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89 lines
3.5 KiB
Python
89 lines
3.5 KiB
Python
#
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# Copyright (c) 2017 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import ngraph.frontends.neon as neon
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import ngraph as ng
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from ngraph.util.names import name_scope
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class InputEmbedder(object):
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def __init__(self, input_size, batch_size=None, activation_function=neon.Rectlin(), name="embedder"):
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self.name = name
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self.input_size = input_size
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self.batch_size = batch_size
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self.activation_function = activation_function
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self.weights_init = neon.GlorotInit()
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self.biases_init = neon.ConstantInit()
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self.input = None
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self.output = None
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def __call__(self, prev_input_placeholder=None):
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with name_scope(self.get_name()):
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# create the input axes
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axes = []
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if len(self.input_size) == 2:
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axis_names = ['H', 'W']
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else:
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axis_names = ['C', 'H', 'W']
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for axis_size, axis_name in zip(self.input_size, axis_names):
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axes.append(ng.make_axis(axis_size, name=axis_name))
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batch_axis_full = ng.make_axis(self.batch_size, name='N')
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input_axes = ng.make_axes(axes)
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if prev_input_placeholder is None:
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self.input = ng.placeholder(input_axes + [batch_axis_full])
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else:
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self.input = prev_input_placeholder
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self._build_module()
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return self.input, self.output(self.input)
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def _build_module(self):
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pass
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def get_name(self):
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return self.name
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class ImageEmbedder(InputEmbedder):
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def __init__(self, input_size, batch_size=None, input_rescaler=255.0, activation_function=neon.Rectlin(), name="embedder"):
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InputEmbedder.__init__(self, input_size, batch_size, activation_function, name)
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self.input_rescaler = input_rescaler
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def _build_module(self):
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# image observation
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self.output = neon.Sequential([
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neon.Preprocess(functor=lambda x: x / self.input_rescaler),
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neon.Convolution((8, 8, 32), strides=4, activation=self.activation_function,
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filter_init=self.weights_init, bias_init=self.biases_init),
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neon.Convolution((4, 4, 64), strides=2, activation=self.activation_function,
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filter_init=self.weights_init, bias_init=self.biases_init),
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neon.Convolution((3, 3, 64), strides=1, activation=self.activation_function,
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filter_init=self.weights_init, bias_init=self.biases_init)
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])
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class VectorEmbedder(InputEmbedder):
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def __init__(self, input_size, batch_size=None, activation_function=neon.Rectlin(), name="embedder"):
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InputEmbedder.__init__(self, input_size, batch_size, activation_function, name)
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def _build_module(self):
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# vector observation
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self.output = neon.Sequential([
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neon.Affine(nout=256, activation=self.activation_function,
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weight_init=self.weights_init, bias_init=self.biases_init)
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])
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