added support for neural odes

Author: FabricioArendTorres

Dockerfile-pytorchlatest

@@ -0,0 +1,11 @@
+# Dockerfile-pytorch1.13.1
+FROM pytorch/pytorch:latest
+
+WORKDIR /app
+
+# Copy the source code and install the package
+COPY . /flowc
+RUN pip install /flowc
+
+CMD ["/bin/bash"]
+

README.md

@@ -99,8 +99,9 @@ log_prob = flow.log_prob(inputs)
 ```
 
 To sample from the flow:
+
 ```python
-samples = flow.sample(num_samples)
+samples = flow.sample_like(num_samples)
 ```
 
 Additional examples of the workflow are provided in [examples folder](examples/).

docker/Dockerfile-devel

@@ -0,0 +1,13 @@
+# Dockerfile-pytorch1.13.1
+FROM pytorch/pytorch:latest
+
+WORKDIR /app
+
+# Copy the source code and install the package
+COPY . /app
+RUN pip install pdoc3
+RUN pip install -e .
+
+CMD ["/bin/bash"]
+
+EXPOSE 8080

docker/Dockerfile-pytorchlatest

@@ -3,9 +3,13 @@ FROM pytorch/pytorch:latest
 
 WORKDIR /app
 
-# Copy the source code and install the package
+RUN apt update && apt install build-essential -y --no-install-recommends
+
+# Copy the source code and install the packagepdoc
 COPY . /flowc
 RUN pip install /flowc
 
 CMD ["/bin/bash"]
 
+EXPOSE 8080
+

docker/requirements.txt

@@ -0,0 +1,15 @@
+matplotlib
+numpy
+pandas
+torchdiffeq
+umnn
+tqdm
+ninja
+scikit-learn
+h5py
+torchtestcase
+parameterized
+testingflake8
+pytest
+pytestcov
+black

examples/conditional_toy_2d.py

@@ -133,7 +133,8 @@ def plot_model(flow: Flow, dataset: PlaneDataset):
     plt.title('iteration {}'.format(i + 1))
     plt.tight_layout()
     # plt.show()
-    plt.show()
+    plt.savefig(f"figures/conditional_{selected_data}.png")
+    plt.close()
 
 
 if __name__ == "__main__":

flowcon/CNF/neural_odes/__init__.py

@@ -50,9 +50,9 @@ def _log_prob(self, inputs, context):
     def _sample(self, num_samples, context):
         embedded_context = self._embedding_net(context)
         if self._context_used_in_base:
-            noise = self._distribution.sample(num_samples, context=embedded_context)
+            noise = self._distribution.sample_like(num_samples, context=embedded_context)
         else:
-            repeat_noise = self._distribution.sample(num_samples * embedded_context.shape[0])
+            repeat_noise = self._distribution.sample_like(num_samples * embedded_context.shape[0])
             noise = torch.reshape(
                 repeat_noise,
                 (embedded_context.shape[0], -1, repeat_noise.shape[1])

flowcon/CNF/neural_odes/squeeze.py

@@ -1,5 +1,5 @@
 from flowcon.nn.nets import *
-
+from flowcon.nn.neural_odes import odefunc
 __all__ = ['DenseNet',
            'MixedConditionalDenseNet',
            'InputConditionalDenseNet',
@@ -17,5 +17,6 @@
            'ConvResidualNet',
            'ResidualNet',
            'MLP',
-           'FCBlock'
+           'FCBlock',
+
            ]