[feat] Adding SegFormer

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)
- - 10 models architectures for binary and multi class segmentation (including legendary Unet)
+ - 11 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
@@ -95,6 +95,7 @@ Congratulations! You are done! Now you can train your model with your favorite f
  - 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)]
+ - Segformer [[paper](https://arxiv.org/abs/2105.15203)] [[docs](https://smp.readthedocs.io/en/latest/models.html#segformer)]
 
 #### Encoders <a name="encoders"></a>
 

docs/models.rst

@@ -73,3 +73,11 @@ PAN
 UPerNet
 ~~~~~~~
 .. autoclass:: segmentation_models_pytorch.UPerNet
+
+
+.. _segformer:
+
+Segformer
+~~~~~~~~~
+.. autoclass:: segmentation_models_pytorch.Segformer
+

segmentation_models_pytorch/__init__.py

@@ -15,6 +15,7 @@
 from .decoders.deeplabv3 import DeepLabV3, DeepLabV3Plus
 from .decoders.pan import PAN
 from .decoders.upernet import UPerNet
+from .decoders.segformer import Segformer
 from .base.hub_mixin import from_pretrained
 
 from .__version__ import __version__
@@ -50,6 +51,7 @@ def create_model(
         DeepLabV3Plus,
         PAN,
         UPerNet,
+        Segformer,
     ]
     archs_dict = {a.__name__.lower(): a for a in archs}
     try:
@@ -85,6 +87,7 @@ def create_model(
     "DeepLabV3Plus",
     "PAN",
     "UPerNet",
+    "Segformer",
     "from_pretrained",
     "create_model",
     "__version__",

segmentation_models_pytorch/decoders/segformer/__init__.py

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

segmentation_models_pytorch/decoders/segformer/convert_original_to_smp.py

@@ -0,0 +1,148 @@
+import torch
+import argparse
+import requests
+import numpy as np
+import huggingface_hub
+import albumentations as A
+import matplotlib.pyplot as plt
+
+from PIL import Image
+import segmentation_models_pytorch as smp
+
+
+def convert_state_dict_to_smp(state_dict: dict):
+    # fmt: off
+
+    if "state_dict" in state_dict:
+        state_dict = state_dict["state_dict"]
+
+    new_state_dict = {}
+
+    # Map the backbone components to the encoder
+    keys = list(state_dict.keys())
+    for key in keys:
+        if key.startswith("backbone"):
+            new_key = key.replace("backbone", "encoder")
+            new_state_dict[new_key] = state_dict.pop(key)
+
+
+    # Map the linear_cX layers to MLP stages
+    for i in range(4):
+        base = f"decode_head.linear_c{i+1}.proj"
+        new_state_dict[f"decoder.mlp_stage.{3-i}.linear.weight"] = state_dict.pop(f"{base}.weight")
+        new_state_dict[f"decoder.mlp_stage.{3-i}.linear.bias"] = state_dict.pop(f"{base}.bias")
+
+    # Map fuse_stage components
+    fuse_base = "decode_head.linear_fuse"
+    fuse_weights = {
+        "decoder.fuse_stage.0.weight": state_dict.pop(f"{fuse_base}.conv.weight"),
+        "decoder.fuse_stage.1.weight": state_dict.pop(f"{fuse_base}.bn.weight"),
+        "decoder.fuse_stage.1.bias": state_dict.pop(f"{fuse_base}.bn.bias"),
+        "decoder.fuse_stage.1.running_mean": state_dict.pop(f"{fuse_base}.bn.running_mean"),
+        "decoder.fuse_stage.1.running_var": state_dict.pop(f"{fuse_base}.bn.running_var"),
+        "decoder.fuse_stage.1.num_batches_tracked": state_dict.pop(f"{fuse_base}.bn.num_batches_tracked"),
+    }
+    new_state_dict.update(fuse_weights)
+
+    # Map final layer components
+    new_state_dict["segmentation_head.0.weight"] = state_dict.pop("decode_head.linear_pred.weight")
+    new_state_dict["segmentation_head.0.bias"] = state_dict.pop("decode_head.linear_pred.bias")
+
+    del state_dict["decode_head.conv_seg.weight"]
+    del state_dict["decode_head.conv_seg.bias"]
+
+    assert len(state_dict) == 0, f"Unmapped keys: {state_dict.keys()}"
+
+    # fmt: on
+    return new_state_dict
+
+
+def get_np_image():
+    url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
+    image = Image.open(requests.get(url, stream=True).raw)
+    return np.array(image)
+
+
+def main(args):
+    original_checkpoint = torch.load(args.path, map_location="cpu", weights_only=True)
+    smp_state_dict = convert_state_dict_to_smp(original_checkpoint)
+
+    config = original_checkpoint["meta"]["config"]
+    num_classes = int(config.split("num_classes=")[1].split(",\n")[0])
+    decoder_dims = int(config.split("embed_dim=")[1].split(",\n")[0])
+    height, width = [
+        int(x) for x in config.split("crop_size=(")[1].split("), ")[0].split(",")
+    ]
+    model_size = args.path.split("segformer.")[1][:2]
+
+    # Create the model
+    model = smp.create_model(
+        in_channels=3,
+        classes=num_classes,
+        arch="segformer",
+        encoder_name=f"mit_{model_size}",
+        encoder_weights=None,
+        decoder_segmentation_channels=decoder_dims,
+    ).eval()
+
+    # Load the converted state dict
+    model.load_state_dict(smp_state_dict, strict=True)
+
+    # Preprocessing params
+    preprocessing = A.Compose(
+        [
+            A.Resize(height, width, p=1),
+            A.Normalize(
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                max_pixel_value=1.0,
+                p=1,
+            ),
+        ]
+    )
+
+    # Prepare the input
+    image = get_np_image()
+    normalized_image = preprocessing(image=image)["image"]
+    tensor = torch.tensor(normalized_image).permute(2, 0, 1).unsqueeze(0).float()
+
+    # Forward pass
+    with torch.no_grad():
+        mask = model(tensor)
+
+    # Postprocessing
+    mask = torch.nn.functional.interpolate(
+        mask, size=(image.shape[0], image.shape[1]), mode="bilinear"
+    )
+    mask = torch.argmax(mask, dim=1)
+    mask = mask.squeeze().cpu().numpy()
+
+    model_name = args.path.split("/")[-1].replace(".pth", "").replace(".", "-")
+
+    model.save_pretrained(model_name)
+    preprocessing.save_pretrained(model_name)
+
+    # fmt: off
+    plt.subplot(121), plt.axis('off'), plt.imshow(image), plt.title('Input Image')
+    plt.subplot(122), plt.axis('off'), plt.imshow(mask), plt.title('Output Mask')
+    plt.savefig(f"{model_name}/example_mask.png")
+    # fmt: on
+
+    if args.push_to_hub:
+        repo_id = f"smp-hub/{model_name}"
+        api = huggingface_hub.HfApi()
+        api.create_repo(repo_id=repo_id, repo_type="model")
+        api.upload_folder(folder_path=model_name, repo_id=repo_id)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--path",
+        type=str,
+        default="weights/trained_models/segformer.b2.512x512.ade.160k.pth",
+    )
+    parser.add_argument("--push_to_hub", action="store_true")
+    args = parser.parse_args()
+
+    main(args)

segmentation_models_pytorch/decoders/segformer/decoder.py

@@ -0,0 +1,72 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from segmentation_models_pytorch.base import modules as md
+
+
+class MLP(nn.Module):
+    def __init__(self, skip_channels, segmentation_channels):
+        super().__init__()
+
+        self.linear = nn.Linear(skip_channels, segmentation_channels)
+
+    def forward(self, x: torch.Tensor):
+        batch, _, height, width = x.shape
+        x = x.flatten(2).transpose(1, 2)
+        x = self.linear(x)
+        x = x.transpose(1, 2).reshape(batch, -1, height, width).contiguous()
+        return x
+
+
+class SegformerDecoder(nn.Module):
+    def __init__(
+        self,
+        encoder_channels,
+        encoder_depth=5,
+        segmentation_channels=256,
+    ):
+        super().__init__()
+
+        if encoder_depth < 3:
+            raise ValueError(
+                "Encoder depth for Segformer decoder cannot be less than 3, got {}.".format(
+                    encoder_depth
+                )
+            )
+
+        if encoder_channels[1] == 0:
+            encoder_channels = tuple(
+                channel for index, channel in enumerate(encoder_channels) if index != 1
+            )
+        encoder_channels = encoder_channels[::-1]
+
+        self.mlp_stage = nn.ModuleList(
+            [MLP(channel, segmentation_channels) for channel in encoder_channels[:-1]]
+        )
+
+        self.fuse_stage = md.Conv2dReLU(
+            in_channels=(len(encoder_channels) - 1) * segmentation_channels,
+            out_channels=segmentation_channels,
+            kernel_size=1,
+            use_batchnorm=True,
+        )
+
+    def forward(self, *features):
+        # Resize all features to the size of the largest feature
+        target_size = [dim // 4 for dim in features[0].shape[2:]]
+
+        features = features[2:] if features[1].size(1) == 0 else features[1:]
+        features = features[::-1]  # reverse channels to start from head of encoder
+
+        resized_features = []
+        for feature, stage in zip(features, self.mlp_stage):
+            feature = stage(feature)
+            resized_feature = F.interpolate(
+                feature, size=target_size, mode="bilinear", align_corners=False
+            )
+            resized_features.append(resized_feature)
+
+        output = self.fuse_stage(torch.cat(resized_features, dim=1))
+
+        return output

segmentation_models_pytorch/decoders/segformer/model.py

@@ -0,0 +1,93 @@
+from typing import Any, Optional, Union, Callable
+
+from segmentation_models_pytorch.base import (
+    ClassificationHead,
+    SegmentationHead,
+    SegmentationModel,
+)
+from segmentation_models_pytorch.encoders import get_encoder
+
+from .decoder import SegformerDecoder
+
+
+class Segformer(SegmentationModel):
+    """Segformer is simple and efficient design for semantic segmentation with Transformers
+
+    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_segmentation_channels: A number of convolution filters in segmentation blocks, 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**
+        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)
+        kwargs: Arguments passed to the encoder class ``__init__()`` function. Applies only to ``timm`` models. Keys with ``None`` values are pruned before passing.
+
+    Returns:
+        ``torch.nn.Module``: **Segformer**
+
+    .. _Segformer:
+        https://arxiv.org/abs/2105.15203
+
+    """
+
+    def __init__(
+        self,
+        encoder_name: str = "resnet34",
+        encoder_depth: int = 5,
+        encoder_weights: Optional[str] = "imagenet",
+        decoder_segmentation_channels: int = 256,
+        in_channels: int = 3,
+        classes: int = 1,
+        activation: Optional[Union[str, Callable]] = None,
+        aux_params: Optional[dict] = None,
+        **kwargs: dict[str, Any],
+    ):
+        super().__init__()
+
+        self.encoder = get_encoder(
+            encoder_name,
+            in_channels=in_channels,
+            depth=encoder_depth,
+            weights=encoder_weights,
+            **kwargs,
+        )
+
+        self.decoder = SegformerDecoder(
+            encoder_channels=self.encoder.out_channels,
+            encoder_depth=encoder_depth,
+            segmentation_channels=decoder_segmentation_channels,
+        )
+
+        self.segmentation_head = SegmentationHead(
+            in_channels=decoder_segmentation_channels,
+            out_channels=classes,
+            activation=activation,
+            kernel_size=1,
+            upsampling=4,
+        )
+
+        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 = "segformer-{}".format(encoder_name)
+        self.initialize()