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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Im2Hands | Im2Hands-main/im2mesh/common.py | # import multiprocessing
import torch
from im2mesh.utils.libkdtree import KDTree
import numpy as np
def compute_iou(occ1, occ2):
''' Computes the Intersection over Union (IoU) value for two sets of
occupancy values.
Args:
occ1 (tensor): first set of occupancy values
occ2 (tensor): second ... | 9,273 | 28.163522 | 78 | py |
Im2Hands | Im2Hands-main/im2mesh/preprocess.py | import torch
from im2mesh import config
from im2mesh.checkpoints import CheckpointIO
from im2mesh.utils.io import export_pointcloud
class PSGNPreprocessor:
''' Point Set Generation Networks (PSGN) preprocessor class.
Args:
cfg_path (str): path to config file
pointcloud_n (int): number of outp... | 1,773 | 31.254545 | 74 | py |
Im2Hands | Im2Hands-main/im2mesh/r2n2/training.py | import os
from tqdm import trange
import numpy as np
import torch
import torch.nn.functional as F
from im2mesh.training import BaseTrainer
from im2mesh.common import compute_iou
from im2mesh.utils import visualize as vis
from im2mesh.utils.voxels import VoxelGrid
class Trainer(BaseTrainer):
''' Trainer class for ... | 4,360 | 31.066176 | 76 | py |
Im2Hands | Im2Hands-main/im2mesh/r2n2/config.py | import os
from im2mesh.encoder import encoder_dict
from im2mesh.r2n2 import models, training, generation
from im2mesh import data
def get_model(cfg, device=None, **kwargs):
''' Return the model.
Args:
cfg (dict): loaded yaml config
device (device): pytorch device
'''
decoder = cfg['mo... | 2,505 | 24.571429 | 57 | py |
Im2Hands | Im2Hands-main/im2mesh/r2n2/generation.py | import torch
import numpy as np
from im2mesh.utils.voxels import VoxelGrid
class VoxelGenerator3D(object):
''' Generator class for R2N2 model.
The output of the model is transformed to a voxel grid and returned as a
mesh.
Args:
model (nn.Module): (trained) R2N2 model
threshold (float... | 1,414 | 24.267857 | 76 | py |
Im2Hands | Im2Hands-main/im2mesh/r2n2/models/decoder.py | import torch.nn as nn
import torch.nn.functional as F
class Decoder(nn.Module):
''' Decoder network class for the R2N2 model.
It consists of 4 transposed 3D-convolutional layers.
Args:
dim (int): input dimension
c_dim (int): dimension of latent conditioned code c
'''
def __init_... | 1,260 | 31.333333 | 72 | py |
Im2Hands | Im2Hands-main/im2mesh/r2n2/models/__init__.py | import torch.nn as nn
from im2mesh.r2n2.models.decoder import Decoder
# Decoder dictionary
decoder_dict = {
'simple': Decoder,
}
class R2N2(nn.Module):
''' The 3D Recurrent Reconstruction Neural Network (3D-R2N2) model.
For details regarding the model, please see
https://arxiv.org/abs/1604.00449
... | 757 | 21.294118 | 72 | py |
Im2Hands | Im2Hands-main/im2mesh/dmc/training.py | import os
from tqdm import trange
import torch
from im2mesh.common import chamfer_distance
from im2mesh.training import BaseTrainer
from im2mesh.utils import visualize as vis
import numpy as np
import torch.nn.functional as F
import scipy.ndimage
from im2mesh.dmc.utils.util import gaussian_kernel, offset_to_normal
fro... | 9,194 | 35.78 | 153 | py |
Im2Hands | Im2Hands-main/im2mesh/dmc/generation.py | import torch
import numpy as np
import trimesh
from im2mesh.dmc.utils.pred2mesh import pred_to_mesh_max
from im2mesh.dmc.ops.occupancy_to_topology import OccupancyToTopology
from im2mesh.dmc.ops.table import get_accept_topology
class Generator3D(object):
def __init__(self, model, device=None, num_voxels=32):
... | 1,182 | 29.333333 | 77 | py |
Im2Hands | Im2Hands-main/im2mesh/dmc/models/encoder.py | import torch.nn as nn
import torch
from im2mesh.dmc.ops.grid_pooling import GridPooling
class PointNetLocal(nn.Module):
''' Point Net Local Conditional Network from the Deep Marching Cubes paper.
It applies two fully connected layers to the input points (dim 3) in a
1D Convolutional Layer fashio... | 2,574 | 35.785714 | 83 | py |
Im2Hands | Im2Hands-main/im2mesh/dmc/models/decoder.py | import torch.nn as nn
import torch
from im2mesh.dmc.ops.occupancy_to_topology import OccupancyToTopology
class UNetDecoder(nn.Module):
def __init__(self, input_dim=16, T=256, W=32, H=32, D=32, skip_connection=True):
super().__init__()
self.skip_connection = skip_connection
self.decoder = S... | 6,730 | 37.028249 | 102 | py |
Im2Hands | Im2Hands-main/im2mesh/dmc/models/__init__.py | import torch.nn as nn
from im2mesh.dmc.models import encoder, decoder
decoder_dict = {
'unet': decoder.UNetDecoder
}
encoder_dict = {
'pointnet_local': encoder.PointNetLocal,
}
class DMC(nn.Module):
def __init__(self, decoder, encoder):
super().__init__()
self.decoder = decoder
s... | 495 | 19.666667 | 53 | py |
Im2Hands | Im2Hands-main/im2mesh/dmc/utils/pointTriangleDistance.py | #!/usr/bin/env python
#
# Tests distance between point and triangle in 3D. Aligns and uses 2D technique.
#
# Was originally some code on mathworks
#
# Implemented for pytorch Variable
# Adapted from https://gist.github.com/joshuashaffer/99d58e4ccbd37ca5d96e
import numpy as np
import torch
from torch.autograd import Va... | 11,593 | 32.031339 | 112 | py |
Im2Hands | Im2Hands-main/im2mesh/dmc/utils/util.py | import numpy as np
#import os
#import json
import torch
from torch.autograd import Variable
from im2mesh.dmc.utils.pointTriangleDistance import pointTriangleDistance, pointTriangleDistanceFast
from im2mesh.dmc.ops.table import get_triangle_table, get_unique_triangles, vertices_on_location
#from mpl_toolkits.mplot3d.art... | 8,274 | 36.274775 | 115 | py |
Im2Hands | Im2Hands-main/im2mesh/dmc/utils/pred2mesh.py | import torch
import numpy as np
from im2mesh.dmc.ops.cpp_modules import pred2mesh
def unique_rows(a):
""" Return the matrix with unique rows """
rowtype = np.dtype((np.void, a.dtype.itemsize * a.shape[1]))
b = np.ascontiguousarray(a).view(rowtype)
_, idx, inverse = np.unique(b, return_index=True, retu... | 2,159 | 29.857143 | 103 | py |
Im2Hands | Im2Hands-main/im2mesh/dmc/utils/visualize.py | import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import torch
import os
import numpy as np
from utils.util import write_to_off, unique_rows
from _ext import eval_util
def save_mesh_fig(pts_rnd_, offset, topology, x_grids, y_grids, z_grids, ind, args, phase):
""" save the estimated mesh with... | 4,919 | 33.893617 | 101 | py |
Im2Hands | Im2Hands-main/im2mesh/dmc/ops/setup.py | from setuptools import setup
import torch
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
setup(
name='_cuda_ext',
ext_modules=[
CUDAExtension('_cuda_ext', [
'src/extension.cpp',
'src/curvature_constraint_kernel.cu',
'src/grid_pooling_kernel.cu',
... | 553 | 26.7 | 67 | py |
Im2Hands | Im2Hands-main/im2mesh/dmc/ops/occupancy_connectivity.py | import torch
import math
from torch import nn
from torch.autograd import Function
from torch.autograd import Variable
from ._cuda_ext import occupancy_connectivity_forward, occupancy_connectivity_backward
class OccupancyConnectivityFunction(Function):
@staticmethod
def forward(ctx, occ):
loss = occup... | 1,268 | 23.882353 | 86 | py |
Im2Hands | Im2Hands-main/im2mesh/dmc/ops/occupancy_to_topology.py | import math
from torch import nn
from torch.autograd import Function
import torch
from ._cuda_ext import occupancy_to_topology_forward, occupancy_to_topology_backward
class OccupancyToTopologyFunction(Function):
@staticmethod
def forward(ctx, occupancy):
W = occupancy.size()[0] - 1
H = occupa... | 1,505 | 26.381818 | 97 | py |
Im2Hands | Im2Hands-main/im2mesh/dmc/ops/grid_pooling.py | import torch
import math
from torch import nn
from torch.autograd import Function
from torch.autograd import Variable
from ._cuda_ext import grid_pooling_forward, grid_pooling_backward
class GridPoolingFunction(Function):
""" Perform max-pooling in every cell over the point features
see ../src/extension.c... | 2,244 | 27.782051 | 82 | py |
Im2Hands | Im2Hands-main/im2mesh/dmc/ops/point_triangle_distance.py | import torch
import math
from torch import nn
from torch.autograd import Function
from torch.autograd import Variable
from im2mesh.dmc.ops.table import get_connected_pairs
from ._cuda_ext import point_topology_distance_forward, point_topology_distance_backward
class PointTriangleDistanceFunction(Function):
@stati... | 1,940 | 28.861538 | 88 | py |
Im2Hands | Im2Hands-main/im2mesh/dmc/ops/curvature_constraint.py | import torch
import math
from torch import nn
from torch.autograd import Function
from torch.autograd import Variable
from im2mesh.dmc.ops.table import get_connected_pairs
from ._cuda_ext import curvature_constraint_forward, curvature_constraint_backward
######### TEST FAILS #########
# return connected pairs in x,... | 2,271 | 29.702703 | 104 | py |
Im2Hands | Im2Hands-main/im2mesh/dmc/ops/tests/test_distance.py |
import sys
sys.path.append('../../../..')
import torch
import torch.nn as nn
from torch.autograd import Variable
import time
import numpy as np
import resource
from im2mesh.dmc.ops.tests.loss_autograd import LossAutoGrad
from im2mesh.dmc.ops.point_triangle_distance import PointTriangleDistance
print("Testing CUDA ... | 2,446 | 30.371795 | 119 | py |
Im2Hands | Im2Hands-main/im2mesh/dmc/ops/tests/loss_autograd.py | import torch
from torch.autograd import Variable
import torch.nn.functional as F
import numpy as np
#import settings
from im2mesh.dmc.utils.util import (
offset_to_normal, offset_to_vertices, pts_in_cell, dis_to_meshs)
from im2mesh.dmc.ops.table import (
get_connected_pairs, get_accept_topology)
import scipy.nd... | 11,473 | 47.210084 | 155 | py |
Im2Hands | Im2Hands-main/im2mesh/dmc/ops/tests/test_gridpooling.py | import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.autograd import Function
import time
import numpy as np
import sys
sys.path.append('../../../..')
from im2mesh.dmc.utils.util import pts_in_cell
from torch.autograd import gradcheck
from im2mesh.dmc.ops.grid_pooling import GridPoolingF... | 4,507 | 32.894737 | 138 | py |
Im2Hands | Im2Hands-main/im2mesh/dmc/ops/tests/test_curvature.py | import torch
import torch.nn as nn
from torch.autograd import Variable
import sys
sys.path.append('../../../..')
from im2mesh.dmc.ops.tests.loss_autograd import LossAutoGrad
from im2mesh.dmc.ops.curvature_constraint import CurvatureConstraint
import torch.nn.functional as F
import numpy as np
import time
# check the... | 2,983 | 30.083333 | 100 | py |
Im2Hands | Im2Hands-main/im2mesh/dmc/ops/tests/test_occupancy_connectivity_yiyi.py | import torch
import torch.nn as nn
from torch.autograd import Variable
import sys
sys.path.append('../../../..')
import time
import numpy as np
from .loss import Loss
from .loss_autograd import LossAutoGrad
# check the cuda extension or c extension
print "Testing CUDA extension..."
dtype = torch.cuda.FloatTensor
#... | 2,586 | 29.797619 | 124 | py |
Im2Hands | Im2Hands-main/im2mesh/dmc/ops/tests/test_occupancy_connectivity.py |
import sys
sys.path.append('../../../..')
import torch
import torch.nn as nn
from torch.autograd import Variable
import time
import numpy as np
from im2mesh.dmc.ops.occupancy_connectivity import OccupancyConnectivity
#from loss import Loss
#from loss_autograd import LossAutoGrad
#from parse_args import parse_args
... | 3,106 | 27.245455 | 136 | py |
Im2Hands | Im2Hands-main/im2mesh/dmc/ops/tests/test_occupancy_to_topology.py | import sys
sys.path.append('../../../..')
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import time
import torch.nn.functional as F
from im2mesh.dmc.ops.occupancy_to_topology import OccupancyToTopology
def get_occupancy_table():
"""Return binary occupancy status of 8 ve... | 4,022 | 32.247934 | 127 | py |
Im2Hands | Im2Hands-main/im2mesh/dmc/ops/cpp_modules/setup.py | from setuptools import setup
import torch
from torch.utils.cpp_extension import BuildExtension, CppExtension
setup(
name='pred2mesh',
ext_modules=[
CppExtension('pred2mesh', [
'pred_to_mesh_.cpp',
# 'commons.cpp'
]),
],
cmdclass={
'build_ext': BuildExtensi... | 329 | 21 | 66 | py |
Im2Hands | Im2Hands-main/im2mesh/encoder/r2n2.py | import torch.nn as nn
# import torch.nn.functional as F
from im2mesh.common import normalize_imagenet
class SimpleConv(nn.Module):
''' 3D Recurrent Reconstruction Neural Network (3D-R2-N2) encoder network.
Args:
c_dim: output dimension
'''
def __init__(self, c_dim=1024):
super().__i... | 2,857 | 25.220183 | 79 | py |
Im2Hands | Im2Hands-main/im2mesh/encoder/pointnet.py | import torch
import torch.nn as nn
from im2mesh.layers import ResnetBlockFC
def maxpool(x, dim=-1, keepdim=False):
out, _ = x.max(dim=dim, keepdim=keepdim)
return out
class SimplePointnet(nn.Module):
''' PointNet-based encoder network.
Args:
c_dim (int): dimension of latent code c
d... | 3,420 | 29.008772 | 71 | py |
Im2Hands | Im2Hands-main/im2mesh/encoder/conv.py | import torch.nn as nn
# import torch.nn.functional as F
from torchvision import models
from im2mesh.common import normalize_imagenet
class ConvEncoder(nn.Module):
r''' Simple convolutional encoder network.
It consists of 5 convolutional layers, each downsampling the input by a
factor of 2, and a final fu... | 5,021 | 30.78481 | 75 | py |
Im2Hands | Im2Hands-main/im2mesh/encoder/voxels.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class VoxelEncoder(nn.Module):
''' 3D-convolutional encoder network for voxel input.
Args:
dim (int): input dimension
c_dim (int): output dimension
'''
def __init__(self, dim=3, c_dim=128):
super().__init__()
... | 2,726 | 29.3 | 65 | py |
Im2Hands | Im2Hands-main/im2mesh/encoder/psgn_cond.py | import torch.nn as nn
class PCGN_Cond(nn.Module):
r''' Point Set Generation Network encoding network.
The PSGN conditioning network from the original publication consists of
several 2D convolution layers. The intermediate outputs from some layers
are used as additional input to the encoder network, s... | 2,930 | 36.576923 | 76 | py |
Im2Hands | Im2Hands-main/im2mesh/encoder/pix2mesh_cond.py | import torch.nn as nn
class Pix2mesh_Cond(nn.Module):
r''' Conditioning Network proposed in the authors' Pixel2Mesh implementation.
The network consists of several 2D convolution layers, and several of the
intermediate feature maps are returned to features for the image
projection layer of the encode... | 2,701 | 41.888889 | 81 | py |
Im2Hands | Im2Hands-main/im2mesh/onet/training.py | import os
from tqdm import trange
import torch
from torch.nn import functional as F
from torch import distributions as dist
from im2mesh.common import (
compute_iou, make_3d_grid
)
from im2mesh.utils import visualize as vis
from im2mesh.training import BaseTrainer
class Trainer(BaseTrainer):
''' Trainer objec... | 5,479 | 30.494253 | 78 | py |
Im2Hands | Im2Hands-main/im2mesh/onet/config.py | import torch
import torch.distributions as dist
from torch import nn
import os
from im2mesh.encoder import encoder_dict
from im2mesh.onet import models, training, generation
from im2mesh import data
from im2mesh import config
def get_model(cfg, device=None, dataset=None, **kwargs):
''' Return the Occupancy Networ... | 4,466 | 28.006494 | 76 | py |
Im2Hands | Im2Hands-main/im2mesh/onet/generation.py | import torch
import torch.optim as optim
from torch import autograd
import numpy as np
from tqdm import trange
import trimesh
from im2mesh.utils import libmcubes
from im2mesh.common import make_3d_grid
from im2mesh.utils.libsimplify import simplify_mesh
from im2mesh.utils.libmise import MISE
import time
class Generat... | 10,942 | 33.850318 | 79 | py |
Im2Hands | Im2Hands-main/im2mesh/onet/models/legacy.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from im2mesh.layers import ResnetBlockFC, AffineLayer
class VoxelDecoder(nn.Module):
def __init__(self, dim=3, z_dim=128, c_dim=128, hidden_size=128):
super().__init__()
self.c_dim = c_dim
self.z_dim = z_dim
# Submo... | 4,001 | 31.016 | 72 | py |
Im2Hands | Im2Hands-main/im2mesh/onet/models/encoder_latent.py | import torch
import torch.nn as nn
import torch.nn.functional as F
# Max Pooling operation
def maxpool(x, dim=-1, keepdim=False):
out, _ = x.max(dim=dim, keepdim=keepdim)
return out
class Encoder(nn.Module):
''' Latent encoder class.
It encodes the input points and returns mean and standard deviati... | 2,112 | 26.802632 | 79 | py |
Im2Hands | Im2Hands-main/im2mesh/onet/models/decoder.py |
import torch.nn as nn
import torch.nn.functional as F
from im2mesh.layers import (
ResnetBlockFC, CResnetBlockConv1d,
CBatchNorm1d, CBatchNorm1d_legacy,
ResnetBlockConv1d
)
class Decoder(nn.Module):
''' Decoder class.
It does not perform any form of normalization.
Args:
dim (int): i... | 9,357 | 29.090032 | 75 | py |
Im2Hands | Im2Hands-main/im2mesh/onet/models/__init__.py | import torch
import torch.nn as nn
from torch import distributions as dist
from im2mesh.onet.models import encoder_latent, decoder
# Encoder latent dictionary
encoder_latent_dict = {
'simple': encoder_latent.Encoder,
}
# Decoder dictionary
decoder_dict = {
'simple': decoder.Decoder,
'cbatchnorm': decoder.... | 4,453 | 27.551282 | 77 | py |
Im2Hands | Im2Hands-main/im2mesh/psgn/training.py | import os
from tqdm import trange
import torch
from im2mesh.common import chamfer_distance
from im2mesh.training import BaseTrainer
from im2mesh.utils import visualize as vis
class Trainer(BaseTrainer):
r''' Trainer object for the Point Set Generation Network.
The PSGN network is trained on Chamfer distance.... | 3,775 | 30.466667 | 76 | py |
Im2Hands | Im2Hands-main/im2mesh/psgn/generation.py | import torch
from im2mesh.utils.io import export_pointcloud
import tempfile
import subprocess
import os
import trimesh
class Generator3D(object):
r''' Generator Class for Point Set Generation Network.
While for point cloud generation the output of the network if used, for
mesh generation, we perform surf... | 4,097 | 37.660377 | 348 | py |
Im2Hands | Im2Hands-main/im2mesh/psgn/models/psgn_2branch.py | import torch.nn as nn
import torch
class PCGN_2Branch(nn.Module):
r''' The 2-Branch decoder of the Point Set Generation Network.
The latent embedding of the image is passed through a fully-connected
branch as well as a convolution-based branch which receives additional
input from the conditioning net... | 2,700 | 37.042254 | 79 | py |
Im2Hands | Im2Hands-main/im2mesh/psgn/models/decoder.py | import torch.nn as nn
import torch.nn.functional as F
class Decoder(nn.Module):
r''' Simple decoder for the Point Set Generation Network.
The simple decoder consists of 4 fully-connected layers, resulting in an
output of 3D coordinates for a fixed number of points.
Args:
dim (int): The outpu... | 1,176 | 28.425 | 76 | py |
Im2Hands | Im2Hands-main/im2mesh/psgn/models/__init__.py | import torch.nn as nn
from im2mesh.psgn.models.decoder import Decoder
from im2mesh.psgn.models.psgn_2branch import PCGN_2Branch
decoder_dict = {
'simple': Decoder,
'psgn_2branch': PCGN_2Branch
}
class PCGN(nn.Module):
r''' The Point Set Generation Network.
For the PSGN, the input image is first pass... | 900 | 26.30303 | 76 | py |
Im2Hands | Im2Hands-main/im2mesh/pix2mesh/training.py | import torch.nn.functional as F
import torch
from im2mesh.common import chamfer_distance
import os
from torchvision.utils import save_image
from im2mesh.training import BaseTrainer
from im2mesh.utils import visualize as vis
import im2mesh.common as common
class Trainer(BaseTrainer):
r''' Trainer object for the pi... | 14,112 | 40.026163 | 85 | py |
Im2Hands | Im2Hands-main/im2mesh/pix2mesh/layers.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import im2mesh.common as common
from matplotlib import pyplot as plt
class GraphUnpooling(nn.Module):
''' Graph Unpooling Layer.
Unpools additional vertices following the helper file and uses the
average feature vector from the tw... | 5,588 | 35.529412 | 79 | py |
Im2Hands | Im2Hands-main/im2mesh/pix2mesh/generation.py | import torch
import trimesh
import im2mesh.common as common
class Generator3D(object):
''' Mesh Generator Class for the Pixel2Mesh model.
A forward pass is made with the image and camera matrices to obtain the
predicted vertex locations for the mesh. Subsequently, the faces of the
base mesh of an ell... | 2,604 | 35.180556 | 78 | py |
Im2Hands | Im2Hands-main/im2mesh/pix2mesh/models/decoder.py | import torch
import torch.nn as nn
from im2mesh.pix2mesh.layers import (
GraphConvolution, GraphProjection, GraphUnpooling)
class Decoder(nn.Module):
r""" Decoder class for Pixel2Mesh Model.
Args:
ellipsoid (list): list of helper matrices for the graph convolution
and pooling laye... | 7,640 | 43.684211 | 79 | py |
Im2Hands | Im2Hands-main/im2mesh/pix2mesh/models/__init__.py | import torch.nn as nn
from im2mesh.pix2mesh.models.decoder import Decoder
decoder_dict = {
'simple': Decoder,
}
class Pix2Mesh(nn.Module):
''' Pixel2Mesh model.
First, the input image is passed through a CNN to extract several feature
maps. These feature maps as well as camera matrices are passed t... | 904 | 25.617647 | 77 | py |
Im2Hands | Im2Hands-main/im2mesh/utils/visualize.py | import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from torchvision.utils import save_image
import im2mesh.common as common
def visualize_data(data, data_type, out_file):
r''' Visualizes the data with regard to its type.
Args:
data (tensor): batch of data
... | 3,747 | 30.762712 | 76 | py |
Im2Hands | Im2Hands-main/im2mesh/data/core.py | import os
import logging
from torch.utils import data
import numpy as np
import yaml
logger = logging.getLogger(__name__)
# Fields
class Field(object):
''' Data fields class.
'''
def load(self, data_path, idx, category):
''' Loads a data point.
Args:
data_path (str): path t... | 5,182 | 28.282486 | 77 | py |
Im2Hands | Im2Hands-main/im2mesh/data/real.py | import os
from PIL import Image
import numpy as np
import torch
from torch.utils import data
from torchvision import transforms
class KittiDataset(data.Dataset):
r""" Kitti Instance dataset.
Args:
dataset_folder (str): path to the KITTI dataset
img_size (int): size of the cropped images
... | 7,357 | 27.51938 | 87 | py |
Im2Hands | Im2Hands-main/manopth/tensutils.py | import torch
from manopth import rodrigues_layer
def th_posemap_axisang(pose_vectors):
rot_nb = int(pose_vectors.shape[1] / 3)
pose_vec_reshaped = pose_vectors.contiguous().view(-1, 3)
rot_mats = rodrigues_layer.batch_rodrigues(pose_vec_reshaped)
rot_mats = rot_mats.view(pose_vectors.shape[0], rot_nb... | 1,341 | 26.958333 | 75 | py |
Im2Hands | Im2Hands-main/manopth/manolayer_backup.py | '''
This manolayer.py is a modified version of Yana's Hasson pytorch implementation
of the MANO model (https://github.com/hassony2/manopth).
We made the following changes to the original file to get the transformation matrices
necessary for training the HALO model.
- No logical change has been made to the way the t... | 14,017 | 44.219355 | 116 | py |
Im2Hands | Im2Hands-main/manopth/rot6d.py | import torch
def compute_rotation_matrix_from_ortho6d(poses):
"""
Code from
https://github.com/papagina/RotationContinuity
On the Continuity of Rotation Representations in Neural Networks
Zhou et al. CVPR19
https://zhouyisjtu.github.io/project_rotation/rotation.html
"""
x_raw = poses[:... | 2,212 | 29.736111 | 78 | py |
Im2Hands | Im2Hands-main/manopth/demo.py | from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
import numpy as np
import torch
from manopth.manolayer import ManoLayer
def generate_random_hand(batch_size=1, ncomps=6, mano_root='mano/models'):
nfull_comps = ncomps + 3 # Add g... | 2,832 | 35.320513 | 105 | py |
Im2Hands | Im2Hands-main/manopth/rodrigues_layer.py | """
This part reuses code from https://github.com/MandyMo/pytorch_HMR/blob/master/src/util.py
which is part of a PyTorch port of SMPL.
Thanks to Zhang Xiong (MandyMo) for making this great code available on github !
"""
import argparse
from torch.autograd import gradcheck
import torch
from torch.autograd import Variab... | 2,920 | 31.455556 | 89 | py |
Im2Hands | Im2Hands-main/manopth/manolayer.py | '''
This manolayer.py is a modified version of Yana's Hasson pytorch implementation
of the MANO model (https://github.com/hassony2/manopth).
We made the following changes to the original file to get the transformation matrices
necessary for training the HALO model.
- No logical change has been made to the way the t... | 13,870 | 44.18241 | 116 | py |
Im2Hands | Im2Hands-main/manopth/rotproj.py | import torch
def batch_rotprojs(batches_rotmats):
proj_rotmats = []
for batch_idx, batch_rotmats in enumerate(batches_rotmats):
proj_batch_rotmats = []
for rot_idx, rotmat in enumerate(batch_rotmats):
# GPU implementation of svd is VERY slow
# ~ 2 10^-3 per hit vs 5 10^... | 753 | 33.272727 | 63 | py |
GANterfactual | GANterfactual-main/GANterfactual/discriminator.py | from keras.layers import Input
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import Conv2D
from keras.models import Model
from keras_contrib.layers.normalization.instancenormalization import InstanceNormalization
def build_discriminator(img_shape, df):
def d_layer(layer_i... | 883 | 33 | 90 | py |
GANterfactual | GANterfactual-main/GANterfactual/cyclegan.py | from __future__ import print_function, division
import datetime
import os
import keras
import matplotlib.pyplot as plt
import numpy as np
from skimage.transform import resize
from keras.layers import Input, Dropout, Concatenate
from keras.models import Model
from keras.optimizers import Adam
from keras_contrib.laye... | 14,019 | 42.949843 | 166 | py |
GANterfactual | GANterfactual-main/GANterfactual/dataloader.py | from __future__ import print_function, division
import os
import keras
import numpy as np
class DataLoader():
def __init__(self, dataset_name=None, img_res=(128, 128)):
self.dataset_name = dataset_name
self.img_res = img_res
self.image_gen_config = {
"horizontal_flip": False... | 1,872 | 31.859649 | 104 | py |
GANterfactual | GANterfactual-main/GANterfactual/classifier.py | from __future__ import print_function, division
from keras.layers import Input, Dense, Reshape, Flatten, Dropout, Concatenate
from keras.layers import BatchNormalization, Activation, ZeroPadding2D, Lambda
from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D
from keras.layers.convolutional ... | 3,472 | 30.862385 | 96 | py |
GANterfactual | GANterfactual-main/GANterfactual/train_alexNet.py | import keras
from keras import Input, Model
from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
from keras.layers.normalization import BatchNormalization
import numpy as np
from keras.regularizers import l2
import os
tensorboard_cal... | 4,910 | 30.88961 | 119 | py |
GANterfactual | GANterfactual-main/GANterfactual/generator.py | from keras.layers import Dropout
from keras.layers import Input, Concatenate
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Model
from keras_contrib.layers.normalization.instancenormalization import InstanceNormalization
def... | 1,557 | 32.869565 | 98 | py |
compsensing_dip | compsensing_dip-master/cs_dip.py | import numpy as np
import parser
import torch
from torch.autograd import Variable
import baselines
import utils
import time
args = parser.parse_args('configs.json')
CUDA = torch.cuda.is_available()
dtype = utils.set_dtype(CUDA)
se = torch.nn.MSELoss(reduction='none').type(dtype)
BATCH_SIZE = 1
EXIT_WINDOW = 51
los... | 2,757 | 33.475 | 96 | py |
compsensing_dip | compsensing_dip-master/utils.py | import numpy as np
import os
import errno
import parser
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets,transforms
BATCH_SIZE = 1
class DCGAN_XRAY(nn.Module):
def __init__(self, nz, ngf=64, output_size=256, nc=3, num_measurements=1000):
super(DCGAN_XRA... | 11,104 | 36.265101 | 113 | py |
compsensing_dip | compsensing_dip-master/comp_sensing.py | import numpy as np
import pickle as pkl
import os
import parser
import numpy as np
import torch
from torchvision import datasets
import utils
import cs_dip
import baselines as baselines
import time
NEW_RECONS = False
args = parser.parse_args('configs.json')
print(args)
NUM_MEASUREMENTS_LIST, ALG_LIST = utils.conv... | 1,944 | 28.029851 | 97 | py |
PrivateCovariance | PrivateCovariance-main/functions.py | import torch
import os
import gzip
import argparse
import numpy as np
from exponential.algos import EMCov
from adaptive.algos import GaussCov, LapCov, SeparateCov, SeparateLapCov, AdaptiveCov, AdaptiveLapCov
from coinpress.algos import cov_est
from urllib.request import urlretrieve
from sklearn.feature_extraction.text ... | 39,810 | 39.873717 | 163 | py |
PrivateCovariance | PrivateCovariance-main/coinpress/utils.py | # coding: utf-8
'''
Utilities functions
'''
import torch
import argparse
import os.path as osp
import numpy as np
import math
import pdb
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--total_budget', default=.5, type=float, help='Total privacy budget')
parser.add_argument('--d'... | 4,308 | 29.34507 | 115 | py |
PrivateCovariance | PrivateCovariance-main/coinpress/algos.py |
'''
Privately estimating covariance.
'''
import torch
import coinpress.utils as utils
import numpy as np
import math
def cov_est_step(X, A, rho, cur_iter, args):
"""
One step of multivariate covariance estimation, scale cov a.
"""
W = torch.mm(X, A)
n = args.n
d = args.d
#Hyperparameters... | 3,435 | 28.118644 | 96 | py |
PrivateCovariance | PrivateCovariance-main/adaptive/utils.py | import torch
import numpy as np
from scipy.optimize import root_scalar
def SVT(T,eps,D,func,args):
T_tilde = T + np.random.laplace(scale=2.0/eps)
i = 0
m = len(args)
while i < m:
Qi = func(D,args[i]) + np.random.laplace(scale=4.0/eps)
if Qi >= T_tilde:
break
i = i +... | 3,410 | 26.288 | 87 | py |
PrivateCovariance | PrivateCovariance-main/adaptive/algos.py | import numpy as np
import torch
from adaptive.utils import get_gauss_wigner_matrix, get_lap_wigner_matrix, get_gauss_noise_vector, get_lap_noise_vector, SVT, get_bincounts, gaussian_tailbound, laplace_tailbound, wigner_gauss_fnormbound, wigner_lap_fnormbound, wigner_gauss_tailbound, wigner_lap_tailbound, clip
def Gau... | 8,261 | 34.612069 | 278 | py |
PrivateCovariance | PrivateCovariance-main/exponential/utils.py | import torch
import numpy as np
from scipy.optimize import root_scalar
import time
def root_bisect_dec(x0,x1,func,args,T=10,thres=1e-8):
left = x0
right = x1
mid = 0.5*(left+right)
mid_val = func(mid, args)
t = 0
err = abs(mid_val)
while t<T and err > thres:
if mid_val > 0:
... | 1,821 | 26.19403 | 86 | py |
PrivateCovariance | PrivateCovariance-main/exponential/algos.py | import torch
import numpy as np
from exponential.utils import find_bingham, convert_eps
from scipy.linalg import null_space
def EMCov(X, args, b_budget=False, b_fleig=True):
rho = args.total_budget
delta = args.delta
n = args.n
d = args.d
cov = torch.mm(X.t(),X)
if not(delta > 0.0):
eps... | 1,802 | 27.619048 | 69 | py |
rsp | rsp-master/src/python/rsp/step_05_experiment_run/experiment_run.py | """This library contains all utility functions to help you run your
experiments.
Methods
-------
run_experiment:
Run a single experiment with a specific solver and ExperimentParameters
run_experiment_agenda:
Run a number of experiments defined in the ExperimentAgenda
run_specific_experiments_from_research_agend... | 58,195 | 54.372027 | 160 | py |
rsp | rsp-master/src/python/rsp/step_05_experiment_run/scopers/scoper_online_random.py | import pprint
from typing import Dict
import numpy as np
from flatland.envs.rail_trainrun_data_structures import TrainrunDict
from rsp.scheduling.propagate import verify_consistency_of_route_dag_constraints_for_agent
from rsp.scheduling.scheduling_problem import RouteDAGConstraints
from rsp.scheduling.scheduling_prob... | 4,898 | 42.353982 | 145 | py |
rsp | rsp-master/src/python/rsp/step_05_experiment_run/scopers/scoper_offline_fully_restricted.py | import pprint
from typing import Dict
import numpy as np
from flatland.envs.rail_trainrun_data_structures import TrainrunDict
from rsp.scheduling.propagate import verify_consistency_of_route_dag_constraints_for_agent
from rsp.scheduling.scheduling_problem import RouteDAGConstraints
from rsp.scheduling.scheduling_prob... | 4,060 | 44.122222 | 141 | py |
rsp | rsp-master/src/python/rsp/step_05_experiment_run/scopers/scoper_offline_delta_weak.py | import pprint
from typing import Dict
import numpy as np
from flatland.envs.rail_trainrun_data_structures import TrainrunDict
from rsp.scheduling.propagate import verify_consistency_of_route_dag_constraints_for_agent
from rsp.scheduling.propagate import verify_trainrun_satisfies_route_dag_constraints
from rsp.schedul... | 4,868 | 43.263636 | 149 | py |
rsp | rsp-master/src/python/rsp/step_05_experiment_run/scopers/scoper_agent_wise.py | from enum import Enum
import networkx as nx
from flatland.envs.rail_trainrun_data_structures import Trainrun
from rsp.scheduling.scheduling_problem import ScheduleProblemDescription
from rsp.step_05_experiment_run.experiment_malfunction import ExperimentMalfunction
from rsp.step_05_experiment_run.scopers.scoper_onlin... | 2,810 | 43.619048 | 145 | py |
rsp | rsp-master/src/python/rsp/step_05_experiment_run/scopers/scoper_offline_delta.py | import logging
import pprint
from typing import Dict
import networkx as nx
import numpy as np
from flatland.envs.rail_trainrun_data_structures import Trainrun
from flatland.envs.rail_trainrun_data_structures import TrainrunDict
from rsp.scheduling.propagate import _get_delayed_trainrun_waypoint_after_malfunction
from... | 8,348 | 42.036082 | 141 | py |
rsp | rsp-master/src/python/rsp/step_05_experiment_run/scopers/scoper_online_transmission_chains.py | import pprint
from typing import Dict
from typing import Set
from typing import Tuple
import numpy as np
from flatland.envs.rail_trainrun_data_structures import TrainrunDict
from rsp.resource_occupation.resource_occupation import extract_resource_occupations
from rsp.scheduling.propagate import verify_consistency_of_... | 5,579 | 45.115702 | 132 | py |
rsp | rsp-master/src/python/rsp/step_05_experiment_run/scopers/scoper_online_route_restricted.py | import pprint
from typing import Dict
import numpy as np
from flatland.envs.rail_trainrun_data_structures import TrainrunDict
from rsp.scheduling.propagate import verify_consistency_of_route_dag_constraints_for_agent
from rsp.scheduling.scheduling_problem import RouteDAGConstraintsDict
from rsp.scheduling.scheduling_... | 4,118 | 41.463918 | 119 | py |
Alpha-IoU | Alpha-IoU-main/test.py | import argparse, csv
import json
import os
from pathlib import Path
from threading import Thread
import numpy as np
import torch
import yaml
from tqdm import tqdm
from models.experimental import attempt_load
from utils.datasets import create_dataloader
from utils.general import coco80_to_coco91_class, check_dataset, ... | 17,952 | 47.131367 | 122 | py |
Alpha-IoU | Alpha-IoU-main/detect.py | import argparse
import time
from pathlib import Path
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, check_requirements, check_imshow, non_max... | 8,218 | 45.698864 | 119 | py |
Alpha-IoU | Alpha-IoU-main/hubconf.py | """File for accessing YOLOv5 via PyTorch Hub https://pytorch.org/hub/
Usage:
import torch
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True, channels=3, classes=80)
"""
from pathlib import Path
import torch
from models.yolo import Model
from utils.general import set_logging
from utils.... | 5,274 | 34.884354 | 114 | py |
Alpha-IoU | Alpha-IoU-main/train.py | import argparse
import logging
import math
import os
import random
import time
from copy import deepcopy
from pathlib import Path
from threading import Thread
import numpy as np
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.optim.lr_sche... | 32,757 | 51.4128 | 151 | py |
Alpha-IoU | Alpha-IoU-main/models/yolo.py | import argparse
import logging
import sys
from copy import deepcopy
sys.path.append('./') # to run '$ python *.py' files in subdirectories
logger = logging.getLogger(__name__)
from models.common import *
from models.experimental import *
from utils.autoanchor import check_anchor_order
from utils.general import make_... | 12,060 | 42.699275 | 119 | py |
Alpha-IoU | Alpha-IoU-main/models/export.py | """Exports a YOLOv5 *.pt model to ONNX and TorchScript formats
Usage:
$ export PYTHONPATH="$PWD" && python models/export.py --weights ./weights/yolov5s.pt --img 640 --batch 1
"""
import argparse
import sys
import time
sys.path.append('./') # to run '$ python *.py' files in subdirectories
import torch
import to... | 4,422 | 41.12381 | 117 | py |
Alpha-IoU | Alpha-IoU-main/models/common.py | # This file contains modules common to various models
import math
from pathlib import Path
import numpy as np
import requests
import torch
import torch.nn as nn
from PIL import Image
from utils.datasets import letterbox
from utils.general import non_max_suppression, make_divisible, scale_coords, xyxy2xywh
from utils... | 12,997 | 41.064725 | 120 | py |
Alpha-IoU | Alpha-IoU-main/models/experimental.py | # This file contains experimental modules
import numpy as np
import torch
import torch.nn as nn
from models.common import Conv, DWConv
from utils.google_utils import attempt_download
class CrossConv(nn.Module):
# Cross Convolution Downsample
def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
... | 5,146 | 37.125926 | 114 | py |
Alpha-IoU | Alpha-IoU-main/utils/loss.py | # Loss functions
import torch
import torch.nn as nn
from utils.general import bbox_iou, bbox_alpha_iou
from utils.torch_utils import is_parallel
def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
# return positive, negative label smoothing BCE targets
return ... | 15,771 | 44.191977 | 120 | py |
Alpha-IoU | Alpha-IoU-main/utils/autoanchor.py | # Auto-anchor utils
import numpy as np
import torch
import yaml
from scipy.cluster.vq import kmeans
from tqdm import tqdm
from utils.general import colorstr
def check_anchor_order(m):
# Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
a = m.anchor_grid.prod(-1).... | 13,869 | 45.233333 | 120 | py |
Alpha-IoU | Alpha-IoU-main/utils/plots.py | # Plotting utils
import glob
import math
import os
import random
from copy import copy
from pathlib import Path
import cv2
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import torch
import yaml
from PIL import Image, ImageDraw, ImageFont
from scipy.sign... | 18,126 | 41.254079 | 120 | py |
Alpha-IoU | Alpha-IoU-main/utils/datasets.py | # Dataset utils and dataloaders
import glob
import logging
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from threading import Thread
import cv2
import numpy as np
import torch
import torch.nn.functional ... | 45,239 | 41.201493 | 120 | py |
Alpha-IoU | Alpha-IoU-main/utils/torch_utils.py | # PyTorch utils
import logging
import math
import os
import subprocess
import time
from contextlib import contextmanager
from copy import deepcopy
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torchvision
try:
import thop ... | 11,956 | 39.532203 | 120 | py |
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