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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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| | | |
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| | | import cv2 |
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| | | import matplotlib.pyplot as plt |
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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 = torchvision.models.resnet18() |
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| | | # Parameters of newly constructed modules have requires_grad=True by default |
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| | | num_ftrs = model.fc.in_features |
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| | | model.fc = nn.Linear(num_ftrs, 3) |
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| | | # ResNet50 with 3 outputs. |
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| | | model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 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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| | | # Test the Model |
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| | | model.eval() # Change model to 'eval' mode (BN uses moving mean/var). |
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| | | yaw_correct = 0 |
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| | | pitch_correct = 0 |
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| | | roll_correct = 0 |
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| | | total = 0 |
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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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| | | |
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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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| | | 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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| | | |
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| | | total += labels.size(0) |
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| | | # TODO: There are more efficient ways. |
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| | | yaw_correct += (outputs[:][0] == labels[:][0]) |
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| | | pitch_correct += (outputs[:][]) |
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| | | for idx in xrange(len(outputs)): |
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| | | yaw_correct += (outputs[idx].data[0] == labels[idx].data[0]) |
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| | | pitch_correct += (outputs[idx].data[1] == labels[idx].data[1]) |
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| | | roll_correct += (outputs[idx].data[2] == labels[idx].data[2]) |
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| | | label_yaw = labels[:,0] |
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| | | label_pitch = labels[:,1] |
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| | | label_roll = labels[:,2] |
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| | | |
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| | | yaw, pitch, roll = model(images) |
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| | | # _, yaw_predicted = torch.max(yaw.data, 1) |
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| | | # _, pitch_predicted = torch.max(pitch.data, 1) |
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| | | # _, roll_predicted = torch.max(roll.data, 1) |
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| | | |
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| | | print('Test accuracies of the model on the ' + str(total) + |
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| | | ' test images. Yaw: %.4f %%, Pitch: %.4f %%, Roll: %.4f %%' % (yaw_correct / total, |
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| | | pitch_correct / total, roll_correct / total)) |
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| | | yaw_predicted = F.softmax(yaw) |
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| | | pitch_predicted = F.softmax(pitch) |
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| | | roll_predicted = F.softmax(roll) |
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| | | |
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| | | yaw_predicted = torch.sum(yaw_predicted.data[0] * idx_tensor) |
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| | | pitch_predicted = torch.sum(pitch_predicted.data[0] * idx_tensor) |
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| | | roll_predicted = torch.sum(roll_predicted.data[0] * idx_tensor) |
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| | | |
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| | | yaw_error += abs(yaw_predicted - label_yaw[0]) * 3 |
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| | | pitch_error += abs(pitch_predicted - label_pitch[0]) * 3 |
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| | | roll_error += abs(roll_predicted - label_roll[0]) * 3 |
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| | | |
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| | | # for er in xrange(0,n_margins): |
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| | | # yaw_correct[er] += (label_yaw[0] in range(yaw_predicted[0,0] - er, yaw_predicted[0,0] + er + 1)) |
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| | | # pitch_correct[er] += (label_pitch[0] in range(pitch_predicted[0,0] - er, pitch_predicted[0,0] + er + 1)) |
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| | | # roll_correct[er] += (label_roll[0] in range(roll_predicted[0,0] - er, roll_predicted[0,0] + er + 1)) |
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| | | |
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| | | # print label_yaw[0], yaw_predicted[0,0] |
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| | | # 4 -> 15 |
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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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| | | # 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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