From c13dba86b2dbe581353b72602d7fa6e40991964c Mon Sep 17 00:00:00 2001 From: natanielruiz <nataniel777@hotmail.com> Date: 星期三, 27 九月 2017 04:11:23 +0800 Subject: [PATCH] next --- code/test_preangles.py | 37 ++++++++++++++++++++++++------------- 1 files changed, 24 insertions(+), 13 deletions(-) diff --git a/code/test_preangles.py b/code/test_preangles.py index 1203578..b742195 100644 --- a/code/test_preangles.py +++ b/code/test_preangles.py @@ -67,10 +67,15 @@ if args.dataset == 'AFLW2000': pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list, transformations) + elif args.dataset == 'AFLW2000_ds': + pose_dataset = datasets.AFLW2000_ds(args.data_dir, args.filename_list, + transformations) elif args.dataset == 'BIWI': pose_dataset = datasets.BIWI(args.data_dir, args.filename_list, transformations) elif args.dataset == 'AFLW': pose_dataset = datasets.AFLW(args.data_dir, args.filename_list, transformations) + elif args.dataset == 'Pose_300W_LP': + pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations) elif args.dataset == 'AFW': pose_dataset = datasets.AFW(args.data_dir, args.filename_list, transformations) else: @@ -97,12 +102,12 @@ l1loss = torch.nn.L1Loss(size_average=False) - for i, (images, labels, name) in enumerate(test_loader): + for i, (images, labels, cont_labels, name) in enumerate(test_loader): images = Variable(images).cuda(gpu) - total += labels.size(0) - label_yaw = labels[:,0].float() - label_pitch = labels[:,1].float() - label_roll = labels[:,2].float() + total += cont_labels.size(0) + label_yaw = cont_labels[:,0].float() + label_pitch = cont_labels[:,1].float() + label_roll = cont_labels[:,2].float() yaw, pitch, roll, angles = model(images) @@ -116,21 +121,27 @@ pitch_predicted = utils.softmax_temperature(pitch.data, 1) roll_predicted = utils.softmax_temperature(roll.data, 1) - yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1).cpu() - pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1).cpu() - roll_predicted = torch.sum(roll_predicted * idx_tensor, 1).cpu() + yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1).cpu() * 3 - 99 + pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1).cpu() * 3 - 99 + roll_predicted = torch.sum(roll_predicted * idx_tensor, 1).cpu() * 3 - 99 # Mean absolute error - yaw_error += torch.sum(torch.abs(yaw_predicted - label_yaw) * 3) - pitch_error += torch.sum(torch.abs(pitch_predicted - label_pitch) * 3) - roll_error += torch.sum(torch.abs(roll_predicted - label_roll) * 3) + yaw_error += torch.sum(torch.abs(yaw_predicted - label_yaw)) + pitch_error += torch.sum(torch.abs(pitch_predicted - label_pitch)) + roll_error += torch.sum(torch.abs(roll_predicted - label_roll)) # Save images with pose cube. # TODO: fix for larger batch size if args.save_viz: name = name[0] - cv2_img = cv2.imread(os.path.join(args.data_dir, name + '.jpg')) - utils.plot_pose_cube(cv2_img, yaw_predicted[0] * 3 - 99, pitch_predicted[0] * 3 - 99, roll_predicted[0] * 3 - 99) + if args.dataset == 'BIWI': + cv2_img = cv2.imread(os.path.join(args.data_dir, name + '_rgb.png')) + else: + cv2_img = cv2.imread(os.path.join(args.data_dir, name + '.jpg')) + if args.batch_size == 1: + error_string = 'y %.2f, p %.2f, r %.2f' % (torch.sum(torch.abs(yaw_predicted - label_yaw)), torch.sum(torch.abs(pitch_predicted - label_pitch)), torch.sum(torch.abs(roll_predicted - label_roll))) + cv2.putText(cv2_img, error_string, (30, cv2_img.shape[0]- 30), fontFace=1, fontScale=1, color=(0,0,255), thickness=1) + utils.plot_pose_cube(cv2_img, yaw_predicted[0], pitch_predicted[0], roll_predicted[0]) cv2.imwrite(os.path.join('output/images', name + '.jpg'), cv2_img) print('Test error in degrees of the model on the ' + str(total) + -- Gitblit v1.8.0