Datasets:
jblitzar
/
github-python-corpus

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# <FILESEP>
import os
USED_DEVICES = "-1" # if your want to use CPU in a server with GPU, change "0" to "-1"
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = USED_DEVICES
os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
import alphastarmini
import torch
from alphastarmini.core.arch import entity_encoder
from alphastarmini.core.arch import scalar_encoder
from alphastarmini.core.arch import spatial_encoder
from alphastarmini.core.arch import arch_model
from alphastarmini.core.arch import action_type_head
from alphastarmini.core.arch import selected_units_head
from alphastarmini.core.arch import target_unit_head
from alphastarmini.core.arch import delay_head
from alphastarmini.core.arch import queue_head
from alphastarmini.core.arch import location_head
from alphastarmini.core.arch import agent
from alphastarmini.core.arch import baseline
from alphastarmini.core.sl import load_pickle
from alphastarmini.core.rl import action
from alphastarmini.core.rl import env_utils
from alphastarmini.core.rl import actor
from alphastarmini.core.rl import against_computer
from alphastarmini.core.rl import pseudo_reward
import param as P
if __name__ == '__main__':
# if we don't add this line, it may cause running time error while in Windows
# torch.multiprocessing.freeze_support()
print("run init")
# ------------------------
# 1. we transform the replays to pickle
from alphastarmini.core.sl import transform_replay_data
transform_replay_data.test(on_server=P.on_server)
print('run over')
# <FILESEP>
import json
import numpy as np
import torch
import os
import warnings
import wandb
import random
from torch.utils.data import Sampler, Dataset
import matplotlib.pyplot as plt
import matplotlib
import PIL
# used to sample a subset of the val set during training
class SubsetSampler(Sampler):
def __init__(self, mask):
self.mask = mask
def __iter__(self):
return (self.indices[i] for i in torch.nonzero(self.mask))
def __len__(self):
return len(self.mask)
class ListDataset(Dataset):
def __init__(self, original_list):
self.original_list = original_list
def __len__(self):
return len(self.original_list)
def __getitem__(self, i):
return self.original_list[i]
def load_config(args):
if args.eval != -1:
path = f'./configs/eval_ssm_config_{args.eval}.json'
else:
path = './configs/finetune_ssm_config.json'
f = open(path)
json_data = json.load(f)
f.close()
if args.device != 'None':
json_data['model_device'] = f'cuda:{args.device}'
return json_data