def __init__(self, config: str, pretrained: bool = True):
super().__init__()
self.cfg = parse_config(config)
self.backbone = TextLSTM(BASE_DIR / self.cfg.BACKBONE.NET).cuda()
if getattr(self.cfg.BACKBONE, 'WEIGHT', None):
self.backbone.load_state_dict(
torch.load(BASE_DIR / self.cfg.BACKBONE.WEIGHT))
self.backbone.fc = nn.Identity()
self.backbone.sfm = nn.Identity()
profiling = EliteNet(BASE_DIR / self.cfg.PROFILING.NET).cuda()
if getattr(self.cfg.BACKBONE, 'WEIGHT', None):
profiling.load_state_dict(
torch.load(BASE_DIR / self.cfg.PROFILING.WEIGHT))
self.profiling = nn.Sequential(*list(profiling.children())[:-1])
bottle_neck = self.backbone.cfg.HIDDEN_SIZE + profiling.cfg.FC4
self.fc1 = nn.Linear(bottle_neck, 256, bias=True)
self.bn1 = nn.BatchNorm1d(256)
self.fc2 = nn.Linear(256, 512, bias=True)
self.bn2 = nn.BatchNorm1d(512)
self.out = nn.Linear(512, 2, bias=True)
# freeze if pretrained flag is set
if pretrained:
_freeze(self.backbone)
_freeze(self.profiling)
def get_config(): """ This function fetches the iView "config". Among other things, it tells us an always-metered "fallback" RTMP server, and points us to many of iView's other XML files. """ global iview_config iview_config = parser.parse_config(maybe_fetch(config.config_url))
def __init__(self, config):
"""Initialize a design instance with a condiguration object
Args:
config (object): JSON configuration of the experimental design
"""
parsed_config = parse_config(config)
self.between_subject_factors = parsed_config.get(
FactorType.between_subject.name, [])
self.within_subject_factors = parsed_config.get(
FactorType.within_subject.name, [])
def __init__(self, config):
super().__init__()
# get config
self.cfg = parse_config(config)
# structure
self.input = nn.Linear(self.cfg.INPUT, self.cfg.FC1, bias=True)
self.fc1 = nn.Linear(self.cfg.FC1, self.cfg.FC2, bias=True)
self.bn_1 = nn.BatchNorm1d(self.cfg.FC2)
self.drop_1 = nn.Dropout(self.cfg.DROP1)
self.fc2 = nn.Linear(self.cfg.FC2, self.cfg.FC3, bias=True)
self.bn_2 = nn.BatchNorm1d(self.cfg.FC3)
self.drop_2 = nn.Dropout(self.cfg.DROP2)
self.fc3 = nn.Linear(self.cfg.FC3, self.cfg.FC4, bias=True)
self.out = nn.Linear(self.cfg.FC4, self.cfg.OUTPUT, bias=True)
def __init__(self, config):
super().__init__()
# load config
self.cfg = parse_config(config)
self.output_size = self.cfg.OUTPUT_SIZE
self.hidden_size = self.cfg.HIDDEN_SIZE
self.embedding_length = self.cfg.EMBEDDING_LENGTH
self.word_embeddings = nn.Embedding.from_pretrained(
torch.from_numpy(np.load(BASE_DIR /
self.cfg.EMBEDDING_DIR)).float())
self.lstm = nn.LSTM(self.cfg.EMBEDDING_LENGTH,
self.hidden_size,
bidirectional=True)
self.fc = nn.Linear(self.hidden_size, self.output_size)
self.sfm = nn.Softmax()
def test_parse_reads_file(self):
config = {
"mc_version": "1.16.5",
"mods_dir": "mods",
"mods": [
{
"source": "GITHUB",
"owner": "snallapa",
"repo": "scentfindermod"
}
]
}
with open('config.json', 'w') as outfile:
json.dump(config, outfile)
def cleanup():
os.remove("config.json")
self.addCleanup(cleanup)
sources = parse_config()
self.assertEqual(1, len(sources))
self.assertEqual("GithubSource", sources[0].__class__.__name__)
def get_config(): """ This function fetches the iView "config". Among other things, it tells us an always-metered "fallback" RTMP server, and points us to many of iView's other XML files. """ return parser.parse_config(fetch_url(config.config_url))
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from pytorch_lightning import Trainer
from pytorch_lightning.loggers import WandbLogger
from model import Net
from parser import parse_args, parse_config
from dataset import Dataset
if __name__ == "__main__":
args = parse_args()
args = parse_config(args)
args.model_dir.mkdir(parents=True, exist_ok=True)
args.checkpoints_dir.mkdir(exist_ok=True)
if args.resume_checkpoint == 0:
shutil.copy(args.cfg, args.model_dir / 'experiment_settings.cfg')
else:
shutil.copy(args.cfg,
args.model_dir / 'experiment_settings_resume.cfg')
if args.log_file.exists():
args.log_file.unlink()
logger.add(args.log_file,
format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}",
backtrace=False,
diagnose=False)
02: 473 491 (200 iterations)
03: 641 673 (300 iterations)
04: 1001 1092 (1100 after 100 iterations)
05: 749 801 400 iterations
06: 876 113 200 iterations
07: 885
08: 4437 5362
801 with 5 10 20 population profile on problem 05. 500 iterations.
839 with 5 population profile on problem 05. 500 iterations.
002 with 5 population profile on problem 05. 500 iterations. local optimum..
'''
depots, customers, durations, n_paths_per_depot = loader.load_dataset(filename)
conf = configparser.parse_config('configs/default.conf')
if len(plt.get_fignums()) > 0:
ax0, ax1 = plt.gcf().get_axes()
else:
_, (ax0, ax1) = plt.subplots(1, 2)
model = MDVRPModel(customers, depots, n_paths_per_depot, conf)
optimal_solution = utils.visualize_solution(model, solution_file)
model.evolve(3)
one = model.population[0] # debug
L = [each.fitness_score() for each in model.population]
best = model.population[np.argmin(L)]
