ap = argparse.ArgumentParser()
ap.add_argument("-c", "--checkpoints", required = True,
help = "path to output checkpoint directory")
ap.add_argument("-m", "--model", type = str,
help = "path to specific model checkpoint to load")
ap.add_argument("-s", "--start_epoch", type = int, default = 0,
help = "epoch to restart training at")
args = vars(ap.parse_args())
# construct the training and testing image generators for data
# augmentation, then initialize the image preprocessor
trainAug = ImageDataGenerator(rotation_range = 10, zoom_range = 0.1,
horizontal_flip = True, rescale = 1 / 255.0, fill_mode = "nearest")
valAug = ImageDataGenerator(rescale = 1 / 255.0)
iap = ImageToArrayPreprocessor()
# initialize the training and validation dataset generators
trainGen = HDF5DatasetGenerator(config.TRAIN_HDF5, config.BATCH_SIZE,
aug = trainAug, preprocessors = [iap], classes = config.NUM_CLASSES)
valGen = HDF5DatasetGenerator(config.VAL_HDF5, config.BATCH_SIZE,
aug = valAug, preprocessors = [iap], classes = config.NUM_CLASSES)
# if there is no specific model checkpoint supplied, then initialize
# the network and compile the model
if args["model"] is None:
print("compiling model...")
model = EmotionVGGNet.build(width = 48, height = 48, depth = 1,
classes = config.NUM_CLASSES)
# opt = SGD(lr = 1e-2, momentum = 0.9, nesterov = True)
opt = Adam(lr = 1e-3)
from pipeline.preprocessing import CropPreprocessor
from pipeline.io import HDF5DatasetGenerator
from pipeline.utils.ranked import rank5_accuracy
from keras.models import load_model
import numpy as np
import progressbar
import json
# load the RGB means for the training set
means = json.loads(open(config.DATASET_MEAN).read())
# initialize the image preprocessors
sp = SimplePreprocessor(227, 227)
mp = MeanPreprocessor(means["R"], means["G"], means["B"])
cp = CropPreprocessor(227, 227)
iap = ImageToArrayPreprocessor()
# load the pretrained network
print("[INFO] loading model...")
model = load_model(config.MODEL_PATH)
# initialize the testing dataset generator, then make predictions on the testing data
print("[INFO] predicting on test data (no crops)...")
testGen = HDF5DatasetGenerator(config.TEST_HDF5,
64,
preprocessors=[sp, mp, iap],
classes=2)
predictions = model.predict_generator(testGen.generator(),
steps=testGen.numImages // 64,
max_queue_size=10)