111 lines
3.7 KiB
Python
111 lines
3.7 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from datetime import datetime
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import os.path
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from os import environ
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import time
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import sys
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import random
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import tensorflow as tf
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import numpy as np
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import importlib
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import argparse
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import base_func
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import h5py
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import math
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import tensorflow.contrib.slim as slim
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from tensorflow.python.ops import data_flow_ops
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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import pickle
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from scipy import misc
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def _softmax(x, axis=-1, t=-100.):
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x = x - np.max(x)
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if np.min(x) < t:
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x = x / np.min(x) * t
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e_x = np.exp(x)
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return e_x / e_x.sum(axis, keepdims=True)
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def main(args):
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environ['CUDA_VISIBLE_DEVICES'] = args.gpus
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image_size = (args.image_size, args.image_size)
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dataset = base_func.get_dataset(args.data_dir)
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val_set = dataset
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image_list, label_list = base_func.get_image_paths_and_labels(val_set)
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nrof_classes = len(val_set)
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val_image_num = len(image_list)
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top1 = 0.0
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top5 = 0.0
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with tf.Graph().as_default() as graph:
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with tf.Session() as sess:
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base_func.load_model(args.model)
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input_image = sess.graph.get_tensor_by_name('input:0')
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output = sess.graph.get_tensor_by_name('MobileNetV1/Bottleneck2/BatchNorm/Reshape_1:0')
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if (os.path.isdir(args.model)):
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phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
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for i in range(val_image_num):
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print(image_list[i])
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# images = base_func.load_data([image_list[i]], False, False, args.image_size)
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img = np.array(misc.imread(image_list[i], mode='RGB'))
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# img = base_func.crop(img, False, args.image_size)
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img = misc.imresize(img, image_size, interp='bilinear')
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img = img / 255.
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images = [img]
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feed_dict={input_image:images}
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if (os.path.isdir(args.model)):
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feed_dict={input_image:images,phase_train_placeholder:False}
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logits = sess.run(output,feed_dict=feed_dict)
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pred = _softmax(logits[0,:])
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# print(logits)
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des_idx = np.argsort(pred)
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# des_data = np.sort(logits)
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# print(des_data[0,995:])
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if (des_idx[nrof_classes-1]) == label_list[i]:
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top1 += 1
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for j in range(5):
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if (des_idx[nrof_classes-1-j]) == label_list[i]:
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top5 += 1
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break
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print("%05d th pic have been validated, top1 = %.2f%% top5 = %.2f%% " % (i+1,top1/(i+1)*100.,top5/(i+1)*100.))
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def parse_arguments(argv):
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parser = argparse.ArgumentParser()
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parser.add_argument('model', type=str,
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help='Model definition. ckpt folder or pb file', default='mobilenet_v1.pb')
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parser.add_argument('data_dir', type=str,
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help='Path to the data directory containing aligned face patches.',
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default='~/datasets/iamgenet_val')
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parser.add_argument('--image_size', type=int,
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help='image size.', default=224)
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parser.add_argument('--gpu_memory_fraction', type=float,
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help='Upper bound on the amount of GPU memory that will be used by the process.', default=1.0)
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parser.add_argument('--gpus', type=str,
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help='Indicate the GPUs to be used.', default='3')
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return parser.parse_args(argv)
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if __name__ == '__main__':
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args = parse_arguments(sys.argv[1:])
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print('gpu device ID: %s'%args.gpus)
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main(args)
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