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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direct | direct-main/direct/nn/recurrent/recurrent.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import math
from typing import List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
class Conv2dGRU(nn.Module):
"""2D Convolutional GRU Network."""
def __init__(
self,
in_channels: int,
hidden_cha... | 10,231 | 35.412811 | 118 | py |
direct | direct-main/direct/nn/didn/didn.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
class Subpixel(nn.Module):
"""Subpixel convolution layer for up-scaling of low resolution features at super-resolution as implemented in [1]_.
References
... | 12,235 | 30.781818 | 243 | py |
direct | direct-main/direct/nn/resnet/resnet.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Optional
import torch
from torch import nn
class ResNetBlock(nn.Module):
"""Main block of :class:`ResNet`.
Consisted of a convolutional layer followed by a relu activation, a second convolution, and finally a scaled
skip connection w... | 3,909 | 31.583333 | 117 | py |
direct | direct-main/direct/nn/rim/rim_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Callable, Dict, Optional
import torch
from torch import nn
from torch.cuda.amp import autocast
import direct.data.transforms as T
from direct.config import BaseConfig
from direct.engine import DoIterationOutput
from direct.nn.mri_models import MRI... | 6,258 | 40.726667 | 119 | py |
direct | direct-main/direct/nn/rim/rim.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import warnings
from typing import Callable, Optional, Tuple
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from direct.data import transforms as T
from direct.nn.recurrent.recurrent import Conv2dGRU, NormConv2dGRU
from direct.... | 18,282 | 38.659436 | 183 | py |
direct | direct-main/direct/functionals/psnr.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import torch
import torch.nn as nn
__all__ = ("batch_psnr", "PSNRLoss")
def batch_psnr(input_data, target_data, reduction="mean"):
"""This function is a torch implementation of skimage.metrics.compare_psnr.
Parameters
----------
input_data: torch.Te... | 1,281 | 26.276596 | 86 | py |
direct | direct-main/direct/functionals/nmae.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import torch
import torch.nn as nn
__all__ = ["NMAELoss"]
class NMAELoss(nn.Module):
"""Computes the Normalized Mean Absolute Error (NMAE), i.e.:
.. math::
\frac{||u - v||_1}{||u||_1},
where :math:`u` and :math:`v` denote the target and the in... | 1,237 | 27.136364 | 92 | py |
direct | direct-main/direct/functionals/ssim.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
# Taken from: https://github.com/VainF/pytorch-msssim/blob/master/pytorch_msssim/ssim.py
# Licensed under MIT.
# Copyright 2020 by Gongfan Fang, Zhejiang University.
# All rights reserved.
# Some changes are made to work together with DIRECT.
import torch
import torc... | 1,853 | 28.428571 | 88 | py |
direct | direct-main/direct/functionals/challenges.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import numpy as np
import torch
__all__ = (
"fastmri_ssim",
"fastmri_psnr",
"fastmri_nmse",
"calgary_campinas_ssim",
"calgary_campinas_psnr",
"calgary_campinas_vif",
)
def _to_numpy(tensor):
if isinstance(tensor, np.ndarray):
retu... | 3,243 | 31.44 | 141 | py |
direct | direct-main/direct/functionals/nmse.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import torch
import torch.nn as nn
__all__ = ["NMSELoss", "NRMSELoss"]
class NMSELoss(nn.Module):
"""Computes the Normalized Mean Squared Error (NMSE), i.e.:
.. math::
\frac{||u - v||_2^2}{||u||_2^2},
where :math:`u` and :math:`v` denote the t... | 2,413 | 28.802469 | 103 | py |
direct | direct-main/direct/functionals/grad.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
# Code was borrowed and reformatted from https://github.com/kornia/kornia/blob/master/kornia/filters/sobel.py
# part of "Kornia: an Open Source Differentiable Computer Vision Library for PyTorch" with an Apache License.
from enum import Enum
from typing import Tuple
... | 6,627 | 31.650246 | 120 | py |
direct | direct-main/direct/common/subsample.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
"""DIRECT samplers module."""
# Code and comments can be shared with code of FastMRI under the same MIT license:
# https://github.com/facebookresearch/fastMRI/
# The code can have been adjusted to our needs.
import contextlib
import logging
from abc import abstractm... | 41,930 | 36.10708 | 119 | py |
direct | direct-main/direct/algorithms/optimization.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
"""General mathematical optimization techniques."""
from abc import ABC, abstractmethod
from typing import Callable, Optional
import torch
class Algorithm(ABC):
"""Base class for implementing mathematical optimization algorithms."""
def __init__(self, max... | 3,581 | 25.932331 | 85 | py |
direct | direct-main/direct/algorithms/mri_algorithms.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
"""This module contains mathematical optimization techniques specific to MRI."""
from typing import Any, Callable, Dict
import numpy as np
import torch
from torch import nn
from direct.algorithms.optimization import MaximumEigenvaluePowerMethod
from direct.data.tra... | 6,896 | 36.483696 | 117 | py |
direct | direct-main/direct/utils/events.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
# Inspired on
# https://github.com/facebookresearch/detectron2/blob/45808c0ed68332cdb4c55801f1e2934d58231d35/detectron2/utils/events.py
# https://github.com/facebookresearch/fvcore/blob/master/fvcore/common/history_buffer.py
# Copyright (c) Facebook, Inc. and its affi... | 16,057 | 34.137856 | 317 | py |
direct | direct-main/direct/utils/asserts.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import inspect
from typing import List, Optional
import torch
from direct.utils import is_complex_data
def assert_positive_integer(*variables, strict: bool = False) -> None:
"""Assert if given variables are positive integer.
Parameters
----------
v... | 2,177 | 33.571429 | 118 | py |
direct | direct-main/direct/utils/logging.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import logging
import sys
from os import PathLike
from typing import Optional, Union
def setup(
use_stdout: Optional[bool] = True,
filename: Optional[PathLike] = None,
log_level: Union[int, str] = "INFO",
) -> None:
"""Setup logging for DIRECT.
... | 1,592 | 26 | 90 | py |
direct | direct-main/direct/utils/bbox.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import List, Union
import numpy as np
import torch
def crop_to_bbox(
data: Union[np.ndarray, torch.Tensor], bbox: List[int], pad_value: int = 0
) -> Union[np.ndarray, torch.Tensor]:
"""Extract bbox from images, coordinates can be negative.
P... | 3,068 | 32.358696 | 117 | py |
direct | direct-main/direct/utils/__init__.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import abc
import ast
import functools
import importlib
import logging
import os
import pathlib
import random
import subprocess
import sys
from collections import OrderedDict
from typing import Any, Callable, Dict, KeysView, List, Optional, Tuple, Union
import numpy a... | 13,937 | 25.347826 | 119 | py |
direct | direct-main/direct/utils/io.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
# Several of the utilities here are copied/modified from torchvision under the BSD License.
# https://github.com/pytorch/vision/blob/d367a01a18a3ae6bee13d8be3b63fd6a581ea46f/torchvision/datasets/utils.py
import bz2
import gzip
import hashlib
import json
import loggin... | 16,269 | 29.073937 | 119 | py |
direct | direct-main/direct/utils/communication.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
# Taken from Detectron 2, licensed under Apache 2.0.
# https://github.com/facebookresearch/detectron2/blob/989f52d67d05445ccd030d8f13d6cc53e297fb91/detectron2/utils/comm.py
# Changes:
# - Docstring to match the rest of the library.
# - Calls to other subroutines which... | 10,230 | 31.686901 | 124 | py |
direct | direct-main/direct/utils/writers.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import logging
import pathlib
from typing import Callable, DefaultDict, Dict, Optional, Union
import h5py # type: ignore
import numpy as np
logger = logging.getLogger(__name__)
def write_output_to_h5(
output: Union[Dict, DefaultDict],
output_directory: pa... | 1,950 | 32.067797 | 107 | py |
direct | direct-main/direct/utils/imports.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
"""General utilities for module imports."""
from importlib.util import find_spec
def _module_available(module_path: str) -> bool:
"""Check if a path is available in your environment.
>>> _module_available('os')
True
>>> _module_available('bla.bla')
... | 641 | 28.181818 | 160 | py |
direct | direct-main/direct/data/lr_scheduler.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# Taken from Detectron 2, licensed under Apache 2.0.
# https://github.com/facebookresearch/detectron2/blob/60d7a1fd33cc48e58968659cd3301f3300b2786b/detectron2/solver/lr_scheduler.py
# Changes:
# - ... | 5,419 | 36.123288 | 128 | py |
direct | direct-main/direct/data/mri_transforms.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from __future__ import annotations
import functools
import logging
import random
import warnings
from enum import Enum
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
import torchvision
from d... | 63,562 | 36.94806 | 120 | py |
direct | direct-main/direct/data/bbox.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
"""Utilities to handle images with respect to bounding boxes."""
from typing import List, Union
import numpy as np
import torch
def crop_to_bbox(
data: Union[np.ndarray, torch.Tensor], bbox: List[int], pad_value: int = 0
) -> Union[np.ndarray, torch.Tensor]:
... | 3,324 | 33.278351 | 117 | py |
direct | direct-main/direct/data/datasets.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
"""DIRECT datasets module."""
import bisect
import contextlib
import logging
import pathlib
import sys
import xml.etree.ElementTree as etree # nosec
from enum import Enum
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as ... | 40,588 | 37.509488 | 123 | py |
direct | direct-main/direct/data/h5_data.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import logging
import pathlib
import re
from typing import Any, Dict, List, Optional, Tuple, Union
import h5py
import numpy as np
from torch.utils.data import Dataset
from direct.types import PathOrString
from direct.utils import cast_as_path
from direct.utils.datase... | 13,118 | 43.774744 | 120 | py |
direct | direct-main/direct/data/samplers.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
# `DistributedSampler` below taken from Detectron 2, licensed under Apache 2.0.
# Changes:
# - Docstring to match the rest of the library
# - Calls to other subroutines which do not exist in DIRECT.
"""Module containing all sampler logic."""
import itertools
import log... | 8,209 | 33.93617 | 119 | py |
direct | direct-main/direct/data/transforms.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
# Code and comments can be shared with code of FastMRI under the same MIT license:
# https://github.com/facebookresearch/fastMRI/
# The code can have been adjusted to our needs.
from typing import Callable, List, Optional, Tuple, Union
import numpy as np
import torc... | 29,535 | 30.288136 | 119 | py |
CQL | CQL-master/atari/batch_rl/multi_head/multi_network_dqn_agent.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 9,069 | 41.783019 | 80 | py |
CQL | CQL-master/atari/batch_rl/multi_head/atari_helpers.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 14,541 | 36.479381 | 80 | py |
CQL | CQL-master/atari/batch_rl/multi_head/quantile_agent.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 10,471 | 41.918033 | 97 | py |
CQL | CQL-master/atari/batch_rl/multi_head/multi_head_dqn_agent.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 5,707 | 40.664234 | 79 | py |
CQL | CQL-master/d4rl/examples/sac.py | from gym.envs.mujoco import HalfCheetahEnv
import rlkit.torch.pytorch_util as ptu
from rlkit.data_management.env_replay_buffer import EnvReplayBuffer
from rlkit.envs.wrappers import NormalizedBoxEnv
from rlkit.launchers.launcher_util import setup_logger
from rlkit.samplers.data_collector import MdpPathCollector
from r... | 3,252 | 28.044643 | 74 | py |
CQL | CQL-master/d4rl/examples/ddpg.py | """
Example of running PyTorch implementation of DDPG on HalfCheetah.
"""
import copy
from gym.envs.mujoco import HalfCheetahEnv
from rlkit.data_management.env_replay_buffer import EnvReplayBuffer
from rlkit.envs.wrappers import NormalizedBoxEnv
from rlkit.exploration_strategies.base import (
PolicyWrappedWithExp... | 3,250 | 31.838384 | 76 | py |
CQL | CQL-master/d4rl/examples/dqn_and_double_dqn.py | """
Run DQN on grid world.
"""
import gym
from torch import nn as nn
from rlkit.exploration_strategies.base import \
PolicyWrappedWithExplorationStrategy
from rlkit.exploration_strategies.epsilon_greedy import EpsilonGreedy
from rlkit.policies.argmax import ArgmaxDiscretePolicy
from rlkit.torch.dqn.dqn import DQN... | 2,848 | 27.49 | 70 | py |
CQL | CQL-master/d4rl/examples/td3.py | """
This should results in an average return of ~3000 by the end of training.
Usually hits 3000 around epoch 80-100. Within a see, the performance will be
a bit noisy from one epoch to the next (occasionally dips dow to ~2000).
Note that one epoch = 5k steps, so 200 epochs = 1 million steps.
"""
from gym.envs.mujoco ... | 3,877 | 29.535433 | 76 | py |
CQL | CQL-master/d4rl/examples/cql_antmaze_new.py | import rlkit.torch.pytorch_util as ptu
from rlkit.data_management.env_replay_buffer import EnvReplayBuffer
from rlkit.envs.wrappers import NormalizedBoxEnv
from rlkit.launchers.launcher_util import setup_logger
from rlkit.samplers.data_collector import MdpPathCollector, CustomMDPPathCollector
from rlkit.torch.sac.polic... | 7,015 | 35.352332 | 161 | py |
CQL | CQL-master/d4rl/examples/cql_mujoco_new.py | import rlkit.torch.pytorch_util as ptu
from rlkit.data_management.env_replay_buffer import EnvReplayBuffer
from rlkit.envs.wrappers import NormalizedBoxEnv
from rlkit.launchers.launcher_util import setup_logger
from rlkit.samplers.data_collector import MdpPathCollector, CustomMDPPathCollector
from rlkit.torch.sac.polic... | 7,103 | 35.80829 | 161 | py |
CQL | CQL-master/d4rl/examples/doodad/ec2_example.py | """
Example of running stuff on EC2
"""
import time
from rlkit.core import logger
from rlkit.launchers.launcher_util import run_experiment
from datetime import datetime
from pytz import timezone
import pytz
def example(variant):
import torch
logger.log(torch.__version__)
date_format = '%m/%d/%Y %H:%M:%S ... | 1,543 | 26.571429 | 79 | py |
CQL | CQL-master/d4rl/examples/doodad/gcp_example.py | """
Example of running stuff on GCP
"""
import time
from rlkit.core import logger
from rlkit.launchers.launcher_util import run_experiment
from datetime import datetime
from pytz import timezone
import pytz
def example(variant):
import torch
import rlkit.torch.pytorch_util as ptu
print("Starting")
lo... | 1,889 | 27.208955 | 79 | py |
CQL | CQL-master/d4rl/examples/skewfit/sawyer_pickup.py | import rlkit.util.hyperparameter as hyp
from rlkit.envs.goal_generation.pickup_goal_dataset import (
generate_vae_dataset,
get_image_presampled_goals_from_vae_env,
)
import rlkit.torch.vae.vae_schedules as vae_schedules
from multiworld.envs.mujoco.cameras import (
sawyer_pick_and_place_camera,
)
from rlkit... | 5,778 | 32.994118 | 79 | py |
CQL | CQL-master/d4rl/examples/skewfit/sawyer_door.py | import os.path as osp
import multiworld.envs.mujoco as mwmj
import rlkit.util.hyperparameter as hyp
from multiworld.envs.mujoco.cameras import sawyer_door_env_camera_v0
from rlkit.launchers.launcher_util import run_experiment
import rlkit.torch.vae.vae_schedules as vae_schedules
from rlkit.launchers.skewfit_experiments... | 4,644 | 32.905109 | 72 | py |
CQL | CQL-master/d4rl/examples/skewfit/sawyer_push.py | import rlkit.util.hyperparameter as hyp
from multiworld.envs.mujoco.cameras import sawyer_init_camera_zoomed_in
from rlkit.launchers.launcher_util import run_experiment
import rlkit.torch.vae.vae_schedules as vae_schedules
from rlkit.launchers.skewfit_experiments import skewfit_full_experiment
from rlkit.torch.vae.conv... | 5,650 | 33.882716 | 79 | py |
CQL | CQL-master/d4rl/examples/her/her_sac_gym_fetch_reach.py | import gym
import rlkit.torch.pytorch_util as ptu
from rlkit.data_management.obs_dict_replay_buffer import ObsDictRelabelingBuffer
from rlkit.launchers.launcher_util import setup_logger
from rlkit.samplers.data_collector import GoalConditionedPathCollector
from rlkit.torch.her.her import HERTrainer
from rlkit.torch.ne... | 4,142 | 30.869231 | 80 | py |
CQL | CQL-master/d4rl/examples/her/her_td3_multiworld_sawyer_reach.py | """
This should results in an average return of ~3000 by the end of training.
Usually hits 3000 around epoch 80-100. Within a see, the performance will be
a bit noisy from one epoch to the next (occasionally dips dow to ~2000).
Note that one epoch = 5k steps, so 200 epochs = 1 million steps.
"""
import gym
import rl... | 4,634 | 30.746575 | 80 | py |
CQL | CQL-master/d4rl/examples/her/her_dqn_gridworld.py | """
This should results in an average return of -20 by the end of training.
Usually hits -30 around epoch 50.
Note that one epoch = 5k steps, so 200 epochs = 1 million steps.
"""
import gym
import rlkit.torch.pytorch_util as ptu
from rlkit.data_management.obs_dict_replay_buffer import ObsDictRelabelingBuffer
from rlk... | 3,690 | 30.818966 | 80 | py |
CQL | CQL-master/d4rl/scripts/run_policy.py | from rlkit.samplers.rollout_functions import rollout
from rlkit.torch.pytorch_util import set_gpu_mode
import argparse
import torch
import uuid
from rlkit.core import logger
filename = str(uuid.uuid4())
def simulate_policy(args):
data = torch.load(args.file)
policy = data['evaluation/policy']
env = data[... | 1,081 | 25.390244 | 57 | py |
CQL | CQL-master/d4rl/scripts/run_experiment_from_doodad.py | import doodad as dd
import torch.multiprocessing as mp
from rlkit.launchers.launcher_util import run_experiment_here
if __name__ == "__main__":
import matplotlib
matplotlib.use('agg')
mp.set_start_method('forkserver')
args_dict = dd.get_args()
method_call = args_dict['method_call']
run_experi... | 2,343 | 37.42623 | 83 | py |
CQL | CQL-master/d4rl/scripts/run_goal_conditioned_policy.py | import argparse
import torch
from rlkit.core import logger
from rlkit.samplers.rollout_functions import multitask_rollout
from rlkit.torch import pytorch_util as ptu
from rlkit.envs.vae_wrapper import VAEWrappedEnv
def simulate_policy(args):
data = torch.load(args.file)
policy = data['evaluation/policy']
... | 1,974 | 33.051724 | 69 | py |
CQL | CQL-master/d4rl/rlkit/launchers/skewfit_experiments.py | import time
from multiworld.core.image_env import ImageEnv
from rlkit.core import logger
from rlkit.envs.vae_wrapper import temporary_mode
import cv2
import numpy as np
import os.path as osp
from rlkit.samplers.data_collector.vae_env import (
VAEWrappedEnvPathCollector,
)
from rlkit.torch.her.her import HERTraine... | 23,414 | 37.073171 | 91 | py |
CQL | CQL-master/d4rl/rlkit/launchers/launcher_util.py | import datetime
import json
import os
import os.path as osp
import pickle
import random
import sys
import time
from collections import namedtuple
import __main__ as main
import dateutil.tz
import numpy as np
from rlkit.core import logger
from rlkit.launchers import conf
from rlkit.torch.pytorch_util import set_gpu_mo... | 28,209 | 30.625561 | 96 | py |
CQL | CQL-master/d4rl/rlkit/core/batch_rl_algorithm.py | import abc
import copy
# Visualization
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from rlkit.torch import pytorch_util as ptu
import gtimer as gt
from rlkit.core.rl_algorithm import BaseRLAlgorithm
from rlkit.core.rl_algorithm import eval_util
from rlkit.data_management.replay_buffer impor... | 13,118 | 40.125392 | 148 | py |
CQL | CQL-master/d4rl/rlkit/core/logging.py | """
Based on rllab's logger.
https://github.com/rll/rllab
"""
from enum import Enum
from contextlib import contextmanager
import numpy as np
import os
import os.path as osp
import sys
import datetime
import dateutil.tz
import csv
import json
import pickle
import errno
import torch
from rlkit.core.tabulate import tabu... | 10,305 | 32.570033 | 87 | py |
CQL | CQL-master/d4rl/rlkit/envs/vae_wrapper.py | import copy
import random
import warnings
import torch
import cv2
import numpy as np
from gym.spaces import Box, Dict
import rlkit.torch.pytorch_util as ptu
from multiworld.core.multitask_env import MultitaskEnv
from multiworld.envs.env_util import get_stat_in_paths, create_stats_ordered_dict
from rlkit.envs.wrappers... | 16,221 | 36.550926 | 125 | py |
CQL | CQL-master/d4rl/rlkit/envs/mujoco_image_env.py | import cv2
import numpy as np
import torch
from PIL import Image
from collections.__init__ import deque
from gym import Env
from gym.spaces import Box
from rlkit.envs.wrappers import ProxyEnv
class ImageMujocoEnv(ProxyEnv, Env):
def __init__(self,
wrapped_env,
imsize=32,
... | 5,280 | 34.682432 | 79 | py |
CQL | CQL-master/d4rl/rlkit/data_management/shared_obs_dict_replay_buffer.py | import numpy as np
from rlkit.data_management.obs_dict_replay_buffer import ObsDictRelabelingBuffer
import torch.multiprocessing as mp
import ctypes
class SharedObsDictRelabelingBuffer(ObsDictRelabelingBuffer):
"""
Same as an ObsDictRelabelingBuffer but the obs and next_obs are backed
by multiprocessing... | 4,399 | 33.645669 | 83 | py |
CQL | CQL-master/d4rl/rlkit/data_management/online_vae_replay_buffer.py | import numpy as np
import rlkit.torch.pytorch_util as ptu
from multiworld.core.image_env import normalize_image
from rlkit.core.eval_util import create_stats_ordered_dict
from rlkit.data_management.obs_dict_replay_buffer import flatten_dict
from rlkit.data_management.shared_obs_dict_replay_buffer import \
SharedOb... | 12,107 | 38.058065 | 95 | py |
CQL | CQL-master/d4rl/rlkit/policies/argmax.py | """
Torch argmax policy
"""
import numpy as np
from torch import nn
import rlkit.torch.pytorch_util as ptu
from rlkit.policies.base import Policy
class ArgmaxDiscretePolicy(nn.Module, Policy):
def __init__(self, qf):
super().__init__()
self.qf = qf
def get_action(self, obs):
obs = np... | 517 | 22.545455 | 46 | py |
CQL | CQL-master/d4rl/rlkit/torch/pytorch_util.py | import torch
import numpy as np
def soft_update_from_to(source, target, tau):
for target_param, param in zip(target.parameters(), source.parameters()):
target_param.data.copy_(
target_param.data * (1.0 - tau) + param.data * tau
)
def copy_model_params_from_to(source, target):
for... | 3,009 | 23.672131 | 77 | py |
CQL | CQL-master/d4rl/rlkit/torch/distributions.py | import torch
from torch.distributions import Distribution, Normal
import rlkit.torch.pytorch_util as ptu
class TanhNormal(Distribution):
"""
Represent distribution of X where
X ~ tanh(Z)
Z ~ N(mean, std)
Note: this is not very numerically stable.
"""
def __init__(self, normal_mean... | 2,240 | 27.730769 | 76 | py |
CQL | CQL-master/d4rl/rlkit/torch/torch_rl_algorithm.py | import abc
from collections import OrderedDict
from typing import Iterable
from torch import nn as nn
from rlkit.core.batch_rl_algorithm import BatchRLAlgorithm
from rlkit.core.online_rl_algorithm import OnlineRLAlgorithm
from rlkit.core.trainer import Trainer
from rlkit.torch.core import np_to_pytorch_batch
class ... | 1,380 | 24.109091 | 60 | py |
CQL | CQL-master/d4rl/rlkit/torch/core.py | import numpy as np
import torch
from rlkit.torch import pytorch_util as ptu
def eval_np(module, *args, **kwargs):
"""
Eval this module with a numpy interface
Same as a call to __call__ except all Variable input/outputs are
replaced with numpy equivalents.
Assumes the output is either a single o... | 1,623 | 25.193548 | 72 | py |
CQL | CQL-master/d4rl/rlkit/torch/modules.py | """
Contain some self-contained modules.
"""
import torch
import torch.nn as nn
class HuberLoss(nn.Module):
def __init__(self, delta=1):
super().__init__()
self.huber_loss_delta1 = nn.SmoothL1Loss()
self.delta = delta
def forward(self, x, x_hat):
loss = self.huber_loss_delta1(... | 1,248 | 25.574468 | 73 | py |
CQL | CQL-master/d4rl/rlkit/torch/data.py | import numpy as np
import torch
from torch.utils.data import Dataset, Sampler
# TODO: move this to more reasonable place
from rlkit.data_management.obs_dict_replay_buffer import normalize_image
class ImageDataset(Dataset):
def __init__(self, images, should_normalize=True):
super().__init__()
sel... | 2,192 | 25.421687 | 76 | py |
CQL | CQL-master/d4rl/rlkit/torch/networks.py | """
General networks for pytorch.
Algorithm-specific networks should go else-where.
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
from rlkit.policies.base import Policy
from rlkit.torch import pytorch_util as ptu
from rlkit.torch.core import eval_np
from rlkit.torch.data_management.... | 3,520 | 27.395161 | 71 | py |
CQL | CQL-master/d4rl/rlkit/torch/conv_networks.py | import torch
from torch import nn as nn
from rlkit.pythonplusplus import identity
import numpy as np
class CNN(nn.Module):
def __init__(
self,
input_width,
input_height,
input_channels,
output_size,
kernel_sizes,
n_channels,
... | 9,881 | 36.862069 | 127 | py |
CQL | CQL-master/d4rl/rlkit/torch/data_management/normalizer.py | import torch
import rlkit.torch.pytorch_util as ptu
import numpy as np
from rlkit.data_management.normalizer import Normalizer, FixedNormalizer
class TorchNormalizer(Normalizer):
"""
Update with np array, but de/normalize pytorch Tensors.
"""
def normalize(self, v, clip_range=None):
if not se... | 2,270 | 30.109589 | 72 | py |
CQL | CQL-master/d4rl/rlkit/torch/td3/td3.py | from collections import OrderedDict
import numpy as np
import torch
import torch.optim as optim
from torch import nn as nn
import rlkit.torch.pytorch_util as ptu
from rlkit.core.eval_util import create_stats_ordered_dict
from rlkit.torch.torch_rl_algorithm import TorchTrainer
class TD3Trainer(TorchTrainer):
"""... | 6,314 | 31.219388 | 99 | py |
CQL | CQL-master/d4rl/rlkit/torch/ddpg/ddpg.py | from collections import OrderedDict
import numpy as np
import torch
import torch.optim as optim
from torch import nn as nn
import rlkit.torch.pytorch_util as ptu
from rlkit.core.eval_util import create_stats_ordered_dict
from rlkit.torch.torch_rl_algorithm import TorchTrainer
class DDPGTrainer(TorchTrainer):
""... | 6,759 | 31.344498 | 82 | py |
CQL | CQL-master/d4rl/rlkit/torch/vae/vae_trainer.py | from collections import OrderedDict
from os import path as osp
import numpy as np
import torch
from torch import optim
from torch.distributions import Normal
from torch.utils.data import DataLoader
from torchvision.utils import save_image
from multiworld.core.image_env import normalize_image
from rlkit.core import logg... | 22,100 | 38.18617 | 116 | py |
CQL | CQL-master/d4rl/rlkit/torch/vae/conv_vae.py | import torch
import torch.utils.data
from torch import nn
from torch.nn import functional as F
from rlkit.pythonplusplus import identity
from rlkit.torch import pytorch_util as ptu
import numpy as np
from rlkit.torch.conv_networks import CNN, DCNN
from rlkit.torch.vae.vae_base import GaussianLatentVAE
###### DEFAULT A... | 7,700 | 30.178138 | 93 | py |
CQL | CQL-master/d4rl/rlkit/torch/vae/vae_base.py | import torch
import numpy as np
import abc
from torch.distributions import Normal
from torch.nn import functional as F
from rlkit.torch import pytorch_util as ptu
class VAEBase(torch.nn.Module, metaclass=abc.ABCMeta):
def __init__(
self,
representation_size,
):
super().__init__... | 3,881 | 28.409091 | 89 | py |
CQL | CQL-master/d4rl/rlkit/torch/dqn/double_dqn.py | import numpy as np
import torch
import rlkit.torch.pytorch_util as ptu
from rlkit.core.eval_util import create_stats_ordered_dict
from rlkit.torch.dqn.dqn import DQNTrainer
class DoubleDQNTrainer(DQNTrainer):
def train_from_torch(self, batch):
rewards = batch['rewards']
terminals = batch['termina... | 1,786 | 29.810345 | 79 | py |
CQL | CQL-master/d4rl/rlkit/torch/dqn/dqn.py | from collections import OrderedDict
import numpy as np
import torch
import torch.optim as optim
from torch import nn as nn
import rlkit.torch.pytorch_util as ptu
from rlkit.core.eval_util import create_stats_ordered_dict
from rlkit.torch.torch_rl_algorithm import TorchTrainer
class DQNTrainer(TorchTrainer):
def... | 3,088 | 27.869159 | 79 | py |
CQL | CQL-master/d4rl/rlkit/torch/skewfit/online_vae_algorithm.py | import gtimer as gt
from rlkit.core import logger
from rlkit.data_management.online_vae_replay_buffer import \
OnlineVaeRelabelingBuffer
from rlkit.data_management.shared_obs_dict_replay_buffer \
import SharedObsDictRelabelingBuffer
import rlkit.torch.vae.vae_schedules as vae_schedules
from rlkit.torch.torch_rl... | 7,495 | 34.358491 | 132 | py |
CQL | CQL-master/d4rl/rlkit/torch/her/her.py | import torch
from rlkit.torch.torch_rl_algorithm import TorchTrainer
class HERTrainer(TorchTrainer):
def __init__(self, base_trainer: TorchTrainer):
super().__init__()
self._base_trainer = base_trainer
def train_from_torch(self, data):
obs = data['observations']
next_obs = da... | 889 | 27.709677 | 71 | py |
CQL | CQL-master/d4rl/rlkit/torch/sac/sac.py | from collections import OrderedDict
import numpy as np
import torch
import torch.optim as optim
from torch import nn as nn
import rlkit.torch.pytorch_util as ptu
from rlkit.core.eval_util import create_stats_ordered_dict
from rlkit.torch.torch_rl_algorithm import TorchTrainer
class SACTrainer(TorchTrainer):
def... | 7,211 | 30.631579 | 113 | py |
CQL | CQL-master/d4rl/rlkit/torch/sac/policies.py | import numpy as np
import torch
from torch import nn as nn
from rlkit.policies.base import ExplorationPolicy, Policy
from rlkit.torch.core import eval_np
from rlkit.torch.distributions import TanhNormal
from rlkit.torch.networks import Mlp
LOG_SIG_MAX = 2
LOG_SIG_MIN = -5
MEAN_MIN = -9.0
MEAN_MAX = 9.0
def atanh(x):... | 5,135 | 31.1 | 82 | py |
CQL | CQL-master/d4rl/rlkit/torch/sac/cql.py | from collections import OrderedDict
import numpy as np
import torch
import torch.optim as optim
from torch import nn as nn
import rlkit.torch.pytorch_util as ptu
from rlkit.core.eval_util import create_stats_ordered_dict
from rlkit.torch.torch_rl_algorithm import TorchTrainer
from torch import autograd
class CQLTrai... | 16,713 | 37.334862 | 131 | py |
one_epoch_phenomenon | one_epoch_phenomenon-main/script/utils.py | import numpy as np
from collections import defaultdict
import tensorflow as tf
from tensorflow.python.ops.rnn_cell import *
from tensorflow.python.ops.rnn_cell_impl import _Linear
from tensorflow import keras
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.... | 5,535 | 32.349398 | 124 | py |
KOBE | KOBE-master/setup.py | from setuptools import find_packages, setup
setup(
name="kobe",
version="2.0",
author="Qibin Chen",
author_email="qibinc@andrew.cmu.edu",
license="MIT",
python_requires=">=3.8",
packages=find_packages(exclude=[]),
install_requires=[
"bert-score>=0.3.11",
"black>=21.10b0"... | 742 | 22.967742 | 43 | py |
KOBE | KOBE-master/kobe/train.py | import argparse
import random
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint, TQDMProgressBar
from pytorch_lightning.loggers import WandbLogger
from kobe.data.dataset import KobeDataModule
from kobe.models.model import KobeModel
fr... | 1,953 | 29.061538 | 88 | py |
KOBE | KOBE-master/kobe/models/model.py | from typing import List, Tuple
import numpy as np
import pytorch_lightning as pl
import sentencepiece as spm
import torch
import torch.nn as nn
from bert_score import BERTScorer
from sacrebleu.metrics.bleu import BLEU, _get_tokenizer
from torch import optim
from torch.nn.init import xavier_uniform_
from transformers.m... | 5,448 | 37.373239 | 118 | py |
KOBE | KOBE-master/kobe/models/scheduler.py | import torch
from torch.optim.lr_scheduler import _LRScheduler
class WarmupDecayLR(_LRScheduler):
"""Linear warmup + inverse square root decay.
Described in the T5 paper https://arxiv.org/abs/1910.10683.
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
warmup_steps: ... | 903 | 27.25 | 64 | py |
KOBE | KOBE-master/kobe/models/transformer.py | import math
from typing import Tuple
import torch
import torch.nn as nn
from cached_property import cached_property
from torch.nn.modules.transformer import (
TransformerDecoder,
TransformerDecoderLayer,
TransformerEncoder,
TransformerEncoderLayer,
)
from kobe.data.dataset import Batched, EncodedBatch... | 7,714 | 33.441964 | 88 | py |
KOBE | KOBE-master/kobe/utils/helpers.py | import sentencepiece as spm
import torch
import torch.nn.functional as F
from transformers.models.bert.tokenization_bert import BertTokenizer
BASELINE = "baseline"
KOBE_ATTRIBUTE = "kobe-attr"
KOBE_KNOWLEDGE = "kobe-know"
KOBE_FULL = "kobe-full"
def get_bert_vocab_size(vocab_path: str) -> int:
tokenizer = BertTo... | 2,912 | 35.873418 | 97 | py |
KOBE | KOBE-master/kobe/data/dataset.py | import glob
from dataclasses import dataclass
from typing import List
import pytorch_lightning as pl
import sentencepiece as spm
import torch
import webdataset as wds
from torch.functional import Tensor
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data.dataloader import DataLoader
from kobe.utils impo... | 7,999 | 29.769231 | 87 | py |
light-moco | light-moco-main/main_moco_sne.py | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import builtins
import math
import os
import random
import shutil
import time
import warnings
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distri... | 17,579 | 41.057416 | 130 | py |
light-moco | light-moco-main/main_lincls.py | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import builtins
import os
import random
import shutil
import time
import warnings
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dis... | 19,571 | 37.910537 | 96 | py |
light-moco | light-moco-main/evaluate_sim.py | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import builtins
import math
import os
import random
import shutil
import time
import warnings
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distri... | 15,739 | 43.843305 | 116 | py |
light-moco | light-moco-main/knn_imagenet.py | import torch
from moco.utils import *
import time
from tqdm import tqdm
import torch.nn.functional as F
def kNN(net, trainloader, testloader, K, sigma, dim=512):
net.eval()
net_time = AverageMeter('net_time')
cls_time = AverageMeter('cls_time')
total = 0
testsize = testloader.dataset.__len__()
... | 3,326 | 40.5875 | 122 | py |
light-moco | light-moco-main/shortcut_inspect.py | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import builtins
import math
import os
import random
import shutil
import time
import warnings
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distri... | 14,380 | 44.22327 | 107 | py |
light-moco | light-moco-main/main_lincls_lars.py | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its 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 argparse
import builtins
import math
import os
import random
import shutil
import time
import w... | 23,218 | 39.310764 | 129 | py |
light-moco | light-moco-main/main_moco.py | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import builtins
import math
import os
import random
import shutil
import time
import warnings
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distri... | 16,059 | 41.941176 | 130 | py |
light-moco | light-moco-main/moco/utils.py | import torch
import math
import torch.nn.functional as F
def softmax_cross_entropy_with_softtarget(input, target, reduction='mean'):
"""
:param input: (batch, *)
:param target: (batch, *) same shape as input, each item must be a valid distribution: target[i, :].sum() == 1.
"""
logprobs = torch.nn.... | 5,247 | 32.21519 | 115 | py |
light-moco | light-moco-main/moco/builder.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import torch.nn as nn
from itertools import combinations
from moco.utils import softmax_cross_entropy_with_softtarget
class MoCo(nn.Module):
"""
Build a MoCo model with: a query encoder, a key encoder, and a queue
https://... | 12,376 | 38.044164 | 126 | py |
light-moco | light-moco-main/my_models/dla.py | """ Deep Layer Aggregation and DLA w/ Res2Net
DLA original adapted from Official Pytorch impl at:
DLA Paper: `Deep Layer Aggregation` - https://arxiv.org/abs/1707.06484
Res2Net additions from: https://github.com/gasvn/Res2Net/
Res2Net Paper: `Res2Net: A New Multi-scale Backbone Architecture` - https://arxiv.org/abs/19... | 17,158 | 38.08656 | 127 | py |
light-moco | light-moco-main/my_models/efficientnet_blocks.py | """ EfficientNet, MobileNetV3, etc Blocks
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
import torch.nn as nn
from torch.nn import functional as F
from .layers import create_conv2d, drop_path, get_act_layer
from .layers.activations import sigmoid
# Defaults used for Google/Tensorflow training o... | 14,680 | 35.886935 | 115 | py |
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