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| | | from torch.utils.data import DataLoader |
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| | | from torchvision import transforms |
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| | | import torch.backends.cudnn as cudnn |
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| | | import torchvision |
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| | | import torch.nn.functional as F |
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| | | import cv2 |
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| | | import matplotlib.pyplot as plt |
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| | | import os |
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| | | import argparse |
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| | | from datasets import AFLW2000 |
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| | | import datasets |
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| | | import hopenet |
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| | | import utils |
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| | | default='', type=str) |
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| | | parser.add_argument('--batch_size', dest='batch_size', help='Batch size.', |
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| | | default=1, type=int) |
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| | | parser.add_argument('--save_viz', dest='save_viz', help='Save images with pose cube.', |
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| | | default=False, type=bool) |
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| | | args = parser.parse_args() |
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| | | args = parse_args() |
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| | | cudnn.enabled = True |
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| | | batch_size = 1 |
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| | | gpu = args.gpu_id |
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| | | snapshot_path = os.path.join('output/snapshots', args.snapshot + '.pkl') |
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| | | |
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| | | model = hopenet.Simple_CNN() |
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| | | # ResNet101 with 3 outputs. |
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| | | # model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 23, 3], 66) |
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| | | # ResNet50 |
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| | | model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66) |
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| | | # ResNet18 |
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| | | # model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66) |
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| | | |
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| | | print 'Loading snapshot.' |
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| | | # Load snapshot |
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| | | |
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| | | print 'Loading data.' |
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| | | |
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| | | transformations = transforms.Compose([transforms.Scale(302),transforms.CenterCrop(302),transforms.ToTensor()]) |
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| | | transformations = transforms.Compose([transforms.Scale(224), |
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| | | transforms.RandomCrop(224), transforms.ToTensor()]) |
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| | | |
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| | | pose_dataset = AFLW2000(args.data_dir, args.filename_list, |
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| | | pose_dataset = datasets.AFLW2000_binned(args.data_dir, args.filename_list, |
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| | | transformations) |
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| | | test_loader = torch.utils.data.DataLoader(dataset=pose_dataset, |
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| | | batch_size=batch_size, |
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| | | batch_size=args.batch_size, |
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| | | num_workers=2) |
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| | | model.cuda(gpu) |
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| | | |
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| | | # Test the Model |
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| | | model.eval() # Change model to 'eval' mode (BN uses moving mean/var). |
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| | | error = .0 |
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| | | total = 0 |
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| | | for i, (images, labels, path) in enumerate(test_loader): |
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| | | n_margins = 20 |
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| | | yaw_correct = np.zeros(n_margins) |
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| | | pitch_correct = np.zeros(n_margins) |
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| | | roll_correct = np.zeros(n_margins) |
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| | | idx_tensor = [idx for idx in xrange(66)] |
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| | | idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu) |
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| | | |
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| | | yaw_error = .0 |
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| | | pitch_error = .0 |
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| | | roll_error = .0 |
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| | | |
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| | | l1loss = torch.nn.L1Loss(size_average=False) |
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| | | |
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| | | for i, (images, labels, name) in enumerate(test_loader): |
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| | | images = Variable(images).cuda(gpu) |
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| | | labels = Variable(labels).cuda(gpu) |
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| | | outputs = model(images) |
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| | | _, predicted = torch.max(outputs.data, 1) |
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| | | total += labels.size(0) |
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| | | # TODO: There are more efficient ways. |
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| | | for idx in xrange(len(outputs)): |
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| | | error += utils.mse_loss(outputs[idx], labels[idx]) |
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| | | label_yaw = labels[:,0].float() |
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| | | label_pitch = labels[:,1].float() |
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| | | label_roll = labels[:,2].float() |
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| | | yaw, pitch, roll = model(images) |
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| | | print('Test MSE error of the model on the ' + str(total) + |
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| | | ' test images: %.4f' % (error / total)) |
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| | | # Binned predictions |
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| | | _, yaw_bpred = torch.max(yaw.data, 1) |
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| | | _, pitch_bpred = torch.max(pitch.data, 1) |
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| | | _, roll_bpred = torch.max(roll.data, 1) |
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| | | |
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| | | # Continuous predictions |
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| | | yaw_predicted = utils.softmax_temperature(yaw.data, 1) |
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| | | pitch_predicted = utils.softmax_temperature(pitch.data, 1) |
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| | | roll_predicted = utils.softmax_temperature(roll.data, 1) |
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| | | |
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| | | yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1).cpu() |
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| | | pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1).cpu() |
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| | | roll_predicted = torch.sum(roll_predicted * idx_tensor, 1).cpu() |
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| | | |
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| | | # Mean absolute error |
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| | | yaw_error += torch.sum(torch.abs(yaw_predicted - label_yaw) * 3) |
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| | | pitch_error += torch.sum(torch.abs(pitch_predicted - label_pitch) * 3) |
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| | | roll_error += torch.sum(torch.abs(roll_predicted - label_roll) * 3) |
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| | | |
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| | | # Binned Accuracy |
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| | | # for er in xrange(n_margins): |
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| | | # yaw_bpred[er] += (label_yaw[0] in range(yaw_bpred[0,0] - er, yaw_bpred[0,0] + er + 1)) |
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| | | # pitch_bpred[er] += (label_pitch[0] in range(pitch_bpred[0,0] - er, pitch_bpred[0,0] + er + 1)) |
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| | | # roll_bpred[er] += (label_roll[0] in range(roll_bpred[0,0] - er, roll_bpred[0,0] + er + 1)) |
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| | | # print label_yaw[0], yaw_bpred[0,0] |
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| | | |
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| | | # Save images with pose cube. |
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| | | # TODO: fix for larger batch size |
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| | | if args.save_viz: |
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| | | name = name[0] |
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| | | cv2_img = cv2.imread(os.path.join(args.data_dir, name + '.jpg')) |
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| | | #print os.path.join('output/images', name + '.jpg') |
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| | | #print label_yaw[0] * 3 - 99, label_pitch[0] * 3 - 99, label_roll[0] * 3 - 99 |
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| | | #print yaw_predicted * 3 - 99, pitch_predicted * 3 - 99, roll_predicted * 3 - 99 |
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| | | utils.plot_pose_cube(cv2_img, yaw_predicted[0] * 3 - 99, pitch_predicted[0] * 3 - 99, roll_predicted[0] * 3 - 99) |
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| | | cv2.imwrite(os.path.join('output/images', name + '.jpg'), cv2_img) |
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| | | |
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| | | print('Test error in degrees of the model on the ' + str(total) + |
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| | | ' test images. Yaw: %.4f, Pitch: %.4f, Roll: %.4f' % (yaw_error / total, |
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| | | pitch_error / total, roll_error / total)) |
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| | | |
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| | | # Binned accuracy |
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| | | # for idx in xrange(len(yaw_correct)): |
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| | | # print yaw_correct[idx] / total, pitch_correct[idx] / total, roll_correct[idx] / total |
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