# Trying to understand this small piece of code

Hi,

I’m using the following two functions to find the accuracy of my semantic segmentation network, I found this code on github and they seem to work but I dont exactly know how. I am trying to understand what each line is doing.

I have commented each line with what I think is going on, if I am wrong in my understanding can you please correct me.

The lines I need help in understanding are: `values,indices = tensor.cpu().max(1)` and `incorrect=preds.ne(targets).cpu().sum()`

``````def get_predictions(output_batch):
bs, c, h, w = output_batch.size()           # size returns [batchsize, channels, rows, columns]
tensor = output_batch.data
values, indices = tensor.cpu().max(1)       # get the values and indices of the max values in every channel (dim=1),  why are we finding the maximum value in RGB channels?
indices = indices.view(bs, h, w)            # reshape it to this, as this is how 'targets' is shaped
return indices

def error(preds, targets):
assert preds.size() == targets.size()
bs, h, w = preds.size()
n_pixels = bs*h*w
incorrect = preds.ne(targets).cpu().sum()       # I cannot find out what 'ne' is doing here and what are we summing?
err = incorrect.numpy()/n_pixels                # converted this tensor to numpy as the tensor was int and division was giving 0 everytime
# return err
return round(err, 5)
``````

Many Thanks

Let’s walk through the code using your explanations:

``````def get_predictions(output_batch):
bs, c, h, w = output_batch.size()           # size returns [batchsize, channels, rows, columns]
# Get's the underlying data. I would prefer to use .detach(), but that shouldn't be a problem here.
tensor = output_batch.data
# Gets the maximal value in every channel, right.
# As this will most likely be your model's prediction, you the channels correspond to the classes, i.e.
# channel0 represents the logits of class0. indices will therefore contain the predicted class for each pixel location.
values, indices = tensor.cpu().max(1)       # get the values and indices of the max values in every channel (dim=1),  why are we finding the maximum value in RGB channels?
# .squeeze() would probably do the same.
# Basically you want to get rid of dim1 which is a single channel now with the class predictions.
indices = indices.view(bs, h, w)            # reshape it to this, as this is how 'targets' is shaped
return indices

def error(preds, targets):
assert preds.size() == targets.size()
bs, h, w = preds.size()
n_pixels = bs*h*w
# You are comparing the predictions of your model with the target tensor element-wise
# using the "not equal" operation. In other words, you'll bet a ByteTensor with 1s for all pixel locations,
# where the predictions do not equal the target. Summing it will give you the number of falsely predicted pixels.
incorrect = preds.ne(targets).cpu().sum()       # I cannot find out what 'ne' is doing here and what are we summing?
# Divide the number of incorrectly classified pixel by the number of all pixels.
err = incorrect.numpy()/n_pixels                # converted this tensor to numpy as the tensor was int and division was giving 0 everytime
# return err
return round(err, 5)
``````

Let me know, if some aspects are still unclear.

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