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| # Copyright (c) OpenMMLab. All rights reserved. | |
| from typing import List, Optional | |
| import torch.nn.functional as F | |
| from torch import Tensor | |
| from mmseg.registry import MODELS | |
| from mmseg.utils import (ConfigType, OptConfigType, OptMultiConfig, | |
| OptSampleList, SampleList, add_prefix) | |
| from .base import BaseSegmentor | |
| class MultimodalEncoderDecoder(BaseSegmentor): | |
| """Multimodal Encoder-Decoder segmentors. | |
| Multimodal segmentation architecture is used for open-vocabulary | |
| semantic segmentation with combining the visual and language | |
| pretrain models. It consists of a image_encoder (backbone) to extract | |
| visual feature, a text encoder to extract text feature, and a decode | |
| head to generate semantic maps. | |
| Note that the deep supervision during training is implemented in decode head. | |
| 1. The ``loss`` method is used to calculate the loss of model, | |
| which includes two steps: (1) Extracts features to obtain the feature maps | |
| (2) Call the decode head loss function to forward decode head model and | |
| calculate losses. | |
| .. code:: text | |
| loss(): extract_feat() -> _decode_head_forward_train() | |
| _decode_head_forward_train(): decode_head.loss() | |
| 2. The ``predict`` method is used to predict segmentation results, | |
| which includes two steps: (1) Run inference function to obtain the list of | |
| seg_logits (2) Call post-processing function to obtain list of | |
| ``SegDataSampel`` including ``pred_sem_seg`` and ``seg_logits``. | |
| .. code:: text | |
| predict(): inference() -> postprocess_result() | |
| inference(): whole_inference()/slide_inference() | |
| whole_inference()/slide_inference(): encoder_decoder() | |
| encoder_decoder(): extract_feat() -> decode_head.predict() | |
| 3. The ``_forward`` method is used to output the tensor by running the model, | |
| which includes two steps: (1) Extracts features to obtain the feature maps | |
| (2)Call the decode head forward function to forward decode head model. | |
| .. code:: text | |
| _forward(): extract_feat() -> _decode_head.forward() | |
| Args: | |
| image_encoder (ConfigType): The config for the visual encoder of segmentor. | |
| text_encoder ((ConfigType): The config for the text encoder of segmentor. | |
| decode_head (ConfigType): The config for the decode head of segmentor. | |
| train_cfg (OptConfigType): The config for training. Defaults to None. | |
| test_cfg (OptConfigType): The config for testing. Defaults to None. | |
| data_preprocessor (dict, optional): The pre-process config of | |
| :class:`BaseDataPreprocessor`. | |
| pretrained (str, optional): The path for pretrained model. | |
| Defaults to None. | |
| asymetric_input (bool): whether to use different size of input for image encoder | |
| and decode head. Defaults to False. | |
| encoder_resolution (float): resize scale of input images for image encoder. | |
| Defaults to None. | |
| init_cfg (dict, optional): The weight initialized config for | |
| :class:`BaseModule`. | |
| """ # noqa: E501 | |
| def __init__(self, | |
| image_encoder: ConfigType, | |
| text_encoder: ConfigType, | |
| decode_head: ConfigType, | |
| train_cfg: OptConfigType = None, | |
| test_cfg: OptConfigType = None, | |
| data_preprocessor: OptConfigType = None, | |
| pretrained: Optional[str] = None, | |
| asymetric_input: bool = True, | |
| encoder_resolution: float = None, | |
| init_cfg: OptMultiConfig = None): | |
| super().__init__( | |
| data_preprocessor=data_preprocessor, init_cfg=init_cfg) | |
| if pretrained is not None: | |
| image_encoder.init_cfg = dict( | |
| type='Pretrained_Part', checkpoint=pretrained) | |
| text_encoder.init_cfg = dict( | |
| type='Pretrained_Part', checkpoint=pretrained) | |
| decode_head.init_cfg = dict( | |
| type='Pretrained_Part', checkpoint=pretrained) | |
| if asymetric_input: | |
| assert encoder_resolution is not None, \ | |
| 'if asymetric_input set True, ' \ | |
| 'clip_resolution must be a certain value' | |
| self.asymetric_input = asymetric_input | |
| self.encoder_resolution = encoder_resolution | |
| self.image_encoder = MODELS.build(image_encoder) | |
| self.text_encoder = MODELS.build(text_encoder) | |
| self._init_decode_head(decode_head) | |
| self.train_cfg = train_cfg | |
| self.test_cfg = test_cfg | |
| assert self.with_decode_head | |
| def _init_decode_head(self, decode_head: ConfigType) -> None: | |
| """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 | |
| self.out_channels = self.decode_head.out_channels | |
| def extract_feat(self, inputs: Tensor) -> List[Tensor]: | |
| """Extract visual features from images.""" | |
| x = self.image_encoder(inputs) | |
| return x | |
| def encode_decode(self, inputs: Tensor, | |
| batch_img_metas: List[dict]) -> Tensor: | |
| """Encode the name of classes with text_encoder and encode images with | |
| image_encoder. | |
| Then decode the class embedding and visual feature into a semantic | |
| segmentation map of the same size as input. | |
| """ | |
| classifier_embeds = self.text_encoder() | |
| clip_inputs = inputs | |
| if self.asymetric_input: | |
| clip_inputs = F.interpolate( | |
| inputs, scale_factor=self.encoder_resolution, mode='bilinear') | |
| x = self.image_encoder(clip_inputs) | |
| seg_logits = self.decode_head.predict([inputs, x, classifier_embeds], | |
| batch_img_metas, self.test_cfg) | |
| return seg_logits | |
| def _decode_head_forward_train(self, inputs: List[Tensor], | |
| data_samples: SampleList) -> dict: | |
| """Run forward function and calculate loss for decode head in | |
| training.""" | |
| losses = dict() | |
| loss_decode = self.decode_head.loss(inputs, data_samples, | |
| self.train_cfg) | |
| losses.update(add_prefix(loss_decode, 'decode')) | |
| return losses | |
| def loss(self, inputs: Tensor, data_samples: SampleList) -> dict: | |
| """Calculate losses from a batch of inputs and data samples. | |
| Args: | |
| inputs (Tensor): Input images. | |
| data_samples (list[:obj:`SegDataSample`]): The seg data samples. | |
| It usually includes information such as `metainfo` and | |
| `gt_sem_seg`. | |
| Returns: | |
| dict[str, Tensor]: a dictionary of loss components | |
| """ | |
| classifier_embeds = self.text_encoder() | |
| clip_inputs = inputs | |
| if self.asymetric_input: | |
| clip_inputs = F.interpolate( | |
| inputs, scale_factor=self.encoder_resolution, mode='bilinear') | |
| x = self.image_encoder(clip_inputs) | |
| losses = dict() | |
| loss_decode = self._decode_head_forward_train( | |
| [inputs, x, classifier_embeds], data_samples) | |
| losses.update(loss_decode) | |
| return losses | |
| def predict(self, | |
| inputs: Tensor, | |
| data_samples: OptSampleList = None) -> SampleList: | |
| """Predict results from a batch of inputs and data samples with post- | |
| processing. | |
| Args: | |
| inputs (Tensor): Inputs with shape (N, C, H, W). | |
| data_samples (List[:obj:`SegDataSample`], optional): The seg data | |
| samples. It usually includes information such as `metainfo` | |
| and `gt_sem_seg`. | |
| Returns: | |
| list[:obj:`SegDataSample`]: Segmentation results of the | |
| input images. Each SegDataSample usually contain: | |
| - ``pred_sem_seg``(PixelData): Prediction of semantic segmentation. | |
| - ``seg_logits``(PixelData): Predicted logits of semantic | |
| segmentation before normalization. | |
| """ | |
| if data_samples is not None: | |
| batch_img_metas = [ | |
| data_sample.metainfo for data_sample in data_samples | |
| ] | |
| else: | |
| batch_img_metas = [ | |
| dict( | |
| ori_shape=inputs.shape[2:], | |
| img_shape=inputs.shape[2:], | |
| pad_shape=inputs.shape[2:], | |
| padding_size=[0, 0, 0, 0]) | |
| ] * inputs.shape[0] | |
| seg_logits = self.inference(inputs, batch_img_metas) | |
| return self.postprocess_result(seg_logits, data_samples) | |
| def _forward(self, | |
| inputs: Tensor, | |
| data_samples: OptSampleList = None) -> Tensor: | |
| """Network forward process. | |
| Args: | |
| inputs (Tensor): Inputs with shape (N, C, H, W). | |
| data_samples (List[:obj:`SegDataSample`]): The seg | |
| data samples. It usually includes information such | |
| as `metainfo` and `gt_sem_seg`. | |
| Returns: | |
| Tensor: Forward output of model without any post-processes. | |
| """ | |
| x = self.extract_feat(inputs) | |
| return self.decode_head.forward(x) | |
| def slide_inference(self, inputs: Tensor, | |
| batch_img_metas: List[dict]) -> Tensor: | |
| """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: | |
| inputs (tensor): the tensor should have a shape NxCxHxW, | |
| which contains all images in the batch. | |
| batch_img_metas (List[dict]): List of image metainfo where each may | |
| also contain: 'img_shape', 'scale_factor', 'flip', 'img_path', | |
| 'ori_shape', and 'pad_shape'. | |
| For details on the values of these keys see | |
| `mmseg/datasets/pipelines/formatting.py:PackSegInputs`. | |
| Returns: | |
| Tensor: The segmentation results, seg_logits from model of each | |
| input image. | |
| """ | |
| h_stride, w_stride = self.test_cfg.stride | |
| h_crop, w_crop = self.test_cfg.crop_size | |
| batch_size, _, h_img, w_img = inputs.size() | |
| out_channels = self.out_channels | |
| 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 = inputs.new_zeros((batch_size, out_channels, h_img, w_img)) | |
| count_mat = 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 = inputs[:, :, y1:y2, x1:x2] | |
| # change the image shape to patch shape | |
| batch_img_metas[0]['img_shape'] = crop_img.shape[2:] | |
| # the output of encode_decode is seg logits tensor map | |
| # with shape [N, C, H, W] | |
| 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 | |
| seg_logits = preds / count_mat | |
| return seg_logits | |
| def whole_inference(self, inputs: Tensor, | |
| batch_img_metas: List[dict]) -> Tensor: | |
| """Inference with full image. | |
| Args: | |
| inputs (Tensor): The tensor should have a shape NxCxHxW, which | |
| contains all images in the batch. | |
| batch_img_metas (List[dict]): List of image metainfo where each may | |
| also contain: 'img_shape', 'scale_factor', 'flip', 'img_path', | |
| 'ori_shape', and 'pad_shape'. | |
| For details on the values of these keys see | |
| `mmseg/datasets/pipelines/formatting.py:PackSegInputs`. | |
| Returns: | |
| Tensor: The segmentation results, seg_logits from model of each | |
| input image. | |
| """ | |
| seg_logits = self.encode_decode(inputs, batch_img_metas) | |
| return seg_logits | |
| def inference(self, inputs: Tensor, batch_img_metas: List[dict]) -> Tensor: | |
| """Inference with slide/whole style. | |
| Args: | |
| inputs (Tensor): The input image of shape (N, 3, H, W). | |
| batch_img_metas (List[dict]): List of image metainfo where each may | |
| also contain: 'img_shape', 'scale_factor', 'flip', 'img_path', | |
| 'ori_shape', 'pad_shape', and 'padding_size'. | |
| For details on the values of these keys see | |
| `mmseg/datasets/pipelines/formatting.py:PackSegInputs`. | |
| Returns: | |
| Tensor: The segmentation results, seg_logits from model of each | |
| input image. | |
| """ | |
| 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(inputs, batch_img_metas) | |
| else: | |
| seg_logit = self.whole_inference(inputs, batch_img_metas) | |
| return seg_logit | |
| def aug_test(self, 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(inputs[0], batch_img_metas[0], rescale) | |
| for i in range(1, len(inputs)): | |
| cur_seg_logit = self.inference(inputs[i], batch_img_metas[i], | |
| rescale) | |
| seg_logit += cur_seg_logit | |
| seg_logit /= len(inputs) | |
| seg_pred = seg_logit.argmax(dim=1) | |
| # unravel batch dim | |
| seg_pred = list(seg_pred) | |
| return seg_pred | |