with open(config.DATASET_MEAN, 'r') as f:
means = json.load(f)
simple_preprocessor = SimplePreprocessor(64, 64)
mean_preprocessor = MeanPreprocessor(means['R'], means['G'], means['B'])
image_to_array_preprocessor = ImageToArrayPreprocessor()
test_generator = HDF5DatasetGenerator(config.TEST_HDF5,
64,
preprocessors=[
simple_preprocessor,
mean_preprocessor,
image_to_array_preprocessor
],
classes=config.NUM_CLASSES)
print('[INFO] Loading model...')
model = load_model(config.MODEL_PATH)
print('[INFO] Predicting on test data...')
predictions = model.predict_generator(test_generator.generator(),
steps=test_generator.num_images // 64,
max_queue_size=10)
rank1, rank5 = rank5_accuracy(predictions, test_generator.db['labels'])
print(f'[INFO] Rank-1: {rank1 * 100:.2f}%')
print(f'[INFO] Rank-5: {rank5 * 100:.2f}%')
test_generator.close()
import argparse
import pickle
import h5py
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--db", required=True, help="path HDF5 database")
ap.add_argument("-m",
"--model",
required=True,
help="path to pre-trained model")
args = vars(ap.parse_args())
# load the pre-trained model
print("[INFO] loading pre-trained model...")
model = pickle.loads(open(args["model"], "rb").read())
# open the HDF5 database for reading then determine the index of
# the training and testing split, provided that this data was
# already shuffled *prior* to writing it to disk
db = h5py.File(args["db"], "r")
i = int(db["labels"].shape[0] * 0.75)
# make predictions on the testing set then compute the rank-1
# and rank-5 accuracies
print("[INFO] predicting...")
preds = model.predict_proba(db["features"][i:])
(rank1, rank5) = rank5_accuracy(preds, db["labels"][i:])
# display the rank-1 and rank-5 accuracies
print("[INFO] rank-1: {:.2f}%".format(rank1 * 100))
print("[INFO] rank-5: {:.2f}%".format(rank5 * 100))
# close the database
db.close()
import json
# load RGB channel mean values
means = json.loads(open(config.DATASET_MEAN_PATH).read())
# initialization the image preprocessors
sp = SimplePreprocessor(64, 64)
mp = MeanPreprocessor(means["R"], means["G"], means["B"])
iap = ImageToArrayPreprocessor()
# initialize testing dataset generator
testGen = 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 prediction on test data
print("[INFO] predicting on test data........")
preds = model.predict_generator(testGen.generator(),
steps=(testGen.numImages / 64))
# compute rank-1 and rank-5 accuracies
(rank1, rank5) = rank5_accuracy(preds, testGen.db["labels"].generator())
print("[INFO] Rank - 1 : {:.2f}%".format(rank1 * 100))
print("[INFO] Rank - 5 : {:.2f}%".format(rank5 * 100))
testGen.close()
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=64 * 2)
# compute the rank-1 and rank-5 accuracies
(rank1, _) = rank5_accuracy(predictions, testGen.db["labels"])
print("[INFO] rank-1: {:.2f}%".format(rank1 * 100))
testGen.close()
# re-initialize the testing set generator, this time excluding the
# ‘SimplePreprocessor‘
testGen = HDF5DatasetGenerator(config.TEST_HDF5,
64,
preprocessors=[mp],
classes=2)
predictions = []
# initialize the progress bar
widgets = [
"Evaluating: ",
progressbar.Percentage(), " ",
progressbar.Bar(), " ",
progressbar.ETA()
from utils.ranked import rank5_accuracy
argument_parser = argparse.ArgumentParser()
argument_parser.add_argument('-d',
'--db',
required=True,
help='Path to the HDF5 database.')
argument_parser.add_argument('-m',
'--model',
required=True,
help='Path to pre-trained model.')
arguments = vars(argument_parser.parse_args())
print('[INFO] Loading pre-trained model...')
with open(arguments['model'], 'rb') as f:
model = pickle.loads(f.read())
db = h5py.File(arguments['db'], 'r')
i = int(db['labels'].shape[0] * 0.75)
print('[INFO] Predicting...')
predictions = model.predict_proba(db['features'][i:])
rank1, rank5 = rank5_accuracy(predictions, db['labels'][i:])
print(f'[INFO] rank-1: {rank1 * 100:.2f}%')
print(f'[INFO] rank-5: {rank5 * 100:.2f}%')
db.close()