#!/usr/bin/env python
# -*- coding: UTF-8 -*-
# File: imagenet-resnet.py
# Author: Yuxin Wu <ppwwyyxx@gmail.com>

import cv2
import sys
import argparse
import numpy as np
import os
import multiprocessing

import tensorflow as tf
from tensorflow.contrib.layers import variance_scaling_initializer
from tensorpack import *
from tensorpack.utils.stats import RatioCounter
from tensorpack.tfutils.symbolic_functions import *
from tensorpack.tfutils.summary import *

"""
Training code of Pre-Activation version of ResNet on ImageNet.
It mainly follows the setup in fb.resnet.torch, and get similar performance.
"""

TOTAL_BATCH_SIZE = 256
INPUT_SHAPE = 224
DEPTH = None


class Model(ModelDesc):
    def _get_inputs(self):
        return [InputVar(tf.float32, [None, INPUT_SHAPE, INPUT_SHAPE, 3], 'input'),
                InputVar(tf.int32, [None], 'label')]

    def _build_graph(self, inputs):
        image, label = inputs

        def shortcut(l, n_in, n_out, stride):
            if n_in != n_out:
                return Conv2D('convshortcut', l, n_out, 1, stride=stride)
            else:
                return l

        def basicblock(l, ch_out, stride, preact):
            ch_in = l.get_shape().as_list()[-1]
            if preact == 'both_preact':
                l = BNReLU('preact', l)
                input = l
            elif preact != 'no_preact':
                input = l
                l = BNReLU('preact', l)
            else:
                input = l
            l = Conv2D('conv1', l, ch_out, 3, stride=stride, nl=BNReLU)
            l = Conv2D('conv2', l, ch_out, 3)
            return l + shortcut(input, ch_in, ch_out, stride)

        def bottleneck(l, ch_out, stride, preact):
            ch_in = l.get_shape().as_list()[-1]
            if preact == 'both_preact':
                l = BNReLU('preact', l)
                input = l
            elif preact != 'no_preact':
                input = l
                l = BNReLU('preact', l)
            else:
                input = l
            l = Conv2D('conv1', l, ch_out, 1, nl=BNReLU)
            l = Conv2D('conv2', l, ch_out, 3, stride=stride, nl=BNReLU)
            l = Conv2D('conv3', l, ch_out * 4, 1)
            return l + shortcut(input, ch_in, ch_out * 4, stride)

        def layer(l, layername, block_func, features, count, stride, first=False):
            with tf.variable_scope(layername):
                with tf.variable_scope('block0'):
                    l = block_func(l, features, stride,
                                   'no_preact' if first else 'both_preact')
                for i in range(1, count):
                    with tf.variable_scope('block{}'.format(i)):
                        l = block_func(l, features, 1, 'default')
                return l

        cfg = {
            18: ([2, 2, 2, 2], basicblock),
            34: ([3, 4, 6, 3], basicblock),
            50: ([3, 4, 6, 3], bottleneck),
            101: ([3, 4, 23, 3], bottleneck)
        }
        defs, block_func = cfg[DEPTH]

        with argscope(Conv2D, nl=tf.identity, use_bias=False,
                      W_init=variance_scaling_initializer(mode='FAN_OUT')):
            logits = (LinearWrap(image)
                      .Conv2D('conv0', 64, 7, stride=2, nl=BNReLU)
                      .MaxPooling('pool0', shape=3, stride=2, padding='SAME')
                      .apply(layer, 'group0', block_func, 64, defs[0], 1, first=True)
                      .apply(layer, 'group1', block_func, 128, defs[1], 2)
                      .apply(layer, 'group2', block_func, 256, defs[2], 2)
                      .apply(layer, 'group3', block_func, 512, defs[3], 2)
                      .BNReLU('bnlast')
                      .GlobalAvgPooling('gap')
                      .FullyConnected('linear', 1000, nl=tf.identity)())

        loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label)
        loss = tf.reduce_mean(loss, name='xentropy-loss')

        wrong = prediction_incorrect(logits, label, 1, name='wrong-top1')
        add_moving_summary(tf.reduce_mean(wrong, name='train-error-top1'))

        wrong = prediction_incorrect(logits, label, 5, name='wrong-top5')
        add_moving_summary(tf.reduce_mean(wrong, name='train-error-top5'))

        wd_cost = regularize_cost('.*/W', l2_regularizer(1e-4), name='l2_regularize_loss')
        add_moving_summary(loss, wd_cost)
        self.cost = tf.add_n([loss, wd_cost], name='cost')


def get_data(train_or_test):
    isTrain = train_or_test == 'train'

    datadir = args.data
    ds = dataset.ILSVRC12(datadir, train_or_test,
                          shuffle=True if isTrain else False, dir_structure='original')
    image_mean = np.array([0.485, 0.456, 0.406], dtype='float32')
    image_std = np.array([0.229, 0.224, 0.225], dtype='float32')

    if isTrain:
        class Resize(imgaug.ImageAugmentor):
            """
            crop 8%~100% of the original image
            See `Going Deeper with Convolutions` by Google.
            """

            def _augment(self, img, _):
                h, w = img.shape[:2]
                area = h * w
                for _ in range(10):
                    targetArea = self.rng.uniform(0.08, 1.0) * area
                    aspectR = self.rng.uniform(0.75, 1.333)
                    ww = int(np.sqrt(targetArea * aspectR))
                    hh = int(np.sqrt(targetArea / aspectR))
                    if self.rng.uniform() < 0.5:
                        ww, hh = hh, ww
                    if hh <= h and ww <= w:
                        x1 = 0 if w == ww else self.rng.randint(0, w - ww)
                        y1 = 0 if h == hh else self.rng.randint(0, h - hh)
                        out = img[y1:y1 + hh, x1:x1 + ww]
                        out = cv2.resize(out, (224, 224), interpolation=cv2.INTER_CUBIC)
                        return out
                out = cv2.resize(img, (224, 224), interpolation=cv2.INTER_CUBIC)
                return out

        augmentors = [
            Resize(),
            imgaug.RandomOrderAug(
                [imgaug.Brightness(30, clip=False),
                 imgaug.Contrast((0.8, 1.2), clip=False),
                 imgaug.Saturation(0.4),
                 imgaug.Lighting(0.1,
                                 eigval=[0.2175, 0.0188, 0.0045],
                                 eigvec=[[-0.5675, 0.7192, 0.4009],
                                         [-0.5808, -0.0045, -0.8140],
                                         [-0.5836, -0.6948, 0.4203]]
                                 )]),
            imgaug.Clip(),
            imgaug.Flip(horiz=True),
            imgaug.MapImage(lambda x: (x * (1.0 / 255) - image_mean) / image_std),
        ]
    else:
        augmentors = [
            imgaug.ResizeShortestEdge(256),
            imgaug.CenterCrop((224, 224)),
            imgaug.MapImage(lambda x: (x * (1.0 / 255) - image_mean) / image_std),
        ]
    ds = AugmentImageComponent(ds, augmentors)
    ds = BatchData(ds, BATCH_SIZE, remainder=not isTrain)
    if isTrain:
        ds = PrefetchDataZMQ(ds, min(30, multiprocessing.cpu_count()))
    return ds


def get_config():
    # prepare dataset
    dataset_train = get_data('train')
    dataset_val = get_data('val')

    lr = get_scalar_var('learning_rate', 0.1, summary=True)
    return TrainConfig(
        dataflow=dataset_train,
        optimizer=tf.train.MomentumOptimizer(lr, 0.9, use_nesterov=True),
        callbacks=[
            ModelSaver(),
            InferenceRunner(dataset_val, [
                ClassificationError('wrong-top1', 'val-error-top1'),
                ClassificationError('wrong-top5', 'val-error-top5')]),
            ScheduledHyperParamSetter('learning_rate',
                                      [(30, 1e-2), (60, 1e-3), (85, 1e-4), (95, 1e-5)]),
            HumanHyperParamSetter('learning_rate'),
        ],
        model=Model(),
        steps_per_epoch=5000,
        max_epoch=110,
    )


def eval_on_ILSVRC12(model_file, data_dir):
    ds = get_data('val')
    pred_config = PredictConfig(
        model=Model(),
        session_init=get_model_loader(model_file),
        input_names=['input', 'label'],
        output_names=['wrong-top1', 'wrong-top5']
    )
    pred = SimpleDatasetPredictor(pred_config, ds)
    acc1, acc5 = RatioCounter(), RatioCounter()
    for o in pred.get_result():
        batch_size = o[0].shape[0]
        acc1.feed(o[0].sum(), batch_size)
        acc5.feed(o[1].sum(), batch_size)
    print("Top1 Error: {}".format(acc1.ratio))
    print("Top5 Error: {}".format(acc5.ratio))


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
    parser.add_argument('--data', help='ILSVRC dataset dir')
    parser.add_argument('--load', help='load model')
    parser.add_argument('-d', '--depth', help='resnet depth',
                        type=int, default=18, choices=[18, 34, 50, 101])
    parser.add_argument('--eval', action='store_true')
    args = parser.parse_args()

    DEPTH = args.depth
    if args.gpu:
        os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
    if args.eval:
        BATCH_SIZE = 128    # something that can run on one gpu
        eval_on_ILSVRC12(args.load, args.data)
        sys.exit()

    assert args.gpu is not None, "Need to specify a list of gpu for training!"
    NR_GPU = len(args.gpu.split(','))
    BATCH_SIZE = TOTAL_BATCH_SIZE // NR_GPU

    logger.auto_set_dir()
    config = get_config()
    if args.load:
        config.session_init = SaverRestore(args.load)
    config.nr_tower = NR_GPU
    SyncMultiGPUTrainer(config).train()
