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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relative-entropy | relative-entropy-main/chemtrain/neural_networks.py | """Neural network models for potential energy and molecular property
prediction.
"""
from functools import partial
from typing import Callable, Dict, Any, Tuple
import haiku as hk
from jax import numpy as jnp, nn as jax_nn
from jax_md import smap, space, partition, nn, util
from chemtrain import layers, sparse_graph... | 14,117 | 48.191638 | 80 | py |
relative-entropy | relative-entropy-main/chemtrain/reweighting.py | """Implementation of the reweighting formalism.
Allows re-using existing trajectories.
p(S) = ...
"""
from abc import abstractmethod
import time
import warnings
from jax import (checkpoint, lax, jit, random, grad, tree_util, vmap,
numpy as jnp)
from jax_md import util as jax_md_util
from chemtrain i... | 17,923 | 44.492386 | 80 | py |
relative-entropy | relative-entropy-main/chemtrain/trainers.py | """This file contains several Trainer classes as a quickstart for users."""
from jax import numpy as jnp
from jax_sgmc import data
from jax_sgmc.data import numpy_loader
from chemtrain import (util, force_matching, reweighting,
max_likelihood)
class ForceMatching(max_likelihood.DataParallelTra... | 12,808 | 50.441767 | 80 | py |
relative-entropy | relative-entropy-main/chemtrain/data_processing.py | """Functions that facilitate common data processing operations for machine
learning.
"""
import numpy as onp
from jax import lax
from jax.tree_util import tree_flatten
from jax_sgmc.data import numpy_loader
from chemtrain.jax_md_mod import custom_space
from chemtrain import util
def get_dataset(data_location_str, re... | 5,020 | 36.470149 | 80 | py |
relative-entropy | relative-entropy-main/chemtrain/layers.py | # Copyright 2022 Multiscale Modeling of Fluid Materials, TU Munich
#
# 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 appl... | 20,944 | 40.55754 | 80 | py |
relative-entropy | relative-entropy-main/chemtrain/dropout.py | """Customn function for enabling dropout applications in haiku."""
from functools import wraps
import haiku as hk
from jax import random, vmap, numpy as jnp
# Note: This implementation currently stores the RNG key as float32
# rather than uint32. This way, both energy_params and the RNG key
# can be treated analog... | 8,399 | 38.810427 | 80 | py |
relative-entropy | relative-entropy-main/chemtrain/force_matching.py | """Functions for learning via direct matching of per-snapshot quantities,
such as energy, forces and virial pressure.
"""
from collections import namedtuple
from jax import vmap, value_and_grad, numpy as jnp
from chemtrain import max_likelihood
from chemtrain.jax_md_mod import custom_quantity
# Note:
# Computing th... | 6,673 | 38.258824 | 80 | py |
relative-entropy | relative-entropy-main/chemtrain/traj_util.py | """Utility functions to process whole MD trajectories rather than
single snapshots.
"""
from functools import partial
from typing import Any, Dict
import chex
from jax import lax, jit, vmap, numpy as jnp
from jax_md import simulate, util as jax_md_util
from chemtrain import util
from chemtrain.jax_md_mod import custo... | 13,097 | 39.93125 | 80 | py |
relative-entropy | relative-entropy-main/chemtrain/util.py | """Utility functions helpful in designing new trainers."""
import abc
from functools import partial
import pathlib
from typing import Any
import chex
import cloudpickle as pickle
from jax import tree_map, device_count, numpy as jnp
from jax.tree_util import tree_flatten, tree_leaves
from jax_md import simulate
import ... | 9,189 | 32.663004 | 79 | py |
relative-entropy | relative-entropy-main/chemtrain/traj_quantity.py | """Molecular dynamics observable functions acting on trajectories rather than
single snapshots.
Builds on the TrajectoryState object defined in traj_util.py.
"""
from jax import numpy as jnp
def init_traj_mean_fn(quantity_key):
"""Initializes the 'traj_fn' for the DiffTRe 'target' dict for simple
trajectory-... | 1,558 | 40.026316 | 78 | py |
relative-entropy | relative-entropy-main/chemtrain/max_likelihood.py | """A collection of functions to facilitate learning maximum likelihood /
single point estimate models.
"""
import abc
import copy
from functools import partial
import time
from jax import (lax, jit, vmap, pmap, value_and_grad, tree_map, device_count,
numpy as jnp)
from jax_sgmc import data
import nu... | 27,573 | 41.81677 | 80 | py |
relative-entropy | relative-entropy-main/chemtrain/jax_md_mod/custom_interpolate.py | import jax.numpy as jnp
from jax.tree_util import register_pytree_node_class
@register_pytree_node_class
class MonotonicInterpolate:
"""
Piecewise cubic, monotonic interpolation via Steffens method [1].
The interpolation curve is monotonic within each interval such that extrema
can only occur at grid ... | 19,047 | 36.718812 | 110 | py |
relative-entropy | relative-entropy-main/chemtrain/jax_md_mod/custom_energy.py | """Custom definition of some potential energy functions."""
from functools import partial
from typing import Callable, Any
from jax import vmap
import jax.numpy as jnp
from jax_md import space, partition, util, energy, smap
from chemtrain.jax_md_mod import custom_interpolate, custom_quantity
from chemtrain import spa... | 10,505 | 33.559211 | 101 | py |
relative-entropy | relative-entropy-main/chemtrain/jax_md_mod/custom_space.py | """Custom functions simplifying the handling of fractional coordinates."""
from typing import Union, Tuple, Callable
from jax_md import space, util
import jax.numpy as jnp
Box = Union[float, util.Array]
def _rectangular_boxtensor(box: Box) -> Box:
"""Transforms a 1-dimensional box to a 2D box tensor."""
spa... | 1,155 | 32.028571 | 78 | py |
relative-entropy | relative-entropy-main/chemtrain/jax_md_mod/io.py | """Functions for io: Loading data to and from Jax M.D."""
import jax.numpy as jnp
import mdtraj
import numpy as onp
def load_box(filename):
"""Loads initial configuration using the file loader from MDTraj.
Args:
filename: String providing the location of the file to load.
Returns:
Tuple ... | 866 | 27.9 | 70 | py |
relative-entropy | relative-entropy-main/chemtrain/jax_md_mod/custom_quantity.py | """A collection of functions evaluating quantiities of trajectories.
For easiest integration into chemtain, functions should be compatible with
traj_util.quantity_traj. Functions provided to quantity_traj need to take the
state and additional kwargs.
"""
from functools import partial
from jax import grad, vmap, lax, n... | 28,968 | 41.538913 | 80 | py |
relative-entropy | relative-entropy-main/util/Postprocessing.py | import jax.numpy as jnp
from jax import lax
import pickle
from matplotlib import pyplot as plt
from functools import partial
def box_density(R_snapshot, bin_edges, axis=0):
# assumes all particles are wrapped into the same box
profile, _ = jnp.histogram(R_snapshot[:, axis], bins=bin_edges)
# norm via n_b... | 4,851 | 37.507937 | 80 | py |
relative-entropy | relative-entropy-main/util/Initialization.py | """A collection of functions to initialize Jax, M.D. simulations."""
from functools import partial
import chex
import haiku as hk
from jax import random, vmap, numpy as jnp
from jax_md import util, simulate, partition, space, energy
import numpy as onp
from scipy import interpolate as sci_interpolate
from chemtrain i... | 22,831 | 41.998117 | 80 | py |
ngp_pl | ngp_pl-master/losses.py | import torch
from torch import nn
import vren
class DistortionLoss(torch.autograd.Function):
"""
Distortion loss proposed in Mip-NeRF 360 (https://arxiv.org/pdf/2111.12077.pdf)
Implementation is based on DVGO-v2 (https://arxiv.org/pdf/2206.05085.pdf)
Inputs:
ws: (N) sample point weights
... | 2,112 | 33.639344 | 83 | py |
ngp_pl | ngp_pl-master/utils.py | import torch
def extract_model_state_dict(ckpt_path, model_name='model', prefixes_to_ignore=[]):
checkpoint = torch.load(ckpt_path, map_location='cpu')
checkpoint_ = {}
if 'state_dict' in checkpoint: # if it's a pytorch-lightning checkpoint
checkpoint = checkpoint['state_dict']
for k, v in che... | 1,353 | 32.85 | 85 | py |
ngp_pl | ngp_pl-master/show_gui.py | import torch
from opt import get_opts
import numpy as np
from einops import rearrange
import dearpygui.dearpygui as dpg
from scipy.spatial.transform import Rotation as R
import time
from datasets import dataset_dict
from datasets.ray_utils import get_ray_directions, get_rays
from models.networks import NGP
from models... | 6,939 | 34.773196 | 93 | py |
ngp_pl | ngp_pl-master/metrics.py | import torch
def mse(image_pred, image_gt, valid_mask=None, reduction='mean'):
value = (image_pred-image_gt)**2
if valid_mask is not None:
value = value[valid_mask]
if reduction == 'mean':
return torch.mean(value)
return value
@torch.no_grad()
def psnr(image_pred, image_gt, valid_mas... | 424 | 25.5625 | 76 | py |
ngp_pl | ngp_pl-master/train.py | import torch
from torch import nn
from opt import get_opts
import os
import glob
import imageio
import numpy as np
import cv2
from einops import rearrange
# data
from torch.utils.data import DataLoader
from datasets import dataset_dict
from datasets.ray_utils import axisangle_to_R, get_rays
# models
from kornia.utils... | 11,972 | 39.72449 | 116 | py |
ngp_pl | ngp_pl-master/models/networks.py | import torch
from torch import nn
import tinycudann as tcnn
import vren
from einops import rearrange
from .custom_functions import TruncExp
import numpy as np
from .rendering import NEAR_DISTANCE
class NGP(nn.Module):
def __init__(self, scale, rgb_act='Sigmoid'):
super().__init__()
self.rgb_act ... | 10,391 | 37.488889 | 103 | py |
ngp_pl | ngp_pl-master/models/custom_functions.py | import torch
import vren
from torch.cuda.amp import custom_fwd, custom_bwd
from torch_scatter import segment_csr
from einops import rearrange
class RayAABBIntersector(torch.autograd.Function):
"""
Computes the intersections of rays and axis-aligned voxels.
Inputs:
rays_o: (N_rays, 3) ray origins
... | 6,186 | 34.557471 | 83 | py |
ngp_pl | ngp_pl-master/models/rendering.py | import torch
from .custom_functions import \
RayAABBIntersector, RayMarcher, VolumeRenderer
from einops import rearrange
import vren
MAX_SAMPLES = 1024
NEAR_DISTANCE = 0.01
@torch.cuda.amp.autocast()
def render(model, rays_o, rays_d, **kwargs):
"""
Render rays by
1. Compute the intersection of the ra... | 6,192 | 36.762195 | 89 | py |
ngp_pl | ngp_pl-master/models/csrc/setup.py | import glob
import os.path as osp
from setuptools import setup
from torch.utils.cpp_extension import CUDAExtension, BuildExtension
ROOT_DIR = osp.dirname(osp.abspath(__file__))
include_dirs = [osp.join(ROOT_DIR, "include")]
# "helper_math.h" is copied from https://github.com/NVIDIA/cuda-samples/blob/master/Common/hel... | 893 | 26.090909 | 104 | py |
ngp_pl | ngp_pl-master/datasets/base.py | from torch.utils.data import Dataset
import numpy as np
class BaseDataset(Dataset):
"""
Define length and sampling method
"""
def __init__(self, root_dir, split='train', downsample=1.0):
self.root_dir = root_dir
self.split = split
self.downsample = downsample
def read_intr... | 1,704 | 37.75 | 88 | py |
ngp_pl | ngp_pl-master/datasets/colmap.py | import torch
import numpy as np
import os
import glob
from tqdm import tqdm
from .ray_utils import *
from .color_utils import read_image
from .colmap_utils import \
read_cameras_binary, read_images_binary, read_points3d_binary
from .base import BaseDataset
class ColmapDataset(BaseDataset):
def __init__(self... | 7,505 | 46.207547 | 97 | py |
ngp_pl | ngp_pl-master/datasets/nerf.py | import torch
import json
import numpy as np
import os
from tqdm import tqdm
from .ray_utils import get_ray_directions
from .color_utils import read_image
from .base import BaseDataset
class NeRFDataset(BaseDataset):
def __init__(self, root_dir, split='train', downsample=1.0, **kwargs):
super().__init__(... | 3,180 | 33.956044 | 89 | py |
ngp_pl | ngp_pl-master/datasets/nsvf.py | import torch
import glob
import numpy as np
import os
from tqdm import tqdm
from .ray_utils import get_ray_directions
from .color_utils import read_image
from .base import BaseDataset
class NSVFDataset(BaseDataset):
def __init__(self, root_dir, split='train', downsample=1.0, **kwargs):
super().__init__(... | 4,232 | 40.910891 | 93 | py |
ngp_pl | ngp_pl-master/datasets/ray_utils.py | import torch
import numpy as np
from kornia import create_meshgrid
from einops import rearrange
@torch.cuda.amp.autocast(dtype=torch.float32)
def get_ray_directions(H, W, K, device='cpu', random=False, return_uv=False, flatten=True):
"""
Get ray directions for all pixels in camera coordinate [right down front... | 7,289 | 32.906977 | 97 | py |
ngp_pl | ngp_pl-master/datasets/nerfpp.py | import torch
import glob
import numpy as np
import os
from PIL import Image
from tqdm import tqdm
from .ray_utils import get_ray_directions
from .color_utils import read_image
from .base import BaseDataset
class NeRFPPDataset(BaseDataset):
def __init__(self, root_dir, split='train', downsample=1.0, **kwargs):
... | 2,341 | 38.033333 | 92 | py |
ngp_pl | ngp_pl-master/datasets/rtmv.py | import torch
import glob
import json
import numpy as np
import os
from tqdm import tqdm
from .ray_utils import get_ray_directions
from .color_utils import read_image
from .base import BaseDataset
class RTMVDataset(BaseDataset):
def __init__(self, root_dir, split='train', downsample=1.0, **kwargs):
super... | 2,582 | 35.380282 | 97 | py |
DeepSpeech | DeepSpeech-master/training/deepspeech_training/train.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function
import os
import sys
LOG_LEVEL_INDEX = sys.argv.index('--log_level') + 1 if '--log_level' in sys.argv else 0
DESIRED_LOG_LEVEL = sys.argv[LOG_LEVEL_INDEX] if 0 < LOG_LEVEL_INDEX < len(sys.argv) else '3'
os.e... | 46,010 | 43.369335 | 211 | py |
DeepSpeech | DeepSpeech-master/doc/conf.py | # -*- coding: utf-8 -*-
#
# DeepSpeech documentation build configuration file, created by
# sphinx-quickstart on Thu Feb 2 21:20:39 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
... | 6,579 | 30.333333 | 109 | py |
FGAHOI | FGAHOI-main/generate_vcoco_official.py | # ------------------------------------------------------------------------
# QAHOI
# Copyright (c) 2021 Junwen Chen. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Copyright (c) Hitachi, Ltd. Al... | 19,612 | 46.146635 | 183 | py |
FGAHOI | FGAHOI-main/main.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import datetime
import json
import random
import time
from pathlib import Path
import os, sys
from typing import Optional
from util.logger import setup_logger
import numpy as np
import torch
from torch.utils.data import DataLoa... | 22,680 | 49.740492 | 184 | py |
FGAHOI | FGAHOI-main/engine.py |
import math
import os
import sys
from typing import Iterable
import numpy as np
import copy
import itertools
import torch
import util.misc as utils
from datasets.hico_eval import HICOEvaluator
from datasets.vcoco_eval import VCOCOEvaluator
from datasets.hake_eval import HAKEEvaluator
from loguru import logger
# fr... | 7,080 | 44.683871 | 211 | py |
FGAHOI | FGAHOI-main/make_param.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import datetime
import json
import random
import time
from pathlib import Path
import os, sys
from typing import Optional
from util.logger import setup_logger
import numpy as np
import torch
from torch.utils.data import DataLoa... | 15,107 | 50.040541 | 155 | py |
FGAHOI | FGAHOI-main/coco_param.py | import argparse
import torch
from torch import nn
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
'--save_path', type=str, default="/mnt/gluster/home/mashuailei/DAB-DETR-main/dahoi/param/pretrained_357_coco_obj.pth",
)
parser.add_argument(
'--load_path', type=... | 2,582 | 38.136364 | 128 | py |
FGAHOI | FGAHOI-main/models/dab_deformable_detr/test.py | import torch
a = torch.rand([2,1])
b = torch.rand([2,1])
c = torch.max(a,b)
print(a.shape) | 93 | 10.75 | 21 | py |
FGAHOI | FGAHOI-main/models/dab_deformable_detr/swin_transformer.py | # ------------------------------------------------------------------------
# QAHOI
# Copyright (c) 2021 Junwen Chen. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# --------------------------------------------------------
# Modified from Swin Transformer (https://github... | 29,754 | 38.202899 | 123 | py |
FGAHOI | FGAHOI-main/models/dab_deformable_detr/matcher.py | # ------------------------------------------------------------------------
# QAHOI
# Copyright (c) 2021 Junwen Chen. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from QPIC (https://gi... | 4,478 | 48.766667 | 121 | py |
FGAHOI | FGAHOI-main/models/dab_deformable_detr/segmentation.py | # ------------------------------------------------------------------------
# DAB-DETR
# Copyright (c) 2022 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# DModified from eformable DETR
# Co... | 16,148 | 42.179144 | 120 | py |
FGAHOI | FGAHOI-main/models/dab_deformable_detr/deformable_transformer.py | # ------------------------------------------------------------------------
# DAB-DETR
# Copyright (c) 2022 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# DModified from eformable DETR
# Co... | 27,364 | 47.093146 | 173 | py |
FGAHOI | FGAHOI-main/models/dab_deformable_detr/position_encoding.py | # ------------------------------------------------------------------------
# DAB-DETR
# Copyright (c) 2022 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# DModified from eformable DETR
# Co... | 4,011 | 38.333333 | 103 | py |
FGAHOI | FGAHOI-main/models/dab_deformable_detr/backbone.py | # ------------------------------------------------------------------------
# QAHOI
# Copyright (c) 2021 Junwen Chen. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from Deformable DETR ... | 6,974 | 42.867925 | 201 | py |
FGAHOI | FGAHOI-main/models/dab_deformable_detr/dab_deformable_detr.py | # ------------------------------------------------------------------------
# DAB-DETR
# Copyright (c) 2022 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# DModified from eformable DETR
# Co... | 38,148 | 48.224516 | 164 | py |
FGAHOI | FGAHOI-main/models/dab_deformable_detr/ops/test.py | # ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# -------------------------------------------------------------------------... | 4,087 | 44.422222 | 172 | py |
FGAHOI | FGAHOI-main/models/dab_deformable_detr/ops/setup.py | # ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# -------------------------------------------------------------------------... | 2,596 | 34.094595 | 98 | py |
FGAHOI | FGAHOI-main/models/dab_deformable_detr/ops/functions/ms_deform_attn_func.py | # ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# -------------------------------------------------------------------------... | 3,298 | 52.209677 | 138 | py |
FGAHOI | FGAHOI-main/models/dab_deformable_detr/ops/functions/__init__.py | # ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# -------------------------------------------------------------------------... | 598 | 53.454545 | 98 | py |
FGAHOI | FGAHOI-main/models/dab_deformable_detr/ops/modules/__init__.py | # ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# -------------------------------------------------------------------------... | 584 | 57.5 | 98 | py |
FGAHOI | FGAHOI-main/models/dab_deformable_detr/ops/modules/attention.py | # ------------------------------------------------------------------------
# Modified from codes in torch.nn
# ------------------------------------------------------------------------
# ------------------------------------------------------------------------
# Modified from Conditional DETR (https://github.com/Atten4Vi... | 20,197 | 53.295699 | 120 | py |
FGAHOI | FGAHOI-main/models/dab_deformable_detr/ops/modules/ms_deform_attn.py |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import warnings
import math
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.init import xavier_uniform_, constant_
import einops
from ..functions import MSDeformAttnFunction
im... | 20,416 | 56.838527 | 156 | py |
FGAHOI | FGAHOI-main/models/DAB_DETR/DABDETR.py | # ------------------------------------------------------------------------
# DAB-DETR
# Copyright (c) 2022 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from Conditional DETR (ht... | 23,292 | 44.494141 | 137 | py |
FGAHOI | FGAHOI-main/models/DAB_DETR/swin_transformer.py | # ------------------------------------------------------------------------
# DAB-DETR
# Copyright (c) 2022 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from Swin Transformer
# C... | 26,290 | 39.76124 | 119 | py |
FGAHOI | FGAHOI-main/models/DAB_DETR/matcher.py | # ------------------------------------------------------------------------
# DAB-DETR
# Copyright (c) 2022 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from Conditional DETR (ht... | 5,105 | 51.102041 | 119 | py |
FGAHOI | FGAHOI-main/models/DAB_DETR/segmentation.py | # ------------------------------------------------------------------------
# DAB-DETR
# Copyright (c) 2022 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from Conditional DETR (ht... | 16,281 | 42.303191 | 120 | py |
FGAHOI | FGAHOI-main/models/DAB_DETR/position_encoding.py | # ------------------------------------------------------------------------
# DAB-DETR
# Copyright (c) 2022 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from Conditional DETR (ht... | 6,204 | 39.555556 | 110 | py |
FGAHOI | FGAHOI-main/models/DAB_DETR/backbone.py | # ------------------------------------------------------------------------
# DAB-DETR
# Copyright (c) 2022 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from Conditional DETR (ht... | 5,971 | 39.080537 | 113 | py |
FGAHOI | FGAHOI-main/models/DAB_DETR/transformer.py | # ------------------------------------------------------------------------
# DAB-DETR
# Copyright (c) 2022 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from Conditional DETR (ht... | 20,612 | 41.153374 | 145 | py |
FGAHOI | FGAHOI-main/models/DAB_DETR/attention.py | # ------------------------------------------------------------------------
# DAB-DETR
# Copyright (c) 2022 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from Conditional DETR (ht... | 20,785 | 51.357683 | 130 | py |
FGAHOI | FGAHOI-main/util/plot_utils.py | """
Plotting utilities to visualize training logs.
"""
import torch
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from pathlib import Path, PurePath
def plot_logs(logs, fields=('class_error', 'loss_bbox_unscaled', 'mAP'), ewm_col=0, log_name='log.txt'):
'''
Func... | 4,658 | 40.230088 | 120 | py |
FGAHOI | FGAHOI-main/util/misc.py | # ------------------------------------------------------------------------
# QAHOI
# Copyright (c) 2021 Junwen Chen. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from Deformable DETR ... | 18,119 | 32.680297 | 113 | py |
FGAHOI | FGAHOI-main/util/utils.py | from collections import OrderedDict
from copy import deepcopy
import json
import warnings
import torch
import numpy as np
def slprint(x, name='x'):
if isinstance(x, (torch.Tensor, np.ndarray)):
print(f'{name}.shape:', x.shape)
elif isinstance(x, (tuple, list)):
print('type x:', type(x))
... | 13,342 | 32.441103 | 814 | py |
FGAHOI | FGAHOI-main/util/visualizer.py | # -*- coding: utf-8 -*-
'''
@File : visualizer.py
@Time : 2022/04/05 11:39:33
@Author : Shilong Liu
@Contact : slongliu86@gmail.com
'''
import os, sys
from textwrap import wrap
import torch
import numpy as np
import cv2
import datetime
import matplotlib.pyplot as plt
from matplotlib.collections import... | 8,740 | 40.037559 | 139 | py |
FGAHOI | FGAHOI-main/util/get_param_dicts.py | import json
import torch
import torch.nn as nn
def match_name_keywords(n: str, name_keywords: list):
out = False
for b in name_keywords:
if b in n:
out = True
break
return out
def get_param_dict(args, model_without_ddp: nn.Module):
try:
param_dict_type = args.... | 3,315 | 38.47619 | 157 | py |
FGAHOI | FGAHOI-main/util/box_ops.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Utilities for bounding box manipulation and GIoU.
"""
import torch, os
from torchvision.ops.boxes import box_area
import numpy as np
from scipy.optimize import linear_sum_assignment
def sort_with_idx(source,idx):
n,_ = source.size()
afte... | 5,221 | 30.269461 | 141 | py |
FGAHOI | FGAHOI-main/util/box_loss.py | # borrow from https://github.com/Zzh-tju/CIoU/blob/master/layers/modules/multibox_loss.py
import torch, math
def ciou(bboxes1, bboxes2):
bboxes1 = torch.sigmoid(bboxes1)
bboxes2 = torch.sigmoid(bboxes2)
rows = bboxes1.shape[0]
cols = bboxes2.shape[0]
cious = torch.zeros((rows, cols))
if rows... | 3,884 | 33.380531 | 96 | py |
FGAHOI | FGAHOI-main/datasets/test.py | import torch
import torch.nn.functional as F
verb_labels = torch.tensor([0], dtype=torch.int64)
verb_labels = F.one_hot(verb_labels, num_classes=117)
list = ['a','b']
index = list.index('a')
a = 0 | 198 | 21.111111 | 53 | py |
FGAHOI | FGAHOI-main/datasets/gen_npy.py | from pathlib import Path
from PIL import Image
import json
from collections import defaultdict
import numpy as np
import copy
import torch
import torch.utils.data
import torchvision
import math
import torch.nn.functional as F
import os
with open("/mnt/gluster/ssd/datasets/HAKE/proposed_challenge/dc_hoi_list.json", 'r'... | 595 | 22.84 | 90 | py |
FGAHOI | FGAHOI-main/datasets/hico_eval.py | # ------------------------------------------------------------------------
# QAHOI
# Copyright (c) 2021 Junwen Chen. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
import numpy as np
import os
imp... | 24,349 | 37.650794 | 143 | py |
FGAHOI | FGAHOI-main/datasets/hico.py | # ------------------------------------------------------------------------
# QAHOI
# Copyright (c) 2021 Junwen Chen. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from QPIC (https://gi... | 11,053 | 39.196364 | 125 | py |
FGAHOI | FGAHOI-main/datasets/__init__.py | # ------------------------------------------------------------------------
# QAHOI
# Copyright (c) 2021 Junwen Chen. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from Deformable DETR ... | 989 | 38.6 | 86 | py |
FGAHOI | FGAHOI-main/datasets/hake.py | """
HAKE detection dataset.
"""
from pathlib import Path
from PIL import Image
import json
from collections import defaultdict
import numpy as np
import torch
import torch.utils.data
import torchvision
import math
import torch.nn.functional as F
import datasets.transforms_hake as T
import os
def gaussian2D(shape, si... | 9,548 | 40.517391 | 163 | py |
FGAHOI | FGAHOI-main/datasets/hake_eval.py | # ------------------------------------------------------------------------
# QAHOI
# Copyright (c) 2021 Junwen Chen. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
import numpy as np
import os
imp... | 19,890 | 38.310277 | 159 | py |
FGAHOI | FGAHOI-main/datasets/vcoco.py | # ------------------------------------------------------------------------
# QAHOI
# Copyright (c) 2021 Junwen Chen. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Copyright (c) Hitachi, Ltd. Al... | 8,044 | 41.342105 | 123 | py |
FGAHOI | FGAHOI-main/datasets/transforms_hake.py | # ------------------------------------------------------------------------
# QAHOI
# Copyright (c) 2021 Junwen Chen. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from Deformable DETR ... | 9,703 | 31.13245 | 104 | py |
FGAHOI | FGAHOI-main/datasets/transforms.py | # ------------------------------------------------------------------------
# QAHOI
# Copyright (c) 2021 Junwen Chen. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from Deformable DETR ... | 9,214 | 30.558219 | 104 | py |
differentiable-learning-under-triage | differentiable-learning-under-triage-main/Galaxy-zoo/prepare_data.py | import os
import pandas as pd
import numpy as np
import skimage
from skimage import io
import csv
from skimage.transform import rescale, resize, downscale_local_mean
import torch
import torch.nn as nn
import copy
# entire dataset size = 61577
'''
The dataset is downloaded from https://www.kaggle.com/c/galaxy-zoo-the-g... | 5,062 | 28.436047 | 131 | py |
differentiable-learning-under-triage | differentiable-learning-under-triage-main/Synthetic/generate_synthetic.py | import pickle
import sys
import numpy as np
import torch
import os
def save_data(data, file_path):
with open(file_path + '.pkl','wb') as f:
pickle.dump(data,f,pickle.HIGHEST_PROTOCOL)
def load_data(file_name):
assert(os.path.exists(file_name+'.pkl'))
with open(file_name + '.pkl', 'rb') as ... | 3,535 | 28.714286 | 129 | py |
differentiable-learning-under-triage | differentiable-learning-under-triage-main/Hatespeech/process_text_data.py | import numpy.random as rand
import codecs
import csv
import random
import numpy as np
import numpy.linalg as LA
import fasttext
import copy
import pickle5 as pickle
import os
import torch
'''
The data can be downloaded from https://github.com/t-davidson/hate-speech-and-offensive-language. put the labeled_data.csv in t... | 8,410 | 34.944444 | 195 | py |
craves.ai | craves.ai-master/train_arm.py | from __future__ import print_function, absolute_import
import sys
import os
import argparse
import time
import matplotlib.pyplot as plt
import scipy
import json
import numpy as np
import cv2
import torch
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torchvision.datasets as d... | 21,397 | 41.710579 | 206 | py |
craves.ai | craves.ai-master/data_generation/load_data_and_vis.py | import sys
sys.path.append('..')
from unreal.arm import get_joint_2d
import unreal.virtual_db as vdb
import numpy as np
import cv2
import torch
import time
import pose.datasets as datasets
from pose.utils.imutils import *
from pose.utils.transforms import multi_scale_merge
from pose.utils.evaluation import *
if __na... | 1,046 | 25.175 | 122 | py |
craves.ai | craves.ai-master/pose/models/preresnet.py | '''Pre-activated Resnet for cifar dataset.
Ported form https://github.com/facebook/fb.resnet.torch/blob/master/models/preresnet.lua
(c) YANG, Wei
'''
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
__all__ = ['PreResNet', 'preresnet20', 'preresnet32', 'preresnet44', 'preresnet56',
... | 5,650 | 29.38172 | 95 | py |
craves.ai | craves.ai-master/pose/models/hourglass.py | '''
Hourglass network inserted in the pre-activated Resnet
Use lr=0.01 for current version
(c) YANG, Wei
'''
import torch.nn as nn
import torch.nn.functional as F
# from .preresnet import BasicBlock, Bottleneck
__all__ = ['HourglassNet', 'hg', 'hg_feat']
class Bottleneck(nn.Module):
expansion = 2
def __i... | 9,704 | 33.172535 | 107 | py |
craves.ai | craves.ai-master/pose/datasets/concat.py | import numpy as np
import torch
import torch.utils.data as data
class Concat(data.Dataset):
def __init__(self, datasets, ratio = None):
self.datasets = datasets
self.ratio = ratio
if self.ratio is not None:
self.ratio = self.ratio / np.sum(self.ratio)
self.reset()
... | 3,030 | 34.244186 | 128 | py |
craves.ai | craves.ai-master/pose/datasets/arm_realtime_loader.py | import numpy as np
import torch
import scipy
from utils.transforms import crop, color_normalize
def to_torch(ndarray):
if type(ndarray).__module__ == 'numpy':
return torch.from_numpy(ndarray)
elif not torch.is_tensor(ndarray):
raise ValueError("Cannot convert {} to torch tensor"
... | 1,309 | 26.87234 | 126 | py |
craves.ai | craves.ai-master/pose/datasets/mpii.py | from __future__ import print_function, absolute_import
import os
import numpy as np
import json
import random
import math
import torch
import torch.utils.data as data
from pose.utils.osutils import *
from pose.utils.imutils import *
from pose.utils.transforms import *
class Mpii(data.Dataset):
def __init__(sel... | 4,650 | 34.234848 | 110 | py |
craves.ai | craves.ai-master/pose/datasets/arm.py | from __future__ import print_function, absolute_import
import os
import numpy as np
import json
import random
import math
import cv2
import torch
import torch.utils.data as data
import imageio as io
import json
from pose.utils.osutils import *
from pose.utils.imutils import *
from pose.utils.transforms import *
from... | 11,865 | 37.904918 | 157 | py |
craves.ai | craves.ai-master/pose/datasets/arm_resnet.py | from __future__ import print_function, absolute_import
import os
import numpy as np
import json
import random
import math
import cv2
import torch
import torch.utils.data as data
import imageio as io
import json
from pose.utils.osutils import *
from pose.utils.imutils import *
from pose.utils.transforms import *
imp... | 4,105 | 31.330709 | 126 | py |
craves.ai | craves.ai-master/pose/datasets/lsp.py | from __future__ import print_function, absolute_import
import os
import numpy as np
import json
import random
import math
import torch
import torch.utils.data as data
from pose.utils.osutils import *
from pose.utils.imutils import *
from pose.utils.transforms import *
class LSP(data.Dataset):
"""
LSP exten... | 4,969 | 34.755396 | 115 | py |
craves.ai | craves.ai-master/pose/datasets/mscoco.py | from __future__ import print_function, absolute_import
import os
import numpy as np
import json
import random
import math
import torch
import torch.utils.data as data
from pose.utils.osutils import *
from pose.utils.imutils import *
from pose.utils.transforms import *
class Mscoco(data.Dataset):
def __init__(s... | 4,816 | 34.947761 | 110 | py |
craves.ai | craves.ai-master/pose/utils/misc.py | from __future__ import absolute_import
import os
import shutil
import torch
import math
import numpy as np
import scipy.io
import matplotlib.pyplot as plt
def to_numpy(tensor):
if torch.is_tensor(tensor):
return tensor.cpu().numpy()
elif type(tensor).__module__ != 'numpy':
raise ValueError("C... | 1,899 | 28.6875 | 107 | py |
craves.ai | craves.ai-master/pose/utils/logger.py | # A simple torch style logger
# (C) Wei YANG 2017
from __future__ import absolute_import
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
__all__ = ['Logger', 'LoggerMonitor', 'savefig']
def savefig(fname, dpi=None):
dpi = 150 if dpi == None else dpi
plt.savefig(fname, dpi=dpi)
de... | 4,399 | 33.375 | 100 | py |
craves.ai | craves.ai-master/pose/utils/imutils.py | from __future__ import absolute_import
import torch
import torch.nn as nn
import numpy as np
import scipy.misc
import cv2
import os
import json
import time
from .misc import *
def im_to_numpy(img):
img = to_numpy(img)
img = np.transpose(img, (1, 2, 0)) # H*W*C
return img
def im_to_torch(img):
img =... | 8,327 | 31.15444 | 108 | py |
craves.ai | craves.ai-master/pose/utils/evaluation.py | from __future__ import absolute_import
import math
import numpy as np
import matplotlib.pyplot as plt
from random import randint
import torch
from .misc import *
from .transforms import transform, transform_preds
__all__ = ['accuracy', 'AverageMeter']
def get_preds(scores):
''' get predictions from score maps i... | 4,865 | 28.313253 | 114 | py |
craves.ai | craves.ai-master/pose/utils/transforms.py | from __future__ import absolute_import
import os
import numpy as np
import scipy.misc
import matplotlib.pyplot as plt
import torch
import cv2
import json
import time
from .misc import *
from .imutils import *
def color_normalize(x, mean, std):
if x.size(0) == 1:
x = x.repeat(3, 1, 1)
for t, m, s in... | 8,363 | 28.347368 | 145 | py |
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