Update to v0.4.11, fix tests related to numpy2 & add Newton's law of cooling example

examples/MDF/abcd_torch.py

@@ -86,7 +86,7 @@ def forward(self, input: torch.Tensor):
 
 # Export the model
 fn = "ABCD_from_torch.onnx"
-torch_out = torch.onnx._export(
+torch.onnx.export(
     m_abcd,  # model being run
     input,  # model input (or a tuple for multiple inputs)
     fn,  # where to save the model (can be a file or file-like object)

examples/PsyNeuLink/stroop_conflict_monitoring.py

@@ -5,14 +5,14 @@
 
 # Construct the color naming pathway:
 color_input = pnl.ProcessingMechanism(
-    name="color_input", size=2
+    name="color_input", input_shapes=2
 )  # Note:  default function is Linear
 color_input_to_hidden_wts = np.array([[2, -2], [-2, 2]])
 color_hidden = pnl.ProcessingMechanism(
-    name="color_hidden", size=2, function=pnl.Logistic(bias=-4)
+    name="color_hidden", input_shapes=2, function=pnl.Logistic(bias=-4)
 )
 color_hidden_to_output_wts = np.array([[2, -2], [-2, 2]])
-output = pnl.ProcessingMechanism(name="OUTPUT", size=2, function=pnl.Logistic)
+output = pnl.ProcessingMechanism(name="OUTPUT", input_shapes=2, function=pnl.Logistic)
 color_pathway = [
     color_input,
     color_input_to_hidden_wts,
@@ -22,10 +22,10 @@
 ]
 
 # Construct the word reading pathway (using the same output_layer)
-word_input = pnl.ProcessingMechanism(name="word_input", size=2)
+word_input = pnl.ProcessingMechanism(name="word_input", input_shapes=2)
 word_input_to_hidden_wts = np.array([[3, -3], [-3, 3]])
 word_hidden = pnl.ProcessingMechanism(
-    name="word_hidden", size=2, function=pnl.Logistic(bias=-4)
+    name="word_hidden", input_shapes=2, function=pnl.Logistic(bias=-4)
 )
 word_hidden_to_output_wts = np.array([[3, -3], [-3, 3]])
 word_pathway = [
@@ -37,8 +37,8 @@
 ]
 
 # Construct the task specification pathways
-task_input = pnl.ProcessingMechanism(name="task_input", size=2)
-task = pnl.LCAMechanism(name="TASK", size=2, initial_value=[0.5, 0.5])
+task_input = pnl.ProcessingMechanism(name="task_input", input_shapes=2)
+task = pnl.LCAMechanism(name="TASK", input_shapes=2, initial_value=[0.5, 0.5])
 task_color_wts = np.array([[4, 4], [0, 0]])
 task_word_wts = np.array([[0, 0], [4, 4]])
 task_color_pathway = [task_input, task, task_color_wts, color_hidden]
@@ -53,7 +53,7 @@
     name="CONTROL",
     objective_mechanism=pnl.ObjectiveMechanism(
         name="Conflict Monitor",
-        function=pnl.Energy(size=2, matrix=[[0, -2.5], [-2.5, 0]]),
+        function=pnl.Energy(input_shapes=2, matrix=[[0, -2.5], [-2.5, 0]]),
         monitor=output,
     ),
     default_allocation=[0.5],

setup.cfg

@@ -55,7 +55,7 @@ package_dir =
 
 [options.extras_require]
 psyneulink =
-    psyneulink<=0.15.1.0
+    psyneulink>=0.15.3
 
 neuroml =
     pyNeuroML>=1.0.10
@@ -92,7 +92,7 @@ optional =
     Jinja2<3.1
     torchviz
     netron
-    torch<2.2.0,>=1.11.0
+    torch>=1.11.0
     torchvision
     h5py
 

src/modeci_mdf/interfaces/pytorch/importer.py

@@ -5,6 +5,7 @@
 
     Mike He, "From Models to Computation Graphs (Part I)", https://ad1024.space/articles/22
 """
+
 import inspect
 import logging
 
@@ -13,6 +14,7 @@
 
 import torch
 
+
 # We need to monkey patch the torch._C.Node class to add a __getitem__ method
 # This is for torch 2.0
 # From https://github.com/openai/CLIP/issues/79#issuecomment-1624202950
@@ -523,7 +525,16 @@ def pytorch_to_mdf(
     # Call out to a part of the ONNX exporter that simiplifies the graph before ONNX export.
     from torch.onnx.utils import _model_to_graph
     from torch.onnx import TrainingMode
-    from torch.onnx.symbolic_helper import _set_opset_version
+
+    # Seems they got rid of _set_opset_version in 2.2 or something, can't find a better way to do this
+    try:
+        from torch.onnx.symbolic_helper import _set_opset_version
+    except ImportError:
+
+        def _set_opset_version(version):
+            from torch.onnx._globals import GLOBALS
+
+            GLOBALS.export_onnx_opset_version = version
 
     try:
         from torch.onnx.symbolic_helper import _export_onnx_opset_version