deep-head-pose
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natanielruiz
input sizes mismatch for nn.CrossEntropyLoss()
Hi @natanielruiz thanks for sharing your work,
while trying to adapt it for a project i stumbled upon a problem. In lines 160 to 161 of your train_hopenet.py file stand:
# Forward pass
yaw, pitch, roll = model(images)
If i am not mistaken the size of each by the model predicted angle is (batch_size, num_bins) so for example (128, 66).
Which makes absolute fine sense. Because the fully connected layer is of output_size 66.
While investigating the datahandling in the datasets.py there is the following codeblock
# We get the pose in radians
pose = utils.get_ypr_from_mat(mat_path)
# And convert to degrees.
pitch = pose[0] * 180 / np.pi
yaw = pose[1] * 180 / np.pi
roll = pose[2] * 180 / np.pi
# Bin values
bins = np.array(range(-99, 102, 3))
labels = torch.LongTensor(np.digitize([yaw, pitch, roll], bins) - 1)
While assuming that the pose of the head has 3 values, one for each angle.
Then i would get the bin of each angle in the labels variable, like [30, 33, 33].
The first codeblock is followed combined with:
label_yaw = Variable(labels[:,0]).cuda(gpu)
label_pitch = Variable(labels[:,1]).cuda(gpu)
label_roll = Variable(labels[:,2]).cuda(gpu)
# Continuous labels
label_yaw_cont = Variable(cont_labels[:,0]).cuda(gpu)
label_pitch_cont = Variable(cont_labels[:,1]).cuda(gpu)
label_roll_cont = Variable(cont_labels[:,2]).cuda(gpu)
# Cross entropy loss
loss_yaw = criterion(yaw, label_yaw)
loss_pitch = criterion(pitch, label_pitch)
loss_roll = criterion(roll, label_roll)
with the criterion being nn.CrossEntropyLoss().cuda(gpu).
This is where i get confused, because the sizes of the inputs do not match? We have yaw with (128, 66) but the label_yaw is of size (128, 1).
Could you please tell me where i am doing something wrong? Any help is appreciated.
Kind regards
