mirror of
https://github.com/gryf/coach.git
synced 2025-12-17 19:20:19 +01:00
1b095aeeca
Till now, most of the modules were importing all of the module objects (variables, classes, functions, other imports) into module namespace, which potentially could (and was) cause of unintentional use of class or methods, which was indirect imported. With this patch, all the star imports were substituted with top-level module, which provides desired class or function. Besides, all imports where sorted (where possible) in a way pep8[1] suggests - first are imports from standard library, than goes third party imports (like numpy, tensorflow etc) and finally coach modules. All of those sections are separated by one empty line. [1] https://www.python.org/dev/peps/pep-0008/#imports
88 lines
3.5 KiB
Python
88 lines
3.5 KiB
Python
#
|
|
# 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 as ng
|
|
import ngraph.frontends.neon as neon
|
|
import ngraph.util.names as ngraph_names
|
|
|
|
|
|
class InputEmbedder(object):
|
|
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 ngraph_names.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)
|
|
])
|