autodl-projects/exps/basic/basic-eval.py

116 lines
3.8 KiB
Python

#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
#####################################################
import os, sys, time, torch, random, argparse
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
from copy import deepcopy
from xautodl.config_utils import load_config, dict2config
from xautodl.procedures import get_procedures, get_optim_scheduler
from xautodl.datasets import get_datasets
from xautodl.models import obtain_model
from xautodl.utils import get_model_infos
from xautodl.log_utils import PrintLogger, time_string
def main(args):
assert os.path.isdir(args.data_path), "invalid data-path : {:}".format(
args.data_path
)
assert os.path.isfile(args.checkpoint), "invalid checkpoint : {:}".format(
args.checkpoint
)
checkpoint = torch.load(args.checkpoint)
xargs = checkpoint["args"]
train_data, valid_data, xshape, class_num = get_datasets(
xargs.dataset, args.data_path, xargs.cutout_length
)
valid_loader = torch.utils.data.DataLoader(
valid_data,
batch_size=xargs.batch_size,
shuffle=False,
num_workers=xargs.workers,
pin_memory=True,
)
logger = PrintLogger()
model_config = dict2config(checkpoint["model-config"], logger)
base_model = obtain_model(model_config)
flop, param = get_model_infos(base_model, xshape)
logger.log("model ====>>>>:\n{:}".format(base_model))
logger.log("model information : {:}".format(base_model.get_message()))
logger.log("-" * 50)
logger.log(
"Params={:.2f} MB, FLOPs={:.2f} M ... = {:.2f} G".format(
param, flop, flop / 1e3
)
)
logger.log("-" * 50)
logger.log("valid_data : {:}".format(valid_data))
optim_config = dict2config(checkpoint["optim-config"], logger)
_, _, criterion = get_optim_scheduler(base_model.parameters(), optim_config)
logger.log("criterion : {:}".format(criterion))
base_model.load_state_dict(checkpoint["base-model"])
_, valid_func = get_procedures(xargs.procedure)
logger.log("initialize the CNN done, evaluate it using {:}".format(valid_func))
network = torch.nn.DataParallel(base_model).cuda()
try:
valid_loss, valid_acc1, valid_acc5 = valid_func(
valid_loader,
network,
criterion,
optim_config,
"pure-evaluation",
xargs.print_freq_eval,
logger,
)
except:
_, valid_func = get_procedures("basic")
valid_loss, valid_acc1, valid_acc5 = valid_func(
valid_loader,
network,
criterion,
optim_config,
"pure-evaluation",
xargs.print_freq_eval,
logger,
)
num_bytes = torch.cuda.max_memory_cached(next(network.parameters()).device) * 1.0
logger.log(
"***{:s}*** EVALUATION loss = {:.6f}, accuracy@1 = {:.2f}, accuracy@5 = {:.2f}, error@1 = {:.2f}, error@5 = {:.2f}".format(
time_string(),
valid_loss,
valid_acc1,
valid_acc5,
100 - valid_acc1,
100 - valid_acc5,
)
)
logger.log(
"[GPU-Memory-Usage on {:} is {:} bytes, {:.2f} KB, {:.2f} MB, {:.2f} GB.]".format(
next(network.parameters()).device,
int(num_bytes),
num_bytes / 1e3,
num_bytes / 1e6,
num_bytes / 1e9,
)
)
logger.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser("Evaluate-CNN")
parser.add_argument("--data_path", type=str, help="Path to dataset.")
parser.add_argument(
"--checkpoint", type=str, help="Choose between Cifar10/100 and ImageNet."
)
args = parser.parse_args()
assert torch.cuda.is_available(), "torch.cuda is not available"
main(args)