Datasets:
jblitzar
/
github-python-corpus

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optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr, betas=(0.9, 0.999), weight_decay=args.wd)
# load parameters
start_epoch = 0
if args.resume:
saved_models = [fn for fn in os.listdir(args.logdir) if fn.endswith(".ckpt")]
saved_models = sorted(saved_models, key=lambda x: int(x.split('_')[-1].split('.')[0]))
# use the latest checkpoint file
loadckpt = os.path.join(args.logdir, saved_models[-1])
print("resuming", loadckpt)
state_dict = torch.load(loadckpt, map_location=torch.device("cpu"))
model.load_state_dict(state_dict['model'])
optimizer.load_state_dict(state_dict['optimizer'])
start_epoch = state_dict['epoch'] + 1
elif args.loadckpt:
# load checkpoint file specified by args.loadckpt
print("loading model {}".format(args.loadckpt))
state_dict = torch.load(args.loadckpt, map_location=torch.device("cpu"))
model.load_state_dict(state_dict['model'])
if (not is_distributed) or (dist.get_rank() == 0):
print("start at epoch {}".format(start_epoch))
print('Number of model parameters: {}'.format(sum([p.data.nelement() for p in model.parameters()])))
if args.using_apex:
# Initialize Amp
model, optimizer = amp.initialize(model, optimizer,
opt_level=args.opt_level,
keep_batchnorm_fp32=args.keep_batchnorm_fp32,
loss_scale=args.loss_scale
)
if is_distributed:
print("Let's use", torch.cuda.device_count(), "GPUs!")
model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.local_rank], output_device=args.local_rank,
# find_unused_parameters=True,
# this should be removed if we update BatchNorm stats
# broadcast_buffers=False,
)
else:
if torch.cuda.is_available():
print("Let's use", torch.cuda.device_count(), "GPUs!")
model = nn.DataParallel(model)
# dataset, dataloader
MVSDataset = find_dataset_def(args.dataset)
train_dataset = MVSDataset(args.trainpath, args.pseudopath, args.trainlist, "train", args.nviews, args.numdepth, args.interval_scale)
TESTDataset = find_dataset_def('dtu_yao')
test_dataset = TESTDataset(args.testpath, args.testlist, "test", args.nviews, args.numdepth, args.interval_scale)
if is_distributed:
train_sampler = torch.utils.data.DistributedSampler(train_dataset, num_replicas=dist.get_world_size(),
rank=dist.get_rank())
test_sampler = torch.utils.data.DistributedSampler(test_dataset, num_replicas=dist.get_world_size(),
rank=dist.get_rank())
TrainImgLoader = DataLoader(train_dataset, args.batch_size, sampler=train_sampler, num_workers=2,
drop_last=True,
pin_memory=args.pin_m)
TestImgLoader = DataLoader(test_dataset, args.batch_size, sampler=test_sampler, num_workers=2, drop_last=False,
pin_memory=args.pin_m)
else:
TrainImgLoader = DataLoader(train_dataset, args.batch_size, shuffle=True, num_workers=0, drop_last=True,
pin_memory=args.pin_m)
TestImgLoader = DataLoader(test_dataset, args.batch_size, shuffle=False, num_workers=0, drop_last=False,
pin_memory=args.pin_m)
if args.mode == "train":
train(model, model_loss, optimizer, TrainImgLoader, TestImgLoader, start_epoch, args)
elif args.mode == "test":
test(model, model_loss, TestImgLoader, args)
elif args.mode == "profile":
profile()
else:
raise NotImplementedError
# <FILESEP>
import logging
import os
from datetime import datetime
import paho.mqtt.client as mqtt
from dotenv import load_dotenv
from constants import callback_api_version_map, protocol_map
from exporter.metric.node_configuration_metrics import NodeConfigurationMetrics
from exporter.metric_cleanup_job import MetricTrackingRegistry
try:
from meshtastic.mesh_pb2 import MeshPacket
from meshtastic.mqtt_pb2 import ServiceEnvelope
except ImportError:
from meshtastic.protobuf.mesh_pb2 import MeshPacket
from meshtastic.protobuf.mqtt_pb2 import ServiceEnvelope
from prometheus_client import start_http_server
from psycopg_pool import ConnectionPool