def infer_from_model(model: Model, image: np.ndarray) -> InferenceResult:
image = np.expand_dims(image, axis=0)
start = time.time()
result = model.predict_on_batch(image)
result = np.argmax(np.squeeze(result, axis=0), axis=-1)
inference_time = time.time() - start
result_colors = InferenceWrapper.map_color(class_map=result)
return InferenceResult(
result=result,
result_colors=result_colors,
inference_time=inference_time
)
train_loader, test_loader = data_loader.run()
"""
Evaluate and check
"""
if evaluate:
progress = tqdm.tqdm(test_loader, total=len(test_loader))
inp = Input(shape=(2048, ), name="dense")
dense_layer = Model(inp, motion_model_restored.layers[-1].layers[-1](inp))
video_level_preds_np = np.zeros(
(len(progress), num_actions)) # each video per 101 class (prediction)
video_level_labels_np = np.zeros((len(progress), 1))
for index, (video_frames, video_label
) in enumerate(progress): # i don't need frame level labels
feature_field, frame_preds = motion_model_with_2_outputs.predict_on_batch(
video_frames)
assert np.allclose(frame_preds, dense_layer.predict(feature_field))
video_level_preds_np[index, :] = np.mean(frame_preds, axis=0)
video_level_labels_np[index, 0] = video_label
video_level_loss, video_level_accuracy_1, video_level_accuracy_5 = keras.backend.get_session(
).run(
[val_loss_op, acc_top_1_op, acc_top_5_op],
feed_dict={
video_level_labels_k: video_level_labels_np,
video_level_preds_k: video_level_preds_np
})
print("Motion Model validation", "prec@1", video_level_accuracy_1,
"prec@5", video_level_accuracy_5, "loss", video_level_loss)
save_model(model, 'keras-logos-gen-xception-ep%d.model' % EPOCHS)
else:
model = load_model('keras-logos-gen-xception-ep%d.model' % EPOCHS)
all_predictions = []
with open(
"keras-logos-gen-xception-ep%d-thresh%0.2f-predictions.txt" %
(EPOCHS, THRESHOLD), 'w') as out:
idx = 0
for batch in test_dir_iterator:
filenames = test_dir_iterator.filenames[idx:idx +
test_dir_iterator.batch_size]
if len(filenames) == 0:
break
idx += test_dir_iterator.batch_size
predictions = model.predict_on_batch(batch[0])
for i in range(len(predictions)):
truth = test_class_map[np.argmax(batch[1][i])]
conf = np.max(predictions[i])
if conf >= THRESHOLD:
predicted = train_class_map[np.argmax(predictions[i])]
else:
predicted = "no-logo"
all_predictions.append(test_dir_iterator.class_indices[predicted])
m = re.match(r".*/(\d+)\.jpg", filenames[i])
fid = m.group(1)
print("file = %s\ttruth = %s\tpredicted = %s\tconfidence = %f" %
(filenames[i], truth, predicted, conf))
out.write("%s, %s, %f\n" % (fid, predicted, conf))
# generate confusion matrix