repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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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/count_rings.py | import sys
import numpy as np
import argparse
import time
from data.parallel import ProgressParallel
from data.data_loading import load_graph_dataset
from data.utils import get_rings
from joblib import delayed
parser = argparse.ArgumentParser(description='Ring counting experiment.')
parser.add_argument('--dataset', t... | 3,857 | 31.15 | 99 | py |
cwn | cwn-main/exp/prepare_sr_tests.py | import os
import sys
import pickle
from data.data_loading import load_dataset, load_graph_dataset
from data.perm_utils import permute_graph, generate_permutation_matrices
from definitions import ROOT_DIR
__families__ = [
'sr16622',
'sr251256',
'sr261034',
'sr281264',
'sr291467',
'sr351668',
... | 1,765 | 33.627451 | 115 | py |
cwn | cwn-main/exp/prepare_tu_tuning.py | import sys
import yaml
from data.data_loading import load_dataset
if __name__ == "__main__":
# standard args
passed_args = sys.argv[1:]
conf_path = passed_args[0]
# parse grid from yaml
with open(conf_path, 'r') as handle:
conf = yaml.safe_load(handle)
dataset = conf['dataset'... | 694 | 27.958333 | 105 | py |
cwn | cwn-main/exp/run_sr_exp.py | import os
import sys
import copy
import time
import numpy as np
import subprocess
from definitions import ROOT_DIR
from exp.parser import get_parser
from exp.run_exp import main
# python3 -m exp.run_sr_exp --task_type isomorphism --eval_metric isomorphism --untrained --model sparse_cin --nonlinearity id --emb_dim 16 ... | 3,774 | 33.633028 | 168 | py |
cwn | cwn-main/exp/run_mol_exp.py | import sys
import os
import copy
import numpy as np
import subprocess
from exp.parser import get_parser
from exp.run_exp import main
from itertools import product
def exp_main(passed_args):
# Extract the commit sha so we can check the code that was used for each experiment
sha = subprocess.check_output(["git... | 4,759 | 43.90566 | 100 | 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/test_run_exp.py | from exp.parser import get_parser
from exp.run_exp import main
def get_args_for_dummym():
args = list()
args += ['--use_coboundaries', 'True']
args += ['--graph_norm', 'id']
args += ['--lr_scheduler', 'None']
args += ['--num_layers', '3']
args += ['--emb_dim', '8']
args += ['--batch_size', ... | 916 | 32.962963 | 60 | py |
cwn | cwn-main/exp/run_ring_exp.py | import os
import sys
import copy
import subprocess
import numpy as np
from exp.parser import get_parser
from exp.run_exp import main
RING_SIZES = list(range(10, 32, 2))
def exp_main(passed_args):
# Extract the commit sha so we can check the code that was used for each experiment
sha = subprocess.check_outpu... | 2,783 | 35.631579 | 93 | py |
cwn | cwn-main/exp/run_tu_exp.py | import sys
import os
import copy
import time
import numpy as np
from exp.parser import get_parser
from exp.run_exp import main
# python3 -m exp.run_tu_exp --dataset IMDBBINARY --model cin --drop_rate 0.0 --lr 0.0001 --max_dim 2 --emb_dim 32 --dump_curves --epochs 30 --num_layers 1 --lr_scheduler StepLR --lr_scheduler_... | 2,719 | 32.170732 | 205 | py |
cwn | cwn-main/exp/__init__.py | 0 | 0 | 0 | py | |
cwn | cwn-main/exp/plot_sr_cwn_results.py | import os
import sys
import matplotlib
matplotlib.use('Agg')
import numpy as np
import seaborn as sns
sns.set_style("whitegrid", {'legend.frameon': False})
from matplotlib import cm
from matplotlib import pyplot as plt
from definitions import ROOT_DIR
def run(exps, codenames, plot_name):
# Meta
family_names ... | 4,012 | 35.816514 | 147 | 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/run_tu_tuning.py | import itertools
import os
import copy
import yaml
import argparse
from definitions import ROOT_DIR
from exp.parser import get_parser
from exp.run_tu_exp import exp_main
__max_devices__ = 8
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='CWN tuning.')
parser.add_argument('--conf'... | 1,712 | 30.145455 | 113 | 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 |
ECG-Heartbeat-Classification-seq2seq-model | ECG-Heartbeat-Classification-seq2seq-model-master/seq_seq_annot_aami.py | import numpy as np
import matplotlib.pyplot as plt
import scipy.io as spio
from sklearn.preprocessing import MinMaxScaler
import random
import time
import os
from datetime import datetime
from sklearn.metrics import confusion_matrix
import tensorflow as tf
from imblearn.over_sampling import SMOTE
from sklearn.model_se... | 19,548 | 44.043779 | 197 | py |
ECG-Heartbeat-Classification-seq2seq-model | ECG-Heartbeat-Classification-seq2seq-model-master/seq_seq_annot_DS1DS2.py | import numpy as np
import matplotlib.pyplot as plt
import scipy.io as spio
from sklearn.preprocessing import MinMaxScaler
import random
import time
import os
from datetime import datetime
from sklearn.metrics import confusion_matrix
import tensorflow as tf
from imblearn.over_sampling import SMOTE
from sklearn.model_se... | 18,958 | 41.508969 | 157 | py |
robust-selection | robust-selection-main/setup.py | from setuptools import setup, Extension, find_packages
from setuptools.command.build_ext import build_ext
with open("README.md", "r") as fh:
long_description = fh.read()
# inject numpy headers
class build_ext_robsel(build_ext):
def finalize_options(self):
build_ext.finalize_options(self)
# Pr... | 1,398 | 32.309524 | 98 | py |
robust-selection | robust-selection-main/robsel/robsel.py | import numpy as np
from sklearn.utils import resample
def RWP(X, orig_cov, with_diag=False):
"""
Robust Wasserstein Profile function.
Parameters
----------
X : ndarray of shape (n_samples, n_features)
Data from which to compute the covariance estimate from bootrap sample.
orig_cov: ndar... | 2,074 | 28.642857 | 80 | py |
robust-selection | robust-selection-main/robsel/__init__.py | from . import robsel
from .robsel import * | 42 | 20.5 | 21 | py |
LearningSPH | LearningSPH-main/learning_dns_data_Re80/hierarchy_post_process/volume_plots_py.py | import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np
X, Y, Z = np.mgrid[0:2*np.pi:16j, 0:2*np.pi:16j, 0:2*np.pi:16j]
values = np.sin(X) * np.cos(Z) * np.sin(Y)
m_phys = ["phys_inf_W2ab_theta_po_liv_Pi", "phys_inf_Wab_theta_po_liv_Pi",
"phys_inf_Wliu_theta_po_l... | 9,569 | 29.477707 | 113 | py |
LearningSPH | LearningSPH-main/learning_dns_data_Re80/hierarchy_post_process/volume_plots_t20_lf_sequence_pngs.py | import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np
X, Y, Z = np.mgrid[0:2*np.pi:16j, 0:2*np.pi:16j, 0:2*np.pi:16j]
#methods and file names
m_phys = ["phys_inf_W2ab_theta_po_liv_Pi", "phys_inf_Wab_theta_po_liv_Pi",
"phys_inf_Wliu_theta_po_liv_Pi", "phys_inf_... | 9,697 | 31.763514 | 99 | py |
LearningSPH | LearningSPH-main/learning_dns_data_Re80/hierarchy_post_process/volume_plots_t20_convergence.py | import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np
X, Y, Z = np.mgrid[0:2*np.pi:16j, 0:2*np.pi:16j, 0:2*np.pi:16j]
#methods and file names
m_phys = ["phys_inf_W2ab_theta_po_liv_Pi", "phys_inf_Wab_theta_po_liv_Pi",
"phys_inf_Wliu_theta_po_liv_Pi", "phys_inf_... | 3,315 | 28.345133 | 94 | py |
LearningSPH | LearningSPH-main/learning_dns_data_Re80/hierarchy_post_process/animate.py | import os
def save():
os.system("ffmpeg -framerate 16 -pattern_type glob -i '*.png' -c:v libx264 -pix_fmt yuv420p u_over_t.mp4")
save() | 142 | 19.428571 | 110 | py |
LearningSPH | LearningSPH-main/learning_dns_data_Re80/hierarchy_post_process/volume_plots_py_t50_lf.py | import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np
X, Y, Z = np.mgrid[0:2*np.pi:16j, 0:2*np.pi:16j, 0:2*np.pi:16j]
values = np.sin(X) * np.cos(Z) * np.sin(Y)
m_phys = ["phys_inf_W2ab_theta_po_liv_Pi", "phys_inf_Wab_theta_po_liv_Pi",
"phys_inf_Wliu_theta_po_l... | 9,587 | 29.535032 | 113 | py |
LearningSPH | LearningSPH-main/learning_dns_data_Re80/hierarchy_post_process/volume_plots_all.py | import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np
X, Y, Z = np.mgrid[0:2*np.pi:16j, 0:2*np.pi:16j, 0:2*np.pi:16j]
values = np.sin(X) * np.cos(Z) * np.sin(Y)
m_phys = ["phys_inf_W2ab_theta_po_liv_Pi", "phys_inf_Wab_theta_po_liv_Pi",
"phys_inf_Wliu_theta_po_l... | 31,012 | 30.840862 | 117 | py |
imsat | imsat-master/calculate_distance.py | import argparse
import sys
import cPickle as pickle
import datetime, math, sys, time
from sklearn.datasets import fetch_mldata
import numpy as np
import cupy as cp
import chainer
import chainer.functions as F
import chainer.links as L
from chainer import FunctionSet, Variable, optimizers, cuda, serializers
parser = ... | 1,293 | 23.415094 | 94 | py |
imsat | imsat-master/imsat_hash.py | import argparse, sys
import numpy as np
import chainer
import chainer.functions as F
from chainer import FunctionSet, Variable, optimizers, cuda, serializers
from sklearn import metrics
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, help='which gpu device to use', default=0)
parser.add_argum... | 8,948 | 32.267658 | 119 | py |
imsat | imsat-master/imsat_cluster.py | import argparse, sys
import numpy as np
import chainer
import chainer.functions as F
from chainer import FunctionSet, Variable, optimizers, cuda, serializers
from munkres import Munkres, print_matrix
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, help='which gpu device to use', default=1)
pa... | 5,794 | 29.824468 | 117 | py |
imsat | imsat-master/mnist/load_mnist.py | import sys
import cPickle as pickle
import datetime, math, sys, time
from sklearn.datasets import fetch_mldata
import numpy as np
from chainer import cuda
class Data:
def __init__(self, data, label):
self.data = data
self.label = label
self.index = np.arange(len(data))
def get_index... | 1,173 | 30.72973 | 160 | 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/__init__.py | from vlnce_baselines import ss_trainer_CMA, ss_trainer_VLNBERT
from vlnce_baselines.common import environments
from vlnce_baselines.models import (
Policy_ViewSelection_CMA,
Policy_ViewSelection_VLNBERT,
)
| 215 | 26 | 62 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/vlnce_baselines/config/__init__.py | 0 | 0 | 0 | py | |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/vlnce_baselines/config/default.py | from typing import List, Optional, Union
import habitat_baselines.config.default
from habitat.config.default import CONFIG_FILE_SEPARATOR
from habitat.config.default import Config as CN
from habitat_extensions.config.default import (
get_extended_config as get_task_config,
)
# -----------------------------------... | 8,920 | 37.786957 | 79 | 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/environments.py | from typing import Any, Dict, Optional, Tuple, List, Union
import habitat
import numpy as np
from habitat import Config, Dataset
from habitat.core.simulator import Observations
from habitat.tasks.utils import cartesian_to_polar
from habitat.utils.geometry_utils import quaternion_rotate_vector
from habitat_baselines.co... | 5,996 | 38.453947 | 115 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/vlnce_baselines/common/env_utils.py | import os
import random
from typing import List, Optional, Type, Union
import habitat
from habitat import Config, Env, RLEnv, VectorEnv, make_dataset
from habitat_baselines.utils.env_utils import make_env_fn
random.seed(0)
SLURM_JOBID = os.environ.get("SLURM_JOB_ID", None)
def is_slurm_job() -> bool:
return SL... | 7,426 | 34.033019 | 85 | 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/__init__.py | 0 | 0 | 0 | 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, [email protected]
# Modified in Recurrent VLN-BERT, 2020, [email protected]
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/vlnce_baselines/models/vlnbert/vlnbert_init.py | # Recurrent VLN-BERT, 2020, by [email protected]
from transformers import (BertConfig, BertTokenizer)
def get_vlnbert_models(config=None):
config_class = BertConfig
from vlnce_baselines.models.vlnbert.vlnbert_PREVALENT import VLNBert
model_class = VLNBert
model_name_or_path = 'data/pretrained_mo... | 685 | 35.105263 | 97 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/habitat_extensions/shortest_path_follower.py | # Copied from https://github.com/facebookresearch/habitat-lab/blob/v0.1.4/habitat/tasks/nav/shortest_path_follower.py
# Use the Habitat v0.1.4 ShortestPathFollower for compatibility with
# the dataset generation oracle.
from typing import Optional, Union
import habitat_sim
import numpy as np
from habitat.sims.habitat... | 7,219 | 35.1 | 117 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/habitat_extensions/task.py | import gzip
import json
import os
from typing import Dict, List, Optional, Union
import attr
from habitat.config import Config
from habitat.core.dataset import Dataset
from habitat.core.registry import registry
from habitat.core.utils import not_none_validator
from habitat.datasets.pointnav.pointnav_dataset import ALL... | 8,711 | 34.851852 | 95 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/habitat_extensions/nav.py | from typing import Any, List, Optional, Tuple
import math
import numpy as np
from habitat.core.embodied_task import (
SimulatorTaskAction,
)
from habitat.core.registry import registry
from habitat.sims.habitat_simulator.actions import HabitatSimActions
from habitat.utils.geometry_utils import quaternion_rotate_ve... | 7,067 | 40.093023 | 96 | 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/utils.py | from typing import Dict
import numpy as np
from habitat.core.utils import try_cv2_import
from habitat.utils.visualizations import maps as habitat_maps
from habitat.utils.visualizations.utils import draw_collision
from habitat_extensions import maps
cv2 = try_cv2_import()
def observations_to_image(observation: Dict... | 3,056 | 31.870968 | 75 | 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/habitat_extensions/measures.py | import gzip
import json
import pickle
from typing import Any, List, Union
import numpy as np
from dtw import dtw
from fastdtw import fastdtw
from habitat.config import Config
from habitat.core.embodied_task import EmbodiedTask, Measure
from habitat.core.registry import registry
from habitat.core.simulator import Simul... | 18,287 | 30.860627 | 79 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/habitat_extensions/sensors.py | from typing import Any, Dict
import numpy as np
from gym import spaces
from habitat.config import Config
from habitat.core.registry import registry
from habitat.core.simulator import Observations, Sensor, SensorTypes, Simulator
from habitat.sims.habitat_simulator.actions import HabitatSimActions
from habitat.tasks.nav... | 6,291 | 31.266667 | 84 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/habitat_extensions/maps.py | from typing import Dict, List, Optional, Tuple, Union
import networkx as nx
import numpy as np
from habitat.core.simulator import Simulator
from habitat.core.utils import try_cv2_import
from habitat.tasks.vln.vln import VLNEpisode
from habitat.utils.visualizations import maps as habitat_maps
cv2 = try_cv2_import()
A... | 9,290 | 29.86711 | 96 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/habitat_extensions/__init__.py | from habitat_extensions import measures, obs_transformers, sensors, nav
from habitat_extensions.config.default import get_extended_config
from habitat_extensions.task import VLNCEDatasetV1
from habitat_extensions.habitat_simulator import Simulator
| 248 | 48.8 | 71 | py |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/habitat_extensions/config/__init__.py | 0 | 0 | 0 | py | |
Discrete-Continuous-VLN | Discrete-Continuous-VLN-main/habitat_extensions/config/default.py | from typing import List, Optional, Union
from habitat.config.default import Config as CN
from habitat.config.default import get_config
_C = get_config()
_C.defrost()
# ----------------------------------------------------------------------------
# CUSTOM ACTION: HIGHTOLOWINFER ACTION
# -------------------------------... | 7,363 | 46.818182 | 134 | 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, [email protected]
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/test.py | import os
from options.test_options import TestOptions
from dataloader.data_loader import dataloader
from model.models import create_model
from util.visualizer import Visualizer
from util import html
opt = TestOptions().parse()
dataset = dataloader(opt)
dataset_size = len(dataset) * opt.batchSize
print ('testing imag... | 737 | 29.75 | 113 | py |
Synthetic2Realistic | Synthetic2Realistic-master/train.py | import time
from options.train_options import TrainOptions
from dataloader.data_loader import dataloader
from model.models import create_model
from util.visualizer import Visualizer
opt = TrainOptions().parse()
dataset = dataloader(opt)
dataset_size = len(dataset) * opt.batchSize
print('training images = %d' % datase... | 1,916 | 34.5 | 98 | py |
Synthetic2Realistic | Synthetic2Realistic-master/options/train_options.py | from .base_options import BaseOptions
class TrainOptions(BaseOptions):
def initialize(self):
BaseOptions.initialize(self)
# training epoch
self.parser.add_argument('--epoch_count', type=int, default=1,
help='the starting epoch count')
self.parser.ad... | 3,503 | 62.709091 | 105 | 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/options/__init__.py | 0 | 0 | 0 | py | |
Synthetic2Realistic | Synthetic2Realistic-master/options/test_options.py | from .base_options import BaseOptions
class TestOptions(BaseOptions):
def initialize(self):
BaseOptions.initialize(self)
self.parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples')
self.parser.add_argument('--results_dir', type=str, default='./results/', ... | 478 | 42.545455 | 110 | 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/html.py | import dominate
from dominate.tags import *
import os
class HTML:
def __init__(self, web_dir, title, reflesh=0):
self.title = title
self.web_dir = web_dir
self.img_dir = os.path.join(self.web_dir, 'images')
if not os.path.exists(self.web_dir):
os.makedirs(self.web_dir)
... | 1,912 | 28.430769 | 95 | 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/util/visualizer.py | import numpy as np
import os
import ntpath
import time
from . import util
from . import html
class Visualizer():
def __init__(self, opt):
# self.opt = opt
self.display_id = opt.display_id
self.use_html = opt.isTrain and not opt.no_html
self.win_size = opt.display_winsize
sel... | 6,117 | 42.7 | 96 | py |
Synthetic2Realistic | Synthetic2Realistic-master/util/util.py | import numpy as np
import os
import imageio
# convert a tensor into a numpy array
def tensor2im(image_tensor, bytes=255.0, imtype=np.uint8):
if image_tensor.dim() == 3:
image_numpy = image_tensor.cpu().float().numpy()
else:
image_numpy = image_tensor[0].cpu().float().numpy()
image_numpy = (... | 864 | 27.833333 | 85 | py |
Synthetic2Realistic | Synthetic2Realistic-master/util/__init__.py | 0 | 0 | 0 | py | |
Synthetic2Realistic | Synthetic2Realistic-master/util/evaluation.py | import argparse
from data_kitti import *
parser = argparse.ArgumentParser(description='Evaluation ont the dataset')
parser.add_argument('--split', type=str, default='eigen', help='data split')
parser.add_argument('--predicted_depth_path', type=str, default='../dataset/KITTI31_predicted_lsgan/', help='path to estimated... | 5,327 | 49.742857 | 158 | py |
Synthetic2Realistic | Synthetic2Realistic-master/util/visual_result.py | import matplotlib.pyplot as plt
import sys,os
sys.path.append('/home/asus/lyndon/program/Image2Depth/dataloader')
from dataloader.image_folder import make_dataset
import numpy as np
import scipy.misc
dataRoot = '/data/dataset/Image2Depth31_KITTI/testB'
dispairtyRoot = '/data/result/disparities_eigen_godard/disparities... | 1,366 | 28.717391 | 93 | py |
Synthetic2Realistic | Synthetic2Realistic-master/util/data_kitti.py | import numpy as np
import os
import cv2
from collections import Counter
from scipy.interpolate import LinearNDInterpolator
from PIL import Image
from dataloader.image_folder import make_dataset
def compute_errors(ground_truth, predication):
# accuracy
threshold = np.maximum((ground_truth / predication),(predi... | 7,828 | 33.337719 | 123 | 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/models.py |
def create_model(opt):
print(opt.model)
if opt.model == 'wsupervised':
from .T2model import T2NetModel
model = T2NetModel()
elif opt.model == 'supervised':
from .TaskModel import TNetModel
model = TNetModel()
elif opt.model == 'test':
from .test_model import Test... | 527 | 30.058824 | 66 | 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/__init__.py | 0 | 0 | 0 | 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 |
Synthetic2Realistic | Synthetic2Realistic-master/dataloader/image_folder.py | import os
import os.path
IMG_EXTENSIONS = [
'.jpg', '.JPG', '.jpeg', '.JPEG',
'.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
]
def is_image_file(filename):
return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
def make_dataset(path_files):
if path_files.find('.txt') != -1:
... | 1,085 | 22.106383 | 76 | py |
Synthetic2Realistic | Synthetic2Realistic-master/dataloader/__init__.py | 0 | 0 | 0 | py |
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