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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furniture-bench | furniture-bench-main/vip/vip/examples/plot_reward_curves.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import cv2
import glob
from matplotlib import pyplot as plt
import numpy as np
import os
import torch
import torchvision... | 4,021 | 34.280702 | 131 | py |
furniture-bench | furniture-bench-main/vip/vip/examples/encoder_example.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import omegaconf
import hydra
import torch
import torchvision.transforms as T
import numpy as np
from PIL import Image
fro... | 934 | 25.714286 | 97 | py |
furniture-bench | furniture-bench-main/vip/vip/models/model_vip.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
from numpy.core.numeric import full
import torch
import torch.nn as nn
from torch.nn.modules.activation ... | 3,280 | 35.054945 | 138 | py |
furniture-bench | furniture-bench-main/vip/vip/utils/data_loaders.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import warnings
import torchvision
warnings.filterwarnings('ignore', category=DeprecationWarning)
import os
os.environ['M... | 4,246 | 32.976 | 91 | py |
furniture-bench | furniture-bench-main/vip/vip/utils/utils.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import random
import re
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
... | 4,974 | 29.151515 | 78 | py |
furniture-bench | furniture-bench-main/vip/vip/utils/logger.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import csv
import datetime
from collections import defaultdict
import numpy as np
import torch
import torchvision
import w... | 6,131 | 32.326087 | 91 | py |
furniture-bench | furniture-bench-main/implicit_q_learning/dataset_utils.py | import collections
import pickle
from typing import Optional, Dict
# import d4rl
import gym
import numpy as np
import jax.numpy as jnp
from tqdm import tqdm
Batch = collections.namedtuple("Batch",
["observations", "actions", "rewards", "masks", "next_observations"])
def split_into_tra... | 13,173 | 39.411043 | 127 | py |
furniture-bench | furniture-bench-main/implicit_q_learning/actor.py | from typing import Tuple
import jax
import jax.numpy as jnp
from common import Batch, InfoDict, Model, Params, PRNGKey
def update(key: PRNGKey, actor: Model, critic: Model, value: Model,
batch: Batch, temperature: float) -> Tuple[Model, InfoDict]:
v = value(batch.observations)
q1, q2 = critic(ba... | 986 | 30.83871 | 76 | py |
furniture-bench | furniture-bench-main/implicit_q_learning/learner.py | """Implementations of algorithms for continuous control."""
from typing import Optional, Sequence, Tuple
import jax
import jax.numpy as jnp
import numpy as np
import optax
import policy
import value_net
from actor import update as awr_update_actor
from common import Batch, InfoDict, Model, PRNGKey
from critic import... | 6,219 | 29.640394 | 111 | py |
furniture-bench | furniture-bench-main/implicit_q_learning/policy.py | import functools
from typing import Optional, Sequence, Tuple, Dict
import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
from tensorflow_probability.substrates import jax as tfp
tfd = tfp.distributions
tfb = tfp.bijectors
from common import MLP, Encoder, ResNet18
from common import Params
fr... | 3,105 | 31.694737 | 103 | py |
furniture-bench | furniture-bench-main/implicit_q_learning/common.py | import collections
from functools import partial
import os
from typing import Any, Callable, Dict, Optional, Sequence, Tuple
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
import optax
Batch = collections.namedtuple('Batch',
['observations', 'actions', 'rewards',... | 6,800 | 31.855072 | 100 | py |
furniture-bench | furniture-bench-main/implicit_q_learning/value_net.py | from typing import Callable, Sequence, Tuple, Dict
import jax.numpy as jnp
from flax import linen as nn
from common import MLP, Encoder, Encoder
class ValueCritic(nn.Module):
hidden_dims: Sequence[int]
use_encoder: bool = False
@nn.compact
def __call__(self, observations: Dict[str, jnp.ndarray]) ->... | 2,292 | 33.742424 | 119 | py |
furniture-bench | furniture-bench-main/implicit_q_learning/evaluation.py | from typing import Dict
import flax.linen as nn
import gym
import numpy as np
def evaluate(
agent: nn.Module, env: gym.Env, num_episodes: int, temperature: float = 0.00
) -> Dict[str, float]:
stats = {"return": [], "length": []}
for _ in range(num_episodes):
observation, done = env.reset(), Fals... | 648 | 23.961538 | 80 | py |
furniture-bench | furniture-bench-main/implicit_q_learning/extract_feature.py | import pickle
from pathlib import Path
import furniture_bench
import numpy as np
import torch
import gym
from absl import app, flags
FLAGS = flags.FLAGS
flags.DEFINE_string("furniture", None, "Furniture name.")
flags.DEFINE_string("demo_dir", "square_table_parts_state", "Demonstration dir.")
flags.DEFINE_string("o... | 4,979 | 34.571429 | 155 | py |
furniture-bench | furniture-bench-main/implicit_q_learning/critic.py | from typing import Tuple
import jax.numpy as jnp
from common import Batch, InfoDict, Model, Params
def loss(diff, expectile=0.8):
weight = jnp.where(diff > 0, expectile, (1 - expectile))
return weight * (diff**2)
def update_v(critic: Model, value: Model, batch: Batch,
expectile: float) -> Tup... | 1,574 | 29.882353 | 78 | py |
furniture-bench | furniture-bench-main/r3m/setup.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import sys
from setuptools import setup, find_packages
if sys.version_info.major != 3:
print("This Python i... | 1,009 | 30.5625 | 108 | py |
furniture-bench | furniture-bench-main/r3m/r3m/example.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import omegaconf
import hydra
import torch
import torchvision.transforms as T
import numpy as np
from PIL import Image
fr... | 966 | 27.441176 | 97 | py |
furniture-bench | furniture-bench-main/r3m/r3m/setup.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import sys
from setuptools import setup, find_packages
if sys.version_info.major != 3:
print("This Python i... | 1,009 | 30.5625 | 108 | py |
furniture-bench | furniture-bench-main/r3m/r3m/train_representation.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import warnings
import torchvision
warnings.filterwarnings('ignore', category=DeprecationWarning)
import os
os.environ['... | 5,279 | 33.285714 | 120 | py |
furniture-bench | furniture-bench-main/r3m/r3m/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from r3m.models.models_r3m import R3M
import os
from os.path import expanduser
import omegaconf
import hydra
import gdow... | 4,689 | 40.140351 | 130 | py |
furniture-bench | furniture-bench-main/r3m/r3m/trainer.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import hydra
import numpy as np
from numpy.core.numeric import full
import torch
import torch.nn as nn
import torch.nn.fun... | 6,734 | 40.574074 | 148 | py |
furniture-bench | furniture-bench-main/r3m/r3m/models/models_language.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
from numpy.core.numeric import full
import torch
import torch.nn as nn
from torch.nn.modules.activation... | 2,188 | 38.8 | 93 | py |
furniture-bench | furniture-bench-main/r3m/r3m/models/models_r3m.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
from numpy.core.numeric import full
import torch
import torch.nn as nn
from torch.nn.modules.activation... | 4,115 | 37.111111 | 138 | py |
furniture-bench | furniture-bench-main/r3m/r3m/r3m/example.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import omegaconf
import hydra
import torch
import torchvision.transforms as T
import numpy as np
from PIL import Image
fr... | 966 | 27.441176 | 97 | py |
furniture-bench | furniture-bench-main/r3m/r3m/r3m/train_representation.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import warnings
import torchvision
warnings.filterwarnings('ignore', category=DeprecationWarning)
import os
os.environ['... | 5,279 | 33.285714 | 120 | py |
furniture-bench | furniture-bench-main/r3m/r3m/r3m/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from r3m.models.models_r3m import R3M
import os
from os.path import expanduser
import omegaconf
import hydra
import gdow... | 4,689 | 40.140351 | 130 | py |
furniture-bench | furniture-bench-main/r3m/r3m/r3m/trainer.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import hydra
import numpy as np
from numpy.core.numeric import full
import torch
import torch.nn as nn
import torch.nn.fun... | 6,734 | 40.574074 | 148 | py |
furniture-bench | furniture-bench-main/r3m/r3m/r3m/models/models_language.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
from numpy.core.numeric import full
import torch
import torch.nn as nn
from torch.nn.modules.activation... | 2,188 | 38.8 | 93 | py |
furniture-bench | furniture-bench-main/r3m/r3m/r3m/models/models_r3m.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
from numpy.core.numeric import full
import torch
import torch.nn as nn
from torch.nn.modules.activation... | 4,115 | 37.111111 | 138 | py |
furniture-bench | furniture-bench-main/r3m/r3m/r3m/utils/data_loaders.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import warnings
import torchvision
warnings.filterwarnings('ignore', category=DeprecationWarning)
import os
os.environ['... | 3,522 | 31.321101 | 101 | py |
furniture-bench | furniture-bench-main/r3m/r3m/r3m/utils/utils.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import random
import re
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F... | 4,975 | 29.341463 | 78 | py |
furniture-bench | furniture-bench-main/r3m/r3m/r3m/utils/logger.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import csv
import datetime
from collections import defaultdict
import numpy as np
import torch
import torchvision
import ... | 6,165 | 32.51087 | 91 | py |
furniture-bench | furniture-bench-main/r3m/r3m/utils/data_loaders.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import warnings
import torchvision
warnings.filterwarnings('ignore', category=DeprecationWarning)
import os
os.environ['... | 3,522 | 31.321101 | 101 | py |
furniture-bench | furniture-bench-main/r3m/r3m/utils/utils.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import random
import re
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F... | 4,975 | 29.341463 | 78 | py |
furniture-bench | furniture-bench-main/r3m/r3m/utils/logger.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import csv
import datetime
from collections import defaultdict
import numpy as np
import torch
import torchvision
import ... | 6,165 | 32.51087 | 91 | py |
LF-DAnet | LF-DAnet-main/test.py | import argparse
from utils.utility import *
from model.DAnet import Net
import numpy as np
import imageio
import torch
import os
from einops import rearrange
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cuda:0')
parser.add_argument("--angRes", typ... | 5,251 | 48.54717 | 163 | py |
LF-DAnet | LF-DAnet-main/validation.py | import argparse
import torch.backends.cudnn as cudnn
from utils.utility import *
from utils.dataloader import *
from model.DAnet import Net
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cuda:0')
parser.add_argument('--num_workers', type=int, default=4)
parser.add_argument('--mo... | 4,944 | 41.264957 | 152 | py |
LF-DAnet | LF-DAnet-main/train.py | import time
import argparse
import torch.backends.cudnn as cudnn
from tqdm import tqdm
import random
from utils.utility import *
from utils.dataloader import *
from model.DAnet import Net
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cuda:0')
parser.add_argument('--parallel', t... | 8,576 | 43.905759 | 152 | py |
LF-DAnet | LF-DAnet-main/utils/utility.py | import torch
import numpy as np
from skimage import metrics
import torch.nn.functional as F
from einops import rearrange
def save_ckpt(args, net, idx_epoch):
if args.parallel:
torch.save({'epoch': idx_epoch, 'state_dict': net.module.state_dict()},
'./log/' + args.model_name + '_' + str(... | 4,266 | 38.146789 | 129 | py |
LF-DAnet | LF-DAnet-main/utils/dataloader.py | import os
from torch.utils.data.dataset import Dataset
from utils.degrade import *
import numpy as np
import h5py
from torch.utils.data import DataLoader
from einops import rearrange
class TrainSetLoader():
def __init__(self, args):
super(TrainSetLoader, self).__init__()
self.args = args
s... | 2,600 | 33.68 | 105 | py |
LF-DAnet | LF-DAnet-main/utils/degrade.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
class MultiDegrade(object):
def __init__(self, scale=4, kernel_size=21, sig_min=0, sig_max=4.0, noise=None, sig=None):
self.kernel_size = kernel_size
self.scale = scale
if sig... | 6,311 | 40.801325 | 117 | py |
LF-DAnet | LF-DAnet-main/model/DAnet.py | import torch
import torch.nn as nn
from einops import rearrange
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self, factor, angRes):
super(Net, self).__init__()
channels = 64
n_group = 4
n_block = 4
self.factor = factor
self.gen_code = Gen_Code(... | 10,452 | 37.149635 | 114 | py |
saic_depth_completion | saic_depth_completion-master/tools/train_matterport.py | import torch
import argparse
import numpy as np
from saic_depth_completion.data.datasets.matterport import Matterport
from saic_depth_completion.engine.train import train
from saic_depth_completion.utils.tensorboard import Tensorboard
from saic_depth_completion.utils.logger import setup_logger
from saic_depth_comple... | 3,793 | 30.616667 | 106 | py |
saic_depth_completion | saic_depth_completion-master/tools/test_net.py | import torch
import argparse
from saic_depth_completion.data.datasets.matterport import Matterport
from saic_depth_completion.data.datasets.nyuv2_test import NyuV2Test
from saic_depth_completion.engine.inference import inference
from saic_depth_completion.utils.tensorboard import Tensorboard
from saic_depth_completio... | 4,177 | 32.424 | 93 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/metrics/absolute.py | import torch
from torch import nn
###### LOSSES #######
class BerHuLoss(nn.Module):
def __init__(self, scale=0.5, eps=1e-5):
super(BerHuLoss, self).__init__()
self.scale = scale
self.eps = eps
def forward(self, pred, gt):
img1 = torch.zeros_like(pred)
img2 = torch.zero... | 1,943 | 26.380282 | 91 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/metrics/relative.py | from functools import partial
import torch
from torch import nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from math import exp
###### METRICS #######
class DepthRel(nn.Module):
def __init__(self, eps=1e-5):
super(DepthRel, self).__init__()
self.eps = e... | 3,570 | 32.688679 | 118 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/engine/inference.py | import os
import time
import datetime
import torch
from tqdm import tqdm
import matplotlib.pyplot as plt
from saic_depth_completion.utils.meter import AggregatedMeter
from saic_depth_completion.utils.meter import Statistics as LossMeter
from saic_depth_completion.utils import visualize
def inference(
model,... | 1,649 | 31.352941 | 95 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/engine/val.py | import time
import datetime
import torch
from tqdm import tqdm
from saic_depth_completion.utils.meter import AggregatedMeter
from saic_depth_completion.utils.meter import Statistics as LossMeter
def validate(
model, val_loaders, metrics, epoch=0, logger=None, tensorboard=None, tracker=None
):
model.eval... | 1,366 | 32.341463 | 109 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/engine/train.py | import time
import datetime
import torch
from saic_depth_completion.utils.meter import AggregatedMeter
from saic_depth_completion.utils.meter import Statistics as LossMeter
from saic_depth_completion.engine.val import validate
def train(
model, trainloader, optimizer, val_loaders={}, scheduler=None, snapshoter=No... | 2,859 | 33.047619 | 100 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/utils/snapshoter.py | import logging
import os
import torch
import numpy as np
class Snapshoter:
def __init__(
self,
model,
optimizer=None,
scheduler=None,
save_dir="",
period=10,
logger=None,
):
self.model = model
self.optimize... | 2,265 | 31.84058 | 99 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/utils/model_zoo.py | import os
import re
import torch
from torch.hub import load_state_dict_from_url
model_resnet_imagenet = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resne... | 2,795 | 54.92 | 126 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/utils/tensorboard.py | import torch.utils.tensorboard as tb
from saic_depth_completion.utils import visualize
class Tensorboard:
def __init__(self, tb_dir, max_figures=10):
self.tb_dir = tb_dir
self.max_figures = max_figures
def update(self, metrics_dict, epoch, tag="train"):
with tb.SummaryWriter(self.tb_di... | 1,087 | 37.857143 | 66 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/utils/meter.py | from collections import deque
import torch
class Statistics(object):
def __init__(self, maxlen=20):
self.enum = deque(maxlen=maxlen)
self.denum = deque(maxlen=maxlen)
self.total = 0.0
self.count = 0
def reset(self):
self.total = 0.0
self.count = 0
self... | 2,265 | 23.630435 | 71 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/data/collate.py | import torch
def default_collate(samples):
batch = dict()
for k in samples[0].keys():
batch[k] = list()
for sample in samples:
for k, v in sample.items():
batch[k].append(v)
for k, v in batch.items():
batch[k] = torch.stack(v)
return batch | 299 | 19 | 35 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/data/datasets/nyuv2_test.py | import os
import torch
from torch.nn import functional as F
import numpy as np
from PIL import Image
import cv2
# ROOT = '/Vol1/dbstore/datasets/depth_completion/Matterport3D/'
ROOT = "/Vol0/user/d.senushkin/datasets/nyuv2_test"
class NyuV2Test:
def __init__(
self, root=ROOT, split="1gr10pv1pd", tran... | 3,757 | 38.978723 | 103 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/data/datasets/nyu_raw.py | import os
import torch
import numpy as np
from PIL import Image
import random
ROOT = '/Vol1/dbstore/datasets/depth_completion/NYUv2_raw'
class NYURaw():
def __init__(self, split, dt=0.01, valid_split=0.05,
focal=None, image_aug=None,
depth_aug=None, geometry_aug=None,
... | 4,875 | 29.666667 | 108 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/data/datasets/matterport.py | import os
import torch
import numpy as np
from PIL import Image
# ROOT = '/Vol1/dbstore/datasets/depth_completion/Matterport3D/'
ROOT = "/Vol0/user/d.senushkin/datasets/matterport3d"
class Matterport:
def __init__(
self, root=ROOT, split="train", transforms=None
):
self.transforms = trans... | 4,048 | 41.177083 | 104 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/data/datasets/completion_dataset.py | import numpy as np
from skimage.filters import gaussian
import torch
def rgb2gray(rgb):
return np.dot(rgb[..., :3], [0.2989, 0.5870, 0.1140])
def create_holes_mask(layer, granularity, percentile):
gaussian_layer = np.random.uniform(size=layer.shape[1:])
gaussian_layer = gaussian(gaussian_layer, sigma=gr... | 2,917 | 35.024691 | 91 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/modeling/dm_lrn.py | from functools import partial
import torch
from torch import nn
import torch.nn.functional as F
from saic_depth_completion.modeling.backbone import build_backbone
from saic_depth_completion.modeling.blocks import AdaptiveBlock, MaskEncoder, FusionBlock, CRPBlock, SharedEncoder
from saic_depth_completion.utils import... | 6,260 | 37.176829 | 114 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/modeling/lrn.py | from functools import partial
import torch
from torch import nn
import torch.nn.functional as F
from saic_depth_completion.modeling.backbone import build_backbone
from saic_depth_completion.modeling.blocks import AdaptiveBlock, MaskEncoder, FusionBlock, CRPBlock
from saic_depth_completion.utils import registry
from ... | 4,692 | 37.154472 | 103 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/modeling/meta.py | import torch
import torch.nn.functional as F
from saic_depth_completion.utils import registry
# refactor this to
class MetaModel(torch.nn.Module):
def __init__(self, cfg, device):
super(MetaModel, self).__init__()
self.model = registry.MODELS[cfg.model.arch](cfg.model)
self.model.to(device... | 1,369 | 30.860465 | 86 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/modeling/blocks.py | from functools import partial
import torch
from torch import nn
import torch.nn.functional as F
from saic_depth_completion import ops
from saic_depth_completion.modeling.backbone.res_blocks import Bottleneck
class CRPBlock(nn.Module):
def conv1x1(self, in_planes, out_planes, stride=1, bias=False):
retur... | 5,751 | 35.405063 | 114 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/modeling/backbone/hrnet.py | import sys
import torch
from torch import nn
from torch.nn import Conv2d
from collections import namedtuple
from saic_depth_completion.modeling.backbone import res_blocks
from saic_depth_completion import ops
StageSpec = namedtuple(
"StageSpec",
[
"num_channels", # tuple
"num_blocks",... | 17,294 | 39.790094 | 100 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/modeling/backbone/res_blocks.py | import torch
import torch.nn as nn
from collections import namedtuple
import sys
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, groups=groups, b... | 3,147 | 30.168317 | 90 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/modeling/backbone/efficientnet.py | import sys
import torch.nn as nn
from efficientnet_pytorch import EfficientNet as _EfficientNet
from efficientnet_pytorch.utils import url_map, get_model_params
from collections import namedtuple
StageSpec = namedtuple("StageSpec", ["num_channels", "stage_stamp"],)
efficientnet_b0 = tuple(StageSpec(num_channels=nc,... | 2,822 | 34.734177 | 99 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/modeling/backbone/resnet.py | import sys
from collections import namedtuple
from torch import nn
from saic_depth_completion.modeling.backbone import res_blocks
from saic_depth_completion import ops
StageSpec = namedtuple("StageSpec", ["block_count", "block"],)
resnet18 = tuple(StageSpec(block_count=c, block=b)
for (c, b) in ((2, "BasicBlock... | 5,712 | 38.951049 | 106 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/ops/batch_norm.py | import torch
from torch import nn
from torch.nn import functional as F
class FrozenBatchNorm2d(nn.Module):
"""
BatchNorm2d where the batch statistics and the affine parameters
are fixed
"""
def __init__(self, channels, eps=1e-5):
super(FrozenBatchNorm2d, self).__init__()
self.eps ... | 1,460 | 36.461538 | 81 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/ops/spade.py | import torch
from torch import nn
from torch.nn import functional as F
class SPADE(nn.Module):
def __init__(self, x_ch, y_ch, kernel_size=3, upsample='nearest'):
super(SPADE, self).__init__()
self.eps = 1e-5
assert upsample in ['nearest', 'bilinear']
self.upsample = upsample
... | 2,290 | 28.371795 | 81 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/ops/sean.py | import torch
from torch import nn
from torch.nn import functional as F
class SEAN(nn.Module):
def __init__(self, x_ch, y_ch, kernel_size=3, upsample='nearest'):
super(SEAN, self).__init__()
assert upsample in ['nearest', 'bilinear']
self.upsample = upsample
padding = (kernel_size ... | 1,678 | 29.527273 | 76 | py |
saic_depth_completion | saic_depth_completion-master/saic_depth_completion/ops/__init__.py | from functools import partial
import torch
from .batch_norm import FrozenBatchNorm2d
from .spade import SPADE, SelfSPADE
from .sean import SEAN
from saic_depth_completion.utils.registry import Registry
MODULATION_LAYERS = Registry()
NORM_LAYERS = Registry()
ACTIVATION_LAYERS = Registry()
ACTIVATION_LAYERS["ReLU"] ... | 603 | 25.26087 | 57 | py |
dwave-neal | dwave-neal-master/docs/conf.py | # -*- coding: utf-8 -*-
#
# dwave_neal documentation build configuration file, created by
# sphinx-quickstart on Wed Jul 26 10:55:26 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
... | 6,219 | 29.945274 | 83 | py |
IdealGPT | IdealGPT-master/blip_gpt_main.py | import os
import yaml
import argparse
import torch
import openai
from tqdm import tqdm
import pdb
from data import VCRSampler
from data import VESampler
from chat import VCRConversationTwoAgent
from chat import VEConversationTwoAgent
import random
def IdealGPT(vqa_model, dataset, data_ids, model, save_path='', max_... | 11,536 | 45.708502 | 171 | py |
IdealGPT | IdealGPT-master/misc/blip_caption.py | import argparse
import torch
import yaml
import json
from tqdm import tqdm
import jsonlines
import os
import sys
# Get the absolute path to the parent directory
parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Add the parent directory to sys.path
sys.path.insert(0, parent_dir)
from lib.blip2_l... | 5,378 | 38.262774 | 131 | py |
IdealGPT | IdealGPT-master/lib/llava_lib.py | import argparse
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
import torch
import os
import json
from tqdm import tqdm
import shortuuid
from llava.conversation import conv_templates
from llava.utils import disable_torch_init
from transformers import CLIPVisionModel, CLIPImageProcessor, Stopp... | 8,493 | 42.336735 | 154 | py |
ccvae | ccvae-master/ss_vae.py | import argparse
import torch
import torchvision.utils
import pyro
from pyro.infer import SVI, Trace_ELBO
from pyro.optim import Adam
from utils.custom_loss import TraceCCVAE_ELBO
from utils.dataset_cached import setup_data_loaders, CELEBACached, CELEBA_EASY_LABELS
from models.semisup_vae import SSVAE_CCVAE
import nump... | 5,949 | 37.387097 | 98 | py |
ccvae | ccvae-master/models/networks.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class View(nn.Module):
def __init__(self, size):
super(View, self).__init__()
self.size = size
def forward(self, tensor):
return tensor.view(self.size)
class CELEBAEncoder(nn.Module):
def __init__(s... | 3,017 | 30.4375 | 96 | py |
ccvae | ccvae-master/models/semisup_vae.py | import numpy as np
import torch
from torchvision.utils import make_grid, save_image
import pyro
import pyro.distributions as dist
import os
from .networks import (CondPrior, Classifier,
CELEBADecoder, CELEBAEncoder)
class SSVAE_CCVAE(torch.nn.Module):
"""
Class that deals with the propos... | 7,853 | 40.776596 | 130 | py |
ccvae | ccvae-master/utils/custom_loss.py | import torch
import pyro
from pyro.infer import Trace_ELBO
from pyro.util import warn_if_nan
from pyro.infer.util import torch_backward, torch_item
class TraceCCVAE_ELBO(Trace_ELBO):
"""
Custom loss
"""
def __init__(self, vae, *args, **kwargs):
super(TraceCCVAE_ELBO, self).__init__(*args, **kw... | 1,504 | 28.509804 | 85 | py |
ccvae | ccvae-master/utils/dataset_cached.py | import os
import PIL
from functools import reduce
import numpy as np
import torch
from torch.utils.data import DataLoader
from torchvision.datasets import CelebA
import torchvision.transforms as transforms
from pyro.contrib.examples.util import get_data_directory
def split_celeba(X, y, sup_frac, validation_num):
... | 5,783 | 35.377358 | 190 | py |
SIFT | SIFT-master/inference.py | import argparse
import csv
import json
import os
import re
from types import SimpleNamespace
import pytorch_lightning as pl
import torch
from data_readers import output_modes, processors
from data_readers.base_reader import convert_examples_to_features, get_graphs
from models import RGCNSemanticEncoder
def load_pre... | 6,050 | 36.351852 | 167 | py |
SIFT | SIFT-master/train.py | import argparse
from importlib import reload
import logging
import os
import json
from pathlib import Path
import random
import numpy as np
import pytorch_lightning as pl
import torch
reload(logging)
from models import RGCNSemanticEncoder
logger = logging.getLogger(__name__)
class LoggingCallback(pl.Callback):
... | 6,356 | 32.81383 | 138 | py |
SIFT | SIFT-master/models/pretrained_transformer.py | import logging
import pytorch_lightning as pl
from torch import nn
from transformers import AutoConfig, AutoModel, AutoTokenizer
logger = logging.getLogger(__name__)
class PretrainedTransformer(pl.LightningModule):
def __init__(self, args, num_labels=None, mode="base", **config_kwargs):
"Initialize a ... | 2,916 | 38.418919 | 151 | py |
SIFT | SIFT-master/models/gnn.py | import torch.nn.functional as F
import torch.nn as nn
from dgl.nn.pytorch import RelGraphConv
class GNN(nn.Module):
def __init__(self, h_dim, num_relations, num_hidden_layers=1, dropout=0, activation=F.relu):
super().__init__()
self.h_dim = h_dim
self.num_relations = num_relations
... | 1,704 | 30 | 98 | py |
SIFT | SIFT-master/models/rgcn_semantic_encoder.py | import logging
from allennlp.modules.matrix_attention import BilinearMatrixAttention
from allennlp.nn.util import masked_softmax, masked_max
import torch
from torch import nn
import torch.nn.functional as F
from .semantic_encoder import SemanticEncoder
from .gnn import RGCN
logger = logging.getLogger(__name__)
cl... | 13,926 | 47.866667 | 256 | py |
SIFT | SIFT-master/models/base_lightning_model.py | import numpy as np
import pytorch_lightning as pl
import torch
from transformers import get_linear_schedule_with_warmup
from transformers import AdamW
class BaseLightningModel(pl.LightningModule):
def __init__(self, args):
super().__init__()
self.args = args
def train_dataloader(self, shuffl... | 5,148 | 39.865079 | 148 | py |
SIFT | SIFT-master/models/semantic_encoder.py | import logging
import os
import pickle
from types import GeneratorType
from allennlp.nn.util import masked_mean
from dgl import DGLGraph
from pytorch_lightning.utilities import move_data_to_device
import torch
from torch import nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from transformer... | 16,081 | 40.448454 | 174 | py |
SIFT | SIFT-master/data_readers/base_reader.py | from collections import Counter
import dataclasses
from dataclasses import dataclass
import json
import logging
import os
import pickle
from typing import Any, List, Optional, Tuple, Union
from dgl import DGLGraph
from rdflib import Graph as RDFGraph
import torch
from tqdm import tqdm
from .rdf2dgl import relations_i... | 14,054 | 40.58284 | 177 | py |
SIFT | SIFT-master/data_readers/semantic_dataset.py | import dgl
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset
from torch.utils.data._utils.collate import default_collate
def _transpose(l):
return list(zip(*l))
class SemanticDataset(Dataset):
"""When labels and/or graphs are not available, pass in a list of Nones"""
def... | 7,461 | 42.637427 | 166 | py |
SIFT | SIFT-master/data_readers/rdf2dgl.py | # some methods/classes adpated from https://github.com/dmlc/dgl/blob/ca48787ae49e9d73ebf17b1c882cb66fb66fb828/python/dgl/contrib/data/knowledge_graph.py
from collections import Counter
from dataclasses import dataclass
import pickle
import sys
from typing import Dict, Optional
from dgl import DGLGraph
from rdflib imp... | 3,734 | 29.867769 | 152 | py |
hexaconv | hexaconv-master/groupy/gconv/gconv_tensorflow/keras/layers.py | """Contains the group equivariant convolutional Keras layers."""
import tensorflow as tf
import groupy.gconv.gconv_tensorflow.layers as tf_layers
class SplitGConv2D(tf_layers.SplitGConv2D, tf.keras.layers.Layer):
"""Group convolution base class for split plane groups.
A plane group (aka wallpaper group) is a... | 31,761 | 55.116608 | 119 | py |
pixel-deflection | pixel-deflection-master/main.py | from __future__ import division
import os, sys, argparse, multiprocessing
from joblib import Parallel, delayed
from glob import glob
import numpy as np
from utils import *
from methods import pixel_deflection, denoiser
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # silences the TF INFO messages
def get_arguments():
p... | 4,565 | 48.630435 | 138 | py |
pixel-deflection | pixel-deflection-master/utils.py | import matplotlib
import numpy as np
from keras.preprocessing import image
from scipy.misc import imread
import json
def batches(array, batch_size):
return (array[i:i + batch_size] for i in range(0, len(array), batch_size))
def rgb2ycc(im):
xform = np.array([[.299, .587, .114], [-.1687, -.3313, .5], [.5, -.41... | 924 | 25.428571 | 86 | py |
pixel-deflection | pixel-deflection-master/methods.py | from __future__ import division
from random import randint, uniform
import os, sys, argparse, matplotlib, multiprocessing
from keras.preprocessing import image
from scipy.misc import imread
from joblib import Parallel, delayed
from glob import glob
import numpy as np
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
norm = mat... | 1,707 | 38.72093 | 120 | py |
DeepFormableTag | DeepFormableTag-master/setup.py | """
!/usr/bin/env python
Copyright (c) Facebook, Inc. and its affiliates.
Edited by Mustafa B. Yaldiz (VCLAB, KAIST)
"""
import glob
import os
import shutil
from os import path
from setuptools import find_packages, setup
from typing import List
import torch
# from torch.utils.cpp_extension import CUDA_HOME, CppExtensio... | 1,768 | 31.163636 | 97 | py |
DeepFormableTag | DeepFormableTag-master/tools/generate_board_pdf.py | # Copyright (c) Mustafa B. Yaldiz (VCLAB, KAIST) All Rights Reserved.
"""
This code creates pdfs for given configs.
"""
import os, json, argparse
from pathlib import Path
from collections import OrderedDict
import cairo
import numpy as np
from cv2 import aruco
import torch
from deepformable.utils import (
get_aruc... | 9,512 | 41.28 | 117 | py |
DeepFormableTag | DeepFormableTag-master/tools/training_visualizer.py | # Copyright (c) Mustafa B. Yaldiz (VCLAB, KAIST) All Rights Reserved.
import cv2
from cv2 import aruco
import random
import matplotlib.pyplot as plt
import json
import numpy as np
import argparse
import math
from pathlib import Path
from typing import Union
from copy import deepcopy
import datetime
import os
import ran... | 8,608 | 35.790598 | 108 | py |
DeepFormableTag | DeepFormableTag-master/tools/predictor_demo.py | # Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import glob
from pathlib import Path
import multiprocessing as mp
import os
import tempfile
import time
import warnings
import tqdm
import json
import cv2
import numpy as np
import torch
from detectron2.data.detection_utils import read_image
from detec... | 7,927 | 36.396226 | 95 | py |
DeepFormableTag | DeepFormableTag-master/tools/train.py | # Copyright (c) Mustafa B. Yaldiz (VCLAB, KAIST) All Rights Reserved.
from pathlib import Path
import json
import torch
import detectron2
import detectron2.utils.comm as comm
from detectron2.engine import default_argument_parser, default_setup, launch
import deepformable
from deepformable.engine import DeepformableTr... | 3,491 | 36.148936 | 128 | py |
DeepFormableTag | DeepFormableTag-master/deepformable/evaluation/evaluation.py | """
This code is modified from detectron2 implementation,
changes are logged in comments.
Copyright (c) Mustafa B. Yaldiz (VCLAB, KAIST) All Rights Reserved.
"""
import os
import copy
import logging
import itertools
import datetime
from collections import OrderedDict
import numpy as np
import torch
import pycocotoo... | 16,742 | 41.820972 | 108 | py |
DeepFormableTag | DeepFormableTag-master/deepformable/layers/adaptive_loss.py | # Copyright (c) Mustafa B. Yaldiz (VCLAB, KAIST) All Rights Reserved.
import torch
import torch.distributed as dist
from torch import nn
from torch.nn import functional as F
from detectron2.utils.comm import get_world_size, is_main_process
from .dist_ops import AllReduce
class AdaptiveLoss(nn.Module):
"""
Th... | 3,133 | 40.786667 | 94 | py |
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