rtest.py

import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import numpy as np
from models import *
from utils.datasets import *
from utils.utils import *
from tqdm import tnrange, tqdm_notebook

def test(cfg, data_cfg, weights=None, batch_size=16, img_size=416, iou_thres=0.5, conf_thres=0.001, nms_thres=0.5, model=None):
    if model is None:
        device = torch_utils.select_device()

        # Initialize model
        model = Darknet(cfg, img_size).to(device)

        # Load weights
        if weights.endswith('.pt'):  # pytorch format
            model.load_state_dict(torch.load(weights, map_location=device)['model'])
        else:  # darknet format
            _ = load_darknet_weights(model, weights)

        if torch.cuda.device_count() > 1:
            model = nn.DataParallel(model)
    else:
        device = next(model.parameters()).device  # get model device

    # Configure run
    data_cfg = parse_data_cfg(data_cfg)
    nc = int(data_cfg['classes'])  # number of classes
    test_path = data_cfg['valid']  # path to test images
    names = load_classes(data_cfg['names'])  # class names

    # Dataloader
    dataset = LoadDataset(test_path, img_size=img_size)
    dataloader = DataLoader(dataset,
                            batch_size=batch_size,
                            num_workers=4,
                            pin_memory=True,
                            collate_fn=dataset.collate_fn)

    seen = 0
    model.eval()
    #print(('%20s' + '%10s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP', 'F1'))
    loss, p, r, f1, mp, mr, map, mf1 = 0., 0., 0., 0., 0., 0., 0., 0.
    jdict, stats, ap, ap_class = [], [], [], []
    for batch_i, (imgs, targets, paths, shapes) in enumerate(tqdm_notebook(dataloader)):
        targets = targets.to(device)
        imgs = imgs.to(device)

        # Plot images with bounding boxes
        if batch_i == 0 and not os.path.exists('test_batch0.jpg'):
            plot_images(imgs=imgs, targets=targets, fname='test_batch0.jpg')

        # Run model
        inf_out, train_out = model(imgs)  # inference and training outputs

        # Build targets
        target_list = build_targets(model, targets)

        # Compute loss
        loss_i, _ = compute_loss(train_out, target_list)
        loss += loss_i.item()

        # Run NMS
        output = non_max_suppression(inf_out, conf_thres=conf_thres, nms_thres=nms_thres)

        # Statistics per image
        for si, pred in enumerate(output):
            labels = targets[targets[:, 0] == si, 1:]
            nl = len(labels)
            tcls = labels[:, 0].tolist() if nl else []  # target class
            seen += 1

            if pred is None:
                if nl:
                    stats.append(([], torch.Tensor(), torch.Tensor(), tcls))
                continue

            # Assign all predictions as incorrect
            correct = [0] * len(pred)
            if nl:
                detected = []
                tbox = xywh2xyxy(labels[:, 1:5]) * img_size  # target boxes

                # Search for correct predictions
                for i, (*pbox, pconf, pcls_conf, pcls) in enumerate(pred):

                    # Break if all targets already located in image
                    if len(detected) == nl:
                        break

                    # Continue if predicted class not among image classes
                    if pcls.item() not in tcls:
                        continue

                    # Best iou, index between pred and targets
                    iou, bi = bbox_iou(pbox, tbox).max(0)

                    # If iou > threshold and class is correct mark as correct
                    if iou > iou_thres and bi not in detected:
                        correct[i] = 1
                        detected.append(bi)

            # Append statistics (correct, conf, pcls, tcls)
            stats.append((correct, pred[:, 4].cpu(), pred[:, 6].cpu(), tcls))

    # Compute statistics
    stats_np = [np.concatenate(x, 0) for x in list(zip(*stats))]
    nt = np.bincount(stats_np[3].astype(np.int64), minlength=nc)  # number of targets per class
    if len(stats_np):
        p, r, ap, f1, ap_class = ap_per_class(*stats_np)
        mp, mr, map, mf1 = p.mean(), r.mean(), ap.mean(), f1.mean()

    # Print results
    #pf = '%20s' + '%10.3g' * 6  # print format
    #print(pf % ('all', seen, nt.sum(), mp, mr, map, mf1), end='\n\n')

    # Print results per class
    #if nc > 1 and len(stats_np):
        #for i, c in enumerate(ap_class):
            #print(pf % (names[c], seen, nt[c], p[i], r[i], ap[i], f1[i]))

    # Return results
    return mp, mr, map, mf1, loss / len(dataloader)