sensitivity_anal.py

# Copyright (c) 2021 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import sys

__dir__ = os.path.dirname(__file__)
sys.path.append(__dir__)
sys.path.append(os.path.join(__dir__, '..', '..', '..'))
sys.path.append(os.path.join(__dir__, '..', '..', '..', 'tools'))

import paddle
import paddle.distributed as dist
from ppocr.data import build_dataloader
from ppocr.modeling.architectures import build_model
from ppocr.losses import build_loss
from ppocr.optimizer import build_optimizer
from ppocr.postprocess import build_post_process
from ppocr.metrics import build_metric
from ppocr.utils.save_load import init_model
import tools.program as program

dist.get_world_size()


def get_pruned_params(parameters):
    params = []

    for param in parameters:
        if len(
                param.shape
        ) == 4 and 'depthwise' not in param.name and 'transpose' not in param.name and "conv2d_57" not in param.name and "conv2d_56" not in param.name:
            params.append(param.name)
    return params


def main(config, device, logger, vdl_writer):
    # init dist environment
    if config['Global']['distributed']:
        dist.init_parallel_env()

    global_config = config['Global']

    # build dataloader
    train_dataloader = build_dataloader(config, 'Train', device, logger)
    if config['Eval']:
        valid_dataloader = build_dataloader(config, 'Eval', device, logger)
    else:
        valid_dataloader = None

    # build post process
    post_process_class = build_post_process(config['PostProcess'],
                                            global_config)

    # build model
    # for rec algorithm
    if hasattr(post_process_class, 'character'):
        char_num = len(getattr(post_process_class, 'character'))
        config['Architecture']["Head"]['out_channels'] = char_num
    model = build_model(config['Architecture'])

    flops = paddle.flops(model, [1, 3, 640, 640])
    logger.info(f"FLOPs before pruning: {flops}")

    from paddleslim.dygraph import FPGMFilterPruner
    model.train()
    pruner = FPGMFilterPruner(model, [1, 3, 640, 640])

    # build loss
    loss_class = build_loss(config['Loss'])

    # build optim
    optimizer, lr_scheduler = build_optimizer(
        config['Optimizer'],
        epochs=config['Global']['epoch_num'],
        step_each_epoch=len(train_dataloader),
        parameters=model.parameters())

    # build metric
    eval_class = build_metric(config['Metric'])
    # load pretrain model
    pre_best_model_dict = init_model(config, model, logger, optimizer)

    logger.info('train dataloader has {} iters, valid dataloader has {} iters'.
                format(len(train_dataloader), len(valid_dataloader)))
    # build metric
    eval_class = build_metric(config['Metric'])

    logger.info('train dataloader has {} iters, valid dataloader has {} iters'.
                format(len(train_dataloader), len(valid_dataloader)))

    def eval_fn():
        metric = program.eval(model, valid_dataloader, post_process_class,
                              eval_class)
        logger.info(f"metric['hmean']: {metric['hmean']}")
        return metric['hmean']

    params_sensitive = pruner.sensitive(
        eval_func=eval_fn,
        sen_file="./sen.pickle",
        skip_vars=[
            "conv2d_57.w_0", "conv2d_transpose_2.w_0", "conv2d_transpose_3.w_0"
        ])

    logger.info(
        "The sensitivity analysis results of model parameters saved in sen.pickle"
    )
    # calculate pruned params's ratio
    params_sensitive = pruner._get_ratios_by_loss(params_sensitive, loss=0.02)
    for key in params_sensitive.keys():
        logger.info(f"{key}, {params_sensitive[key]}")

    plan = pruner.prune_vars(params_sensitive, [0])
    for param in model.parameters():
        if ("weights" in param.name and "conv" in param.name) or (
                "w_0" in param.name and "conv2d" in param.name):
            logger.info(f"{param.name}: {param.shape}")

    flops = paddle.flops(model, [1, 3, 640, 640])
    logger.info(f"FLOPs after pruning: {flops}")

    # start train

    program.train(config, train_dataloader, valid_dataloader, device, model,
                  loss_class, optimizer, lr_scheduler, post_process_class,
                  eval_class, pre_best_model_dict, logger, vdl_writer)
    mode = 'infer'
    if mode == 'infer':
        from paddle.jit import to_static

        infer_shape = [3, -1, -1]
        if config['Architecture']['model_type'] == "rec":
            infer_shape = [3, 32, -1]  # for rec model, H must be 32
            if 'Transform' in config['Architecture'] and config['Architecture'][
                    'Transform'] is not None and config['Architecture'][
                        'Transform']['name'] == 'TPS':
                logger.info(
                    'When there is tps in the network, variable length input is not supported, and the input size needs to be the same as during training'
                )
                infer_shape[-1] = 100
        model = to_static(
            model,
            input_spec=[
                paddle.static.InputSpec(
                    shape=[None] + infer_shape, dtype='float32')
            ])

        save_path = '{}/inference'.format(config['Global'][
            'save_inference_dir'])
        paddle.jit.save(model, save_path)
        logger.info('inference model is saved to {}'.format(save_path))


if __name__ == '__main__':
    config, device, logger, vdl_writer = program.preprocess(is_train=True)
    main(config, device, logger, vdl_writer)