p2ch11: label_g[:,1] as target label in computeBatchLoss() ?

[4179e1]

Hi,

I'm having trouble understanding the label_g[:,1] used in computeBatchLoss():

dlwpt-code/p2ch11/training.py

Lines 225 to 238 in d6c0210

	def computeBatchLoss(self, batch_ndx, batch_tup, batch_size, metrics_g):
	input_t, label_t, _series_list, _center_list = batch_tup
	input_g = input_t.to(self.device, non_blocking=True)
	label_g = label_t.to(self.device, non_blocking=True)
	logits_g, probability_g = self.model(input_g)
	loss_func = nn.CrossEntropyLoss(reduction='none')
	loss_g = loss_func(
	logits_g,
	label_g[:,1],
	)
	start_ndx = batch_ndx * batch_size

Assume that batch size is 32, the logits_g will have shape [32, 2].
And the label_g have the same size [32, 2], if I didn't get it wrong, it should be the one-hot vector defined in

dlwpt-code/p2ch11/dsets.py

Lines 203 to 210 in d6c0210

	pos_t = torch.tensor([
	not candidateInfo_tup.isNodule_bool,
	candidateInfo_tup.isNodule_bool
	],
	dtype=torch.long,
	)
	return candidate_t, pos_t, candidateInfo_tup.series_uid, torch.tensor(center_irc)

My quesetion is that in the CrossEntropyLoss function, should we use label_g instead of label_g[:,1] ( which take the 2nd column for each item)? Something like:

        loss_g = loss_func(
            logits_g,
            label_g, # the one-hot vector instead of label_g[:,1]
        )

or

        loss_g = loss_func(
            logits_g,
            torch.argmax(label_g, dim=1), # if we want to use the index 
        )

Thanks

[4179e1]

hmm, just realized that it's a shortcut only apply to binary classification: label_g[:,1] and torch.argmax(label_g, dim=1) produce the same result:

torch.argmax(t, dim
t = torch.tensor([
    [1, 0],
    [0, 1],
    [1, 0],
])
​
print (torch.argmax(t, dim=1))  # tensor([0, 1, 0])
print (t[:,1])                                # tensor([0, 1, 0])

However it doesn't hold true for mulit classification

t = torch.tensor([
    [1, 0, 0],
    [0, 1, 0],
    [0, 0, 1],
])
​
print (torch.argmax(t, dim=1))  # tensor([0, 1, 2])
print (t[:,1])                                # tensor([0, 1, 0])