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inference_v0.py
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#-----*******************************************************-----#
#
# Authors:
# Vittorio Fra (VitF), Politecnico di Torino, Italy
# Andrea Pignata (andreapignaz), Politecnico di Torino, Italy
# Last modified: 2023.10.12
#
#-----*******************************************************-----#
import numpy as np
import onnx
import onnxruntime as ort
from playsound import playsound
from time import sleep
torch2numpy_dataset = True # if True, data and labels are loaded from stored .pt file and converted to numpy array
torch2numpy_save = False # if True (with torch2numpy_dataset=True), data and labels are loaded from stored .pt file, converted to numpy array and saved as .npy
whole_set = True # if False, random single-sampole inference is run from the test set
Ns = 100 # number of single-sample inferences, used only if whole_set is False
experiment_id = "rebsp36h"
trial_id = "Qmabv"
net = "./models/pretrained_Neu-BrAuER_v0_{}_{}_ref.onnx".format(experiment_id,trial_id)
if onnx.checker.check_model(net, full_check=True) == None:
print("#######################")
print("ONNX model check: done!")
print("#######################")
device = "cpu"
if torch2numpy_dataset:
import torch
dataset_filename = "./data/braille_letters_digits_40Hz_augmented_ds_test.pt"
dataset = torch.load(dataset_filename, map_location=device)
input_tensors = []
input_labels = []
for i in dataset:
input_tensors.append(i[0].numpy())
input_labels.append(i[1].numpy())
data = np.array(input_tensors)
labels = np.array(input_labels)
if torch2numpy_save:
np.save('./data/numpy_data_braille_letters_digits_40Hz_augmented_ds_test', data)
np.save('./data/numpy_labels_braille_letters_digits_40Hz_augmented_ds_test', labels)
else:
data = np.load('./data/numpy_data_braille_letters_digits_40Hz_augmented_ds_test.npy')
labels = np.load('./data/numpy_labels_braille_letters_digits_40Hz_augmented_ds_test.npy')
letter_written = ['Space', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K',
'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']
providers = ['CPUExecutionProvider']
session = ort.InferenceSession(net, providers=providers)
outname = [i.name for i in session.get_outputs()]
inname = [i.name for i in session.get_inputs()]
if whole_set:
preds = []
for num,el in enumerate(data):
spk_out = session.run(outname, {inname[0]:np.expand_dims(el,1)})
preds.append( np.argmax( np.sum(spk_out[0], 0) ) )
print("-----------------")
print("Accuracy: {}%".format( np.round( np.mean( (np.array(preds)==labels) )*100,2 )) )
print("-----------------")
else:
check_preds = np.zeros(Ns)
for ii in range(Ns):
rnd_idx = np.random.randint(data.shape[0])
spk_out = session.run(outname, {inname[0]:np.expand_dims(data[rnd_idx],1)})
pred = np.argmax(np.sum(spk_out[0],0))
if pred == labels[rnd_idx]:
check_preds[ii] = 1
print("Run {}/{}:".format(ii+1,Ns))
print("\tSample: {} \t Prediction: {}".format(letter_written[labels[rnd_idx]],letter_written[pred]))
playsound("./data/character_playback/{}.wav".format(letter_written[pred]))
sleep(1)
print("------------------------------------------")
print("Overall accuracy from the {} runs: {}%".format(Ns,np.round(np.mean(check_preds)*100,2)))
print("------------------------------------------")