171 lines
7.9 KiB
Python
171 lines
7.9 KiB
Python
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
|
"""
|
|
Validate a trained YOLOv5 classification model on a classification dataset
|
|
|
|
Usage:
|
|
$ bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images)
|
|
$ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate ImageNet
|
|
|
|
Usage - formats:
|
|
$ python classify/val.py --weights yolov5s-cls.pt # PyTorch
|
|
yolov5s-cls.torchscript # TorchScript
|
|
yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn
|
|
yolov5s-cls_openvino_model # OpenVINO
|
|
yolov5s-cls.engine # TensorRT
|
|
yolov5s-cls.mlmodel # CoreML (macOS-only)
|
|
yolov5s-cls_saved_model # TensorFlow SavedModel
|
|
yolov5s-cls.pb # TensorFlow GraphDef
|
|
yolov5s-cls.tflite # TensorFlow Lite
|
|
yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU
|
|
yolov5s-cls_paddle_model # PaddlePaddle
|
|
"""
|
|
|
|
import argparse
|
|
import os
|
|
import sys
|
|
from pathlib import Path
|
|
|
|
import torch
|
|
from tqdm import tqdm
|
|
|
|
FILE = Path(__file__).resolve()
|
|
ROOT = FILE.parents[1] # YOLOv5 root directory
|
|
if str(ROOT) not in sys.path:
|
|
sys.path.append(str(ROOT)) # add ROOT to PATH
|
|
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
|
|
|
from models.common import DetectMultiBackend
|
|
from utils.dataloaders import create_classification_dataloader
|
|
from utils.general import (LOGGER, TQDM_BAR_FORMAT, Profile, check_img_size, check_requirements, colorstr,
|
|
increment_path, print_args)
|
|
from utils.torch_utils import select_device, smart_inference_mode
|
|
|
|
|
|
@smart_inference_mode()
|
|
def run(
|
|
data=ROOT / '../datasets/mnist', # dataset dir
|
|
weights=ROOT / 'yolov5s-cls.pt', # model.pt path(s)
|
|
batch_size=128, # batch size
|
|
imgsz=224, # inference size (pixels)
|
|
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
|
workers=8, # max dataloader workers (per RANK in DDP mode)
|
|
verbose=False, # verbose output
|
|
project=ROOT / 'runs/val-cls', # save to project/name
|
|
name='exp', # save to project/name
|
|
exist_ok=False, # existing project/name ok, do not increment
|
|
half=False, # use FP16 half-precision inference
|
|
dnn=False, # use OpenCV DNN for ONNX inference
|
|
model=None,
|
|
dataloader=None,
|
|
criterion=None,
|
|
pbar=None,
|
|
):
|
|
# Initialize/load model and set device
|
|
training = model is not None
|
|
if training: # called by train.py
|
|
device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model
|
|
half &= device.type != 'cpu' # half precision only supported on CUDA
|
|
model.half() if half else model.float()
|
|
else: # called directly
|
|
device = select_device(device, batch_size=batch_size)
|
|
|
|
# Directories
|
|
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
|
|
save_dir.mkdir(parents=True, exist_ok=True) # make dir
|
|
|
|
# Load model
|
|
model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half)
|
|
stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
|
|
imgsz = check_img_size(imgsz, s=stride) # check image size
|
|
half = model.fp16 # FP16 supported on limited backends with CUDA
|
|
if engine:
|
|
batch_size = model.batch_size
|
|
else:
|
|
device = model.device
|
|
if not (pt or jit):
|
|
batch_size = 1 # export.py models default to batch-size 1
|
|
LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
|
|
|
|
# Dataloader
|
|
data = Path(data)
|
|
test_dir = data / 'test' if (data / 'test').exists() else data / 'val' # data/test or data/val
|
|
dataloader = create_classification_dataloader(path=test_dir,
|
|
imgsz=imgsz,
|
|
batch_size=batch_size,
|
|
augment=False,
|
|
rank=-1,
|
|
workers=workers)
|
|
|
|
model.eval()
|
|
pred, targets, loss, dt = [], [], 0, (Profile(), Profile(), Profile())
|
|
n = len(dataloader) # number of batches
|
|
action = 'validating' if dataloader.dataset.root.stem == 'val' else 'testing'
|
|
desc = f"{pbar.desc[:-36]}{action:>36}" if pbar else f"{action}"
|
|
bar = tqdm(dataloader, desc, n, not training, bar_format=TQDM_BAR_FORMAT, position=0)
|
|
with torch.cuda.amp.autocast(enabled=device.type != 'cpu'):
|
|
for images, labels in bar:
|
|
with dt[0]:
|
|
images, labels = images.to(device, non_blocking=True), labels.to(device)
|
|
|
|
with dt[1]:
|
|
y = model(images)
|
|
|
|
with dt[2]:
|
|
pred.append(y.argsort(1, descending=True)[:, :5])
|
|
targets.append(labels)
|
|
if criterion:
|
|
loss += criterion(y, labels)
|
|
|
|
loss /= n
|
|
pred, targets = torch.cat(pred), torch.cat(targets)
|
|
correct = (targets[:, None] == pred).float()
|
|
acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1) # (top1, top5) accuracy
|
|
top1, top5 = acc.mean(0).tolist()
|
|
|
|
if pbar:
|
|
pbar.desc = f"{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}"
|
|
if verbose: # all classes
|
|
LOGGER.info(f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}")
|
|
LOGGER.info(f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}")
|
|
for i, c in model.names.items():
|
|
aci = acc[targets == i]
|
|
top1i, top5i = aci.mean(0).tolist()
|
|
LOGGER.info(f"{c:>24}{aci.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}")
|
|
|
|
# Print results
|
|
t = tuple(x.t / len(dataloader.dataset.samples) * 1E3 for x in dt) # speeds per image
|
|
shape = (1, 3, imgsz, imgsz)
|
|
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}' % t)
|
|
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
|
|
|
|
return top1, top5, loss
|
|
|
|
|
|
def parse_opt():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--data', type=str, default=ROOT / '../datasets/mnist', help='dataset path')
|
|
parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-cls.pt', help='model.pt path(s)')
|
|
parser.add_argument('--batch-size', type=int, default=128, help='batch size')
|
|
parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=224, help='inference size (pixels)')
|
|
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
|
parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
|
|
parser.add_argument('--verbose', nargs='?', const=True, default=True, help='verbose output')
|
|
parser.add_argument('--project', default=ROOT / 'runs/val-cls', help='save to project/name')
|
|
parser.add_argument('--name', default='exp', help='save to project/name')
|
|
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
|
|
parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
|
|
parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
|
|
opt = parser.parse_args()
|
|
print_args(vars(opt))
|
|
return opt
|
|
|
|
|
|
def main(opt):
|
|
check_requirements(exclude=('tensorboard', 'thop'))
|
|
run(**vars(opt))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
opt = parse_opt()
|
|
main(opt)
|