img_file_path = sys.argv[1] # the index should be 1, 0 is the 'eval.py'
img = Image.open(img_file_path)
img_norm = (img - IMG_MEAN) / IMG_STD
img_np = np.asarray([img_norm], dtype="float32")
img_tensor = torch.from_numpy(img_np)
prior_bboxes = generate_prior_bboxes(prior_layer_cfg = prior_layer_cfg)
if WILL_TEST:
if USE_GPU:
test_net_state = torch.load(path_to_trained_model)
else:
test_net_state = torch.load(path_to_trained_model, map_location='cpu')
test_net = SSD(num_classes=3)
test_net.load_state_dict(test_net_state)
test_net.eval()
test_image_permuted = img_tensor.permute(0, 3, 1, 2)
test_image_permuted = Variable(test_image_permuted.float())
test_conf_preds, test_loc_preds = test_net.forward(test_image_permuted)
test_bbox_priors = prior_bboxes.unsqueeze(0)
test_bbox_preds = loc2bbox(test_loc_preds.cpu(), test_bbox_priors.cpu(), center_var=0.1, size_var=0.2)
sel_bbox_preds = nms_bbox(test_bbox_preds.squeeze().detach(), test_conf_preds.squeeze().detach().cpu(), overlap_threshold=0.5, prob_threshold=0.5)
rects = []
classes = []
for key in sel_bbox_preds.keys():
for value in sel_bbox_preds[key]:
classes.append(key)
test_data_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=16,
shuffle=False,
num_workers=0)
print('test items:', len(test_dataset))
file_name = 'SSD'
test_net_state = torch.load(os.path.join('.', file_name + '.pth'))
net = SSD(3)
if use_gpu:
net = net.cuda()
net.load_state_dict(test_net_state)
itr = 0
net.eval()
for test_batch_idx, (loc_targets, conf_targets,
imgs) in enumerate(test_data_loader):
itr += 1
imgs = imgs.permute(0, 3, 1, 2).contiguous()
if use_gpu:
imgs = imgs.cuda()
imgs = Variable(imgs)
conf, loc = net.forward(imgs)
conf = conf[0, ...]
loc = loc[0, ...].cpu()
prior = test_dataset.get_prior_bbox()
prior = torch.unsqueeze(prior, 0)
# prior = prior.cuda()
real_bounding_box = loc2bbox(loc, prior, center_var=0.1, size_var=0.2)
def test_net(test_dataset, class_labels, results_path):
if torch.cuda.is_available():
torch.set_default_tensor_type('torch.cuda.FloatTensor')
# Load the save model and deploy
test_net = SSD(len(class_labels))
test_net_state = torch.load(os.path.join(results_path))
test_net.load_state_dict(test_net_state)
test_net.cuda()
test_net.eval()
# accuracy
count_matched = 0
count_gt = 0
for test_item_idx in range(0, len(test_dataset)):
# test_item_idx = random.choice(range(0, len(test_dataset)))
test_image_tensor, test_label_tensor, test_bbox_tensor, prior_bbox = test_dataset[
test_item_idx]
# run Forward
with torch.no_grad():
pred_scores_tensor, pred_bbox_tensor = test_net.forward(
test_image_tensor.unsqueeze(0).cuda()) # N C H W
# scores -> Prob # because I deleted F.softmax~ at the ssd_net for net.eval
pred_scores_tensor = F.softmax(pred_scores_tensor, dim=2)
# bbox loc -> bbox (center)
pred_bbox_tensor = loc2bbox(pred_bbox_tensor, prior_bbox.unsqueeze(0))
# NMS : return tensor dictionary (bbo
pred_picked = nms_bbox(
pred_bbox_tensor[0],
pred_scores_tensor[0]) # not tensor, corner form
# Show the result
test_image = test_image_tensor.cpu().numpy().astype(
np.float32).transpose().copy() # H, W, C
test_image = ((test_image + 1) / 2)
gt_label = test_label_tensor.cpu().numpy().astype(np.uint8).copy()
gt_bbox_tensor = torch.cat([
test_bbox_tensor[..., :2] - test_bbox_tensor[..., 2:] / 2,
test_bbox_tensor[..., :2] + test_bbox_tensor[..., 2:] / 2
],
dim=-1)
gt_bbox = gt_bbox_tensor.detach().cpu().numpy().astype(
np.float32).reshape((-1, 4)).copy() * 300
gt_idx = gt_label > 0
# Calculate accuracy
pred_scores = pred_scores_tensor.detach().cpu().numpy().astype(
np.float32).copy()
pred_label = pred_scores[0].argmax(axis=1)
n_matched = 0
for gt, pr in zip(gt_label, pred_label):
if gt > 0 and gt == pr:
n_matched += 1
acc_per_image = 100 * n_matched / gt_idx.sum()
count_matched += n_matched
count_gt += gt_idx.sum()
# Show the results
gt_bbox = gt_bbox[gt_idx]
gt_label = gt_label[gt_idx]
if False:
for idx in range(gt_bbox.shape[0]):
cv2.rectangle(test_image, (gt_bbox[idx][0], gt_bbox[idx][1]),
(gt_bbox[idx][2], gt_bbox[idx][3]), (255, 0, 0),
1)
cv2.putText(test_image, str(gt_label[idx]),
(gt_bbox[idx][0], gt_bbox[idx][1]),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (200, 0, 0), 1,
cv2.LINE_AA)
#--------------------
# cv2.rectangle(test_image, (pred_bbox[idx][0], pred_bbox[idx][1]), (pred_bbox[idx][2], pred_bbox[idx][3]),
# (0, 255, 0), 1)
#-----------------------
for cls_dict in pred_picked:
for p_score, p_bbox in zip(cls_dict['picked_scores'],
cls_dict['picked_bboxes']):
p_lbl = '%d | %.2f' % (cls_dict['class'], p_score)
p_bbox = p_bbox * 300
print(p_bbox, p_lbl)
cv2.rectangle(test_image, (p_bbox[0], p_bbox[1]),
(p_bbox[2], p_bbox[3]), (0, 0, 255), 2)
cv2.putText(test_image, p_lbl, (p_bbox[0], p_bbox[1]),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1,
cv2.LINE_AA)
plt.imshow(test_image)
plt.suptitle(class_labels)
plt.title('Temp Accuracy: {} %'.format(acc_per_image))
plt.show()
acc = 100 * count_matched / count_gt
print('Classification acc: ', '%')