coach v0.8.0

architectures/neon_components/embedders.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.
+#
+
+import ngraph.frontends.neon as neon
+import ngraph as ng
+from ngraph.util.names import name_scope
+
+
+class InputEmbedder:
+    def __init__(self, input_size, batch_size=None, activation_function=neon.Rectlin(), name="embedder"):
+        self.name = name
+        self.input_size = input_size
+        self.batch_size = batch_size
+        self.activation_function = activation_function
+        self.weights_init = neon.GlorotInit()
+        self.biases_init = neon.ConstantInit()
+        self.input = None
+        self.output = None
+
+    def __call__(self, prev_input_placeholder=None):
+        with name_scope(self.get_name()):
+            # create the input axes
+            axes = []
+            if len(self.input_size) == 2:
+                axis_names = ['H', 'W']
+            else:
+                axis_names = ['C', 'H', 'W']
+            for axis_size, axis_name in zip(self.input_size, axis_names):
+                axes.append(ng.make_axis(axis_size, name=axis_name))
+            batch_axis_full = ng.make_axis(self.batch_size, name='N')
+            input_axes = ng.make_axes(axes)
+
+            if prev_input_placeholder is None:
+                self.input = ng.placeholder(input_axes + [batch_axis_full])
+            else:
+                self.input = prev_input_placeholder
+            self._build_module()
+
+        return self.input, self.output(self.input)
+
+    def _build_module(self):
+        pass
+
+    def get_name(self):
+        return self.name
+
+
+class ImageEmbedder(InputEmbedder):
+    def __init__(self, input_size, batch_size=None, input_rescaler=255.0, activation_function=neon.Rectlin(), name="embedder"):
+        InputEmbedder.__init__(self, input_size, batch_size, activation_function, name)
+        self.input_rescaler = input_rescaler
+
+    def _build_module(self):
+        # image observation
+        self.output = neon.Sequential([
+            neon.Preprocess(functor=lambda x: x / self.input_rescaler),
+            neon.Convolution((8, 8, 32), strides=4, activation=self.activation_function,
+                             filter_init=self.weights_init, bias_init=self.biases_init),
+            neon.Convolution((4, 4, 64), strides=2, activation=self.activation_function,
+                             filter_init=self.weights_init, bias_init=self.biases_init),
+            neon.Convolution((3, 3, 64), strides=1, activation=self.activation_function,
+                             filter_init=self.weights_init, bias_init=self.biases_init)
+        ])
+
+
+class VectorEmbedder(InputEmbedder):
+    def __init__(self, input_size, batch_size=None, activation_function=neon.Rectlin(), name="embedder"):
+        InputEmbedder.__init__(self, input_size, batch_size, activation_function, name)
+
+    def _build_module(self):
+        # vector observation
+        self.output = neon.Sequential([
+                neon.Affine(nout=256, activation=self.activation_function,
+                            weight_init=self.weights_init, bias_init=self.biases_init)
+            ])