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
value |
|---|---|---|---|---|---|---|
w2ot | w2ot-main/w2ot/external/benchmark/icnn.py | # This file is from
# https://github.com/iamalexkorotin/Wasserstein1Benchmark/commit/647a1acc85f88e207733d087cbe87987cc0dea06
# and remains under the original licensing.
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
from .unet import UNet
from .resnet2 import Res... | 12,359 | 37.504673 | 116 | py |
w2ot | w2ot-main/w2ot/external/benchmark/unet.py | # This file is from
# https://github.com/iamalexkorotin/Wasserstein1Benchmark/commit/647a1acc85f88e207733d087cbe87987cc0dea06
# and remains under the original licensing.
import torch
import torch.nn as nn
import torch.nn.functional as F
import functools
class DoubleConv(nn.Module):
"""(convolution => [BN] => ... | 4,065 | 34.666667 | 122 | py |
w2ot | w2ot-main/w2ot/external/benchmark/metrics.py | # This file is from
# https://github.com/iamalexkorotin/Wasserstein1Benchmark/commit/647a1acc85f88e207733d087cbe87987cc0dea06
# and remains under the original licensing.
import torch
import numpy as np
import jax.numpy as jnp
from .tools import freeze
import gc
def score_fitted_maps(benchmark, D, D_conj, size=4096... | 3,293 | 41.230769 | 116 | py |
w2ot | w2ot-main/w2ot/external/benchmark/map_benchmark.py | # This file is from
# https://github.com/iamalexkorotin/Wasserstein1Benchmark/commit/647a1acc85f88e207733d087cbe87987cc0dea06
# and remains under the original licensing.
import torch
import torch.nn as nn
import numpy as np
from scipy.linalg import sqrtm
from . import potentials
from . import distributions
from .ic... | 8,579 | 33.736842 | 128 | py |
w2ot | w2ot-main/w2ot/external/benchmark/fid_score.py | #!/usr/bin/env python3
# This file is from
# https://github.com/iamalexkorotin/Wasserstein1Benchmark/commit/647a1acc85f88e207733d087cbe87987cc0dea06
# and remains under the original licensing.
"""Calculates the Frechet Inception Distance (FID) to evalulate GANs
The FID metric calculates the distance between two distr... | 9,926 | 36.041045 | 105 | py |
w2ot | w2ot-main/w2ot/models/init_nn.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
from functools import partial
import numpy as np
import jax
import jax.numpy as jnp
from jax import dtypes
from jax.random import PRNGKey
from flax import linen as nn
from flax.linen.linear import default_kernel_init, \
_conv_dimension_numbers
from dataclass... | 1,059 | 22.043478 | 66 | py |
w2ot | w2ot-main/w2ot/models/potential_nn.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
from functools import partial
import numpy as np
import jax
import jax.numpy as jnp
from jax import dtypes
from jax.random import PRNGKey
from flax import linen as nn
from typing import Any, Optional, Callable, Sequence, Tuple, Union
from w2ot import utils
clas... | 1,154 | 22.571429 | 71 | py |
w2ot | w2ot-main/w2ot/models/potential_conv.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
from functools import partial
import numpy as np
import jax
import jax.numpy as jnp
from jax import dtypes
from jax.random import PRNGKey
from flax import linen as nn
from typing import Any, Optional, Callable, Sequence, Tuple, Union
from w2ot import utils
clas... | 1,794 | 24.642857 | 67 | py |
mganprior | mganprior-master/inpainting.py | import os
import argparse
import math, time
import torch
import cv2
from utils.file_utils import image_files, load_as_tensor, Tensor2PIL, split_to_batches
from utils.image_precossing import _sigmoid_to_tanh, _tanh_to_sigmoid, _add_batch_one
from derivable_models.derivable_generator import get_derivable_generator
from ... | 6,547 | 51.384 | 162 | py |
mganprior | mganprior-master/face_semantic_editing.py | import os
import argparse
import torch
import cv2
from utils.manipulate import convert_array_to_images, get_interpolated_wp, get_boundary, BOUNDARY_DIR
from utils.file_utils import image_files, load_as_tensor
from utils.image_precossing import _sigmoid_to_tanh, _add_batch_one
from derivable_models.derivable_generator ... | 7,017 | 49.128571 | 123 | py |
mganprior | mganprior-master/multi_code_inversion.py | import os
import argparse
import torch
import cv2
from utils.file_utils import image_files, load_as_tensor, Tensor2PIL, split_to_batches
from utils.image_precossing import _sigmoid_to_tanh, _tanh_to_sigmoid, _add_batch_one
from derivable_models.derivable_generator import get_derivable_generator
from inversion.inversio... | 5,683 | 49.300885 | 129 | py |
mganprior | mganprior-master/colorization.py | import os
import argparse
import numpy as np
import torch
from PIL import Image
import math, time
import cv2
from utils.file_utils import image_files, load_as_tensor, Tensor2PIL, split_to_batches
from utils.image_precossing import _sigmoid_to_tanh, _tanh_to_sigmoid, _add_batch_one
from derivable_models.derivable_gener... | 6,673 | 51.140625 | 140 | py |
mganprior | mganprior-master/super_resolution.py | import os
import argparse
import torch
import cv2
from utils.file_utils import image_files, load_as_tensor, Tensor2PIL, split_to_batches
from utils.image_precossing import _sigmoid_to_tanh, _tanh_to_sigmoid, _add_batch_one
from derivable_models.derivable_generator import get_derivable_generator
from utils.manipulate i... | 6,432 | 49.257813 | 129 | py |
mganprior | mganprior-master/inversion/losses.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models.vgg import vgg16, vgg19
def get_loss(loss_name, args):
if loss_name == 'VGG':
return VGGLoss(args.vgg_layer, args)
elif loss_name == 'L1':
return nn.L1Loss(reduction='mean')
elif loss_name == 'L2':
... | 4,260 | 39.580952 | 120 | py |
mganprior | mganprior-master/inversion/inversion_methods.py | from tqdm import tqdm
import copy
import torch
import torch.nn as nn
import torch.optim as optim
def get_inversion(inversion_type, args):
if inversion_type == 'GD':
return GradientDescent(args.iterations, args.lr, optimizer=optim.SGD, args=args)
elif inversion_type == 'Adam':
return GradientD... | 2,004 | 35.454545 | 94 | py |
mganprior | mganprior-master/models/model_settings.py | # python 3.7
"""Contains basic configurations for models used in this project.
Please download the public released models from the following repositories
OR train your own models, and then put them into the folder
`pretrain/tensorflow`.
PGGAN: https://github.com/tkarras/progressive_growing_of_gans
StyleGAN: https://g... | 26,659 | 42.633388 | 114 | py |
mganprior | mganprior-master/models/stylegan2_generator.py | # python 3.7
"""Contains the generator class of StyleGAN2.
This class is derived from the `BaseGenerator` class defined in
`base_generator.py`.
"""
import numpy as np
import torch
from . import model_settings
from .base_generator import BaseGenerator
from .stylegan2_generator_network import StyleGAN2GeneratorNet
_... | 11,859 | 39.477816 | 80 | py |
mganprior | mganprior-master/models/pggan_generator.py | # python 3.7
"""Contains the generator class of PGGAN.
This class is derived from the `BaseGenerator` class defined in
`base_generator.py`.
"""
import numpy as np
import torch
from .base_generator import BaseGenerator
from .pggan_generator_network import PGGANGeneratorNet
__all__ = ['PGGANGenerator']
class PGGAN... | 5,314 | 36.167832 | 80 | py |
mganprior | mganprior-master/models/base_generator.py | # python 3.7
"""Contains the base class for generator in a GAN model."""
import os.path
import sys
import logging
import numpy as np
import torch
from . import model_settings
__all__ = ['BaseGenerator']
def get_temp_logger(logger_name='logger'):
"""Gets a temporary logger.
This logger will print all levels o... | 11,057 | 33.664577 | 80 | py |
mganprior | mganprior-master/models/stylegan_generator_network.py | # python 3.7
"""Contains the implementation of generator described in StyleGAN.
Different from the official tensorflow version in folder `stylegan_tf_official`,
this is a simple pytorch version which only contains the generator part. This
class is specially used for inference.
For more details, please check the origi... | 30,448 | 37.494311 | 104 | py |
mganprior | mganprior-master/models/pggan_generator_network.py | # python 3.7
"""Contains the implementation of generator described in PGGAN.
Different from the official tensorflow version in folder `pggan_tf_official`,
this is a simple pytorch version which only contains the generator part. This
class is specially used for inference.
For more details, please check the original pa... | 11,276 | 35.97377 | 80 | py |
mganprior | mganprior-master/models/stylegan_generator.py | # python 3.7
"""Contains the generator class of StyleGAN.
This class is derived from the `BaseGenerator` class defined in
`base_generator.py`.
"""
import numpy as np
import torch
from . import model_settings
from .base_generator import BaseGenerator
from .stylegan_generator_network import StyleGANGeneratorNet
__al... | 12,441 | 40.062706 | 80 | py |
mganprior | mganprior-master/models/stylegan2_generator_network.py | # python 3.7
"""Contains the implementation of generator described in StyleGAN2.
Different from the official tensorflow version in folder
`stylegan2_tf_official`, this is a simple pytorch version which only contains
the generator part. This class is specially used for inference.
NOTE: Compared to that of StyleGAN, th... | 31,291 | 37.395092 | 83 | py |
mganprior | mganprior-master/derivable_models/gan_utils.py | import numpy as np
import os
import torch
import torch.nn as nn
from models.helper import build_generator
PGGAN_Inter_Output_Layer_256 = [-1, 17, 14, 11, 8, 5, 2]
PGGAN_Inter_Output_Layer_1024 = [-1, 23, 20, 17, 14, 11, 8, 5, 2]
def standard_z_sample(size, depth, device=None):
'''
Generate a standard set o... | 1,393 | 29.304348 | 120 | py |
mganprior | mganprior-master/derivable_models/derivable_generator.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .gan_utils import get_gan_model
PGGAN_LATENT_1024 = [(512, 1, 1),
(512, 4, 4), (512, 4, 4),
(512, 8, 8), (512, 8, 8),
(512, 16, 16), (512, 16, 16),
(512, 32, 32), (512, 32, 32),
... | 4,875 | 34.852941 | 145 | py |
mganprior | mganprior-master/utils/manipulate.py | import numpy as np
import cv2
from math import sqrt, ceil
from PIL import Image
import torch
import torch.nn.functional as F
from .file_utils import Tensor2PIL, PIL2Tensor
from .image_precossing import _add_batch_one
BOUNDARY_DIR = './boundaries'
LEVEL = { # for style mixing
'coarse': (0, 4),
'middle': (4, ... | 7,697 | 31.897436 | 103 | py |
mganprior | mganprior-master/utils/file_utils.py | import os
from PIL import Image
import torchvision
IMG_EXTENSIONS = ['jpg', 'jpeg', 'png', 'ppm', 'bmp', 'pgm']
def mkdir(path):
if not os.path.exists(path):
os.makedirs(path)
def pil_loader(path, mode='RGB'):
"""
open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pil... | 2,146 | 27.25 | 126 | py |
GLIP | GLIP-main/setup.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#!/usr/bin/env python
import glob
import os
import torch
from setuptools import find_packages
from setuptools import setup
from torch.utils.cpp_extension import CUDA_HOME
from torch.utils.cpp_extension import CppExtension
from torch.utils.cpp_ext... | 1,967 | 28.373134 | 99 | py |
GLIP | GLIP-main/tools/visualize_grounding_net.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Set up custom environment before nearly anything else is imported
# NOTE: this should be the first import (no not reorder)
from maskrcnn_benchmark.utils.env import setup_environment # noqa F401 isort:skip
import argparse
import os
import matplo... | 12,352 | 37.009231 | 124 | py |
GLIP | GLIP-main/tools/test_net.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Set up custom environment before nearly anything else is imported
# NOTE: this should be the first import (no not reorder)
from maskrcnn_benchmark.utils.env import setup_environment # noqa F401 isort:skip
import argparse
import os
import torch... | 4,839 | 36.230769 | 114 | py |
GLIP | GLIP-main/tools/eval_all.py | """Script to evaluate all checkpoints for the trained model.
OUTPUT_DIR has to contain trained checkpoints.
MODEL.WEIGHT parameter will be ignored.
"""
# NOTE: this should be the first import (no not reorder)
from maskrcnn_benchmark.utils.env import setup_environment # noqa F401 isort:skip
import argparse
import os
... | 5,201 | 34.148649 | 82 | py |
GLIP | GLIP-main/tools/finetune.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
r"""
Basic training script for PyTorch
"""
# Set up custom environment before nearly anything else is imported
# NOTE: this should be the first import (no not reorder)
from maskrcnn_benchmark.utils.env import setup_environment # noqa F401 isort:s... | 18,331 | 38.508621 | 232 | py |
GLIP | GLIP-main/tools/test_grounding_net.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Set up custom environment before nearly anything else is imported
# NOTE: this should be the first import (no not reorder)
from maskrcnn_benchmark.utils.env import setup_environment # noqa F401 isort:skip
import argparse
import os
import torch... | 8,649 | 37.789238 | 128 | py |
GLIP | GLIP-main/tools/train_net.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
r"""
Basic training script for PyTorch
"""
# Set up custom environment before nearly anything else is imported
# NOTE: this should be the first import (no not reorder)
from maskrcnn_benchmark.utils.env import setup_environment # noqa F401 isort:s... | 8,349 | 31.617188 | 102 | py |
GLIP | GLIP-main/maskrcnn_benchmark/solver/lr_scheduler.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from bisect import bisect_right
import math
import torch
# FIXME ideally this would be achieved with a CombinedLRScheduler,
# separating MultiStepLR with WarmupLR
# but the current LRScheduler design doesn't allow it
class WarmupMultiStepLR(torc... | 5,800 | 34.371951 | 144 | py |
GLIP | GLIP-main/maskrcnn_benchmark/solver/build.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import itertools
from .lr_scheduler import WarmupMultiStepLR, WarmupCosineAnnealingLR, WarmupReduceLROnPlateau
def make_optimizer(cfg, model):
def maybe_add_full_model_gradient_clipping(optim): # optim: the optimizer class
... | 4,457 | 37.102564 | 117 | py |
GLIP | GLIP-main/maskrcnn_benchmark/layers/nms.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from maskrcnn_benchmark import _C
try:
import torchvision
from torchvision.ops import nms
except:
nms = _C.nms
ml_nms = _C.ml_nms
soft_nms = _C.soft_nms
# nms.__doc__ = """
# This function performs Non-maximum suppresion"""
| 311 | 19.8 | 71 | py |
GLIP | GLIP-main/maskrcnn_benchmark/layers/batch_norm.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
import torch.distributed as dist
import maskrcnn_benchmark.utils.comm as comm
from torch.autograd.function import Function
class FrozenBatchNorm2d(nn.Module):
"""
BatchNorm2d where the batch statistics an... | 4,925 | 41.102564 | 99 | py |
GLIP | GLIP-main/maskrcnn_benchmark/layers/deform_pool.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .deform_conv import DeformConv2d
def add_conv(in_ch, out_ch, ksize, stride, leaky=True):
"""
Add a conv2d / batchnorm / leaky ReLU block.
Args:
in_ch (int): number of input channels of the convolution layer.
out_ch (in... | 17,079 | 39.283019 | 122 | py |
GLIP | GLIP-main/maskrcnn_benchmark/layers/deform_conv.py | import torch
import math
from torch import nn
from torch.nn import init
from torch.nn.modules.utils import _pair
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from maskrcnn_benchmark.utils.amp import custom_fwd, custom_bwd
from maskrcnn_benchmark import _C
class DeformCon... | 14,204 | 31.505721 | 83 | py |
GLIP | GLIP-main/maskrcnn_benchmark/layers/iou_loss.py | import torch
from torch import nn
class IOULoss(nn.Module):
def __init__(self, loss_type="iou"):
super(IOULoss, self).__init__()
self.loss_type = loss_type
def forward(self, pred, target, weight=None):
pred_left = pred[:, 0]
pred_top = pred[:, 1]
pred_right = pred[:, 2... | 2,849 | 33.337349 | 95 | py |
GLIP | GLIP-main/maskrcnn_benchmark/layers/roi_pool.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from maskrcnn_benchmark import _C
class _ROIPool(Function):
@staticmethod
... | 1,855 | 28 | 74 | py |
GLIP | GLIP-main/maskrcnn_benchmark/layers/roi_align.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from maskrcnn_benchmark import _C
class _ROIAlign(Function):
@staticmethod
... | 2,944 | 31.722222 | 96 | py |
GLIP | GLIP-main/maskrcnn_benchmark/layers/dropblock.py | import torch
import torch.nn.functional as F
from torch import nn
class DropBlock2D(nn.Module):
r"""Randomly zeroes 2D spatial blocks of the input tensor.
As described in the paper
`DropBlock: A regularization method for convolutional networks`_ ,
dropping whole blocks of feature map allows to remove... | 4,439 | 29.410959 | 98 | py |
GLIP | GLIP-main/maskrcnn_benchmark/layers/smooth_l1_loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
# TODO maybe push this to nn?
def smooth_l1_loss(input, target, beta=1. / 9, size_average=True):
"""
very similar to the smooth_l1_loss from pytorch, but with
the extra beta parameter
"""
n = torch.abs(input - tar... | 481 | 27.352941 | 71 | py |
GLIP | GLIP-main/maskrcnn_benchmark/layers/sigmoid_focal_loss.py | import torch
from torch import nn
import torch.nn.functional as F
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from maskrcnn_benchmark import _C
# TODO: Use JIT to replace CUDA implementation in the future.
class _SigmoidFocalLoss(Function):
@staticmethod
def fo... | 7,335 | 36.050505 | 120 | py |
GLIP | GLIP-main/maskrcnn_benchmark/layers/evonorm.py | import torch
import torch.nn as nn
class EvoNorm2d(nn.Module):
__constants__ = ['num_features', 'eps', 'nonlinearity']
def __init__(self, num_features, eps=1e-5, nonlinearity=True, group=32):
super(EvoNorm2d, self).__init__()
self.num_features = num_features
self.eps = eps
se... | 1,305 | 31.65 | 80 | py |
GLIP | GLIP-main/maskrcnn_benchmark/layers/set_loss.py | import torch
import torch.nn.functional as F
import torch.distributed as dist
from torch import nn
from scipy.optimize import linear_sum_assignment
from torch.cuda.amp import custom_fwd, custom_bwd
def box_area(boxes):
return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
# modified from torchvision... | 16,542 | 43.47043 | 130 | py |
GLIP | GLIP-main/maskrcnn_benchmark/layers/misc.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
helper class that supports empty tensors on some nn functions.
Ideally, add support directly in PyTorch to empty tensors in
those functions.
This can be removed once https://github.com/pytorch/pytorch/issues/12013
is implemented
"""
import m... | 6,666 | 31.364078 | 88 | py |
GLIP | GLIP-main/maskrcnn_benchmark/layers/dyrelu.py | import torch
import torch.nn as nn
import torch.nn.functional as F
def _make_divisible(v, divisor, min_value=None):
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_v... | 3,836 | 30.710744 | 102 | py |
GLIP | GLIP-main/maskrcnn_benchmark/layers/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .batch_norm import FrozenBatchNorm2d, NaiveSyncBatchNorm2d
from .misc import Conv2d, _NewEmptyTensorOp
from .misc import ConvTranspose2d
from .misc import DFConv2d
from .misc import interpolate
from .misc import Scale
from .nms i... | 1,510 | 42.171429 | 111 | py |
GLIP | GLIP-main/maskrcnn_benchmark/layers/dyhead.py | import torch
import torch.nn.functional as F
from torch import nn
from .deform_conv import ModulatedDeformConv
from .dyrelu import h_sigmoid, DYReLU
class Conv3x3Norm(torch.nn.Module):
def __init__(self,
in_channels,
out_channels,
stride,
deform... | 5,055 | 32.483444 | 111 | py |
GLIP | GLIP-main/maskrcnn_benchmark/layers/se.py | from torch import nn
class SELayer(nn.Module):
def __init__(self, channel, reduction=16):
super(SELayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction, bias=False),
nn.ReLU(inplace=True),
... | 1,770 | 33.057692 | 99 | py |
GLIP | GLIP-main/maskrcnn_benchmark/engine/inference.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import datetime
import logging
import time
import os
import re
import torch
from tqdm import tqdm
from collections import defaultdict
from maskrcnn_benchmark.data.datasets.evaluation import evaluate, im_detect_bbox_aug
from ..utils.comm import is... | 24,835 | 38.801282 | 194 | py |
GLIP | GLIP-main/maskrcnn_benchmark/engine/stage_trainer.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import datetime
import logging
import time
import torch
import torch.distributed as dist
from maskrcnn_benchmark.utils.comm import get_world_size
from maskrcnn_benchmark.utils.metric_logger import MetricLogger
def reduce_loss_dict(all_loss_dict... | 7,270 | 38.302703 | 106 | py |
GLIP | GLIP-main/maskrcnn_benchmark/engine/evolution.py |
import time
import pickle
import logging
import os
import numpy as np
import torch
import torch.nn as nn
from collections import OrderedDict
from yaml import safe_dump
from yacs.config import load_cfg, CfgNode#, _to_dict
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.engine.inference import _accum... | 12,687 | 34.441341 | 136 | py |
GLIP | GLIP-main/maskrcnn_benchmark/engine/singlepath_trainer.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import datetime
import logging
import time
import random
import torch
import torch.distributed as dist
from maskrcnn_benchmark.utils.comm import get_world_size, synchronize, broadcast_data
from maskrcnn_benchmark.utils.metric_logger import MetricLo... | 4,935 | 33.760563 | 145 | py |
GLIP | GLIP-main/maskrcnn_benchmark/engine/predictor.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import cv2
import torch
import numpy as np
from torchvision import transforms as T
from maskrcnn_benchmark.modeling.detector import build_detection_model
from maskrcnn_benchmark.utils.checkpoint import DetectronCheckpointer
from maskrcnn_benchmark... | 20,346 | 34.822183 | 107 | py |
GLIP | GLIP-main/maskrcnn_benchmark/engine/predictor_glip.py | import cv2
import torch
import re
import numpy as np
from typing import List, Union
import nltk
import inflect
from transformers import AutoTokenizer
from torchvision import transforms as T
import pdb
from maskrcnn_benchmark.modeling.detector import build_detection_model
from maskrcnn_benchmark.utils.checkpoint import ... | 18,662 | 38.624204 | 183 | py |
GLIP | GLIP-main/maskrcnn_benchmark/engine/alter_trainer.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import datetime
import logging
import time
import torch
import torch.distributed as dist
from maskrcnn_benchmark.utils.comm import get_world_size
from maskrcnn_benchmark.utils.metric_logger import MetricLogger
def reduce_loss_dict(all_loss_dict... | 4,431 | 33.625 | 93 | py |
GLIP | GLIP-main/maskrcnn_benchmark/engine/trainer.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import datetime
import logging
import sys
import os
import math
import time
import torch
import torch.distributed as dist
from maskrcnn_benchmark.utils.comm import get_world_size, all_gather, is_main_process, broadcast_data, get_rank
from maskrcn... | 15,719 | 42.545706 | 130 | py |
GLIP | GLIP-main/maskrcnn_benchmark/utils/c2_model_loading.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import logging
import pickle
from collections import OrderedDict
import torch
from maskrcnn_benchmark.utils.model_serialization import load_state_dict
from maskrcnn_benchmark.utils.registry import Registry
def _rename_basic_resnet_weights(layer... | 8,396 | 39.370192 | 129 | py |
GLIP | GLIP-main/maskrcnn_benchmark/utils/metric_logger.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from collections import defaultdict
from collections import deque
import torch
import time
from datetime import datetime
from .comm import is_main_process
class SmoothedValue(object):
"""Track a series of values and provide access to smoothe... | 3,687 | 27.152672 | 86 | py |
GLIP | GLIP-main/maskrcnn_benchmark/utils/checkpoint.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import logging
import os
import torch
from maskrcnn_benchmark.utils.model_serialization import load_state_dict
from maskrcnn_benchmark.utils.c2_model_loading import load_c2_format
from maskrcnn_benchmark.utils.big_model_loading import load_big_fo... | 6,117 | 36.304878 | 98 | py |
GLIP | GLIP-main/maskrcnn_benchmark/utils/fuse_helper.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import pdb
import math
from maskrcnn_benchmark.modeling.utils import cat, concat_box_prediction_layers, permute_and_flatten
from timm.models.layers import DropPath
from transformers.activations import ACT2FN
class BertPredictionHeadTransform(nn.Module)... | 26,691 | 42.829228 | 153 | py |
GLIP | GLIP-main/maskrcnn_benchmark/utils/flops.py | import argparse
import logging
import torch
import torch.nn as nn
import timeit
from maskrcnn_benchmark.layers import *
from maskrcnn_benchmark.modeling.backbone.resnet_big import StdConv2d
from maskrcnn_benchmark.modeling.backbone.fpn import *
from maskrcnn_benchmark.modeling.rpn.inference import *
from maskrcnn_benc... | 7,203 | 27.931727 | 109 | py |
GLIP | GLIP-main/maskrcnn_benchmark/utils/comm.py | """
This file contains primitives for multi-gpu communication.
This is useful when doing distributed training.
"""
import pickle
import time
import functools
import logging
import torch
import torch.distributed as dist
import numpy as np
def get_world_size():
if not dist.is_available():
return 1
if n... | 4,423 | 27.178344 | 84 | py |
GLIP | GLIP-main/maskrcnn_benchmark/utils/stats.py | '''
Copyright (C) 2019 Sovrasov V. - All Rights Reserved
* You may use, distribute and modify this code under the
* terms of the MIT license.
* You should have received a copy of the MIT license with
* this file. If not visit https://opensource.org/licenses/MIT
'''
import sys
from functools import partial
import ... | 17,622 | 33.554902 | 90 | py |
GLIP | GLIP-main/maskrcnn_benchmark/utils/pretrain_model_loading.py | import numpy as np
import torch
import torch.nn as nn
from collections import OrderedDict
def _remove_bn_statics(state_dict):
layer_keys = sorted(state_dict.keys())
remove_list = []
for key in layer_keys:
if 'running_mean' in key or 'running_var' in key or 'num_batches_tracked' in key:
... | 1,647 | 31.96 | 89 | py |
GLIP | GLIP-main/maskrcnn_benchmark/utils/model_zoo.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import os
import sys
try:
from torch.hub import _download_url_to_file
from torch.hub import urlparse
from torch.hub import HASH_REGEX
except ImportError:
from torch.utils.model_zoo import _download_url_to_file
from torch.utils.... | 3,041 | 48.064516 | 135 | py |
GLIP | GLIP-main/maskrcnn_benchmark/utils/mdetr_dist.py | # Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Utilities related to distributed mode.
By default, the reduce of metrics and such are done on GPU, since it's more straightforward (we... | 6,818 | 28.647826 | 119 | py |
GLIP | GLIP-main/maskrcnn_benchmark/utils/big_model_loading.py | import numpy as np
import torch
import torch.nn as nn
from collections import OrderedDict
def tf2th(conv_weights):
"""Possibly convert HWIO to OIHW."""
if conv_weights.ndim == 4:
conv_weights = conv_weights.transpose([3, 2, 0, 1])
return torch.from_numpy(conv_weights)
def _rename_conv_weights_f... | 3,188 | 38.37037 | 104 | py |
GLIP | GLIP-main/maskrcnn_benchmark/utils/collect_env.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import PIL
from torch.utils.collect_env import get_pretty_env_info
def get_pil_version():
return "\n Pillow ({})".format(PIL.__version__)
def collect_env_info():
env_str = get_pretty_env_info()
env_str += get_pil_version()
... | 338 | 21.6 | 71 | py |
GLIP | GLIP-main/maskrcnn_benchmark/utils/model_serialization.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from collections import OrderedDict, defaultdict
import logging
import math
import torch
from maskrcnn_benchmark.utils.imports import import_file
def resize_2d(posemb, shape_new):
# Rescale the grid of position embeddings when loading from st... | 7,256 | 45.22293 | 132 | py |
GLIP | GLIP-main/maskrcnn_benchmark/utils/shallow_contrastive_loss_helper.py | import torch
import maskrcnn_benchmark.utils.dist as dist
def normalized_positive_map(positive_map):
positive_map = positive_map.float()
positive_map_num_pos = positive_map.sum(2)
positive_map_num_pos[positive_map_num_pos == 0] = 1e-6
positive_map = positive_map / positive_map_num_pos.unsqueeze(-1)
... | 2,101 | 35.241379 | 99 | py |
GLIP | GLIP-main/maskrcnn_benchmark/utils/amp.py | from contextlib import contextmanager
@contextmanager
def nullcontext(enter_result=None, **kwargs):
yield enter_result
try:
from torch.cuda.amp import autocast, GradScaler, custom_fwd, custom_bwd
except:
print('[Warning] Library for automatic mixed precision is not found, AMP is disabled!!')
GradScale... | 420 | 29.071429 | 92 | py |
GLIP | GLIP-main/maskrcnn_benchmark/utils/dist.py | # Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Utilities related to distributed mode.
By default, the reduce of metrics and such are done on GPU, since it's more straightforward (we... | 6,744 | 28.454148 | 119 | py |
GLIP | GLIP-main/maskrcnn_benchmark/utils/ema.py | from copy import deepcopy
from collections import OrderedDict
import torch
class ModelEma:
def __init__(self, model, decay=0.9999, device=''):
self.ema = deepcopy(model)
self.ema.eval()
self.decay = decay
self.device = device
if device:
self.ema.to(device=device... | 1,644 | 34 | 84 | py |
GLIP | GLIP-main/maskrcnn_benchmark/utils/imports.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
if torch._six.PY37:
import importlib
import importlib.util
import sys
# from https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path?utm_medium=organic&utm_source=google_rich_qa&utm_campa... | 844 | 34.208333 | 168 | py |
GLIP | GLIP-main/maskrcnn_benchmark/data/collate_batch.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from maskrcnn_benchmark.structures.image_list import to_image_list
import pdb
class BatchCollator(object):
"""
From a list of samples from the dataset,
returns the batched images and targets.
This should be passed to t... | 3,860 | 40.074468 | 111 | py |
GLIP | GLIP-main/maskrcnn_benchmark/data/build.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import bisect
import copy
import logging
import os
import torch.utils.data
import torch.distributed as dist
from maskrcnn_benchmark.utils.comm import get_world_size
from maskrcnn_benchmark.utils.imports import import_file
from . import datasets a... | 22,626 | 45.177551 | 182 | py |
GLIP | GLIP-main/maskrcnn_benchmark/data/datasets/modulated_coco.py | import logging
import os
import os.path
import math
from PIL import Image, ImageDraw
import random
import numpy as np
import torch
import torchvision
import torch.utils.data as data
from pycocotools import mask as coco_mask
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.struct... | 26,513 | 39.479389 | 182 | py |
GLIP | GLIP-main/maskrcnn_benchmark/data/datasets/caption.py | import torch
import torch.distributed as dist
import time
from torchvision.ops import nms
import random
import numpy as np
from PIL import Image, ImageDraw
import pdb
from maskrcnn_benchmark.structures.bounding_box import BoxList
from .modulated_coco import ConvertCocoPolysToMask
from .tsv import ODTSVDataset, TSVYamlD... | 13,112 | 45.832143 | 861 | py |
GLIP | GLIP-main/maskrcnn_benchmark/data/datasets/voc.py | import os
import torch
import torch.utils.data
from PIL import Image
import sys
if sys.version_info[0] == 2:
import xml.etree.cElementTree as ET
else:
import xml.etree.ElementTree as ET
from maskrcnn_benchmark.structures.bounding_box import BoxList
class PascalVOCDataset(torch.utils.data.Dataset):
CL... | 4,121 | 29.533333 | 118 | py |
GLIP | GLIP-main/maskrcnn_benchmark/data/datasets/object365.py | import torch
import torchvision
import torch.utils.data as data
from maskrcnn_benchmark.data.datasets.coco_dt import CocoDetectionTSV
class Object365DetectionTSV(CocoDetectionTSV):
pass
| 192 | 20.444444 | 69 | py |
GLIP | GLIP-main/maskrcnn_benchmark/data/datasets/vg.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import collections
import json
import os.path as op
import numpy as np
import torch
from .tsv import TSVYamlDataset, find_file_path_in_yaml
from .box_label_loader import BoxLabelLoader
from maskrcnn_benchmark.data.datasets.coco_dt import CocoDete... | 10,744 | 39.093284 | 112 | py |
GLIP | GLIP-main/maskrcnn_benchmark/data/datasets/refexp.py | import copy
from collections import defaultdict
from pathlib import Path
import torch
import torch.utils.data
import maskrcnn_benchmark.utils.dist as dist
from maskrcnn_benchmark.layers.set_loss import generalized_box_iou
from .modulated_coco import ModulatedDataset
class RefExpDataset(ModulatedDataset):
pass
... | 3,270 | 35.752809 | 101 | py |
GLIP | GLIP-main/maskrcnn_benchmark/data/datasets/custom_distributed_sampler.py | import math
from typing import TypeVar, Optional, Iterator
import torch
from torch.utils.data import Sampler, Dataset
import torch.distributed as dist
import random
import numpy as np
import torch
class DistributedSamplerChunkByNode(torch.utils.data.Sampler):
def __init__(self,
dataset,
... | 7,776 | 40.811828 | 146 | py |
GLIP | GLIP-main/maskrcnn_benchmark/data/datasets/concat_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import bisect
from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
class ConcatDataset(_ConcatDataset):
"""
Same as torch.utils.data.dataset.ConcatDataset, but exposes an extra
method for querying the sizes of the ima... | 766 | 30.958333 | 72 | py |
GLIP | GLIP-main/maskrcnn_benchmark/data/datasets/box_label_loader.py | import torch
import numpy as np
import math
import base64
import collections
import pycocotools.mask as mask_utils
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask
class LabelLoader(object):
def __init__(self, labelmap, ex... | 11,214 | 43.503968 | 118 | py |
GLIP | GLIP-main/maskrcnn_benchmark/data/datasets/mixup.py | """Mixup detection dataset wrapper."""
from __future__ import absolute_import
import numpy as np
import torch
import torch.utils.data as data
class MixupDetection(data.Dataset):
"""Detection dataset wrapper that performs mixup for normal dataset.
Parameters
----------
dataset : mx.gluon.data.Dataset
... | 4,884 | 38.08 | 92 | py |
GLIP | GLIP-main/maskrcnn_benchmark/data/datasets/background.py | import os
import os.path
import json
from PIL import Image
import torch
import torchvision
import torch.utils.data as data
from maskrcnn_benchmark.structures.bounding_box import BoxList
class Background(data.Dataset):
""" Background
Args:
root (string): Root directory where images are downloaded to.
... | 1,577 | 28.773585 | 96 | py |
GLIP | GLIP-main/maskrcnn_benchmark/data/datasets/coco_dt.py | """
COCO dataset which returns image_id for evaluation.
Mostly copy-paste from https://github.com/pytorch/vision/blob/13b35ff/references/detection/coco_utils.py
"""
import torch
import json
from PIL import Image, ImageDraw
from .modulated_coco import ConvertCocoPolysToMask
from .tsv import ODTSVDataset
from pycocoto... | 6,130 | 38.554839 | 176 | py |
GLIP | GLIP-main/maskrcnn_benchmark/data/datasets/flickr.py | import torch
import torchvision
import torch.utils.data as data
from maskrcnn_benchmark.data.datasets.modulated_coco import ModulatedDataset
class FlickrDataset(ModulatedDataset):
pass
| 191 | 20.333333 | 76 | py |
GLIP | GLIP-main/maskrcnn_benchmark/data/datasets/mixed.py | import os
import os.path
from pathlib import Path
from typing import Any, Callable, Optional, Tuple
import torch
from maskrcnn_benchmark.structures.bounding_box import BoxList
from PIL import Image, ImageDraw
from torchvision.datasets.vision import VisionDataset
from .modulated_coco import ConvertCocoPolysToMask, ha... | 5,286 | 35.212329 | 124 | py |
GLIP | GLIP-main/maskrcnn_benchmark/data/datasets/lvis.py | # Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import json
import os
import time
from collections import defaultdict
import pycocotools.mask as mask_utils
import torchvision
from PIL im... | 8,869 | 32.097015 | 116 | py |
GLIP | GLIP-main/maskrcnn_benchmark/data/datasets/pseudo_data.py | import torch
import torch.distributed as dist
import time
from torchvision.ops import nms
import random
import numpy as np
from PIL import Image, ImageDraw
import pdb
from maskrcnn_benchmark.structures.bounding_box import BoxList
from .modulated_coco import ConvertCocoPolysToMask
from .tsv import ODTSVDataset, TSVYamlD... | 9,133 | 38.886463 | 117 | py |
GLIP | GLIP-main/maskrcnn_benchmark/data/datasets/gqa.py | import json
from pathlib import Path
import torch
import torchvision
from .modulated_coco import ConvertCocoPolysToMask, ModulatedDataset
class GQADataset(ModulatedDataset):
pass
class GQAQuestionAnswering(torchvision.datasets.CocoDetection):
def __init__(self, img_folder, ann_file, transforms, return_mas... | 3,662 | 38.815217 | 111 | py |
GLIP | GLIP-main/maskrcnn_benchmark/data/datasets/imagenet.py | import os
import os.path
import json
from PIL import Image
import torch.utils.data as data
def pil_loader(path):
# open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB')
class Im... | 1,965 | 30.206349 | 118 | py |
GLIP | GLIP-main/maskrcnn_benchmark/data/datasets/phrasecut.py | import torch
import torchvision
import torch.utils.data as data
from maskrcnn_benchmark.data.datasets.modulated_coco import ModulatedDataset
class PhrasecutDetection(ModulatedDataset):
pass
| 196 | 20.888889 | 76 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.