car-detection-bayes/test.py

import argparse

from models import *
from utils.datasets import *
from utils.utils import *

parser = argparse.ArgumentParser()
parser.add_argument('-epochs', type=int, default=200, help='number of epochs')
parser.add_argument('-batch_size', type=int, default=32, help='size of each image batch')
parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='path to model config file')
parser.add_argument('-data_config_path', type=str, default='cfg/coco.data', help='path to data config file')
parser.add_argument('-weights_path', type=str, default='checkpoints/yolov3.weights', help='path to weights file')
parser.add_argument('-class_path', type=str, default='data/coco.names', help='path to class label file')
parser.add_argument('-iou_thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
parser.add_argument('-conf_thres', type=float, default=0.5, help='object confidence threshold')
parser.add_argument('-nms_thres', type=float, default=0.45, help='iou threshold for non-maximum suppression')
parser.add_argument('-n_cpu', type=int, default=0, help='number of cpu threads to use during batch generation')
parser.add_argument('-img_size', type=int, default=416, help='size of each image dimension')
parser.add_argument('-use_cuda', type=bool, default=True, help='whether to use cuda if available')
opt = parser.parse_args()
print(opt)

cuda = torch.cuda.is_available() and opt.use_cuda
device = torch.device('cuda:0' if cuda else 'cpu')

# Get data configuration
data_config = parse_data_config(opt.data_config_path)
test_path = data_config['valid']
num_classes = int(data_config['classes'])

# Initiate model
model = Darknet(opt.cfg, opt.img_size)

# Load weights
weights_path = 'checkpoints/yolov3.weights'
if weights_path.endswith('.weights'):  # darknet format
    load_weights(model, weights_path)
elif weights_path.endswith('.pt'):  # pytorch format
    checkpoint = torch.load(weights_path, map_location='cpu')
    model.load_state_dict(checkpoint['model'])
    del checkpoint

model.to(device).eval()

# Get dataloader
# dataset = ListDataset(test_path)
# dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batch_size, shuffle=False, num_workers=opt.n_cpu)
dataloader = ListDataset(test_path, batch_size=opt.batch_size, img_size=opt.img_size)

Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor

n_gt = 0
correct = 0

print('Compute mAP...')

outputs = []
targets = None
APs = []
for batch_i, (imgs, targets) in enumerate(dataloader):
    imgs = imgs.to(device)

    with torch.no_grad():
        output = model(imgs)
        output = non_max_suppression(output, conf_thres=opt.conf_thres, nms_thres=opt.nms_thres)

    # Compute average precision for each sample
    for sample_i in range(len(targets)):
        correct = []

        # Get labels for sample where width is not zero (dummies)
        annotations = targets[sample_i]
        # Extract detections
        detections = output[sample_i]

        if detections is None:
            # If there are no detections but there are annotations mask as zero AP
            if annotations.size(0) != 0:
                APs.append(0)
            continue

        # Get detections sorted by decreasing confidence scores
        detections = detections[np.argsort(-detections[:, 4])]

        # If no annotations add number of detections as incorrect
        if annotations.size(0) == 0:
            correct.extend([0 for _ in range(len(detections))])
        else:
            # Extract target boxes as (x1, y1, x2, y2)
            # target_boxes = torch.FloatTensor(annotations[:, 1:].shape)
            # target_boxes[:, 0] = (annotations[:, 1] - annotations[:, 3] / 2)
            # target_boxes[:, 1] = (annotations[:, 2] - annotations[:, 4] / 2)
            # target_boxes[:, 2] = (annotations[:, 1] + annotations[:, 3] / 2)
            # target_boxes[:, 3] = (annotations[:, 2] + annotations[:, 4] / 2)
            target_boxes = xywh2xyxy(annotations[:,1:5])
            target_boxes *= opt.img_size

            detected = []
            for *pred_bbox, conf, obj_conf, obj_pred in detections:

                pred_bbox = torch.FloatTensor(pred_bbox).view(1, -1)
                # Compute iou with target boxes
                iou = bbox_iou(pred_bbox, target_boxes)
                # Extract index of largest overlap
                best_i = np.argmax(iou)
                # If overlap exceeds threshold and classification is correct mark as correct
                if iou[best_i] > opt.iou_thres and obj_pred == annotations[best_i, 0] and best_i not in detected:
                    correct.append(1)
                    detected.append(best_i)
                else:
                    correct.append(0)

        # Extract true and false positives
        true_positives = np.array(correct)
        false_positives = 1 - true_positives

        # Compute cumulative false positives and true positives
        false_positives = np.cumsum(false_positives)
        true_positives = np.cumsum(true_positives)

        # Compute recall and precision at all ranks
        recall = true_positives / annotations.size(0) if annotations.size(0) else true_positives
        precision = true_positives / np.maximum(true_positives + false_positives, np.finfo(np.float64).eps)

        # Compute average precision
        AP = compute_ap(recall, precision)
        APs.append(AP)

        print("+ Sample [%d/%d] AP: %.4f (%.4f)" % (len(APs), len(dataloader) * opt.batch_size, AP, np.mean(APs)))

print("Mean Average Precision: %.4f" % np.mean(APs))