Introduce code from Rust monorepo. This is a mess

tts/nemo/collections/cv/modules/__init__.py

@@ -0,0 +1,15 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from nemo.collections.cv.modules.lenet5 import LeNet5

tts/nemo/collections/cv/modules/lenet5.py

@@ -0,0 +1,114 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Optional
+
+from torch import nn
+
+from nemo.core.classes import NeuralModule, typecheck
+from nemo.core.config import Config
+from nemo.core.neural_types import AxisKind, AxisType, ImageValue, LogprobsType, NeuralType
+
+
+class LeNet5(NeuralModule):
+    """
+    Classical LeNet-5 model for MNIST image classification.
+    """
+
+    def __init__(self, cfg: Config = Config()):
+        """
+        Creates the LeNet-5 model.
+
+        Args:
+            cfg: Default NeMo config containing name.
+        """
+        # Call the base class constructor.
+        super().__init__()  # name=name)
+
+        # Create the LeNet-5 model.
+        self.model = nn.Sequential(
+            nn.Conv2d(1, 6, kernel_size=(5, 5)),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=(2, 2), stride=2),
+            nn.Conv2d(6, 16, kernel_size=(5, 5)),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=(2, 2), stride=2),
+            nn.Conv2d(16, 120, kernel_size=(5, 5)),
+            nn.ReLU(),
+            # reshape to [-1, 120]
+            nn.Flatten(),
+            nn.Linear(120, 84),
+            nn.ReLU(),
+            nn.Linear(84, 10),
+            nn.LogSoftmax(dim=1),
+        )
+
+    @property
+    def input_types(self):
+        """ Returns definitions of module input ports. """
+        return {
+            "images": NeuralType(
+                axes=(
+                    AxisType(kind=AxisKind.Batch),
+                    AxisType(kind=AxisKind.Channel, size=1),
+                    AxisType(kind=AxisKind.Height, size=32),
+                    AxisType(kind=AxisKind.Width, size=32),
+                ),
+                elements_type=ImageValue(),
+            )
+        }
+
+    @property
+    def output_types(self):
+        """ Returns definitions of module output ports. """
+        return {
+            "predictions": NeuralType(
+                axes=(AxisType(kind=AxisKind.Batch), AxisType(kind=AxisKind.Dimension)), elements_type=LogprobsType()
+            )
+        }
+
+    @typecheck()
+    def forward(self, images):
+        """
+        Performs the forward step of the LeNet-5 model.
+
+        Args:
+            images: Batch of images to be classified.
+
+        Returns:
+            Batch of predictions.
+        """
+
+        predictions = self.model(images)
+        return predictions
+
+    def save_to(self, save_path: str):
+        """Not implemented yet.
+           Serialize model.
+
+        Args:
+            save_path (str): path to save serialization.
+        """
+        pass
+
+    @classmethod
+    def restore_from(cls, restore_path: str):
+        """ Not implemented yet.
+            Restore module from serialization.
+
+        Args:
+            restore_path (str): path to serialization
+        """
+        pass