# Ultralytics YOLOv5 🚀, AGPL-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,
):
"""Validates a YOLOv5 classification model on a dataset, computing metrics like top1 and top5 accuracy."""
# 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(device=device), Profile(device=device), Profile(device=device))
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():
acc_i = acc[targets == i]
top1i, top5i = acc_i.mean(0).tolist()
LOGGER.info(f"{c:>24}{acc_i.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():
"""Parses and returns command line arguments for YOLOv5 model evaluation and inference settings."""
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):
"""Executes the YOLOv5 model prediction workflow, handling argument parsing and requirement checks."""
check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
run(**vars(opt))
if __name__ == "__main__":
opt = parse_opt()
main(opt)