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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
127 lines
4.3 KiB
Python
127 lines
4.3 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 as ng
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import numpy as np
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from architectures import architecture
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import utils
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class NeonArchitecture(architecture.Architecture):
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def __init__(self, tuning_parameters, name="", global_network=None, network_is_local=True):
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architecture.Architecture.__init__(self, tuning_parameters, name)
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assert tuning_parameters.agent.neon_support, 'Neon is not supported for this agent'
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self.clip_error = tuning_parameters.clip_gradients
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self.total_loss = None
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self.epoch = 0
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self.inputs = []
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self.outputs = []
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self.targets = []
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self.losses = []
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self.transformer = tuning_parameters.sess
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self.network = self.get_model(tuning_parameters)
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self.accumulated_gradients = []
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# training and inference ops
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train_output = ng.sequential([
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self.optimizer(self.total_loss),
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self.total_loss
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])
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placeholders = self.inputs + self.targets
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self.train_op = self.transformer.add_computation(
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ng.computation(
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train_output, *placeholders
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)
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)
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self.predict_op = self.transformer.add_computation(
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ng.computation(
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self.outputs, self.inputs[0]
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)
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)
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# update weights from array op
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self.weights = [ng.placeholder(w.axes) for w in self.total_loss.variables()]
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self.set_weights_ops = []
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for target_variable, variable in zip(self.total_loss.variables(), self.weights):
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self.set_weights_ops.append(self.transformer.add_computation(
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ng.computation(
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ng.assign(target_variable, variable), variable
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)
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))
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# get weights op
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self.get_variables = self.transformer.add_computation(
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ng.computation(
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self.total_loss.variables()
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)
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)
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def predict(self, inputs):
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batch_size = inputs.shape[0]
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# move batch axis to the end
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inputs = inputs.swapaxes(0, -1)
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prediction = self.predict_op(inputs) # TODO: problem with multiple inputs
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if type(prediction) != tuple:
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prediction = (prediction)
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# process all the outputs from the network
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output = []
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for p in prediction:
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output.append(p.transpose()[:batch_size].copy())
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# if there is only one output then we don't need a list
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if len(output) == 1:
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output = output[0]
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return output
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def train_on_batch(self, inputs, targets):
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loss = self.accumulate_gradients(inputs, targets)
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self.apply_and_reset_gradients(self.accumulated_gradients)
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return loss
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def get_weights(self):
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return self.get_variables()
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def set_weights(self, weights, rate=1.0):
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if rate != 1:
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current_weights = self.get_weights()
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updated_weights = [(1 - rate) * t + rate * o for t, o in zip(current_weights, weights)]
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else:
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updated_weights = weights
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for update_function, variable in zip(self.set_weights_ops, updated_weights):
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update_function(variable)
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def accumulate_gradients(self, inputs, targets):
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# Neon doesn't currently allow separating the grads calculation and grad apply operations
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# so this feature is not currently available. instead we do a full training iteration
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inputs = utils.force_list(inputs)
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targets = utils.force_list(targets)
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for idx, input in enumerate(inputs):
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inputs[idx] = input.swapaxes(0, -1)
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for idx, target in enumerate(targets):
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targets[idx] = np.rollaxis(target, 0, len(target.shape))
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all_inputs = inputs + targets
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loss = np.mean(self.train_op(*all_inputs))
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return [loss]
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