model_stat.py

# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
'''
Example:
    >>from paddle.fluid.contrib.model_stat import summary
    >>main_program = ...
    >>summary(main_program)
    +-----+------------+----------------+----------------+---------+------------+
    | No. |       TYPE |          INPUT |         OUTPUT |  PARAMs |      FLOPs |
    +-----+------------+----------------+----------------+---------+------------+
    |   0 |     conv2d |  (3, 200, 200) | (64, 100, 100) |    9408 |  188160000 |
    |   1 | batch_norm | (64, 100, 100) | (64, 100, 100) |     256 |     640000 |
    |   2 |       relu | (64, 100, 100) | (64, 100, 100) |       0 |     640000 |
    |   3 |     pool2d | (64, 100, 100) |   (64, 50, 50) |       0 |    1440000 |
    ...
    | 176 |     conv2d |    (512, 7, 7) |    (512, 7, 7) | 2359296 |  231211008 |
    | 177 |       relu |    (512, 7, 7) |    (512, 7, 7) |       0 |      25088 |
    | 178 |     conv2d |    (512, 7, 7) |   (2048, 7, 7) | 1048576 |  102760448 |
    | 179 |       relu |   (2048, 7, 7) |   (2048, 7, 7) |       0 |     100352 |
    | 180 |     pool2d |   (2048, 7, 7) |   (2048, 1, 1) |       0 |     100352 |
    +-----+------------+----------------+----------------+---------+------------+
    Total PARAMs: 48017344(0.0480G)
    Total FLOPs: 11692747751(11.69G)
'''
from collections import OrderedDict


def summary(main_prog):
    '''
    It can summary model's PARAMS, FLOPs until now.
    It support common operator like conv, fc, pool, relu, sigmoid, bn etc. 
    Args:
        main_prog: main program 
    Returns:
        print summary on terminal
    '''
    collected_ops_list = []
    for one_b in main_prog.blocks:
        block_vars = one_b.vars
        for one_op in one_b.ops:
            op_info = OrderedDict()
            spf_res = _summary_model(block_vars, one_op)
            if spf_res is None:
                continue
            # TODO: get the operator name
            op_info['type'] = one_op.type
            op_info['input_shape'] = spf_res[0][1:]
            op_info['out_shape'] = spf_res[1][1:]
            op_info['PARAMs'] = spf_res[2]
            op_info['FLOPs'] = spf_res[3]
            collected_ops_list.append(op_info)

    summary_table, total = _format_summary(collected_ops_list)
    _print_summary(summary_table, total)


def _summary_model(block_vars, one_op):
    '''
    Compute operator's params and flops.
    Args:
        block_vars: all vars of one block
        one_op: one operator to count
    Returns:
        in_data_shape: one operator's input data shape
        out_data_shape: one operator's output data shape
        params: one operator's PARAMs 
        flops: : one operator's FLOPs
    '''
    if one_op.type in ['conv2d', 'depthwise_conv2d']:
        k_arg_shape = block_vars[one_op.input("Filter")[0]].shape
        in_data_shape = block_vars[one_op.input("Input")[0]].shape
        out_data_shape = block_vars[one_op.output("Output")[0]].shape
        c_out, c_in, k_h, k_w = k_arg_shape
        _, c_out_, h_out, w_out = out_data_shape
        assert c_out == c_out_, 'shape error!'
        k_groups = one_op.attr("groups")
        kernel_ops = k_h * k_w * (c_in / k_groups)
        bias_ops = 0 if one_op.input("Bias") == [] else 1
        params = c_out * (kernel_ops + bias_ops)
        flops = h_out * w_out * c_out * (kernel_ops + bias_ops)
        # base nvidia paper, include mul and add
        flops = 2 * flops

    elif one_op.type == 'pool2d':
        in_data_shape = block_vars[one_op.input("X")[0]].shape
        out_data_shape = block_vars[one_op.output("Out")[0]].shape
        _, c_out, h_out, w_out = out_data_shape
        k_size = one_op.attr("ksize")
        params = 0
        flops = h_out * w_out * c_out * (k_size[0] * k_size[1])

    elif one_op.type == 'mul':
        k_arg_shape = block_vars[one_op.input("Y")[0]].shape
        in_data_shape = block_vars[one_op.input("X")[0]].shape
        out_data_shape = block_vars[one_op.output("Out")[0]].shape
        # TODO: fc has mul ops
        # add attr to mul op, tell us whether it belongs to 'fc'
        # this's not the best way
        if 'fc' not in one_op.output("Out")[0]:
            return None
        k_in, k_out = k_arg_shape
        # bias in sum op
        params = k_in * k_out + 1
        flops = k_in * k_out

    elif one_op.type in ['sigmoid', 'tanh', 'relu', 'leaky_relu', 'prelu']:
        in_data_shape = block_vars[one_op.input("X")[0]].shape
        out_data_shape = block_vars[one_op.output("Out")[0]].shape
        params = 0
        if one_op.type == 'prelu':
            params = 1
        flops = 1
        for one_dim in in_data_shape:
            flops *= one_dim

    elif one_op.type == 'batch_norm':
        in_data_shape = block_vars[one_op.input("X")[0]].shape
        out_data_shape = block_vars[one_op.output("Y")[0]].shape
        _, c_in, h_out, w_out = in_data_shape
        # gamma, beta
        params = c_in * 2
        # compute mean and std
        flops = h_out * w_out * c_in * 2

    else:
        return None

    return in_data_shape, out_data_shape, params, flops


def _format_summary(collected_ops_list):
    '''
    Format summary report.
    Args:
        collected_ops_list: the collected operator with summary
    Returns:
        summary_table: summary report format
        total: sum param and flops
    '''
    _verify_dependent_package()

    summary_table = PrettyTable(
        ["No.", "TYPE", "INPUT", "OUTPUT", "PARAMs", "FLOPs"])
    summary_table.align = 'r'

    total = {}
    total_params = []
    total_flops = []
    for i, one_op in enumerate(collected_ops_list):
        # notice the order
        table_row = [
            i,
            one_op['type'],
            one_op['input_shape'],
            one_op['out_shape'],
            int(one_op['PARAMs']),
            int(one_op['FLOPs']),
        ]
        summary_table.add_row(table_row)
        total_params.append(int(one_op['PARAMs']))
        total_flops.append(int(one_op['FLOPs']))

    total['params'] = total_params
    total['flops'] = total_flops

    return summary_table, total


def _verify_dependent_package():
    """
    Verify whether `prettytable` is installed.
    """
    try:
        from prettytable import PrettyTable
    except ImportError:
        raise ImportError(
            "paddle.summary() requires package `prettytable`, place install it firstly using `pip install prettytable`. "
        )


def _print_summary(summary_table, total):
    '''
    Print all the summary on terminal.
    Args:
        summary_table: summary report format
        total: sum param and flops
    '''
    parmas = total['params']
    flops = total['flops']
    print(summary_table)
    print('Total PARAMs: {}({:.4f}M)'.format(
        sum(parmas), sum(parmas) / (10**6)))
    print('Total FLOPs: {}({:.2f}G)'.format(sum(flops), sum(flops) / 10**9))
    print(
        "Notice: \n now supported ops include [Conv, DepthwiseConv, FC(mul), BatchNorm, Pool, Activation(sigmoid, tanh, relu, leaky_relu, prelu)]"
    )