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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CRFP | CRFP-main/dataset/reds.py |
import os
import random
import pickle
import logging
import numpy as np
import PIL
import pdb
import math
import torch
import torch.utils.data as data
import torchvision.transforms.functional as F
logger = logging.getLogger('base')
def fovea_generator(GT_imgs, method='Rscan', step=0.1, FV_HW=(32, 32)):
len_sp ... | 21,515 | 40.94152 | 142 | py |
CRFP | CRFP-main/loss/loss.py | import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Returns:
... | 6,337 | 33.445652 | 78 | py |
CRFP | CRFP-main/model/CRFP_runtime.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import time
from memory_profiler import profile
from dcn_v2 import DCNv2
from model import LTE
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
def conv1x1(in_channels, out_channels, stride=1):... | 418,709 | 43.969391 | 165 | py |
CRFP | CRFP-main/model/LTE.py | import torch
import torch.nn as nn
import torch.nn.functional as F
def pixel_unshuffle(input, downscale_factor):
'''
input: batchSize * c * k*w * k*h
kdownscale_factor: k
batchSize * c * k*w * k*h -> batchSize * k*k*c * w * h
'''
c = input.shape[1]
kernel = torch.zeros(size=[downscale_fact... | 10,500 | 37.324818 | 92 | py |
CRFP | CRFP-main/model/CRFP_test.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from dcn_v2 import DCNv2
from model import LTE
def rgb2yuv(rgb):
# rgb_ = rgb.permute(0,2,3,1)
# A = torch.tensor([[0.299, -0.14714119,0.61497538],
# [0.587, -0.28886916, -0.51496512],
# [0.114, 0.43... | 157,001 | 44.089604 | 165 | py |
CRFP | CRFP-main/model/CRFP.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import time
from dcn_v2 import DCNv2
from model import LTE
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
def rgb2yuv(rgb):
# rgb_ = rgb.permute(0,2,3,1)
# A = torch.tensor([[0.299, -... | 116,485 | 42.709568 | 167 | py |
CRFP | CRFP-main/pytorch_ssim/__init__.py | import torch
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from math import exp
def gaussian(window_size, sigma):
gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])
return gauss/gauss.sum()
def create_window(window_size,... | 2,809 | 35.493506 | 104 | py |
cotengra | cotengra-main/examples/ex_jax.py | """This script shows how to manually use jax to jit-compile the core
contraction.
"""
import jax
import tqdm
import numpy as np
import cotengra as ctg
from concurrent.futures import ThreadPoolExecutor
# generate a random contraction
inputs, output, shapes, size_dict = ctg.utils.rand_equation(
140, 3, n_out=2, seed... | 2,618 | 28.761364 | 79 | py |
TD-DMN | TD-DMN-master/loss.py | from utils.utils import *
from torch.nn import functional as F
from math import ceil
from utils.utils import doc_flat_mask
def cal_loss_with_attn(logits, event_labels, sent_len, neg_pos_ratio=0,
neg_label=None, pad_label=None, partial=False):
"""
logits -> (N, S, W, L)
sent_len -> (... | 2,527 | 36.731343 | 100 | py |
TD-DMN | TD-DMN-master/iterator.py | """
Iterator reads through the processed
tsv files and generate batches that could
be directly passed to the model
"""
from torchtext import data
from torchtext.vocab import Vectors
class BaseIterator(object):
def __init__(self):
from utils.info_field import InfoField, NestedInfoField
self.InfoFie... | 5,202 | 46.733945 | 115 | py |
TD-DMN | TD-DMN-master/config.py | import torch
# Constants
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
ANCHOR_NUM = 35
ENTITY_NUM = 57
PAD_TOKEN = "<pad>"
WORD_EMBED_SIZE = 300
KFOLD_NUM = 5
# Model hyper-parameters
params = {
"train_batch_size": 10,
"entity_embed_size": 50,
"word_gru_hidden_siz... | 1,283 | 26.913043 | 85 | py |
TD-DMN | TD-DMN-master/eval.py | import torch
class Evaluator(object):
def __init__(self, fold):
self.golden_dict = {
i: {
"train": [],
"test": []
} for i in range(fold)
}
for i in range(fold):
self.load_golden(i)
return
def doc_evaluate(se... | 3,668 | 34.621359 | 111 | py |
TD-DMN | TD-DMN-master/train.py | from iterator import DMNIterator
from loss import cal_loss_with_attn
from torch import optim
from eval import Evaluator
from config import *
import torch
import logging
from utils.ResultWriter import ResultWriter
from utils.utils import init_logging
import datetime
from tensorboardX import SummaryWriter
from models.TDD... | 9,293 | 43.898551 | 106 | py |
TD-DMN | TD-DMN-master/models/TDDMN.py | import torch.nn as nn
from torch.nn import functional
import logging
import numpy as np
from utils.utils import *
class WordAttention(nn.Module):
def __init__(self, config):
super(WordAttention, self).__init__()
self.attention_word = nn.Sequential(
nn.Linear(in_features=int(config.get(... | 22,933 | 38.815972 | 116 | py |
TD-DMN | TD-DMN-master/utils/info_field.py | """
Info fields do nothing to the data and
return the passed data untouched.
This is useful if we need to store additional
information of the example but we do not wish
to numericalize it.
"""
from torchtext import data
class InfoField(data.Field):
def __init__(self, **kwargs):
super(InfoField, self).__i... | 863 | 21.736842 | 62 | py |
TD-DMN | TD-DMN-master/utils/utils.py | from config import *
import torch
import logging
def sequence_mask(sequence_length, max_len=None):
if max_len is None:
max_len = sequence_length.data.max()
batch_size = sequence_length.size(0)
seq_range = torch.range(0, max_len - 1).long().to(DEVICE)
seq_range_expand = seq_range.unsqueeze(0).e... | 2,432 | 31.013158 | 91 | py |
DeepClawBenchmark | DeepClawBenchmark-master/deepclaw/modules/end2end/efficientdet/backbone.py | # Author: Zylo117
import math
import torch
from torch import nn
from .efficientdet.model import BiFPN, Regressor, Classifier, EfficientNet
from .efficientdet.utils import Anchors
class EfficientDetBackbone(nn.Module):
def __init__(self, num_classes=80, compound_coef=0, load_weights=False, **kwargs):
su... | 3,197 | 37.53012 | 115 | py |
DeepClawBenchmark | DeepClawBenchmark-master/deepclaw/modules/end2end/efficientdet/efficientdet_predictor.py | # Author: Fang Wan
"""
Simple Inference Script of EfficientDet-Pytorch
"""
import time
import torch
from torch.backends import cudnn
import cv2
import numpy as np
from .backbone import EfficientDetBackbone
from .efficientdet.utils import BBoxTransform, ClipBoxes
from .utils.utils import preprocess_image, invert_affi... | 7,114 | 70.868687 | 3,162 | py |
DeepClawBenchmark | DeepClawBenchmark-master/deepclaw/modules/end2end/efficientdet/train.py | # original author: signatrix
# adapted from https://github.com/signatrix/efficientdet/blob/master/train.py
# modified by Zylo117
import datetime
import os
import argparse
import traceback
import torch
import yaml
from torch import nn
from torch.utils.data import DataLoader
from torchvision import transforms
from effi... | 15,544 | 44.720588 | 194 | py |
DeepClawBenchmark | DeepClawBenchmark-master/deepclaw/modules/end2end/efficientdet/efficientnet/utils_extra.py | # Author: Zylo117
import math
from torch import nn
import torch.nn.functional as F
class Conv2dStaticSamePadding(nn.Module):
"""
created by Zylo117
The real keras/tensorflow conv2d with same padding
"""
def __init__(self, in_channels, out_channels, kernel_size, stride=1, bias=True, groups=1, di... | 2,923 | 29.14433 | 116 | py |
DeepClawBenchmark | DeepClawBenchmark-master/deepclaw/modules/end2end/efficientdet/efficientnet/utils.py | """
This file contains helper functions for building the model and for loading model parameters.
These helper functions are built to mirror those in the official TensorFlow implementation.
"""
import re
import math
import collections
from functools import partial
import torch
from torch import nn
from torch.nn import ... | 12,844 | 39.907643 | 115 | py |
DeepClawBenchmark | DeepClawBenchmark-master/deepclaw/modules/end2end/efficientdet/efficientnet/model.py | import torch
from torch import nn
from torch.nn import functional as F
from .utils import (
round_filters,
round_repeats,
drop_connect,
get_same_padding_conv2d,
get_model_params,
efficientnet_params,
load_pretrained_weights,
Swish,
MemoryEfficientSwish,
)
class MBConvBlock(nn.Modul... | 9,850 | 40.390756 | 107 | py |
DeepClawBenchmark | DeepClawBenchmark-master/deepclaw/modules/end2end/efficientdet/utils/utils.py | # Author: Zylo117
import os
import cv2
import numpy as np
import torch
from glob import glob
from torch import nn
from torchvision.ops import nms
from typing import Union
import uuid
from .sync_batchnorm import SynchronizedBatchNorm2d
def invert_affine(metas: Union[float, list, tuple], preds):
for i in range(l... | 7,910 | 35.625 | 119 | py |
DeepClawBenchmark | DeepClawBenchmark-master/deepclaw/modules/end2end/efficientdet/utils/sync_batchnorm/replicate.py | # -*- coding: utf-8 -*-
# File : replicate.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import functools
from torch.nn.parallel.da... | 3,226 | 32.968421 | 115 | py |
DeepClawBenchmark | DeepClawBenchmark-master/deepclaw/modules/end2end/efficientdet/utils/sync_batchnorm/unittest.py | # -*- coding: utf-8 -*-
# File : unittest.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import unittest
import torch
class TorchTes... | 746 | 23.9 | 59 | py |
DeepClawBenchmark | DeepClawBenchmark-master/deepclaw/modules/end2end/efficientdet/utils/sync_batchnorm/batchnorm.py | # -*- coding: utf-8 -*-
# File : batchnorm.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import collections
import contextlib
import... | 15,859 | 39.151899 | 116 | py |
DeepClawBenchmark | DeepClawBenchmark-master/deepclaw/modules/end2end/efficientdet/utils/sync_batchnorm/batchnorm_reimpl.py | #! /usr/bin/env python3
# -*- coding: utf-8 -*-
# File : batchnorm_reimpl.py
# Author : acgtyrant
# Date : 11/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import torch
import torch.nn as nn
import torch... | 2,385 | 30.813333 | 95 | py |
DeepClawBenchmark | DeepClawBenchmark-master/deepclaw/modules/end2end/efficientdet/efficientdet/loss.py | import torch
import torch.nn as nn
import cv2
import numpy as np
from efficientdet.utils import BBoxTransform, ClipBoxes
from utils.utils import postprocess, invert_affine, display
def calc_iou(a, b):
# a(anchor) [boxes, (y1, x1, y2, x2)]
# b(gt, coco-style) [boxes, (x1, y1, x2, y2)]
area = (b[:, 2] - b... | 6,729 | 40.801242 | 132 | py |
DeepClawBenchmark | DeepClawBenchmark-master/deepclaw/modules/end2end/efficientdet/efficientdet/utils.py | import itertools
import torch
import torch.nn as nn
import numpy as np
class BBoxTransform(nn.Module):
def forward(self, anchors, regression):
"""
decode_box_outputs adapted from https://github.com/google/automl/blob/master/efficientdet/anchors.py
Args:
anchors: [batchsize, bo... | 5,026 | 35.427536 | 109 | py |
DeepClawBenchmark | DeepClawBenchmark-master/deepclaw/modules/end2end/efficientdet/efficientdet/model.py | import torch.nn as nn
import torch
from torchvision.ops.boxes import nms as nms_torch
from ..efficientnet import EfficientNet as EffNet
from ..efficientnet.utils import MemoryEfficientSwish, Swish
from ..efficientnet.utils_extra import Conv2dStaticSamePadding, MaxPool2dStaticSamePadding
def nms(dets, thresh):
re... | 17,348 | 39.440559 | 119 | py |
DeepClawBenchmark | DeepClawBenchmark-master/deepclaw/modules/end2end/efficientdet/efficientdet/dataset.py | import os
import torch
import numpy as np
from torch.utils.data import Dataset, DataLoader
from pycocotools.coco import COCO
import cv2, json, copy
class WasteDataset(Dataset):
"""Waste dataset."""
def __init__(self, root_dir, type="simple", set_name='train', transform=None):
"""
Args:
... | 14,660 | 34.758537 | 120 | py |
manifoldflasso_jmlr | manifoldflasso_jmlr-master/codes/experimentclasses/AtomicRegression.py | from codes.flasso.FlassoManifold import FlassoManifold
import autograd.numpy as np
import scipy.stats
#import autograd.numpy as np
from autograd import grad
from scipy import sparse
import itertools
import collections
class AtomicRegression(FlassoManifold):
"""
Parameters
----------
filename : string,... | 13,275 | 38.162242 | 130 | py |
manifoldflasso_jmlr | manifoldflasso_jmlr-master/codes/experimentclasses/.ipynb_checkpoints/AtomicRegression-checkpoint.py | from codes.flasso.FlassoManifold import FlassoManifold
import autograd.numpy as np
import scipy.stats
#import autograd.numpy as np
from autograd import grad
from scipy import sparse
import itertools
import collections
class AtomicRegression(FlassoManifold):
"""
Parameters
----------
filename : string,... | 11,070 | 38.967509 | 130 | py |
manifoldflasso_jmlr | manifoldflasso_jmlr-master/codes/geometer/ShapeSpace.py | import matplotlib.pyplot as plt
from megaman.embedding import spectral_embedding
from megaman.geometry import Geometry
from megaman.geometry import RiemannMetric
from scipy import sparse
from scipy.sparse.linalg import norm
from codes.geometer import TangentBundle
import numpy as np
from pathos.multiprocessing import P... | 5,558 | 46.110169 | 130 | py |
gym-unrealcv | gym-unrealcv-master/example/dqn/dqn.py | import time
import random
import numpy as np
from keras.models import Sequential, load_model
from keras.layers import Convolution2D, Flatten, ZeroPadding2D
from keras.layers.core import Dense, Dropout, Activation
from keras.layers.pooling import MaxPooling2D
from keras.optimizers import SGD , Adam
import tensorflow as ... | 6,235 | 31.310881 | 126 | py |
gym-unrealcv | gym-unrealcv-master/example/dqn/io_util.py |
import csv
import os
import cv2
import keras.backend as K
from constants import *
import matplotlib.pyplot as plt
def detect_monitor_files(training_dir):
return [os.path.join(training_dir, f) for f in os.listdir(training_dir) if f.startswith('openaigym')]
def clear_monitor_files(training_dir):
files = detect... | 4,299 | 36.068966 | 124 | py |
devign | devign-master/configs.py | import json
import torch
class Config(object):
def __init__(self, config, file_path="configs.json"):
with open(file_path) as config_file:
self._config = json.load(config_file)
self._config = self._config.get(config)
self.device = torch.device("cuda" if torch.cuda.is_availab... | 3,123 | 19.688742 | 82 | py |
devign | devign-master/src/process/devign.py | import torch.optim as optim
import torch.nn.functional as F
from ..utils import log
from .step import Step
from .model import Net
class Devign(Step):
def __init__(self,
path: str,
device: str,
model: dict,
learning_rate: float,
... | 1,224 | 30.410256 | 109 | py |
devign | devign-master/src/process/step.py | import torch
from ..utils.objects import stats
def softmax_accuracy(probs, all_labels):
acc = (torch.argmax(probs) == all_labels).sum()
acc = torch.div(acc, len(all_labels) + 0.0)
return acc
class Step:
# Performs a step on the loader and returns the result
def __init__(self, model, loss_functio... | 1,121 | 27.769231 | 76 | py |
devign | devign-master/src/process/modeling.py | from ..utils.objects.metrics import Metrics
import torch
import time
from ..utils import log as logger
class Train(object):
def __init__(self, step, epochs, verbose=True):
self.epochs = epochs
self.step = step
self.history = History()
self.verbose = verbose
def __call__(self, ... | 2,363 | 29.307692 | 92 | py |
devign | devign-master/src/process/model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn.conv import GatedGraphConv
torch.manual_seed(2020)
def get_conv_mp_out_size(in_size, last_layer, mps):
size = in_size
for mp in mps:
size = round((size - mp["kernel_size"]) / mp["stride"] + 1)
size = siz... | 2,884 | 29.368421 | 91 | py |
devign | devign-master/src/process/stopping.py | import numpy as np
# Author: https://github.com/Bjarten/early-stopping-pytorch
class EarlyStopping:
"""Early stops the training if validation loss doesn't improve after a given patience."""
def __init__(self, model, patience=7, verbose=False, delta=0):
"""
Args:
patience (int): Ho... | 1,884 | 33.272727 | 111 | py |
devign | devign-master/src/prepare/embeddings.py | import numpy as np
import torch
from torch_geometric.data import Data
from src.utils.functions.parse import tokenizer
from src.utils import log as logger
from gensim.models.keyedvectors import Word2VecKeyedVectors
class NodesEmbedding:
def __init__(self, nodes_dim: int, w2v_keyed_vectors: Word2VecKeyedVectors):
... | 4,012 | 36.157407 | 106 | py |
devign | devign-master/src/utils/objects/input_dataset.py | from torch.utils.data import Dataset as TorchDataset
from torch_geometric.data import DataLoader
class InputDataset(TorchDataset):
def __init__(self, dataset):
self.dataset = dataset
def __len__(self):
return len(self.dataset)
def __getitem__(self, index):
return self.dataset.ilo... | 468 | 26.588235 | 79 | py |
RCT | RCT-main/recipes/dcase2021_task4_baseline/train_sed_rct.py | import argparse
import numpy as np
import os
import pandas as pd
import random
import torch
import yaml
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.loggers import TensorBoardLogger
from desed_task.dataio import ConcatDatasetBatchSampler
... | 11,019 | 32.700306 | 114 | py |
RCT | RCT-main/recipes/dcase2021_task4_baseline/local/sed_trainer_rct.py | import os
import random
from copy import deepcopy
from pathlib import Path
import pandas as pd
import pytorch_lightning as pl
import torch
from torchaudio.transforms import AmplitudeToDB, MelSpectrogram
from desed_task.utils.scaler import TorchScaler
import numpy as np
from desed_task.data_augm.rand_augm_agg import Ra... | 32,787 | 39.629492 | 182 | py |
RCT | RCT-main/desed_task/data_augm/rand_augm_agg.py | import torch
import random
import yaml
import sox
import numpy as np
import torchaudio
import matplotlib.pyplot as plt
import torch.nn as nn
import soundfile as sf
from collections import defaultdict
from torchaudio.transforms import MelSpectrogram, MelScale
from torchaudio.sox_effects import apply_effects_tensor
from ... | 15,451 | 49.006472 | 116 | py |
MLT-LE | MLT-LE-main/mltle/graph_training.py | import tensorflow as tf
import numpy as np
from keras import backend as K
from keras.utils.layer_utils import count_params
class GraphModel:
"""
This is a multi-task drug-target binding strength prediction model:
MuLti-Task with Label Encoding and Graph Encoding.
Parameters
----------
prote... | 12,083 | 39.686869 | 130 | py |
MLT-LE | MLT-LE-main/mltle/datagen.py | import tensorflow as tf
import numpy as np
class DataGen:
"""
This is data generator.
This generator expects variable length drug and protein inputs.
Data should be passed as pandas.DataFrame,
columns order should be the following:
[drug_sequence, protein_sequence, target_1, target_2, ..... | 6,333 | 34.58427 | 123 | py |
MLT-LE | MLT-LE-main/mltle/training.py | import tensorflow as tf
import numpy as np
from keras import backend as K
from keras.utils.layer_utils import count_params
class Model:
"""
This is multi-task drug-target binding strength prediction model:
MuLti-Task with Label Encoding.
Parameters
----------
drug_emb_size : Int, default=64... | 14,682 | 42.440828 | 113 | py |
MLT-LE | MLT-LE-main/mltle/training_utils.py | import tensorflow as tf
import numpy as np
from keras import backend as K
from sklearn.metrics import mean_squared_error
from lifelines.utils import concordance_index
from scipy import stats
def get_scores(y_true, y_pred):
mse = np.round(mean_squared_error(y_true, y_pred), 3)
rmse = np.round(mse**0.5, 3)
... | 2,951 | 30.073684 | 90 | py |
MLT-LE | MLT-LE-main/mltle/__init__.py | try:
import tensorflow as tf
from keras import backend as K
except ImportError:
print("Failed to import tensorflow")
raise
from mltle import (training, graph_training, datagen, datamap)
try:
from mltle import chem_utils
except Exception as e:
print('Failed to load `mltle.chem_utils`. This modu... | 506 | 20.125 | 76 | py |
MLT-LE | MLT-LE-main/MLFlow/run.py | import sys
sys.path.insert(0, '../')
import tensorflow as tf
import mlflow
import mlflow.tensorflow
from mlflow import log_metrics
import argparse
from tqdm.auto import tqdm
from tqdm.keras import TqdmCallback
from keras import backend as K
import numpy as np
from collections import defaultdict
from sklearn.mode... | 6,848 | 32.905941 | 144 | py |
ODPP | ODPP-main/ODPP_Locomotion_Number/main.py | import os
import gym
import torch
import datetime
import random
import numpy as np
from configs import get_common_args
from runner import Runner
from hierarchical_runner import HierRunner
from spectral_DPP_agent.laprepr import LapReprLearner
from utils.env_wrapper import EnvWrapper
import robo_env
def main():
# pr... | 2,151 | 31.606061 | 102 | py |
ODPP | ODPP-main/ODPP_Locomotion_Number/hierarchical_runner.py | import os
import torch
import numpy as np
from tqdm import tqdm
import torch.nn.functional as F
from configs import get_hierarchical_args
from option_agent.hierarchical_policy import HierPolicy
from utils.buffer import OneHot, EpisodeBatch, ReplayBuffer
from runner import Runner
class HierRunner(object):
def __ini... | 15,283 | 51.522337 | 163 | py |
ODPP | ODPP-main/ODPP_Locomotion_Number/runner.py | import os
import torch
from tqdm import tqdm
import numpy as np
from configs import get_rl_args
from option_agent import REGISTRY as agent_REGISTRY
from utils.buffer import OneHot, EpisodeBatch, ReplayBuffer
from spectral_DPP_agent.laprepr import LapReprLearner
from visualization.draw_trajectory import draw_traj
class... | 10,290 | 47.772512 | 148 | py |
ODPP | ODPP-main/ODPP_Locomotion_Number/option_agent/ODPP_bk.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import policy, value, rnn_decoder, prior
from utils.summary_tools import write_summary
from spectral_DPP_agent.laprepr import LapReprLearner
from spectral... | 20,928 | 52.802057 | 180 | py |
ODPP | ODPP-main/ODPP_Locomotion_Number/option_agent/hierarchical_policy.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import prior, value, policy
from utils.summary_tools import write_summary
def get_final_state_value(args, target_v, horizons):
target_v_array = targ... | 14,848 | 46.9 | 197 | py |
ODPP | ODPP-main/ODPP_Locomotion_Number/option_agent/VALOR.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import policy, value, rnn_decoder
from utils.summary_tools import write_summary
from option_agent.base_option_agent import Option_Agent
class VALOR_Agent... | 10,983 | 52.580488 | 167 | py |
ODPP | ODPP-main/ODPP_Locomotion_Number/option_agent/VIC.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import prior, policy, value, decoder
from utils.summary_tools import write_summary, write_hist
from option_agent.base_option_agent import Option_Agent
cl... | 14,805 | 52.84 | 172 | py |
ODPP | ODPP-main/ODPP_Locomotion_Number/option_agent/DCO.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import policy, value
from utils.summary_tools import write_summary
from spectral_DPP_agent.laprepr import LapReprLearner
from option_agent.hierarchical_po... | 13,006 | 50.208661 | 180 | py |
ODPP | ODPP-main/ODPP_Locomotion_Number/option_agent/base_option_agent.py | import os
import torch
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
from utils.summary_tools import write_summary
from option_agent.hierarchical_policy import get_final_state_value, get_return_array, get_advantage
class Option_Agent(object):
def __init__(self, args):
... | 10,002 | 50.035714 | 156 | py |
ODPP | ODPP-main/ODPP_Locomotion_Number/option_agent/ODPP.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import policy, value, rnn_decoder, prior
from utils.summary_tools import write_summary
from spectral_DPP_agent.laprepr import LapReprLearner
from spectral... | 21,158 | 52.977041 | 180 | py |
ODPP | ODPP-main/ODPP_Locomotion_Number/option_agent/DIAYN.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import policy, value, decoder
from utils.summary_tools import write_summary
from option_agent.base_option_agent import Option_Agent
class DIAYN_Agent(Op... | 10,651 | 53.907216 | 183 | py |
ODPP | ODPP-main/ODPP_Locomotion_Number/utils/buffer.py | import torch as th
import numpy as np
from types import SimpleNamespace as SN
class OneHot(object):
def __init__(self, out_dim):
self.out_dim = out_dim
def transform(self, tensor): # check
y_onehot = tensor.new(*tensor.shape[:-1], self.out_dim).zero_()
y_onehot.scatter_(-1, tensor.lon... | 7,036 | 40.394118 | 159 | py |
ODPP | ODPP-main/ODPP_Locomotion_Number/utils/torch_tools.py | import numpy as np
import torch
def to_tensor(x, device):
"""return a torch.Tensor, assume x is an np array."""
if x.dtype in [np.float32, np.float64]:
return torch.tensor(x, dtype=torch.float32, device=device)
elif x.dtype in [np.int32, np.int64, np.uint8]:
return torch.tensor(x, dtype=tor... | 421 | 37.363636 | 66 | py |
ODPP | ODPP-main/ODPP_Locomotion_Number/utils/summary_tools.py | import numpy as np
def get_summary_str(step=None, info=None, prefix=''):
summary_str = prefix
if step is not None:
summary_str += 'Step {}; '.format(step)
for key, val in info.items():
if isinstance(val, (int, np.int32, np.int64)):
summary_str += '{} {}; '.format(key, val)
... | 834 | 35.304348 | 85 | py |
ODPP | ODPP-main/ODPP_Locomotion_Number/learner/value.py | import torch.nn as nn
from learner.base_mlp import MLP
# TODO: share layer with the policy netwrok
class ValueFuntion(nn.Module):
def __init__(self, input_dim, hidden_dim):
super(ValueFuntion, self).__init__()
self.mlp = MLP(layers=[input_dim, hidden_dim, hidden_dim, 1])
def forward(self, x):... | 385 | 28.692308 | 69 | py |
ODPP | ODPP-main/ODPP_Locomotion_Number/learner/base_mlp.py | import torch
import torch.nn as nn
class MLP(nn.Module):
def __init__(self, layers, activation=torch.tanh, output_activation=None, init=True): # TODO：relu
super(MLP, self).__init__()
self.layers = nn.ModuleList()
self.activation = activation
self.output_activation = output_activatio... | 1,136 | 34.53125 | 101 | py |
ODPP | ODPP-main/ODPP_Locomotion_Number/learner/rnn_decoder.py | import torch
import torch.nn as nn
from torch.distributions.categorical import Categorical
class RNN_Decoder(nn.Module):
def __init__(self, input_dim, hidden_dim, code_dim):
super(RNN_Decoder, self).__init__()
self.lstm = nn.LSTM(input_size=input_dim, hidden_size=hidden_dim, batch_first=True, bidir... | 1,253 | 40.8 | 111 | py |
ODPP | ODPP-main/ODPP_Locomotion_Number/learner/prior.py | import torch.nn as nn
from torch.distributions.categorical import Categorical
from learner.base_mlp import MLP
class Prior(nn.Module):
def __init__(self, input_dim, hidden_dim, code_dim, is_high=False):
super(Prior, self).__init__()
self.is_high = is_high
self.mlp = MLP(layers=[input_dim,... | 1,189 | 33 | 91 | py |
ODPP | ODPP-main/ODPP_Locomotion_Number/learner/policy.py | import torch
import torch.nn as nn
from torch.distributions.normal import Normal
from torch.distributions.categorical import Categorical
import numpy as np
from learner.base_mlp import MLP
class GaussianPolicy(nn.Module):
def __init__(self, input_dim, hidden_dim, action_dim, output_activation=None, act_range=None... | 1,980 | 36.377358 | 153 | py |
ODPP | ODPP-main/ODPP_Locomotion_Number/learner/decoder.py | import torch.nn as nn
from torch.distributions.categorical import Categorical
from learner.base_mlp import MLP
class Decoder(nn.Module):
def __init__(self, input_dim, hidden_dim, code_dim):
super(Decoder, self).__init__()
self.mlp = MLP(layers=[input_dim, hidden_dim, hidden_dim, code_dim])
de... | 861 | 29.785714 | 76 | py |
ODPP | ODPP-main/ODPP_Locomotion_Number/spectral_DPP_agent/laprepr.py | import os
import collections
import numpy as np
from tqdm import tqdm
import torch
from torch import optim
from torch.utils.tensorboard import SummaryWriter
from configs import get_laprepr_args
from utils import torch_tools, timer_tools, summary_tools
from spectral_DPP_agent.spectral_buffer import EpisodicReplayBuffer... | 13,324 | 40 | 132 | py |
ODPP | ODPP-main/ODPP_Locomotion_Number/spectral_DPP_agent/laprepr_bk.py | import os
import collections
import numpy as np
from tqdm import tqdm
import torch
from torch import optim
from torch.utils.tensorboard import SummaryWriter
from configs import get_laprepr_args
from utils import torch_tools, timer_tools, summary_tools
from spectral_DPP_agent.spectral_buffer import EpisodicReplayBuffer... | 13,324 | 40 | 132 | py |
ODPP | ODPP-main/ODPP_Ablation/main.py | import os
import gym
import torch
import datetime
import random
import numpy as np
from configs import get_common_args
from runner import Runner
from hierarchical_runner import HierRunner
from spectral_DPP_agent.laprepr import LapReprLearner
from utils.env_wrapper import EnvWrapper
import robo_env
def main():
# pr... | 2,147 | 31.545455 | 102 | py |
ODPP | ODPP-main/ODPP_Ablation/hierarchical_runner.py | import os
import torch
import numpy as np
from tqdm import tqdm
import torch.nn.functional as F
from configs import get_hierarchical_args
from option_agent.hierarchical_policy import HierPolicy
from utils.buffer import OneHot, EpisodeBatch, ReplayBuffer
from runner import Runner
class HierRunner(object):
def __ini... | 15,283 | 51.522337 | 163 | py |
ODPP | ODPP-main/ODPP_Ablation/runner.py | import os
import torch
from tqdm import tqdm
import numpy as np
from configs import get_rl_args
from option_agent import REGISTRY as agent_REGISTRY
from utils.buffer import OneHot, EpisodeBatch, ReplayBuffer
from spectral_DPP_agent.laprepr import LapReprLearner
from visualization.draw_trajectory import draw_traj
class... | 9,374 | 48.08377 | 148 | py |
ODPP | ODPP-main/ODPP_Ablation/option_agent/hierarchical_policy.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import prior, value, policy
from utils.summary_tools import write_summary
def get_final_state_value(args, target_v, horizons):
target_v_array = targ... | 14,848 | 46.9 | 197 | py |
ODPP | ODPP-main/ODPP_Ablation/option_agent/VALOR.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import policy, value, rnn_decoder
from utils.summary_tools import write_summary
from option_agent.base_option_agent import Option_Agent
class VALOR_Agent... | 10,874 | 52.308824 | 167 | py |
ODPP | ODPP-main/ODPP_Ablation/option_agent/VIC.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import prior, policy, value, decoder
from utils.summary_tools import write_summary, write_hist
from option_agent.base_option_agent import Option_Agent
cl... | 14,633 | 52.801471 | 172 | py |
ODPP | ODPP-main/ODPP_Ablation/option_agent/DCO.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import policy, value
from utils.summary_tools import write_summary
from spectral_DPP_agent.laprepr import LapReprLearner
from option_agent.hierarchical_po... | 12,888 | 50.146825 | 180 | py |
ODPP | ODPP-main/ODPP_Ablation/option_agent/base_option_agent.py | import os
import torch
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
from utils.summary_tools import write_summary
from option_agent.hierarchical_policy import get_final_state_value, get_return_array, get_advantage
class Option_Agent(object):
def __init__(self, args):
... | 10,002 | 50.035714 | 156 | py |
ODPP | ODPP-main/ODPP_Ablation/option_agent/ODPP.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import policy, value, rnn_decoder, prior
from utils.summary_tools import write_summary
from spectral_DPP_agent.laprepr import LapReprLearner
from spectral... | 20,750 | 52.759067 | 180 | py |
ODPP | ODPP-main/ODPP_Ablation/option_agent/DIAYN.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import policy, value, decoder
from utils.summary_tools import write_summary
from option_agent.base_option_agent import Option_Agent
class DIAYN_Agent(Op... | 10,541 | 53.90625 | 183 | py |
ODPP | ODPP-main/ODPP_Ablation/utils/buffer.py | import torch as th
import numpy as np
from types import SimpleNamespace as SN
class OneHot(object):
def __init__(self, out_dim):
self.out_dim = out_dim
def transform(self, tensor): # check
y_onehot = tensor.new(*tensor.shape[:-1], self.out_dim).zero_()
y_onehot.scatter_(-1, tensor.lon... | 7,036 | 40.394118 | 159 | py |
ODPP | ODPP-main/ODPP_Ablation/utils/torch_tools.py | import numpy as np
import torch
def to_tensor(x, device):
"""return a torch.Tensor, assume x is an np array."""
if x.dtype in [np.float32, np.float64]:
return torch.tensor(x, dtype=torch.float32, device=device)
elif x.dtype in [np.int32, np.int64, np.uint8]:
return torch.tensor(x, dtype=tor... | 421 | 37.363636 | 66 | py |
ODPP | ODPP-main/ODPP_Ablation/utils/summary_tools.py | import numpy as np
def get_summary_str(step=None, info=None, prefix=''):
summary_str = prefix
if step is not None:
summary_str += 'Step {}; '.format(step)
for key, val in info.items():
if isinstance(val, (int, np.int32, np.int64)):
summary_str += '{} {}; '.format(key, val)
... | 834 | 35.304348 | 85 | py |
ODPP | ODPP-main/ODPP_Ablation/learner/value.py | import torch.nn as nn
from learner.base_mlp import MLP
# TODO: share layer with the policy netwrok
class ValueFuntion(nn.Module):
def __init__(self, input_dim, hidden_dim):
super(ValueFuntion, self).__init__()
self.mlp = MLP(layers=[input_dim, hidden_dim, hidden_dim, 1])
def forward(self, x):... | 385 | 28.692308 | 69 | py |
ODPP | ODPP-main/ODPP_Ablation/learner/base_mlp.py | import torch
import torch.nn as nn
class MLP(nn.Module):
def __init__(self, layers, activation=torch.tanh, output_activation=None, init=True): # TODO：relu
super(MLP, self).__init__()
self.layers = nn.ModuleList()
self.activation = activation
self.output_activation = output_activatio... | 1,136 | 34.53125 | 101 | py |
ODPP | ODPP-main/ODPP_Ablation/learner/rnn_decoder.py | import torch
import torch.nn as nn
from torch.distributions.categorical import Categorical
class RNN_Decoder(nn.Module):
def __init__(self, input_dim, hidden_dim, code_dim):
super(RNN_Decoder, self).__init__()
self.lstm = nn.LSTM(input_size=input_dim, hidden_size=hidden_dim, batch_first=True, bidir... | 1,253 | 40.8 | 111 | py |
ODPP | ODPP-main/ODPP_Ablation/learner/prior.py | import torch.nn as nn
from torch.distributions.categorical import Categorical
from learner.base_mlp import MLP
class Prior(nn.Module):
def __init__(self, input_dim, hidden_dim, code_dim, is_high=False):
super(Prior, self).__init__()
self.is_high = is_high
self.mlp = MLP(layers=[input_dim,... | 1,189 | 33 | 91 | py |
ODPP | ODPP-main/ODPP_Ablation/learner/policy.py | import torch
import torch.nn as nn
from torch.distributions.normal import Normal
from torch.distributions.categorical import Categorical
import numpy as np
from learner.base_mlp import MLP
class GaussianPolicy(nn.Module):
def __init__(self, input_dim, hidden_dim, action_dim, output_activation=None, act_range=None... | 1,980 | 36.377358 | 153 | py |
ODPP | ODPP-main/ODPP_Ablation/learner/decoder.py | import torch.nn as nn
from torch.distributions.categorical import Categorical
from learner.base_mlp import MLP
class Decoder(nn.Module):
def __init__(self, input_dim, hidden_dim, code_dim):
super(Decoder, self).__init__()
self.mlp = MLP(layers=[input_dim, hidden_dim, hidden_dim, code_dim])
de... | 861 | 29.785714 | 76 | py |
ODPP | ODPP-main/ODPP_Ablation/spectral_DPP_agent/laprepr.py | import os
import collections
import numpy as np
from tqdm import tqdm
import torch
from torch import optim
from torch.utils.tensorboard import SummaryWriter
from configs import get_laprepr_args
from utils import torch_tools, timer_tools, summary_tools
from spectral_DPP_agent.spectral_buffer import EpisodicReplayBuffer... | 13,322 | 39.993846 | 132 | py |
ODPP | ODPP-main/ODPP_Relation/main.py | import os
import gym
import torch
import datetime
import random
import numpy as np
from configs import get_common_args
from runner import Runner
from hierarchical_runner import HierRunner
from spectral_DPP_agent.laprepr import LapReprLearner
from utils.env_wrapper import EnvWrapper
import robo_env
def main():
# pr... | 2,147 | 31.545455 | 102 | py |
ODPP | ODPP-main/ODPP_Relation/hierarchical_runner.py | import os
import torch
import numpy as np
from tqdm import tqdm
import torch.nn.functional as F
from configs import get_hierarchical_args
from option_agent.hierarchical_policy import HierPolicy
from utils.buffer import OneHot, EpisodeBatch, ReplayBuffer
from runner import Runner
class HierRunner(object):
def __ini... | 15,283 | 51.522337 | 163 | py |
ODPP | ODPP-main/ODPP_Relation/runner.py | import os
import torch
from tqdm import tqdm
import numpy as np
from configs import get_rl_args
from option_agent import REGISTRY as agent_REGISTRY
from utils.buffer import OneHot, EpisodeBatch, ReplayBuffer
from spectral_DPP_agent.laprepr import LapReprLearner
from visualization.draw_trajectory import draw_traj
class... | 11,592 | 48.331915 | 148 | py |
ODPP | ODPP-main/ODPP_Relation/option_agent/hierarchical_policy.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import prior, value, policy
from utils.summary_tools import write_summary
def get_final_state_value(args, target_v, horizons):
target_v_array = targ... | 14,848 | 46.9 | 197 | py |
ODPP | ODPP-main/ODPP_Relation/option_agent/VALOR.py | import os
import torch
from torch.optim import Adam
import torch.nn.functional as F
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from learner import policy, value, rnn_decoder
from utils.summary_tools import write_summary
from option_agent.base_option_agent import Option_Agent
class VALOR_Agent... | 10,865 | 52.527094 | 167 | py |
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