Merge branch 'master' into pre-commit-ci-update-config

CHANGELOG.md

@@ -1,3 +1,27 @@
+# 1.2.0 (Fri Mar 22 2024)
+
+#### 🚀 Enhancement
+
+- Dev [#295](https://github.com/neuronets/nobrainer/pull/295) ([@hvgazula](https://github.com/hvgazula) [@pre-commit-ci[bot]](https://github.com/pre-commit-ci[bot]) [@satra](https://github.com/satra))
+- Update setup.cfg [#299](https://github.com/neuronets/nobrainer/pull/299) ([@satra](https://github.com/satra))
+
+#### 🐛 Bug Fix
+
+- Update release.yml ([@satra](https://github.com/satra))
+- change hyphenation [#275](https://github.com/neuronets/nobrainer/pull/275) ([@satra](https://github.com/satra))
+- update precommit checks [#275](https://github.com/neuronets/nobrainer/pull/275) ([@satra](https://github.com/satra))
+- fix docker syntax [#275](https://github.com/neuronets/nobrainer/pull/275) ([@satra](https://github.com/satra))
+- remove trained models [#275](https://github.com/neuronets/nobrainer/pull/275) ([@satra](https://github.com/satra))
+- [pre-commit.ci] pre-commit autoupdate [#269](https://github.com/neuronets/nobrainer/pull/269) ([@pre-commit-ci[bot]](https://github.com/pre-commit-ci[bot]))
+
+#### Authors: 3
+
+- [@pre-commit-ci[bot]](https://github.com/pre-commit-ci[bot])
+- H Gazula ([@hvgazula](https://github.com/hvgazula))
+- Satrajit Ghosh ([@satra](https://github.com/satra))
+
+---
+
 # 1.1.1 (Sat Oct 07 2023)
 
 #### 🐛 Bug Fix

nobrainer/dataset.py

@@ -121,6 +121,9 @@ def from_tfrecords(
         )
         block_length = len([0 for _ in first_shard])
 
+        if not n_volumes:
+            n_volumes = block_length * len(files)
+
         dataset = dataset.interleave(
             map_func=lambda x: tf.data.TFRecordDataset(
                 x, compression_type=compression_type

nobrainer/models/__init__.py

@@ -1,10 +1,32 @@
+from pprint import pprint
+
+from .attention_unet import attention_unet
+from .attention_unet_with_inception import attention_unet_with_inception
 from .autoencoder import autoencoder
+from .bayesian_meshnet import variational_meshnet
 from .dcgan import dcgan
 from .highresnet import highresnet
 from .meshnet import meshnet
 from .progressiveae import progressiveae
 from .progressivegan import progressivegan
 from .unet import unet
+from .unetr import unetr
+
+__all__ = ["get", "list_available_models"]
+
+_models = {
+    "highresnet": highresnet,
+    "meshnet": meshnet,
+    "unet": unet,
+    "autoencoder": autoencoder,
+    "progressivegan": progressivegan,
+    "progressiveae": progressiveae,
+    "dcgan": dcgan,
+    "attention_unet": attention_unet,
+    "attention_unet_with_inception": attention_unet_with_inception,
+    "unetr": unetr,
+    "variational_meshnet": variational_meshnet,
+}
 
 
 def get(name):
@@ -21,20 +43,18 @@ def get(name):
     if not isinstance(name, str):
         raise ValueError("Model name must be a string.")
 
-    models = {
-        "highresnet": highresnet,
-        "meshnet": meshnet,
-        "unet": unet,
-        "autoencoder": autoencoder,
-        "progressivegan": progressivegan,
-        "progressiveae": progressiveae,
-        "dcgan": dcgan,
-    }
-
     try:
-        return models[name.lower()]
+        return _models[name.lower()]
     except KeyError:
-        avail = ", ".join(models.keys())
+        avail = ", ".join(_models.keys())
         raise ValueError(
             "Unknown model: '{}'. Available models are {}.".format(name, avail)
         )
+
+
+def available_models():
+    return list(_models)
+
+
+def list_available_models():
+    pprint(available_models())

nobrainer/models/attention_unet.py

@@ -0,0 +1,80 @@
+"""Model definition for Attention U-Net.
+Adapted from https://github.com/nikhilroxtomar/Semantic-Segmentation-Architecture/blob/main/TensorFlow/attention-unet.py
+"""  # noqa: E501
+
+from tensorflow.keras import layers
+import tensorflow.keras.layers as L
+from tensorflow.keras.models import Model
+
+
+def conv_block(x, num_filters):
+    x = L.Conv3D(num_filters, 3, padding="same")(x)
+    x = L.BatchNormalization()(x)
+    x = L.Activation("relu")(x)
+
+    x = L.Conv3D(num_filters, 3, padding="same")(x)
+    x = L.BatchNormalization()(x)
+    x = L.Activation("relu")(x)
+
+    return x
+
+
+def encoder_block(x, num_filters):
+    x = conv_block(x, num_filters)
+    p = L.MaxPool3D()(x)
+    return x, p
+
+
+def attention_gate(g, s, num_filters):
+    Wg = L.Conv3D(num_filters, 1, padding="same")(g)
+    Wg = L.BatchNormalization()(Wg)
+
+    Ws = L.Conv3D(num_filters, 1, padding="same")(s)
+    Ws = L.BatchNormalization()(Ws)
+
+    out = L.Activation("relu")(Wg + Ws)
+    out = L.Conv3D(num_filters, 1, padding="same")(out)
+    out = L.Activation("sigmoid")(out)
+
+    return out * s
+
+
+def decoder_block(x, s, num_filters):
+    x = L.UpSampling3D()(x)
+    s = attention_gate(x, s, num_filters)
+    x = L.Concatenate()([x, s])
+    x = conv_block(x, num_filters)
+    return x
+
+
+def attention_unet(n_classes, input_shape):
+    """Inputs"""
+    inputs = L.Input(input_shape)
+
+    """ Encoder """
+    s1, p1 = encoder_block(inputs, 64)
+    s2, p2 = encoder_block(p1, 128)
+    s3, p3 = encoder_block(p2, 256)
+
+    b1 = conv_block(p3, 512)
+
+    """ Decoder """
+    d1 = decoder_block(b1, s3, 256)
+    d2 = decoder_block(d1, s2, 128)
+    d3 = decoder_block(d2, s1, 64)
+
+    """ Outputs """
+    outputs = L.Conv3D(n_classes, 1, padding="same")(d3)
+
+    final_activation = "sigmoid" if n_classes == 1 else "softmax"
+    outputs = layers.Activation(final_activation)(outputs)
+
+    """ Model """
+    return Model(inputs=inputs, outputs=outputs, name="Attention_U-Net")
+
+
+if __name__ == "__main__":
+    n_classes = 50
+    input_shape = (256, 256, 256, 3)
+    model = attention_unet(n_classes, input_shape)
+    model.summary()