def report_format()

in tensorwatch/model_graph/torchstat/reporter.py [0:0]

• 56 lines of code
• 5 McCabe index (conditional complexity)

def report_format(collected_nodes):
    data = list()
    for node in collected_nodes:
        name = node.name
        input_shape = ' '.join(['{:>3d}'] * len(node.input_shape)).format(
            *[e for e in node.input_shape])
        output_shape = ' '.join(['{:>3d}'] * len(node.output_shape)).format(
            *[e for e in node.output_shape])
        parameter_quantity = node.parameter_quantity
        inference_memory = node.inference_memory
        MAdd = node.MAdd
        Flops = node.Flops
        mread, mwrite = [i for i in node.Memory]
        duration = node.duration
        data.append([name, input_shape, output_shape, parameter_quantity,
                     inference_memory, MAdd, duration, Flops, mread,
                     mwrite])
    df = pd.DataFrame(data)
    df.columns = ['module name', 'input shape', 'output shape',
                  'params', 'memory(MB)',
                  'MAdd', 'duration', 'Flops', 'MemRead(B)', 'MemWrite(B)']
    df['duration[%]'] = df['duration'] / (df['duration'].sum() + 1e-7)
    df['MemR+W(B)'] = df['MemRead(B)'] + df['MemWrite(B)']
    total_parameters_quantity = df['params'].sum()
    total_memory = df['memory(MB)'].sum()
    total_operation_quantity = df['MAdd'].sum()
    total_flops = df['Flops'].sum()
    total_duration = df['duration[%]'].sum()
    total_mread = df['MemRead(B)'].sum()
    total_mwrite = df['MemWrite(B)'].sum()
    total_memrw = df['MemR+W(B)'].sum()
    del df['duration']

    # Add Total row
    total_df = pd.Series([total_parameters_quantity, total_memory,
                          total_operation_quantity, total_flops,
                          total_duration, mread, mwrite, total_memrw],
                         index=['params', 'memory(MB)', 'MAdd', 'Flops', 'duration[%]',
                                'MemRead(B)', 'MemWrite(B)', 'MemR+W(B)'],
                         name='total')
    df = df.append(total_df)

    df = df.fillna(' ')
    df['memory(MB)'] = df['memory(MB)'].apply(
        lambda x: '{:.2f}'.format(x))
    df['duration[%]'] = df['duration[%]'].apply(lambda x: '{:.2%}'.format(x))
    df['MAdd'] = df['MAdd'].apply(lambda x: '{:,}'.format(x))
    df['Flops'] = df['Flops'].apply(lambda x: '{:,}'.format(x))

    summary = str(df) + '\n'
    summary += "=" * len(str(df).split('\n')[0])
    summary += '\n'
    summary += "Total params: {:,}\n".format(total_parameters_quantity)

    summary += "-" * len(str(df).split('\n')[0])
    summary += '\n'
    summary += "Total memory: {:.2f}MB\n".format(total_memory)
    summary += "Total MAdd: {}MAdd\n".format(round_value(total_operation_quantity))
    summary += "Total Flops: {}Flops\n".format(round_value(total_flops))
    summary += "Total MemR+W: {}B\n".format(round_value(total_memrw, True))
    return summary