# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F

from mmseg.core import add_prefix
from mmseg.ops import resize
from mmseg.registry import MODELS
from .base import BaseSegmentor


@MODELS.register_module()
class EncoderDecoder(BaseSegmentor):
    """Encoder Decoder segmentors.

    EncoderDecoder typically consists of backbone, decode_head, auxiliary_head.
    Note that auxiliary_head is only used for deep supervision during training,
    which could be dumped during inference.
    """

    def __init__(self,
                 backbone,
                 decode_head,
                 neck=None,
                 auxiliary_head=None,
                 train_cfg=None,
                 test_cfg=None,
                 preprocess_cfg=None,
                 pretrained=None,
                 init_cfg=None):
        super(EncoderDecoder, self).__init__(
            preprocess_cfg=preprocess_cfg, init_cfg=init_cfg)
        if pretrained is not None:
            assert backbone.get('pretrained') is None, \
                'both backbone and segmentor set pretrained weight'
            backbone.pretrained = pretrained
        self.backbone = MODELS.build(backbone)
        if neck is not None:
            self.neck = MODELS.build(neck)
        self._init_decode_head(decode_head)
        self._init_auxiliary_head(auxiliary_head)

        self.train_cfg = train_cfg
        self.test_cfg = test_cfg

        assert self.with_decode_head

    def _init_decode_head(self, decode_head):
        """Initialize ``decode_head``"""
        self.decode_head = MODELS.build(decode_head)
        self.align_corners = self.decode_head.align_corners
        self.num_classes = self.decode_head.num_classes

    def _init_auxiliary_head(self, auxiliary_head):
        """Initialize ``auxiliary_head``"""
        if auxiliary_head is not None:
            if isinstance(auxiliary_head, list):
                self.auxiliary_head = nn.ModuleList()
                for head_cfg in auxiliary_head:
                    self.auxiliary_head.append(MODELS.build(head_cfg))
            else:
                self.auxiliary_head = MODELS.build(auxiliary_head)

    def extract_feat(self, batch_inputs):
        """Extract features from images."""
        x = self.backbone(batch_inputs)
        if self.with_neck:
            x = self.neck(x)
        return x

    def encode_decode(self, batch_inputs, batch_img_metas):
        """Encode images with backbone and decode into a semantic segmentation
        map of the same size as input."""
        x = self.extract_feat(batch_inputs)
        out = self._decode_head_forward_test(x, batch_img_metas)
        out = resize(
            input=out,
            size=batch_inputs.shape[2:],
            mode='bilinear',
            align_corners=self.align_corners)
        return out

    def _decode_head_forward_train(self, batch_inputs, batch_data_samples):
        """Run forward function and calculate loss for decode head in
        training."""
        losses = dict()
        loss_decode = self.decode_head.forward_train(batch_inputs,
                                                     batch_data_samples,
                                                     self.train_cfg)

        losses.update(add_prefix(loss_decode, 'decode'))
        return losses

    def _decode_head_forward_test(self, batch_inputs, batch_img_metas):
        """Run forward function and calculate loss for decode head in
        inference."""
        seg_logits = self.decode_head.forward_test(batch_inputs,
                                                   batch_img_metas,
                                                   self.test_cfg)
        return seg_logits

    def _auxiliary_head_forward_train(self, batch_inputs, batch_data_samples):
        """Run forward function and calculate loss for auxiliary head in
        training."""
        losses = dict()
        if isinstance(self.auxiliary_head, nn.ModuleList):
            for idx, aux_head in enumerate(self.auxiliary_head):
                loss_aux = aux_head.forward_train(batch_inputs,
                                                  batch_data_samples,
                                                  self.train_cfg)
                losses.update(add_prefix(loss_aux, f'aux_{idx}'))
        else:
            loss_aux = self.auxiliary_head.forward_train(
                batch_inputs, batch_data_samples, self.train_cfg)
            losses.update(add_prefix(loss_aux, 'aux'))

        return losses

    def forward_dummy(self, batch_inputs, batch_img_metas):
        """Dummy forward function."""
        seg_logit = self.encode_decode(batch_inputs, batch_img_metas)

        return seg_logit

    def forward_train(self, batch_inputs, batch_data_samples):
        """Forward function for training.

        Args:
            img (Tensor): Input images.
            batch_data_samples (list[:obj:`SegDataSample`]): The seg
                data samples. It usually includes information such
                as `img_metas` or `gt_semantic_seg`.

        Returns:
            dict[str, Tensor]: a dictionary of loss components
        """

        x = self.extract_feat(batch_inputs)

        losses = dict()

        loss_decode = self._decode_head_forward_train(x, batch_data_samples)
        losses.update(loss_decode)

        if self.with_auxiliary_head:
            loss_aux = self._auxiliary_head_forward_train(
                x, batch_data_samples)
            losses.update(loss_aux)

        return losses

    # TODO refactor
    def slide_inference(self, batch_inputs, batch_img_metas, rescale):
        """Inference by sliding-window with overlap.

        If h_crop > h_img or w_crop > w_img, the small patch will be used to
        decode without padding.

        Args:
            batch_inputs (tensor): the tensor should have a shape NxCxHxW,
                which contains all images in the batch.
            batch_img_metas (list[dict]): Meta information of each image, e.g.,
                image size, scaling factor, etc.

        Returns:
            tensor: get seg_logit.
        """

        h_stride, w_stride = self.test_cfg.stride
        h_crop, w_crop = self.test_cfg.crop_size
        batch_size, _, h_img, w_img = batch_inputs.size()
        num_classes = self.num_classes
        h_grids = max(h_img - h_crop + h_stride - 1, 0) // h_stride + 1
        w_grids = max(w_img - w_crop + w_stride - 1, 0) // w_stride + 1
        preds = batch_inputs.new_zeros((batch_size, num_classes, h_img, w_img))
        count_mat = batch_inputs.new_zeros((batch_size, 1, h_img, w_img))
        for h_idx in range(h_grids):
            for w_idx in range(w_grids):
                y1 = h_idx * h_stride
                x1 = w_idx * w_stride
                y2 = min(y1 + h_crop, h_img)
                x2 = min(x1 + w_crop, w_img)
                y1 = max(y2 - h_crop, 0)
                x1 = max(x2 - w_crop, 0)
                crop_img = batch_inputs[:, :, y1:y2, x1:x2]
                crop_seg_logit = self.encode_decode(crop_img, batch_img_metas)
                preds += F.pad(crop_seg_logit,
                               (int(x1), int(preds.shape[3] - x2), int(y1),
                                int(preds.shape[2] - y2)))

                count_mat[:, :, y1:y2, x1:x2] += 1
        assert (count_mat == 0).sum() == 0
        preds = preds / count_mat
        if rescale:
            preds = resize(
                preds,
                size=batch_img_metas[0]['ori_shape'][:2],
                mode='bilinear',
                align_corners=self.align_corners,
                warning=False)
        return preds

    def whole_inference(self, batch_inputs, batch_img_metas, rescale):
        """Inference with full image."""

        seg_logit = self.encode_decode(batch_inputs, batch_img_metas)
        if rescale:
            size = batch_img_metas[0]['ori_shape'][:2]
            seg_logit = resize(
                seg_logit,
                size=size,
                mode='bilinear',
                align_corners=self.align_corners,
                warning=False)

        return seg_logit

    def inference(self, batch_inputs, batch_img_metas, rescale):
        """Inference with slide/whole style.

        Args:
            batch_inputs (Tensor): The input image of shape (N, 3, H, W).
            batch_img_metas (dict): Image info dict where each dict has:
                'img_shape', 'scale_factor', 'flip', and may also contain
                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
                For details on the values of these keys see
                `mmseg/datasets/pipelines/formatting.py:Collect`.
            rescale (bool): Whether rescale back to original shape.

        Returns:
            Tensor: The output segmentation map.
        """

        assert self.test_cfg.mode in ['slide', 'whole']
        ori_shape = batch_img_metas[0]['ori_shape']
        assert all(_['ori_shape'] == ori_shape for _ in batch_img_metas)
        if self.test_cfg.mode == 'slide':
            seg_logit = self.slide_inference(batch_inputs, batch_img_metas,
                                             rescale)
        else:
            seg_logit = self.whole_inference(batch_inputs, batch_img_metas,
                                             rescale)
        output = F.softmax(seg_logit, dim=1)
        flip = batch_img_metas[0].get('flip', None)
        if flip:
            flip_direction = batch_img_metas[0]['flip_direction']
            assert flip_direction in ['horizontal', 'vertical']
            if flip_direction == 'horizontal':
                output = output.flip(dims=(3, ))
            elif flip_direction == 'vertical':
                output = output.flip(dims=(2, ))

        return output

    def simple_test(self, batch_inputs, batch_img_metas, rescale=True):
        """Simple test with single image."""
        results_dict = dict()
        seg_logit = self.inference(batch_inputs, batch_img_metas, rescale)
        results_dict['seg_logits'] = seg_logit
        seg_pred = seg_logit.argmax(dim=1)
        seg_pred = seg_pred.cpu().numpy()
        results_dict['pred_sem_seg'] = seg_pred
        results_list = self.postprocess_result(results_dict)
        return results_list

    def aug_test(self, batch_inputs, batch_img_metas, rescale=True):
        """Test with augmentations.

        Only rescale=True is supported.
        """
        # aug_test rescale all imgs back to ori_shape for now
        assert rescale
        # to save memory, we get augmented seg logit inplace
        seg_logit = self.inference(batch_inputs[0], batch_img_metas[0],
                                   rescale)
        for i in range(1, len(batch_inputs)):
            cur_seg_logit = self.inference(batch_inputs[i], batch_img_metas[i],
                                           rescale)
            seg_logit += cur_seg_logit
        seg_logit /= len(batch_inputs)
        seg_pred = seg_logit.argmax(dim=1)
        # unravel batch dim
        seg_pred = list(seg_pred)
        return seg_pred