Updating mne_bids version (#586)

* Updating mne_bids version * Accepting mne 1.7 * Updating whats new file * Enforce moabb 1.7 * Enforce moabb 1.7 * Adding edfio as a dependency * Update pyproject.toml Co-authored-by: Bru <[email protected]> * Update pyproject.toml * Updating versions: keras, scikeras, tensorflow, h5py * Updating versions: keras, scikeras, tensorflow, h5py * Defining tensorflow-io-gcs-filesystem version * Defining tensorflow-io-gcs-filesystem version * Updating to Keras 3 optimizers * Updating code Keras3 * [pre-commit.ci] auto fixes from pre-commit.com hooks * Updating code Keras3 * Updating to Keras 3 functions * [pre-commit.ci] auto fixes from pre-commit.com hooks --------- Co-authored-by: Bru <[email protected]> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
NeuroTechX · May 29, 2024 · 03b1f48 · 03b1f48
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diff --git a/docs/source/whats_new.rst b/docs/source/whats_new.rst
@@ -49,6 +49,7 @@ Bugs
 - Fix number of sessions in doc of :class:`moabb.datasets.Sosulski2019` (:gh:`565` by `Pierre Guetschel`_)
 - Fix `code` column of :class:`moabb.datasets.CastillosCVEP100` and :class:`moabb.datasets.CastillosCVEP100` (:gh:`567` by `Pierre Guetschel`_)
 - MAINT updating the packages pre-release (:gh:`578` by `Bruno Aristimunha`_)
+- Fix mne_bids version incompatibility with mne (:gh:`586` by `Bruna Lopes`_)
 - Updating the parameters of the SSVEP_TRCA method (:gh:`589` by `Bruno Aristimunha`_)
 - Fix and updating the parameters for the benchmark function (:gh:`588` by `Bruno Aristimunha`_)
 - Fix :class:`moabb.datasets.preprocessing.SetRawAnnotations` setting incorrect annotations when the dataset's interval does not start at 0 (:gh:`607` by `Pierre Guetschel`_)
@@ -449,4 +450,5 @@ API changes
 .. _Jordy Thielen: https://github.com/thijor
 .. _Sebastien Velut: https://github.com/swetbear
 .. _Brian Irvine: https://github.com/brianjohannes
+.. _Bruna Lopes: https://github.com/brunaafl
 .. _Yash Chauhan: https://github.com/jiggychauhi
diff --git a/examples/pipelines_DL/Keras_DeepConvNet.yml b/examples/pipelines_DL/Keras_DeepConvNet.yml
@@ -25,7 +25,7 @@ pipeline:
       loss: "sparse_categorical_crossentropy"
       optimizer:
         - name: Adam
-          from: tensorflow.keras.optimizers.legacy
+          from: tensorflow.keras.optimizers
           parameters:
             learning_rate: 0.001
       epochs: 2

diff --git a/examples/pipelines_DL/Keras_EEGITNet.yml b/examples/pipelines_DL/Keras_EEGITNet.yml
@@ -25,7 +25,7 @@ pipeline:
       loss: "sparse_categorical_crossentropy"
       optimizer:
         - name: Adam
-          from: tensorflow.keras.optimizers.legacy
+          from: tensorflow.keras.optimizers
           parameters:
             learning_rate: 0.001
       epochs: 2

diff --git a/examples/pipelines_DL/Keras_EEGNeX.yml b/examples/pipelines_DL/Keras_EEGNeX.yml
@@ -25,7 +25,7 @@ pipeline:
       loss: "sparse_categorical_crossentropy"
       optimizer:
         - name: Adam
-          from: tensorflow.keras.optimizers.legacy
+          from: tensorflow.keras.optimizers
           parameters:
             learning_rate: 0.001
       epochs: 2

diff --git a/examples/pipelines_DL/Keras_EEGNet_8_2.yml b/examples/pipelines_DL/Keras_EEGNet_8_2.yml
@@ -25,7 +25,7 @@ pipeline:
       loss: "sparse_categorical_crossentropy"
       optimizer:
         - name: Adam
-          from: tensorflow.keras.optimizers.legacy
+          from: tensorflow.keras.optimizers
           parameters:
             learning_rate: 0.001
       epochs: 2

diff --git a/examples/pipelines_DL/Keras_EEGTCNet.yml b/examples/pipelines_DL/Keras_EEGTCNet.yml
@@ -25,7 +25,7 @@ pipeline:
       loss: "sparse_categorical_crossentropy"
       optimizer:
         - name: Adam
-          from: tensorflow.keras.optimizers.legacy
+          from: tensorflow.keras.optimizers
           parameters:
             learning_rate: 0.001
       epochs: 2

diff --git a/examples/pipelines_DL/Keras_ShallowConvNet.yml b/examples/pipelines_DL/Keras_ShallowConvNet.yml
@@ -25,7 +25,7 @@ pipeline:
       loss: "sparse_categorical_crossentropy"
       optimizer:
         - name: Adam
-          from: tensorflow.keras.optimizers.legacy
+          from: tensorflow.keras.optimizers
           parameters:
             learning_rate: 0.001
       epochs: 2

diff --git a/moabb/pipelines/deep_learning.py b/moabb/pipelines/deep_learning.py
@@ -10,7 +10,6 @@
 from typing import Any, Dict
 
 import tensorflow as tf
-from keras import backend as K
 from keras.constraints import max_norm
 from keras.layers import (
     Activation,
@@ -31,7 +30,9 @@
     Permute,
 )
 from keras.models import Model, Sequential
+from keras.ops import pad
 from scikeras.wrappers import KerasClassifier
+from tensorflow.keras import backend as K
 
 from moabb.pipelines.utils_deep_model import EEGNet, EEGNet_TC, TCN_block
 
@@ -728,15 +729,15 @@ def _keras_build_fn(self, compile_kwargs: Dict[str, Any]):
 
         # ================================
 
-        paddings = tf.constant([[0, 0], [0, 0], [3, 0], [0, 0]])
-        block = tf.pad(block_in, paddings, "CONSTANT")
+        paddings = [[0, 0], [0, 0], [3, 0], [0, 0]]
+        block = pad(block_in, paddings, "constant")
         block = DepthwiseConv2D(
             (1, 4), padding="valid", depth_multiplier=1, dilation_rate=(1, 1)
         )(block)
         block = BatchNormalization()(block)
         block = Activation("elu")(block)
         block = Dropout(0.4)(block)
-        block = tf.pad(block, paddings, "CONSTANT")
+        block = pad(block, paddings, "constant")
         block = DepthwiseConv2D(
             (1, 4), padding="valid", depth_multiplier=1, dilation_rate=(1, 1)
         )(block)
@@ -745,15 +746,16 @@ def _keras_build_fn(self, compile_kwargs: Dict[str, Any]):
         block = Dropout(self.drop_rate)(block)
         block_out = Add()([block_in, block])
 
-        paddings = tf.constant([[0, 0], [0, 0], [6, 0], [0, 0]])
-        block = tf.pad(block_out, paddings, "CONSTANT")
+        paddings = [[0, 0], [0, 0], [6, 0], [0, 0]]
+        block = pad(block_out, paddings, "constant")
         block = DepthwiseConv2D(
             (1, 4), padding="valid", depth_multiplier=1, dilation_rate=(1, 2)
         )(block)
         block = BatchNormalization()(block)
         block = Activation("elu")(block)
         block = Dropout(self.drop_rate)(block)
-        block = tf.pad(block, paddings, "CONSTANT")
+        print(block.dtype)
+        block = pad(block, paddings, "constant")
         block = DepthwiseConv2D(
             (1, 4), padding="valid", depth_multiplier=1, dilation_rate=(1, 2)
         )(block)
@@ -762,15 +764,15 @@ def _keras_build_fn(self, compile_kwargs: Dict[str, Any]):
         block = Dropout(self.drop_rate)(block)
         block_out = Add()([block_out, block])
 
-        paddings = tf.constant([[0, 0], [0, 0], [12, 0], [0, 0]])
-        block = tf.pad(block_out, paddings, "CONSTANT")
+        paddings = [[0, 0], [0, 0], [12, 0], [0, 0]]
+        block = pad(block_out, paddings, "constant")
         block = DepthwiseConv2D(
             (1, 4), padding="valid", depth_multiplier=1, dilation_rate=(1, 4)
         )(block)
         block = BatchNormalization()(block)
         block = Activation("elu")(block)
         block = Dropout(self.drop_rate)(block)
-        block = tf.pad(block, paddings, "CONSTANT")
+        block = pad(block, paddings, "constant")
         block = DepthwiseConv2D(
             (1, 4), padding="valid", depth_multiplier=1, dilation_rate=(1, 4)
         )(block)
@@ -779,15 +781,15 @@ def _keras_build_fn(self, compile_kwargs: Dict[str, Any]):
         block = Dropout(self.drop_rate)(block)
         block_out = Add()([block_out, block])
 
-        paddings = tf.constant([[0, 0], [0, 0], [24, 0], [0, 0]])
-        block = tf.pad(block_out, paddings, "CONSTANT")
+        paddings = [[0, 0], [0, 0], [24, 0], [0, 0]]
+        block = pad(block_out, paddings, "constant")
         block = DepthwiseConv2D(
             (1, 4), padding="valid", depth_multiplier=1, dilation_rate=(1, 8)
         )(block)
         block = BatchNormalization()(block)
         block = Activation("elu")(block)
         block = Dropout(self.drop_rate)(block)
-        block = tf.pad(block, paddings, "CONSTANT")
+        block = pad(block, paddings, "constant")
         block = DepthwiseConv2D(
             (1, 4), padding="valid", depth_multiplier=1, dilation_rate=(1, 8)
         )(block)

diff --git a/pipelines/Keras_DeepConvNet.yml b/pipelines/Keras_DeepConvNet.yml
@@ -27,7 +27,7 @@ pipeline:
       loss: "sparse_categorical_crossentropy"
       optimizer:
         - name: Adam
-          from: tensorflow.keras.optimizers.legacy
+          from: tensorflow.keras.optimizers
           parameters:
             learning_rate: 0.001
       epochs: 300

diff --git a/pipelines/Keras_EEGITNet.yml b/pipelines/Keras_EEGITNet.yml
@@ -27,7 +27,7 @@ pipeline:
       loss: "sparse_categorical_crossentropy"
       optimizer:
         - name: Adam
-          from: tensorflow.keras.optimizers.legacy
+          from: tensorflow.keras.optimizers
           parameters:
             learning_rate: 0.001
       epochs: 300

diff --git a/pipelines/Keras_EEGNeX.yml b/pipelines/Keras_EEGNeX.yml
@@ -27,7 +27,7 @@ pipeline:
       loss: "sparse_categorical_crossentropy"
       optimizer:
         - name: Adam
-          from: tensorflow.keras.optimizers.legacy
+          from: tensorflow.keras.optimizers
           parameters:
             learning_rate: 0.001
       epochs: 300

diff --git a/pipelines/Keras_EEGNet_8_2.yml b/pipelines/Keras_EEGNet_8_2.yml
@@ -27,7 +27,7 @@ pipeline:
       loss: "sparse_categorical_crossentropy"
       optimizer:
         - name: Adam
-          from: tensorflow.keras.optimizers.legacy
+          from: tensorflow.keras.optimizers
           parameters:
             learning_rate: 0.001
       epochs: 300

diff --git a/pipelines/Keras_EEGTCNet.yml b/pipelines/Keras_EEGTCNet.yml
@@ -27,7 +27,7 @@ pipeline:
       loss: "sparse_categorical_crossentropy"
       optimizer:
         - name: Adam
-          from: tensorflow.keras.optimizers.legacy
+          from: tensorflow.keras.optimizers
           parameters:
             learning_rate: 0.001
       epochs: 300

diff --git a/pipelines/Keras_ShallowConvNet.yml b/pipelines/Keras_ShallowConvNet.yml
@@ -27,7 +27,7 @@ pipeline:
       loss: "sparse_categorical_crossentropy"
       optimizer:
         - name: Adam
-          from: tensorflow.keras.optimizers.legacy
+          from: tensorflow.keras.optimizers
           parameters:
             learning_rate: 0.001
       epochs: 300