PaddleOcr_v4/deploy/slim/prune/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, set_signal_handlers
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 load_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
    set_signal_handlers()
    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"])
    if config["Architecture"]["model_type"] == "det":
        input_shape = [1, 3, 640, 640]
    elif config["Architecture"]["model_type"] == "rec":
        input_shape = [1, 3, 32, 320]
    flops = paddle.flops(model, input_shape)

    logger.info("FLOPs before pruning: {}".format(flops))

    from paddleslim.dygraph import FPGMFilterPruner

    model.train()

    pruner = FPGMFilterPruner(model, input_shape)

    # 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),
        model=model,
    )

    # build metric
    eval_class = build_metric(config["Metric"])
    # load pretrain model
    pre_best_model_dict = load_model(config, model, 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, False
        )
        if config["Architecture"]["model_type"] == "det":
            main_indicator = "hmean"
        else:
            main_indicator = "acc"

        logger.info("metric[{}]: {}".format(main_indicator, metric[main_indicator]))
        return metric[main_indicator]

    run_sensitive_analysis = False
    """
    run_sensitive_analysis=True:
        Automatically compute the sensitivities of convolutions in a model.
        The sensitivity of a convolution is the losses of accuracy on test dataset in
        different pruned ratios. The sensitivities can be used to get a group of best
        ratios with some condition.

    run_sensitive_analysis=False:
        Set prune trim ratio to a fixed value, such as 10%. The larger the value,
        the more convolution weights will be cropped.

    """

    if run_sensitive_analysis:
        params_sensitive = pruner.sensitive(
            eval_func=eval_fn,
            sen_file="./deploy/slim/prune/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("{}, {}".format(key, params_sensitive[key]))
    else:
        params_sensitive = {}
        for param in model.parameters():
            if "transpose" not in param.name and "linear" not in param.name:
                # set prune ratio as 10%. The larger the value, the more convolution weights will be cropped
                params_sensitive[param.name] = 0.1

    plan = pruner.prune_vars(params_sensitive, [0])

    flops = paddle.flops(model, input_shape)
    logger.info("FLOPs after pruning: {}".format(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,
    )


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