solver.py

# coding: utf8
# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
# 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.

import sys
import paddle.fluid as fluid
import numpy as np
import importlib
from utils.config import cfg
from paddle.fluid.contrib.mixed_precision.fp16_utils import create_master_params_grads, master_param_to_train_param


class Solver(object):
    def __init__(self, main_prog, start_prog):
        total_images = cfg.DATASET.TRAIN_TOTAL_IMAGES
        self.weight_decay = cfg.SOLVER.WEIGHT_DECAY
        self.momentum = cfg.SOLVER.MOMENTUM
        self.momentum2 = cfg.SOLVER.MOMENTUM2
        self.step_per_epoch = total_images // cfg.BATCH_SIZE
        if total_images % cfg.BATCH_SIZE != 0:
            self.step_per_epoch += 1
        self.total_step = cfg.SOLVER.NUM_EPOCHS * self.step_per_epoch
        self.main_prog = main_prog
        self.start_prog = start_prog

    def piecewise_decay(self):
        gamma = cfg.SOLVER.GAMMA
        bd = [self.step_per_epoch * e for e in cfg.SOLVER.DECAY_EPOCH]
        lr = [cfg.SOLVER.LR * (gamma**i) for i in range(len(bd) + 1)]
        decayed_lr = fluid.layers.piecewise_decay(boundaries=bd, values=lr)
        return decayed_lr

    def poly_decay(self):
        power = cfg.SOLVER.POWER
        decayed_lr = fluid.layers.polynomial_decay(
            cfg.SOLVER.LR, self.total_step, end_learning_rate=0, power=power)
        return decayed_lr

    def cosine_decay(self):
        decayed_lr = fluid.layers.cosine_decay(
            cfg.SOLVER.LR, self.step_per_epoch, cfg.SOLVER.NUM_EPOCHS)
        return decayed_lr

    def get_lr(self, lr_policy):
        if lr_policy.lower() == 'poly':
            decayed_lr = self.poly_decay()
        elif lr_policy.lower() == 'piecewise':
            decayed_lr = self.piecewise_decay()
        elif lr_policy.lower() == 'cosine':
            decayed_lr = self.cosine_decay()
        else:
            raise Exception(
                "unsupport learning decay policy! only support poly,piecewise,cosine"
            )
        return decayed_lr

    def sgd_optimizer(self, lr_policy, loss):
        decayed_lr = self.get_lr(lr_policy)
        optimizer = fluid.optimizer.Momentum(
            learning_rate=decayed_lr,
            momentum=self.momentum,
            regularization=fluid.regularizer.L2Decay(
                regularization_coeff=self.weight_decay),
        )
        if cfg.MODEL.FP16:
            params_grads = optimizer.backward(loss, self.start_prog)
            master_params_grads = create_master_params_grads(
                params_grads, self.main_prog, self.start_prog,
                cfg.MODEL.SCALE_LOSS)
            optimizer.apply_gradients(master_params_grads)
            master_param_to_train_param(master_params_grads, params_grads,
                                        self.main_prog)
        else:
            optimizer.minimize(loss)
        return decayed_lr

    def adam_optimizer(self, lr_policy, loss):
        decayed_lr = self.get_lr(lr_policy)
        optimizer = fluid.optimizer.Adam(
            learning_rate=decayed_lr,
            beta1=self.momentum,
            beta2=self.momentum2,
            regularization=fluid.regularizer.L2Decay(
                regularization_coeff=self.weight_decay),
        )
        optimizer.minimize(loss)
        return decayed_lr

    def optimise(self, loss):
        lr_policy = cfg.SOLVER.LR_POLICY
        opt = cfg.SOLVER.OPTIMIZER

        if opt.lower() == 'adam':
            return self.adam_optimizer(lr_policy, loss)
        elif opt.lower() == 'sgd':
            return self.sgd_optimizer(lr_policy, loss)
        else:
            raise Exception(
                "unsupport optimizer solver, only support adam and sgd")