test_coord_3d_mode.py | import numpy as np
import torch
from mmdet3d.core.bbox import (CameraInstance3DBoxes, Coord3DMode,
DepthInstance3DBoxes, LiDARInstance3DBoxes)
from mmdet3d.core.points import CameraPoints, DepthPoints, LiDARPoints
def test_points_conversion():
"""Test the conversion of points betwe... | 15,932 | 49.580952 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_utils/test_box3d.py | import numpy as np
import pytest
import torch
import unittest
from mmdet3d.core.bbox import (BaseInstance3DBoxes, Box3DMode,
CameraInstance3DBoxes, DepthInstance3DBoxes,
LiDARInstance3DBoxes, bbox3d2roi,
bbox3d_mapping_back)
f... | 64,120 | 47.356712 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_utils/test_merge_augs.py | import mmcv
import pytest
import torch
from mmdet3d.core import merge_aug_bboxes_3d
from mmdet3d.core.bbox import DepthInstance3DBoxes
def test_merge_aug_bboxes_3d():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
img_meta_0 = dict(
pcd_horizontal_flip=False... | 2,555 | 40.225806 | 78 | py |
fcaf3d | fcaf3d-master/tests/test_utils/test_anchors.py | """
CommandLine:
pytest tests/test_utils/test_anchor.py
xdoctest tests/test_utils/test_anchor.py zero
"""
import torch
from mmdet3d.core.anchor import build_anchor_generator
def test_anchor_3d_range_generator():
if torch.cuda.is_available():
device = 'cuda'
else:
device = 'cpu'
a... | 9,299 | 37.912134 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_utils/test_utils.py | import torch
from mmdet3d.core import draw_heatmap_gaussian
def test_gaussian():
heatmap = torch.zeros((128, 128))
ct_int = torch.tensor([64, 64], dtype=torch.int32)
radius = 2
draw_heatmap_gaussian(heatmap, ct_int, radius)
assert torch.isclose(torch.sum(heatmap), torch.tensor(4.3505), atol=1e-3)... | 321 | 25.833333 | 77 | py |
fcaf3d | fcaf3d-master/tests/test_metrics/test_kitti_eval.py | import numpy as np
import pytest
import torch
from mmdet3d.core.evaluation.kitti_utils.eval import (do_eval, eval_class,
kitti_eval)
def test_do_eval():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and CUDA')
gt_name = np.array... | 12,008 | 48.419753 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_metrics/test_losses.py | import pytest
import torch
def test_chamfer_disrance():
from mmdet3d.models.losses import ChamferDistance, chamfer_distance
with pytest.raises(AssertionError):
# test invalid mode
ChamferDistance(mode='smoothl1')
# test invalid type of reduction
ChamferDistance(mode='l2', redu... | 3,348 | 45.513889 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_metrics/test_seg_eval.py | import numpy as np
import pytest
import torch
from mmdet3d.core.evaluation.seg_eval import seg_eval
def test_indoor_eval():
if not torch.cuda.is_available():
pytest.skip()
seg_preds = [
torch.Tensor([
0, 0, 1, 0, 0, 2, 1, 3, 1, 2, 1, 0, 2, 2, 2, 2, 1, 3, 0, 3, 3, 3, 3
])
... | 1,038 | 25.641026 | 79 | py |
fcaf3d | fcaf3d-master/tests/test_metrics/test_indoor_eval.py | import numpy as np
import pytest
import torch
from mmdet3d.core.evaluation.indoor_eval import average_precision, indoor_eval
def test_indoor_eval():
if not torch.cuda.is_available():
pytest.skip()
from mmdet3d.core.bbox.structures import Box3DMode, DepthInstance3DBoxes
det_infos = [{
'lab... | 6,582 | 34.015957 | 79 | py |
fcaf3d | fcaf3d-master/configs/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune.py | _base_ = './fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d.py'
# model settings
model = dict(
train_cfg=dict(
code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.05, 0.05]))
# optimizer
optimizer = dict(lr=0.001)
load_from = 'work_dirs/fcos3d_nus/latest.pth'
| 274 | 29.555556 | 69 | py |
fcaf3d | fcaf3d-master/configs/imvotenet/imvotenet-v2_stage2_16x8_sunrgbd-3d-10class.py | _base_ = [
'../_base_/datasets/sunrgbd-3d-10class.py',
'../_base_/schedules/schedule_3x.py', '../_base_/default_runtime.py',
'../_base_/models/imvotenet_image.py'
]
class_names = ('bed', 'table', 'sofa', 'chair', 'toilet', 'desk', 'dresser',
'night_stand', 'bookshelf', 'bathtub')
# use caff... | 8,126 | 33.004184 | 225 | py |
fcaf3d | fcaf3d-master/configs/imvotenet/imvotenet_stage2_16x8_sunrgbd-3d-10class.py | _base_ = [
'../_base_/datasets/sunrgbd-3d-10class.py',
'../_base_/schedules/schedule_3x.py', '../_base_/default_runtime.py',
'../_base_/models/imvotenet_image.py'
]
class_names = ('bed', 'table', 'sofa', 'chair', 'toilet', 'desk', 'dresser',
'night_stand', 'bookshelf', 'bathtub')
# use caff... | 9,250 | 34.444444 | 225 | py |
fcaf3d | fcaf3d-master/configs/imvotenet/imvotenet_faster_rcnn_r50_fpn_2x4_sunrgbd-3d-10class.py | _base_ = [
'../_base_/datasets/sunrgbd-3d-10class.py', '../_base_/default_runtime.py',
'../_base_/models/imvotenet_image.py'
]
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type... | 1,986 | 32.677966 | 227 | py |
fcaf3d | fcaf3d-master/configs/nuimages/mask_rcnn_r50_caffe_fpn_1x_nuim.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/nuim_instance.py',
'../_base_/schedules/mmdet_schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe'),... | 1,636 | 33.829787 | 78 | py |
fcaf3d | fcaf3d-master/configs/nuimages/mask_rcnn_r50_caffe_fpn_coco-3x_20e_nuim.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/nuim_instance.py',
'../_base_/schedules/mmdet_schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe'),... | 1,945 | 35.716981 | 227 | py |
fcaf3d | fcaf3d-master/configs/nuimages/mask_rcnn_r50_caffe_fpn_coco-3x_1x_nuim.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/nuim_instance.py',
'../_base_/schedules/mmdet_schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe'),... | 1,866 | 37.102041 | 228 | py |
fcaf3d | fcaf3d-master/configs/nuimages/htc_without_semantic_r50_fpn_1x_nuim.py | _base_ = [
'../_base_/datasets/nuim_instance.py',
'../_base_/schedules/mmdet_schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='HybridTaskCascade',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
... | 7,693 | 33.657658 | 79 | py |
fcaf3d | fcaf3d-master/configs/nuimages/mask_rcnn_x101_32x4d_fpn_1x_nuim.py | _base_ = './mask_rcnn_r50_fpn_1x_nuim.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN',... | 368 | 25.357143 | 53 | py |
fcaf3d | fcaf3d-master/configs/nuimages/mask_rcnn_r101_fpn_1x_nuim.py | _base_ = './mask_rcnn_r50_fpn_1x_nuim.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 119 | 39 | 76 | py |
fcaf3d | fcaf3d-master/configs/nuimages/cascade_mask_rcnn_r101_fpn_1x_nuim.py | _base_ = './cascade_mask_rcnn_r50_fpn_1x_nuim.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 127 | 41.666667 | 76 | py |
fcaf3d | fcaf3d-master/configs/nuimages/cascade_mask_rcnn_x101_32x4d_fpn_1x_nuim.py | _base_ = './cascade_mask_rcnn_r50_fpn_1x_nuim.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(ty... | 376 | 25.928571 | 53 | py |
fcaf3d | fcaf3d-master/configs/nuimages/htc_x101_64x4d_fpn_dconv_c3-c5_coco-20e_16x1_20e_nuim.py | _base_ = './htc_r50_fpn_1x_nuim.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requi... | 859 | 34.833333 | 218 | py |
fcaf3d | fcaf3d-master/configs/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d.py | _base_ = [
'../_base_/models/hv_pointpillars_fpn_nus.py',
'../_base_/datasets/nus-3d.py',
'../_base_/schedules/schedule_2x.py',
'../_base_/default_runtime.py',
]
# model settings
model = dict(
type='MVXFasterRCNN',
pretrained=dict(pts='open-mmlab://regnetx_400mf'),
pts_backbone=dict(
... | 752 | 30.375 | 79 | py |
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