TripleCartesianProd and QuadrupleCartesianProd support mini-batch for…

… both branch and trunk nets (lululxvi#977)
amostof · Oct 22, 2022 · 87d63a6 · 87d63a6
1 parent 3101bcb
commit 87d63a6
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Showing 3 changed files with 37 additions and 16 deletions.
diff --git a/deepxde/data/quadruple.py b/deepxde/data/quadruple.py
@@ -72,20 +72,29 @@ def __init__(self, X_train, y_train, X_test, y_test):
         self.train_x, self.train_y = X_train, y_train
         self.test_x, self.test_y = X_test, y_test
 
-        self.train_sampler = BatchSampler(len(X_train[0]), shuffle=True)
+        self.branch_sampler = BatchSampler(len(X_train[0]), shuffle=True)
+        self.trunk_sampler = BatchSampler(len(X_train[2]), shuffle=True)
 
     def losses(self, targets, outputs, loss_fn, inputs, model, aux=None):
         return loss_fn(targets, outputs)
 
     def train_next_batch(self, batch_size=None):
         if batch_size is None:
             return self.train_x, self.train_y
-        indices = self.train_sampler.get_next(batch_size)
+        if not isinstance(batch_size, (tuple, list)):
+            indices = self.branch_sampler.get_next(batch_size)
+            return (
+                self.train_x[0][indices],
+                self.train_x[1][indices],
+                self.train_x[2],
+            ), self.train_y[indices]
+        indices_branch = self.branch_sampler.get_next(batch_size[0])
+        indices_trunk = self.trunk_sampler.get_next(batch_size[1])
         return (
-            self.train_x[0][indices],
-            self.train_x[1][indices],
-            self.train_x[2],
-        ), self.train_y[indices]
+            self.train_x[0][indices_branch],
+            self.train_x[1][indices_branch],
+            self.train_x[2][indices_trunk],
+        ), self.train_y[indices_branch, indices_trunk]
 
     def test(self):
         return self.test_x, self.test_y
diff --git a/deepxde/data/triple.py b/deepxde/data/triple.py
@@ -54,8 +54,7 @@ class TripleCartesianProd(Data):
 
     Args:
         X_train: A tuple of two NumPy arrays. The first element has the shape (`N1`,
-            `dim1`), and the second element has the shape (`N2`, `dim2`). The mini-batch
-            is only applied to `N1`.
+            `dim1`), and the second element has the shape (`N2`, `dim2`).
         y_train: A NumPy array of shape (`N1`, `N2`).
     """
 
@@ -71,16 +70,24 @@ def __init__(self, X_train, y_train, X_test, y_test):
         self.train_x, self.train_y = X_train, y_train
         self.test_x, self.test_y = X_test, y_test
 
-        self.train_sampler = BatchSampler(len(X_train[0]), shuffle=True)
+        self.branch_sampler = BatchSampler(len(X_train[0]), shuffle=True)
+        self.trunk_sampler = BatchSampler(len(X_train[1]), shuffle=True)
 
     def losses(self, targets, outputs, loss_fn, inputs, model, aux=None):
         return loss_fn(targets, outputs)
 
     def train_next_batch(self, batch_size=None):
         if batch_size is None:
             return self.train_x, self.train_y
-        indices = self.train_sampler.get_next(batch_size)
-        return (self.train_x[0][indices], self.train_x[1]), self.train_y[indices]
+        if not isinstance(batch_size, (tuple, list)):
+            indices = self.branch_sampler.get_next(batch_size)
+            return (self.train_x[0][indices], self.train_x[1]), self.train_y[indices]
+        indices_branch = self.branch_sampler.get_next(batch_size[0])
+        indices_trunk = self.trunk_sampler.get_next(batch_size[1])
+        return (
+            self.train_x[0][indices_branch],
+            self.train_x[1][indices_trunk],
+        ), self.train_y[indices_branch, indices_trunk]
 
     def test(self):
         return self.test_x, self.test_y
diff --git a/deepxde/model.py b/deepxde/model.py
@@ -533,11 +533,16 @@ def train(
         Args:
             iterations (Integer): Number of iterations to train the model, i.e., number
                 of times the network weights are updated.
-            batch_size: Integer or ``None``. If you solve PDEs via ``dde.data.PDE`` or
-                ``dde.data.TimePDE``, do not use `batch_size`, and instead use
-                `dde.callbacks.PDEResidualResampler
-                <https://deepxde.readthedocs.io/en/latest/modules/deepxde.html#deepxde.callbacks.PDEResidualResampler>`_,
-                see an `example <https://github.com/lululxvi/deepxde/blob/master/examples/diffusion_1d_resample.py>`_.
+            batch_size: Integer, tuple, or ``None``.
+
+                - If you solve PDEs via ``dde.data.PDE`` or ``dde.data.TimePDE``, do not use `batch_size`, and instead use
+                  `dde.callbacks.PDEResidualResampler
+                  <https://deepxde.readthedocs.io/en/latest/modules/deepxde.html#deepxde.callbacks.PDEResidualResampler>`_,
+                  see an `example <https://github.com/lululxvi/deepxde/blob/master/examples/diffusion_1d_resample.py>`_.
+                - For DeepONet in the format of Cartesian product, if `batch_size` is an Integer,
+                  then it is the batch size for the branch input; if you want to also use mini-batch for the trunk net input,
+                  set `batch_size` as a tuple, where the fist number is the batch size for the branch net input
+                  and the second number is the batch size for the trunk net input.
             display_every (Integer): Print the loss and metrics every this steps.
             disregard_previous_best: If ``True``, disregard the previous saved best
                 model.