mindspore/model_zoo/official/cv/mobilenetv2/src/models.py

# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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 time
import numpy as np
from mindspore import Tensor
from mindspore import nn
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.common import dtype as mstype
from mindspore.nn.loss.loss import _Loss
from mindspore.train.callback import Callback
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from src.mobilenetV2 import MobileNetV2Backbone, MobileNetV2Head, mobilenet_v2

class CrossEntropyWithLabelSmooth(_Loss):
    """
    CrossEntropyWith LabelSmooth.

    Args:
        smooth_factor (float): smooth factor, default=0.
        num_classes (int): num classes

    Returns:
        None.

    Examples:
        >>> CrossEntropyWithLabelSmooth(smooth_factor=0., num_classes=1000)
    """

    def __init__(self, smooth_factor=0., num_classes=1000):
        super(CrossEntropyWithLabelSmooth, self).__init__()
        self.onehot = P.OneHot()
        self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
        self.off_value = Tensor(1.0 * smooth_factor /
                                (num_classes - 1), mstype.float32)
        self.ce = nn.SoftmaxCrossEntropyWithLogits()
        self.mean = P.ReduceMean(False)
        self.cast = P.Cast()

    def construct(self, logit, label):
        one_hot_label = self.onehot(self.cast(label, mstype.int32), F.shape(logit)[1],
                                    self.on_value, self.off_value)
        out_loss = self.ce(logit, one_hot_label)
        out_loss = self.mean(out_loss, 0)
        return out_loss

class Monitor(Callback):
    """
    Monitor loss and time.

    Args:
        lr_init (numpy array): train lr

    Returns:
        None

    Examples:
        >>> Monitor(100,lr_init=Tensor([0.05]*100).asnumpy())
    """

    def __init__(self, lr_init=None):
        super(Monitor, self).__init__()
        self.lr_init = lr_init
        self.lr_init_len = len(lr_init)

    def epoch_begin(self, run_context):
        self.losses = []
        self.epoch_time = time.time()

    def epoch_end(self, run_context):
        cb_params = run_context.original_args()

        epoch_mseconds = (time.time() - self.epoch_time) * 1000
        per_step_mseconds = epoch_mseconds / cb_params.batch_num
        print("epoch time: {:5.3f}, per step time: {:5.3f}, avg loss: {:5.3f}".format(epoch_mseconds,
                                                                                      per_step_mseconds,
                                                                                      np.mean(self.losses)))

    def step_begin(self, run_context):
        self.step_time = time.time()

    def step_end(self, run_context):
        cb_params = run_context.original_args()
        step_mseconds = (time.time() - self.step_time) * 1000
        step_loss = cb_params.net_outputs

        if isinstance(step_loss, (tuple, list)) and isinstance(step_loss[0], Tensor):
            step_loss = step_loss[0]
        if isinstance(step_loss, Tensor):
            step_loss = np.mean(step_loss.asnumpy())

        self.losses.append(step_loss)
        cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num

        print("epoch: [{:3d}/{:3d}], step:[{:5d}/{:5d}], loss:[{:5.3f}/{:5.3f}], time:[{:5.3f}], lr:[{:5.3f}]".format(
            cb_params.cur_epoch_num -
            1, cb_params.epoch_num, cur_step_in_epoch, cb_params.batch_num, step_loss,
            np.mean(self.losses), step_mseconds, self.lr_init[cb_params.cur_step_num - 1]))

def load_ckpt(network, pretrain_ckpt_path, trainable=True):
    """
    incremental_learning or not
    """
    param_dict = load_checkpoint(pretrain_ckpt_path)
    load_param_into_net(network, param_dict)
    if not trainable:
        for param in network.get_parameters():
            param.requires_grad = False

def define_net(args, config):
    backbone_net = MobileNetV2Backbone(platform=args.platform)
    head_net = MobileNetV2Head(input_channel=backbone_net.out_channels, num_classes=config.num_classes)
    net = mobilenet_v2(backbone_net, head_net)

    # load the ckpt file to the network for fine tune or incremental leaning
    if args.pretrain_ckpt:
        if args.train_method == "fine_tune":
            load_ckpt(net, args.pretrain_ckpt)
        elif args.train_method == "incremental_learn":
            load_ckpt(backbone_net, args.pretrain_ckpt, trainable=False)
        elif args.train_method == "train":
            pass
        else:
            raise ValueError("must input the usage of pretrain_ckpt when the pretrain_ckpt isn't None")

    return backbone_net, head_net, net