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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DiffBEV | DiffBEV-main/configs/_base_/models/gcnet_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,326 | 27.234043 | 74 | py |
DiffBEV | DiffBEV-main/configs/_base_/models/encnet_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,435 | 28.306122 | 74 | py |
DiffBEV | DiffBEV-main/configs/_base_/models/danet_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,261 | 27.044444 | 74 | py |
DiffBEV | DiffBEV-main/configs/_base_/models/dnl_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,316 | 27.021277 | 74 | py |
DiffBEV | DiffBEV-main/configs/_base_/models/pspnet_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,271 | 27.266667 | 74 | py |
DiffBEV | DiffBEV-main/configs/_base_/models/upernet_r50.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 1, 1),
strides=... | 1,301 | 27.933333 | 74 | py |
DiffBEV | DiffBEV-main/configs/_base_/models/apcnet_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,302 | 27.955556 | 74 | py |
DiffBEV | DiffBEV-main/configs/_base_/models/psanet_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,406 | 27.14 | 74 | py |
DiffBEV | DiffBEV-main/configs/_base_/models/fastfcn_r50-d32_jpu_psp.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
dilations=(1, 1, 2, 4),
strides=(1, 2, 2, 2),
out_indices=... | 1,502 | 26.833333 | 74 | py |
DiffBEV | DiffBEV-main/configs/_base_/models/deeplabv3plus_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,343 | 27.595745 | 74 | py |
DiffBEV | DiffBEV-main/configs/_base_/models/emanet_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,329 | 26.708333 | 74 | py |
DiffBEV | DiffBEV-main/configs/_base_/models/dmnet_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,302 | 27.955556 | 74 | py |
DiffBEV | DiffBEV-main/configs/_base_/models/fpn_r50.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 1, 1),
strides=... | 1,056 | 27.567568 | 74 | py |
DiffBEV | DiffBEV-main/configs/_base_/models/deeplabv3_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,273 | 27.311111 | 74 | py |
DiffBEV | DiffBEV-main/configs/_base_/models/bisenetv1_r18-d32.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
backbone=dict(
type='BiSeNetV1',
in_channels=3,
context_channels=(128, 256, 512),
spatial_channels=(64, 64, 64, 128),
out_indices=(0, 1, 2),
out_channels=256,
... | 2,014 | 28.202899 | 78 | py |
DiffBEV | DiffBEV-main/configs/_base_/models/pointrend_r50.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='CascadeEncoderDecoder',
num_stages=2,
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1... | 1,704 | 28.912281 | 78 | py |
DiffBEV | DiffBEV-main/configs/_base_/models/ocrnet_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='CascadeEncoderDecoder',
num_stages=2,
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1... | 1,385 | 27.875 | 78 | py |
DiffBEV | DiffBEV-main/configs/_base_/models/isanet_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,291 | 27.086957 | 74 | py |
DiffBEV | DiffBEV-main/configs/_base_/models/nonlocal_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,315 | 27 | 74 | py |
DiffBEV | DiffBEV-main/configs/_base_/models/fcn_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,285 | 26.956522 | 74 | py |
DiffBEV | DiffBEV-main/docs/conf.py | # Copyright (c) OpenMMLab. All rights reserved.
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup -----------------------... | 6,019 | 29.871795 | 79 | py |
viser | viser-main/render_vis.py | import sys, os
sys.path.append(os.path.dirname(os.path.dirname(sys.path[0])))
os.environ["PYOPENGL_PLATFORM"] = "egl" #opengl seems to only work with TPU
sys.path.insert(0,'third_party')
import subprocess
import imageio
import glob
import matplotlib.pyplot as plt
import numpy as np
import torch
import cv2
import pdb
i... | 14,811 | 46.780645 | 240 | py |
viser | viser-main/extract.py | # Copyright 2021 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | 12,708 | 39.346032 | 168 | py |
viser | viser-main/eval_pck.py | import time
import sys, os
import pdb
import torch
import torch.nn as nn
from torch.autograd import Variable
sys.path.insert(0,'third_party')
from ext_utils.badja_data import BADJAData
from ext_utils.joint_catalog import SMALJointInfo
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn.funct... | 13,078 | 52.823045 | 187 | py |
viser | viser-main/optimize.py | # Copyright 2021 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | 1,673 | 26.9 | 112 | py |
viser | viser-main/third_party/PerceptualSimilarity/test_network.py | # import sys; sys.path += ['models']
import torch
from util import util
from models import dist_model as dm
#from IPython import embed
use_gpu = True # Whether to use GPU
spatial = False # Return a spatial map of perceptual distance.
# Optional args spatial_shape and spatial_orde... | 1,940 | 38.612245 | 154 | py |
viser | viser-main/third_party/PerceptualSimilarity/train.py | import torch.backends.cudnn as cudnn
cudnn.benchmark=False
import numpy as np
import time
import os
from models import dist_model as dm
from data import data_loader as dl
import argparse
from util.visualizer import Visualizer
#from IPython import embed
parser = argparse.ArgumentParser()
parser.add_argument('--dataset... | 4,956 | 50.103093 | 269 | py |
viser | viser-main/third_party/PerceptualSimilarity/perceptual_loss.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
from torch.autograd import Variable
from models import dist_model
class PerceptualLoss(torch.nn.Module):
def __init__(self, model='net-lin', net='vgg', use_gpu=True): # VGG using our perceptua... | 2,966 | 32.337079 | 125 | py |
viser | viser-main/third_party/PerceptualSimilarity/models/base_model.py | import os
import torch
import torch.nn as nn
from ..util import util as util
from torch.autograd import Variable
from pdb import set_trace as st
#from IPython import embed
class BaseModel(nn.Module):
def __init__(self):
super(BaseModel, self).__init__()
pass;
def name(self):
re... | 1,829 | 27.59375 | 78 | py |
viser | viser-main/third_party/PerceptualSimilarity/models/pretrained_networks.py | from collections import namedtuple
import torch
from torchvision import models
#from IPython import embed
class squeezenet(torch.nn.Module):
def __init__(self, requires_grad=False, pretrained=True):
super(squeezenet, self).__init__()
pretrained_features = models.squeezenet1_1(pretrained=pretrained)... | 6,560 | 35.049451 | 109 | py |
viser | viser-main/third_party/PerceptualSimilarity/models/networks_basic.py | import torch
import torch.nn as nn
import torch.nn.init as init
from torch.autograd import Variable
import numpy as np
from pdb import set_trace as st
from ..util import util as util
from skimage import color
#from IPython import embed
from . import pretrained_networks as pn
# Off-the-shelf deep network
class PNet(nn.... | 9,835 | 38.187251 | 122 | py |
viser | viser-main/third_party/PerceptualSimilarity/models/dist_model.py | import numpy as np
import torch
from torch import nn
import os
import os.path as osp
from collections import OrderedDict
from torch.autograd import Variable
import itertools
from ..util import util as util
from .base_model import BaseModel
from . import networks_basic as networks
from scipy.ndimage import zoom
import f... | 12,925 | 39.905063 | 229 | py |
viser | viser-main/third_party/PerceptualSimilarity/util/util.py | from __future__ import print_function
import numpy as np
from PIL import Image
import inspect
import re
import numpy as np
import os
import collections
import matplotlib.pyplot as plt
from scipy.ndimage.interpolation import zoom
#from skimage.measure import compare_ssim
import torch
#from IPython import embed
import cv... | 14,037 | 30.057522 | 153 | py |
viser | viser-main/third_party/PerceptualSimilarity/data/custom_dataset_data_loader.py | import torch.utils.data
from data.base_data_loader import BaseDataLoader
import os
def CreateDataset(dataroots,dataset_mode='2afc',load_size=64,):
dataset = None
if dataset_mode=='2afc': # human judgements
from dataset.twoafc_dataset import TwoAFCDataset
dataset = TwoAFCDataset()
elif datas... | 1,482 | 36.075 | 138 | py |
viser | viser-main/third_party/PerceptualSimilarity/data/image_folder.py | ################################################################################
# Code from
# https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py
# Modified the original code so that it also loads images from the current
# directory as well as the subdirectories
###############################... | 2,261 | 30.416667 | 94 | py |
viser | viser-main/third_party/PerceptualSimilarity/data/dataset/twoafc_dataset.py | import os.path
import torchvision.transforms as transforms
from data.dataset.base_dataset import BaseDataset
from data.image_folder import make_dataset
from PIL import Image
import numpy as np
import torch
# from IPython import embed
class TwoAFCDataset(BaseDataset):
def initialize(self, dataroots, load_size=64):
... | 2,411 | 35.545455 | 99 | py |
viser | viser-main/third_party/PerceptualSimilarity/data/dataset/base_dataset.py | import torch.utils.data as data
class BaseDataset(data.Dataset):
def __init__(self):
super(BaseDataset, self).__init__()
def name(self):
return 'BaseDataset'
def initialize(self):
pass
| 237 | 17.307692 | 43 | py |
viser | viser-main/third_party/PerceptualSimilarity/data/dataset/jnd_dataset.py | import os.path
import torchvision.transforms as transforms
from data.dataset.base_dataset import BaseDataset
from data.image_folder import make_dataset
from PIL import Image
import numpy as np
import torch
#from IPython import embed
class JNDDataset(BaseDataset):
def initialize(self, dataroot, load_size=64):
... | 1,793 | 32.222222 | 75 | py |
viser | viser-main/third_party/ext_nnutils/loss_utils.py | # MIT License
#
# Copyright (c) 2017 Hiroharu Kato
# Copyright (c) 2018 Nikos Kolotouros
# Copyright (c) 2019 Shichen Liu
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restricti... | 5,472 | 34.538961 | 113 | py |
viser | viser-main/third_party/ext_nnutils/net_blocks.py | # MIT License
#
# Copyright (c) 2018 akanazawa
# Copyright (c) 2021 Google LLC
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights... | 16,619 | 35.28821 | 145 | py |
viser | viser-main/third_party/ext_nnutils/conv4d.py | # MIT License
#
# Copyright (c) 2019 Carnegie Mellon University
# Copyright (c) 2021 Google LLC
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limi... | 13,409 | 40.645963 | 191 | py |
viser | viser-main/third_party/ext_nnutils/submodule.py | # MIT License
#
# Copyright (c) 2019 Carnegie Mellon University
# Copyright (c) 2021 Google LLC
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limi... | 34,937 | 45.646195 | 164 | py |
viser | viser-main/third_party/ext_nnutils/mesh_net.py | # MIT License
#
# Copyright (c) 2018 akanazawa
# Copyright (c) 2021 Google LLC
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights... | 4,448 | 34.592 | 119 | py |
viser | viser-main/third_party/ext_nnutils/train_utils.py | # MIT License
#
# Copyright (c) 2018 akanazawa
# Copyright (c) 2021 Google LLC
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights... | 4,597 | 32.808824 | 103 | py |
viser | viser-main/third_party/ext_nnutils/VCNplus.py | # MIT License
#
# Copyright (c) 2019 Carnegie Mellon University
# Copyright (c) 2021 Google LLC
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limi... | 27,220 | 49.038603 | 208 | py |
viser | viser-main/third_party/nerf/train_utils.py | import pdb
import torch
from .nerf_helpers import get_minibatches, ndc_rays
from .nerf_helpers import sample_pdf_2 as sample_pdf
from .volume_rendering_utils import volume_render_radiance_field
local_chunksize=131072
def run_network(network_fn, pts, viewdirs, chunksize, embed_fn, embeddirs_fn, code=None):
pts_fla... | 6,093 | 30.412371 | 106 | py |
viser | viser-main/third_party/nerf/nerf_helpers.py | import pdb
import math
from typing import Optional
import torch
def img2mse(img_src, img_tgt):
return torch.nn.functional.mse_loss(img_src, img_tgt)
def mse2psnr(mse):
# For numerical stability, avoid a zero mse loss.
if mse == 0:
mse = 1e-5
return -10.0 * math.log10(mse)
def get_minibatc... | 17,886 | 35.0625 | 105 | py |
viser | viser-main/third_party/nerf/models.py | import torch
import pdb
class VeryTinyNeRFModel(torch.nn.Module):
r"""Define a "very tiny" NeRF model comprising three fully connected layers.
"""
def __init__(self, filter_size=128, num_encoding_functions=6, use_viewdirs=True):
super(VeryTinyNeRFModel, self).__init__()
self.num_encoding_... | 18,208 | 36.621901 | 122 | py |
viser | viser-main/third_party/nerf/load_blender.py | import json
import os
import cv2
import imageio
import numpy as np
import torch
def translate_by_t_along_z(t):
tform = np.eye(4).astype(np.float32)
tform[2][3] = t
return tform
def rotate_by_phi_along_x(phi):
tform = np.eye(4).astype(np.float32)
tform[1, 1] = tform[2, 2] = np.cos(phi)
tform... | 3,270 | 26.957265 | 83 | py |
viser | viser-main/third_party/nerf/load_llff.py | import os
import imageio
import numpy as np
# Implementation from:
# https://github.com/yenchenlin/nerf-pytorch/blob/master/load_llff.py
# Slightly modified version of LLFF data loading code
# see https://github.com/Fyusion/LLFF for original
def _minify(basedir, factors=[], resolutions=[]):
needtoload = False
... | 10,545 | 28.707042 | 88 | py |
viser | viser-main/third_party/nerf/volume_rendering_utils.py | import torch
from .nerf_helpers import cumprod_exclusive
def volume_render_radiance_field(
radiance_field,
depth_values,
ray_directions,
radiance_field_noise_std=0.0,
white_background=False,
):
# TESTED
one_e_10 = torch.tensor(
[1e10], dtype=ray_directions.dtype, device=ray_direct... | 1,625 | 29.111111 | 87 | py |
viser | viser-main/third_party/softras/setup.py | from setuptools import setup, find_packages
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
CUDA_FLAGS = []
ext_modules=[
CUDAExtension('soft_renderer.cuda.load_textures', [
'soft_renderer/cuda/load_textures_cuda.cpp',
'soft_renderer/cuda/load_textures_cuda_kernel.cu',
... | 1,400 | 34.025 | 91 | py |
viser | viser-main/third_party/softras/soft_renderer/renderer.py |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy
import soft_renderer as sr
class Renderer(nn.Module):
def __init__(self, image_size=256, background_color=[0,0,0], near=1, far=100,
anti_aliasing=True, fill_back=True, eps=1e-6,
camera... | 4,366 | 41.813725 | 91 | py |
viser | viser-main/third_party/softras/soft_renderer/losses.py | import torch
import torch.nn as nn
import numpy as np
class LaplacianLoss(nn.Module):
def __init__(self, vertex, faces, average=False):
super(LaplacianLoss, self).__init__()
self.nv = vertex.size(0)
self.nf = faces.size(0)
self.average = average
laplacian = np.zeros([self.n... | 3,795 | 32.298246 | 113 | py |
viser | viser-main/third_party/softras/soft_renderer/rasterizer.py |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import soft_renderer.functional as srf
class SoftRasterizer(nn.Module):
def __init__(self, image_size=256, background_color=[0, 0, 0], near=1, far=100,
anti_aliasing=False, fill_back=False, eps=1e-3,
... | 2,336 | 41.490909 | 94 | py |
viser | viser-main/third_party/softras/soft_renderer/transform.py |
import math
import numpy as np
import torch
import torch.nn as nn
import soft_renderer.functional as srf
class Projection(nn.Module):
def __init__(self, P, dist_coeffs=None, orig_size=512):
super(Projection, self).__init__()
self.P = P
self.dist_coeffs = dist_coeffs
self.orig_siz... | 4,016 | 36.194444 | 114 | py |
viser | viser-main/third_party/softras/soft_renderer/lighting.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import soft_renderer.functional as srf
class AmbientLighting(nn.Module):
def __init__(self, light_intensity=0.5, light_color=(1,1,1)):
super(AmbientLighting, self).__init__()
self.light_intensity = light_intens... | 2,622 | 38.149254 | 90 | py |
viser | viser-main/third_party/softras/soft_renderer/mesh.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import soft_renderer.functional as srf
class Mesh(object):
'''
A simple class for creating and manipulating trimesh objects
'''
def __init__(self, vertices, faces, textures=None, texture_res=1, texture_type='surface... | 6,884 | 37.463687 | 116 | py |
viser | viser-main/third_party/softras/soft_renderer/functional/soft_rasterize.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function
import numpy as np
import soft_renderer.cuda.soft_rasterize as soft_rasterize_cuda
class SoftRasterizeFunction(Function):
@staticmethod
def forward(ctx, face_vertices, textures, image_size=256,
... | 5,728 | 46.347107 | 136 | py |
viser | viser-main/third_party/softras/soft_renderer/functional/vertex_normals.py | import torch
import torch.nn.functional as F
def vertex_normals(vertices, faces):
"""
:param vertices: [batch size, number of vertices, 3]
:param faces: [batch size, number of faces, 3]
:return: [batch size, number of vertices, 3]
"""
assert (vertices.ndimension() == 3)
assert (faces.ndimen... | 1,543 | 39.631579 | 125 | py |
viser | viser-main/third_party/softras/soft_renderer/functional/voxelization.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function
import soft_renderer.cuda.voxelization as voxelization_cuda
def voxelize_sub1(faces, size, dim):
bs = faces.size(0)
nf = faces.size(1)
if dim == 0:
faces = faces[:, :, :, [2, 1, 0]].contiguous()... | 1,694 | 26.33871 | 83 | py |
viser | viser-main/third_party/softras/soft_renderer/functional/ambient_lighting.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def ambient_lighting(light, light_intensity=0.5, light_color=(1,1,1)):
device = light.device
if isinstance(light_color, tuple) or isinstance(light_color, list):
light_color = torch.tensor(light_color, dtype=torch.fl... | 625 | 31.947368 | 83 | py |
viser | viser-main/third_party/softras/soft_renderer/functional/save_obj.py | import os
import torch
from skimage.io import imsave
import soft_renderer.cuda.create_texture_image as create_texture_image_cuda
def create_texture_image(textures, texture_res=16):
num_faces = textures.shape[0]
tile_width = int((num_faces - 1.) ** 0.5) + 1
tile_height = int((num_faces - 1.) / tile_width... | 4,163 | 39.038462 | 99 | py |
viser | viser-main/third_party/softras/soft_renderer/functional/perspective.py | import math
import torch
def perspective(vertices, angle=30.):
'''
Compute perspective distortion from a given angle
'''
if (vertices.ndimension() != 3):
raise ValueError('vertices Tensor should have 3 dimensions')
device = vertices.device
angle = torch.tensor(angle / 180 * math.pi, dt... | 600 | 27.619048 | 83 | py |
viser | viser-main/third_party/softras/soft_renderer/functional/directional_lighting.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def directional_lighting(light, normals, light_intensity=0.5, light_color=(1,1,1),
light_direction=(0,1,0)):
# normals: [nb, :, 3]
device = light.device
if isinstance(light_color, tuple) or is... | 1,210 | 42.25 | 91 | py |
viser | viser-main/third_party/softras/soft_renderer/functional/face_vertices.py | import torch
def face_vertices(vertices, faces):
"""
:param vertices: [batch size, number of vertices, 3]
:param faces: [batch size, number of faces, 3]
:return: [batch size, number of faces, 3, 3]
"""
assert (vertices.ndimension() == 3)
assert (faces.ndimension() == 3)
assert (vertice... | 742 | 31.304348 | 88 | py |
viser | viser-main/third_party/softras/soft_renderer/functional/get_points_from_angles.py | import math
import torch
def get_points_from_angles(distance, elevation, azimuth, degrees=True):
if isinstance(distance, float) or isinstance(distance, int):
if degrees:
elevation = math.radians(elevation)
azimuth = math.radians(azimuth)
return (
distance * math... | 836 | 33.875 | 71 | py |
viser | viser-main/third_party/softras/soft_renderer/functional/load_obj.py | import os
import torch
import numpy as np
from skimage.io import imread
import soft_renderer.cuda.load_textures as load_textures_cuda
def load_mtl(filename_mtl):
'''
load color (Kd) and filename of textures from *.mtl
'''
texture_filenames = {}
colors = {}
material_name = ''
with open(fil... | 5,923 | 34.261905 | 107 | py |
viser | viser-main/third_party/softras/soft_renderer/functional/look.py | import numpy as np
import torch
import torch.nn.functional as F
def look(vertices, eye, direction=[0, 1, 0], up=None):
"""
"Look" transformation of vertices.
"""
if (vertices.ndimension() != 3):
raise ValueError('vertices Tensor should have 3 dimensions')
device = vertices.device
if ... | 1,643 | 30.615385 | 86 | py |
viser | viser-main/third_party/softras/soft_renderer/functional/projection.py | import torch
def projection(vertices, P, dist_coeffs, orig_size):
'''
Calculate projective transformation of vertices given a projection matrix
P: 3x4 projection matrix
dist_coeffs: vector of distortion coefficients
orig_size: original size of image captured by the camera
'''
vertices = to... | 1,198 | 37.677419 | 88 | py |
viser | viser-main/third_party/softras/soft_renderer/functional/orthogonal.py | import torch
def orthogonal(vertices, scale):
'''
Compute orthogonal projection from a given angle
To find equivalent scale to perspective projection
set scale = focal_pixel / object_depth -- to 0~H/W pixel range
= 1 / ( object_depth * tan(half_fov_angle) ) -- to -1~1 pixel range
''... | 584 | 33.411765 | 81 | py |
viser | viser-main/third_party/softras/soft_renderer/functional/look_at.py | import numpy as np
import torch
import torch.nn.functional as F
def look_at(vertices, eye, at=[0, 0, 0], up=[0, 1, 0]):
"""
"Look at" transformation of vertices.
"""
if (vertices.ndimension() != 3):
raise ValueError('vertices Tensor should have 3 dimensions')
device = vertices.device
... | 2,060 | 31.714286 | 86 | py |
viser | viser-main/third_party/ext_utils/quatlib.py | # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 1,541 | 29.235294 | 141 | py |
viser | viser-main/third_party/ext_utils/util_rot.py | # MIT License
#
# Copyright (c) 2019 Yi_Zhou
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, pu... | 1,577 | 39.461538 | 80 | py |
viser | viser-main/third_party/ext_utils/io.py | # MIT License
#
# Copyright (c) 2019 Carnegie Mellon University
# Copyright (c) 2021 Google LLC
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limi... | 1,862 | 30.05 | 80 | py |
viser | viser-main/preprocess/skip_gen.py | from __future__ import print_function
import sys
sys.path.insert(0,'third_party')
import cv2
import pdb
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
from torch.autograd import Vari... | 7,004 | 38.576271 | 108 | py |
viser | viser-main/preprocess/auto_gen.py | # Copyright 2021 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | 9,175 | 40.147982 | 107 | py |
viser | viser-main/nnutils/loss_utils.py | # Copyright 2021 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | 2,451 | 33.535211 | 94 | py |
viser | viser-main/nnutils/predictor.py | # Copyright 2021 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | 15,868 | 46.942598 | 202 | py |
viser | viser-main/nnutils/net_blocks.py | import torch
import torchvision
import torch.nn as nn
import sys
class CodePredictorTex(nn.Module):
def __init__(self, nz_feat=100,tex_code_dim=64, shape_code_dim=64):
super(CodePredictorTex, self).__init__()
self.tex_predictor = nn.Linear(nz_feat, tex_code_dim)
self.shape_predictor = nn.Li... | 525 | 29.941176 | 71 | py |
viser | viser-main/nnutils/mesh_net.py | # Copyright 2021 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | 54,941 | 50.061338 | 207 | py |
viser | viser-main/nnutils/train_utils.py | # Copyright 2021 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | 32,294 | 56.980251 | 249 | py |
viser | viser-main/nnutils/geom_utils.py | # Copyright 2021 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | 7,548 | 26.959259 | 123 | py |
viser | viser-main/nnutils/cenet.py | from __future__ import print_function
import torch
import torch.nn as nn
import torch.utils.data
from torch.autograd import Variable
import torch.nn.functional as F
import math
import numpy as np
import pdb
import kornia
import sys
import sys
sys.path.insert(0,'third_party')
from nerf import (CfgNode, get_embedding_fun... | 17,020 | 39.817746 | 148 | py |
viser | viser-main/dataloader/vid.py | # Copyright 2021 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | 7,391 | 44.073171 | 196 | py |
viser | viser-main/dataloader/vidbase.py | # Copyright 2021 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | 11,958 | 36.725552 | 103 | py |
d3rlpy | d3rlpy-master/setup.py | import os
from setuptools import find_packages, setup
# get __version__ variable
here = os.path.abspath(os.path.dirname(__file__))
exec(open(os.path.join(here, "d3rlpy", "_version.py")).read())
if __name__ == "__main__":
setup(
name="d3rlpy",
version=__version__,
description="An offline d... | 1,825 | 34.803922 | 74 | py |
d3rlpy | d3rlpy-master/tests/test_torch_utility.py | import copy
from typing import Any, Dict, Sequence
from unittest.mock import Mock
import numpy as np
import pytest
import torch
from d3rlpy.dataset import TrajectoryMiniBatch, Transition, TransitionMiniBatch
from d3rlpy.torch_utility import (
Swish,
TorchMiniBatch,
TorchTrajectoryMiniBatch,
View,
... | 11,876 | 26.62093 | 79 | py |
d3rlpy | d3rlpy-master/tests/dummy_scalers.py | from typing import Any, Sequence
import gym
import numpy as np
import torch
from d3rlpy.dataset import (
EpisodeBase,
TrajectorySlicerProtocol,
TransitionPickerProtocol,
)
from d3rlpy.preprocessing import ActionScaler, ObservationScaler, RewardScaler
class DummyObservationScaler(ObservationScaler):
... | 2,891 | 23.508475 | 78 | py |
d3rlpy | d3rlpy-master/tests/models/test_q_functions.py | from typing import Sequence
import pytest
from d3rlpy.models.encoders import VectorEncoderFactory
from d3rlpy.models.q_functions import (
FQFQFunctionFactory,
IQNQFunctionFactory,
MeanQFunctionFactory,
QRQFunctionFactory,
)
from d3rlpy.models.torch import (
ContinuousFQFQFunction,
ContinuousIQ... | 4,021 | 31.699187 | 69 | py |
d3rlpy | d3rlpy-master/tests/models/test_builders.py | from typing import Sequence
import numpy as np
import pytest
import torch
from d3rlpy.models.builders import (
create_categorical_policy,
create_conditional_vae,
create_continuous_decision_transformer,
create_continuous_q_function,
create_deterministic_policy,
create_deterministic_regressor,
... | 15,292 | 31.959052 | 79 | py |
d3rlpy | d3rlpy-master/tests/models/test_encoders.py | # pylint: disable=protected-access
from typing import Sequence
import pytest
from d3rlpy.models.encoders import (
DefaultEncoderFactory,
DenseEncoderFactory,
PixelEncoderFactory,
VectorEncoderFactory,
)
from d3rlpy.models.torch.encoders import (
PixelEncoder,
PixelEncoderWithAction,
Vector... | 4,273 | 30.19708 | 71 | py |
d3rlpy | d3rlpy-master/tests/models/test_optimizers.py | import pytest
import torch
from torch.optim import SGD, Adam, AdamW, RMSprop
from d3rlpy.models.optimizers import (
AdamFactory,
AdamWFactory,
RMSpropFactory,
SGDFactory,
)
@pytest.mark.parametrize("lr", [1e-4])
@pytest.mark.parametrize("module", [torch.nn.Linear(2, 3)])
def test_sgd_factory(lr: floa... | 1,879 | 27.059701 | 69 | py |
d3rlpy | d3rlpy-master/tests/models/torch/test_v_functions.py | import pytest
import torch
import torch.nn.functional as F
from d3rlpy.models.torch.v_functions import ValueFunction
from .model_test import DummyEncoder, check_parameter_updates
@pytest.mark.parametrize("feature_size", [100])
@pytest.mark.parametrize("batch_size", [32])
def test_value_function(feature_size: int, b... | 855 | 24.176471 | 68 | py |
d3rlpy | d3rlpy-master/tests/models/torch/test_q_functions.py | from typing import Sequence
import pytest
import torch
from d3rlpy.models.builders import create_continuous_q_function
from d3rlpy.models.encoders import DefaultEncoderFactory, EncoderFactory
from d3rlpy.models.q_functions import (
MeanQFunctionFactory,
QFunctionFactory,
QRQFunctionFactory,
)
from d3rlpy.... | 1,715 | 30.777778 | 72 | py |
d3rlpy | d3rlpy-master/tests/models/torch/test_imitators.py | import pytest
import torch
import torch.nn.functional as F
from d3rlpy.models.torch.imitators import (
ConditionalVAE,
DeterministicRegressor,
DiscreteImitator,
ProbablisticRegressor,
)
from .model_test import (
DummyEncoder,
DummyEncoderWithAction,
check_parameter_updates,
)
@pytest.mar... | 4,387 | 29.685315 | 75 | py |
d3rlpy | d3rlpy-master/tests/models/torch/test_distributions.py | import math
import pytest
import torch
import torch.nn.functional as F
from torch.distributions import Normal
from d3rlpy.models.torch.distributions import (
GaussianDistribution,
SquashedGaussianDistribution,
)
@pytest.mark.parametrize("action_size", [2])
@pytest.mark.parametrize("batch_size", [32])
@pytes... | 3,513 | 31.841121 | 74 | py |
d3rlpy | d3rlpy-master/tests/models/torch/test_parameters.py | from typing import Sequence
import pytest
import torch
from d3rlpy.models.torch.parameters import Parameter
@pytest.mark.parametrize("shape", [(100,)])
def test_parameter(shape: Sequence[int]) -> None:
data = torch.rand(shape)
parameter = Parameter(data)
assert parameter().shape == shape
assert tor... | 348 | 20.8125 | 52 | py |
d3rlpy | d3rlpy-master/tests/models/torch/test_encoders.py | # pylint: disable=protected-access
from typing import List, Optional, Sequence, Tuple
import pytest
import torch
from d3rlpy.models.torch.encoders import (
PixelEncoder,
PixelEncoderWithAction,
VectorEncoder,
VectorEncoderWithAction,
)
from .model_test import check_parameter_updates
@pytest.mark.pa... | 6,747 | 30.680751 | 78 | py |
d3rlpy | d3rlpy-master/tests/models/torch/test_policies.py | import pytest
import torch
from d3rlpy.models.torch.distributions import (
GaussianDistribution,
SquashedGaussianDistribution,
)
from d3rlpy.models.torch.policies import (
CategoricalPolicy,
DeterministicPolicy,
DeterministicResidualPolicy,
NonSquashedNormalPolicy,
SquashedNormalPolicy,
)
... | 6,449 | 30.009615 | 77 | py |
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