pre-release 0.10.0

rl_coach/architectures/tensorflow_components/embedders/image_embedder.py

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+#
+# Copyright (c) 2017 Intel Corporation
+#
+# 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 List
+
+import tensorflow as tf
+from rl_coach.architectures.tensorflow_components.architecture import Conv2d
+from rl_coach.base_parameters import EmbedderScheme
+
+from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedder
+from rl_coach.core_types import InputImageEmbedding
+
+
+class ImageEmbedder(InputEmbedder):
+    """
+    An input embedder that performs convolutions on the input and then flattens the result.
+    The embedder is intended for image like inputs, where the channels are expected to be the last axis.
+    The embedder also allows custom rescaling of the input prior to the neural network.
+    """
+    schemes = {
+        EmbedderScheme.Empty:
+            [],
+
+        EmbedderScheme.Shallow:
+            [
+                Conv2d([32, 3, 1])
+            ],
+
+        # atari dqn
+        EmbedderScheme.Medium:
+            [
+                Conv2d([32, 8, 4]),
+                Conv2d([64, 4, 2]),
+                Conv2d([64, 3, 1])
+            ],
+
+        # carla
+        EmbedderScheme.Deep: \
+            [
+                Conv2d([32, 5, 2]),
+                Conv2d([32, 3, 1]),
+                Conv2d([64, 3, 2]),
+                Conv2d([64, 3, 1]),
+                Conv2d([128, 3, 2]),
+                Conv2d([128, 3, 1]),
+                Conv2d([256, 3, 2]),
+                Conv2d([256, 3, 1])
+            ]
+    }
+
+    def __init__(self, input_size: List[int], activation_function=tf.nn.relu,
+                 scheme: EmbedderScheme=EmbedderScheme.Medium, batchnorm: bool=False, dropout: bool=False,
+                 name: str= "embedder", input_rescaling: float=255.0, input_offset: float=0.0, input_clipping=None):
+        super().__init__(input_size, activation_function, scheme, batchnorm, dropout, name, input_rescaling,
+                         input_offset, input_clipping)
+        self.return_type = InputImageEmbedding
+        if len(input_size) != 3 and scheme != EmbedderScheme.Empty:
+            raise ValueError("Image embedders expect the input size to have 3 dimensions. The given size is: {}"
+                             .format(input_size))
+
+