next

natanielruiz

2017-09-19 e624d2ace8296e130a4fa4d2d307041798c538e0

 code/test_AFW_preangles.py

@@ -94,12 +94,12 @@
    yaw_correct = .0
    yaw_margin = args.margin

    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() * 3 - 99
        label_pitch = labels[:,1].float() * 3 - 99
        label_roll = labels[:,2].float() * 3 - 99
        label_yaw = cont_labels[:,0]
        label_pitch = cont_labels[:,1].float()
        label_roll = cont_labels[:,2].float()

        yaw, pitch, roll, angles = model(images)

@@ -109,7 +109,7 @@
        _, roll_bpred = torch.max(roll.data, 1)

        # Continuous predictions
        yaw_predicted = utils.softmax_temperature(yaw.data, 0.4)
        yaw_predicted = utils.softmax_temperature(yaw.data, 0.85)
        pitch_predicted = utils.softmax_temperature(pitch.data, 0.8)
        roll_predicted = utils.softmax_temperature(roll.data, 0.8)

@@ -129,14 +129,6 @@

        if yaw_tensor_error[0] > yaw_margin:
            print name[0] + ' ' + str(yaw_predicted[0]) + ' ' + str(label_yaw[0]) + ' ' + str(yaw_tensor_error[0])

        # Binned Accuracy
        # for er in xrange(n_margins):
        #     yaw_bpred[er] += (label_yaw[0] in range(yaw_bpred[0,0] - er, yaw_bpred[0,0] + er + 1))
        #     pitch_bpred[er] += (label_pitch[0] in range(pitch_bpred[0,0] - er, pitch_bpred[0,0] + er + 1))
        #     roll_bpred[er] += (label_roll[0] in range(roll_bpred[0,0] - er, roll_bpred[0,0] + er + 1))

        # print label_yaw[0], yaw_bpred[0,0]

        # Save images with pose cube.
        # TODO: fix for larger batch size