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import os
import onnxruntime
import torch
import torch.utils.data
import torchvision
from torch import nn
from torchvision.transforms.functional import InterpolationMode
import utils
def evaluate(
criterion,
data_loader,
device,
model=None,
model_onnx_path=None,
print_freq=100,
log_suffix="",
):
if model_onnx_path:
session = onnxruntime.InferenceSession(
model_onnx_path, providers=["CPUExecutionProvider"]
)
input_name = session.get_inputs()[0].name
metric_logger = utils.MetricLogger(delimiter=" ")
header = f"Test: {log_suffix}"
num_processed_samples = 0
with torch.inference_mode():
for image, target in metric_logger.log_every(data_loader, print_freq, header):
target = target.to(device, non_blocking=True)
image = image.to(device)
if model_onnx_path:
# from torch to numpy (ort)
input_data = image.cpu().numpy()
output_data = session.run([], {input_name: input_data})[0]
# from numpy to torch
output = torch.from_numpy(output_data).to(device)
elif model:
output = model(image)
loss = criterion(output, target)
acc1, acc5 = utils.accuracy(output, target, topk=(1, 5))
# FIXME need to take into account that the datasets
# could have been padded in distributed setup
batch_size = image.shape[0]
metric_logger.update(loss=loss.item())
metric_logger.meters["acc1"].update(acc1.item(), n=batch_size)
metric_logger.meters["acc5"].update(acc5.item(), n=batch_size)
num_processed_samples += batch_size
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print(
f"{header} Acc@1 {metric_logger.acc1.global_avg:.3f} Acc@5 {metric_logger.acc5.global_avg:.3f}"
)
return metric_logger.acc1.global_avg
def load_data(valdir):
# Data loading code
print("Loading data")
interpolation = InterpolationMode("bilinear")
preprocessing = torchvision.transforms.Compose(
[
torchvision.transforms.Resize(256, interpolation=interpolation),
torchvision.transforms.CenterCrop(224),
torchvision.transforms.PILToTensor(),
torchvision.transforms.ConvertImageDtype(torch.float),
torchvision.transforms.Normalize(
mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)
),
]
)
dataset_test = torchvision.datasets.ImageFolder(
valdir,
preprocessing,
)
print("Creating data loaders")
test_sampler = torch.utils.data.SequentialSampler(dataset_test)
return dataset_test, test_sampler
def main(args):
print(args)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
if torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
val_dir = os.path.join(args.data_path, "val")
dataset_test, test_sampler = load_data(val_dir)
data_loader_test = torch.utils.data.DataLoader(
dataset_test,
batch_size=args.batch_size,
sampler=test_sampler,
num_workers=args.workers,
pin_memory=True,
)
print("Creating model")
criterion = nn.CrossEntropyLoss()
model = None
if args.model_ckpt:
checkpoint = torch.load(args.model_ckpt, map_location="cpu")
model = checkpoint["model_ckpt"]
if "model_ema" in checkpoint:
state_dict = {}
for key, value in checkpoint["model_ema"].items():
if not "module." in key:
continue
state_dict[key.replace("module.", "")] = value
model.load_state_dict(state_dict)
model = model.to(device)
model.eval()
accuracy = evaluate(
model=model,
model_onnx_path=args.model_onnx,
criterion=criterion,
data_loader=data_loader_test,
device=device,
)
print(f"Model accuracy is: {accuracy}")
def get_args_parser(add_help=True):
import argparse
parser = argparse.ArgumentParser(
description="PyTorch Classification Training", add_help=add_help
)
parser.add_argument(
"--data-path", default="datasets/imagenet", type=str, help="dataset path"
)
parser.add_argument(
"-b",
"--batch-size",
default=32,
type=int,
help="images per gpu, the total batch size is $NGPU x batch_size",
)
parser.add_argument(
"-j",
"--workers",
default=16,
type=int,
metavar="N",
help="number of data loading workers (default: 16)",
)
parser.add_argument("--print-freq", default=10, type=int, help="print frequency")
parser.add_argument(
"--model-onnx", default="", type=str, help="path of .onnx checkpoint"
)
parser.add_argument(
"--model-ckpt", default="", type=str, help="path of .pth checkpoint"
)
return parser
if __name__ == "__main__":
args = get_args_parser().parse_args()
main(args)
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