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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cwn | cwn-main/data/sr_utils.py | import networkx as nx
import torch
from torch_geometric.utils import to_undirected
def load_sr_dataset(path):
"""Load the Strongly Regular Graph Dataset from the supplied path."""
nx_graphs = nx.read_graph6(path)
graphs = list()
for nx_graph in nx_graphs:
n = nx_graph.number_of_nodes()
... | 485 | 29.375 | 105 | py |
cwn | cwn-main/data/dummy_complexes.py | import torch
from data.complex import Cochain, Complex
from torch_geometric.data import Data
# TODO: make the features for these dummy complexes disjoint to stress tests even more
def convert_to_graph(complex):
"""Extracts the underlying graph of a cochain complex."""
assert 0 in complex.cochains
assert ... | 23,206 | 39.220104 | 168 | py |
cwn | cwn-main/data/test_utils.py | import torch
from torch_geometric.data import Data
from data.utils import compute_clique_complex_with_gudhi, compute_ring_2complex
from data.utils import convert_graph_dataset_with_gudhi, convert_graph_dataset_with_rings
from data.complex import ComplexBatch
from data.dummy_complexes import convert_to_graph, get_testi... | 31,625 | 48.883281 | 148 | py |
cwn | cwn-main/data/datasets/test_flow.py | import numpy as np
import torch
from scipy.spatial import Delaunay
from data.datasets.flow_utils import load_flow_dataset, create_hole, is_inside_rectangle
def test_create_hole():
# This seed contains some edge cases.
np.random.seed(4)
points = np.random.uniform(size=(400, 2))
tri = Delaunay(points)
... | 2,416 | 36.184615 | 94 | py |
cwn | cwn-main/data/datasets/peptides_structural.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue May 2 21:37:42 2023
@author: renz
"""
import hashlib
import os.path as osp
import os
import pickle
import shutil
import pandas as pd
import torch
from ogb.utils import smiles2graph
from ogb.utils.torch_util import replace_numpy_with_torchtensor
from ... | 11,359 | 41.546816 | 134 | py |
cwn | cwn-main/data/datasets/peptides_functional.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue May 2 21:37:42 2023
@author: renz
"""
import hashlib
import os.path as osp
import os
import pickle
import shutil
import pandas as pd
import torch
from ogb.utils import smiles2graph
from ogb.utils.torch_util import replace_numpy_with_torchtensor
from ... | 10,424 | 39.564202 | 124 | py |
cwn | cwn-main/data/datasets/plot_ringtree_dataset.py | import networkx as nx
import matplotlib.pyplot as plt
from data.datasets.ring_utils import generate_ring_transfer_graph_dataset
from torch_geometric.utils import convert
def visualise_ringtree_dataset():
dataset = generate_ring_transfer_graph_dataset(nodes=10, samples=100, classes=5)
data = dataset[0]
g... | 495 | 23.8 | 84 | py |
cwn | cwn-main/data/datasets/cluster.py | import pickle
from data.datasets import InMemoryComplexDataset
from data.utils import convert_graph_dataset_with_gudhi
from torch_geometric.datasets import GNNBenchmarkDataset
class ClusterDataset(InMemoryComplexDataset):
"""This is the Cluster dataset from the Benchmarking GNNs paper.
The dataset contains ... | 3,283 | 41.102564 | 140 | py |
cwn | cwn-main/data/datasets/csl.py | import os.path as osp
import numpy as np
import torch
from data.datasets import InMemoryComplexDataset
from data.utils import convert_graph_dataset_with_rings
from torch_geometric.datasets import GNNBenchmarkDataset
from torch_geometric.utils import remove_self_loops
class CSLDataset(InMemoryComplexDataset):
"""... | 4,912 | 39.270492 | 127 | py |
cwn | cwn-main/data/datasets/flow_utils.py | import numpy as np
import random
import torch
import networkx as nx
import itertools
from scipy.spatial import Delaunay
from scipy import sparse
from data.complex import Cochain
from data.parallel import ProgressParallel
from joblib import delayed
def is_inside_rectangle(x, rect):
return rect[0, 0] <= x[0] <= re... | 10,807 | 30.976331 | 89 | py |
cwn | cwn-main/data/datasets/sr.py | import os
import torch
import pickle
from data.sr_utils import load_sr_dataset
from data.utils import compute_clique_complex_with_gudhi, compute_ring_2complex
from data.utils import convert_graph_dataset_with_rings, convert_graph_dataset_with_gudhi
from data.datasets import InMemoryComplexDataset
from definitions impo... | 4,522 | 39.747748 | 107 | py |
cwn | cwn-main/data/datasets/ring_utils.py | import numpy as np
import torch
import random
from torch_geometric.data import Data
from sklearn.preprocessing import LabelBinarizer
# TODO: Add a graph dataset for ring lookup.
def generate_ring_lookup_graph(nodes):
"""This generates a dictionary lookup ring. No longer being used for now."""
# Assign all th... | 3,276 | 30.509615 | 82 | py |
cwn | cwn-main/data/datasets/zinc.py | import torch
import os.path as osp
from data.utils import convert_graph_dataset_with_rings
from data.datasets import InMemoryComplexDataset
from torch_geometric.datasets import ZINC
class ZincDataset(InMemoryComplexDataset):
"""This is ZINC from the Benchmarking GNNs paper. This is a graph regression task."""
... | 5,282 | 36.468085 | 91 | py |
cwn | cwn-main/data/datasets/dataset.py | """
The code is based on https://github.com/rusty1s/pytorch_geometric/blob/76d61eaa9fc8702aa25f29dfaa5134a169d0f1f6/torch_geometric/data/dataset.py#L19
and https://github.com/rusty1s/pytorch_geometric/blob/master/torch_geometric/data/in_memory_dataset.py
Copyright (c) 2020 Matthias Fey <matthias.fey@tu-dortmund.de>
Co... | 14,872 | 38.873995 | 147 | py |
cwn | cwn-main/data/datasets/test_zinc.py | import torch
import os.path as osp
import pytest
from data.data_loading import load_dataset
from data.helper_test import (check_edge_index_are_the_same,
check_edge_attr_are_the_same, get_rings,
get_complex_rings)
from torch_geometric.datasets import ZINC
... | 2,395 | 38.933333 | 107 | py |
cwn | cwn-main/data/datasets/tu.py | import os
import torch
import pickle
import numpy as np
from definitions import ROOT_DIR
from data.tu_utils import load_data, S2V_to_PyG, get_fold_indices
from data.utils import convert_graph_dataset_with_gudhi, convert_graph_dataset_with_rings
from data.datasets import InMemoryComplexDataset
def load_tu_graph_datas... | 6,539 | 49.307692 | 110 | py |
cwn | cwn-main/data/datasets/ogb.py | import torch
import os.path as osp
from data.utils import convert_graph_dataset_with_rings
from data.datasets import InMemoryComplexDataset
from ogb.graphproppred import PygGraphPropPredDataset
class OGBDataset(InMemoryComplexDataset):
"""This is OGB graph-property prediction. This are graph-wise classification ... | 4,176 | 42.061856 | 97 | py |
cwn | cwn-main/data/datasets/ringlookup.py | import torch
import os.path as osp
from data.datasets import InMemoryComplexDataset
from data.datasets.ring_utils import generate_ringlookup_graph_dataset
from data.utils import convert_graph_dataset_with_rings
class RingLookupDataset(InMemoryComplexDataset):
"""A dataset where the task is to perform dictionary ... | 3,214 | 32.842105 | 98 | py |
cwn | cwn-main/data/datasets/test_ocean.py | import torch
import pytest
from data.datasets.ocean_utils import load_ocean_dataset
@pytest.mark.data
def test_ocean_dataset_generation():
train, test, _ = load_ocean_dataset()
assert len(train) == 160
assert len(test) == 40
for cochain in train + test:
# checks the upper/lower orientation fe... | 1,247 | 43.571429 | 94 | py |
cwn | cwn-main/data/datasets/ringtransfer.py | import torch
import os.path as osp
from data.datasets import InMemoryComplexDataset
from data.datasets.ring_utils import generate_ring_transfer_graph_dataset
from data.utils import convert_graph_dataset_with_rings
class RingTransferDataset(InMemoryComplexDataset):
"""A dataset where the task is to transfer featu... | 3,322 | 32.908163 | 92 | py |
cwn | cwn-main/data/datasets/dummy.py | import torch
from data.datasets import InMemoryComplexDataset
from data.dummy_complexes import get_testing_complex_list, get_mol_testing_complex_list
class DummyDataset(InMemoryComplexDataset):
"""A dummy dataset using a list of hand-crafted cell complexes with many edge cases."""
def __init__(self, root):
... | 3,221 | 34.021739 | 101 | py |
cwn | cwn-main/exp/parser.py | import os
import time
import argparse
from definitions import ROOT_DIR
def get_parser():
parser = argparse.ArgumentParser(description='CWN experiment.')
parser.add_argument('--seed', type=int, default=43,
help='random seed to set (default: 43, i.e. the non-meaning of life))')
pars... | 11,349 | 59.695187 | 126 | py |
cwn | cwn-main/exp/run_exp.py | import os
import numpy as np
import copy
import pickle
import torch
import torch.optim as optim
import random
from data.data_loading import DataLoader, load_dataset, load_graph_dataset
from torch_geometric.data import DataLoader as PyGDataLoader
from exp.train_utils import train, eval, Evaluator
from exp.parser import... | 26,286 | 52.977413 | 128 | py |
cwn | cwn-main/exp/train_utils.py | import os
import torch
import numpy as np
import logging
from tqdm import tqdm
from sklearn import metrics as met
from data.complex import ComplexBatch
from ogb.graphproppred import Evaluator as OGBEvaluator
cls_criterion = torch.nn.CrossEntropyLoss()
bicls_criterion = torch.nn.BCEWithLogitsLoss()
reg_criterion = torc... | 7,530 | 34.523585 | 100 | py |
cwn | cwn-main/exp/evaluate_sr_cwn_emb_mag.py | import os
import sys
import torch
import numpy as np
import random
from definitions import ROOT_DIR
from exp.prepare_sr_tests import prepare
from mp.models import MessagePassingAgnostic, SparseCIN
from data.data_loading import DataLoader, load_dataset
__families__ = [
'sr16622',
'sr251256',
'sr261034',
... | 3,564 | 31.409091 | 121 | py |
cwn | cwn-main/exp/test_sr.py | import torch
import numpy as np
import random
import pytest
from data.data_loading import DataLoader, load_dataset
from exp.prepare_sr_tests import prepare
from mp.models import MessagePassingAgnostic, SparseCIN
def _get_cwn_sr_embeddings(family, seed, baseline=False):
# Set the seed for everything
torch.man... | 5,473 | 41.434109 | 143 | py |
TCDF | TCDF-master/runTCDF.py | import TCDF
import argparse
import torch
import pandas as pd
import numpy as np
import networkx as nx
import pylab
import copy
import matplotlib.pyplot as plt
import os
import sys
# os.chdir(os.path.dirname(sys.argv[0])) #uncomment this line to run in VSCode
def check_positive(value):
"""Checks if argument is pos... | 13,848 | 39.612903 | 544 | py |
TCDF | TCDF-master/TCDF.py | import torch
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
from model import ADDSTCN
import random
import pandas as pd
import numpy as np
import heapq
import copy
import os
import sys
def preparedata(file, target):
"""Reads data from csv file and transforms it to t... | 5,903 | 33.729412 | 166 | py |
TCDF | TCDF-master/depthwise.py | import torch
import torch.nn as nn
from torch.nn.utils import weight_norm
from torch.autograd import Variable
class Chomp1d(nn.Module):
"""PyTorch does not offer native support for causal convolutions, so it is implemented (with some inefficiency) by simply using a standard convolution with zero padding on both si... | 3,952 | 39.752577 | 232 | py |
TCDF | TCDF-master/model.py | import torch as th
from torch import nn
import torch.nn.functional as F
from torch.autograd import Variable
from depthwise import DepthwiseNet
from torch.nn.utils import weight_norm
import numpy as np
class ADDSTCN(nn.Module):
def __init__(self, target, input_size, num_levels, kernel_size, cuda, dilation_c):
... | 1,175 | 34.636364 | 116 | py |
TCDF | TCDF-master/evaluate_predictions_TCDF.py | import TCDF
import argparse
import torch
import torch.optim as optim
from model import ADDSTCN
import pandas as pd
import numpy as np
import networkx as nx
import pylab
import copy
import matplotlib.pyplot as plt
import os
import sys
# os.chdir(os.path.dirname(sys.argv[0])) #uncomment this line to run in VSCode
def c... | 7,764 | 38.820513 | 212 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/run.py | #!/usr/bin/env python3
import argparse
import random
import os
import numpy as np
import torch
from habitat import logger
from habitat_baselines.common.baseline_registry import baseline_registry
import habitat_extensions # noqa: F401
import vlnce_baselines # noqa: F401
from vlnce_baselines.config.default import ... | 2,787 | 27.742268 | 81 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/vlnce_baselines/ss_trainer_CMA.py | import gc
import os
import random
import warnings
from collections import defaultdict
import lmdb
import msgpack_numpy
import numpy as np
import math
import time
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import tqdm
from habitat import logger
from habitat_baselines.common.baseli... | 18,334 | 39.474614 | 115 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/vlnce_baselines/utils.py | import torch
import torch.distributed as dist
import numpy as np
import math
import copy
class ARGS():
def __init__(self):
self.local_rank = 0
def reduce_loss(tensor, rank, world_size):
with torch.no_grad():
dist.reduce(tensor, dst=0)
if rank == 0:
tensor /= world_size
def... | 5,848 | 34.883436 | 122 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/vlnce_baselines/ss_trainer_VLNBERT.py | import gc
import os
import random
import warnings
from collections import defaultdict
import lmdb
import msgpack_numpy
import numpy as np
import math
import time
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import tqdm
from habitat import logger
from habitat_baselines.common.baseli... | 28,887 | 42.310345 | 116 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/vlnce_baselines/common/aux_losses.py | import torch
class _AuxLosses:
def __init__(self):
self._losses = {}
self._loss_alphas = {}
self._is_active = False
def clear(self):
self._losses.clear()
self._loss_alphas.clear()
def register_loss(self, name, loss, alpha=1.0):
assert self.is_active()
... | 987 | 20.955556 | 70 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/vlnce_baselines/common/recollection_dataset.py | import gzip
import json
from collections import defaultdict, deque
import numpy as np
import torch
import tqdm
from gym import Space
from habitat.config.default import Config
from habitat.sims.habitat_simulator.actions import HabitatSimActions
from habitat_baselines.common.environments import get_env_class
from habita... | 10,692 | 34.88255 | 88 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/vlnce_baselines/common/utils.py | from typing import Any, Dict, List
import torch
import torch.distributed as dist
import numpy as np
import copy
def extract_instruction_tokens(
observations: List[Dict],
instruction_sensor_uuid: str,
tokens_uuid: str = "tokens",
) -> Dict[str, Any]:
r"""Extracts instruction tokens from an instruction s... | 1,716 | 30.218182 | 76 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/vlnce_baselines/common/base_il_trainer.py | import json
import jsonlines
import os
import time
import warnings
from collections import defaultdict
from typing import Dict, List
import torch
import torch.nn.functional as F
from torch.nn.parallel import DistributedDataParallel as DDP
import torch.distributed as distr
import torch.multiprocessing as mp
import gzip... | 45,832 | 40.971612 | 112 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/vlnce_baselines/models/Policy_ViewSelection_CMA.py | import numpy as np
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from gym import Space
from habitat import Config
from habitat_baselines.common.baseline_registry import baseline_registry
from habitat_baselines.rl.models.rnn_state_encoder import (
build_rnn_state_encoder,
)
from hab... | 18,135 | 38.598253 | 142 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/vlnce_baselines/models/Policy_ViewSelection_VLNBERT.py | import numpy as np
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from gym import Space
from habitat import Config
from habitat_baselines.common.baseline_registry import baseline_registry
from habitat_baselines.rl.models.rnn_state_encoder import (
build_rnn_state_encoder,
)
from hab... | 15,286 | 40.204852 | 142 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/vlnce_baselines/models/utils.py | import math
import torch
def angle_feature(headings, device=None):
heading_enc = torch.zeros(len(headings), 64, dtype=torch.float32)
for i, head in enumerate(headings):
heading_enc[i] = torch.tensor(
[math.sin(head), math.cos(head)] * (64 // 2))
return heading_enc.to(device)
def... | 2,129 | 31.769231 | 84 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/vlnce_baselines/models/policy.py | import abc
from typing import Any
from habitat_baselines.rl.ppo.policy import Policy
from habitat_baselines.utils.common import (
CategoricalNet,
CustomFixedCategorical,
)
from torch.distributions import Categorical
class ILPolicy(Policy, metaclass=abc.ABCMeta):
def __init__(self, net, dim_actions):
... | 2,642 | 27.419355 | 78 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/vlnce_baselines/models/encoders/resnet_encoders.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from gym import spaces
from habitat import logger
from habitat_baselines.rl.ddppo.policy import resnet
from habitat_baselines.rl.ddppo.policy.resnet_policy import ResNetEncoder
import torchvision
c... | 8,103 | 32.626556 | 119 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/vlnce_baselines/models/encoders/instruction_encoder.py | import gzip
import json
import torch
import torch.nn as nn
from habitat import Config
class InstructionEncoder(nn.Module):
def __init__(self, config: Config):
r"""An encoder that uses RNN to encode an instruction. Returns
the final hidden state after processing the instruction sequence.
... | 3,647 | 34.764706 | 79 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/vlnce_baselines/models/vlnbert/vlnbert_PREVALENT.py | # PREVALENT, 2020, weituo.hao@duke.edu
# Modified in Recurrent VLN-BERT, 2020, Yicong.Hong@anu.edu.au
from __future__ import absolute_import, division, print_function, unicode_literals
import json
import logging
import math
import os
import sys
from io import open
import torch
from torch import nn
from torch.nn impo... | 19,050 | 41.811236 | 159 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/habitat_extensions/obs_transformers.py | import copy
import numbers
from typing import Dict, List, Tuple, Union
import torch
from gym import spaces
from habitat.config import Config
from habitat.core.logging import logger
from habitat_baselines.common.baseline_registry import baseline_registry
from habitat_baselines.common.obs_transformers import Observation... | 6,642 | 33.598958 | 88 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/habitat_extensions/habitat_simulator.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import (
TYPE_CHECKING,
Any,
Dict,
List,
Optional,
Sequence,
Set,
Union,
... | 2,654 | 27.244681 | 87 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/waypoint_prediction/TRM_net.py | import torch
import torch.nn as nn
import numpy as np
from .utils import get_attention_mask
from .transformer.waypoint_bert import WaypointBert
from pytorch_transformers import BertConfig
class BinaryDistPredictor_TRM(nn.Module):
def __init__(self, hidden_dim=768, n_classes=12, device=None):
super(BinaryD... | 3,269 | 32.030303 | 89 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/waypoint_prediction/utils.py |
import torch
import numpy as np
import sys
import glob
import json
def neighborhoods(mu, x_range, y_range, sigma, circular_x=True, gaussian=False):
""" Generate masks centered at mu of the given x and y range with the
origin in the centre of the output
Inputs:
mu: tensor (N, 2)
Outputs:
... | 3,409 | 32.431373 | 101 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/waypoint_prediction/transformer/waypoint_bert.py | # Copyright (c) 2020 Microsoft Corporation. Licensed under the MIT license.
# Modified in Recurrent VLN-BERT, 2020, Yicong.Hong@anu.edu.au
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import math
import torch
from torch import nn
import torch.nn.functional as F
from... | 8,306 | 37.281106 | 112 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/waypoint_prediction/transformer/pytorch_transformer/modeling_utils.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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 cop... | 44,611 | 48.513873 | 157 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/waypoint_prediction/transformer/pytorch_transformer/modeling_bert.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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 cop... | 67,047 | 52.382166 | 187 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/waypoint_prediction/transformer/pytorch_transformer/file_utils.py | """
Utilities for working with the local dataset cache.
This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
Copyright by the AllenNLP authors.
"""
from __future__ import (absolute_import, division, print_function, unicode_literals)
import sys
import json
import logging
import os
impor... | 8,876 | 33.142308 | 98 | py |
Synthetic2Realistic | Synthetic2Realistic-master/options/base_options.py | import argparse
import os
from util import util
import torch
class BaseOptions():
def __init__(self):
self.parser = argparse.ArgumentParser()
self.initialized = False
def initialize(self):
# basic define
self.parser.add_argument('--name', type=str, default='experiment_name',
... | 7,866 | 57.708955 | 125 | py |
Synthetic2Realistic | Synthetic2Realistic-master/util/image_pool.py | import random
import torch
from torch.autograd import Variable
class ImagePool():
def __init__(self, pool_size):
self.pool_size = pool_size
if self.pool_size > 0:
self.num_imgs = 0
self.images = []
def query(self, images):
if self.pool_size == 0:
r... | 1,083 | 29.111111 | 67 | py |
Synthetic2Realistic | Synthetic2Realistic-master/util/task.py | import torch
import torch.nn.functional as F
###################################################################
# depth function
###################################################################
# calculate the loss
def rec_loss(pred, truth):
mask = truth == -1
mask = mask.float()
errors = torch.abs... | 2,150 | 27.302632 | 96 | py |
Synthetic2Realistic | Synthetic2Realistic-master/model/base_model.py | import os
import torch
from collections import OrderedDict
from util import util
class BaseModel():
def name(self):
return 'BaseModel'
def initialize(self, opt):
self.opt = opt
self.gpu_ids = opt.gpu_ids
self.isTrain = opt.isTrain
self.save_dir = os.path.join(opt.checkp... | 2,424 | 33.642857 | 71 | py |
Synthetic2Realistic | Synthetic2Realistic-master/model/network.py | import torch
import torch.nn as nn
from torch.nn import init
import functools
from torch.autograd import Variable
from torchvision import models
import torch.nn.functional as F
from torch.optim import lr_scheduler
######################################################################################
# Functions
#####... | 24,337 | 37.028125 | 140 | py |
Synthetic2Realistic | Synthetic2Realistic-master/model/TaskModel.py | import torch
from torch.autograd import Variable
import util.task as task
from .base_model import BaseModel
from . import network
class TNetModel(BaseModel):
def name(self):
return 'TNet Model'
def initialize(self, opt):
BaseModel.initialize(self, opt)
self.loss_names = ['lab_s', 'la... | 4,032 | 33.767241 | 122 | py |
Synthetic2Realistic | Synthetic2Realistic-master/model/T2model.py | import torch
from torch.autograd import Variable
import itertools
from util.image_pool import ImagePool
import util.task as task
from .base_model import BaseModel
from . import network
class T2NetModel(BaseModel):
def name(self):
return 'T2Net model'
def initialize(self, opt):
BaseModel.initia... | 9,119 | 37.808511 | 130 | py |
Synthetic2Realistic | Synthetic2Realistic-master/model/test_model.py | import torch
from torch.autograd import Variable
from .base_model import BaseModel
from . import network
from util import util
from collections import OrderedDict
class TestModel(BaseModel):
def name(self):
return 'TestModel'
def initialize(self, opt):
assert (not opt.isTrain)
BaseMod... | 2,883 | 39.619718 | 111 | py |
Synthetic2Realistic | Synthetic2Realistic-master/dataloader/data_loader.py | import random
from PIL import Image
import torchvision.transforms as transforms
import torch.utils.data as data
from .image_folder import make_dataset
import torchvision.transforms.functional as F
class CreateDataset(data.Dataset):
def initialize(self, opt):
self.opt = opt
self.img_source_paths, ... | 4,873 | 41.017241 | 117 | py |
TEC-reduced-model | TEC-reduced-model-main/setup.py | from setuptools import setup, find_packages
install_requires = [
"pybamm == 0.4.0",
"matplotlib",
"prettytable",
"jax",
"jaxlib",
"SciencePlots",
]
setup(
name="tec_reduced_model",
version="0.2",
author="Ferran Brosa Planella",
author_email="Ferran.Brosa-Planella@warwick.ac.uk"... | 692 | 26.72 | 86 | py |
FishFSRNet | FishFSRNet-main/parsing/test_parsingnet.py | from option import args
import os
os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda_name
import torch
import dataset_parsingnet
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import os
import util
import torchvision
import parsingnet
net = parsingnet.ParsingNet()
net = util.prepa... | 1,315 | 36.6 | 108 | py |
FishFSRNet | FishFSRNet-main/parsing/parsingnet.py | import common
import torch.nn as nn
class ParsingNet(nn.Module):
def __init__(self, args, conv=common.default_conv):
super(ParsingNet, self).__init__()
n_resblocks = 8
n_feats = 64
kernel_size = 3
act = nn.ReLU(True)
self.args = args
m_head = [conv(args.n_co... | 906 | 24.914286 | 77 | py |
FishFSRNet | FishFSRNet-main/parsing/cbam.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
class Flatten(nn.Module):
def forward(self, x):
return x.view(x.size(0), -1)
class ChannelGate(nn.Module):
def __init__(self, gate_channels, reduction_ratio=16, pool_types=None):
super(ChannelGate, self).__init__()
... | 3,309 | 34.978261 | 119 | py |
FishFSRNet | FishFSRNet-main/parsing/common.py | import torch.nn as nn
import torch
import cbam
import math
def batched_index_select(values, indices):
last_dim = values.shape[-1]
return values.gather(1, indices[:, :, None].expand(-1, -1, last_dim))
class BasicBlock(nn.Sequential):
def __init__(
self, conv, in_channels, out_channels, kernel... | 8,996 | 34.007782 | 127 | py |
FishFSRNet | FishFSRNet-main/parsing/util.py |
import torch
import numpy as np
import math
import cv2
def prepare(arg):
if torch.cuda.is_available():
# print(1)
arg = arg.cuda()
return arg
def rgb2ycbcr(img, only_y=True):
'''same as matlab rgb2ycbcr
only_y: only return Y channel
Input:
uint8, [0, 255]
float, [... | 4,380 | 28.601351 | 92 | py |
FishFSRNet | FishFSRNet-main/parsing/main_parsingnet.py | import torch
import torch.optim as optim
from option import args
import os
os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda_name
import torch.nn as nn
import dataset_parsingnet
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import util
import torchvision
from parsingnet import P... | 2,264 | 40.181818 | 109 | py |
FishFSRNet | FishFSRNet-main/parsing/dataset_parsingnet.py | from torch.utils import data
import os
from PIL import Image
from torchvision import transforms
from torchvision.transforms import ToTensor
import numpy
import glob
class Data(data.Dataset):
def __init__(self, root, args, train=False):
# 返回指定路径下的文件和文件夹列表。
self.args = args
if args.scale == ... | 1,638 | 31.78 | 88 | py |
FishFSRNet | FishFSRNet-main/fsr/fishfsrnet.py | import common
import torch.nn.functional as F
import torch.nn as nn
import torch
def fish_block(args, conv=common.default_conv, n_feats=64, PCSR1=False):
kernel_size = 3
res = []
act = nn.ReLU(True)
if PCSR1:
res.append(common.PCSR1(
conv, n_feats, kernel_size, act=act, res_scale... | 7,540 | 32.665179 | 107 | py |
FishFSRNet | FishFSRNet-main/fsr/test.py | from option import args
import os
os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda_name
import torch
import dataset_parsing
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import os
import util
import torchvision
from fishfsrnet import FISHNET
net = FISHNET(args)
net = util.prepa... | 1,520 | 39.026316 | 105 | py |
FishFSRNet | FishFSRNet-main/fsr/cbam.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
class Flatten(nn.Module):
def forward(self, x):
return x.view(x.size(0), -1)
class ChannelGate(nn.Module):
def __init__(self, gate_channels, reduction_ratio=16, pool_types=None):
super(ChannelGate, self).__init__()
... | 3,309 | 34.978261 | 119 | py |
FishFSRNet | FishFSRNet-main/fsr/common.py | import torch.nn as nn
import torch
import cbam
import math
def batched_index_select(values, indices):
last_dim = values.shape[-1]
return values.gather(1, indices[:, :, None].expand(-1, -1, last_dim))
class BasicBlock(nn.Sequential):
def __init__(
self, conv, in_channels, out_channels, kernel... | 8,996 | 34.007782 | 127 | py |
FishFSRNet | FishFSRNet-main/fsr/util.py |
import torch
import numpy as np
import math
import cv2
def prepare(arg):
if torch.cuda.is_available():
# print(1)
arg = arg.cuda()
return arg
def rgb2ycbcr(img, only_y=True):
'''same as matlab rgb2ycbcr
only_y: only return Y channel
Input:
uint8, [0, 255]
float, [... | 4,380 | 28.601351 | 92 | py |
FishFSRNet | FishFSRNet-main/fsr/dataset_parsing.py | from torch.utils import data
import os
from PIL import Image
from torchvision.transforms import ToTensor
import numpy
import glob
import random
import numpy as np
def augment(lr, hr, p, hflip=True, rot=True):
# def _augment(img):
# if hflip: img = img[:, ::-1, :]
# if vflip: img = img[::-1, :, :]
... | 3,235 | 31.36 | 88 | py |
FishFSRNet | FishFSRNet-main/fsr/main_parsing.py | from option import args
import os
os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda_name
import torch
import torch.optim as optim
import torch.nn as nn
import dataset_parsing
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import util
from fishfsrnet import FISHNET
net = FISHNE... | 2,671 | 40.75 | 106 | py |
omni3d | omni3d-main/tools/train_net.py | # Copyright (c) Meta Platforms, Inc. and affiliates
import logging
import os
import sys
import numpy as np
import copy
from collections import OrderedDict
import torch
from torch.nn.parallel import DistributedDataParallel
import torch.distributed as dist
import detectron2.utils.comm as comm
from detectron2.checkpoint i... | 18,388 | 35.056863 | 138 | py |
omni3d | omni3d-main/demo/demo.py | # Copyright (c) Meta Platforms, Inc. and affiliates
import logging
import os
import argparse
import sys
import numpy as np
from collections import OrderedDict
import torch
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.engine import default_argument_parser... | 7,175 | 34.349754 | 158 | py |
omni3d | omni3d-main/cubercnn/solver/build.py | # Copyright (c) Meta Platforms, Inc. and affiliates
import torch
from typing import Any, Dict, List, Set
from detectron2.solver.build import maybe_add_gradient_clipping
def build_optimizer(cfg, model):
norm_module_types = (
torch.nn.BatchNorm1d,
torch.nn.BatchNorm2d,
torch.nn.BatchNorm3d,
... | 2,963 | 37.493506 | 100 | py |
omni3d | omni3d-main/cubercnn/evaluation/omni3d_evaluation.py | # Copyright (c) Meta Platforms, Inc. and affiliates
import contextlib
import copy
import datetime
import io
import itertools
import json
import logging
import os
import time
from collections import defaultdict
from typing import List, Union
from typing import Tuple
import numpy as np
import pycocotools.mask as maskUti... | 65,081 | 37.171261 | 168 | py |
omni3d | omni3d-main/cubercnn/vis/vis.py | # Copyright (c) Meta Platforms, Inc. and affiliates
import cv2
import numpy as np
import matplotlib.pyplot as plt
import os
import math
import torch
from copy import deepcopy
from pytorch3d.structures.meshes import join_meshes_as_scene
from pytorch3d.transforms.so3 import (
so3_relative_angle,
)
from matplotlib.pat... | 29,091 | 38.154778 | 206 | py |
omni3d | omni3d-main/cubercnn/util/math_util.py | # Copyright (c) Meta Platforms, Inc. and affiliates
import math
import numpy as np
import pandas as pd
from typing import Tuple, List
from copy import copy
from pytorch3d.renderer.lighting import PointLights
from pytorch3d.renderer.mesh.renderer import MeshRenderer
from pytorch3d.renderer.mesh.shader import SoftPhongSh... | 31,079 | 30.779141 | 167 | py |
omni3d | omni3d-main/cubercnn/data/dataset_mapper.py | # Copyright (c) Meta Platforms, Inc. and affiliates
import copy
import torch
import numpy as np
from detectron2.structures import BoxMode, Keypoints
from detectron2.data import detection_utils
from detectron2.data import transforms as T
from detectron2.data import (
DatasetMapper
)
from detectron2.structures import... | 5,231 | 32.538462 | 128 | py |
omni3d | omni3d-main/cubercnn/data/build.py | # Copyright (c) Meta Platforms, Inc. and affiliates
import itertools
import logging
import numpy as np
import math
from collections import defaultdict
import torch.utils.data
from detectron2.config import configurable
from detectron2.utils.logger import _log_api_usage
from detectron2.data.catalog import DatasetCatalo... | 9,407 | 39.551724 | 128 | py |
omni3d | omni3d-main/cubercnn/modeling/backbone/dla.py | # Copyright (c) Meta Platforms, Inc. and affiliates
import os
import math
import numpy as np
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
import detectron2.utils.comm as comm
from detectron2.layers import ShapeSpec
from detectron2.modeling.backbone impor... | 18,904 | 36.287968 | 98 | py |
omni3d | omni3d-main/cubercnn/modeling/backbone/resnet.py | # Copyright (c) Meta Platforms, Inc. and affiliates
from torchvision import models
from detectron2.layers import ShapeSpec
from detectron2.modeling.backbone import Backbone
from detectron2.modeling.backbone.fpn import LastLevelMaxPool
from detectron2.modeling.backbone.resnet import build_resnet_backbone
from detectron2... | 3,333 | 33.371134 | 113 | py |
omni3d | omni3d-main/cubercnn/modeling/backbone/mnasnet.py | # Copyright (c) Meta Platforms, Inc. and affiliates
from torchvision import models
from detectron2.layers import ShapeSpec
from detectron2.modeling.backbone import Backbone
from detectron2.modeling.backbone.build import BACKBONE_REGISTRY
import torch.nn.functional as F
from detectron2.modeling.backbone.fpn import FPN
... | 1,936 | 29.265625 | 89 | py |
omni3d | omni3d-main/cubercnn/modeling/backbone/densenet.py | # Copyright (c) Meta Platforms, Inc. and affiliates
from torchvision import models
from detectron2.layers import ShapeSpec
from detectron2.modeling.backbone import Backbone
from detectron2.modeling.backbone.build import BACKBONE_REGISTRY
import torch.nn.functional as F
from detectron2.modeling.backbone.fpn import FPN
... | 1,952 | 29.515625 | 95 | py |
omni3d | omni3d-main/cubercnn/modeling/backbone/shufflenet.py | # Copyright (c) Meta Platforms, Inc. and affiliates
from torchvision import models
from detectron2.layers import ShapeSpec
from detectron2.modeling.backbone import Backbone
from detectron2.modeling.backbone.build import BACKBONE_REGISTRY
import torch.nn.functional as F
from detectron2.modeling.backbone.fpn import FPN
... | 2,113 | 29.2 | 91 | py |
omni3d | omni3d-main/cubercnn/modeling/meta_arch/rcnn3d.py | # Copyright (c) Meta Platforms, Inc. and affiliates
from typing import Dict, List, Optional
import torch
import numpy as np
from detectron2.layers import ShapeSpec, batched_nms
from detectron2.utils.visualizer import Visualizer
from detectron2.data.detection_utils import convert_image_to_rgb
from detectron2.structures ... | 11,688 | 41.974265 | 171 | py |
omni3d | omni3d-main/cubercnn/modeling/roi_heads/fast_rcnn.py | # Copyright (c) Meta Platforms, Inc. and affiliates
from re import L
import torch
from torch.nn import functional as F
from typing import List, Tuple
from fvcore.nn import giou_loss, smooth_l1_loss
from detectron2.utils.events import get_event_storage
from detectron2.layers import cat, cross_entropy, nonzero_tuple, ba... | 11,154 | 41.576336 | 113 | py |
omni3d | omni3d-main/cubercnn/modeling/roi_heads/cube_head.py | # Copyright (c) Meta Platforms, Inc. and affiliates
from detectron2.utils.registry import Registry
from typing import Dict
from detectron2.layers import ShapeSpec
from torch import nn
import torch
import numpy as np
import fvcore.nn.weight_init as weight_init
from pytorch3d.transforms.rotation_conversions import _copy... | 8,064 | 38.925743 | 96 | py |
omni3d | omni3d-main/cubercnn/modeling/roi_heads/roi_heads.py | # Copyright (c) Meta Platforms, Inc. and affiliates
import logging
import numpy as np
import cv2
from typing import Dict, List, Tuple
import torch
from torch import nn
import torch.nn.functional as F
from pytorch3d.transforms.so3 import (
so3_relative_angle
)
from detectron2.config import configurable
from detectro... | 41,015 | 42.634043 | 151 | py |
omni3d | omni3d-main/cubercnn/modeling/proposal_generator/rpn.py | # Copyright (c) Meta Platforms, Inc. and affiliates
from typing import Dict, List, Tuple
import torch
from typing import List, Tuple, Union
import torch.nn.functional as F
from detectron2.config import configurable
from detectron2.utils.events import get_event_storage
from detectron2.layers import ShapeSpec, cat
from d... | 15,229 | 42.022599 | 141 | py |
VLC-BERT | VLC-BERT-master/vqa/train_end2end.py | import _init_paths
import os
import argparse
import torch
import subprocess
from vqa.function.config import config, update_config
from vqa.function.train import train_net
from vqa.function.test import test_net
def parse_args():
parser = argparse.ArgumentParser('Train Cognition Network')
parser.add_argument('... | 2,191 | 33.793651 | 113 | py |
VLC-BERT | VLC-BERT-master/vqa/function/val.py | from collections import namedtuple
import torch
from common.trainer import to_cuda
@torch.no_grad()
def do_validation(net, val_loader, metrics, label_index_in_batch):
net.eval()
metrics.reset()
for nbatch, batch in enumerate(val_loader):
batch = to_cuda(batch)
label = batch[label_index_in_... | 528 | 26.842105 | 95 | py |
VLC-BERT | VLC-BERT-master/vqa/function/test.py | import os
import pprint
import shutil
import json
from tqdm import tqdm, trange
import numpy as np
import torch
import torch.nn.functional as F
from common.utils.load import smart_load_model_state_dict
from common.trainer import to_cuda
from common.utils.create_logger import create_logger
from vqa.data.build import m... | 3,359 | 39.481928 | 120 | py |
VLC-BERT | VLC-BERT-master/vqa/function/train.py | import os
import pprint
import shutil
import inspect
from tensorboardX import SummaryWriter
import numpy as np
import torch
import torch.nn
import torch.optim as optim
import torch.distributed as distributed
from torch.nn.parallel import DistributedDataParallel as DDP
from common.utils.create_logger import create_log... | 17,541 | 51.053412 | 147 | py |
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