a0138726cc5814df6cbcade4eccd771d70046106,test.py,,prepare_inputs,#Any#Any#,11

Before Change

        next_begin_index = batch_size
    else:
        inputs = torch.cat(
            [input_buffer[i] for i in range(buffer_index)] +
            [input_buffer[buffer_index][:-n_over_samples]],
            dim=0)
        next_begin_index = input_buffer[buffer_index].size(0) - n_over_samples

After Change

def prepare_inputs(input_buffer, batch_size):
    n_input_samples = 0
    for buffer_index, x in enumerate(input_buffer):
        n_input_samples += x.size(0)
        if n_input_samples >= batch_size:
            n_over_samples = n_input_samples - batch_size
            break
    else:
        n_over_samples = 0

    if n_over_samples == 0:
        inputs = torch.cat(input_buffer[:(buffer_index + 1)], dim=0)
        input_buffer = input_buffer[(buffer_index + 1):]
    else:

Instances