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Source code for torchgeo.models.fcsiam

# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.

"""Fully convolutional change detection (FCCD) implementations."""

from collections.abc import Callable, Sequence
from typing import Any

import segmentation_models_pytorch as smp
import torch
from segmentation_models_pytorch import Unet
from segmentation_models_pytorch.base.model import SegmentationModel
from torch import Tensor


[docs]class FCSiamConc(SegmentationModel): # type: ignore[misc] """Fully-convolutional Siamese Concatenation (FC-Siam-conc). If you use this model in your research, please cite the following paper: * https://doi.org/10.1109/ICIP.2018.8451652 """
[docs] def __init__( self, encoder_name: str = "resnet34", encoder_depth: int = 5, encoder_weights: str | None = "imagenet", decoder_use_batchnorm: bool = True, decoder_channels: Sequence[int] = (256, 128, 64, 32, 16), decoder_attention_type: str | None = None, in_channels: int = 3, classes: int = 1, activation: str | Callable[[Tensor], Tensor] | None = None, ): """Initialize a new FCSiamConc model. 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]. two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features. Each stage generate 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: List of integers which specify **in_channels** parameter for convolutions used in decoder. Length of the list should be the same as **encoder_depth** decoder_use_batchnorm: If **True**, BatchNorm2d layer between Conv2D and Activation layers is used. If **"inplace"** InplaceABN will be used, allows to decrease memory consumption. Available options are **True, False, "inplace"** decoder_attention_type: Attention module used in decoder of the model. Available options are **None** and **scse**. SCSE paper https://arxiv.org/abs/1808.08127 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 n layer. Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**. Default is **None** """ super().__init__() self.encoder = smp.encoders.get_encoder( encoder_name, in_channels=in_channels, depth=encoder_depth, weights=encoder_weights, ) encoder_out_channels = [c * 2 for c in self.encoder.out_channels[1:]] encoder_out_channels.insert(0, self.encoder.out_channels[0]) try: # smp 0.3+ UnetDecoder = smp.decoders.unet.decoder.UnetDecoder except AttributeError: # smp 0.2 UnetDecoder = smp.unet.decoder.UnetDecoder self.decoder = UnetDecoder( encoder_channels=encoder_out_channels, decoder_channels=decoder_channels, n_blocks=encoder_depth, use_batchnorm=decoder_use_batchnorm, center=True if encoder_name.startswith("vgg") else False, attention_type=decoder_attention_type, ) self.segmentation_head = smp.base.SegmentationHead( in_channels=decoder_channels[-1], out_channels=classes, activation=activation, kernel_size=3, ) self.classification_head = None self.name = f"u-{encoder_name}" self.initialize()
[docs] def forward(self, x: Tensor) -> Tensor: """Forward pass of the model. Args: x: input images of shape (b, t, c, h, w) Returns: predicted change masks of size (b, classes, h, w) """ x1 = x[:, 0] x2 = x[:, 1] features1, features2 = self.encoder(x1), self.encoder(x2) features = [ torch.cat([features2[i], features1[i]], dim=1) for i in range(1, len(features1)) ] features.insert(0, features2[0]) decoder_output = self.decoder(*features) masks: Tensor = self.segmentation_head(decoder_output) return masks
[docs]class FCSiamDiff(Unet): # type: ignore[misc] """Fully-convolutional Siamese Difference (FC-Siam-diff). If you use this model in your research, please cite the following paper: * https://doi.org/10.1109/ICIP.2018.8451652 """
[docs] def __init__(self, *args: Any, **kwargs: Any) -> None: """Initialize a new FCSiamConc model. 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]. two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features. Each stage generate 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: List of integers which specify **in_channels** parameter for convolutions used in decoder. Length of the list should be the same as **encoder_depth** decoder_use_batchnorm: If **True**, BatchNorm2d layer between Conv2D and Activation layers is used. If **"inplace"** InplaceABN will be used, allows to decrease memory consumption. Available options are **True, False, "inplace"** decoder_attention_type: Attention module used in decoder of the model. Available options are **None** and **scse**. SCSE paper https://arxiv.org/abs/1808.08127 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 n layer. Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**. Default is **None** """ kwargs["aux_params"] = None super().__init__(*args, **kwargs)
[docs] def forward(self, x: Tensor) -> Tensor: """Forward pass of the model. Args: x: input images of shape (b, t, c, h, w) Returns: predicted change masks of size (b, classes, h, w) """ x1 = x[:, 0] x2 = x[:, 1] features1, features2 = self.encoder(x1), self.encoder(x2) features = [features2[i] - features1[i] for i in range(1, len(features1))] features.insert(0, features2[0]) decoder_output = self.decoder(*features) masks: Tensor = self.segmentation_head(decoder_output) return masks

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