blob: 1aaae1b5fdad4a75a770b5b48863cbf2df1107d6 [file] [log] [blame]
# Licensed to the Apache Software Foundation (ASF) under one
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# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
# pylint: skip-file
import mxnet as mx
import numpy as np
import logging
from solver import Solver, Monitor
try:
import cPickle as pickle
except:
import pickle
def extract_feature(sym, args, auxs, data_iter, N, xpu=mx.cpu()):
input_buffs = [mx.nd.empty(shape, ctx=xpu) for k, shape in data_iter.provide_data]
input_names = [k for k, shape in data_iter.provide_data]
args = dict(args, **dict(zip(input_names, input_buffs)))
exe = sym.bind(xpu, args=args, aux_states=auxs)
outputs = [[] for i in exe.outputs]
output_buffs = None
data_iter.hard_reset()
for batch in data_iter:
for data, buff in zip(batch.data, input_buffs):
data.copyto(buff)
exe.forward(is_train=False)
if output_buffs is None:
output_buffs = [mx.nd.empty(i.shape, ctx=mx.cpu()) for i in exe.outputs]
else:
for out, buff in zip(outputs, output_buffs):
out.append(buff.asnumpy())
for out, buff in zip(exe.outputs, output_buffs):
out.copyto(buff)
for out, buff in zip(outputs, output_buffs):
out.append(buff.asnumpy())
outputs = [np.concatenate(i, axis=0)[:N] for i in outputs]
return dict(zip(sym.list_outputs(), outputs))
class MXModel(object):
def __init__(self, xpu=mx.cpu(), *args, **kwargs):
self.xpu = xpu
self.loss = None
self.args = {}
self.args_grad = {}
self.args_mult = {}
self.auxs = {}
self.setup(*args, **kwargs)
def save(self, fname):
args_save = {key: v.asnumpy() for key, v in self.args.items()}
with open(fname, 'wb') as fout:
pickle.dump(args_save, fout)
def load(self, fname):
with open(fname, 'rb') as fin:
args_save = pickle.load(fin)
for key, v in args_save.items():
if key in self.args:
self.args[key][:] = v
def setup(self, *args, **kwargs):
raise NotImplementedError("must override this")