modules/img2plane/deeplabv3/decoders/my_model.py

import torch
from torch import nn

from typing import Optional

from modules.img2plane.deeplabv3.encoders import get_encoder
from modules.img2plane.deeplabv3.base import initialization as init


from .my_decoder import DeepLabV3Decoder


class DeepLabV3(nn.Module):
    """DeepLabV3_ implementation from "Rethinking Atrous Convolution for Semantic Image Segmentation"

    Args:
        encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone)
            to extract features of different spatial resolution
        encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features
            two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features
            with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on).
            Default is 5
        encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and
            other pretrained weights (see table with available weights for each encoder_name)
        decoder_channels: A number of convolution filters in ASPP module. Default is 256
        in_channels: A number of input channels for the model, default is 3 (RGB images)
        classes: A number of classes for output mask (or you can think as a number of channels of output mask)
        activation: An activation function to apply after the final convolution layer.
            Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**,
                **callable** and **None**.
            Default is **None**
        upsampling: Final upsampling factor. Default is 8 to preserve input-output spatial shape identity
        aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build
            on top of encoder if **aux_params** is not **None** (default). Supported params:
                - classes (int): A number of classes
                - pooling (str): One of "max", "avg". Default is "avg"
                - dropout (float): Dropout factor in [0, 1)
                - activation (str): An activation function to apply "sigmoid"/"softmax"
                    (could be **None** to return logits)
    Returns:
        ``torch.nn.Module``: **DeepLabV3**

    .. _DeeplabV3:
        https://arxiv.org/abs/1706.05587

    """
    
    def initialize(self):
        init.initialize_decoder(self.decoder)

    def __init__(
        self,
        encoder_name: str = "resnet34",
        encoder_depth: int = 5,
        encoder_weights: Optional[str] = "imagenet",
        decoder_channels: int = 256,
        in_channels: int = 5, # 3 for rgb, and 2 for pixel coordinates
    ):
        super().__init__()

        self.encoder = get_encoder(
            encoder_name,
            in_channels=in_channels,
            depth=encoder_depth,
            weights=encoder_weights,
            output_stride=8,
        )

        self.decoder = DeepLabV3Decoder(
            in_channels=self.encoder.out_channels[-1],
            out_channels=decoder_channels,
        )

    def check_input_shape(self, x):

        h, w = x.shape[-2:]
        output_stride = self.encoder.output_stride
        if h % output_stride != 0 or w % output_stride != 0:
            new_h = (h // output_stride + 1) * output_stride if h % output_stride != 0 else h
            new_w = (w // output_stride + 1) * output_stride if w % output_stride != 0 else w
            raise RuntimeError(
                f"Wrong input shape height={h}, width={w}. Expected image height and width "
                f"divisible by {output_stride}. Consider pad your images to shape ({new_h}, {new_w})."
            )

    def forward(self, x):
        """Sequentially pass `x` trough model`s encoder, decoder and heads"""

        self.check_input_shape(x)

        features = self.encoder(x)
        decoder_output = self.decoder(*features)

        return decoder_output

    @torch.no_grad()
    def predict(self, x):
        """Inference method. Switch model to `eval` mode, call `.forward(x)` with `torch.no_grad()`

        Args:
            x: 4D torch tensor with shape (batch_size, channels, height, width)

        Return:
            prediction: 4D torch tensor with shape (batch_size, classes, height, width)

        """
        if self.training:
            self.eval()

        x = self.forward(x)

        return x