Genet from timm

.github/workflows/tests.yml

@@ -29,6 +29,7 @@ jobs:
         python -m pip install codecov pytest mock
         pip3 install torch==1.9.0+cpu torchvision==0.10.0+cpu torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
         pip install .
+        pip install -U git+https://github.com/rwightman/pytorch-image-models
     - name: Test
       run: |
         python -m pytest -s tests

README.md

@@ -12,7 +12,7 @@ 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)
- - 106 available encoders
+ - 109 available encoders
  - All encoders have pre-trained weights for faster and better convergence
 
 ### [📚 Project Documentation 📚](http://smp.readthedocs.io/)
@@ -188,6 +188,19 @@ The following is a list of supported encoders in the SMP. Select the appropriate
 </div>
 </details>
 
+<details>
+<summary style="margin-left: 25px;">GERNet</summary>
+<div style="margin-left: 25px;">
+
+|Encoder                         |Weights                         |Params, M                       |
+|--------------------------------|:------------------------------:|:------------------------------:|
+|timm-gernet_s                   |imagenet                        |6M                              |
+|timm-gernet_m                   |imagenet                        |18M                             |
+|timm-gernet_l                   |imagenet                        |28M                             |
+
+</div>
+</details>
+
 <details>
 <summary style="margin-left: 25px;">SE-Net</summary>
 <div style="margin-left: 25px;">

docs/encoders.rst

@@ -136,6 +136,19 @@ RegNet(x/y)
 | timm-regnety\_320   | imagenet   | 141M        |
 +---------------------+------------+-------------+
 
+GERNet
+~~~~~~
+
++-------------------------+------------+-------------+
+| Encoder                 | Weights    | Params, M   |
++=========================+============+=============+
+| timm-gernet\_s          | imagenet   | 6M          |
++-------------------------+------------+-------------+
+| timm-gernet\_m          | imagenet   | 18M         |
++-------------------------+------------+-------------+
+| timm-gernet\_l          | imagenet   | 28M         |
++-------------------------+------------+-------------+
+
 SE-Net
 ~~~~~~
 

segmentation_models_pytorch/encoders/__init__.py

@@ -17,6 +17,13 @@
 from .timm_res2net import timm_res2net_encoders
 from .timm_regnet import timm_regnet_encoders
 from .timm_sknet import timm_sknet_encoders
+try:
+    from .timm_gernet import timm_gernet_encoders
+except ImportError as e:
+    timm_gernet_encoders = {}
+    print("Current timm version doesn't support GERNet."
+          "If GERNet support is needed please update timm")
+
 from ._preprocessing import preprocess_input
 
 encoders = {}
@@ -36,6 +43,7 @@
 encoders.update(timm_res2net_encoders)
 encoders.update(timm_regnet_encoders)
 encoders.update(timm_sknet_encoders)
+encoders.update(timm_gernet_encoders)
 
 
 def get_encoder(name, in_channels=3, depth=5, weights=None):

segmentation_models_pytorch/encoders/timm_gernet.py

@@ -0,0 +1,121 @@
+from timm.models import ByobCfg, BlocksCfg, ByobNet
+
+from ._base import EncoderMixin
+import torch.nn as nn
+
+
+class GERNetEncoder(ByobNet, EncoderMixin):
+    def __init__(self, out_channels, depth=5, **kwargs):
+        super().__init__(**kwargs)
+        self._depth = depth
+        self._out_channels = out_channels
+        self._in_channels = 3
+
+        del self.head
+
+    def get_stages(self):
+        return [
+            nn.Identity(),
+            self.stem,
+            self.stages[0],
+            self.stages[1],
+            self.stages[2],
+            nn.Sequential(self.stages[3], self.stages[4], self.final_conv)
+        ]
+
+    def forward(self, x):
+        stages = self.get_stages()
+
+        features = []
+        for i in range(self._depth + 1):
+            x = stages[i](x)
+            features.append(x)
+
+        return features
+
+    def load_state_dict(self, state_dict, **kwargs):
+        state_dict.pop("head.fc.weight")
+        state_dict.pop("head.fc.bias")
+        super().load_state_dict(state_dict, **kwargs)
+
+
+regnet_weights = {
+    'timm-gernet_s': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-ger-weights/gernet_s-756b4751.pth',
+    },
+    'timm-gernet_m': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-ger-weights/gernet_m-0873c53a.pth',
+    },
+    'timm-gernet_l': {
+        'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-ger-weights/gernet_l-f31e2e8d.pth',
+    },
+}
+
+pretrained_settings = {}
+for model_name, sources in regnet_weights.items():
+    pretrained_settings[model_name] = {}
+    for source_name, source_url in sources.items():
+        pretrained_settings[model_name][source_name] = {
+            "url": source_url,
+            'input_range': [0, 1],
+            'mean': [0.485, 0.456, 0.406],
+            'std': [0.229, 0.224, 0.225],
+            'num_classes': 1000
+        }
+
+timm_gernet_encoders = {
+    'timm-gernet_s': {
+        'encoder': GERNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-gernet_s"],
+        'params': {
+            'out_channels': (3, 13, 48, 48, 384, 1920),
+            'cfg': ByobCfg(
+                blocks=(
+                    BlocksCfg(type='basic', d=1, c=48, s=2, gs=0, br=1.),
+                    BlocksCfg(type='basic', d=3, c=48, s=2, gs=0, br=1.),
+                    BlocksCfg(type='bottle', d=7, c=384, s=2, gs=0, br=1 / 4),
+                    BlocksCfg(type='bottle', d=2, c=560, s=2, gs=1, br=3.),
+                    BlocksCfg(type='bottle', d=1, c=256, s=1, gs=1, br=3.),
+                ),
+                stem_chs=13,
+                num_features=1920,
+            )
+        },
+    },
+    'timm-gernet_m': {
+        'encoder': GERNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-gernet_m"],
+        'params': {
+            'out_channels': (3, 32, 128, 192, 640, 2560),
+            'cfg': ByobCfg(
+                blocks=(
+                    BlocksCfg(type='basic', d=1, c=128, s=2, gs=0, br=1.),
+                    BlocksCfg(type='basic', d=2, c=192, s=2, gs=0, br=1.),
+                    BlocksCfg(type='bottle', d=6, c=640, s=2, gs=0, br=1 / 4),
+                    BlocksCfg(type='bottle', d=4, c=640, s=2, gs=1, br=3.),
+                    BlocksCfg(type='bottle', d=1, c=640, s=1, gs=1, br=3.),
+                ),
+                stem_chs=32,
+                num_features=2560,
+            )
+        },
+    },
+    'timm-gernet_l': {
+        'encoder': GERNetEncoder,
+        "pretrained_settings": pretrained_settings["timm-gernet_l"],
+        'params': {
+            'out_channels': (3, 32, 128, 192, 640, 2560),
+            'cfg': ByobCfg(
+                blocks=(
+                    BlocksCfg(type='basic', d=1, c=128, s=2, gs=0, br=1.),
+                    BlocksCfg(type='basic', d=2, c=192, s=2, gs=0, br=1.),
+                    BlocksCfg(type='bottle', d=6, c=640, s=2, gs=0, br=1 / 4),
+                    BlocksCfg(type='bottle', d=5, c=640, s=2, gs=1, br=3.),
+                    BlocksCfg(type='bottle', d=4, c=640, s=1, gs=1, br=3.),
+                ),
+                stem_chs=32,
+                num_features=2560,
+            )
+        },
+    },
+}

segmentation_models_pytorch/manet/decoder.py

@@ -56,18 +56,19 @@ def __init__(self, in_channels, skip_channels, out_channels, use_batchnorm=True,
                 use_batchnorm=use_batchnorm,
             )
         )
+        reduced_channels = max(1, skip_channels // reduction)
         self.SE_ll = nn.Sequential(
             nn.AdaptiveAvgPool2d(1),
-            nn.Conv2d(skip_channels, skip_channels // reduction, 1),
+            nn.Conv2d(skip_channels, reduced_channels, 1),
             nn.ReLU(inplace=True),
-            nn.Conv2d(skip_channels // reduction, skip_channels, 1),
+            nn.Conv2d(reduced_channels, skip_channels, 1),
             nn.Sigmoid(),
         )
         self.SE_hl = nn.Sequential(
             nn.AdaptiveAvgPool2d(1),
-            nn.Conv2d(skip_channels, skip_channels // reduction, 1),
+            nn.Conv2d(skip_channels, reduced_channels, 1),
             nn.ReLU(inplace=True),
-            nn.Conv2d(skip_channels // reduction, skip_channels, 1),
+            nn.Conv2d(reduced_channels, skip_channels, 1),
             nn.Sigmoid(),
         )
         self.conv1 = md.Conv2dReLU(