def __init__(self):
# 加载配置文件
self.config = utils.loadYaml('../config/config.yaml')
# 训练结果保存路径
self.output_model_path = os.path.join(
'../output/', self.config['Misc']['OutputFolderName'])
# gpu使用
self.device = utils.set_gpu(self.config)
def __init__(self):
# 加载配置文件
self.config = utils.loadYaml('../config/config.yaml')
# 训练结果保存路径
self.output_model_path = os.path.join(
'../output/', self.config['Misc']['OutputFolderName'])
if self.config['Misc']['OutputFolderName']:
utils.mkdir(self.output_model_path)
else:
raise IOError('请输入训练结果保存路径...')
# gpu使用
self.device = utils.set_gpu(self.config)
self.summaryWriter = SummaryWriter(log_dir=self.output_model_path)
# logger日志
setup_logger(self.output_model_path)
self.logger = get_logger()
def __init__(self):
# 加载配置文件
self.config = utils.loadYaml('../config/config.yaml')
# 训练结果保存路径
self.output_model_path = os.path.join('../output/', self.config['Misc']['OutputFolderName'])
# gpu使用
self.device = utils.set_gpu(self.config)
self.img_shape = (256, 256)
self.overlap_piexl = 200
self.imgs_index_dict, self.rois_start_xy_index_dict, self.rois_xyxy_index_dict = self.getRefInfo(
ref_img_path='/home/pi/Desktop/df1b_dataset/20191024/ref_deploy')
print(self.imgs_index_dict)
print("Creating model")
self.model = getModels(model_name=self.config['Model']['Name'],
num_classes=self.config['Model']['NumClass']).to(self.device)
checkpoint = torch.load(os.path.join(self.output_model_path, self.config['Misc']['BestModelName']),
map_location='cpu')
self.model.load_state_dict(checkpoint['model'])
self.model.eval()
import numpy as np
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config', default="exps/exp-v1/config.yaml")
args = parser.parse_args()
pprint(vars(args))
with open(args.config, 'r') as f:
cfg = AttrDict(yaml.load(f))
cfg = AttrDict(cfg.test)
set_gpu(cfg.gpu)
ensure_path(cfg.result)
dataset = MiniImageNet(cfg.datapath, 'test')
sampler = CategoriesSampler(dataset.label, cfg.batch, cfg.way,
cfg.shot + cfg.query)
loader = DataLoader(dataset,
batch_sampler=sampler,
num_workers=8,
pin_memory=True)
trainset = MiniImageNet(cfg.datapath, 'train')
propagate_loader = DataLoader(dataset=trainset,
batch_size=1280,
shuffle=True,
num_workers=24,
from utils.utils import set_torch_seed, set_gpu, get_tasks, get_data, get_model, get_backbone, get_strategy
from utils.utils import compress_args, get_args, torch_summarize
from utils.builder import ExperimentBuilder
from utils.bunch import bunch
import sys
import pprint
if __name__ == '__main__':
args, excluded_args, parser = get_args()
args = bunch.bunchify(args)
set_torch_seed(args.seed)
device = set_gpu(args.gpu)
datasets = get_data(args)
tasks = get_tasks(args)
backbone = get_backbone(args, device)
strategy = get_strategy(args, device)
model = get_model(backbone, tasks, datasets, strategy, args, device)
compressed_args = compress_args(bunch.unbunchify(args), parser)
print(" ----------------- FULL ARGS (COMPACT) ----------------")
pprint.pprint(compressed_args, indent=2)
print(" ------------------------------------------------------")
print(" ------------------ UNRECOGNISED ARGS -----------------")
pprint.pprint(excluded_args, indent=2)
print(" ------------------------------------------------------")
system = ExperimentBuilder(model, tasks, datasets, device, args)
system.load_pretrained()
parser.add_argument('--inner_lr', type=float, default=1e-2)
parser.add_argument('--inner_opt', type=str, default='SGD')
parser.add_argument('--outer_lr', type=float, default=1e-3)
parser.add_argument('--outer_opt', type=str, default='Adam')
parser.add_argument('--lr_sched', type=lambda x: (str(x).lower() == 'true'), default=False)
# imaml specific settings
parser.add_argument('--lambda', type=float, default=2.0)
parser.add_argument('--version', type=str, default='GD')
parser.add_argument('--cg_steps', type=int, default=5)
# network settings
parser.add_argument('--net', type=str, default='ConvNet')
parser.add_argument('--n_conv', type=int, default=4)
parser.add_argument('--n_dense', type=int, default=0)
parser.add_argument('--hidden_dim', type=int, default=64)
parser.add_argument('--in_channels', type=int, default=1)
parser.add_argument('--hidden_channels', type=int, default=64,
help='Number of channels for each convolutional layer (default: 64).')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
set_seed(args.seed)
set_gpu(args.device)
check_dir(args)
main(args)
def setup_algorithms(server_args):
"""
Load datasets and pretrained models
"""
loaded_models = {}
datasets = None
abspath = os.path.abspath(".")
set_torch_seed(server_args.seed)
if "exp_path" in server_args and server_args["exp_path"] is not None:
abspath = os.path.abspath(server_args["exp_path"])
for builder_args in server_args.models:
original_args = copy.copy(builder_args)
assert 'continue_from' in builder_args, 'All "models" should have a "continue_from" entry.'
assert 'gpu' in builder_args, 'All "models" should have a specified "gpu" entry or "cpu" device.'
stdin_list = [
"--args_file",
os.path.join(abspath, builder_args["continue_from"], 'configs',
'config.json'), "--continue_from",
os.path.join(abspath, builder_args["continue_from"]), "--gpu",
builder_args['gpu'], "--seed", server_args.seed, "--dataset",
server_args.dataset, "--dataset_args",
json.dumps({
'dataset_version': server_args.version,
'data_path': server_args.data_path
})
]
builder_args, excluded_args, parser = get_args(stdin_list)
builder_args = bunch.bunchify(builder_args)
compressed_args = compress_args(bunch.unbunchify(builder_args), parser)
device = set_gpu(builder_args.gpu)
tasks = get_tasks(builder_args)
datasets = get_data(builder_args) if datasets is None else datasets
backbone = get_backbone(builder_args, device)
strategy = get_strategy(builder_args, device)
model = get_model(backbone, tasks, datasets, strategy, builder_args,
device)
compressed_args = compress_args(bunch.unbunchify(builder_args), parser)
print(" ----------------- FULL ARGS (COMPACT) ----------------")
pprint.pprint(compressed_args, indent=2)
print(" ------------------------------------------------------")
print(" ------------------ UNRECOGNISED ARGS -----------------")
pprint.pprint(excluded_args, indent=2)
print(" ------------------------------------------------------")
system = ExperimentBuilder(model, tasks, datasets, device,
builder_args)
system.load_pretrained()
model.set_mode('test')
if builder_args["model"] == 'simpleshot':
system.model.set_train_mean(system.datasets['train'])
name = original_args[
'name'] if 'name' in original_args else builder_args['model']
tie_breaker = 0
name_proposal = name
while name_proposal in loaded_models:
tie_breaker += 1
name_proposal = "{}({})".format(name, tie_breaker)
loaded_models[name_proposal] = system
return loaded_models, datasets
