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gap_predict.py
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gap_predict.py
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# coding:utf-8
import h5py
import numpy as np
from sklearn.utils import shuffle
from keras.models import *
from keras.layers import *
from keras.utils import np_utils
from keras.optimizers import *
from keras.layers import *
from keras.preprocessing.image import ImageDataGenerator
import pandas as pd
np.random.seed(2018)
csv_sample_path = './predict_csv/sample_submission.csv'
csv_out_path = './predict_csv/13_hdropori_1024_10000.csv'
# test_data_dir = '/home/fenglf/data/dog/kaggle/cropped_test_jpg'
test_data_dir = '/home/fenglf/data/dog/kaggle/test'
h5path = '/home/fenglf/PycharmProjects/keras-finetuning-master/gap_feature/'
model_best_path = './model/output/mul/model_13_hdropori_1024_best.h5'
# model_json_path = './model/output/mul/model_13_ndropcop_2048.json'
nb_classes = 120
batch_size = 32
# img_height, img_width = 299, 299
eval_switch = True
bottleneck_feature_test_list = ["gap_InceptionResNetV2_test.h5",
"gap_DenseNet121_test.h5",
"gap_DenseNet169_test.h5",
"gap_DenseNet161_test.h5",
"gap_DenseNet201_test.h5",
"gap_Xception_test.h5",
"gap_ResNet50_test.h5",
"gap_ResNet101_test.h5",
"gap_ResNet152_test.h5",
"gap_InceptionV3_test.h5",
"gap_InceptionV4_test.h5",
"gap_VGG16_test.h5",
"gap_VGG19_test.h5"]
def predict():
print("start...")
test_datagen = ImageDataGenerator(rescale=1. / 255)
print "test_generator creating..."
# 注意：使用此方法时，test_data_dir必须有子文件夹
test_generator = test_datagen.flow_from_directory(
test_data_dir,
target_size=(img_height, img_width),
batch_size=batch_size,
shuffle=False,
class_mode=None)
# define the input_shape
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
# setup model
# load json and create model
json_file = open('model_json_3.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
# load weights into new model
model.load_weights("model_3.h5")
print("Loaded model from disk")
# if os.path.exists(final_weights_path):
# model.load_weights(final_weights_path)
# print ("Checkpoint " + final_weights_path + " loaded.")
print 'predicting...'
# print test_generator.filenames
test = model.predict_generator(
test_generator,
steps=(test_generator.samples // batch_size) + 1, # 25
verbose=1)
print test
# y_pred = model.predict(X_test, verbose=1)
# top1_class = y_pred.max(axis=1)
# print top1_class
def gap_predict():
X_test = []
for filename in bottleneck_feature_test_list:
with h5py.File(h5path + filename, 'r') as h:
X_test.append(np.array(h['test']))
# setup model
# load json and create model
# json_file = open(model_json_path, 'r')
# loaded_model_json = json_file.read()
# json_file.close()
# model = model_from_json(loaded_model_json)
# # load weights into new model
# model.load_weights(model_best_path)
model = load_model(model_best_path)
print("Loaded model from {}".format(model_best_path))
# 拼接特征
X_test = np.concatenate(X_test, axis=1)
res0 = model.predict(X_test)
return res0
def write_csv(y_pred):
n = 0
gen = ImageDataGenerator()
test_generator = gen.flow_from_directory(test_data_dir, shuffle=False, batch_size=batch_size, class_mode=None)
if os.path.exists(csv_out_path):
os.remove(csv_out_path)
with open(csv_sample_path, 'r') as f:
id = f.readline()
# print id
with open(csv_out_path, 'a') as f:
f.writelines(id)
for i, file_dir in enumerate(test_generator.filenames):
file_name = file_dir.split('/')[-1]
file_name, file_ext = file_name.split('.')
# print file_name, file_ext
pred_test = y_pred[i]
if file_ext == 'png' or file_ext == 'jpg':
f.write(file_name)
for str in pred_test:
f.write(',')
# f.write(str)
f.write(str.astype("str"))
f.write('\n')
n += 1
print("count_image:", n)
if __name__ == '__main__':
y_pred = gap_predict()
write_csv(y_pred)