# 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 logging
import math
import time
import unittest
import numpy as np

import paddle
import paddle.fluid as fluid
from paddle.fluid.dygraph.base import to_variable
from paddle.fluid.dygraph.nn import BatchNorm, Conv2D, Linear, Pool2D
from paddle.fluid.dygraph import declarative
from paddle.fluid.dygraph import ProgramTranslator
from paddle.fluid.dygraph.io import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX

from predictor_utils import PredictorTools

SEED = 2020
np.random.seed(SEED)

BATCH_SIZE = 8
EPOCH_NUM = 1
PRINT_STEP = 2
STEP_NUM = 10
MODEL_SAVE_DIR = "./inference"
MODEL_SAVE_PREFIX = "./inference/se_resnet"
MODEL_FILENAME = "se_resnet" + INFER_MODEL_SUFFIX
PARAMS_FILENAME = "se_resnet" + INFER_PARAMS_SUFFIX
DY_STATE_DICT_SAVE_PATH = "./se_resnet.dygraph"

place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda() \
    else fluid.CPUPlace()

# Note: Set True to eliminate randomness.
#     1. For one operation, cuDNN has several algorithms,
#        some algorithm results are non-deterministic, like convolution algorithms.
if fluid.is_compiled_with_cuda():
    fluid.set_flags({'FLAGS_cudnn_deterministic': True})

train_parameters = {
    "learning_strategy": {
        "name": "cosine_decay",
        "batch_size": BATCH_SIZE,
        "epochs": [40, 80, 100],
        "steps": [0.1, 0.01, 0.001, 0.0001]
    },
    "lr": 0.0125,
    "total_images": 6149,
    "momentum_rate": 0.9,
    "l2_decay": 1.2e-4,
    "num_epochs": 1,
}


def optimizer_setting(params, parameter_list):
    ls = params["learning_strategy"]
    if "total_images" not in params:
        total_images = 6149
    else:
        total_images = params["total_images"]

    batch_size = ls["batch_size"]
    l2_decay = params["l2_decay"]
    momentum_rate = params["momentum_rate"]

    step = int(math.ceil(float(total_images) / batch_size))
    bd = [step * e for e in ls["epochs"]]
    lr = params["lr"]
    num_epochs = params["num_epochs"]
    optimizer = fluid.optimizer.Momentum(
        learning_rate=fluid.layers.cosine_decay(
            learning_rate=lr, step_each_epoch=step, epochs=num_epochs),
        momentum=momentum_rate,
        regularization=fluid.regularizer.L2Decay(l2_decay),
        parameter_list=parameter_list)

    return optimizer


class ConvBNLayer(fluid.dygraph.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 filter_size,
                 stride=1,
                 groups=1,
                 act=None):
        super(ConvBNLayer, self).__init__()

        self._conv = Conv2D(
            num_channels=num_channels,
            num_filters=num_filters,
            filter_size=filter_size,
            stride=stride,
            padding=(filter_size - 1) // 2,
            groups=groups,
            act=None,
            bias_attr=False)

        self._batch_norm = BatchNorm(num_filters, act=act)

    def forward(self, inputs):
        y = self._conv(inputs)
        y = self._batch_norm(y)

        return y


class SqueezeExcitation(fluid.dygraph.Layer):
    def __init__(self, num_channels, reduction_ratio):

        super(SqueezeExcitation, self).__init__()
        self._num_channels = num_channels
        self._pool = Pool2D(pool_size=0, pool_type='avg', global_pooling=True)
        stdv = 1.0 / math.sqrt(num_channels * 1.0)
        self._fc = Linear(
            num_channels,
            num_channels // reduction_ratio,
            param_attr=fluid.ParamAttr(
                initializer=fluid.initializer.Uniform(-stdv, stdv)),
            act='relu')
        stdv = 1.0 / math.sqrt(num_channels / 16.0 * 1.0)
        self._excitation = Linear(
            num_channels // reduction_ratio,
            num_channels,
            param_attr=fluid.ParamAttr(
                initializer=fluid.initializer.Uniform(-stdv, stdv)),
            act='sigmoid')

    def forward(self, input):
        y = self._pool(input)
        y = fluid.layers.reshape(y, shape=[-1, self._num_channels])
        y = self._fc(y)
        y = self._excitation(y)
        y = fluid.layers.elementwise_mul(x=input, y=y, axis=0)
        return y


class BottleneckBlock(fluid.dygraph.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 stride,
                 cardinality,
                 reduction_ratio,
                 shortcut=True):
        super(BottleneckBlock, self).__init__()

        self.conv0 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=num_filters,
            filter_size=1,
            act="relu")
        self.conv1 = ConvBNLayer(
            num_channels=num_filters,
            num_filters=num_filters,
            filter_size=3,
            stride=stride,
            groups=cardinality,
            act="relu")
        self.conv2 = ConvBNLayer(
            num_channels=num_filters,
            num_filters=num_filters * 2,
            filter_size=1,
            act=None)

        self.scale = SqueezeExcitation(
            num_channels=num_filters * 2, reduction_ratio=reduction_ratio)

        if not shortcut:
            self.short = ConvBNLayer(
                num_channels=num_channels,
                num_filters=num_filters * 2,
                filter_size=1,
                stride=stride)

        self.shortcut = shortcut

        self._num_channels_out = num_filters * 2

    def forward(self, inputs):
        y = self.conv0(inputs)
        conv1 = self.conv1(y)
        conv2 = self.conv2(conv1)
        scale = self.scale(conv2)

        if self.shortcut:
            short = inputs
        else:
            short = self.short(inputs)

        y = fluid.layers.elementwise_add(x=short, y=scale, act='relu')
        return y


class SeResNeXt(fluid.dygraph.Layer):
    def __init__(self, layers=50, class_dim=102):
        super(SeResNeXt, self).__init__()

        self.layers = layers
        supported_layers = [50, 101, 152]
        assert layers in supported_layers, \
            "supported layers are {} but input layer is {}".format(supported_layers, layers)

        if layers == 50:
            cardinality = 32
            reduction_ratio = 16
            depth = [3, 4, 6, 3]
            num_filters = [128, 256, 512, 1024]
            self.conv0 = ConvBNLayer(
                num_channels=3,
                num_filters=64,
                filter_size=7,
                stride=2,
                act='relu')
            self.pool = Pool2D(
                pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
        elif layers == 101:
            cardinality = 32
            reduction_ratio = 16
            depth = [3, 4, 23, 3]
            num_filters = [128, 256, 512, 1024]
            self.conv0 = ConvBNLayer(
                num_channels=3,
                num_filters=64,
                filter_size=7,
                stride=2,
                act='relu')
            self.pool = Pool2D(
                pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
        elif layers == 152:
            cardinality = 64
            reduction_ratio = 16
            depth = [3, 8, 36, 3]
            num_filters = [128, 256, 512, 1024]
            self.conv0 = ConvBNLayer(
                num_channels=3,
                num_filters=64,
                filter_size=3,
                stride=2,
                act='relu')
            self.conv1 = ConvBNLayer(
                num_channels=64,
                num_filters=64,
                filter_size=3,
                stride=1,
                act='relu')
            self.conv2 = ConvBNLayer(
                num_channels=64,
                num_filters=128,
                filter_size=3,
                stride=1,
                act='relu')
            self.pool = Pool2D(
                pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')

        self.bottleneck_block_list = []
        num_channels = 64
        if layers == 152:
            num_channels = 128
        for block in range(len(depth)):
            shortcut = False
            for i in range(depth[block]):
                bottleneck_block = self.add_sublayer(
                    'bb_%d_%d' % (block, i),
                    BottleneckBlock(
                        num_channels=num_channels,
                        num_filters=num_filters[block],
                        stride=2 if i == 0 and block != 0 else 1,
                        cardinality=cardinality,
                        reduction_ratio=reduction_ratio,
                        shortcut=shortcut))
                num_channels = bottleneck_block._num_channels_out
                self.bottleneck_block_list.append(bottleneck_block)
                shortcut = True

        self.pool2d_avg = Pool2D(
            pool_size=7, pool_type='avg', global_pooling=True)
        stdv = 1.0 / math.sqrt(2048 * 1.0)

        self.pool2d_avg_output = num_filters[len(num_filters) - 1] * 2 * 1 * 1

        self.out = Linear(
            self.pool2d_avg_output,
            class_dim,
            param_attr=fluid.param_attr.ParamAttr(
                initializer=fluid.initializer.Uniform(-stdv, stdv)))

    @declarative
    def forward(self, inputs, label):
        if self.layers == 50 or self.layers == 101:
            y = self.conv0(inputs)
            y = self.pool(y)
        elif self.layers == 152:
            y = self.conv0(inputs)
            y = self.conv1(y)
            y = self.conv2(y)
            y = self.pool(y)

        for bottleneck_block in self.bottleneck_block_list:
            y = bottleneck_block(y)

        y = self.pool2d_avg(y)
        y = fluid.layers.dropout(y, dropout_prob=0.5, seed=100)
        y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_output])
        out = self.out(y)

        softmax_out = fluid.layers.softmax(out, use_cudnn=False)
        loss = fluid.layers.cross_entropy(input=softmax_out, label=label)
        avg_loss = fluid.layers.mean(x=loss)

        acc_top1 = fluid.layers.accuracy(input=softmax_out, label=label, k=1)
        acc_top5 = fluid.layers.accuracy(input=softmax_out, label=label, k=5)
        return out, avg_loss, acc_top1, acc_top5


def train(train_reader, to_static):
    program_translator = ProgramTranslator()
    program_translator.enable(to_static)

    np.random.seed(SEED)

    with fluid.dygraph.guard(place):
        paddle.seed(SEED)
        paddle.framework.random._manual_program_seed(SEED)
        se_resnext = SeResNeXt()
        optimizer = optimizer_setting(train_parameters, se_resnext.parameters())

        for epoch_id in range(EPOCH_NUM):
            total_loss = 0.0
            total_acc1 = 0.0
            total_acc5 = 0.0
            total_sample = 0
            step_idx = 0
            speed_list = []
            for step_id, data in enumerate(train_reader()):
                dy_x_data = np.array([x[0].reshape(3, 224, 224)
                                      for x in data]).astype('float32')
                y_data = np.array([x[1] for x in data]).astype('int64').reshape(
                    BATCH_SIZE, 1)

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

                pred, avg_loss, acc_top1, acc_top5 = se_resnext(img, label)

                dy_out = avg_loss.numpy()
                avg_loss.backward()

                optimizer.minimize(avg_loss)
                se_resnext.clear_gradients()

                lr = optimizer._global_learning_rate().numpy()
                total_loss += dy_out
                total_acc1 += acc_top1.numpy()
                total_acc5 += acc_top5.numpy()
                total_sample += 1
                if step_id % PRINT_STEP == 0:
                    if step_id == 0:
                        logging.info( "epoch %d | step %d, loss %0.3f, acc1 %0.3f, acc5 %0.3f" % \
                                      ( epoch_id, step_id, total_loss / total_sample, \
                                        total_acc1 / total_sample, total_acc5 / total_sample))
                        avg_batch_time = time.time()
                    else:
                        speed = PRINT_STEP / (time.time() - avg_batch_time)
                        speed_list.append(speed)
                        logging.info( "epoch %d | step %d, loss %0.3f, acc1 %0.3f, acc5 %0.3f, speed %.3f steps/s" % \
                                      ( epoch_id, step_id, total_loss / total_sample, \
                                        total_acc1 / total_sample, total_acc5 / total_sample, speed))
                        avg_batch_time = time.time()

                step_idx += 1
                if step_idx == STEP_NUM:
                    if to_static:
                        fluid.dygraph.jit.save(
                            se_resnext,
                            MODEL_SAVE_PREFIX, [img],
                            output_spec=[pred])
                    else:
                        fluid.dygraph.save_dygraph(se_resnext.state_dict(),
                                                   DY_STATE_DICT_SAVE_PATH)
                    break
        return pred.numpy(), avg_loss.numpy(), acc_top1.numpy(), acc_top5.numpy(
        )


def predict_dygraph(data):
    program_translator = ProgramTranslator()
    program_translator.enable(False)
    with fluid.dygraph.guard(place):
        se_resnext = SeResNeXt()

        model_dict, _ = fluid.dygraph.load_dygraph(DY_STATE_DICT_SAVE_PATH)
        se_resnext.set_dict(model_dict)
        se_resnext.eval()

        label = np.random.random([1, 1]).astype("int64")
        img = fluid.dygraph.to_variable(data)
        label = fluid.dygraph.to_variable(label)
        pred_res, _, _, _ = se_resnext(img, label)

        return pred_res.numpy()


def predict_static(data):
    paddle.enable_static()
    exe = fluid.Executor(place)
    [inference_program, feed_target_names,
     fetch_targets] = fluid.io.load_inference_model(
         MODEL_SAVE_DIR,
         executor=exe,
         model_filename=MODEL_FILENAME,
         params_filename=PARAMS_FILENAME)

    pred_res = exe.run(inference_program,
                       feed={feed_target_names[0]: data},
                       fetch_list=fetch_targets)

    return pred_res[0]


def predict_dygraph_jit(data):
    with fluid.dygraph.guard(place):
        se_resnext = fluid.dygraph.jit.load(MODEL_SAVE_PREFIX)
        se_resnext.eval()

        pred_res = se_resnext(data)

        return pred_res.numpy()


def predict_analysis_inference(data):
    output = PredictorTools(MODEL_SAVE_DIR, MODEL_FILENAME, PARAMS_FILENAME,
                            [data])
    out = output()
    return out


class TestSeResnet(unittest.TestCase):
    def setUp(self):
        self.train_reader = paddle.batch(
            paddle.dataset.flowers.train(
                use_xmap=False, cycle=True),
            batch_size=BATCH_SIZE,
            drop_last=True)

    def verify_predict(self):
        image = np.random.random([1, 3, 224, 224]).astype('float32')
        dy_pre = predict_dygraph(image)
        st_pre = predict_static(image)
        dy_jit_pre = predict_dygraph_jit(image)
        predictor_pre = predict_analysis_inference(image)
        self.assertTrue(
            np.allclose(dy_pre, st_pre),
            msg="dy_pre:\n {}\n, st_pre: \n{}.".format(dy_pre, st_pre))
        self.assertTrue(
            np.allclose(dy_jit_pre, st_pre),
            msg="dy_jit_pre:\n {}\n, st_pre: \n{}.".format(dy_jit_pre, st_pre))

        flat_st_pre = st_pre.flatten()
        flat_predictor_pre = np.array(predictor_pre).flatten()
        for i in range(len(flat_predictor_pre)):
            # modify precision to 1e-6, avoid unittest failed
            self.assertAlmostEqual(
                flat_predictor_pre[i],
                flat_st_pre[i],
                delta=1e-6,
                msg="predictor_pre:\n {}\n, st_pre: \n{}.".format(
                    flat_predictor_pre[i], flat_st_pre[i]))

    def test_check_result(self):
        pred_1, loss_1, acc1_1, acc5_1 = train(
            self.train_reader, to_static=False)
        pred_2, loss_2, acc1_2, acc5_2 = train(
            self.train_reader, to_static=True)

        self.assertTrue(
            np.allclose(pred_1, pred_2),
            msg="static pred: {} \ndygraph pred: {}".format(pred_1, pred_2))
        self.assertTrue(
            np.allclose(loss_1, loss_2),
            msg="static loss: {} \ndygraph loss: {}".format(loss_1, loss_2))
        self.assertTrue(
            np.allclose(acc1_1, acc1_2),
            msg="static acc1: {} \ndygraph acc1: {}".format(acc1_1, acc1_2))
        self.assertTrue(
            np.allclose(acc5_1, acc5_2),
            msg="static acc5: {} \ndygraph acc5: {}".format(acc5_1, acc5_2))

        self.verify_predict()


if __name__ == '__main__':
    unittest.main()
