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523 lines
24 KiB
Python
523 lines
24 KiB
Python
3 weeks ago
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# Ultralytics YOLOv5 🚀, AGPL-3.0 license
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"""
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Validate a trained YOLOv5 segment model on a segment dataset.
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Usage:
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$ bash data/scripts/get_coco.sh --val --segments # download COCO-segments val split (1G, 5000 images)
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$ python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate COCO-segments
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Usage - formats:
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$ python segment/val.py --weights yolov5s-seg.pt # PyTorch
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yolov5s-seg.torchscript # TorchScript
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yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn
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yolov5s-seg_openvino_label # OpenVINO
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yolov5s-seg.engine # TensorRT
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yolov5s-seg.mlmodel # CoreML (macOS-only)
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yolov5s-seg_saved_model # TensorFlow SavedModel
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yolov5s-seg.pb # TensorFlow GraphDef
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yolov5s-seg.tflite # TensorFlow Lite
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yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU
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yolov5s-seg_paddle_model # PaddlePaddle
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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 subprocess
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import sys
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from multiprocessing.pool import ThreadPool
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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[1] # 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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import torch.nn.functional as F
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from models.common import DetectMultiBackend
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from models.yolo import SegmentationModel
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from utils.callbacks import Callbacks
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from utils.general import (
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LOGGER,
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NUM_THREADS,
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TQDM_BAR_FORMAT,
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Profile,
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check_dataset,
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check_img_size,
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check_requirements,
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check_yaml,
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coco80_to_coco91_class,
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colorstr,
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increment_path,
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non_max_suppression,
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print_args,
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scale_boxes,
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xywh2xyxy,
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xyxy2xywh,
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)
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from utils.metrics import ConfusionMatrix, box_iou
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from utils.plots import output_to_target, plot_val_study
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from utils.segment.dataloaders import create_dataloader
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from utils.segment.general import mask_iou, process_mask, process_mask_native, scale_image
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from utils.segment.metrics import Metrics, ap_per_class_box_and_mask
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from utils.segment.plots import plot_images_and_masks
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from utils.torch_utils import de_parallel, select_device, smart_inference_mode
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def save_one_txt(predn, save_conf, shape, file):
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"""Saves detection results in txt format; includes class, xywh (normalized), optionally confidence if `save_conf` is
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True.
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"""
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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, pred_masks):
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"""
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Saves a JSON file with detection results including bounding boxes, category IDs, scores, and segmentation masks.
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Example JSON result: {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}.
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"""
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from pycocotools.mask import encode
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def single_encode(x):
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"""Encodes binary mask arrays into RLE (Run-Length Encoding) format for JSON serialization."""
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rle = encode(np.asarray(x[:, :, None], order="F", dtype="uint8"))[0]
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rle["counts"] = rle["counts"].decode("utf-8")
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return rle
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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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pred_masks = np.transpose(pred_masks, (2, 0, 1))
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with ThreadPool(NUM_THREADS) as pool:
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rles = pool.map(single_encode, pred_masks)
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for i, (p, b) in enumerate(zip(predn.tolist(), box.tolist())):
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jdict.append(
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{
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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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"segmentation": rles[i],
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}
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)
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def process_batch(detections, labels, iouv, pred_masks=None, gt_masks=None, overlap=False, masks=False):
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"""
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Return correct prediction matrix
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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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if masks:
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if overlap:
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nl = len(labels)
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index = torch.arange(nl, device=gt_masks.device).view(nl, 1, 1) + 1
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gt_masks = gt_masks.repeat(nl, 1, 1) # shape(1,640,640) -> (n,640,640)
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gt_masks = torch.where(gt_masks == index, 1.0, 0.0)
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if gt_masks.shape[1:] != pred_masks.shape[1:]:
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gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode="bilinear", align_corners=False)[0]
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gt_masks = gt_masks.gt_(0.5)
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iou = mask_iou(gt_masks.view(gt_masks.shape[0], -1), pred_masks.view(pred_masks.shape[0], -1))
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else: # boxes
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iou = box_iou(labels[:, 1:], detections[:, :4])
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correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)
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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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max_det=300, # maximum detections per image
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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-seg", # 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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overlap=False,
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mask_downsample_ratio=1,
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compute_loss=None,
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callbacks=Callbacks(),
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):
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"""Validates a YOLOv5 segmentation model on specified dataset, producing metrics, plots, and optional JSON
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output.
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"""
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if save_json:
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check_requirements("pycocotools>=2.0.6")
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process = process_mask_native # more accurate
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else:
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process = process_mask # faster
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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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nm = de_parallel(model).model[-1].nm # number of masks
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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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nm = de_parallel(model).model.model[-1].nm if isinstance(model, SegmentationModel) else 32 # number of masks
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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, (
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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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)
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model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup
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pad, rect = (0.0, False) if task == "speed" else (0.5, 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(
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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}: "),
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overlap_mask=overlap,
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mask_downsample_ratio=mask_downsample_ratio,
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)[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" * 10) % (
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"Class",
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"Images",
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"Instances",
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"Box(P",
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"R",
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"mAP50",
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"mAP50-95)",
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"Mask(P",
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"R",
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"mAP50",
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"mAP50-95)",
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)
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dt = Profile(device=device), Profile(device=device), Profile(device=device)
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metrics = Metrics()
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loss = torch.zeros(4, device=device)
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jdict, stats = [], []
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# callbacks.run('on_val_start')
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pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar
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for batch_i, (im, targets, paths, shapes, masks) 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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masks = masks.to(device)
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masks = masks.float()
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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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preds, protos, train_out = model(im) if compute_loss else (*model(im, augment=augment)[:2], None)
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# Loss
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if compute_loss:
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loss += compute_loss((train_out, protos), targets, masks)[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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preds = non_max_suppression(
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preds, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls, max_det=max_det, nm=nm
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)
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# Metrics
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plot_masks = [] # masks for plotting
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for si, (pred, proto) in enumerate(zip(preds, protos)):
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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]
|
||
|
|
correct_masks = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init
|
||
|
|
correct_bboxes = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init
|
||
|
|
seen += 1
|
||
|
|
|
||
|
|
if npr == 0:
|
||
|
|
if nl:
|
||
|
|
stats.append((correct_masks, correct_bboxes, *torch.zeros((2, 0), device=device), labels[:, 0]))
|
||
|
|
if plots:
|
||
|
|
confusion_matrix.process_batch(detections=None, labels=labels[:, 0])
|
||
|
|
continue
|
||
|
|
|
||
|
|
# Masks
|
||
|
|
midx = [si] if overlap else targets[:, 0] == si
|
||
|
|
gt_masks = masks[midx]
|
||
|
|
pred_masks = process(proto, pred[:, 6:], pred[:, :4], shape=im[si].shape[1:])
|
||
|
|
|
||
|
|
# Predictions
|
||
|
|
if single_cls:
|
||
|
|
pred[:, 5] = 0
|
||
|
|
predn = pred.clone()
|
||
|
|
scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred
|
||
|
|
|
||
|
|
# Evaluate
|
||
|
|
if nl:
|
||
|
|
tbox = xywh2xyxy(labels[:, 1:5]) # target boxes
|
||
|
|
scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels
|
||
|
|
labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels
|
||
|
|
correct_bboxes = process_batch(predn, labelsn, iouv)
|
||
|
|
correct_masks = process_batch(predn, labelsn, iouv, pred_masks, gt_masks, overlap=overlap, masks=True)
|
||
|
|
if plots:
|
||
|
|
confusion_matrix.process_batch(predn, labelsn)
|
||
|
|
stats.append((correct_masks, correct_bboxes, pred[:, 4], pred[:, 5], labels[:, 0])) # (conf, pcls, tcls)
|
||
|
|
|
||
|
|
pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)
|
||
|
|
if plots and batch_i < 3:
|
||
|
|
plot_masks.append(pred_masks[:15]) # filter top 15 to plot
|
||
|
|
|
||
|
|
# Save/log
|
||
|
|
if save_txt:
|
||
|
|
save_one_txt(predn, save_conf, shape, file=save_dir / "labels" / f"{path.stem}.txt")
|
||
|
|
if save_json:
|
||
|
|
pred_masks = scale_image(
|
||
|
|
im[si].shape[1:], pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape, shapes[si][1]
|
||
|
|
)
|
||
|
|
save_one_json(predn, jdict, path, class_map, pred_masks) # append to COCO-JSON dictionary
|
||
|
|
# callbacks.run('on_val_image_end', pred, predn, path, names, im[si])
|
||
|
|
|
||
|
|
# Plot images
|
||
|
|
if plots and batch_i < 3:
|
||
|
|
if len(plot_masks):
|
||
|
|
plot_masks = torch.cat(plot_masks, dim=0)
|
||
|
|
plot_images_and_masks(im, targets, masks, paths, save_dir / f"val_batch{batch_i}_labels.jpg", names)
|
||
|
|
plot_images_and_masks(
|
||
|
|
im,
|
||
|
|
output_to_target(preds, max_det=15),
|
||
|
|
plot_masks,
|
||
|
|
paths,
|
||
|
|
save_dir / f"val_batch{batch_i}_pred.jpg",
|
||
|
|
names,
|
||
|
|
) # pred
|
||
|
|
|
||
|
|
# callbacks.run('on_val_batch_end')
|
||
|
|
|
||
|
|
# Compute metrics
|
||
|
|
stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy
|
||
|
|
if len(stats) and stats[0].any():
|
||
|
|
results = ap_per_class_box_and_mask(*stats, plot=plots, save_dir=save_dir, names=names)
|
||
|
|
metrics.update(results)
|
||
|
|
nt = np.bincount(stats[4].astype(int), minlength=nc) # number of targets per class
|
||
|
|
|
||
|
|
# Print results
|
||
|
|
pf = "%22s" + "%11i" * 2 + "%11.3g" * 8 # print format
|
||
|
|
LOGGER.info(pf % ("all", seen, nt.sum(), *metrics.mean_results()))
|
||
|
|
if nt.sum() == 0:
|
||
|
|
LOGGER.warning(f"WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels")
|
||
|
|
|
||
|
|
# Print results per class
|
||
|
|
if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
|
||
|
|
for i, c in enumerate(metrics.ap_class_index):
|
||
|
|
LOGGER.info(pf % (names[c], seen, nt[c], *metrics.class_result(i)))
|
||
|
|
|
||
|
|
# Print speeds
|
||
|
|
t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image
|
||
|
|
if not training:
|
||
|
|
shape = (batch_size, 3, imgsz, imgsz)
|
||
|
|
LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}" % t)
|
||
|
|
|
||
|
|
# Plots
|
||
|
|
if plots:
|
||
|
|
confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
|
||
|
|
# callbacks.run('on_val_end')
|
||
|
|
|
||
|
|
mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask = metrics.mean_results()
|
||
|
|
|
||
|
|
# Save JSON
|
||
|
|
if save_json and len(jdict):
|
||
|
|
w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else "" # weights
|
||
|
|
anno_json = str(Path("../datasets/coco/annotations/instances_val2017.json")) # annotations
|
||
|
|
pred_json = str(save_dir / f"{w}_predictions.json") # predictions
|
||
|
|
LOGGER.info(f"\nEvaluating pycocotools mAP... saving {pred_json}...")
|
||
|
|
with open(pred_json, "w") as f:
|
||
|
|
json.dump(jdict, f)
|
||
|
|
|
||
|
|
try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
|
||
|
|
from pycocotools.coco import COCO
|
||
|
|
from pycocotools.cocoeval import COCOeval
|
||
|
|
|
||
|
|
anno = COCO(anno_json) # init annotations api
|
||
|
|
pred = anno.loadRes(pred_json) # init predictions api
|
||
|
|
results = []
|
||
|
|
for eval in COCOeval(anno, pred, "bbox"), COCOeval(anno, pred, "segm"):
|
||
|
|
if is_coco:
|
||
|
|
eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # img ID to evaluate
|
||
|
|
eval.evaluate()
|
||
|
|
eval.accumulate()
|
||
|
|
eval.summarize()
|
||
|
|
results.extend(eval.stats[:2]) # update results (mAP@0.5:0.95, mAP@0.5)
|
||
|
|
map_bbox, map50_bbox, map_mask, map50_mask = results
|
||
|
|
except Exception as e:
|
||
|
|
LOGGER.info(f"pycocotools unable to run: {e}")
|
||
|
|
|
||
|
|
# Return results
|
||
|
|
model.float() # for training
|
||
|
|
if not training:
|
||
|
|
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ""
|
||
|
|
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
|
||
|
|
final_metric = mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask
|
||
|
|
return (*final_metric, *(loss.cpu() / len(dataloader)).tolist()), metrics.get_maps(nc), t
|
||
|
|
|
||
|
|
|
||
|
|
def parse_opt():
|
||
|
|
"""Parses command line arguments for configuring YOLOv5 options like dataset path, weights, batch size, and
|
||
|
|
inference settings.
|
||
|
|
"""
|
||
|
|
parser = argparse.ArgumentParser()
|
||
|
|
parser.add_argument("--data", type=str, default=ROOT / "data/coco128-seg.yaml", help="dataset.yaml path")
|
||
|
|
parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-seg.pt", help="model path(s)")
|
||
|
|
parser.add_argument("--batch-size", type=int, default=32, help="batch size")
|
||
|
|
parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="inference size (pixels)")
|
||
|
|
parser.add_argument("--conf-thres", type=float, default=0.001, help="confidence threshold")
|
||
|
|
parser.add_argument("--iou-thres", type=float, default=0.6, help="NMS IoU threshold")
|
||
|
|
parser.add_argument("--max-det", type=int, default=300, help="maximum detections per image")
|
||
|
|
parser.add_argument("--task", default="val", help="train, val, test, speed or study")
|
||
|
|
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("--single-cls", action="store_true", help="treat as single-class dataset")
|
||
|
|
parser.add_argument("--augment", action="store_true", help="augmented inference")
|
||
|
|
parser.add_argument("--verbose", action="store_true", help="report mAP by class")
|
||
|
|
parser.add_argument("--save-txt", action="store_true", help="save results to *.txt")
|
||
|
|
parser.add_argument("--save-hybrid", action="store_true", help="save label+prediction hybrid results to *.txt")
|
||
|
|
parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels")
|
||
|
|
parser.add_argument("--save-json", action="store_true", help="save a COCO-JSON results file")
|
||
|
|
parser.add_argument("--project", default=ROOT / "runs/val-seg", help="save results 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()
|
||
|
|
opt.data = check_yaml(opt.data) # check YAML
|
||
|
|
# opt.save_json |= opt.data.endswith('coco.yaml')
|
||
|
|
opt.save_txt |= opt.save_hybrid
|
||
|
|
print_args(vars(opt))
|
||
|
|
return opt
|
||
|
|
|
||
|
|
|
||
|
|
def main(opt):
|
||
|
|
"""Executes YOLOv5 tasks including training, validation, testing, speed, and study with configurable options."""
|
||
|
|
check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
|
||
|
|
|
||
|
|
if opt.task in ("train", "val", "test"): # run normally
|
||
|
|
if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466
|
||
|
|
LOGGER.warning(f"WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results")
|
||
|
|
if opt.save_hybrid:
|
||
|
|
LOGGER.warning("WARNING ⚠️ --save-hybrid returns high mAP from hybrid labels, not from predictions alone")
|
||
|
|
run(**vars(opt))
|
||
|
|
|
||
|
|
else:
|
||
|
|
weights = opt.weights if isinstance(opt.weights, list) else [opt.weights]
|
||
|
|
opt.half = torch.cuda.is_available() and opt.device != "cpu" # FP16 for fastest results
|
||
|
|
if opt.task == "speed": # speed benchmarks
|
||
|
|
# python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt...
|
||
|
|
opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False
|
||
|
|
for opt.weights in weights:
|
||
|
|
run(**vars(opt), plots=False)
|
||
|
|
|
||
|
|
elif opt.task == "study": # speed vs mAP benchmarks
|
||
|
|
# python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt...
|
||
|
|
for opt.weights in weights:
|
||
|
|
f = f"study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt" # filename to save to
|
||
|
|
x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis
|
||
|
|
for opt.imgsz in x: # img-size
|
||
|
|
LOGGER.info(f"\nRunning {f} --imgsz {opt.imgsz}...")
|
||
|
|
r, _, t = run(**vars(opt), plots=False)
|
||
|
|
y.append(r + t) # results and times
|
||
|
|
np.savetxt(f, y, fmt="%10.4g") # save
|
||
|
|
subprocess.run(["zip", "-r", "study.zip", "study_*.txt"])
|
||
|
|
plot_val_study(x=x) # plot
|
||
|
|
else:
|
||
|
|
raise NotImplementedError(f'--task {opt.task} not in ("train", "val", "test", "speed", "study")')
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
opt = parse_opt()
|
||
|
|
main(opt)
|