Enable any resolution for Unet

segmentation_models_pytorch/base/model.py

@@ -1,8 +1,11 @@
 import torch
+from typing import TypeVar, Type
 
 from . import initialization as init
 from .hub_mixin import SMPHubMixin
 
+T = TypeVar("T", bound="SegmentationModel")
+
 
 class SegmentationModel(torch.nn.Module, SMPHubMixin):
     """Base class for all segmentation models."""
@@ -11,6 +14,11 @@ class SegmentationModel(torch.nn.Module, SMPHubMixin):
     # set to False
     requires_divisible_input_shape = True
 
+    # Fix type-hint for models, to avoid HubMixin signature
+    def __new__(cls: Type[T], *args, **kwargs) -> T:
+        instance = super().__new__(cls, *args, **kwargs)
+        return instance
+
     def initialize(self):
         init.initialize_decoder(self.decoder)
         init.initialize_head(self.segmentation_head)
@@ -42,7 +50,7 @@ def check_input_shape(self, x):
     def forward(self, x):
         """Sequentially pass `x` trough model`s encoder, decoder and heads"""
 
-        if not torch.jit.is_tracing() or self.requires_divisible_input_shape:
+        if torch.jit.is_tracing() or self.requires_divisible_input_shape:
             self.check_input_shape(x)
 
         features = self.encoder(x)

segmentation_models_pytorch/decoders/unet/decoder.py

@@ -2,19 +2,22 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
+from typing import Optional, Sequence
 from segmentation_models_pytorch.base import modules as md
 
 
 class DecoderBlock(nn.Module):
     def __init__(
         self,
-        in_channels,
-        skip_channels,
-        out_channels,
-        use_batchnorm=True,
-        attention_type=None,
+        in_channels: int,
+        skip_channels: int,
+        out_channels: int,
+        use_batchnorm: bool = True,
+        attention_type: Optional[str] = None,
+        interpolation_mode: str = "nearest",
     ):
         super().__init__()
+        self.interpolate_mode = interpolation_mode
         self.conv1 = md.Conv2dReLU(
             in_channels + skip_channels,
             out_channels,
@@ -34,19 +37,32 @@ def __init__(
         )
         self.attention2 = md.Attention(attention_type, in_channels=out_channels)
 
-    def forward(self, x, skip=None):
-        x = F.interpolate(x, scale_factor=2, mode="nearest")
-        if skip is not None:
-            x = torch.cat([x, skip], dim=1)
-            x = self.attention1(x)
-        x = self.conv1(x)
-        x = self.conv2(x)
-        x = self.attention2(x)
-        return x
+    def forward(
+        self,
+        feature_map: torch.Tensor,
+        target_height: int,
+        target_width: int,
+        skip_connection: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """Upsample feature map to the given spatial shape, concatenate with skip connection,
+        apply attention block (if specified) and then apply two convolutions.
+        """
+        feature_map = F.interpolate(
+            feature_map, size=(target_height, target_width), mode=self.interpolate_mode
+        )
+        if skip_connection is not None:
+            feature_map = torch.cat([feature_map, skip_connection], dim=1)
+            feature_map = self.attention1(feature_map)
+        feature_map = self.conv1(feature_map)
+        feature_map = self.conv2(feature_map)
+        feature_map = self.attention2(feature_map)
+        return feature_map
 
 
 class CenterBlock(nn.Sequential):
-    def __init__(self, in_channels, out_channels, use_batchnorm=True):
+    """Center block of the Unet decoder. Applied to the last feature map of the encoder."""
+
+    def __init__(self, in_channels: int, out_channels: int, use_batchnorm: bool = True):
         conv1 = md.Conv2dReLU(
             in_channels,
             out_channels,
@@ -67,12 +83,12 @@ def __init__(self, in_channels, out_channels, use_batchnorm=True):
 class UnetDecoder(nn.Module):
     def __init__(
         self,
-        encoder_channels,
-        decoder_channels,
-        n_blocks=5,
-        use_batchnorm=True,
-        attention_type=None,
-        center=False,
+        encoder_channels: Sequence[int],
+        decoder_channels: Sequence[int],
+        n_blocks: int = 5,
+        use_batchnorm: bool = True,
+        attention_type: Optional[str] = None,
+        add_center_block: bool = False,
     ):
         super().__init__()
 
@@ -94,31 +110,44 @@ def __init__(
         skip_channels = list(encoder_channels[1:]) + [0]
         out_channels = decoder_channels
 
-        if center:
+        if add_center_block:
             self.center = CenterBlock(
                 head_channels, head_channels, use_batchnorm=use_batchnorm
             )
         else:
             self.center = nn.Identity()
 
         # combine decoder keyword arguments
-        kwargs = dict(use_batchnorm=use_batchnorm, attention_type=attention_type)
-        blocks = [
-            DecoderBlock(in_ch, skip_ch, out_ch, **kwargs)
-            for in_ch, skip_ch, out_ch in zip(in_channels, skip_channels, out_channels)
-        ]
-        self.blocks = nn.ModuleList(blocks)
-
-    def forward(self, *features):
+        self.blocks = nn.ModuleList()
+        for block_in_channels, block_skip_channels, block_out_channels in zip(
+            in_channels, skip_channels, out_channels
+        ):
+            block = DecoderBlock(
+                block_in_channels,
+                block_skip_channels,
+                block_out_channels,
+                use_batchnorm=use_batchnorm,
+                attention_type=attention_type,
+            )
+            self.blocks.append(block)
+
+    def forward(self, *features: torch.Tensor) -> torch.Tensor:
+        # spatial shapes of features: [hw, hw/2, hw/4, hw/8, ...]
+        spatial_shapes = [feature.shape[2:] for feature in features]
+        spatial_shapes = spatial_shapes[::-1]
+
         features = features[1:]  # remove first skip with same spatial resolution
         features = features[::-1]  # reverse channels to start from head of encoder
 
         head = features[0]
-        skips = features[1:]
+        skip_connections = features[1:]
 
         x = self.center(head)
+
         for i, decoder_block in enumerate(self.blocks):
-            skip = skips[i] if i < len(skips) else None
-            x = decoder_block(x, skip)
+            # upsample to the next spatial shape
+            height, width = spatial_shapes[i + 1]
+            skip_connection = skip_connections[i] if i < len(skip_connections) else None
+            x = decoder_block(x, height, width, skip_connection=skip_connection)
 
         return x

segmentation_models_pytorch/decoders/unet/model.py

@@ -1,4 +1,4 @@
-from typing import Any, Optional, Union, Tuple, Callable
+from typing import Any, Optional, Union, Callable, Sequence
 
 from segmentation_models_pytorch.base import (
     ClassificationHead,
@@ -12,10 +12,21 @@
 
 
 class Unet(SegmentationModel):
-    """Unet_ is a fully convolution neural network for image semantic segmentation. Consist of *encoder*
-    and *decoder* parts connected with *skip connections*. Encoder extract features of different spatial
-    resolution (skip connections) which are used by decoder to define accurate segmentation mask. Use *concatenation*
-    for fusing decoder blocks with skip connections.
+    """
+    U-Net is a fully convolutional neural network architecture designed for semantic image segmentation.
+
+    It consists of two main parts:
+
+    1. An encoder (downsampling path) that extracts increasingly abstract features
+    2. A decoder (upsampling path) that gradually recovers spatial details
+
+    The key is the use of skip connections between corresponding encoder and decoder layers.
+    These connections allow the decoder to access fine-grained details from earlier encoder layers,
+    which helps produce more precise segmentation masks.
+
+    The skip connections work by concatenating feature maps from the encoder directly into the decoder
+    at corresponding resolutions. This helps preserve important spatial information that would
+    otherwise be lost during the encoding process.
 
     Args:
         encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone)
@@ -51,19 +62,39 @@ class Unet(SegmentationModel):
     Returns:
         ``torch.nn.Module``: Unet
 
+    Example:
+        .. code-block:: python
+
+            import torch
+            import segmentation_models_pytorch as smp
+
+            model = smp.Unet("resnet18", encoder_weights="imagenet", classes=5)
+            model.eval()
+
+            # generate random images
+            images = torch.rand(2, 3, 256, 256)
+
+            with torch.inference_mode():
+                mask = model(images)
+
+            print(mask.shape)
+            # torch.Size([2, 5, 256, 256])
+
     .. _Unet:
         https://arxiv.org/abs/1505.04597
 
     """
 
+    requires_divisible_input_shape = False
+
     @supports_config_loading
     def __init__(
         self,
         encoder_name: str = "resnet34",
         encoder_depth: int = 5,
         encoder_weights: Optional[str] = "imagenet",
         decoder_use_batchnorm: bool = True,
-        decoder_channels: Tuple[int, ...] = (256, 128, 64, 32, 16),
+        decoder_channels: Sequence[int] = (256, 128, 64, 32, 16),
         decoder_attention_type: Optional[str] = None,
         in_channels: int = 3,
         classes: int = 1,
@@ -81,12 +112,13 @@ def __init__(
             **kwargs,
         )
 
+        add_center_block = encoder_name.startswith("vgg")
         self.decoder = UnetDecoder(
             encoder_channels=self.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,
+            add_center_block=add_center_block,
             attention_type=decoder_attention_type,
         )
 

tests/encoders/base.py

@@ -82,7 +82,7 @@ def test_in_channels(self):
                 encoder.eval()
 
                 # forward
-                with torch.no_grad():
+                with torch.inference_mode():
                     encoder.forward(sample)
 
     def test_depth(self):
@@ -110,7 +110,7 @@ def test_depth(self):
                 encoder.eval()
 
                 # forward
-                with torch.no_grad():
+                with torch.inference_mode():
                     features = encoder.forward(sample)
 
                 # check number of features
@@ -187,7 +187,7 @@ def test_dilated(self):
                 encoder.eval()
 
                 # forward
-                with torch.no_grad():
+                with torch.inference_mode():
                     features = encoder.forward(sample)
 
                 height_strides, width_strides = self.get_features_output_strides(