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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unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/training/dataset.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any ... | 10,285 | 36.268116 | 158 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/training/networks.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any ... | 74,128 | 46.488149 | 168 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/training/facial_recognition/model_irse.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module
from training.facial_recognition.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm
"""
Modified Backbone implementation fr... | 2,920 | 32.574713 | 109 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/training/facial_recognition/helpers.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from collections import namedtuple
import torch
from torch.nn import Conv2d, BatchNorm2d, PReLU, ReLU, Sigmoid, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module
"""
ArcFace implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytor... | 3,628 | 28.745902 | 112 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/torch_utils/custom_ops.py | # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this sof... | 6,666 | 41.196203 | 146 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/torch_utils/training_stats.py | # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this sof... | 10,720 | 38.855019 | 118 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/torch_utils/persistence.py | # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this sof... | 9,752 | 37.702381 | 144 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/torch_utils/misc.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications t... | 12,213 | 39.177632 | 133 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/torch_utils/distributed_utils.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import logging
import os
import pickle
import random
import socket
import struct
import subprocess
import warnings
import tempfile
import uuid
from datetime import date
from pathlib import Path
from collections import OrderedDict
from typing impo... | 6,869 | 31.102804 | 107 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/torch_utils/ops/hash_sample.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# Please refer to original code: https://github.com/NVlabs/instant-ngp
# and the pytorch wrapper from https://github.com/ashawkey/torch-ngp
import os
import torch
from .. import custom_ops
from torch.cuda.amp import custom_bwd, custom_fwd
_plugi... | 4,112 | 34.456897 | 162 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/torch_utils/ops/bias_act.py | # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this sof... | 9,813 | 45.733333 | 185 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/torch_utils/ops/grid_sample_gradfix.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications ... | 3,245 | 37.642857 | 132 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/torch_utils/ops/conv2d_gradfix.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications t... | 9,537 | 46.452736 | 197 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/torch_utils/ops/upfirdn2d.py | # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this sof... | 16,400 | 41.053846 | 120 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/torch_utils/ops/filtered_lrelu.py | # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this sof... | 12,884 | 45.854545 | 164 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/torch_utils/ops/conv2d_resample.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications t... | 6,837 | 45.835616 | 130 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/torch_utils/ops/nerf_utils.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import torch
from .. import custom_ops
_plugin = None
def _init():
global _plugin
if _plugin is None:
_plugin = custom_ops.get_plugin(
module_name='nerf_utils_plugin',
sources=['nerf_utils.cu'],... | 1,007 | 25.526316 | 97 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/torch_utils/ops/fma.py | # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this sof... | 2,047 | 32.57377 | 105 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/viz/renderer.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications t... | 17,215 | 39.508235 | 164 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/dnnlib/camera.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import numpy as np
from numpy.lib.function_base import angle
import torch
import torch.nn.functional as F
import math
from scipy.spatial.transform import Rotation as Rot
HUGE_NUMBER = 1e10
TINY_NUMBER = 1e-6 # float32 only has 7 decimal digi... | 25,027 | 35.377907 | 140 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/dnnlib/util.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any ... | 18,031 | 32.958569 | 151 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/dnnlib/geometry.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import torch.nn.functional as F
import math
import random
import numpy as np
def positional_encoding(p, size, pe='normal', use_pos=False):
if pe == 'gauss':
p_transformed = np.pi * p @ size
p_transformed = torch.... | 16,292 | 39.031941 | 163 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/dnnlib/filters.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import math
import torch
from torch import nn
from torch.nn import functional as F
def kaiser_attenuation(n_taps, f_h, sr):
df = (2 * f_h) / (sr / 2)
return 2.285 * (n_taps - 1) * math.pi * df + 7.95
def kaiser_beta(n_taps, f_h, sr):
... | 1,946 | 22.743902 | 87 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/tools/inception.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
try:
from torchvision.models.utils import load_state_dict_from_url
except ImportError:
from torch.utils.model_zoo import load_url as load_state_dict_from_url
# Inception weights ported to Pytorch from
# http://download.tenso... | 12,193 | 36.06383 | 140 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/tools/fid_score.py | """Calculates the Frechet Inception Distance (FID) to evalulate GANs
The FID metric calculates the distance between two distributions of images.
Typically, we have summary statistics (mean & covariance matrix) of one
of these distributions, while the 2nd distribution is given by a GAN.
When run as a stand-alone progr... | 9,538 | 35.547893 | 98 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/core/criterions/base.py | import core.utils.managers as managers
from core.utils import global_device, diagnose
import torch
class Criterion(object):
def __init__(self,
wrapper,
models: managers.ModelsManager,
optimizers: managers.OptimizersManager,
lr_schedulers: manager... | 1,996 | 31.737705 | 93 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/core/criterions/ddpm.py | __all__ = ["DTDSM", "DTDSDM", "DTDSDMErr", "CTDSDM", "CTDSDMErr", "CTDSM"]
import torch
from .base import NaiveCriterion
import core.func as func
import logging
def dt_dsm(x0, wrapper, schedule):
n, eps, xn = schedule.sample(x0)
eps_pred = wrapper(xn, n)
return func.sos(eps - eps_pred)
def dt_dsdm(x0,... | 5,444 | 36.040816 | 118 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/core/func/functions.py | import numpy as np
import torch.nn.functional as F
import torch
import math
def bipartition(ts):
if ts.dim() == 4: # bs * 2c * w * w
assert ts.size(1) % 2 == 0
c = ts.size(1) // 2
return ts.split(c, dim=1)
else:
raise NotImplementedError
def stp(s, ts: torch.Tensor): # scal... | 3,977 | 28.466667 | 105 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/core/func/differential.py |
__all__ = ["RequiresGradContext", "differential"]
import torch
import torch.nn as nn
import torch.autograd as autograd
from typing import Union, List
def judge_requires_grad(obj: Union[torch.Tensor, nn.Module]):
if isinstance(obj, torch.Tensor):
return obj.requires_grad
elif isinstance(obj, nn.Modu... | 1,741 | 31.867925 | 102 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/core/diffusion/likelihood.py | import core.func as func
import torch
from .trajectory import _choice_steps
import logging
from .dtdpm import DDPM
from .utils import report_statistics
import numpy as np
from core.evaluate.score import score_on_dataset
from .sde import ReverseSDE, ODE
from scipy import integrate
@ torch.no_grad()
def nelbo_dtdpm(dtd... | 6,853 | 38.165714 | 111 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/core/diffusion/sample.py | import torch
import logging
import math
import numpy as np
import core.func as func
from .dtdpm import DTDPM
from .sde import ReverseSDE, ODE
from .utils import report_statistics
from .trajectory import _choice_steps
@ torch.no_grad()
def sample_dtdpm(dtdpm, x_init, rev_var_type, trajectory='linear', sample_steps=Non... | 2,908 | 40.557143 | 131 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/core/diffusion/wrapper.py | __all__ = ["DTWrapper", "DTCTWrapper", "CTWrapper", "SplitDTWrapper"]
import numpy as np
import torch
import torch.nn as nn
import logging
from core.utils.compatible import Wrapper
import core.func as func
def _rescale_timesteps(n, N, flag):
if flag:
return n * 1000.0 / float(N)
return n
class DTWr... | 4,167 | 33.163934 | 111 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/core/diffusion/dtdpm.py | __all__ = ["DTDPM", "DDPM", "DDIM"]
import numpy as np
import logging
import core.func as func
from core.utils import global_device
import torch
class DTDPM(object): # diffusion with discrete timesteps
r"""
E[xs|xt] = E[ E[xs|xt,x0] |xt] = E[xs|xt,x0=E[x0|xt]] in DDPM or DDIM forward process
"""
... | 9,619 | 43.331797 | 139 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/core/diffusion/schedule.py | import numpy as np
import math
import torch
import core.func as func
def get_skip(alphas, betas):
N = len(betas) - 1
skip_alphas = np.ones([N + 1, N + 1], dtype=betas.dtype)
for s in range(N + 1):
skip_alphas[s, s + 1:] = alphas[s + 1:].cumprod()
skip_betas = np.zeros([N + 1, N + 1], dtype=bet... | 4,543 | 37.184874 | 94 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/core/diffusion/sde.py | import torch
import numpy as np
import math
import core.func as func
from .schedule import Schedule
class SDE(object):
r"""
dx = f(x, t)dt + g(t) dw with 0 <= t <= 1
f(x, t) is the drift
g(t) is the diffusion
"""
def drift(self, x, t):
raise NotImplementedError
def dif... | 4,501 | 29.214765 | 93 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/core/evaluate/score.py |
__all__ = ["score_on_dataset", "cat_score_on_dataset"]
import torch
from torch.utils.data import DataLoader, Dataset
def score_on_dataset(dataset: Dataset, score_fn, batch_size):
r"""
Args:
dataset: an instance of Dataset
score_fn: a batch of data -> a batch of scalars
batch_size: th... | 1,525 | 32.911111 | 98 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/core/evaluate/sample.py |
__all__ = ["grid_sample", "sample2dir"]
import os
import torch
from torchvision.utils import make_grid, save_image
from core.utils import amortize
def grid_sample(fname, nrow, ncol, sample_fn, unpreprocess_fn=None):
r""" Sample images in a grid
Args:
fname: the file name
nrow: the number of ... | 1,530 | 33.022222 | 94 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/core/utils/clip_grad.py |
__all__ = ["clip_grad_norm_", "clip_grad_element_wise_"]
import torch
from typing import List, Union
import math
def clip_grad_norm_(grads: Union[torch.Tensor, List[torch.Tensor]], max_norm: float, norm_type: float = 2.):
if isinstance(grads, torch.Tensor):
grads = [grads]
max_norm = float(max_norm... | 1,056 | 29.2 | 108 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/core/utils/compatible.py | __all__ = ["Wrapper"]
import torch.nn as nn
class Wrapper(object):
def __init__(self, model: [nn.Module, None]):
self.model_ = model
self.model = None if model is None else nn.DataParallel(model)
def __call__(self, *args, **kwargs):
raise NotImplementedError
| 294 | 23.583333 | 70 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/core/utils/managers.py | r""" Sometimes, we need manage multiple pytorch objects in a script, e.g., multiple models, multiple optimizers
Manager provide a interface to manage them together
"""
import torch.nn as nn
import torch.optim as optim
from .ema import ema
import logging
from core.utils.compatible import Wrapper
class Manager(obje... | 4,808 | 29.630573 | 111 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/core/utils/diagnose.py | import torch.nn as nn
from typing import Union, Iterator
import torch
from .device_utils import device_of, global_device
def grad_norm_inf(inputs: Union[nn.Module, Iterator[torch.Tensor]]) -> float:
if isinstance(inputs, nn.Module):
inputs = inputs.parameters()
s = float("-inf")
for p in inputs:
... | 1,796 | 32.90566 | 119 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/core/utils/device_utils.py |
__all__ = ["device_of", "global_device"]
import torch.nn as nn
import torch
from typing import Union
from .managers import ModelsManager
def device_of(inputs: Union[nn.Module, torch.Tensor, ModelsManager]) -> torch.device:
if isinstance(inputs, nn.Module):
return next(inputs.parameters()).device
el... | 643 | 25.833333 | 85 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/core/utils/ema.py | import torch.nn as nn
def ema(model_dest: nn.Module, model_src: nn.Module, rate):
param_dict_src = dict(model_src.named_parameters())
for p_name, p_dest in model_dest.named_parameters():
p_src = param_dict_src[p_name]
assert p_src is not p_dest
p_dest.data.mul_(rate).add_((1 - rate) * ... | 332 | 32.3 | 60 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/interface/datasets/dataset_factory.py | from .utils import is_labelled, UnlabeledDataset
from torch.utils.data import ConcatDataset
import numpy as np
class DatasetFactory(object):
r""" Output dataset after two transformations to the raw data:
1. distribution transform (e.g. binarized, adding noise), often irreversible, a part of which is implement... | 3,059 | 30.546392 | 112 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/interface/datasets/imagenet64.py | from PIL import Image
import os
import torchvision.transforms as transforms
from .dataset_factory import DatasetFactory
from .utils import *
class ImageDataset(Dataset):
def __init__(self, path):
super().__init__()
names = os.listdir(path)
self.local_images = [os.path.join(path, name) for ... | 1,569 | 26.068966 | 85 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/interface/datasets/lsun_bedroom.py | import torchvision.transforms as transforms
from .dataset_factory import DatasetFactory
from .lsun.lsun import LSUN
from .utils import *
import os
class LSUNBedroom(DatasetFactory):
def __init__(self, data_path):
super().__init__()
_transform = transforms.Compose([transforms.Resize(256), transform... | 1,192 | 33.085714 | 116 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/interface/datasets/utils.py | import torch
from torch.utils.data import Dataset
import torchvision.transforms.functional as F
def pad22pow(a):
assert a % 2 == 0
bits = a.bit_length()
ub = 2 ** bits
pad = (ub - a) // 2
return pad, ub
def is_labelled(dataset):
labelled = False
if isinstance(dataset[0], tuple) and len(d... | 2,402 | 22.330097 | 82 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/interface/datasets/cifar10.py | from torch.utils.data import Subset
from torchvision import datasets
import torchvision.transforms as transforms
from .dataset_factory import DatasetFactory
from .utils import *
class CIFAR10(DatasetFactory):
r""" CIFAR10 dataset
Information of the raw dataset:
train: 40,000
val: 10,000
... | 1,658 | 31.529412 | 111 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/interface/datasets/celeba.py | from torchvision.datasets.utils import verify_str_arg
import torchvision.transforms as transforms
from .dataset_factory import DatasetFactory
from .utils import *
import numpy as np
from collections import namedtuple
import csv
from functools import partial
import PIL
import os
import torch
import torch.utils.data as d... | 15,119 | 38.477807 | 121 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/interface/datasets/mnist.py | from torch.utils.data import Subset
from torchvision import datasets
import torchvision.transforms as transforms
from .dataset_factory import DatasetFactory
from .utils import *
class Mnist(DatasetFactory):
r""" Mnist dataset
Information of the raw dataset:
train: 50,000
val: 10,000
... | 3,257 | 35.2 | 109 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/interface/datasets/lsun/lsun.py | from .vision import VisionDataset
from PIL import Image
import os
import os.path
import io
from collections.abc import Iterable
import pickle
import torch
def iterable_to_str(iterable: Iterable) -> str:
return "'" + "', '".join([str(item) for item in iterable]) + "'"
def verify_str_arg(
value, arg=None, val... | 6,426 | 30.048309 | 90 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/interface/datasets/lsun/vision.py | import os
import torch
import torch.utils.data as data
class VisionDataset(data.Dataset):
_repr_indent = 4
def __init__(self, root, transforms=None, transform=None, target_transform=None):
if isinstance(root, torch._six.string_classes):
root = os.path.expanduser(root)
self.root = ... | 3,266 | 37.435294 | 86 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/interface/evaluators/dtdpm_evaluator.py | import os
from core.evaluate import grid_sample, sample2dir
from .base import Evaluator
from core.diffusion.sample import sample_dtdpm
from core.diffusion.dtdpm import DDPM, DDIM, DTDPM
from core.diffusion.likelihood import nelbo_dtdpm, get_nelbo_terms
from core.evaluate.score import score_on_dataset, cat_score_on_data... | 7,964 | 45.578947 | 154 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/interface/evaluators/utils.py | import torch
def linear_interpolate(a, b, steps):
a_shape = a.shape
a = a.detach().cpu().view(-1)
b = b.detach().cpu().view(-1)
res = []
for aa, bb in zip(a, b):
res.append(torch.linspace(aa, bb, steps=steps).unsqueeze(dim=1))
res = torch.cat(res, dim=1)
res = res.view(len(res), *a... | 658 | 23.407407 | 72 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/interface/evaluators/sde_evaluator.py | import os
from core.evaluate import grid_sample, sample2dir
from .base import Evaluator
from core.diffusion.sample import euler_maruyama
from core.diffusion.likelihood import ode_nll
from core.diffusion.sde import ReverseSDE, ODE
from core.evaluate.score import score_on_dataset, cat_score_on_dataset
from core.diffusion... | 5,096 | 44.508929 | 123 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/interface/runner/fit.py | from torch.utils.data import DataLoader
from interface.utils.ckpt import CKPT
import os
import logging
import math
from core.utils.managers import ModelsManager
def infinite_loader(dataset, batch_size):
loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
while True:
for data in loader:
... | 3,385 | 37.044944 | 129 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/interface/utils/reproducibility.py |
__all__ = ['set_seed', 'set_deterministic', 'backup_codes', 'backup_config']
import torch
import numpy as np
import os
import shutil
import pprint
def set_seed(seed: int):
if seed is not None:
torch.manual_seed(seed)
np.random.seed(seed)
def set_deterministic(flag: bool):
if flag:
... | 1,317 | 27.652174 | 84 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/interface/utils/config_utils.py | import core.utils.managers as managers
import torch.optim as optim
from .interact import Interact
from core.evaluate import score_on_dataset
import functools
import torch
import logging
def create_instance(config):
kwargs = config.get("kwargs", {})
return config.cls(**kwargs)
###############################... | 6,786 | 37.782857 | 102 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/interface/utils/interact.py | import os
import logging
from torch.utils.tensorboard import SummaryWriter
import socket
def set_logger(fname):
logger = logging.getLogger()
logger.setLevel(level=logging.INFO)
handler1 = logging.StreamHandler()
handler2 = logging.FileHandler(fname, mode='w')
formatter = logging.Formatter('%(ascti... | 1,988 | 34.517857 | 99 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/interface/utils/ckpt.py | import torch
import os
import core.utils.managers as managers
import logging
def load_from_dir(path: str):
dct = {}
files = os.listdir(path)
for file in files:
p = os.path.join(path, file)
if file.endswith("pth"):
key = os.path.splitext(file)[0]
dct[key] = torch.loa... | 4,016 | 32.198347 | 122 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/configs/default.py | import ml_collections
import interface.datasets as datasets
import torch.optim as optim
import os
import datetime
import core.criterions as criterions
import interface.evaluators as evaluators
import core.diffusion.wrapper as wrapper
from torch.optim.lr_scheduler import LambdaLR
######################################... | 12,962 | 34.225543 | 95 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/libs/ddpm/customized.py | import logging
import torch
from .model import Model, ResnetBlock, Normalize, get_timestep_embedding, nonlinearity
class Model4Pretrained(Model):
def __init__(self, head_out_ch, mode="simple", **kwargs):
super().__init__(**kwargs)
self.requires_grad_(False)
self.mode = mode
logging... | 2,713 | 37.771429 | 86 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/libs/ddpm/model.py | import math
import torch
import torch.nn as nn
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
... | 12,381 | 36.295181 | 93 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/libs/ddpm/__init__.py | # codes from https://github.com/pesser/pytorch_diffusion
from .model import Model
from .customized import Model4Pretrained
| 124 | 24 | 56 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/libs/score_sde/customized.py | # the code added by us
from .models import utils, layers, layerspp, normalization
import torch.nn as nn
import functools
import torch
import numpy as np
from .models.ncsnpp import NCSNpp
ResnetBlockDDPM = layerspp.ResnetBlockDDPMpp
ResnetBlockBigGAN = layerspp.ResnetBlockBigGANpp
Combine = layerspp.Combine
conv3x3 = ... | 15,107 | 32.648107 | 113 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/libs/score_sde/__init__.py | # codes from https://github.com/yang-song/score_sde_pytorch
from .customized import get_nscnpp_model
| 102 | 24.75 | 59 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/libs/score_sde/models/up_or_down_sampling.py | """Layers used for up-sampling or down-sampling images.
Many functions are ported from https://github.com/NVlabs/stylegan2.
"""
import torch.nn as nn
import torch
import torch.nn.functional as F
import numpy as np
from ..op import upfirdn2d
# Function ported from StyleGAN2
def get_weight(module,
shap... | 8,902 | 33.507752 | 91 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/libs/score_sde/models/utils.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# 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 applicab... | 1,872 | 26.144928 | 105 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/libs/score_sde/models/layers.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# 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 applicab... | 22,687 | 33.271903 | 112 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/libs/score_sde/models/ddpm.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# 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 applicab... | 6,082 | 32.423077 | 113 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/libs/score_sde/models/ncsnv2.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# 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 applicab... | 16,043 | 37.567308 | 120 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/libs/score_sde/models/normalization.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# 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 applicab... | 7,657 | 34.453704 | 106 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/libs/score_sde/models/ema.py | # Modified from https://raw.githubusercontent.com/fadel/pytorch_ema/master/torch_ema/ema.py
from __future__ import division
from __future__ import unicode_literals
import torch
# Partially based on: https://github.com/tensorflow/tensorflow/blob/r1.13/tensorflow/python/training/moving_averages.py
class ExponentialMo... | 3,414 | 33.846939 | 119 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/libs/score_sde/models/ncsnpp.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# 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 applicab... | 13,653 | 34.743455 | 113 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/libs/score_sde/models/layerspp.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# 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 applicab... | 9,001 | 31.734545 | 99 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/libs/score_sde/op/upfirdn2d.py | import os
import torch
from torch.nn import functional as F
from torch.autograd import Function
from torch.utils.cpp_extension import load
module_path = os.path.dirname(__file__)
upfirdn2d_op = load(
"upfirdn2d",
sources=[
os.path.join(module_path, "upfirdn2d.cpp"),
os.path.join(module_path, ... | 5,672 | 27.223881 | 108 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/libs/score_sde/op/fused_act.py | import os
import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Function
from torch.utils.cpp_extension import load
module_path = os.path.dirname(__file__)
fused = load(
"fused",
sources=[
os.path.join(module_path, "fused_bias_act.cpp"),
os.path.joi... | 2,690 | 26.459184 | 83 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/libs/iddpm/customized.py | from .unet import UNetModel, TimestepEmbedSequential, ResBlock
import logging
import torch as th
import torch.nn as nn
from .nn import (
SiLU,
conv_nd,
zero_module,
normalization,
timestep_embedding,
)
class UNetModel3OutChannels(UNetModel):
def __init__(self, **kwargs):
super().__init... | 2,991 | 32.244444 | 107 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/libs/iddpm/nn.py | """
Various utilities for neural networks.
"""
import math
import torch as th
import torch.nn as nn
# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
class SiLU(nn.Module):
def forward(self, x):
return x * th.sigmoid(x)
class GroupNorm32(nn.GroupNorm):
def forward(self, x):
return super(... | 4,544 | 27.765823 | 88 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/libs/iddpm/fp16_util.py | """
Helpers to train with 16-bit precision.
"""
import torch.nn as nn
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
def convert_module_to_f16(l):
"""
Convert primitive modules to float16.
"""
if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
l.weight.data = l.we... | 2,282 | 28.649351 | 114 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/extended_adpm/libs/iddpm/unet.py | from abc import abstractmethod
import math
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from .fp16_util import convert_module_to_f16, convert_module_to_f32
from .nn import (
SiLU,
conv_nd,
linear,
avg_pool_nd,
zero_module,
normalization,
time... | 18,907 | 33.315789 | 124 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylesdf/losses.py | import math
import torch
from torch import autograd
from torch.nn import functional as F
def viewpoints_loss(viewpoint_pred, viewpoint_target):
loss = F.smooth_l1_loss(viewpoint_pred, viewpoint_target)
return loss
def eikonal_loss(eikonal_term, sdf=None, beta=100):
if eikonal_term == None:
eiko... | 1,786 | 27.822581 | 83 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylesdf/utils.py | import torch
import random
import trimesh
import numpy as np
from torch import nn
from torch.nn import functional as F
from torch.utils import data
from scipy.spatial import Delaunay
from skimage.measure import marching_cubes
from pytorch3d.structures import Meshes
from pytorch3d.renderer import (
look_at_view_tra... | 11,382 | 36.444079 | 111 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylesdf/model.py | import math
import random
import torch
import numpy as np
from torch import nn
from torch.nn import functional as F
from .volume_renderer import VolumeFeatureRenderer
from .op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d
from .utils import (
create_cameras,
add_textures,
create_depth_mesh_renderer,
)... | 33,662 | 32.06778 | 152 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylesdf/generate_shapes_and_images.py | import os
import torch
import numpy as np
from munch import *
from tqdm import tqdm
from torchvision import utils
from .options import BaseOptions
from .model import Generator
from .utils import (
generate_camera_params,
align_volume,
extract_mesh_with_marching_cubes,
xyz2mesh,
)
def generate(opt, g_e... | 11,520 | 47.817797 | 177 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylesdf/volume_renderer.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.autograd as autograd
import numpy as np
# Basic SIREN fully connected layer
class LinearLayer(nn.Module):
def __init__(self, in_dim, out_dim, bias=True, bias_init=0, std_init=1, freq_init=False, is_first=False):
super().__init... | 15,063 | 41.196078 | 153 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylesdf/op/upfirdn2d.py | import os
import torch
from torch.nn import functional as F
from torch.autograd import Function
from torch.utils.cpp_extension import load
from pdb import set_trace as st
module_path = os.path.dirname(__file__)
upfirdn2d_op = load(
"upfirdn2d",
sources=[
os.path.join(module_path, "upfirdn2d.cpp"),
... | 5,704 | 27.242574 | 108 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylesdf/op/fused_act.py | import os
import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Function
from torch.utils.cpp_extension import load
module_path = os.path.dirname(__file__)
fused = load(
"fused",
sources=[
os.path.join(module_path, "fused_bias_act.cpp"),
os.path.joi... | 3,143 | 25.2 | 86 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/diffusion_stylegan/legacy.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 16,577 | 50.325077 | 154 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/diffusion_stylegan/training/diffusion.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 7,703 | 37.328358 | 102 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/diffusion_stylegan/training/adaaug.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 27,527 | 60.173333 | 366 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/diffusion_stylegan/training/networks.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 37,595 | 50.081522 | 164 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/diffusion_stylegan/training/diffaug.py | # Differentiable Augmentation for Data-Efficient GAN Training
# Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han
# https://arxiv.org/pdf/2006.10738
import torch
import torch.nn.functional as F
class DiffAugment(torch.nn.Module):
def __init__(self, policy='color,translation,cutout', channels_first=True... | 3,586 | 37.569892 | 110 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/diffusion_stylegan/torch_utils/custom_ops.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 5,644 | 43.448819 | 146 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/diffusion_stylegan/torch_utils/training_stats.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 10,707 | 38.806691 | 118 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/diffusion_stylegan/torch_utils/persistence.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 9,708 | 37.527778 | 144 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/diffusion_stylegan/torch_utils/misc.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and re... | 11,089 | 40.535581 | 133 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/diffusion_stylegan/torch_utils/ops/bias_act.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 10,047 | 46.173709 | 185 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/diffusion_stylegan/torch_utils/ops/grid_sample_gradfix.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 3,299 | 38.285714 | 138 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/diffusion_stylegan/torch_utils/ops/conv2d_gradfix.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 7,677 | 43.900585 | 197 | py |
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