[feat] Adding UPerNet

README.md

@@ -19,7 +19,7 @@ Segmentation based on [PyTorch](https://pytorch.org/).**
 The main features of this library are:
 
  - High-level API (just two lines to create a neural network)
- - 9 models architectures for binary and multi class segmentation (including legendary Unet)
+ - 10 models architectures for binary and multi class segmentation (including legendary Unet)
  - 124 available encoders (and 500+ encoders from [timm](https://github.com/rwightman/pytorch-image-models))
  - All encoders have pre-trained weights for faster and better convergence
  - Popular metrics and losses for training routines
@@ -94,6 +94,7 @@ Congratulations! You are done! Now you can train your model with your favorite f
  - PAN [[paper](https://arxiv.org/abs/1805.10180)] [[docs](https://smp.readthedocs.io/en/latest/models.html#pan)]
  - DeepLabV3 [[paper](https://arxiv.org/abs/1706.05587)] [[docs](https://smp.readthedocs.io/en/latest/models.html#deeplabv3)]
  - DeepLabV3+ [[paper](https://arxiv.org/abs/1802.02611)] [[docs](https://smp.readthedocs.io/en/latest/models.html#id9)]
+ - UPerNet [[paper](https://arxiv.org/abs/1807.10221)] [[docs](https://smp.readthedocs.io/en/latest/models.html#upernet)]
 
 #### Encoders <a name="encoders"></a>
 

docs/models.rst

@@ -66,3 +66,10 @@ MAnet
 PAN
 ~~~
 .. autoclass:: segmentation_models_pytorch.PAN
+
+
+.. _upernet:
+
+UPerNet
+~~~
+.. autoclass:: segmentation_models_pytorch.UPerNet

segmentation_models_pytorch/__init__.py

@@ -14,6 +14,7 @@
 from .decoders.pspnet import PSPNet
 from .decoders.deeplabv3 import DeepLabV3, DeepLabV3Plus
 from .decoders.pan import PAN
+from .decoders.upernet import UPerNet
 from .base.hub_mixin import from_pretrained
 
 from .__version__ import __version__
@@ -48,6 +49,7 @@ def create_model(
         DeepLabV3,
         DeepLabV3Plus,
         PAN,
+        UPerNet,
     ]
     archs_dict = {a.__name__.lower(): a for a in archs}
     try:
@@ -82,6 +84,7 @@ def create_model(
     "DeepLabV3",
     "DeepLabV3Plus",
     "PAN",
+    "UPerNet",
     "from_pretrained",
     "create_model",
     "__version__",

segmentation_models_pytorch/decoders/upernet/__init__.py

@@ -0,0 +1,3 @@
+from .model import UPerNet
+
+__all__ = ["UPerNet"]

segmentation_models_pytorch/decoders/upernet/decoder.py

@@ -0,0 +1,142 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from segmentation_models_pytorch.base import modules as md
+
+
+class PSPModule(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        sizes=(1, 2, 3, 6),
+        use_batchnorm=True,
+    ):
+        super().__init__()
+        self.blocks = nn.ModuleList(
+            [
+                nn.Sequential(
+                    nn.AdaptiveAvgPool2d(size),
+                    md.Conv2dReLU(
+                        in_channels,
+                        in_channels // len(sizes),
+                        kernel_size=1,
+                        use_batchnorm=use_batchnorm,
+                    ),
+                )
+                for size in sizes
+            ]
+        )
+        self.out_conv = md.Conv2dReLU(
+            in_channels=in_channels * 2,
+            out_channels=out_channels,
+            kernel_size=1,
+            use_batchnorm=True,
+        )
+
+    def forward(self, x):
+        _, _, height, weight = x.shape
+        out = [x] + [
+            F.interpolate(
+                block(x), size=(height, weight), mode="bilinear", align_corners=False
+            )
+            for block in self.blocks
+        ]
+        out = self.out_conv(torch.cat(out, dim=1))
+        return out
+
+
+class FPNBlock(nn.Module):
+    def __init__(self, skip_channels, pyramid_channels, use_bathcnorm=True):
+        super().__init__()
+        self.skip_conv = (
+            md.Conv2dReLU(
+                skip_channels,
+                pyramid_channels,
+                kernel_size=1,
+                use_batchnorm=use_bathcnorm,
+            )
+            if skip_channels != 0
+            else nn.Identity()
+        )
+
+    def forward(self, x, skip):
+        _, channels, height, weight = skip.shape
+        x = F.interpolate(
+            x, size=(height, weight), mode="bilinear", align_corners=False
+        )
+        if channels != 0:
+            skip = self.skip_conv(skip)
+            x = x + skip
+        return x
+
+
+class UPerNetDecoder(nn.Module):
+    def __init__(
+        self,
+        encoder_channels,
+        encoder_depth=5,
+        pyramid_channels=256,
+        segmentation_channels=64,
+    ):
+        super().__init__()
+        self.out_channels = segmentation_channels
+        if encoder_depth < 3:
+            raise ValueError(
+                "Encoder depth for UPerNet decoder cannot be less than 3, got {}.".format(
+                    encoder_depth
+                )
+            )
+
+        encoder_channels = encoder_channels[::-1]
+
+        # PSP Module
+        self.psp = PSPModule(
+            in_channels=encoder_channels[0],
+            out_channels=pyramid_channels,
+            sizes=(1, 2, 3, 6),
+            use_batchnorm=True,
+        )
+
+        # FPN Module
+        self.fpn_stages = nn.ModuleList(
+            [FPNBlock(ch, pyramid_channels) for ch in encoder_channels[1:]]
+        )
+
+        self.fpn_bottleneck = md.Conv2dReLU(
+            in_channels=(len(encoder_channels) - 1) * pyramid_channels,
+            out_channels=segmentation_channels,
+            kernel_size=3,
+            padding=1,
+            use_batchnorm=True,
+        )
+
+    def forward(self, *features):
+        output_size = features[0].shape[2:]
+        target_size = [size // 4 for size in output_size]
+
+        features = features[1:]  # remove first skip with same spatial resolution
+        features = features[::-1]  # reverse channels to start from head of encoder
+
+        psp_out = self.psp(features[0])
+
+        fpn_features = [psp_out]
+        for feature, stage in zip(features[1:], self.fpn_stages):
+            fpn_feature = stage(fpn_features[-1], feature)
+            fpn_features.append(fpn_feature)
+
+        # Resize all FPN features to 1/4 of the original resolution.
+        resized_fpn_features = []
+        for feature in fpn_features:
+            resized_feature = F.interpolate(
+                feature, size=target_size, mode="bilinear", align_corners=False
+            )
+            resized_fpn_features.append(resized_feature)
+
+        output = self.fpn_bottleneck(torch.cat(resized_fpn_features, dim=1))
+        output = F.interpolate(
+            output, size=output_size, mode="bilinear", align_corners=False
+        )
+
+        return output

segmentation_models_pytorch/decoders/upernet/model.py

@@ -0,0 +1,91 @@
+from typing import Optional, Union
+
+from segmentation_models_pytorch.encoders import get_encoder
+from segmentation_models_pytorch.base import (
+    SegmentationModel,
+    SegmentationHead,
+    ClassificationHead,
+)
+from .decoder import UPerNetDecoder
+
+
+class UPerNet(SegmentationModel):
+    """UPerNet is a unified perceptual parsing network for 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_pyramid_channels: A number of convolution filters in Feature Pyramid, default is 256
+        decoder_segmentation_channels: A number of convolution filters in segmentation blocks, default is 64
+        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**
+        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``: **UPerNet**
+
+    .. _UPerNet:
+        https://arxiv.org/abs/1807.10221
+
+    """
+
+    def __init__(
+        self,
+        encoder_name: str = "resnet34",
+        encoder_depth: int = 5,
+        encoder_weights: Optional[str] = "imagenet",
+        decoder_pyramid_channels: int = 256,
+        decoder_segmentation_channels: int = 64,
+        in_channels: int = 3,
+        classes: int = 1,
+        activation: Optional[Union[str, callable]] = None,
+        aux_params: Optional[dict] = None,
+    ):
+        super().__init__()
+
+        self.encoder = get_encoder(
+            encoder_name,
+            in_channels=in_channels,
+            depth=encoder_depth,
+            weights=encoder_weights,
+        )
+
+        self.decoder = UPerNetDecoder(
+            encoder_channels=self.encoder.out_channels,
+            encoder_depth=encoder_depth,
+            pyramid_channels=decoder_pyramid_channels,
+            segmentation_channels=decoder_segmentation_channels,
+        )
+
+        self.segmentation_head = SegmentationHead(
+            in_channels=self.decoder.out_channels,
+            out_channels=classes,
+            activation=activation,
+            kernel_size=3,
+        )
+
+        if aux_params is not None:
+            self.classification_head = ClassificationHead(
+                in_channels=self.encoder.out_channels[-1], **aux_params
+            )
+        else:
+            self.classification_head = None
+
+        self.name = "upernet-{}".format(encoder_name)
+        self.initialize()

tests/test_models.py

@@ -29,6 +29,7 @@ def get_sample(model_class):
         smp.PSPNet,
         smp.UnetPlusPlus,
         smp.MAnet,
+        smp.UPerNet,
     ]:
         sample = torch.ones([1, 3, 64, 64])
     elif model_class == smp.PAN:
@@ -57,7 +58,8 @@ def _test_forward_backward(model, sample, test_shape=False):
 @pytest.mark.parametrize("encoder_name", ENCODERS)
 @pytest.mark.parametrize("encoder_depth", [3, 5])
 @pytest.mark.parametrize(
-    "model_class", [smp.FPN, smp.PSPNet, smp.Linknet, smp.Unet, smp.UnetPlusPlus]
+    "model_class",
+    [smp.FPN, smp.PSPNet, smp.Linknet, smp.Unet, smp.UnetPlusPlus, smp.UPerNet],
 )
 def test_forward(model_class, encoder_name, encoder_depth, **kwargs):
     if (