# Copyright (c) 2016-present, Facebook, Inc.
#
# 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.
##############################################################################

## @package net_construct_bench
# Module caffe2.experiments.python.net_construct_bench





import argparse
import logging
import time

from caffe2.python import workspace, data_parallel_model
from caffe2.python import cnn

import caffe2.python.models.resnet as resnet

'''
Simple benchmark that creates a data-parallel resnet-50 model
and measures the time.
'''


logging.basicConfig()
log = logging.getLogger("net_construct_bench")
log.setLevel(logging.DEBUG)


def AddMomentumParameterUpdate(train_model, LR):
    '''
    Add the momentum-SGD update.
    '''
    params = train_model.GetParams()
    assert(len(params) > 0)
    ONE = train_model.param_init_net.ConstantFill(
        [], "ONE", shape=[1], value=1.0,
    )
    NEGONE = train_model.param_init_net.ConstantFill(
        [], 'NEGONE', shape=[1], value=-1.0,
    )

    for param in params:
        param_grad = train_model.param_to_grad[param]
        param_momentum = train_model.param_init_net.ConstantFill(
            [param], param + '_momentum', value=0.0
        )

        # Update param_grad and param_momentum in place
        train_model.net.MomentumSGD(
            [param_grad, param_momentum, LR],
            [param_grad, param_momentum],
            momentum=0.9,
            nesterov=1
        )

        # Update parameters by applying the moment-adjusted gradient
        train_model.WeightedSum(
            [param, ONE, param_grad, NEGONE],
            param
        )


def Create(args):
    gpus = list(range(args.num_gpus))
    log.info("Running on gpus: {}".format(gpus))

    # Create CNNModeLhelper object
    train_model = cnn.CNNModelHelper(
        order="NCHW",
        name="resnet50",
        use_cudnn=True,
        cudnn_exhaustive_search=False
    )

    # Model building functions
    def create_resnet50_model_ops(model, loss_scale):
        [softmax, loss] = resnet.create_resnet50(
            model,
            "data",
            num_input_channels=3,
            num_labels=1000,
            label="label",
        )
        model.Accuracy([softmax, "label"], "accuracy")
        return [loss]

    # SGD
    def add_parameter_update_ops(model):
        model.AddWeightDecay(1e-4)
        ITER = model.Iter("ITER")
        stepsz = int(30)
        LR = model.net.LearningRate(
            [ITER],
            "LR",
            base_lr=0.1,
            policy="step",
            stepsize=stepsz,
            gamma=0.1,
        )
        AddMomentumParameterUpdate(model, LR)

    def add_image_input(model):
        pass

    start_time = time.time()

    # Create parallelized model
    data_parallel_model.Parallelize_GPU(
        train_model,
        input_builder_fun=add_image_input,
        forward_pass_builder_fun=create_resnet50_model_ops,
        param_update_builder_fun=add_parameter_update_ops,
        devices=gpus,
    )

    ct = time.time() - start_time
    train_model.net._CheckLookupTables()

    log.info("Model create for {} gpus took: {} secs".format(len(gpus), ct))


def main():
    # TODO: use argv
    parser = argparse.ArgumentParser(
        description="Caffe2: Benchmark for net construction"
    )
    parser.add_argument("--num_gpus", type=int, default=1,
                        help="Number of GPUs.")
    args = parser.parse_args()

    Create(args)


if __name__ == '__main__':
    workspace.GlobalInit(['caffe2', '--caffe2_log_level=2'])
    import cProfile

    cProfile.run('main()', sort="cumulative")