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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Relation-CZSL | Relation-CZSL-master/model/pygcn.py | import math
import numpy as np
from scipy.sparse import diags
import torch
from torch.nn import Module, Parameter
def normalize(mx):
"""Row-normalize sparse matrix"""
rowsum = np.array(mx.sum(1))
r_inv = np.power(rowsum, -1).flatten()
r_inv[np.isinf(r_inv)] = 0.
r_mat_inv = diags(r_inv)
mx = ... | 2,132 | 31.318182 | 103 | py |
Relation-CZSL | Relation-CZSL-master/model/datasets/CompositionDataset.py | from PIL import Image
import random
import numpy as np
import torch
import torch.utils.data as tdata
import torchvision.transforms as transforms
class ImageLoader:
def __init__(self, root):
self.img_dir = root
def __call__(self, img):
file = '%s/%s' % (self.img_dir, img)
img = Image.... | 12,963 | 41.785479 | 165 | py |
Relation-CZSL | Relation-CZSL-master/model/datasets/CompositionDatasetGrouped.py | from PIL import Image
import numpy as np
import torch
import torch.utils.data as tdata
import torchvision.transforms as transforms
class ImageLoader:
def __init__(self, root):
self.img_dir = root
def __call__(self, img):
file = '%s/%s' % (self.img_dir, img)
img = Image.open(file).con... | 6,104 | 36.22561 | 127 | py |
Relation-CZSL | Relation-CZSL-master/model/misc/utils.py | import sys
import os
import time
import subprocess
import inspect
import logging
import argparse
from contextlib import contextmanager
from timeit import default_timer
import matplotlib.pyplot as plt
import torch
import random
import uuid
import numpy as np
import xmltodict
# ---------- debugging ---------- #
def pl... | 19,599 | 34.571688 | 153 | py |
IVR | IVR-main/actor.py | from typing import Tuple
import jax
import jax.numpy as jnp
from common import Batch, InfoDict, Model, Params, PRNGKey
def update_actor(key: PRNGKey, actor: Model, critic: Model, value: Model,
batch: Batch, alpha: float, alg: str) -> Tuple[Model, InfoDict]:
v = value(batch.observations)
q1,... | 1,100 | 31.382353 | 81 | py |
IVR | IVR-main/learner.py | """Implementations of algorithms for continuous control."""
from typing import Optional, Sequence, Tuple
import jax
import jax.numpy as jnp
import numpy as np
import optax
import policy
import value_net
from common import Batch, InfoDict, Model, PRNGKey
from actor import update_actor
from critic import update_q, u... | 6,552 | 37.547059 | 109 | py |
IVR | IVR-main/policy.py | import functools
from typing import Optional, Sequence, Tuple
import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
from tensorflow_probability.substrates import jax as tfp
tfd = tfp.distributions
tfb = tfp.bijectors
from common import MLP, Params, PRNGKey, default_init
LOG_STD_MIN = -10.0
L... | 2,998 | 34.702381 | 79 | py |
IVR | IVR-main/common.py | import collections
import os
from typing import Any, Callable, Dict, Optional, Sequence, Tuple
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
import optax
Batch = collections.namedtuple(
'Batch',
['observations', 'actions', 'rewards', 'masks', 'next_observations', 'next_actions'])
de... | 3,375 | 31.152381 | 89 | py |
IVR | IVR-main/value_net.py | from typing import Callable, Sequence, Tuple, Optional
import jax.numpy as jnp
from flax import linen as nn
from common import MLP
class ValueCritic(nn.Module):
hidden_dims: Sequence[int]
layer_norm: bool = False
dropout_rate: Optional[float] = 0.0
@nn.compact
def __call__(self, observatio... | 1,717 | 32.686275 | 118 | py |
IVR | IVR-main/evaluation.py | from typing import Dict
import flax.linen as nn
import gym
import numpy as np
import d4rl
# from mingpt.utils import sample
# import atari_py
from collections import deque
import random
# import cv2
# import torch
def evaluate(env_name: str, agent: nn.Module, env: gym.Env,
num_episodes: int) -> Dict[st... | 906 | 26.484848 | 81 | py |
IVR | IVR-main/critic.py | from typing import Tuple
import jax.numpy as jnp
from common import PRNGKey
import policy
import jax
from common import Batch, InfoDict, Model, Params
def update_v(critic: Model, value: Model, batch: Batch,
alpha: float, alg: str) -> Tuple[Model, InfoDict]:
q1, q2 = critic(batch.observations, batch... | 2,107 | 35.982456 | 100 | py |
id-reveal | id-reveal-main/network.py | import torch
def add_tensor_1d(x, y):
s1 = (y.shape[-1] - x.shape[-1]) // 2
e1 = s1 + x.shape[-1]
y = y[..., s1:e1]
if x.shape[1] > y.shape[1]:
d = [int(i) for i in y.shape]
d[1] = int(x.shape[1] - y.shape[1])
y = torch.cat((y, torch.zeros(d, dtype=y.dtype, device=y.device)), -3... | 5,638 | 40.463235 | 135 | py |
DeepMoji | DeepMoji-master/deepmoji/class_avg_finetuning.py | """ Class average finetuning functions. Before using any of these finetuning
functions, ensure that the model is set up with nb_classes=2.
"""
from __future__ import print_function
import sys
import uuid
import numpy as np
from os.path import dirname
from time import sleep
from keras.optimizers import Adam
from g... | 13,300 | 39.675841 | 94 | py |
DeepMoji | DeepMoji-master/deepmoji/model_def.py | """ Model definition functions and weight loading.
"""
from __future__ import print_function, division
from keras.models import Model, Sequential
from keras.layers.merge import concatenate
from keras.layers import Input, Bidirectional, Embedding, Dense, Dropout, SpatialDropout1D, LSTM, Activation
from keras.regulariz... | 11,369 | 41.58427 | 162 | py |
DeepMoji | DeepMoji-master/deepmoji/attlayer.py | # -*- coding: utf-8 -*-
from __future__ import absolute_import, division
import sys
from os.path import dirname
sys.path.append(dirname(dirname(__file__)))
from keras import initializers
from keras.engine import InputSpec, Layer
from keras import backend as K
class AttentionWeightedAverage(Layer):
"""
Comput... | 2,792 | 35.75 | 86 | py |
DeepMoji | DeepMoji-master/deepmoji/finetuning.py | """ Finetuning functions for doing transfer learning to new datasets.
"""
from __future__ import print_function
import sys
import uuid
from time import sleep
import h5py
import math
import pickle
import numpy as np
from keras.layers.wrappers import Bidirectional, TimeDistributed
from sklearn.metrics import f1_score
... | 23,552 | 36.267405 | 87 | py |
DeepMoji | DeepMoji-master/examples/imdb_from_scratch.py | """Trains the DeepMoji architecture on the IMDB sentiment classification task.
This is a simple example of using the architecture without the pretrained model.
The architecture is designed for transfer learning - it should normally
be used with the pretrained model for optimal performance.
"""
from __future__ ... | 1,492 | 32.177778 | 83 | py |
DeepMoji | DeepMoji-master/scripts/analyze_all_results.py | from __future__ import print_function
# allow us to import the codebase/keras directory
import sys
import glob
import numpy as np
from os.path import dirname, abspath
sys.path.insert(0, dirname(dirname(abspath(__file__))))
DATASETS = ['SE0714', 'Olympic', 'PsychExp', 'SS-Twitter', 'SS-Youtube',
'SCv1', 'S... | 1,221 | 27.418605 | 88 | py |
separator | separator-main/scripts/generate_3way_wikianswers.py | import jsonlines, os, json
import numpy as np
from flair.models import SequenceTagger
from flair.data import Sentence
from collections import defaultdict, Counter
from tqdm import tqdm
from copy import deepcopy
import torch
# predictor = Predictor.from_archive(load_archive("https://storage.googleapis.com/allennlp-p... | 15,891 | 37.293976 | 1,024 | py |
separator | separator-main/scripts/train_vq_code_predictor.py | # MLP code prediction
import argparse, json, os
parser = argparse.ArgumentParser(
description="MLP code prediction trainer",
)
parser.add_argument(
"--data_dir", type=str, default='./data/', help="Path to data folder"
)
parser.add_argument(
"--model_path", type=str, default='./runs/sep_ae/20201230_132811_vae_... | 16,433 | 32.133065 | 225 | py |
linmix | linmix-master/docs/conf.py | # -*- coding: utf-8 -*-
#
# linmix documentation build configuration file, created by
# sphinx-quickstart on Tue May 12 10:44:33 2015.
#
# 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.
#
# Al... | 8,902 | 29.806228 | 98 | py |
leela-zero | leela-zero-master/training/elf/elf_convert.py | #!/usr/bin/env python3
import numpy as np
import sys
import torch
net = torch.load(sys.argv[1])
state = net['state_dict']
def tensor_to_str(t):
return ' '.join(map(str, np.array(t).flatten()))
def convert_block(t, name):
weight = np.array(t[name + '.0.weight'])
bias = np.array(t[name + '.0.bias'])
bn... | 2,191 | 30.314286 | 84 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./train.py | import argparse
import importlib.util
import torch
from iharm.utils.exp import init_experiment
def main():
args = parse_args()
model_script = load_module(args.model_path)
cfg = init_experiment(args)
torch.backends.cudnn.benchmark = True
torch.multiprocessing.set_sharing_strategy('file_system')
... | 3,105 | 36.878049 | 120 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./models/fixed256/improved_ssam.py | from functools import partial
import torch
from torchvision import transforms
from easydict import EasyDict as edict
from albumentations import HorizontalFlip, Resize, RandomResizedCrop
from iharm.data.compose import ComposeDataset
from iharm.data.hdataset import HDataset, MyDataset
from iharm.data.transforms import ... | 4,667 | 29.913907 | 106 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/engine/simple_trainer.py | import os
import logging
from copy import deepcopy
from collections import defaultdict
import argparse
import cv2
import torch
import numpy as np
from tqdm import tqdm
from torch.utils.data import DataLoader
from torchvision.transforms import Normalize
from iharm.utils.log import logger, TqdmToLogger, SummaryWriterAv... | 13,411 | 40.9125 | 119 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/engine/optimizer.py | import torch
import math
from iharm.utils.log import logger
def get_optimizer(model, opt_name, opt_kwargs):
params = []
base_lr = opt_kwargs['lr']
for name, param in model.named_parameters():
param_group = {'params': [param]}
if not param.requires_grad:
params.append(param_grou... | 797 | 27.5 | 82 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/utils/exp.py | import os
import sys
import shutil
import pprint
from pathlib import Path
from datetime import datetime
import yaml
import torch
from easydict import EasyDict as edict
from .log import logger, add_new_file_output_to_logger
def init_experiment(args):
model_path = Path(args.model_path)
ftree = get_model_famil... | 4,641 | 27.832298 | 113 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/utils/misc.py | import torch
from .log import logger
def get_dims_with_exclusion(dim, exclude=None):
dims = list(range(dim))
if exclude is not None:
dims.remove(exclude)
return dims
def save_checkpoint(net, checkpoints_path, epoch=None, prefix='', verbose=True, multi_gpu=False):
if epoch is None:
... | 1,192 | 27.404762 | 97 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/utils/log.py | import io
import time
import logging
from datetime import datetime
import numpy as np
from torch.utils.tensorboard import SummaryWriter
LOGGER_NAME = 'root'
LOGGER_DATEFMT = '%Y-%m-%d %H:%M:%S'
handler = logging.StreamHandler()
logger = logging.getLogger(LOGGER_NAME)
logger.setLevel(logging.INFO)
logger.addHandler(... | 2,809 | 27.1 | 89 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/data/base.py | import random
import numpy as np
import torch
class BaseHDataset(torch.utils.data.dataset.Dataset):
def __init__(self,
augmentator=None,
input_transform=None,
keep_background_prob=0.0,
with_image_info=False,
epoch_len=-1):
... | 3,701 | 35.653465 | 153 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/model/losses.py | import torch
import torch.nn as nn
from iharm.utils import misc
class Loss(nn.Module):
def __init__(self, pred_outputs, gt_outputs):
super().__init__()
self.pred_outputs = pred_outputs
self.gt_outputs = gt_outputs
class MSE(Loss):
def __init__(self, pred_name='images', gt_image_name... | 1,347 | 32.7 | 99 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/model/metrics.py | import torch
import torch.nn.functional as F
class TrainMetric(object):
def __init__(self, pred_outputs, gt_outputs, epsilon=1e-6):
self.pred_outputs = pred_outputs
self.gt_outputs = gt_outputs
self.epsilon = epsilon
self._last_batch_metric = 0.0
self._epoch_metric_sum = 0.... | 3,379 | 32.8 | 95 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/model/ops.py | import torch
from torch import nn as nn
class SimpleInputFusion(nn.Module):
def __init__(self, add_ch=1, rgb_ch=3, ch=8, norm_layer=nn.BatchNorm2d):
super(SimpleInputFusion, self).__init__()
self.fusion_conv = nn.Sequential(
nn.Conv2d(in_channels=add_ch + rgb_ch, out_channels=ch, kern... | 4,695 | 32.784173 | 101 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/model/initializer.py | import torch
import torch.nn as nn
import numpy as np
class Initializer(object):
def __init__(self, local_init=True, gamma=None):
self.local_init = local_init
self.gamma = gamma
def __call__(self, m):
if getattr(m, '__initialized', False):
return
if isinstance(m, ... | 3,408 | 31.160377 | 98 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/model/syncbn/modules/nn/syncbn.py | """
/*****************************************************************************/
BatchNorm2dSync with multi-gpu
/*****************************************************************************/
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
try:
... | 5,187 | 33.818792 | 79 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/model/syncbn/modules/functional/syncbn.py | """
/*****************************************************************************/
BatchNorm2dSync with multi-gpu
code referenced from : https://github.com/mapillary/inplace_abn
/*****************************************************************************/
"""
from __future__ import absolute_import
from __future__... | 5,291 | 37.347826 | 79 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/model/syncbn/modules/functional/_csrc.py | """
/*****************************************************************************/
Extension module loader
code referenced from : https://github.com/facebookresearch/maskrcnn-benchmark
/*****************************************************************************/
"""
from __future__ import absolute_import
from __f... | 1,586 | 27.854545 | 79 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/model/backboned/hrnet.py | import torch.nn as nn
from iharm.model.modeling.hrnet_ocr import HighResolutionNet
from iharm.model.backboned.ih_model import IHModelWithBackbone
from iharm.model.modifiers import LRMult
from iharm.model.modeling.basic_blocks import MaxPoolDownSize
class HRNetIHModel(IHModelWithBackbone):
def __init__(
s... | 5,787 | 43.523077 | 117 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/model/backboned/ih_model.py | import torch
import torch.nn as nn
from iharm.model.ops import SimpleInputFusion, ScaleLayer
class IHModelWithBackbone(nn.Module):
def __init__(
self,
model, backbone,
downsize_backbone_input=False,
mask_fusion='sum',
backbone_conv1_channels=64,
):
"""
... | 3,309 | 37.045977 | 118 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/model/backboned/deeplab.py | from torch import nn as nn
from iharm.model.modeling.deeplab_v3 import DeepLabV3Plus
from iharm.model.backboned.ih_model import IHModelWithBackbone
from iharm.model.modifiers import LRMult
from iharm.model.modeling.basic_blocks import MaxPoolDownSize
class DeepLabIHModel(IHModelWithBackbone):
def __init__(
... | 4,474 | 41.619048 | 117 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/model/base/dih_model.py | import torch
import torch.nn as nn
from iharm.model.modeling.conv_autoencoder import ConvEncoder, DeconvDecoder
class DeepImageHarmonization(nn.Module):
def __init__(
self,
depth,
norm_layer=nn.BatchNorm2d, batchnorm_from=0,
attend_from=-1,
image_fusion=False,
ch=6... | 1,049 | 32.870968 | 112 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/model/base/iseunet_v1.py | import torch
import torch.nn as nn
from iharm.model.modeling.unet import UNetEncoder, UNetDecoder
from iharm.model.ops import MaskedChannelAttention
class ISEUNetV1(nn.Module):
def __init__(
self,
depth,
norm_layer=nn.BatchNorm2d, batchnorm_from=2,
attend_from=3,
image_fus... | 1,191 | 30.368421 | 66 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/model/base/ssam_model.py | import torch
from functools import partial
from torch import nn as nn
from iharm.model.modeling.basic_blocks import ConvBlock, GaussianSmoothing
from iharm.model.modeling.unet import UNetEncoder, UNetDecoder
from iharm.model.ops import ChannelAttention
class SSAMImageHarmonization(nn.Module):
def __init__(
... | 2,771 | 35 | 86 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/model/modeling/basic_blocks.py | import math
import numbers
import torch
import torch.nn.functional as F
from torch import nn as nn
class ConvHead(nn.Module):
def __init__(self, out_channels, in_channels=32, num_layers=1,
kernel_size=3, padding=1,
norm_layer=nn.BatchNorm2d):
super(ConvHead, self).__init... | 6,923 | 36.225806 | 117 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/model/modeling/deeplab_v3.py | from contextlib import ExitStack
import torch
from torch import nn
import torch.nn.functional as F
from iharm.model.modeling.basic_blocks import select_activation_function
from .basic_blocks import SeparableConv2d
from .resnet import ResNetBackbone
class DeepLabV3Plus(nn.Module):
def __init__(self, backbone='re... | 6,392 | 35.118644 | 103 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/model/modeling/resnet.py | import torch
from .resnetv1b import resnet34_v1b, resnet50_v1s, resnet101_v1s, resnet152_v1s
class ResNetBackbone(torch.nn.Module):
def __init__(self, backbone='resnet50', pretrained_base=True, dilated=True, **kwargs):
super(ResNetBackbone, self).__init__()
if backbone == 'resnet34':
... | 1,552 | 35.97619 | 93 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/model/modeling/hrnet_ocr.py | import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch._utils
from .ocr import SpatialOCR_Module, SpatialGather_Module
from .resnetv1b import BasicBlockV1b, BottleneckV1b
from iharm.utils.log import logger
relu_inplace = True
class HighResolutionModule(nn.Module... | 17,393 | 42.376559 | 111 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/model/modeling/ocr.py | import torch
import torch.nn as nn
import torch._utils
import torch.nn.functional as F
class SpatialGather_Module(nn.Module):
"""
Aggregate the context features according to the initial
predicted probability distribution.
Employ the soft-weighted method to aggregate the context.
"""
... | 5,740 | 39.429577 | 100 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/model/modeling/conv_autoencoder.py | import torch
from torch import nn as nn
from iharm.model.modeling.basic_blocks import ConvBlock
from iharm.model.ops import MaskedChannelAttention, FeaturesConnector
class ConvEncoder(nn.Module):
def __init__(
self,
depth, ch,
norm_layer, batchnorm_from, max_channels,
backbone_fro... | 4,940 | 37.601563 | 120 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/model/modeling/unet.py | import torch
from torch import nn as nn
from functools import partial
from iharm.model.modeling.basic_blocks import ConvBlock
from iharm.model.ops import FeaturesConnector
class UNetEncoder(nn.Module):
def __init__(
self,
depth, ch,
norm_layer, batchnorm_from, max_channels,
backbo... | 7,240 | 38.140541 | 110 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/model/modeling/resnetv1b.py | import torch
import torch.nn as nn
GLUON_RESNET_TORCH_HUB = 'rwightman/pytorch-pretrained-gluonresnet'
class BasicBlockV1b(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None,
previous_dilation=1, norm_layer=nn.BatchNorm2d):
super(Basi... | 10,805 | 38.01083 | 112 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/inference/predictor.py | import torch
from iharm.inference.transforms import NormalizeTensor, PadToDivisor, ToTensor, AddFlippedTensor
class Predictor(object):
def __init__(self, net, device, with_flip=False,
mean=(.485, .456, .406), std=(.229, .224, .225)):
self.device = device
self.net = net.to(self.dev... | 1,474 | 31.777778 | 96 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/inference/evaluation.py | from time import time
from tqdm import trange
import torch
def evaluate_dataset(dataset, predictor, metrics_hub):
for sample_i in trange(len(dataset), desc=f'Testing on {metrics_hub.name}'):
sample = dataset.get_sample(sample_i)
sample = dataset.augment_sample(sample)
sample_mask = sample... | 841 | 39.095238 | 104 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/Huang et al./iharm/inference/transforms.py | import cv2
import torch
from collections import namedtuple
class EvalTransform:
def __init__(self):
pass
def transform(self, image, mask):
raise NotImplementedError
def inv_transform(self, image):
raise NotImplementedError
class PadToDivisor(EvalTransform):
"""
Pad side... | 3,462 | 31.980952 | 114 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/scripts/evaluate_flow.py | import os
import cv2
import numpy as np
import torch
import sys
sys.path.insert(0, '.')
from flownet import *
from flownet.resample2d_package.resample2d import Resample2d
import os
import time
import argparse
from skimage.measure import compare_mse as mse
from iharm.data.transforms import HCompose, LongestMaxSizeIfLarg... | 6,396 | 34.148352 | 166 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/scripts/evaluate_model.py | import argparse
import sys
sys.path.insert(0, '.')
import torch
import os
import time
import numpy as np
import cv2
from iharm.utils import pytorch_ssim
from iharm.utils.misc import load_weights
from skimage.measure import compare_mse as mse
from skimage.measure import compare_psnr as psnr
from skimage.measure impo... | 13,539 | 37.356941 | 123 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/scripts/my_train.py | import argparse
import sys
sys.path.insert(0, '.')
import torch
import os
from functools import partial
import time
import numpy as np
import cv2
from iharm.utils.misc import load_weights
from skimage.measure import compare_mse as mse
from skimage.measure import compare_psnr as psnr
from skimage.measure import compar... | 10,799 | 43.081633 | 152 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/engine/simple_trainer.py | import os
import logging
from copy import deepcopy
from collections import defaultdict
import cv2
import torch
import numpy as np
from tqdm import tqdm
from torch.utils.data import DataLoader
from torchvision.transforms import Normalize
from iharm.inference.transforms import NormalizeTensor, PadToDivisor, ToTensor, Ad... | 26,698 | 42.413008 | 187 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/engine/optimizer.py | import torch
import math
from iharm.utils.log import logger
def get_optimizer(model, opt_name, opt_kwargs):
params = []
base_lr = opt_kwargs['lr']
for name, param in model.named_parameters():
param_group = {'params': [param]}
if not param.requires_grad:
params.append(param_grou... | 797 | 27.5 | 82 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/utils/pytorch_ssim.py | import torch
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from math import exp
import os
def gaussian(window_size, sigma):
gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])
return gauss/gauss.sum()
def create_window(w... | 2,979 | 34.47619 | 104 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/utils/exp.py | import os
import sys
import shutil
import pprint
from pathlib import Path
from datetime import datetime
import yaml
import torch
from easydict import EasyDict as edict
from .log import logger, add_new_file_output_to_logger
def init_experiment(args):
model_path = Path(args.model_path)
ftree = get_model_famil... | 4,654 | 28.09375 | 113 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/utils/misc.py | import torch
from .log import logger
def get_dims_with_exclusion(dim, exclude=None):
dims = list(range(dim))
if exclude is not None:
dims.remove(exclude)
return dims
def save_checkpoint(net, checkpoints_path, epoch=None, prefix='', verbose=True, multi_gpu=False):
if epoch is None:
... | 1,191 | 27.380952 | 97 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/utils/log.py | import io
import time
import logging
from datetime import datetime
import numpy as np
from torch.utils.tensorboard import SummaryWriter
LOGGER_NAME = 'root'
LOGGER_DATEFMT = '%Y-%m-%d %H:%M:%S'
handler = logging.StreamHandler()
logger = logging.getLogger(LOGGER_NAME)
logger.setLevel(logging.INFO)
logger.addHandler(... | 2,809 | 27.1 | 89 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/data/base.py | import random
import numpy as np
import torch
import cv2
import os
class BaseHDataset(torch.utils.data.dataset.Dataset):
def __init__(self,
augmentator=None,
input_transform=None,
keep_background_prob=0.0,
with_image_info=False,
... | 4,758 | 36.179688 | 119 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/data/compose.py | from .base import BaseHDataset
import cv2
import numpy as np
import copy
import os
import time
import torch
class MyDirectDataset:
def __init__(self, val_list, dataset_path, backbone_type = 'issam', input_transform=None, augmentator=None, lut_map_dir='', lut_output_dir=''):
start_time = time.time()
... | 20,372 | 45.09276 | 157 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/losses.py | import torch
import torch.nn as nn
from iharm.utils import misc
class Loss(nn.Module):
def __init__(self, pred_outputs, gt_outputs):
super().__init__()
self.pred_outputs = pred_outputs
self.gt_outputs = gt_outputs
class MSE(Loss):
def __init__(self, pred_name='images', gt_image_name... | 2,073 | 35.385965 | 99 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/metrics.py | import torch
import torch.nn.functional as F
class TrainMetric(object):
def __init__(self, pred_outputs, gt_outputs, epsilon=1e-6):
self.pred_outputs = pred_outputs
self.gt_outputs = gt_outputs
self.epsilon = epsilon
self._last_batch_metric = 0.0
self._epoch_metric_sum = 0.... | 3,732 | 31.745614 | 95 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/ops.py | import torch
from torch import nn as nn
class SimpleInputFusion(nn.Module):
def __init__(self, add_ch=1, rgb_ch=3, ch=8, norm_layer=nn.BatchNorm2d):
super(SimpleInputFusion, self).__init__()
self.fusion_conv = nn.Sequential(
nn.Conv2d(in_channels=add_ch + rgb_ch, out_channels=ch, kern... | 4,695 | 32.784173 | 101 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/initializer.py | import torch
import torch.nn as nn
import numpy as np
class Initializer(object):
def __init__(self, local_init=True, gamma=None):
self.local_init = local_init
self.gamma = gamma
def __call__(self, m):
if getattr(m, '__initialized', False):
return
if isinstance(m, ... | 3,408 | 31.160377 | 98 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/syncbn/modules/nn/syncbn.py | """
/*****************************************************************************/
BatchNorm2dSync with multi-gpu
/*****************************************************************************/
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
try:
... | 5,187 | 33.818792 | 79 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/syncbn/modules/functional/syncbn.py | """
/*****************************************************************************/
BatchNorm2dSync with multi-gpu
code referenced from : https://github.com/mapillary/inplace_abn
/*****************************************************************************/
"""
from __future__ import absolute_import
from __future__... | 5,291 | 37.347826 | 79 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/syncbn/modules/functional/_csrc.py | """
/*****************************************************************************/
Extension module loader
code referenced from : https://github.com/facebookresearch/maskrcnn-benchmark
/*****************************************************************************/
"""
from __future__ import absolute_import
from __f... | 1,586 | 27.854545 | 79 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/backboned/hrnet.py | import torch.nn as nn
from iharm.model.modeling.hrnet_ocr import HighResolutionNet
from iharm.model.backboned.ih_model import IHModelWithBackbone
from iharm.model.modifiers import LRMult
from iharm.model.modeling.basic_blocks import MaxPoolDownSize
class HRNetIHModel(IHModelWithBackbone):
def __init__(
s... | 5,787 | 43.523077 | 117 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/backboned/ih_model.py | import torch
import torch.nn as nn
from iharm.model.ops import SimpleInputFusion, ScaleLayer
class IHModelWithBackbone(nn.Module):
def __init__(
self,
model, backbone,
downsize_backbone_input=False,
mask_fusion='sum',
backbone_conv1_channels=64,
):
"""
... | 3,463 | 37.488889 | 118 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/backboned/deeplab.py | from torch import nn as nn
from iharm.model.modeling.deeplab_v3 import DeepLabV3Plus
from iharm.model.backboned.ih_model import IHModelWithBackbone
from iharm.model.modifiers import LRMult
from iharm.model.modeling.basic_blocks import MaxPoolDownSize
class DeepLabIHModel(IHModelWithBackbone):
def __init__(
... | 4,474 | 41.619048 | 117 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/base/ssam_video_lut_dbp.py | import torch
from functools import partial
from torch import nn as nn
import torch.nn.functional as F
import numpy as np
from iharm.model.base.rain.util.config import cfg
from iharm.model.base.rain.models.networks import RainNet
from iharm.model.base.rain.models.normalize import RAIN
from iharm.model.base.rain.util ... | 9,579 | 46.192118 | 144 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/base/ssam_video_lut_withoutdbp.py | import torch
from functools import partial
from torch import nn as nn
import torch.nn.functional as F
import numpy as np
import cv2
import os
import copy
from skimage.measure import compare_mse as mse
from iharm.model.base.rain.util.config import cfg
from iharm.model.base.rain.models.networks import RainNet
from ihar... | 9,471 | 44.538462 | 173 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/base/ssam_model.py | import torch
from functools import partial
from torch import nn as nn
import time
from iharm.model.modeling.basic_blocks import ConvBlock, GaussianSmoothing
from iharm.model.modeling.unet import UNetEncoder, UNetDecoder
from iharm.model.ops import ChannelAttention
class SSAMImageHarmonization(nn.Module):
def __i... | 3,972 | 36.130841 | 90 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/base/rain/models/base_model.py | import os
import torch
from collections import OrderedDict
from abc import ABC, abstractmethod
from . import networks
class BaseModel(ABC):
"""This class is an abstract base class (ABC) for models.
To create a subclass, you need to implement the following five functions:
-- <__init__>: ... | 10,345 | 43.787879 | 260 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/base/rain/models/rainnet_model.py | import torch
from .base_model import BaseModel
from . import networks
import torch.nn.functional as F
from torch import nn, cuda
from torch.autograd import Variable
class RainNetModel(BaseModel):
def __init__(self, opt):
BaseModel.__init__(self, opt)
# specify the training losses you want to print... | 5,980 | 47.626016 | 150 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/base/rain/models/networks.py | import torch
import torch.nn as nn
from torch.nn import init
import torch.nn.functional as F
import functools
from torch.optim import lr_scheduler
from iharm.model.base.rain.models.normalize import RAIN
from torch.nn.utils import spectral_norm
class Identity(nn.Module):
def forward(self, x):
return x
def ... | 35,535 | 43.812106 | 143 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/base/rain/models/normalize.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class RAIN(nn.Module):
def __init__(self, dims_in, eps=1e-5):
'''Compute the instance normalization within only the background region, in which
the mean and standard variance are measured from the features in background region.... | 2,025 | 49.65 | 108 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/base/rain/util/image_pool.py | import random
import torch
class ImagePool():
"""This class implements an image buffer that stores previously generated images.
This buffer enables us to update discriminators using a history of generated images
rather than the ones produced by the latest generators.
"""
def __init__(self, pool_... | 2,226 | 39.490909 | 140 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/base/rain/util/spectral_norm.py | """
Spectral Normalization from https://arxiv.org/abs/1802.05957
"""
import torch
from torch.nn.functional import normalize
class SpectralNorm(object):
# Invariant before and after each forward call:
# u = normalize(W @ v)
# NB: At initialization, this invariant is not enforced
_version = 1
# A... | 13,029 | 46.039711 | 118 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/base/rain/util/util.py | """This module contains simple helper functions """
from __future__ import print_function
import torch
import numpy as np
from PIL import Image
import os
def tensor2im(input_image, imtype=np.uint8):
""""Converts a Tensor array into a numpy image array.
Parameters:
input_image (tensor) -- the input i... | 3,846 | 30.276423 | 119 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/modeling/basic_blocks.py | import math
import numbers
import torch
import torch.nn.functional as F
from torch import nn as nn
from torch.nn.utils import spectral_norm
class ConvBlock(nn.Module):
def __init__(
self,
in_channels, out_channels,
kernel_size=4, stride=2, padding=1,
norm_layer=nn.... | 17,780 | 38.425721 | 126 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/modeling/deeplab_v3.py | from contextlib import ExitStack
import torch
from torch import nn
import torch.nn.functional as F
from iharm.model.modeling.basic_blocks import select_activation_function
from .basic_blocks import SeparableConv2d
from .resnet import ResNetBackbone
class DeepLabV3Plus(nn.Module):
def __init__(self, backbone='re... | 6,392 | 35.118644 | 103 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/modeling/resnet.py | import torch
from .resnetv1b import resnet34_v1b, resnet50_v1s, resnet101_v1s, resnet152_v1s
class ResNetBackbone(torch.nn.Module):
def __init__(self, backbone='resnet50', pretrained_base=True, dilated=True, **kwargs):
super(ResNetBackbone, self).__init__()
if backbone == 'resnet34':
... | 1,552 | 35.97619 | 93 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/modeling/lut.py | import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch
import time
import numpy as np
import math
import trilinear
import cv2
import random
import tridistribute
class TridistributeGeneraotrFunction(torch.autograd.Function):
@staticmethod
def forward(ctx,... | 7,045 | 28.855932 | 108 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/modeling/hrnet_ocr.py | import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch._utils
from .ocr import SpatialOCR_Module, SpatialGather_Module
from .resnetv1b import BasicBlockV1b, BottleneckV1b
from iharm.utils.log import logger
relu_inplace = True
class HighResolutionModule(nn.Module... | 17,393 | 42.376559 | 111 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/modeling/ocr.py | import torch
import torch.nn as nn
import torch._utils
import torch.nn.functional as F
class SpatialGather_Module(nn.Module):
"""
Aggregate the context features according to the initial
predicted probability distribution.
Employ the soft-weighted method to aggregate the context.
"""
... | 5,740 | 39.429577 | 100 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/modeling/conv_autoencoder.py | import torch
from torch import nn as nn
from iharm.model.modeling.basic_blocks import ConvBlock
from iharm.model.ops import MaskedChannelAttention, FeaturesConnector
class ConvEncoder(nn.Module):
def __init__(
self,
depth, ch,
norm_layer, batchnorm_from, max_channels,
backbone_fro... | 4,940 | 37.601563 | 120 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/modeling/unet.py | import torch
from torch import nn as nn
from functools import partial
from iharm.model.modeling.basic_blocks import ConvBlock
from iharm.model.ops import FeaturesConnector
class UNetEncoder(nn.Module):
def __init__(
self,
depth, ch,
norm_layer, batchnorm_from, max_channels,
backbo... | 7,279 | 38.351351 | 110 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/modeling/resnetv1b.py | import torch
import torch.nn as nn
GLUON_RESNET_TORCH_HUB = 'rwightman/pytorch-pretrained-gluonresnet'
class BasicBlockV1b(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None,
previous_dilation=1, norm_layer=nn.BatchNorm2d):
super(Basi... | 10,805 | 38.01083 | 112 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/model/modeling/dbp.py | import torch
from torch import nn as nn
from iharm.model.modeling.basic_blocks import ConvBlock, DBDownsample, DBUpsample, UpBlock, DownBlock
import torch.nn.functional as F
class SimpleRefine(nn.Module):
def __init__(self, feature_channels = 0, in_channel = 6, inner_channel = 32,
norm_layer = nn... | 11,477 | 45.469636 | 154 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/inference/predictor.py | import torch
from iharm.inference.transforms import NormalizeTensor, PadToDivisor, ToTensor, AddFlippedTensor
class Predictor(object):
def __init__(self, net, device, with_flip=False,
mean=(.485, .456, .406), std=(.229, .224, .225)):
self.device = device
self.net = net.to(self.dev... | 1,432 | 32.325581 | 96 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/inference/evaluation.py | from time import time
from tqdm import trange
import torch
def evaluate_dataset(dataset, predictor, metrics_hub):
for sample_i in trange(len(dataset), desc=f'Testing on {metrics_hub.name}'):
sample = dataset.get_sample(sample_i)
sample = dataset.augment_sample(sample)
sample_mask = sample... | 841 | 39.095238 | 104 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/iharm/inference/transforms.py | import cv2
import torch
from collections import namedtuple
class EvalTransform:
def __init__(self):
pass
def transform(self, image, mask):
raise NotImplementedError
def inv_transform(self, image):
raise NotImplementedError
class PadToDivisor(EvalTransform):
"""
Pad side... | 3,462 | 31.980952 | 114 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/tridistribute/setup.py | from setuptools import setup
import torch
from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CppExtension
if torch.cuda.is_available():
print('Including CUDA code.')
setup(
name='tridistribute',
ext_modules=[
CUDAExtension('tridistribute', [
'src/tr... | 701 | 29.521739 | 81 | py |
Video-Harmonization-Dataset-HYouTube | Video-Harmonization-Dataset-HYouTube-master/CO2Net/trilinear/setup.py | from setuptools import setup
import torch
from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CppExtension
if torch.cuda.is_available():
print('Including CUDA code.')
setup(
name='trilinear',
ext_modules=[
CUDAExtension('trilinear', [
'src/trilinear_... | 673 | 28.304348 | 81 | py |
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