"Evaluating: ",
progressbar.Percentage(), " ",
progressbar.Bar(), " ",
progressbar.ETA()
]
pbar = progressbar.ProgressBar(maxval=testGen.numImages // 64,
widgets=widgets).start()
# loop over a single pass of the test data
for (i, (images, labels)) in enumerate(testGen.generator(passes=1)):
# loop over each of the individual images
for image in images:
# apply the crop preprocessor to the image to generate 10
# separate crops, then convert them from images to arrays
crops = cp.preprocess(image)
crops = np.array([iap.preprocess(c) for c in crops], dtype="float32")
# make predictions on the crops and then average them
# together to obtain the final prediction
pred = model.predict(crops)
predictions.append(pred.mean(axis=0))
# update the progress bar
pbar.update(i)
# compute the rank-1 accuracy
pbar.finish()
print("[INFO] predicting on test data (with crops)...")
(rank1, _) = rank5_accuracy(predictions, testGen.db["labels"])
print("[INFO] rank-1: {:.2f}%".format(rank1 * 100))
testGen.close()
pbar = progressbar.ProgressBar(widgets=widgets,
max_value=test_gen.num_images //
configs.BATCH_SIZE).start()
# re-initialize generator and prediction
# list and loop over batches of data in generator
test_gen = HDF5DatasetGenerator(configs.TEST_HDF5,
feature_ref_name="data",
batch_size=configs.BATCH_SIZE,
preprocessors=[sp, mp, ip])
predictions = []
for (i, (images, label)) in enumerate(test_gen.generate(passes=1)):
for image in images:
# generate ten cropped copies of each images
# and appl image-to-array preprocessor
cropped_images = cp.preprocess(image)
cropped_image = np.array(
[ip.preprocess(crop) for crop in cropped_images])
# predicts the image class and append
# the average predictions to prediction list
pred = model.predict(cropped_images)
predictions.append(np.mean(pred, axis=0))
pbar.update(i)
# compute the rank-1 accuracy
pbar.finish()
print("[INFO] predicting on test data (with crops)...")
(rank1, _) = rank5_accuracy(y_true=test_gen.db["labels"], y_pred=predictions)
print("[INFO] rank-1: {:.2f}%".format(rank1 * 100))
test_gen.close()