質問編集履歴
4
コードの修正
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ソースコード
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train_imagenet.py
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from __future__ import print_function
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import argparse
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import datetime
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import json
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import multiprocessing
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import os
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import random
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import sys
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import threading
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import time
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import numpy as np
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from PIL import Image
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import six
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import six.moves.cPickle as pickle
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from six.moves import queue
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import chainer
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from chainer import serializers
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from chainer import computational_graph
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from chainer import cuda
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from chainer import optimizers
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from chainer import serializers
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#追加
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import alex
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import googlenet
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import googlenetbn
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import nin
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parser = argparse.ArgumentParser(description='Learning convnet from ILSVRC2012 dataset')
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else:
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denominator = 100000
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def load_image_list(path, root): **←**
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tuples = []
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for line in open(path):
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pair = line.strip().split()
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tuples.append((os.path.join(root, pair[0]), np.int32(pair[1])))
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return tuples
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# Prepare dataset
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train_list = load_image_list(args.train, args.root) **←**
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val_list = load_image_list(args.val, args.root)
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mean_image = np.load(args.mean)
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# Prepare model
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if args.arch == 'nin':
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import nin
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model = nin.NIN()
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elif args.arch == 'i2vvgg':
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import i2vvgg
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model = i2vvgg.i2vVGG()
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elif args.arch == 'alex':
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import alex
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model = alex.Alex()
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elif args.arch == 'alexbn':
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import alexbn
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model = alexbn.AlexBN()
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elif args.arch == 'googlenet':
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import googlenet
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model = googlenet.GoogLeNet()
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elif args.arch == 'googlenetbn':
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import googlenetbn
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model = googlenetbn.GoogLeNetBN()
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else:
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raise ValueError('Invalid architecture name')
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if args.gpu >= 0:
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cuda.get_device(args.gpu).use()
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model.to_gpu()
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# Setup optimizer
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optimizer = optimizers.MomentumSGD(lr=0.01, momentum=0.9)
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optimizer.setup(model)
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# Init/Resume
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if args.initmodel:
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print('Load model from', args.initmodel)
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serializers.load_npz(args.initmodel, model)
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if args.resume:
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print('Load optimizer state from', args.resume)
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serializers.load_npz(args.resume, optimizer)
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# ------------------------------------------------------------------------------
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# This example consists of three threads: data feeder, logger and trainer.
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# These communicate with each other via Queue.
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data_q = queue.Queue(maxsize=1)
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res_q = queue.Queue()
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cropwidth = 256 - model.insize
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def read_image(path, center=False, flip=False):
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# Data loading routine
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image = np.asarray(Image.open(path)).transpose(2, 0, 1)
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if center:
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top = left = cropwidth // 2
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else:
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top = random.randint(0, cropwidth - 1)
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left = random.randint(0, cropwidth - 1)
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bottom = model.insize + top
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right = model.insize + left
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image = image[:, top:bottom, left:right].astype(np.float32)
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image -= mean_image[:, top:bottom, left:right]
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image /= 255
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if flip and random.randint(0, 1) == 0:
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return image[:, :, ::-1]
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else:
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return image
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def feed_data():
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# Data feeder
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i = 0
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count = 0
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x_batch = np.ndarray(
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(args.batchsize, 3, model.insize, model.insize), dtype=np.float32)
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y_batch = np.ndarray((args.batchsize,), dtype=np.float32)
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val_x_batch = np.ndarray(
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(args.val_batchsize, 3, model.insize, model.insize), dtype=np.float32)
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val_y_batch = np.ndarray((args.val_batchsize,), dtype=np.float32)
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batch_pool = [None] * args.batchsize
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val_batch_pool = [None] * args.val_batchsize
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pool = multiprocessing.Pool(args.loaderjob)
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data_q.put('train')
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for epoch in six.moves.range(1, 1 + args.epoch):
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print('epoch', epoch, file=sys.stderr)
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print('learning rate', optimizer.lr, file=sys.stderr)
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perm = np.random.permutation(len(train_list))
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for idx in perm:
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path, label = train_list[idx]
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batch_pool[i] = pool.apply_async(read_image, (path, False, True))
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y_batch[i] = label
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i += 1
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if i == args.batchsize:
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for j, x in enumerate(batch_pool):
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x_batch[j] = x.get()
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data_q.put((x_batch.copy(), y_batch.copy()))
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i = 0
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count += 1
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if count % denominator == 0:
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data_q.put('val')
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j = 0
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for path, label in val_list:
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val_batch_pool[j] = pool.apply_async(
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read_image, (path, True, False))
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val_y_batch[j] = label
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j += 1
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if j == args.val_batchsize:
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for k, x in enumerate(val_batch_pool):
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val_x_batch[k] = x.get()
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data_q.put((val_x_batch.copy(), val_y_batch.copy()))
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j = 0
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data_q.put('train')
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optimizer.lr *= 0.97
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pool.close()
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pool.join()
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data_q.put('end')
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3
エラー箇所を分かりやすくした
test
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test
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denominator = 100000
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def load_image_list(path, root):
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def load_image_list(path, root): **←**
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tuples = []
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for line in open(path):
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pair = line.strip().split()
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# Prepare dataset
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train_list = load_image_list(args.train, args.root)
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train_list = load_image_list(args.train, args.root) **←**
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val_list = load_image_list(args.val, args.root)
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題名の変更
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###
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### Chainerでの機械学習
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Chainerでの機械学習の実装中でしたが、コマンドプロンプトでpython train_imagenet.py -g 0 train.txt test.txt (2>&1 | tee log)と打ち込んだ際にエラーが出てしまいました。インターネットなどで調べましたがどうすればいいか分かりませんでした。
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初歩的な問題だと思いますがどうかよろしくお願いします。
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見にくい文章であったので見やすくなるように改良を加えた。
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### 前提
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Chainerでの機械学習の実装中でしたが、コマンドプロンプトでpython train_imagenet.py -g 0 train.txt test.txt (2>&1 | tee log)と打ち込んだ際にエラーが出てしまいました。インターネットなどで調べましたがどうすればいいか分かりませんでした。
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（例）
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初歩的な問題だと思いますがどうかよろしくお願いします。
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※文字数の都合上後半のコードを省いてあります。
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```python
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エラーメッセージ
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```
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File "train_imagenet.py", line 96, in <module>
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train_list = load_image_list(args.train, args.root)
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File "train_imagenet.py", line 89, in load_image_list
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for line in open(path):
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TypeError: invalid file: None
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ソースコード
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```
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#!/usr/bin/env python
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"""Example code of learning a large scale convnet from ILSVRC2012 dataset.
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Prerequisite: To run this example, crop the center of ILSVRC2012 training and
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separated CSV whose first column is full path to image and second column is
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zero-origin label (this format is same as that used by Caffe's ImageDataLayer).
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train_imagenet.py
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from __future__ import print_function
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import argparse
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import datetime
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pool.join()
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data_q.put('end')
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