import argparse
import logging
import pprint
import mxnet as mx
import numpy as np
import os.path as osp
import pickle as pkl

from rcnn.config import config, default, generate_config
from rcnn.symbol import *
from rcnn.core import callback, metric
from rcnn.core.loader import MaskROIIter
from rcnn.core.module import MutableModule
from rcnn.processing.bbox_regression import add_bbox_regression_targets, add_mask_targets
from rcnn.processing.assign_levels import add_assign_targets
from rcnn.utils.load_data import load_proposal_roidb, merge_roidb #, filter_roidb
from rcnn.utils.load_model import load_param


def train_net(network, dataset, image_set, root_path, dataset_path,
               frequent, kvstore, work_load_list, no_flip, no_shuffle, resume,
               ctx, pretrained, epoch, prefix, begin_epoch, end_epoch,
               train_shared, lr, lr_step, proposal, maskrcnn_stage=None):

    # set up logger
    logging.basicConfig()
    logger = logging.getLogger()
    logger.setLevel(logging.INFO)

    # setup config
    config.TRAIN.BATCH_IMAGES = 1
    config.TRAIN.BATCH_ROIS = 128
    config.TRAIN.END2END = True
    config.TRAIN.BBOX_NORMALIZATION_PRECOMPUTED = True

    # load symbol
    sym = eval('get_' + network + '_train')(num_classes=config.NUM_CLASSES, num_anchors=config.NUM_ANCHORS)
    feat_sym = []
    for stride in config.RPN_FEAT_STRIDE:
        feat_sym.append(sym.get_internals()['rpn_cls_score_stride%s_output' % stride])

    # setup multi-gpu
    batch_size = len(ctx)
    input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size

    # print config
    logger.info(pprint.pformat(config))

    roidb_file = root_path + '/cache/' + dataset + '_roidb_with_mask.pkl'
    mean_file = root_path + '/cache/' + dataset + '_roidb_mean.pkl'
    std_file = root_path + '/cache/' + dataset + '_roidb_std.pkl'
    if maskrcnn_stage is not None:
        roidb_file = root_path + '/cache/' + dataset + '_roidb_with_mask_' + maskrcnn_stage + '.pkl'
        mean_file = root_path + '/cache/' + dataset + '_roidb_mean_' + maskrcnn_stage + '.pkl'
        std_file = root_path + '/cache/' + dataset + '_roidb_std_' + maskrcnn_stage + '.pkl'

    if osp.exists(roidb_file) and osp.exists(mean_file) and osp.exists(std_file):
        print('Load ' + roidb_file)
        with open(roidb_file, 'r') as f:
            roidb = pkl.load(f)
        print('Load ' + mean_file)
        with open(mean_file, 'r') as f:
            means = pkl.load(f)
        print('Load ' + std_file)
        with open(std_file, 'r') as f:
            stds = pkl.load(f)
    else:
        # load dataset and prepare imdb for training
        image_sets = [iset for iset in image_set.split('+')]
        roidbs = [load_proposal_roidb(dataset, image_set, root_path, dataset_path,
                                      proposal=proposal, append_gt=True, flip=not no_flip)
                  for image_set in image_sets]
        roidb = merge_roidb(roidbs)

        def filter_roidb(roidb):
            """ remove roidb entries without usable rois """

            def is_valid(entry):
                """ valid images have at least 1 fg or bg roi """
                overlaps = entry['max_overlaps']
                fg_inds = np.where(overlaps >= config.TRAIN.FG_THRESH)[0]
                bg_inds = np.where((overlaps < config.TRAIN.BG_THRESH_HI) & (overlaps >= config.TRAIN.BG_THRESH_LO))[0]
                valid = len(fg_inds) > 0 and len(bg_inds) > 0
                return valid

            num = len(roidb)
            filtered_roidb = [entry for entry in roidb if is_valid(entry)]
            num_after = len(filtered_roidb)
            print('filtered %d roidb entries: %d -> %d' % (num - num_after, num, num_after))

            return filtered_roidb

        roidb = filter_roidb(roidb)
        means, stds = add_bbox_regression_targets(roidb)
        add_assign_targets(roidb)
        add_mask_targets(roidb)
        for file, obj in zip([roidb_file, mean_file, std_file], [roidb, means, stds]):
            with open(file, 'w') as f:
                pkl.dump(obj, f, -1)

    # load training data
    train_data = MaskROIIter(roidb, batch_size=input_batch_size, shuffle=not no_shuffle,
                             ctx=ctx, work_load_list=work_load_list, aspect_grouping=config.TRAIN.ASPECT_GROUPING)
    # infer max shape
    max_data_shape = [('data', (input_batch_size, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES])))]
    max_label_shape = []
    for s in config.RCNN_FEAT_STRIDE:
        max_data_shape.append(('rois_stride%s' % s, (input_batch_size, config.TRAIN.BATCH_ROIS, 5)))
        max_label_shape.append(('label_stride%s' % s, (input_batch_size, config.TRAIN.BATCH_ROIS)))
        max_label_shape.append(('bbox_target_stride%s' % s, (input_batch_size, config.TRAIN.BATCH_ROIS*config.NUM_CLASSES*4)))
        max_label_shape.append(('bbox_weight_stride%s' % s, (input_batch_size, config.TRAIN.BATCH_ROIS*config.NUM_CLASSES*4)))
        max_label_shape.append(('mask_target_stride%s' % s, (input_batch_size, config.TRAIN.BATCH_ROIS, config.NUM_CLASSES, 28, 28)))
        max_label_shape.append(('mask_weight_stride%s' % s, (input_batch_size, config.TRAIN.BATCH_ROIS, config.NUM_CLASSES, 1, 1)))
    # infer shape
    data_shape_dict = dict(train_data.provide_data + train_data.provide_label)

    arg_shape, out_shape, aux_shape = sym.infer_shape(**data_shape_dict)
    arg_shape_dict = dict(zip(sym.list_arguments(), arg_shape))
    out_shape_dict = zip(sym.list_outputs(), out_shape)
    aux_shape_dict = dict(zip(sym.list_auxiliary_states(), aux_shape))
    print('output shape:')
    pprint.pprint(out_shape_dict)

    # load and initialize params
    if resume:
        arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
    else:
        arg_params, aux_params = load_param(pretrained, epoch, convert=True)
        init_bbox_pred = mx.init.Normal(sigma=0.001)
        init_internal = mx.init.Normal(sigma=0.01)
        init = mx.init.Xavier(factor_type="in", rnd_type='gaussian', magnitude=2)
        for k in sym.list_arguments():
            if k in data_shape_dict:
                continue
            if k not in arg_params:
                print('init', k)
                arg_params[k] = mx.nd.zeros(shape=arg_shape_dict[k])
                init_internal(k, arg_params[k])
                if k in ['rcnn_fc_bbox_weight', 'bbox_pred_weight']:
                    init_bbox_pred(k, arg_params[k])
                if k.endswith('bias'):
                    arg_params[k] = mx.nd.zeros(shape=arg_shape_dict[k])
                if 'ctx_red_weight' in k:
                    ctx_shape = np.array(arg_shape_dict[k])
                    ctx_shape[1] /= 2
                    arg_params[k][:] = np.concatenate((np.eye(ctx_shape[1]).reshape(ctx_shape), np.zeros(ctx_shape)), axis=1)

        for k in sym.list_auxiliary_states():
            if k not in aux_params:
                print('init', k)
                aux_params[k] = mx.nd.zeros(shape=aux_shape_dict[k])
                init(k, aux_params[k])

    # check parameter shapes
    for k in sym.list_arguments():
        if k in data_shape_dict:
            continue
        assert k in arg_params, k + ' not initialized'
        assert arg_params[k].shape == arg_shape_dict[k], \
            'shape inconsistent for ' + k + ' inferred ' + str(arg_shape_dict[k]) + ' provided ' + str(arg_params[k].shape)
    for k in sym.list_auxiliary_states():
        assert k in aux_params, k + ' not initialized'
        assert aux_params[k].shape == aux_shape_dict[k], \
            'shape inconsistent for ' + k + ' inferred ' + str(aux_shape_dict[k]) + ' provided ' + str(aux_params[k].shape)

    # create solver
    data_names = [k[0] for k in train_data.provide_data]
    label_names = [k[0] for k in train_data.provide_label]
    if train_shared:
        fixed_param_prefix = config.FIXED_PARAMS_SHARED
    else:
        fixed_param_prefix = config.FIXED_PARAMS
    mod = MutableModule(sym, data_names=data_names, label_names=label_names,
                        logger=logger, context=ctx, work_load_list=work_load_list,
                        max_data_shapes=max_data_shape, max_label_shapes=max_label_shape,
                        fixed_param_prefix=fixed_param_prefix)

    # decide training params
    # metric
    eval_metric = metric.RCNNAccMetric()
    cls_metric = metric.RCNNLogLossMetric()
    bbox_metric = metric.RCNNRegLossMetric()
    mask_acc_metric = metric.MaskAccMetric()
    mask_log_metric = metric.MaskLogLossMetric()
    eval_metrics = mx.metric.CompositeEvalMetric()
    for child_metric in [eval_metric, cls_metric, bbox_metric, mask_acc_metric, mask_log_metric]:
        eval_metrics.add(child_metric)
    # callback
    batch_end_callback = callback.Speedometer(train_data.batch_size, frequent=frequent)
    epoch_end_callback = callback.do_checkpoint(prefix, means, stds)
    # decide learning rate
    base_lr = lr
    lr_factor = 0.1
    lr_epoch = [int(epoch) for epoch in lr_step.split(',')]
    lr_epoch_diff = [epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch]
    lr = base_lr * (lr_factor ** (len(lr_epoch) - len(lr_epoch_diff)))
    lr_iters = [int(epoch * len(roidb) / batch_size) for epoch in lr_epoch_diff]
    print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
    lr_scheduler = mx.lr_scheduler.MultiFactorScheduler(lr_iters, lr_factor)
    # optimizer
    optimizer_params = {'momentum': 0.9,
                        'wd': 0.0001,
                        'learning_rate': lr,
                        'lr_scheduler': lr_scheduler,
                        'rescale_grad': (1.0 / batch_size),
                        'clip_gradient': 5}

    # train
    mod.fit(train_data, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback,
            batch_end_callback=batch_end_callback, kvstore=kvstore,
            optimizer='sgd', optimizer_params=optimizer_params,
            arg_params=arg_params, aux_params=aux_params, begin_epoch=begin_epoch, num_epoch=end_epoch)


def parse_args():
    parser = argparse.ArgumentParser(description='Train Faster R-CNN network')
    # general
    parser.add_argument('--network', help='network name', default=default.network, type=str)
    parser.add_argument('--dataset', help='dataset name', default=default.dataset, type=str)
    args, rest = parser.parse_known_args()
    generate_config(args.network, args.dataset)
    parser.add_argument('--image_set', help='image_set name', default=default.image_set, type=str)
    parser.add_argument('--root_path', help='output data folder', default=default.root_path, type=str)
    parser.add_argument('--dataset_path', help='dataset path', default=default.dataset_path, type=str)
    # training
    parser.add_argument('--frequent', help='frequency of logging', default=default.frequent, type=int)
    parser.add_argument('--kvstore', help='the kv-store type', default=default.kvstore, type=str)
    parser.add_argument('--work_load_list', help='work load for different devices', default=None, type=list)
    parser.add_argument('--no_flip', help='disable flip images', action='store_true')
    parser.add_argument('--no_shuffle', help='disable random shuffle', action='store_true')
    parser.add_argument('--resume', help='continue training', action='store_true')
    # basic training config
    parser.add_argument('--gpus', help='GPU device to train with', default='0', type=str)
    parser.add_argument('--pretrained', help='pretrained model prefix', default=default.pretrained, type=str)
    parser.add_argument('--pretrained_epoch', help='pretrained model epoch', default=default.pretrained_epoch, type=int)
    parser.add_argument('--prefix', help='new model prefix', default=default.rpn_prefix, type=str)
    parser.add_argument('--begin_epoch', help='begin epoch of training', default=0, type=int)
    parser.add_argument('--end_epoch', help='end epoch of training', default=default.rpn_epoch, type=int)
    parser.add_argument('--lr', help='base learning rate', default=default.rpn_lr, type=float)
    parser.add_argument('--lr_step', help='learning rate steps (in epoch)', default=default.rpn_lr_step, type=str)

    args = parser.parse_args()
    return args

def main():
    args = parse_args()
    logger.info('Called with argument: %s' % args)
    ctx = [mx.gpu(int(i)) for i in args.gpus.split(',')]
    train_net(args.network, args.dataset, args.image_set, args.root_path, args.dataset_path,
               args.frequent, args.kvstore, args.work_load_list, args.no_flip, args.no_shuffle, args.resume,
               ctx, pretrained, epoch, model_path+'/rcnn1', begin_epoch, rcnn_epoch,
               train_shared=False, lr=rcnn_lr, lr_step=rcnn_lr_step)



if __name__ == '__main__':
    main()
