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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CEMNet | CEMNet-main/cems/base_cem.py | import torch, cemnet_lib
from utils.euler2mat import euler2mat_torch
from utils.commons import stack_transforms_seq
from utils.transform_pc import transform_pc_torch
from utils.batch_icp import batch_icp
class BaseCEM(torch.nn.Module):
def __init__(self, opts):
super(BaseCEM, self).__init__()
self.... | 6,114 | 53.598214 | 171 | py |
CEMNet | CEMNet-main/cemnet_lib/setup.py | #python3 setup.py install
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
libname = "cemnet_lib"
setup(
name='cemnet_lib',
ext_modules=[
CUDAExtension("cemnet_lib_cuda", [
"src/cemnet_lib_api.cpp",
"src/ops/ops_cuda.cpp",
... | 523 | 28.111111 | 105 | py |
CEMNet | CEMNet-main/cemnet_lib/functions/distances.py | import torch, cemnet_lib_cuda
from torch.autograd import Function
# closest point
class ClosestPoint(Function):
@staticmethod
def forward(ctx, srcs, tgt):
"""
input: srcs: (B, 3, N), tgt: (3, N)
return: closest_points: (B, 3, N)
"""
closest_points = torch.zeros_like(srcs... | 2,417 | 28.487805 | 90 | py |
CEMNet | CEMNet-main/batch_svd/setup.py | from setuptools import setup, find_packages
from torch.utils.cpp_extension import CUDAExtension, BuildExtension
import os
import glob
libname = "torch_batch_svd"
ext_src = glob.glob(os.path.join(libname, 'csrc/*.cpp'))
print(ext_src)
setup(name=libname,
packages=find_packages(exclude=('tests', 'build', 'csrc', ... | 809 | 34.217391 | 104 | py |
CEMNet | CEMNet-main/batch_svd/tests/tests.py | import torch
from torch import testing
from torch_batch_svd import svd
def test_float():
torch.manual_seed(0)
a = torch.randn(1000000, 9, 3).cuda()
b = a.clone()
a.requires_grad = True
b.requires_grad = True
U, S, V = svd(a)
loss = U.sum() + S.sum() + V.sum()
loss.backward()
u, ... | 2,017 | 28.246377 | 103 | py |
CEMNet | CEMNet-main/batch_svd/torch_batch_svd/batch_svd.py | import torch, torch_batch_svd_cuda
# from . import _c
class BatchSVDFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, input: torch.Tensor, some=True, compute_uv=True, out=None):
"""
This function returns `(U, S, V)`
which is the singular value decomposition
... | 2,393 | 31.351351 | 90 | py |
CEMNet | CEMNet-main/utils/losses.py | import torch, torch.nn as nn, pdb
class CDLoss(nn.Module):
def __init__(self, opts):
super(CDLoss, self).__init__()
self.device = opts.device
def forward(self, srcs, tgts):
P = self.pairwise_distance(srcs, tgts)
return torch.min(P, 1)[0].mean() + torch.min(P, 2)[0].mean()
... | 2,803 | 53.980392 | 144 | py |
CEMNet | CEMNet-main/utils/test.py | from tqdm import tqdm
import torch, numpy as np, os
def test(opts, model, test_loader, epoch):
cal_score = lambda x: np.mean(np.concatenate(x, 0)).item()
with torch.no_grad():
rs_mse, ts_mse, rs_mae, ts_mae = [], [], [], []
rs_prior_mse, ts_prior_mse, rs_prior_mae, ts_prior_mae = [], [], [], []... | 2,329 | 58.74359 | 121 | py |
CEMNet | CEMNet-main/utils/transform_pc.py | import torch, numpy as np
from utils.euler2mat import euler2mat_torch, euler2mat_np
def transform_pc_torch(pcs, Rs, ts):
"""
:param pcs: point clouds, (B, 3, N),
:param Rs: rotation matrix, (B, 3, 3)
:param ts: translation vector, (B, 3)
:return: transformed pcs, (B, 3, N)
"""
return torch.... | 1,181 | 29.307692 | 58 | py |
CEMNet | CEMNet-main/utils/batch_icp.py | import torch, pdb
from utils.transform_pc import transform_pc_torch
from torch_batch_svd import svd
from cemnet_lib.functions import closest_point
def one_step(srcs, tgt, Rs, ts):
xs_mean = srcs.mean(2, keepdim=True) # [B, 3, 1]
xs_centered = srcs - xs_mean # [B, 3, N]
ys = closest_point(transform_pc_to... | 1,467 | 30.234043 | 78 | py |
CEMNet | CEMNet-main/utils/commons.py | import pickle, numpy as np, matplotlib, pdb, torch, os
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from utils.mat2euler import mat2euler_torch
from utils.euler2mat import euler2mat_torch
def load_data(path):
file = open(path, "rb")
data = pickle.load(file)
file.close()
return data
def save_d... | 3,458 | 30.733945 | 122 | py |
CEMNet | CEMNet-main/utils/options.py | from utils.attr_dict import AttrDict
import numpy as np, torch
opts = AttrDict()
## general setting
opts.db_nm = "scene7" # "modelnet40", "scene7", "icl_nuim
opts.is_debug = False
opts.device=torch.device("cuda")
opts.seed = 123
opts.batch_size = 35
opts.minibatch_size = 35
opts.n_epochs = 30
## dataset
opts.db = Att... | 2,082 | 25.0375 | 91 | py |
CEMNet | CEMNet-main/utils/euler2mat.py | import numpy as np, torch
def euler2mat_np(rs, seq="zyx"):
assert seq == "zyx", "Invalid euler seq."
rs_z, rs_y, rs_x = rs[:, [0]], rs[:, [1]], rs[:, [2]]
sinx, siny, sinz = np.sin(rs_x), np.sin(rs_y), np.sin(rs_z)
cosx, cosy, cosz = np.cos(rs_x), np.cos(rs_y), np.cos(rs_z)
R = np.concatenate([cosy... | 1,264 | 49.6 | 112 | py |
CEMNet | CEMNet-main/utils/mat2euler.py | import numpy as np, torch
from scipy.spatial.transform import Rotation
def mat2euler_np(mats, seq='zyx', is_degrees=True):
eulers = []
for i in range(mats.shape[0]):
r = Rotation.from_dcm(mats[i])
eulers.append(r.as_euler(seq, degrees=is_degrees))
return np.asarray(eulers, dtype='float32')
... | 1,405 | 31.697674 | 81 | py |
IncrementalDimensionalityReduction | IncrementalDimensionalityReduction-master/Code/prepare_data.py | import numpy as np
from sklearn.utils import gen_batches
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
import keras
if __name__ == '__main__':
np.random.seed(12227)
X, y = make_classification(n_samples=1500, n_features=400, n_classes=3, n_clusters_per_cl... | 742 | 29.958333 | 99 | py |
Fairface-Recognition-Solution | Fairface-Recognition-Solution-master/test/detect_face.py | from __future__ import print_function
import sys
import os
import datetime
import time
import numpy as np
import mxnet as mx
from mxnet import ndarray as nd
import cv2
from bbox_transform import clip_boxes, nms_wrapper
from generate_anchor import generate_anchors_fpn, anchors_plane
class RetinaFace:
def __init__(se... | 26,958 | 37.623209 | 121 | py |
Fairface-Recognition-Solution | Fairface-Recognition-Solution-master/test/gen_eccv.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from datetime import datetime
import os.path
from easydict import EasyDict as edict
import time
import json
import sys
import numpy as np
import importlib
import itertools
import argparse
import stru... | 6,244 | 29.763547 | 94 | py |
Fairface-Recognition-Solution | Fairface-Recognition-Solution-master/train/pair_wise_loss.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import math
import random
import logging
import sklearn
import pickle
import numpy as np
import mxnet as mx
from mxnet import ndarray as nd
import argparse
import mxnet.optimizer as optimiz... | 5,648 | 36.410596 | 117 | py |
Fairface-Recognition-Solution | Fairface-Recognition-Solution-master/train/image_iter_gluon.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import random
import logging
import sys
import numbers
import math
import sklearn
import datetime
import numpy as np
import cv2
import mxnet as mx
from mxnet import ndarray as nd
from mxnet import io... | 8,684 | 35.1875 | 122 | py |
Fairface-Recognition-Solution | Fairface-Recognition-Solution-master/train/triplet_image_iter.py | # THIS FILE IS FOR EXPERIMENTS, USE image_iter.py FOR NORMAL IMAGE LOADING.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import random
import logging
import sys
import numbers
import math
import sklearn
import datetime
import numpy as np
import ... | 28,039 | 38.382022 | 153 | py |
Fairface-Recognition-Solution | Fairface-Recognition-Solution-master/train/pair_fusion_class_image_iter.py | # THIS FILE IS FOR EXPERIMENTS, USE image_iter.py FOR NORMAL IMAGE LOADING.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import random
import logging
import sys
import numbers
import math
import sklearn
import datetime
import numpy as np
import ... | 27,569 | 38.498567 | 153 | py |
Fairface-Recognition-Solution | Fairface-Recognition-Solution-master/train/class_level_loss.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import math
import random
import logging
import sklearn
import pickle
import numpy as np
import mxnet as mx
from mxnet import ndarray as nd
import argparse
import mxnet.optimizer as optimiz... | 14,194 | 50.431159 | 153 | py |
Fairface-Recognition-Solution | Fairface-Recognition-Solution-master/train/train.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import math
import random
import logging
import sklearn
import pickle
import numpy as np
import mxnet as mx
from mxnet import ndarray as nd
import argparse
import mxnet.optimizer as optimiz... | 22,287 | 41.372624 | 176 | py |
Fairface-Recognition-Solution | Fairface-Recognition-Solution-master/train/metric.py | import numpy as np
import mxnet as mx
import pdb
class AccMetric(mx.metric.EvalMetric):
def __init__(self, acc_name = 'acc', label_index = 0, pred_index = 1):
self.axis = 1
super(AccMetric, self).__init__(
acc_name, axis=self.axis,
output_names=None, label_names=None)
self.losses = []
... | 2,626 | 31.8375 | 117 | py |
Fairface-Recognition-Solution | Fairface-Recognition-Solution-master/train/common/flops_counter.py |
'''
@author: insightface
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import os
import json
import argparse
import numpy as np
import mxnet as mx
def is_no_bias(attr):
ret = False
if 'no_bias' in attr and (attr['no_bias']==True or a... | 3,537 | 29.239316 | 114 | py |
Fairface-Recognition-Solution | Fairface-Recognition-Solution-master/train/eval/verification.py | """Helper for evaluation on the Labeled Faces in the Wild dataset
"""
# MIT License
#
# Copyright (c) 2016 David Sandberg
#
# 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 restric... | 22,776 | 37.088629 | 151 | py |
Fairface-Recognition-Solution | Fairface-Recognition-Solution-master/train/symbol/dropblock.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang
## Email: zhanghang0704@gmail.com
## Copyright (c) 2020
##
## LICENSE file in the root directory of this source tree
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
import mxnet as mx
from ... | 2,799 | 36.837838 | 125 | py |
Fairface-Recognition-Solution | Fairface-Recognition-Solution-master/train/symbol/symbol_utils.py | import sys
import os
import mxnet as mx
#sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from config import config, default
import numpy as np
from mxnet.gluon import nn
swish_index = 0
def gluon_act(act_type):
if act_type=='prelu':
return nn.PReLU()
else:
return nn.Activation(a... | 18,880 | 54.369501 | 187 | py |
Fairface-Recognition-Solution | Fairface-Recognition-Solution-master/train/symbol/fresnet.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 15,937 | 44.930836 | 135 | py |
Fairface-Recognition-Solution | Fairface-Recognition-Solution-master/train/symbol/resnest.py | """ResNets, implemented in Gluon."""
# pylint: disable=arguments-differ,unused-argument,missing-docstring
from __future__ import division
import sys
import os
import math
import mxnet as mx
import mxnet.gluon as gluon
from mxnet.gluon.block import HybridBlock
from mxnet.gluon import nn
import numpy as np
from symbol ... | 20,997 | 51.233831 | 162 | py |
Fairface-Recognition-Solution | Fairface-Recognition-Solution-master/train/symbol/splat.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang
## Email: zhanghang0704@gmail.com
## Copyright (c) 2020
##
## LICENSE file in the root directory of this source tree
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
import mxnet as mx
from ... | 3,910 | 34.234234 | 97 | py |
Fairface-Recognition-Solution | Fairface-Recognition-Solution-master/train/symbol/fmobilefacenet.py |
import sys
import os
import mxnet as mx
from symbol import symbol_utils
from symbol.symbol_utils import Act
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from config import config
import numpy as np
#def Act(data, act_type, name):
# #ignore param act_type, set it in this function
# if act_typ... | 5,267 | 56.89011 | 201 | py |
Fairface-Recognition-Solution | Fairface-Recognition-Solution-master/train/data_process/face2rec2.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 12,081 | 41.542254 | 136 | py |
transferability-advdnn-pub | transferability-advdnn-pub-master/models/helper.py | import sys
import os.path as osp
import numpy as np
import tensorflow as tf
import os
# Add the kaffe module to the import path
sys.path.append(osp.realpath(osp.join(osp.dirname(__file__), './')))
from inception.inception import inception_model
from googlenet import GoogleNet
from vgg import VGG16
from alexnet import... | 9,493 | 32.666667 | 102 | py |
transferability-advdnn-pub | transferability-advdnn-pub-master/models/inception/inception/data/build_imagenet_data.py | # Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 27,456 | 37.240947 | 89 | py |
transferability-advdnn-pub | transferability-advdnn-pub-master/models/kaffe/transformers.py | '''
A collection of graph transforms.
A transformer is a callable that accepts a graph and returns a transformed version.
'''
import numpy as np
from .caffe import get_caffe_resolver, has_pycaffe
from .errors import KaffeError, print_stderr
from .layers import NodeKind
class DataInjector(object):
'''
Assoc... | 10,908 | 35.242525 | 95 | py |
transferability-advdnn-pub | transferability-advdnn-pub-master/models/kaffe/graph.py | from google.protobuf import text_format
from .caffe import get_caffe_resolver
from .errors import KaffeError, print_stderr
from .layers import LayerAdapter, LayerType, NodeKind, NodeDispatch
from .shapes import TensorShape
class Node(object):
def __init__(self, name, kind, layer=None):
self.name = name
... | 11,776 | 36.626198 | 99 | py |
transferability-advdnn-pub | transferability-advdnn-pub-master/models/kaffe/caffe/caffepb.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
# source: caffe.proto
from google.protobuf.internal import enum_type_wrapper
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import... | 262,982 | 43.770684 | 28,180 | py |
transferability-advdnn-pub | transferability-advdnn-pub-master/models/kaffe/caffe/resolver.py | import sys
SHARED_CAFFE_RESOLVER = None
class CaffeResolver(object):
def __init__(self):
self.import_caffe()
def import_caffe(self):
self.caffe = None
try:
# Try to import PyCaffe first
import caffe
self.caffe = caffe
except ImportError:
... | 1,427 | 25.444444 | 68 | py |
transferability-advdnn-pub | transferability-advdnn-pub-master/models/kaffe/caffe/__init__.py | from .resolver import get_caffe_resolver, has_pycaffe
| 54 | 26.5 | 53 | py |
transferability-advdnn-pub | transferability-advdnn-pub-master/models/kaffe/tensorflow/transformer.py | import numpy as np
from ..errors import KaffeError, print_stderr
from ..graph import GraphBuilder, NodeMapper
from ..layers import NodeKind
from ..transformers import (
DataInjector,
DataReshaper,
NodeRenamer,
ReLUFuser,
BatchNormScaleBiasFuser,
BatchNormPreprocessor,
ParameterNamer)
from ... | 10,505 | 32.673077 | 88 | py |
qadra | qadra-main/Tiny-Imagenet/loadingutils.py | import matplotlib.pyplot as plt
from zipfile import ZipFile
import os, sys, wget
import numpy as np
import cv2
import glob
def download_dataset():
url = 'http://cs231n.stanford.edu/tiny-imagenet-200.zip'
tiny_imgdataset = wget.download('http://cs231n.stanford.edu/tiny-imagenet-200.zip', out = os.getcwd())
... | 1,436 | 30.933333 | 106 | py |
tf-faster-rcnn | tf-faster-rcnn-master/lib/roi_data_layer/minibatch.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Xinlei Chen
# --------------------------------------------------------
"""Compute minibatch blobs for training a Fast R-CNN ne... | 2,923 | 35.098765 | 114 | py |
tf-faster-rcnn | tf-faster-rcnn-master/lib/nets/network.py | # --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ impor... | 39,763 | 52.446237 | 198 | py |
tf-faster-rcnn | tf-faster-rcnn-master/lib/model/config.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import os.path as osp
import numpy as np
# `pip install easydict` if you don't have it
from easydict import EasyDict as edict
__C = edict()
# Consumers can get config by:
# from fast_rcnn_config im... | 11,427 | 27.287129 | 91 | py |
tf-faster-rcnn | tf-faster-rcnn-master/lib/model/train_val.py | # --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Xinlei Chen and Zheqi He
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __f... | 24,378 | 40.531516 | 120 | py |
MTCN | MTCN-main/models_av.py | import torch
from torch import nn
from embeddings import FeatureEmbedding
from transformers import TransformerEncoder, TransformerEncoderLayer
class MTCN_AV(nn.Module):
def __init__(self,
num_class,
seq_len=5,
num_clips=10,
visual_input_dim=2304,... | 4,349 | 46.802198 | 109 | py |
MTCN | MTCN-main/transformers.py | import copy
from typing import Optional, Any
import torch
from torch import Tensor
import torch.nn.functional as F
from torch.nn import Module
from torch.nn import MultiheadAttention
from torch.nn import ModuleList
from torch.nn.init import xavier_uniform_
from torch.nn import Dropout
from torch.nn import Linear
from ... | 18,360 | 45.133166 | 127 | py |
MTCN | MTCN-main/corpus.py | import pandas as pd
import numpy as np
import torch
import random
from torch.utils.data import Dataset
class Dictionary(object):
def __init__(self):
self.word2idx = {}
self.idx2word = []
self.idx2count = {}
def add_word(self, word):
if word not in self.word2idx:
se... | 6,233 | 35.034682 | 110 | py |
MTCN | MTCN-main/test_av.py | import argparse
import time
from pathlib import Path
import numpy as np
import torch
import torch.nn.parallel
import torch.optim
from sklearn.metrics import confusion_matrix, accuracy_score
from epic_kitchens import EpicKitchens
from egtea import Egtea
from models_av import MTCN_AV
import pickle
_DATASETS = {'epic': ... | 8,075 | 39.38 | 105 | py |
MTCN | MTCN-main/embeddings.py | import torch
from torch import nn
from torch.nn.init import normal_
class FeatureEmbedding(nn.Module):
def __init__(self, seq_len, num_clips, visual_input_dim, audio_input_dim, d_model, audio, embed_actions):
super(FeatureEmbedding, self).__init__()
self.seq_len = seq_len
self.num_clips = ... | 4,625 | 51.568182 | 130 | py |
MTCN | MTCN-main/utils.py | import torch
import math
import numpy as np
import shutil
from pathlib import Path
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
corr... | 5,946 | 36.16875 | 108 | py |
MTCN | MTCN-main/mixup.py | import torch
import torch.nn.functional as F
import numpy as np
def soft_cross_entropy(pred, soft_targets):
logsoftmax = torch.nn.LogSoftmax(dim=1)
return torch.mean(torch.sum(- soft_targets * logsoftmax(pred), 1))
def mixup_data(x, y, alpha=1.0):
'''Returns mixed inputs, pairs of targets, and lambda'''... | 2,722 | 31.035294 | 72 | py |
MTCN | MTCN-main/train_lm.py | import argparse
import time
import wandb
import numpy as np
import pandas as pd
import random
from pathlib import Path
from corpus import EpicCorpus, EgteaCorpus
from models_lm import MTCN_LM
from utils import accuracy, multitask_accuracy, save_checkpoint, AverageMeter
import torch
from torch.optim.lr_scheduler impor... | 17,367 | 41.778325 | 138 | py |
MTCN | MTCN-main/epic_kitchens.py | import torch
from torch.utils import data
import pandas as pd
import numpy as np
import h5py
class EpicKitchens(data.Dataset):
def __init__(self,
hdf5_path,
labels_pickle,
visual_feature_dim=2304,
audio_feature_dim=2304,
window_l... | 4,411 | 50.302326 | 128 | py |
MTCN | MTCN-main/train_av.py | import argparse
from pathlib import Path
import time
import wandb
import torch
from torch.optim.lr_scheduler import MultiStepLR
import numpy as np
from models_av import MTCN_AV
from epic_kitchens import EpicKitchens
from egtea import Egtea
from mixup import mixup_data, mixup_criterion
from utils import accuracy, multit... | 17,557 | 43.676845 | 119 | py |
MTCN | MTCN-main/models_lm.py | # Model
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import TransformerEncoder, TransformerEncoderLayer
class PositionalEncoding(nn.Module):
"""Inject some information about the relative or absolute position of the tokens
in the sequence. The positional ... | 4,515 | 40.814815 | 98 | py |
MTCN | MTCN-main/egtea.py | import torch
from torch.utils import data
import pandas as pd
import numpy as np
import h5py
class Egtea(data.Dataset):
def __init__(self,
hdf5_path,
labels_pickle,
visual_feature_dim=2304,
audio_feature_dim=None,
window_len=5,
... | 3,010 | 42.014286 | 111 | py |
MTCN | MTCN-main/test_av_lm.py | import argparse
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F
from sklearn.metrics import confusion_matrix, accuracy_score
from scipy.special import log_softmax
from collections import OrderedDict
import pandas as pd
import pickle
from models_lm import MTCN_LM
from utils impo... | 12,295 | 41.993007 | 151 | py |
M3VSNet | M3VSNet-master/utils.py | import numpy as np
import torchvision.utils as vutils
import torch
import torch.nn.functional as F
# print arguments
def print_args(args):
print("################################ args ################################")
for k, v in args.__dict__.items():
print("{0: <10}\t{1: <30}\t{2: <20}".format(k,... | 5,334 | 32.136646 | 103 | py |
M3VSNet | M3VSNet-master/eval.py | import argparse
import os
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.autograd import Variable
import torch.nn.functional as F
import numpy as np
import time
from datasets import find_data... | 14,973 | 44.792049 | 139 | py |
M3VSNet | M3VSNet-master/train.py | import argparse
import os
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.autograd import Variable
import torch.nn.functional as F
import numpy as np
import time
from tensorboardX import Summa... | 16,944 | 41.575377 | 239 | py |
M3VSNet | M3VSNet-master/models/vgg16.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
class vggNet(nn.Module):
def __init__(self, pretrained=True):
super(vggNet, self).__init__()
self.net = models.vgg16(pretrained=True).features.eval()
def forward(self, x):
out = []
... | 642 | 24.72 | 64 | py |
M3VSNet | M3VSNet-master/models/mvsnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .module import *
import time
from datasets import ssim
# class CostRegNet(nn.Module):
# def __init__(self):
# super(CostRegNet, self).__init__()
# self.conv0 = ConvBnReLU3D(32, 8)
# self.conv1 = ConvBnReLU3D(... | 54,995 | 45.685908 | 183 | py |
M3VSNet | M3VSNet-master/models/module.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvBnReLU(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, pad=1):
super(ConvBnReLU, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=pad... | 8,621 | 39.28972 | 118 | py |
M3VSNet | M3VSNet-master/datasets/dtu_yao_eval.py | from torch.utils.data import Dataset
import numpy as np
import os
from PIL import Image
from datasets.data_io import *
# the DTU dataset preprocessed by Yao Yao (only for training)
class MVSDataset(Dataset):
def __init__(self, datapath, listfile, mode, nviews, ndepths=192, interval_scale=1.06, **kwargs):
... | 4,535 | 37.769231 | 118 | py |
M3VSNet | M3VSNet-master/datasets/dtu_yao.py | from torch.utils.data import Dataset
import numpy as np
import os
from PIL import Image
from datasets.data_io import *
# the DTU dataset preprocessed by Yao Yao (only for training)
class MVSDataset(Dataset):
def __init__(self, datapath, listfile, mode, nviews, ndepths=192, interval_scale=1.06, **kwargs):
... | 7,008 | 38.376404 | 139 | py |
M3VSNet | M3VSNet-master/datasets/__init__.py | import importlib
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from math import exp
# find the dataset definition by name, for example dtu_yao (dtu_yao.py)
def find_dataset_def(dataset_name):
module_name = 'datasets.{}'.format(dataset_name)
module = importl... | 2,927 | 35.148148 | 104 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/GraphBuilder_NYUD2.py | # GraphBuilder_NYUD2.py
import matplotlib
matplotlib.use("Agg")
from matplotlib import pyplot as plt
import colorcet as cc
import numpy as np
import torch
class GraphBuilder_NYUD2():
"""A class for setting up a grid of images relating to depth experiments.
Purpose is to centralise all code for handling ranges, colou... | 4,438 | 39.724771 | 134 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/evaluate.py | import argparse
import os
import sys
import numpy as np
import torch
import torch.nn as nn
from PIL import Image
from tqdm import tqdm
import model_io
import utils
from dataloader import DepthDataLoader
from models import UnetAdaptiveBins
from utils import RunningAverageDict
from ArgParseWrappers.EvalArgParser impor... | 6,659 | 36 | 218 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/dataloader.py | # This dataloader is a modified version of the original AdaBins one, which itself is mostly derived from the BTS implementation.
import os
import sys
import random
import numpy as np
import torch
import torch.utils.data.distributed
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from torchvisio... | 28,021 | 44.123994 | 158 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/loss.py | import torch
import torch.nn as nn
from pytorch3d.loss import chamfer_distance
from torch.nn.utils.rnn import pad_sequence
class SILogLoss(nn.Module): # Main loss function used in AdaBins paper
def __init__(self):
super(SILogLoss, self).__init__()
self.name = 'SILog'
def forward(self, input, target, mask=None... | 1,646 | 34.042553 | 100 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/utils.py | import os
import shutil
import base64
import math
import re
from io import BytesIO
import matplotlib.cm
import numpy as np
import torch
import torch.nn
from PIL import Image
def setUpExpDir(root, exp_name):
"""Set up experiment dir
Checks for existence of the path in string root
If exp dir exists within it then a... | 5,177 | 27.141304 | 119 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/model_io.py | import os
import torch
def save_weights(model, filename, path="./saved_models"):
if not os.path.isdir(path):
os.makedirs(path)
fpath = os.path.join(path, filename)
torch.save(model.state_dict(), fpath)
return
def save_checkpoint(model, optimizer, epoch, filename, root="./checkpoints"):
... | 2,125 | 28.123288 | 85 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/infer.py | import glob
import os
import numpy as np
import torch
import torch.nn as nn
from PIL import Image
from torchvision import transforms
from tqdm import tqdm
import model_io
import utils
from models import UnetAdaptiveBins
def _is_pil_image(img):
return isinstance(img, Image.Image)
def _is_numpy_image(img):
return... | 5,094 | 30.067073 | 142 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/train.py | import argparse
import os
import sys
import uuid
from datetime import datetime as dt
import numpy as np
from scipy.io import loadmat
import colorcet as cc
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.nn as nn
import torch.optim as optim
import torch.utils.data.distribut... | 27,331 | 41.639626 | 209 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/semantic-segmentation-pytorch/eval_multipro.py | # System libs
import os
import argparse
from distutils.version import LooseVersion
from multiprocessing import Queue, Process
# Numerical libs
import numpy as np
import math
import torch
import torch.nn as nn
from scipy.io import loadmat
# Our libs
from mit_semseg.config import cfg
from mit_semseg.dataset import ValDat... | 7,059 | 30.517857 | 115 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/semantic-segmentation-pytorch/test.py | # System libs
import os
import argparse
from distutils.version import LooseVersion
# Numerical libs
import numpy as np
import torch
import torch.nn as nn
from scipy.io import loadmat
import csv
# Our libs
from mit_semseg.dataset import TestDataset
from mit_semseg.models import ModelBuilder, SegmentationModule
from mit_... | 5,442 | 24.796209 | 76 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/semantic-segmentation-pytorch/setup.py | import setuptools
with open('README.md', 'r') as fh:
long_description = fh.read()
setuptools.setup(
name='mit_semseg',
version='1.0.0',
author='MIT CSAIL',
description='Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset',
long_description=long_description,
... | 817 | 26.266667 | 103 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/semantic-segmentation-pytorch/eval.py | # System libs
import os
import time
import argparse
from distutils.version import LooseVersion
# Numerical libs
import numpy as np
import torch
import torch.nn as nn
from scipy.io import loadmat
# Our libs
from mit_semseg.config import cfg
from mit_semseg.dataset import ValDataset
from mit_semseg.models import ModelBui... | 5,992 | 29.891753 | 100 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/semantic-segmentation-pytorch/train.py | # System libs
import os
import time
# import math
import random
import argparse
from distutils.version import LooseVersion
# Numerical libs
import torch
import torch.nn as nn
# Our libs
from mit_semseg.config import cfg
from mit_semseg.dataset import TrainDataset
from mit_semseg.models import ModelBuilder, Segmentation... | 9,224 | 32.667883 | 107 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/semantic-segmentation-pytorch/mit_semseg/dataset.py | import os
import json
import torch
from torchvision import transforms
import numpy as np
from PIL import Image
def imresize(im, size, interp='bilinear'):
if interp == 'nearest':
resample = Image.NEAREST
elif interp == 'bilinear':
resample = Image.BILINEAR
elif interp == 'bicubic':
... | 11,898 | 39.063973 | 108 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/semantic-segmentation-pytorch/mit_semseg/models/hrnet.py | """
This HRNet implementation is modified from the following repository:
https://github.com/HRNet/HRNet-Semantic-Segmentation
"""
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from .utils import load_url
from mit_semseg.lib.nn import SynchronizedBatchNorm2d
BatchNorm2d = Synchroniz... | 16,811 | 36.695067 | 164 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/semantic-segmentation-pytorch/mit_semseg/models/resnet.py | import torch.nn as nn
import math
from .utils import load_url
from mit_semseg.lib.nn import SynchronizedBatchNorm2d
BatchNorm2d = SynchronizedBatchNorm2d
__all__ = ['ResNet', 'resnet18', 'resnet50', 'resnet101'] # resnet101 is coming soon!
model_urls = {
'resnet18': 'http://sceneparsing.csail.mit.edu/model/pret... | 6,770 | 30.202765 | 99 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/semantic-segmentation-pytorch/mit_semseg/models/utils.py | import sys
import os
try:
from urllib import urlretrieve
except ImportError:
from urllib.request import urlretrieve
import torch
def load_url(url, model_dir='./pretrained', map_location=None):
if not os.path.exists(model_dir):
os.makedirs(model_dir)
filename = url.split('/')[-1]
cached_fil... | 577 | 29.421053 | 78 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/semantic-segmentation-pytorch/mit_semseg/models/resnext.py | import torch.nn as nn
import math
from .utils import load_url
from mit_semseg.lib.nn import SynchronizedBatchNorm2d
BatchNorm2d = SynchronizedBatchNorm2d
__all__ = ['ResNeXt', 'resnext101'] # support resnext 101
model_urls = {
#'resnext50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnext50-im... | 5,367 | 31.731707 | 101 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/semantic-segmentation-pytorch/mit_semseg/models/models.py | import torch
import torch.nn as nn
from . import resnet, resnext, mobilenet, hrnet
from mit_semseg.lib.nn import SynchronizedBatchNorm2d
BatchNorm2d = SynchronizedBatchNorm2d
class SegmentationModuleBase(nn.Module):
def __init__(self):
super(SegmentationModuleBase, self).__init__()
def pixel_acc(self... | 21,185 | 35.091993 | 114 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/semantic-segmentation-pytorch/mit_semseg/models/mobilenet.py | """
This MobileNetV2 implementation is modified from the following repository:
https://github.com/tonylins/pytorch-mobilenet-v2
"""
import torch.nn as nn
import math
from .utils import load_url
from mit_semseg.lib.nn import SynchronizedBatchNorm2d
BatchNorm2d = SynchronizedBatchNorm2d
__all__ = ['mobilenetv2']
mo... | 4,938 | 30.864516 | 100 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/semantic-segmentation-pytorch/mit_semseg/lib/nn/modules/replicate.py | # -*- coding: utf-8 -*-
# File : replicate.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import functools
from torch.nn.parallel.da... | 3,226 | 32.968421 | 115 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/semantic-segmentation-pytorch/mit_semseg/lib/nn/modules/unittest.py | # -*- coding: utf-8 -*-
# File : unittest.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import unittest
import numpy as np
from tor... | 835 | 26.866667 | 157 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/semantic-segmentation-pytorch/mit_semseg/lib/nn/modules/batchnorm.py | # -*- coding: utf-8 -*-
# File : batchnorm.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import collections
import torch
import tor... | 13,813 | 40.860606 | 127 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/semantic-segmentation-pytorch/mit_semseg/lib/nn/modules/tests/test_sync_batchnorm.py | # -*- coding: utf-8 -*-
# File : test_sync_batchnorm.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
import unittest
import torch
import torch.nn as nn
from torch.autograd import Variable
from sync_batchnorm import Synchroniz... | 3,571 | 30.892857 | 109 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/semantic-segmentation-pytorch/mit_semseg/lib/nn/modules/tests/test_numeric_batchnorm.py | # -*- coding: utf-8 -*-
# File : test_numeric_batchnorm.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
import unittest
import torch
import torch.nn as nn
from torch.autograd import Variable
from sync_batchnorm.unittest impor... | 1,615 | 27.350877 | 85 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/semantic-segmentation-pytorch/mit_semseg/lib/nn/parallel/data_parallel.py | # -*- coding: utf8 -*-
import torch.cuda as cuda
import torch.nn as nn
import torch
import collections
from torch.nn.parallel._functions import Gather
__all__ = ['UserScatteredDataParallel', 'user_scattered_collate', 'async_copy_to']
def async_copy_to(obj, dev, main_stream=None):
if torch.is_tensor(obj):
... | 3,399 | 29.088496 | 82 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/semantic-segmentation-pytorch/mit_semseg/lib/utils/th.py | import torch
from torch.autograd import Variable
import numpy as np
import collections
__all__ = ['as_variable', 'as_numpy', 'mark_volatile']
def as_variable(obj):
if isinstance(obj, Variable):
return obj
if isinstance(obj, collections.Sequence):
return [as_variable(v) for v in obj]
elif i... | 1,237 | 28.47619 | 60 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/semantic-segmentation-pytorch/mit_semseg/lib/utils/data/sampler.py | import torch
class Sampler(object):
"""Base class for all Samplers.
Every Sampler subclass has to provide an __iter__ method, providing a way
to iterate over indices of dataset elements, and a __len__ method that
returns the length of the returned iterators.
"""
def __init__(self, data_sourc... | 3,761 | 27.5 | 88 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/semantic-segmentation-pytorch/mit_semseg/lib/utils/data/dataloader.py | import torch
import torch.multiprocessing as multiprocessing
from torch._C import _set_worker_signal_handlers, \
_remove_worker_pids, _error_if_any_worker_fails
try:
from torch._C import _set_worker_pids
except:
from torch._C import _update_worker_pids as _set_worker_pids
from .sampler import SequentialSamp... | 16,207 | 37.046948 | 102 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/semantic-segmentation-pytorch/mit_semseg/lib/utils/data/dataset.py | import bisect
import warnings
from torch._utils import _accumulate
from torch import randperm
class Dataset(object):
"""An abstract class representing a Dataset.
All other datasets should subclass it. All subclasses should override
``__len__``, that provides the size of the dataset, and ``__getitem__``,... | 3,465 | 28.12605 | 118 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/semantic-segmentation-pytorch/mit_semseg/lib/utils/data/distributed.py | import math
import torch
from .sampler import Sampler
from torch.distributed import get_world_size, get_rank
class DistributedSampler(Sampler):
"""Sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with
:class:`torch.nn.parallel.DistributedDataParallel`... | 1,964 | 32.305085 | 86 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/models/layers.py | import torch
import torch.nn as nn
class PatchTransformerEncoder(nn.Module):
def __init__(self, in_channels, patch_size=10, embedding_dim=128, num_heads=4):
super(PatchTransformerEncoder, self).__init__()
encoder_layers = nn.TransformerEncoderLayer(embedding_dim, num_heads, dim_feedforward=1024)
... | 1,686 | 44.594595 | 107 | py |
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