ofa_train.py

# Copyright (c) 2020 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.

import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.fluid.dygraph.nn as nn
from paddle.nn import ReLU
from paddleslim.nas.ofa import OFA, RunConfig, DistillConfig
from paddleslim.nas.ofa import supernet


class Model(fluid.dygraph.Layer):
    def __init__(self):
        super(Model, self).__init__()
        with supernet(
                kernel_size=(3, 5, 7), expand_ratio=[1, 2, 4]) as ofa_super:
            models = []
            models += [nn.Conv2D(1, 6, 3)]
            models += [ReLU()]
            models += [nn.Pool2D(2, 'max', 2)]
            models += [nn.Conv2D(6, 16, 5, padding=0)]
            models += [ReLU()]
            models += [nn.Pool2D(2, 'max', 2)]
            models += [
                nn.Linear(784, 120), nn.Linear(120, 84), nn.Linear(84, 10)
            ]
            models = ofa_super.convert(models)
        self.models = paddle.nn.Sequential(*models)

    def forward(self, inputs, label, depth=None):
        if depth != None:
            assert isinstance(depth, int)
            assert depth < len(self.models)
            models = self.models[:depth]
        else:
            depth = len(self.models)
            models = self.models[:]

        for idx, layer in enumerate(models):
            if idx == 6:
                inputs = fluid.layers.flatten(inputs, 1)
            inputs = layer(inputs)

        inputs = fluid.layers.softmax(inputs)
        return inputs


def test_ofa():

    default_run_config = {
        'train_batch_size': 256,
        'eval_batch_size': 64,
        'n_epochs': [[1], [2, 3], [4, 5]],
        'init_learning_rate': [[0.001], [0.003, 0.001], [0.003, 0.001]],
        'dynamic_batch_size': [1, 1, 1],
        'total_images': 50000,  #1281167,
        'elastic_depth': (2, 5, 8)
    }
    run_config = RunConfig(**default_run_config)

    default_distill_config = {
        'lambda_distill': 0.01,
        'teacher_model': Model,
        'mapping_layers': ['models.0.fn']
    }
    distill_config = DistillConfig(**default_distill_config)

    fluid.enable_dygraph()
    model = Model()
    ofa_model = OFA(model, run_config, distill_config=distill_config)

    train_reader = paddle.fluid.io.batch(
        paddle.dataset.mnist.train(), batch_size=256, drop_last=True)

    start_epoch = 0
    for idx in range(len(run_config.n_epochs)):
        cur_idx = run_config.n_epochs[idx]
        for ph_idx in range(len(cur_idx)):
            cur_lr = run_config.init_learning_rate[idx][ph_idx]
            adam = fluid.optimizer.Adam(
                learning_rate=cur_lr,
                parameter_list=(ofa_model.parameters() + ofa_model.netAs_param))
            for epoch_id in range(start_epoch,
                                  run_config.n_epochs[idx][ph_idx]):
                for batch_id, data in enumerate(train_reader()):
                    dy_x_data = np.array(
                        [x[0].reshape(1, 28, 28)
                         for x in data]).astype('float32')
                    y_data = np.array(
                        [x[1] for x in data]).astype('int64').reshape(-1, 1)

                    img = fluid.dygraph.to_variable(dy_x_data)
                    label = fluid.dygraph.to_variable(y_data)
                    label.stop_gradient = True

                    for model_no in range(run_config.dynamic_batch_size[idx]):
                        output, _ = ofa_model(img, label)
                        loss = fluid.layers.reduce_mean(output)
                        dis_loss = ofa_model.calc_distill_loss()
                        loss += dis_loss
                        loss.backward()

                        if batch_id % 10 == 0:
                            print(
                                'epoch: {}, batch: {}, loss: {}, distill loss: {}'.
                                format(epoch_id, batch_id,
                                       loss.numpy()[0], dis_loss.numpy()[0]))
                    ### accumurate dynamic_batch_size network of gradients for same batch of data
                    ### NOTE: need to fix gradients accumulate in PaddlePaddle
                    adam.minimize(loss)
                    adam.clear_gradients()
            start_epoch = run_config.n_epochs[idx][ph_idx]


test_ofa()