398 lines
19 KiB
Python
398 lines
19 KiB
Python
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Validate a trained YOLOv5 detection model on a detection dataset
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Usage:
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$ python val.py --weights yolov5s.pt --data coco128.yaml --img 640
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Usage - formats:
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$ python val.py --weights yolov5s.pt # PyTorch
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yolov5s.torchscript # TorchScript
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yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
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yolov5s.xml # OpenVINO
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yolov5s.engine # TensorRT
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yolov5s.mlmodel # CoreML (macOS-only)
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yolov5s_saved_model # TensorFlow SavedModel
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yolov5s.pb # TensorFlow GraphDef
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yolov5s.tflite # TensorFlow Lite
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yolov5s_edgetpu.tflite # TensorFlow Edge TPU
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"""
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import argparse
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import json
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import os
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import sys
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from pathlib import Path
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import numpy as np
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import torch
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from tqdm import tqdm
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[0] # YOLOv5 root directory
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if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT)) # add ROOT to PATH
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ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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from app.yolov5.models.common import DetectMultiBackend
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from app.yolov5.utils.callbacks import Callbacks
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from app.yolov5.utils.dataloaders import create_dataloader
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from app.yolov5.utils.general import (LOGGER, Profile, check_dataset, check_img_size, check_requirements, check_yaml,
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coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, print_args,
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scale_coords, xywh2xyxy, xyxy2xywh)
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from app.yolov5.utils.metrics import ConfusionMatrix, ap_per_class, box_iou
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from app.yolov5.utils.plots import output_to_target, plot_images, plot_val_study
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from app.yolov5.utils.torch_utils import select_device, smart_inference_mode
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def save_one_txt(predn, save_conf, shape, file):
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# Save one txt result
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gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh
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for *xyxy, conf, cls in predn.tolist():
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xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
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line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
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with open(file, 'a') as f:
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f.write(('%g ' * len(line)).rstrip() % line + '\n')
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def save_one_json(predn, jdict, path, class_map):
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# Save one JSON result {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
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image_id = int(path.stem) if path.stem.isnumeric() else path.stem
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box = xyxy2xywh(predn[:, :4]) # xywh
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box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
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for p, b in zip(predn.tolist(), box.tolist()):
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jdict.append({
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'image_id': image_id,
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'category_id': class_map[int(p[5])],
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'bbox': [round(x, 3) for x in b],
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'score': round(p[4], 5)})
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def process_batch(detections, labels, iouv):
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"""
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Return correct predictions matrix. Both sets of boxes are in (x1, y1, x2, y2) format.
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Arguments:
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detections (Array[N, 6]), x1, y1, x2, y2, conf, class
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labels (Array[M, 5]), class, x1, y1, x2, y2
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Returns:
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correct (Array[N, 10]), for 10 IoU levels
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"""
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correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)
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iou = box_iou(labels[:, 1:], detections[:, :4])
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correct_class = labels[:, 0:1] == detections[:, 5]
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for i in range(len(iouv)):
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x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match
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if x[0].shape[0]:
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matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou]
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if x[0].shape[0] > 1:
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matches = matches[matches[:, 2].argsort()[::-1]]
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matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
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# matches = matches[matches[:, 2].argsort()[::-1]]
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matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
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correct[matches[:, 1].astype(int), i] = True
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return torch.tensor(correct, dtype=torch.bool, device=iouv.device)
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@smart_inference_mode()
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def run(
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data,
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weights=None, # model.pt path(s)
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batch_size=32, # batch size
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imgsz=640, # inference size (pixels)
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conf_thres=0.001, # confidence threshold
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iou_thres=0.6, # NMS IoU threshold
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task='val', # train, val, test, speed or study
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device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
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workers=8, # max dataloader workers (per RANK in DDP mode)
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single_cls=False, # treat as single-class dataset
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augment=False, # augmented inference
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verbose=False, # verbose output
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save_txt=False, # save results to *.txt
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save_hybrid=False, # save label+prediction hybrid results to *.txt
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save_conf=False, # save confidences in --save-txt labels
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save_json=False, # save a COCO-JSON results file
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project=ROOT / 'runs/val', # save to project/name
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name='exp', # save to project/name
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exist_ok=False, # existing project/name ok, do not increment
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half=True, # use FP16 half-precision inference
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dnn=False, # use OpenCV DNN for ONNX inference
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model=None,
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dataloader=None,
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save_dir=Path(''),
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plots=True,
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callbacks=Callbacks(),
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compute_loss=None,
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):
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# Initialize/load model and set device
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training = model is not None
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if training: # called by train.py
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device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model
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half &= device.type != 'cpu' # half precision only supported on CUDA
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model.half() if half else model.float()
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else: # called directly
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device = select_device(device, batch_size=batch_size)
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# Directories
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save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
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(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
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# Load model
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model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
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stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
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imgsz = check_img_size(imgsz, s=stride) # check image size
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half = model.fp16 # FP16 supported on limited backends with CUDA
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if engine:
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batch_size = model.batch_size
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else:
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device = model.device
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if not (pt or jit):
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batch_size = 1 # export.py models default to batch-size 1
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LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
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# Data
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data = check_dataset(data) # check
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# Configure
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model.eval()
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cuda = device.type != 'cpu'
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is_coco = isinstance(data.get('val'), str) and data['val'].endswith(f'coco{os.sep}val2017.txt') # COCO dataset
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nc = 1 if single_cls else int(data['nc']) # number of classes
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iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95
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niou = iouv.numel()
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# Dataloader
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if not training:
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if pt and not single_cls: # check --weights are trained on --data
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ncm = model.model.nc
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assert ncm == nc, f'{weights} ({ncm} classes) trained on different --data than what you passed ({nc} ' \
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f'classes). Pass correct combination of --weights and --data that are trained together.'
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model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup
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pad = 0.0 if task in ('speed', 'benchmark') else 0.5
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rect = False if task == 'benchmark' else pt # square inference for benchmarks
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task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images
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dataloader = create_dataloader(data[task],
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imgsz,
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batch_size,
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stride,
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single_cls,
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pad=pad,
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rect=rect,
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workers=workers,
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prefix=colorstr(f'{task}: '))[0]
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seen = 0
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confusion_matrix = ConfusionMatrix(nc=nc)
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names = model.names if hasattr(model, 'names') else model.module.names # get class names
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if isinstance(names, (list, tuple)): # old format
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names = dict(enumerate(names))
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class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
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s = ('%22s' + '%11s' * 6) % ('Class', 'Images', 'Instances', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
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dt, p, r, f1, mp, mr, map50, map = (Profile(), Profile(), Profile()), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
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loss = torch.zeros(3, device=device)
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jdict, stats, ap, ap_class = [], [], [], []
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callbacks.run('on_val_start')
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pbar = tqdm(dataloader, desc=s, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar
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for batch_i, (im, targets, paths, shapes) in enumerate(pbar):
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callbacks.run('on_val_batch_start')
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with dt[0]:
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if cuda:
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im = im.to(device, non_blocking=True)
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targets = targets.to(device)
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im = im.half() if half else im.float() # uint8 to fp16/32
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im /= 255 # 0 - 255 to 0.0 - 1.0
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nb, _, height, width = im.shape # batch size, channels, height, width
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# Inference
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with dt[1]:
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out, train_out = model(im) if compute_loss else (model(im, augment=augment), None)
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# Loss
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if compute_loss:
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loss += compute_loss(train_out, targets)[1] # box, obj, cls
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# NMS
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targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels
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lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling
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with dt[2]:
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out = non_max_suppression(out, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls)
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# Metrics
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for si, pred in enumerate(out):
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labels = targets[targets[:, 0] == si, 1:]
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nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions
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path, shape = Path(paths[si]), shapes[si][0]
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correct = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init
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seen += 1
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if npr == 0:
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if nl:
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stats.append((correct, *torch.zeros((2, 0), device=device), labels[:, 0]))
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if plots:
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confusion_matrix.process_batch(detections=None, labels=labels[:, 0])
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continue
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# Predictions
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if single_cls:
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pred[:, 5] = 0
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predn = pred.clone()
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scale_coords(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred
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# Evaluate
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if nl:
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tbox = xywh2xyxy(labels[:, 1:5]) # target boxes
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scale_coords(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels
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labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels
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correct = process_batch(predn, labelsn, iouv)
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if plots:
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confusion_matrix.process_batch(predn, labelsn)
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stats.append((correct, pred[:, 4], pred[:, 5], labels[:, 0])) # (correct, conf, pcls, tcls)
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# Save/log
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if save_txt:
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save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
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if save_json:
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save_one_json(predn, jdict, path, class_map) # append to COCO-JSON dictionary
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callbacks.run('on_val_image_end', pred, predn, path, names, im[si])
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# Plot images
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if plots and batch_i < 3:
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plot_images(im, targets, paths, save_dir / f'val_batch{batch_i}_labels.jpg', names) # labels
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plot_images(im, output_to_target(out), paths, save_dir / f'val_batch{batch_i}_pred.jpg', names) # pred
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callbacks.run('on_val_batch_end')
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# Compute metrics
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stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy
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if len(stats) and stats[0].any():
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tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
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ap50, ap = ap[:, 0], ap.mean(1) # AP@0.5, AP@0.5:0.95
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mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
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nt = np.bincount(stats[3].astype(int), minlength=nc) # number of targets per class
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# Print results
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pf = '%22s' + '%11i' * 2 + '%11.3g' * 4 # print format
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LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
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if nt.sum() == 0:
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LOGGER.warning(f'WARNING: no labels found in {task} set, can not compute metrics without labels ⚠️')
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# Print results per class
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if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
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for i, c in enumerate(ap_class):
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LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
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# Print speeds
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t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image
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if not training:
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shape = (batch_size, 3, imgsz, imgsz)
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LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t)
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# Plots
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if plots:
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confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
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callbacks.run('on_val_end')
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# Save JSON
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if save_json and len(jdict):
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w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights
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anno_json = str(Path(data.get('path', '../coco')) / 'annotations/instances_val2017.json') # annotations json
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pred_json = str(save_dir / f"{w}_predictions.json") # predictions json
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LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...')
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with open(pred_json, 'w') as f:
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json.dump(jdict, f)
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try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
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check_requirements(['pycocotools'])
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from pycocotools.coco import COCO
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from pycocotools.cocoeval import COCOeval
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anno = COCO(anno_json) # init annotations api
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pred = anno.loadRes(pred_json) # init predictions api
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eval = COCOeval(anno, pred, 'bbox')
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if is_coco:
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eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # image IDs to evaluate
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eval.evaluate()
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eval.accumulate()
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eval.summarize()
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map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5)
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except Exception as e:
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LOGGER.info(f'pycocotools unable to run: {e}')
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# Return results
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model.float() # for training
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if not training:
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s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
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LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
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maps = np.zeros(nc) + map
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for i, c in enumerate(ap_class):
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maps[c] = ap[i]
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return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t
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def parse_opt():
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parser = argparse.ArgumentParser()
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parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
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parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model.pt path(s)')
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parser.add_argument('--batch-size', type=int, default=32, help='batch size')
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parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
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parser.add_argument('--conf-thres', type=float, default=0.001, help='confidence threshold')
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parser.add_argument('--iou-thres', type=float, default=0.6, help='NMS IoU threshold')
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parser.add_argument('--task', default='val', help='train, val, test, speed or study')
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parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
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parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')
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parser.add_argument('--augment', action='store_true', help='augmented inference')
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parser.add_argument('--verbose', action='store_true', help='report mAP by class')
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parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
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parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')
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parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
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parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file')
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parser.add_argument('--project', default=ROOT / 'runs/val', help='save to project/name')
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parser.add_argument('--name', default='exp', help='save to project/name')
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parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
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parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
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parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
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opt = parser.parse_args()
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opt.data = check_yaml(opt.data) # check YAML
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opt.save_json |= opt.data.endswith('coco.yaml')
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opt.save_txt |= opt.save_hybrid
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print_args(vars(opt))
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return opt
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def main(opt):
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check_requirements(requirements=ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
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if opt.task in ('train', 'val', 'test'): # run normally
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if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466
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LOGGER.info(f'WARNING: confidence threshold {opt.conf_thres} > 0.001 produces invalid results ⚠️')
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if opt.save_hybrid:
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LOGGER.info('WARNING: --save-hybrid will return high mAP from hybrid labels, not from predictions alone ⚠️')
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run(**vars(opt))
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else:
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weights = opt.weights if isinstance(opt.weights, list) else [opt.weights]
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opt.half = True # FP16 for fastest results
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if opt.task == 'speed': # speed benchmarks
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# python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt...
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opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False
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for opt.weights in weights:
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run(**vars(opt), plots=False)
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elif opt.task == 'study': # speed vs mAP benchmarks
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# python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt...
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for opt.weights in weights:
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f = f'study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt' # filename to save to
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x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis
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for opt.imgsz in x: # img-size
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LOGGER.info(f'\nRunning {f} --imgsz {opt.imgsz}...')
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r, _, t = run(**vars(opt), plots=False)
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y.append(r + t) # results and times
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np.savetxt(f, y, fmt='%10.4g') # save
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os.system('zip -r study.zip study_*.txt')
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plot_val_study(x=x) # plot
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if __name__ == "__main__":
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opt = parse_opt()
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main(opt)
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