# import the necessary packages
from config import tiny_imagenet_config as config
from pyimagesearch.preprocessing import imagetoarraypreprocessor as ITA
from pyimagesearch.preprocessing import simplespreprocessor as SP
from pyimagesearch.preprocessing import meanpreprocessor as MP
from pyimagesearch.io import hdf5datasetgenerator as HDFG
from pyimagesearch.utils.ranked import rank5_accuracy
from keras.models import load_model
import json
# load the RGB means for the training set
means = json.loads(open(config.DATASET_MEAN).read())
# initialize the image preprocessors
sp = SP.SimplePreprocessor(64,64)
mp = MP.MeanPreprocessor(means['R'],means['G'],means['B'])
iap = ITA.ImageToArrayPreprocessor()
# initialize the testing dataset generator
testGen = HDFG.HDF5DatasetGenerator(config.TEST_HDF5, 64,preprocessors=[sp, mp, iap], classes=config.NUM_CLASSES)
# load the pre-trained network
print("[INFO] loading model...")
model = load_model(config.MODEL_PATH)
# make predictions on the testing data
print("[INFO] predicting on test data...")
predictions = model.predict_generator(testGen.generator(),steps=testGen.numImages // 64, max_queue_size=64 * 2)
# compute the rank-1 and rank-5 accuracies
import json
import os
aug = ImageDataGenerator(rotation_range=20,
zoom_range=0.15,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.15,
horizontal_flip=True,
fill_mode="nearest")
means = json.loads(open(config.DATASET_MEAN).read())
sp = simplePreprocessor.SimplePreprocessor(227, 227)
pp = patchpreprocessor.PatchPreprocessor(227, 227)
mp = meanpreprocessor.MeanPreprocessor(means["R"], means["G"], means["B"])
iap = imagetoarraypreprocessor.ImageToArrayPreprocessor()
batchSize = 64
trainGen = hdf5datasetgenerator.HDF5DatasetGenerator(
config.TRAIN_HDF5,
batchSize,
aug=aug,
preprocessors=[pp, mp, iap],
classes=config.NUM_CLASSES)
valGen = hdf5datasetgenerator.HDF5DatasetGenerator(config.VAL_HDF5,
batchSize,
aug=aug,
preprocessors=[sp, mp, iap],
classes=config.NUM_CLASSES)
