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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Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/cfg_helper.py | import os
import os.path as osp
import shutil
import copy
import time
import pprint
import numpy as np
import torch
import matplotlib
import argparse
import json
import yaml
from easydict import EasyDict as edict
from .model_zoo import get_model
############
# cfg_bank #
############
def cfg_solvef(cmd, root):
i... | 20,632 | 29.934033 | 97 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/autokl.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from contextlib import contextmanager
from lib.model_zoo.common.get_model import get_model, register
# from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
from .autokl_modules import Encoder, Decoder
from .distributions impor... | 6,334 | 36.934132 | 118 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/pfd.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import numpy.random as npr
import copy
from functools import partial
from contextlib import contextmanager
from lib.model_zoo.common.get_model import get_model, register
from lib.log_service import print_log
symbol = 'pfd'
from .dif... | 19,895 | 36.610586 | 119 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/distributions.py | import torch
import numpy as np
class AbstractDistribution:
def sample(self):
raise NotImplementedError()
def mode(self):
raise NotImplementedError()
class DiracDistribution(AbstractDistribution):
def __init__(self, value):
self.value = value
def sample(self):
retur... | 2,970 | 30.946237 | 131 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/swin.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import numpy as np
from lib.model_zoo.common.get_model import register
##############################
# timm.models.layers helpers #
##############################
def drop_path(x, drop_prob: float = 0., tr... | 25,644 | 37.856061 | 123 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/controlnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import numpy.random as npr
import copy
from functools import partial
from contextlib import contextmanager
from lib.model_zoo.common.get_model import get_model, register
from lib.log_service import print_log
from .openaimodel import ... | 21,949 | 42.551587 | 135 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/clip.py | import torch
import torch.nn as nn
import numpy as np
from functools import partial
from lib.model_zoo.common.get_model import register
symbol = 'clip'
class AbstractEncoder(nn.Module):
def __init__(self):
super().__init__()
def encode(self, *args, **kwargs):
raise NotImplementedError
from t... | 32,574 | 40.286439 | 132 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/sampler.py | """SAMPLING ONLY."""
import torch
import numpy as np
from tqdm import tqdm
from functools import partial
from .diffusion_utils import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
def append_dims(x, target_dims):
dims_to_append = target_dims - x.ndim
if dims_to_append < 0:
raise Valu... | 3,768 | 34.895238 | 112 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/ddim.py | """SAMPLING ONLY."""
import torch
import numpy as np
from tqdm import tqdm
from functools import partial
from .diffusion_utils import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
class DDIMSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
super().__init__()
... | 13,684 | 44.616667 | 131 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/openaimodel.py | from abc import abstractmethod
from functools import partial
import math
from typing import Iterable
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from .diffusion_utils import \
checkpoint, conv_nd, linear, avg_pool_nd, \
zero_module, normalization, timestep_embed... | 114,771 | 37.56586 | 143 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/seecoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import copy
from lib.model_zoo.common.get_model import get_model, register
symbol = 'seecoder'
###########
# helpers #
###########
def with_pos_embed(x, pos):
return x if pos is None else x + pos
def _get_clones(module, N):
return nn.Module... | 19,958 | 33.471503 | 108 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/autokl_utils.py | import torch
import torch.nn as nn
import functools
class ActNorm(nn.Module):
def __init__(self, num_features, logdet=False, affine=True,
allow_reverse_init=False):
assert affine
super().__init__()
self.logdet = logdet
self.loc = nn.Parameter(torch.zeros(1, num_feat... | 15,988 | 38.872818 | 116 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/ema.py | import torch
from torch import nn
class LitEma(nn.Module):
def __init__(self, model, decay=0.9999, use_num_updates=True):
super().__init__()
if decay < 0.0 or decay > 1.0:
raise ValueError('Decay must be between 0 and 1')
self.m_name2s_name = {}
self.register_buffer('de... | 2,984 | 38.276316 | 102 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/diffusion_utils.py | import os
import math
import torch
import torch.nn as nn
import numpy as np
from einops import repeat
def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if schedule == "linear":
betas = (
torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_... | 9,350 | 36.25498 | 114 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/attention.py | from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from .diffusion_utils import checkpoint
try:
import xformers
import xformers.ops
XFORMERS_IS_AVAILBLE = True
except:
XFORMERS_IS_AVAILBLE = False
... | 20,070 | 36.099815 | 143 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/autokl_modules.py | # pytorch_diffusion + derived encoder decoder
import math
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange
# from .diffusion_utils import instantiate_from_config
from .attention import LinearAttention
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the... | 33,408 | 38.962919 | 125 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/common/get_optimizer.py | import torch
import torch.optim as optim
import numpy as np
import itertools
def singleton(class_):
instances = {}
def getinstance(*args, **kwargs):
if class_ not in instances:
instances[class_] = class_(*args, **kwargs)
return instances[class_]
return getinstance
class get_opt... | 1,566 | 31.645833 | 120 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/common/utils.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import copy
import functools
import itertools
import matplotlib.pyplot as plt
########
# unit #
########
def singleton(class_):
instances = {}
def getinstance(*args, **kwargs):
if class_ not in instances:
... | 8,329 | 27.430034 | 99 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/common/get_model.py | from email.policy import strict
import torch
import torchvision.models
import os.path as osp
import copy
from ...log_service import print_log
from .utils import \
get_total_param, get_total_param_sum, \
get_unit
# def load_state_dict(net, model_path):
# if isinstance(net, dict):
# for ni, neti in ... | 4,100 | 31.808 | 92 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/common/get_scheduler.py | import torch
import torch.optim as optim
import numpy as np
import copy
from ... import sync
from ...cfg_holder import cfg_unique_holder as cfguh
def singleton(class_):
instances = {}
def getinstance(*args, **kwargs):
if class_ not in instances:
instances[class_] = class_(*args, **kwargs)
... | 8,911 | 33.015267 | 139 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/controlnet_annotator/midas/utils.py | """Utils for monoDepth."""
import sys
import re
import numpy as np
import cv2
import torch
def read_pfm(path):
"""Read pfm file.
Args:
path (str): path to file
Returns:
tuple: (data, scale)
"""
with open(path, "rb") as file:
color = None
width = None
heig... | 4,582 | 23.121053 | 88 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/controlnet_annotator/midas/api.py | # based on https://github.com/isl-org/MiDaS
import cv2
import torch
import torch.nn as nn
import os
models_path = 'pretrained/controlnet/preprocess'
from torchvision.transforms import Compose
from .midas.dpt_depth import DPTDepthModel
from .midas.midas_net import MidasNet
from .midas.midas_net_custom import MidasNet... | 6,979 | 31.465116 | 124 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/controlnet_annotator/midas/__init__.py | import cv2
import numpy as np
import torch
from einops import rearrange
from .api import MiDaSInference
model = None
def unload_midas_model():
global model
if model is not None:
model = model.cpu()
def apply_midas(input_image, a=np.pi * 2.0, bg_th=0.1, device='cpu'):
global model
if model is... | 1,494 | 30.808511 | 89 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/controlnet_annotator/midas/midas/base_model.py | import torch
class BaseModel(torch.nn.Module):
def load(self, path):
"""Load model from file.
Args:
path (str): file path
"""
parameters = torch.load(path, map_location=torch.device('cpu'))
if "optimizer" in parameters:
parameters = parameters["mod... | 367 | 20.647059 | 71 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/controlnet_annotator/midas/midas/midas_net.py | """MidashNet: Network for monocular depth estimation trained by mixing several datasets.
This file contains code that is adapted from
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
"""
import torch
import torch.nn as nn
from .base_model import BaseModel
f... | 2,709 | 34.194805 | 130 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/controlnet_annotator/midas/midas/vit.py | import torch
import torch.nn as nn
import timm
import types
import math
import torch.nn.functional as F
class Slice(nn.Module):
def __init__(self, start_index=1):
super(Slice, self).__init__()
self.start_index = start_index
def forward(self, x):
return x[:, self.start_index :]
class... | 14,625 | 28.727642 | 96 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/controlnet_annotator/midas/midas/dpt_depth.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .base_model import BaseModel
from .blocks import (
FeatureFusionBlock,
FeatureFusionBlock_custom,
Interpolate,
_make_encoder,
forward_vit,
)
def _make_fusion_block(features, use_bn):
return FeatureFusionBlock_custom(
... | 3,154 | 27.681818 | 89 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/controlnet_annotator/midas/midas/midas_net_custom.py | """MidashNet: Network for monocular depth estimation trained by mixing several datasets.
This file contains code that is adapted from
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
"""
import torch
import torch.nn as nn
from .base_model import BaseModel
f... | 5,207 | 39.6875 | 168 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/controlnet_annotator/midas/midas/blocks.py | import torch
import torch.nn as nn
from .vit import (
_make_pretrained_vitb_rn50_384,
_make_pretrained_vitl16_384,
_make_pretrained_vitb16_384,
forward_vit,
)
def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ign... | 9,242 | 25.947522 | 150 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/controlnet_annotator/mlsd/utils.py | '''
modified by lihaoweicv
pytorch version
'''
'''
M-LSD
Copyright 2021-present NAVER Corp.
Apache License v2.0
'''
import os
import numpy as np
import cv2
import torch
from torch.nn import functional as F
def deccode_output_score_and_ptss(tpMap, topk_n = 200, ksize = 5):
'''
tpMap:
center: tpMap[1, 0... | 24,198 | 40.507719 | 150 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/controlnet_annotator/mlsd/__init__.py | import cv2
import numpy as np
import torch
import os
from einops import rearrange
from .models.mbv2_mlsd_tiny import MobileV2_MLSD_Tiny
from .models.mbv2_mlsd_large import MobileV2_MLSD_Large
from .utils import pred_lines
models_path = 'pretrained/controlnet/preprocess'
mlsdmodel = None
remote_model_path = "https... | 3,123 | 37.097561 | 119 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/controlnet_annotator/mlsd/models/mbv2_mlsd_tiny.py | import os
import sys
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from torch.nn import functional as F
class BlockTypeA(nn.Module):
def __init__(self, in_c1, in_c2, out_c1, out_c2, upscale = True):
super(BlockTypeA, self).__init__()
self.conv1 = nn.Sequential(
... | 9,180 | 32.385455 | 107 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/controlnet_annotator/mlsd/models/mbv2_mlsd_large.py | import os
import sys
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from torch.nn import functional as F
class BlockTypeA(nn.Module):
def __init__(self, in_c1, in_c2, out_c1, out_c2, upscale = True):
super(BlockTypeA, self).__init__()
self.conv1 = nn.Sequential(
... | 9,678 | 32.14726 | 107 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/controlnet_annotator/hed/__init__.py | # This is an improved version and model of HED edge detection with Apache License, Version 2.0.
# Please use this implementation in your products
# This implementation may produce slightly different results from Saining Xie's official implementations,
# but it generates smoother edges and is more suitable for ControlNe... | 5,817 | 42.096296 | 149 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/controlnet_annotator/pidinet/model.py | """
Author: Zhuo Su, Wenzhe Liu
Date: Feb 18, 2021
"""
import math
import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
def img2tensor(imgs, bgr2rgb=True, float32=True):
"""Numpy array to tensor.
Args:
imgs (list[ndarray] | ndarray): Input images.
... | 21,792 | 31.048529 | 123 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/controlnet_annotator/pidinet/__init__.py | import os
import torch
import numpy as np
from einops import rearrange
from .model import pidinet
models_path = 'pretrained/controlnet/preprocess'
netNetwork = None
remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/table5_pidinet.pth"
modeldir = os.path.join(models_path, "pidinet")
old_mo... | 3,786 | 36.49505 | 116 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/controlnet_annotator/openpose/hand.py | import cv2
import json
import numpy as np
import math
import time
from scipy.ndimage.filters import gaussian_filter
import matplotlib.pyplot as plt
import matplotlib
import torch
from skimage.measure import label
from .model import handpose_model
from . import util
class Hand(object):
def __init__(self, model_pat... | 3,360 | 34.755319 | 110 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/controlnet_annotator/openpose/model.py | import torch
from collections import OrderedDict
import torch
import torch.nn as nn
def make_layers(block, no_relu_layers):
layers = []
for layer_name, v in block.items():
if 'pool' in layer_name:
layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1],
paddi... | 8,744 | 38.931507 | 86 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/controlnet_annotator/openpose/face.py | import logging
import numpy as np
from torchvision.transforms import ToTensor, ToPILImage
import torch
import torch.nn.functional as F
import cv2
from . import util
from torch.nn import Conv2d, Module, ReLU, MaxPool2d, init
class FaceNet(Module):
"""Model the cascading heatmaps. """
def __init__(self):
... | 13,485 | 36.254144 | 113 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/controlnet_annotator/openpose/__init__.py | # Openpose
# Original from CMU https://github.com/CMU-Perceptual-Computing-Lab/openpose
# 2nd Edited by https://github.com/Hzzone/pytorch-openpose
# 3rd Edited by ControlNet
# 4th Edited by ControlNet (added face and correct hands)
# 5th Edited by ControlNet (Improved JSON serialization/deserialization, and lots of bug... | 12,325 | 37.398754 | 116 | py |
Prompt-Free-Diffusion | Prompt-Free-Diffusion-master/lib/model_zoo/controlnet_annotator/openpose/body.py | import cv2
import numpy as np
import math
import time
from scipy.ndimage.filters import gaussian_filter
import matplotlib.pyplot as plt
import matplotlib
import torch
from torchvision import transforms
from typing import NamedTuple, List, Union
from . import util
from .model import bodypose_model
class Keypoint(Named... | 13,042 | 45.917266 | 137 | py |
orphics | orphics-master/orphics/maps.py | from __future__ import print_function
from pixell import enmap, utils, resample, curvedsky as cs, reproject
import numpy as np
from pixell.fft import fft,ifft
from scipy.interpolate import interp1d
import yaml,six
from orphics import io,cosmology,stats
import math
from scipy.interpolate import RectBivariateSpline,inte... | 70,081 | 33.659743 | 203 | py |
ami | ami-master/preprocessing.py | from PIL import Image
from img2vec_pytorch import Img2Vec
import os
import torch
from tqdm import tqdm
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, transforms
from pathlib import Path
img2vec = Img2Vec(cuda=True)
# CelebA
IMG2VEC_PATH = 'data_celebA/celeba_img2vec_resnet/'
IMG_PA... | 2,152 | 31.621212 | 73 | py |
ami | ami-master/ldp/cifar-eval.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
import numpy as np
from torchvision import datasets, transforms
from torch.utils.data import Dataset, DataLoader
import os
import argparse
import multiprocessing
parser = argparse.ArgumentPars... | 9,887 | 32.405405 | 172 | py |
ami | ami-master/ldp/celeba-eval.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
import numpy as np
from torchvision import datasets, transforms
from torch.utils.data import Dataset, DataLoader
import os
import argparse
import multiprocessing
parser = argparse.ArgumentPars... | 9,887 | 32.292929 | 171 | py |
ami | ami-master/ldp/celeba-exp.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
import numpy as np
from torchvision import datasets, transforms
from torch.utils.data import Dataset, DataLoader
from PIL import Image
from img2vec_pytorch import Img2Vec
import os
import argpa... | 18,318 | 32.007207 | 226 | py |
ami | ami-master/ldp/cifar-exp.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
import numpy as np
from torchvision import datasets, transforms
from torch.utils.data import Dataset, DataLoader
from PIL import Image
from img2vec_pytorch import Img2Vec
import os
import argpa... | 16,467 | 32.608163 | 226 | py |
ami | ami-master/ldp/imgnet-eval.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
import numpy as np
from torchvision import datasets, transforms
from torch.utils.data import Dataset, DataLoader
import os
import argparse
import multiprocessing
import glob
parser = argparse.... | 10,338 | 32.459547 | 173 | py |
ami | ami-master/ldp/imgnet-exp.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
import numpy as np
from torchvision import datasets, transforms
from torch.utils.data import Dataset, DataLoader
from PIL import Image
from img2vec_pytorch import Img2Vec
import os
import argpa... | 18,730 | 31.632404 | 226 | py |
ami | ami-master/ldp/img2vec_pytorch/img_to_vec.py | import torch
import torch.nn as nn
import torchvision.models as models
import torchvision.transforms as transforms
import numpy as np
class Img2Vec():
RESNET_OUTPUT_SIZES = {
'resnet18': 512,
'resnet34': 512,
'resnet50': 2048,
'resnet101': 2048,
'resnet152': 2048
}
... | 7,933 | 39.070707 | 117 | py |
ami | ami-master/ldp/img2vec_pytorch/__init__.py | from img2vec_pytorch.img_to_vec import Img2Vec | 46 | 46 | 46 | py |
ami | ami-master/noldp/imgnet-exp-noldp.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
import numpy as np
from torchvision import datasets, transforms
from torch.utils.data import Dataset, DataLoader
from PIL import Image
from img2vec_pytorch import Img2Vec
import os
import argpa... | 9,060 | 32.069343 | 226 | py |
ami | ami-master/noldp/cifar-eval-noldp.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
import numpy as np
from torchvision import datasets, transforms
from torch.utils.data import Dataset, DataLoader
from PIL import Image
from img2vec_pytorch import Img2Vec
import os
import argpa... | 7,718 | 30.765432 | 226 | py |
ami | ami-master/noldp/imgnet-eval-noldp.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
import numpy as np
from torchvision import datasets, transforms
from torch.utils.data import Dataset, DataLoader
from PIL import Image
from img2vec_pytorch import Img2Vec
import os
import argpa... | 8,307 | 31.580392 | 226 | py |
ami | ami-master/noldp/cifar-exp-noldp.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
import numpy as np
from torchvision import datasets, transforms
from torch.utils.data import Dataset, DataLoader
from PIL import Image
from img2vec_pytorch import Img2Vec
import os
import argpa... | 8,394 | 31.792969 | 226 | py |
ami | ami-master/noldp/celeba-eval-noldp.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
import numpy as np
from torchvision import datasets, transforms
from torch.utils.data import Dataset, DataLoader
from PIL import Image
from img2vec_pytorch import Img2Vec
import os
import argpa... | 7,830 | 30.704453 | 226 | py |
ami | ami-master/noldp/celeba-exp-noldp.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
import numpy as np
from torchvision import datasets, transforms
from torch.utils.data import Dataset, DataLoader
from PIL import Image
from img2vec_pytorch import Img2Vec
import os
import argpa... | 9,211 | 31.322807 | 226 | py |
ami | ami-master/noldp/img2vec_pytorch/img_to_vec.py | import torch
import torch.nn as nn
import torchvision.models as models
import torchvision.transforms as transforms
import numpy as np
class Img2Vec():
RESNET_OUTPUT_SIZES = {
'resnet18': 512,
'resnet34': 512,
'resnet50': 2048,
'resnet101': 2048,
'resnet152': 2048
}
... | 7,933 | 39.070707 | 117 | py |
ami | ami-master/noldp/img2vec_pytorch/__init__.py | from img2vec_pytorch.img_to_vec import Img2Vec | 46 | 46 | 46 | py |
ami | ami-master/noldp/.ipynb_checkpoints/celeba-exp-noldp-checkpoint.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
import numpy as np
from torchvision import datasets, transforms
from torch.utils.data import Dataset, DataLoader
from PIL import Image
from img2vec_pytorch import Img2Vec
import os
import argpa... | 9,211 | 31.322807 | 226 | py |
ami | ami-master/img2vec_pytorch/img_to_vec.py | import torch
import torch.nn as nn
import torchvision.models as models
import torchvision.transforms as transforms
import numpy as np
class Img2Vec():
RESNET_OUTPUT_SIZES = {
'resnet18': 512,
'resnet34': 512,
'resnet50': 2048,
'resnet101': 2048,
'resnet152': 2048
}
... | 7,933 | 39.070707 | 117 | py |
ami | ami-master/img2vec_pytorch/__init__.py | from img2vec_pytorch.img_to_vec import Img2Vec | 46 | 46 | 46 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/scripts/data_generate.py | import gym
import sys
import diffuser.environments
import numpy as np
import torch
import diffuser.utils as utils
import numpy as np
import os
from stable_baselines3 import DQN, SAC
os.environ["CUDA_VISIBLE_DEVICES"] = '3'
from stable_baselines3.common.callbacks import BaseCallback
class CustomCallback(BaseCallback... | 6,835 | 40.430303 | 152 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/models/diffusion.py | from collections import namedtuple
import numpy as np
import torch
from torch import nn
import pdb
import diffuser.utils as utils
from .helpers import (
cosine_beta_schedule,
extract,
apply_conditioning,
Losses,
)
Sample = namedtuple('Sample', 'trajectories values costs chains')
@torch.no_grad()
de... | 24,140 | 41.501761 | 151 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/models/TCN.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torch.nn.utils import weight_norm
import einops
from einops.layers.torch import Rearrange
from .helpers import (
SinusoidalPosEmb,
)
class TemporalVauleNet(nn.... | 4,679 | 31.275862 | 143 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/models/temporal.py | import torch
import torch.nn as nn
import einops
from einops.layers.torch import Rearrange
import pdb
from .helpers import (
SinusoidalPosEmb,
Downsample1d,
Upsample1d,
Conv1dBlock,
Residual,
PreNorm,
LinearAttention,
)
class ResidualTemporalBlock(nn.Module):
def __init__(self, inp_c... | 11,157 | 30.519774 | 125 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/models/helpers.py | import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import einops
from einops.layers.torch import Rearrange
import pdb
import diffuser.utils as utils
#-----------------------------------------------------------------------------#
#---------------------------------- module... | 6,844 | 30.255708 | 95 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/sampling/guides.py | import torch
import torch.nn as nn
import pdb
class ValueGuide(nn.Module):
def __init__(self, model):
super().__init__()
self.model = model
def forward(self, x, cond, t, **kargs):
output = self.model(x[:, :, 0:self.model.transition_dim], cond, t, **kargs)
return output.squeez... | 535 | 22.304348 | 83 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/sampling/functions.py | import torch
from diffuser.models.helpers import (
extract,
apply_conditioning,
)
@torch.no_grad()
def n_step_guided_p_sample(
model, x, cond, t, guide, scale=0.001, t_stopgrad=0, n_guide_steps=1, scale_grad_by_std=True, state_grad_mask=False,
):
model_log_variance = extract(model.posterior_log_vari... | 5,313 | 37.507246 | 149 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/sampling/policies.py | from collections import namedtuple
import torch
import einops
import pdb
import numpy as np
import diffuser.utils as utils
from diffuser.datasets.preprocessing import get_policy_preprocess_fn
Trajectories = namedtuple('Trajectories', 'actions observations rewards terminals values costs')
class GuidedPolicy:
d... | 3,630 | 42.22619 | 166 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/datasets/sequence.py | from collections import namedtuple
import numpy as np
import torch
import pdb
from .preprocessing import get_preprocess_fn
from .d4rl import load_environment, sequence_dataset
from .normalization import DatasetNormalizer
from .buffer import ReplayBuffer
Batch = namedtuple('Batch', 'trajectories conditions')
ValueBat... | 6,737 | 37.284091 | 142 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/datasets/d4rl.py | import os
import collections
import numpy as np
import gym
import pdb
from contextlib import (
contextmanager,
redirect_stderr,
redirect_stdout,
)
@contextmanager
def suppress_output():
"""
A context manager that redirects stdout and stderr to devnull
https://stackoverflow.com/a/524423... | 3,880 | 29.801587 | 79 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/utils/colab.py | import os
import numpy as np
import einops
import matplotlib.pyplot as plt
from tqdm import tqdm
try:
import io
import base64
from IPython.display import HTML
from IPython import display as ipythondisplay
except:
print('[ utils/colab ] Warning: not importing colab dependencies')
from .serializatio... | 3,816 | 29.536 | 122 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/utils/setup.py | import os
import importlib
import random
import numpy as np
import torch
from tap import Tap
import pdb
from .serialization import mkdir
from .git_utils import (
get_git_rev,
save_git_diff,
)
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_s... | 5,895 | 34.518072 | 116 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/utils/arrays.py | import collections
import numpy as np
import torch
import pdb
DTYPE = torch.float
DEVICE = 'cuda:0'
#-----------------------------------------------------------------------------#
#------------------------------ numpy <--> torch -----------------------------#
#---------------------------------------------------------... | 3,233 | 27.619469 | 109 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/utils/training.py | import os
import copy
import numpy as np
import torch
import einops
import pdb
import wandb
import time
from .arrays import batch_to_device, to_np, to_device, apply_dict
from .timer import Timer
from .cloud import sync_logs
def cycle(dl):
while True:
for data in dl:
yield data
class EMA():
... | 25,054 | 38.960128 | 135 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/utils/transformations.py | # -*- coding: utf-8 -*-
# transformations.py
# Copyright (c) 2006, Christoph Gohlke
# Copyright (c) 2006-2009, The Regents of the University of California
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions a... | 57,638 | 35.619441 | 79 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/utils/iql.py | import os
import numpy as np
import jax
import jax.numpy as jnp
import functools
import pdb
from diffuser.iql.common import Model
from diffuser.iql.value_net import DoubleCritic
def load_q(env, loadpath, hidden_dims=(256, 256), seed=42):
print(f'[ utils/iql ] Loading Q: {loadpath}')
observations = env.observa... | 1,149 | 26.380952 | 87 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/utils/serialization.py | import os
import pickle
import glob
import torch
import pdb
from collections import namedtuple
DiffusionExperiment = namedtuple('Diffusion', 'dataset renderer model diffusion ema trainer epoch')
def mkdir(savepath):
"""
returns `True` iff `savepath` is created
"""
if not os.path.exists(savepath):... | 4,137 | 37.314815 | 157 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/agent/SACPolicy.py | import numpy as np
import torch
import torch.nn as nn
from copy import deepcopy
from diffuser.models.helpers import (
SinusoidalPosEmb,
)
class MLP(nn.Module):
def __init__(self, input_dim, hidden_dims, output_dim=None, activation=nn.ReLU):
super().__init__()
hidden_dims = [input_dim] + list(... | 10,951 | 38.825455 | 124 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/agent/COMBOPolicy.py | import numpy as np
import torch
import torch.nn as nn
from gym.spaces import Box, Discrete
from copy import deepcopy
from diffuser.agent.CQLPolicy import CQLPolicy
class COMBOPolicy(CQLPolicy, nn.Module):
def __init__(
self,
actor,
critic_input_dim,
actor_lr,
critic_lr,... | 2,813 | 35.076923 | 107 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/agent/CQLPolicy.py | import numpy as np
import torch
import torch.nn as nn
from gym.spaces import Box, Discrete
from copy import deepcopy
from diffuser.agent.SACPolicy import SACPolicy
class CQLPolicy(SACPolicy, nn.Module):
def __init__(
self,
actor,
critic_input_dim,
actor_lr,
critic_lr,
... | 5,838 | 41.007194 | 132 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/environments/double_pendulum.py | import numpy as np
from gym.envs.mujoco import InvertedDoublePendulumEnv
from gym import Env
from diffuser.environments.wrappers import SafeEnv, OfflineEnv
from typing import Dict, Tuple
double_pendulum_cfg = dict(
action_dim=1,
action_range=[
-1,
1],
unsafe_reward=-20... | 2,652 | 38.014706 | 138 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/environments/single_pendulum.py | import gym
from gym import spaces
from gym.utils import seeding
import numpy as np
from typing import Callable, List, Dict, Tuple
import torch
from os import path
from typing import Union
from gym import Env
from diffuser.environments.wrappers import SafeEnv, OfflineEnv
Array = Union[torch.Tensor, np.ndarray]
def ang... | 10,557 | 43.92766 | 166 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/environments/two_step_mdp.py | import numpy as np
import pandas as pd
# 定义非线性系统环境，按照GYM的格式
import gym
from gym import spaces, logger
from gym.utils import seeding
import torch
class TwoStepMDP(gym.Env):
def __init__(self, onehot=True):
self.state = np.array([1, 0, 0, 0])
self.rewards = np.array([0, 0, 2, -3])
self.action... | 1,699 | 27.813559 | 89 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/environments/mycliffwalking.py | import numpy as np
import sys
from contextlib import closing
from io import StringIO
from gym.envs.toy_text import discrete
UP = 0
RIGHT = 1
DOWN = 2
LEFT = 3
class CliffWalkingEnv(discrete.DiscreteEnv):
"""
This is a simple implementation of the Gridworld Cliff
reinforcement learning task.
Adapted ... | 4,989 | 33.413793 | 94 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/environments/reacher.py | from gym import spaces
from gym.utils import seeding
from typing import Tuple, Dict, List
from gym.envs.mujoco.reacher import ReacherEnv
import numpy as np
from diffuser.environments.wrappers import SafeEnv, OfflineEnv
reacher_cfg = {
'action_dim': 1,
'action_range': [-2, 2],
'unsafe_reward': -3.75,
'... | 3,763 | 33.53211 | 119 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/environments/myfrozen_lake.py |
import sys
from contextlib import closing
import numpy as np
from io import StringIO
from gym import utils
from gym.envs.toy_text import discrete
LEFT = 0
DOWN = 1
RIGHT = 2
UP = 3
MAPS = {
"4x4": [
"SFFF",
"FHFH",
"FFFH",
"HFFG"
],
"8x8": [
"SFFFFFFF",
"... | 6,376 | 31.370558 | 146 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/environments/myroulette.py | import gym
from gym import spaces
from gym.utils import seeding
import numpy as np
class RouletteEnv(gym.Env):
"""Simple roulette environment
The roulette wheel has 37 spots. If the bet is 0 and a 0 comes up,
you win a reward of 35. If the parity of your bet matches the parity
of the spin, you win 1. ... | 2,067 | 32.354839 | 88 | py |
Safe-offline-RL-with-diffusion-model | Safe-offline-RL-with-diffusion-model-main/diffuser/environments/ocpm.py | import gym
from gym import spaces
from gym.utils import seeding
import numpy as np
from typing import Callable, List, Dict, Tuple
import torch
from os import path
from typing import Union
from gym import Env
from diffuser.environments.wrappers import SafeEnv, OfflineEnv
Array = Union[torch.Tensor, np.ndarray]
class O... | 2,206 | 35.783333 | 105 | py |
mmselfsup-0.x | mmselfsup-0.x/tools/test.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
import time
import mmcv
import torch
from mmcv import DictAction
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, init_dist, load_checkpoint
from mmselfsup.datasets... | 5,638 | 34.689873 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tools/train.py | # Copyright (c) OpenMMLab. All rights reserved.
from __future__ import division
import argparse
import os
import os.path as osp
import time
import warnings
import mmcv
import torch
import torch.distributed as dist
from mmcv import Config, DictAction
from mmcv.runner import get_dist_info, init_dist
from mmselfsup impo... | 7,454 | 36.089552 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tools/benchmarks/detectron2/convert-pretrain-to-detectron2.py | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import pickle as pkl
import sys
import torch
if __name__ == '__main__':
input = sys.argv[1]
obj = torch.load(input, map_location='cpu')
obj = obj['state_dict']
newmodel = {}
for k, v in obj.items():
... | 979 | 24.789474 | 70 | py |
mmselfsup-0.x | mmselfsup-0.x/tools/benchmarks/classification/knn_imagenet/test_knn.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
import time
import mmcv
import torch
from mmcv import DictAction
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, init_dist, load_checkpoint
from mmselfsup.datasets... | 7,282 | 37.946524 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tools/benchmarks/classification/svm_voc07/extract.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
import time
import mmcv
import numpy as np
import torch
from mmcv import DictAction
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, init_dist, load_checkpoint
from... | 6,563 | 37.611765 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tools/misc/mae_visualization.py | # Copyright (c) OpenMMLab. All rights reserved.
# Copyright (c) Meta Platforms, Inc. and affiliates.
# Modified from https://colab.research.google.com/github/facebookresearch/mae
# /blob/main/demo/mae_visualize.ipynb
from argparse import ArgumentParser
from typing import Tuple
import matplotlib.pyplot as plt
import nu... | 2,976 | 30.336842 | 78 | py |
mmselfsup-0.x | mmselfsup-0.x/tools/model_converters/publish_model.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import datetime
import subprocess
from pathlib import Path
import torch
from mmcv import digit_version
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file',... | 1,745 | 30.178571 | 78 | py |
mmselfsup-0.x | mmselfsup-0.x/tools/model_converters/extract_backbone_weights.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='This script extracts backbone weights from a checkpoint')
parser.add_argument('checkpoint', type=str, help='checkpoint file')
parser.add_argument('output',... | 1,000 | 29.333333 | 78 | py |
mmselfsup-0.x | mmselfsup-0.x/tools/analysis_tools/visualize_tsne.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
import time
import matplotlib.pyplot as plt
import mmcv
import numpy as np
import torch
from mmcv import Config, DictAction
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, in... | 12,798 | 38.260736 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/tests/test_runtime/test_optimizer.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
import torch.nn as nn
from mmselfsup.core import LARS, build_optimizer
class ExampleModel(nn.Module):
def __init__(self):
super(ExampleModel, self).__init__()
self.test_cfg = None
self.predictor = nn.Linear(2, 1)
... | 1,458 | 27.057692 | 77 | py |
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