1# Licensed to the Apache Software Foundation (ASF) under one
2# or more contributor license agreements.  See the NOTICE file
3# distributed with this work for additional information
4# regarding copyright ownership.  The ASF licenses this file
5# to you under the Apache License, Version 2.0 (the
6# "License"); you may not use this file except in compliance
7# with the License.  You may obtain a copy of the License at
8#
9#   http://www.apache.org/licenses/LICENSE-2.0
10#
11# Unless required by applicable law or agreed to in writing,
12# software distributed under the License is distributed on an
13# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
14# KIND, either express or implied.  See the License for the
15# specific language governing permissions and limitations
16# under the License.
17
18# pylint: disable=missing-docstring
19from __future__ import print_function
20
21import numpy as np
22import mxnet as mx
23try:
24    import cPickle as pickle
25except ImportError:
26    import pickle
27
28
29def extract_feature(sym, args, auxs, data_iter, N, xpu=mx.cpu()):
30    input_buffs = [mx.nd.empty(shape, ctx=xpu) for k, shape in data_iter.provide_data]
31    input_names = [k for k, shape in data_iter.provide_data]
32    args = dict(args, **dict(zip(input_names, input_buffs)))
33    exe = sym.bind(xpu, args=args, aux_states=auxs)
34    outputs = [[] for _ in exe.outputs]
35    output_buffs = None
36
37    data_iter.hard_reset()
38    for batch in data_iter:
39        for data, buff in zip(batch.data, input_buffs):
40            data.copyto(buff)
41        exe.forward(is_train=False)
42        if output_buffs is None:
43            output_buffs = [mx.nd.empty(i.shape, ctx=mx.cpu()) for i in exe.outputs]
44        else:
45            for out, buff in zip(outputs, output_buffs):
46                out.append(buff.asnumpy())
47        for out, buff in zip(exe.outputs, output_buffs):
48            out.copyto(buff)
49    for out, buff in zip(outputs, output_buffs):
50        out.append(buff.asnumpy())
51    outputs = [np.concatenate(i, axis=0)[:N] for i in outputs]
52    return dict(zip(sym.list_outputs(), outputs))
53
54
55class MXModel(object):
56    def __init__(self, xpu=mx.cpu(), *args, **kwargs):
57        self.xpu = xpu
58        self.loss = None
59        self.args = {}
60        self.args_grad = {}
61        self.args_mult = {}
62        self.auxs = {}
63        self.setup(*args, **kwargs)
64
65    def save(self, fname):
66        args_save = {key: v.asnumpy() for key, v in self.args.items()}
67        with open(fname, 'wb') as fout:
68            pickle.dump(args_save, fout)
69
70    def load(self, fname):
71        with open(fname, 'rb') as fin:
72            args_save = pickle.load(fin)
73            for key, v in args_save.items():
74                if key in self.args:
75                    self.args[key][:] = v
76
77    def setup(self, *args, **kwargs):
78        raise NotImplementedError("must override this")
79