python - 毎回同じ予測をする BVLC/caffe

Question

BVLC/caffeモデル (CPU のみ)を実行しようとしています。すべてのインストールを完了しました。以下のコマンドを実行して実行すると：

python python/classify.py examples/images/cat.jpg foo

次に、以下の出力を示します。

Classifying 1 inputs.
Done in 2.68 s.
prediction shape: 1000
predicted class: 0
n01440764 tench, Tinca tinca

上記の出力は、どの画像でも同じです。

classify.py ファイル:

#!/usr/bin/env python
"""
classify.py is an out-of-the-box image classifer callable from the command  line.

By default it configures and runs the Caffe reference ImageNet model.
"""

import numpy as np

import os

import sys

import argparse

import glob

import time

import caffe

def main(argv):
    pycaffe_dir = os.path.dirname(__file__)

    parser = argparse.ArgumentParser()
    # Required arguments: input and output files.
    parser.add_argument(
        "input_file",
        help="Input image, directory, or npy."
    )
parser.add_argument(
    "output_file",
    help="Output npy filename."
)
# Optional arguments.
parser.add_argument(
    "--model_def",
    default=os.path.join(pycaffe_dir,
            "../models/bvlc_reference_caffenet/deploy.prototxt"),
    help="Model definition file."
)
parser.add_argument(
    "--pretrained_model",
    default=os.path.join(pycaffe_dir,
            "../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel"),
    help="Trained model weights file."
)
parser.add_argument(
    "--gpu",
    action='store_true',
    help="Switch for gpu computation."
)
parser.add_argument(
    "--center_only",
    action='store_true',
    help="Switch for prediction from center crop alone instead of " +
         "averaging predictions across crops (default)."
)
parser.add_argument(
    "--images_dim",
    default='256,256',
    help="Canonical 'height,width' dimensions of input images."
)
parser.add_argument(
    "--mean_file",
    default=os.path.join(pycaffe_dir,
                         'caffe/imagenet/ilsvrc_2012_mean.npy'),
    help="Data set image mean of [Channels x Height x Width] dimensions " +
         "(numpy array). Set to '' for no mean subtraction."
)
parser.add_argument(
    "--input_scale",
    type=float,
    help="Multiply input features by this scale to finish preprocessing."
)
parser.add_argument(
    "--raw_scale",
    type=float,
    default=255.0,
    help="Multiply raw input by this scale before preprocessing."
)
parser.add_argument(
    "--channel_swap",
    default='2,1,0',
    help="Order to permute input channels. The default converts " +
         "RGB -> BGR since BGR is the Caffe default by way of OpenCV."
)
parser.add_argument(
    "--ext",
    default='jpg',
    help="Image file extension to take as input when a directory " +
         "is given as the input file."
)
parser.add_argument(
"--labels_file",
default=os.path.join(pycaffe_dir,"../data/ilsvrc12/synset_words.txt"),help="Readable label definition file."
)
args = parser.parse_args()

image_dims = [int(s) for s in args.images_dim.split(',')]

mean, channel_swap = None, None
if args.mean_file:
    mean = np.load(args.mean_file)
if args.channel_swap:
    channel_swap = [int(s) for s in args.channel_swap.split(',')]

if args.gpu:
    caffe.set_mode_gpu()
    print("GPU mode")
else:
    caffe.set_mode_cpu()
    print("CPU mode")

# Make classifier.
classifier = caffe.Classifier(args.model_def, args.pretrained_model,
        image_dims=image_dims, mean=mean,
        input_scale=args.input_scale, raw_scale=args.raw_scale,
        channel_swap=channel_swap)

# Load numpy array (.npy), directory glob (*.jpg), or image file.
args.input_file = os.path.expanduser(args.input_file)
if args.input_file.endswith('npy'):
    print("Loading file: %s" % args.input_file)
    inputs = np.load(args.input_file)
elif os.path.isdir(args.input_file):
    print("Loading folder: %s" % args.input_file)
    inputs =[caffe.io.load_image(im_f)
             for im_f in glob.glob(args.input_file + '/*.' + args.ext)]
else:
    print("Loading file: %s" % args.input_file)
    inputs = [caffe.io.load_image(args.input_file)]

print("Classifying %d inputs." % len(inputs))

# Classify.
start = time.time()
predictions = classifier.predict(inputs, not args.center_only)
print("Done in %.2f s." % (time.time() - start))
print 'prediction shape:', predictions[0].shape[0]
print 'predicted class:', predictions[0].argmax()

with open(args.labels_file) as f:
    labels = f.readlines()

print labels[predictions[0].argmax()]

# Save
print("Saving results into %s" % args.output_file)
np.save(args.output_file, predictions)



if __name__ == '__main__':
    main(sys.argv)