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130 lines
4.4 KiB
Python
130 lines
4.4 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 sys
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import copy
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from ngraph.frontends.neon import *
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import ngraph as ng
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from architectures.architecture import *
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import numpy as np
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from utils import *
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class NeonArchitecture(Architecture):
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def __init__(self, tuning_parameters, name="", global_network=None, network_is_local=True):
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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 = force_list(inputs)
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targets = 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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