car-detection-bayes/test.py

146 lines
5.6 KiB
Python

import argparse
from models import *
from utils.datasets import *
from utils.utils import *
from utils import torch_utils
def test(
cfg,
data_cfg,
weights,
batch_size=16,
img_size=416,
iou_thres=0.5,
conf_thres=0.3,
nms_thres=0.45
):
device = torch_utils.select_device()
# Configure run
data_cfg = parse_data_cfg(data_cfg)
nC = int(data_cfg['classes']) # number of classes (80 for COCO)
test_path = data_cfg['valid']
# Initiate model
model = Darknet(cfg, img_size)
# Load weights
if weights.endswith('.pt'): # pytorch format
model.load_state_dict(torch.load(weights, map_location='cpu')['model'])
else: # darknet format
load_darknet_weights(model, weights)
model.to(device).eval()
# Get dataloader
# dataloader = torch.utils.data.DataLoader(load_images_with_labels(test_path), batch_size=batch_size) # pytorch
dataloader = load_images_and_labels(test_path, batch_size=batch_size, img_size=img_size)
mean_mAP, mean_R, mean_P = 0.0, 0.0, 0.0
print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))
outputs, mAPs, mR, mP, TP, confidence, pred_class, target_class = [], [], [], [], [], [], [], []
AP_accum, AP_accum_count = np.zeros(nC), np.zeros(nC)
for batch_i, (imgs, targets) in enumerate(dataloader):
output = model(imgs.to(device))
output = non_max_suppression(output, conf_thres=conf_thres, nms_thres=nms_thres)
# Compute average precision for each sample
for sample_i, (labels, detections) in enumerate(zip(targets, output)):
correct = []
if detections is None:
# If there are no detections but there are labels mask as zero AP
if labels.size(0) != 0:
mAPs.append(0), mR.append(0), mP.append(0)
continue
# Get detections sorted by decreasing confidence scores
detections = detections.cpu().numpy()
detections = detections[np.argsort(-detections[:, 4])]
# If no labels add number of detections as incorrect
if labels.size(0) == 0:
# correct.extend([0 for _ in range(len(detections))])
mAPs.append(0), mR.append(0), mP.append(0)
continue
else:
target_cls = labels[:, 0]
# Extract target boxes as (x1, y1, x2, y2)
target_boxes = xywh2xyxy(labels[:, 1:5]) * 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] > iou_thres and obj_pred == labels[best_i, 0] and best_i not in detected:
correct.append(1)
detected.append(best_i)
else:
correct.append(0)
# Compute Average Precision (AP) per class
AP, AP_class, R, P = ap_per_class(tp=correct, conf=detections[:, 4], pred_cls=detections[:, 6],
target_cls=target_cls)
# Accumulate AP per class
AP_accum_count += np.bincount(AP_class, minlength=nC)
AP_accum += np.bincount(AP_class, minlength=nC, weights=AP)
# Compute mean AP across all classes in this image, and append to image list
mAPs.append(AP.mean())
mR.append(R.mean())
mP.append(P.mean())
# Means of all images
mean_mAP = np.mean(mAPs)
mean_R = np.mean(mR)
mean_P = np.mean(mP)
# Print image mAP and running mean mAP
print(('%11s%11s' + '%11.3g' * 3) % (len(mAPs), dataloader.nF, mean_P, mean_R, mean_mAP))
# Print mAP per class
print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP') + '\n\nmAP Per Class:')
classes = load_classes(data_cfg['names']) # Extracts class labels from file
for i, c in enumerate(classes):
print('%15s: %-.4f' % (c, AP_accum[i] / AP_accum_count[i]))
# Return mAP
return mean_mAP, mean_R, mean_P
if __name__ == '__main__':
parser = argparse.ArgumentParser(prog='test.py')
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-cfg', type=str, default='cfg/coco.data', help='path to data config file')
parser.add_argument('--weights', type=str, default='weights/yolov3.pt', help='path to weights 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=0.3, help='object confidence threshold')
parser.add_argument('--nms-thres', type=float, default=0.45, help='iou threshold for non-maximum suppression')
parser.add_argument('--img-size', type=int, default=416, help='size of each image dimension')
opt = parser.parse_args()
print(opt, end='\n\n')
with torch.no_grad():
mAP = test(
opt.cfg,
opt.data_cfg,
opt.weights,
opt.batch_size,
opt.img_size,
opt.iou_thres,
opt.conf_thres,
opt.nms_thres
)