# -*- coding: utf-8 -*-
# File: config.py

import numpy as np
import os
import pprint
import six

from tensorpack.utils import logger
from tensorpack.utils.gpu import get_num_gpu

__all__ = ['config', 'finalize_configs']


class AttrDict():

    _freezed = False
    """ Avoid accidental creation of new hierarchies. """

    def __getattr__(self, name):
        if self._freezed:
            raise AttributeError(name)
        if name.startswith('_'):
            # Do not mess with internals. Otherwise copy/pickle will fail
            raise AttributeError(name)
        ret = AttrDict()
        setattr(self, name, ret)
        return ret

    def __setattr__(self, name, value):
        if self._freezed and name not in self.__dict__:
            raise AttributeError(
                "Config was freezed! Unknown config: {}".format(name))
        super().__setattr__(name, value)

    def __str__(self):
        return pprint.pformat(self.to_dict(), indent=1, width=100, compact=True)

    __repr__ = __str__

    def to_dict(self):
        """Convert to a nested dict. """
        return {k: v.to_dict() if isinstance(v, AttrDict) else v
                for k, v in self.__dict__.items() if not k.startswith('_')}

    def from_dict(self, d):
        self.freeze(False)
        for k, v in d.items():
            self_v = getattr(self, k)
            if isinstance(self_v, AttrDict):
                self_v.from_dict(v)
            else:
                setattr(self, k, v)

    def update_args(self, args):
        """Update from command line args. """
        for cfg in args:
            keys, v = cfg.split('=', maxsplit=1)
            keylist = keys.split('.')

            dic = self
            for i, k in enumerate(keylist[:-1]):
                assert k in dir(dic), "Unknown config key: {}".format(keys)
                dic = getattr(dic, k)
            key = keylist[-1]

            oldv = getattr(dic, key)
            if not isinstance(oldv, str):
                v = eval(v)
            setattr(dic, key, v)

    def freeze(self, freezed=True):
        self._freezed = freezed
        for v in self.__dict__.values():
            if isinstance(v, AttrDict):
                v.freeze(freezed)

    # avoid silent bugs
    def __eq__(self, _):
        raise NotImplementedError()

    def __ne__(self, _):
        raise NotImplementedError()


config = AttrDict()
_C = config     # short alias to avoid coding

# mode flags ---------------------
_C.TRAINER = 'replicated'  # options: 'horovod', 'replicated'
_C.MODE_MASK = True        # Faster R-CNN or Mask R-CNN
_C.MODE_FPN = True

# dataset -----------------------
_C.DATA.BASEDIR = '/path/to/your/DATA/DIR'
# All available dataset names are defined in `dataset/coco.py:register_coco`.
# All TRAIN dataset will be concatenated for training.
_C.DATA.TRAIN = ('coco_train2017',)   # i.e. trainval35k
# Each VAL dataset will be evaluated separately (instead of concatenated)
_C.DATA.VAL = ('coco_val2017',)  # AKA minival2014

# These two configs will be populated later inside `finalize_configs`.
_C.DATA.NUM_CATEGORY = -1  # without the background class (e.g., 80 for COCO)
_C.DATA.CLASS_NAMES = []  # NUM_CLASS (NUM_CATEGORY+1) strings, the first is "BG".

# whether the coordinates in your registered dataset are
# absolute pixel values in range [0, W or H] or relative values in [0, 1]
_C.DATA.ABSOLUTE_COORD = True
# Filter Negative Samples from dataset
_C.DATA.FILTER_EMPTY_ANNOTATIONS = True
# Number of data loading workers.
# In case of horovod training, this is the number of workers per-GPU (so you may want to use a smaller number).
# Set to 0 to disable parallel data loading
_C.DATA.NUM_WORKERS = 10

# backbone ----------------------
_C.BACKBONE.WEIGHTS = ''
# To train from scratch, set it to empty, and set FREEZE_AT to 0
# To train from ImageNet pre-trained models, use the one that matches your
#   architecture from http://models.tensorpack.com under the 'FasterRCNN' section.
# To train from an existing COCO model, use the path to that file, and change
#   the other configurations according to that model.

_C.BACKBONE.RESNET_NUM_BLOCKS = [3, 4, 6, 3]     # for resnet50
# RESNET_NUM_BLOCKS = [3, 4, 23, 3]    # for resnet101
_C.BACKBONE.FREEZE_AFFINE = False   # do not train affine parameters inside norm layers
_C.BACKBONE.NORM = 'FreezeBN'  # options: FreezeBN, SyncBN, GN, None
_C.BACKBONE.FREEZE_AT = 2  # options: 0, 1, 2. How many stages in backbone to freeze (not training)

# Use a base model with TF-preferred padding mode,
# which may pad more pixels on right/bottom than top/left.
# See https://github.com/tensorflow/tensorflow/issues/18213
# In tensorpack model zoo, ResNet models with TF_PAD_MODE=False are marked with "-AlignPadding".
# All other models under `ResNet/` in the model zoo are using TF_PAD_MODE=True.
# Using either one should probably give the same performance.
# We use the "AlignPadding" one just to be consistent with caffe2.
_C.BACKBONE.TF_PAD_MODE = False
_C.BACKBONE.STRIDE_1X1 = False  # True for MSRA models

# schedule -----------------------
_C.TRAIN.NUM_GPUS = None         # by default, will be set from code
_C.TRAIN.WEIGHT_DECAY = 1e-4
_C.TRAIN.BASE_LR = 1e-2  # defined for total batch size=8. Otherwise it will be adjusted automatically
_C.TRAIN.WARMUP = 1000   # in terms of iterations. This is not affected by #GPUs
_C.TRAIN.WARMUP_INIT_LR = 1e-5  # defined for total batch size=8. Otherwise it will be adjusted automatically
_C.TRAIN.STEPS_PER_EPOCH = 500
_C.TRAIN.STARTING_EPOCH = 1  # the first epoch to start with, useful to continue a training

# LR_SCHEDULE means equivalent steps when the total batch size is 8.
# It can be either a string like "3x" that refers to standard convention, or a list of int.
# LR_SCHEDULE=3x is the same as LR_SCHEDULE=[420000, 500000, 540000], which
# means to decrease LR at steps 420k and 500k and stop training at 540k.
# When the total bs!=8, the actual iterations to decrease learning rate, and
# the base learning rate are computed from BASE_LR and LR_SCHEDULE.
# Therefore, there is *no need* to modify the config if you only change the number of GPUs.
_C.TRAIN.LR_SCHEDULE = "1x"      # "1x" schedule in detectron
_C.TRAIN.EVAL_PERIOD = 50  # period (epochs) to run evaluation
_C.TRAIN.CHECKPOINT_PERIOD = 20  # period (epochs) to save model

# preprocessing --------------------
# Alternative old (worse & faster) setting: 600
_C.PREPROC.TRAIN_SHORT_EDGE_SIZE = [800, 800]  # [min, max] to sample from
_C.PREPROC.TEST_SHORT_EDGE_SIZE = 800
_C.PREPROC.MAX_SIZE = 1333
# mean and std in RGB order.
# Un-scaled version: [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
_C.PREPROC.PIXEL_MEAN = [123.675, 116.28, 103.53]
_C.PREPROC.PIXEL_STD = [58.395, 57.12, 57.375]

# anchors -------------------------
_C.RPN.ANCHOR_STRIDE = 16
_C.RPN.ANCHOR_SIZES = (32, 64, 128, 256, 512)   # sqrtarea of the anchor box
_C.RPN.ANCHOR_RATIOS = (0.5, 1., 2.)
_C.RPN.POSITIVE_ANCHOR_THRESH = 0.7
_C.RPN.NEGATIVE_ANCHOR_THRESH = 0.3

# rpn training -------------------------
_C.RPN.FG_RATIO = 0.5  # fg ratio among selected RPN anchors
_C.RPN.BATCH_PER_IM = 256  # total (across FPN levels) number of anchors that are marked valid
_C.RPN.MIN_SIZE = 0
_C.RPN.PROPOSAL_NMS_THRESH = 0.7
# Anchors which overlap with a crowd box (IOA larger than threshold) will be ignored.
# Setting this to a value larger than 1.0 will disable the feature.
# It is disabled by default because Detectron does not do this.
_C.RPN.CROWD_OVERLAP_THRESH = 9.99
_C.RPN.HEAD_DIM = 1024      # used in C4 only

# RPN proposal selection -------------------------------
# for C4
_C.RPN.TRAIN_PRE_NMS_TOPK = 12000
_C.RPN.TRAIN_POST_NMS_TOPK = 2000
_C.RPN.TEST_PRE_NMS_TOPK = 6000
_C.RPN.TEST_POST_NMS_TOPK = 1000   # if you encounter OOM in inference, set this to a smaller number
# for FPN, #proposals per-level and #proposals after merging are (for now) the same
# if FPN.PROPOSAL_MODE = 'Joint', these options have no effect
_C.RPN.TRAIN_PER_LEVEL_NMS_TOPK = 2000
_C.RPN.TEST_PER_LEVEL_NMS_TOPK = 1000

# fastrcnn training ---------------------
_C.FRCNN.BATCH_PER_IM = 512
_C.FRCNN.BBOX_REG_WEIGHTS = [10., 10., 5., 5.]  # Slightly better setting: 20, 20, 10, 10
_C.FRCNN.FG_THRESH = 0.5
_C.FRCNN.FG_RATIO = 0.25  # fg ratio in a ROI batch

# FPN -------------------------
_C.FPN.ANCHOR_STRIDES = (4, 8, 16, 32, 64)  # strides for each FPN level. Must be the same length as ANCHOR_SIZES
_C.FPN.PROPOSAL_MODE = 'Level'  # 'Level', 'Joint'
_C.FPN.NUM_CHANNEL = 256
_C.FPN.NORM = 'None'  # 'None', 'GN'
# The head option is only used in FPN. For C4 models, the head is C5
_C.FPN.FRCNN_HEAD_FUNC = 'fastrcnn_2fc_head'
# choices: fastrcnn_2fc_head, fastrcnn_4conv1fc_{,gn_}head
_C.FPN.FRCNN_CONV_HEAD_DIM = 256
_C.FPN.FRCNN_FC_HEAD_DIM = 1024
_C.FPN.MRCNN_HEAD_FUNC = 'maskrcnn_up4conv_head'   # choices: maskrcnn_up4conv_{,gn_}head

# Mask R-CNN
_C.MRCNN.HEAD_DIM = 256
_C.MRCNN.ACCURATE_PASTE = True  # slightly more aligned results, but very slow on numpy

# Cascade R-CNN, only available in FPN mode
_C.FPN.CASCADE = False
_C.CASCADE.IOUS = [0.5, 0.6, 0.7]
_C.CASCADE.BBOX_REG_WEIGHTS = [[10., 10., 5., 5.], [20., 20., 10., 10.], [30., 30., 15., 15.]]

# testing -----------------------
_C.TEST.FRCNN_NMS_THRESH = 0.5

# Smaller threshold value gives significantly better mAP. But we use 0.05 for consistency with Detectron.
# mAP with 1e-4 threshold can be found at https://github.com/tensorpack/tensorpack/commit/26321ae58120af2568bdbf2269f32aa708d425a8#diff-61085c48abee915b584027e1085e1043  # noqa
_C.TEST.RESULT_SCORE_THRESH = 0.05
_C.TEST.RESULT_SCORE_THRESH_VIS = 0.5   # only visualize confident results
_C.TEST.RESULTS_PER_IM = 100

_C.freeze()  # avoid typo / wrong config keys


def finalize_configs(is_training):
    """
    Run some sanity checks, and populate some configs from others
    """
    _C.freeze(False)  # populate new keys now
    if isinstance(_C.DATA.VAL, six.string_types):  # support single string (the typical case) as well
        _C.DATA.VAL = (_C.DATA.VAL, )
    if isinstance(_C.DATA.TRAIN, six.string_types):  # support single string
        _C.DATA.TRAIN = (_C.DATA.TRAIN, )

    # finalize dataset definitions ...
    from dataset import DatasetRegistry
    datasets = list(_C.DATA.TRAIN) + list(_C.DATA.VAL)
    _C.DATA.CLASS_NAMES = DatasetRegistry.get_metadata(datasets[0], "class_names")
    _C.DATA.NUM_CATEGORY = len(_C.DATA.CLASS_NAMES) - 1

    assert _C.BACKBONE.NORM in ['FreezeBN', 'SyncBN', 'GN', 'None'], _C.BACKBONE.NORM
    if _C.BACKBONE.NORM != 'FreezeBN':
        assert not _C.BACKBONE.FREEZE_AFFINE
    assert _C.BACKBONE.FREEZE_AT in [0, 1, 2]

    _C.RPN.NUM_ANCHOR = len(_C.RPN.ANCHOR_SIZES) * len(_C.RPN.ANCHOR_RATIOS)
    assert len(_C.FPN.ANCHOR_STRIDES) == len(_C.RPN.ANCHOR_SIZES)
    # image size into the backbone has to be multiple of this number
    _C.FPN.RESOLUTION_REQUIREMENT = _C.FPN.ANCHOR_STRIDES[3]  # [3] because we build FPN with features r2,r3,r4,r5

    if _C.MODE_FPN:
        size_mult = _C.FPN.RESOLUTION_REQUIREMENT * 1.
        _C.PREPROC.MAX_SIZE = np.ceil(_C.PREPROC.MAX_SIZE / size_mult) * size_mult
        assert _C.FPN.PROPOSAL_MODE in ['Level', 'Joint']
        assert _C.FPN.FRCNN_HEAD_FUNC.endswith('_head')
        assert _C.FPN.MRCNN_HEAD_FUNC.endswith('_head')
        assert _C.FPN.NORM in ['None', 'GN']

        if _C.FPN.CASCADE:
            # the first threshold is the proposal sampling threshold
            assert _C.CASCADE.IOUS[0] == _C.FRCNN.FG_THRESH
            assert len(_C.CASCADE.BBOX_REG_WEIGHTS) == len(_C.CASCADE.IOUS)

    if is_training:
        train_scales = _C.PREPROC.TRAIN_SHORT_EDGE_SIZE
        if isinstance(train_scales, (list, tuple)) and train_scales[1] - train_scales[0] > 100:
            # don't autotune if augmentation is on
            os.environ['TF_CUDNN_USE_AUTOTUNE'] = '0'
        os.environ['TF_AUTOTUNE_THRESHOLD'] = '1'
        assert _C.TRAINER in ['horovod', 'replicated'], _C.TRAINER

        lr = _C.TRAIN.LR_SCHEDULE
        if isinstance(lr, six.string_types):
            if lr.endswith("x"):
                LR_SCHEDULE_KITER = {
                    "{}x".format(k):
                    [180 * k - 120, 180 * k - 40, 180 * k]
                    for k in range(2, 10)}
                LR_SCHEDULE_KITER["1x"] = [120, 160, 180]
                _C.TRAIN.LR_SCHEDULE = [x * 1000 for x in LR_SCHEDULE_KITER[lr]]
            else:
                _C.TRAIN.LR_SCHEDULE = eval(lr)

        # setup NUM_GPUS
        if _C.TRAINER == 'horovod':
            import horovod.tensorflow as hvd
            ngpu = hvd.size()
            logger.info("Horovod Rank={}, Size={}, LocalRank={}".format(
                hvd.rank(), hvd.size(), hvd.local_rank()))
        else:
            assert 'OMPI_COMM_WORLD_SIZE' not in os.environ
            ngpu = get_num_gpu()
        assert ngpu > 0, "Has to train with GPU!"
        assert ngpu % 8 == 0 or 8 % ngpu == 0, "Can only train with 1,2,4 or >=8 GPUs, but found {} GPUs".format(ngpu)
    else:
        # autotune is too slow for inference
        os.environ['TF_CUDNN_USE_AUTOTUNE'] = '0'
        ngpu = get_num_gpu()

    if _C.TRAIN.NUM_GPUS is None:
        _C.TRAIN.NUM_GPUS = ngpu
    else:
        if _C.TRAINER == 'horovod':
            assert _C.TRAIN.NUM_GPUS == ngpu
        else:
            assert _C.TRAIN.NUM_GPUS <= ngpu

    _C.freeze()
    logger.info("Config: ------------------------------------------\n" + str(_C))
