def test_tflite_output(model_file, image_size=224):
val_path = 'data/val_official/'
print("Model Name:", model_file, "Create Time:",
get_FileCreateTime(model_file))
interpreter = tf.lite.Interpreter(model_path=str(model_file))
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()[0]
output_details = interpreter.get_output_details()[0]
for dirpath, dirnames, filenames in os.walk(val_path):
filenames.sort(key=lambda x: int(x.split('.')[0]))
for imgname in filenames:
image = load_image(os.path.join(dirpath, imgname))
image = img_preprocess(image, image_size, "per", False)
test_image = image
if input_details['dtype'] == np.uint8:
input_scale, input_zero_point = input_details["quantization"]
test_image = test_image / input_scale + input_zero_point
test_image = np.expand_dims(test_image,
axis=0).astype(input_details["dtype"])
interpreter.set_tensor(input_details["index"], test_image)
interpreter.invoke()
output = interpreter.get_tensor(output_details["index"])[0]
results = np.squeeze(output)
prediction_top_3 = results.argsort()[-3:][::-1]
print(prediction_top_3[0] + 1)
def representative_dataset():
global image_size
data_path = "/data2/competition/classification/represent_data/"
for dirpath, dirnames, filenames in os.walk(data_path):
for imgname in filenames:
image = tf.io.read_file(os.path.join(data_path, imgname))
image = tf.compat.v1.image.decode_jpeg(image)
image = img_preprocess(image, image_size, "per", False)
image = np.array(image)
print(os.path.join(data_path, imgname))
image = np.reshape(image, (1, image_size, image_size, 3))
yield [image.astype(np.float32)]
def test_tflite(model_file,
image_size=224,
save_plabel=False,
show_wrong=False,
save_wrong_img=False):
val_path = '/data2/competition/classification/val_true'
global total_time, top1_correct, top3_correct, total, thread_count
total = total_time = top1_correct = top3_correct = 0
for i in range(len(CLASSES)):
CLASSES[i] = "".join("".join(CLASSES[i].split()).split('/'))
print("Model Name:", model_file, "Create Time:",
get_FileCreateTime(model_file))
tflite = TFLiteModel(str(model_file))
thread_list = []
for dirpath, dirnames, filenames in os.walk(val_path):
for dirname in dirnames:
for path, _, imgnames in os.walk(os.path.join(dirpath, dirname)):
for imgname in imgnames:
total += 1
label = int(dirname.split('_')[0]) - 1
image = load_image(os.path.join(path, imgname))
image = img_preprocess(image, image_size, "per", False)
t = threading.Thread(target=thread_test,
args=(model_file, image, label,
imgname, path, False,
save_plabel, save_wrong_img))
t.start()
thread_count += 1
while thread_count >= 10:
pass
while thread_count > 0:
time.sleep(0.01)
print("Top1 Acc:", top1_correct / total, "Top3 Acc:", top3_correct / total,
"Average time:", total_time / total)
get_FileCreateTime(model_file))
interpreter = tf.lite.Interpreter(model_path=str(model_file))
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()[0]
output_details = interpreter.get_output_details()[0]
total = 0
top1_correct = 0
top3_correct = 0
for dirpath, dirnames, filenames in os.walk(val_path):
for imgname in filenames:
image = load_image(os.path.join(dirpath, imgname))
image = img_preprocess(image, 224, "per", False)
test_image = image
if input_details['dtype'] == np.uint8:
input_scale, input_zero_point = input_details["quantization"]
test_image = test_image / input_scale + input_zero_point
test_image = np.expand_dims(test_image,
axis=0).astype(input_details["dtype"])
interpreter.set_tensor(input_details["index"], test_image)
interpreter.invoke()
output = interpreter.get_tensor(output_details["index"])[0]
results = np.squeeze(output)
prediction_top_3 = results.argsort()[-3:][::-1]
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
floating_model = False
if input_details[0]['dtype'] == type(np.float32(1.0)):
floating_model = True
val_path = 'data/val_official/'
for dirpath, dirnames, filenames in os.walk(val_path):
filenames.sort(key=lambda x: int(x.split('.')[0]))
for imgname in filenames:
image = load_image(os.path.join(dirpath, imgname))
image = img_preprocess(image, IMAGE_SIZE, NORMALIZATION, False)
image = np.reshape(image, (1, IMAGE_SIZE, IMAGE_SIZE, 3))
input_data = image
if floating_model:
input_data = np.float32(input_data)
else:
input_data = np.uint8(input_data)
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
results = np.squeeze(output_data)
prediction = np.argmax(results)
def test_tflite_model(model_file,
image_size=224,
save=False,
show_wrong=False):
val_path = '/data2/competition/classification/val_20'
CLASSES = [
'Portrait', 'Group Portrait', 'Kids / Infants', 'Dog', 'Cat',
'Macro / Close-up', 'Food / Gourmet', 'Beach', 'Mountains',
'Waterfall', 'Snow', 'Landscape', 'Underwater', 'Architecture',
'Sunrise / Sunset', 'Blue Sky', 'Overcast / Cloudy Sky',
'Greenery / Green Plants / Grass', 'Autumn Plants', 'Flower',
'Night Shot', 'Stage / Concert', 'Fireworks', 'Candle light',
'Neon Lights / Neon Signs', 'Indoor', 'Backlight / Contre-jour',
'Text / Document', 'QR Code', 'Monitor Screen'
]
for i in range(len(CLASSES)):
CLASSES[i] = "".join("".join(CLASSES[i].split()).split('/'))
print("Model Name:", model_file, "Create Time:",
get_FileCreateTime(model_file))
interpreter = tf.lite.Interpreter(model_path=str(model_file))
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()[0]
output_details = interpreter.get_output_details()[0]
total = 0
top1_correct = 0
top3_correct = 0
for dirpath, dirnames, filenames in os.walk(val_path):
for dirname in dirnames:
for path, _, imgnames in os.walk(os.path.join(dirpath, dirname)):
for imgname in imgnames:
label = int(dirname.split('_')[0]) - 1
image = load_image(os.path.join(path, imgname))
image = img_preprocess(image, image_size, "per", False)
test_image = image
if input_details['dtype'] == np.uint8:
input_scale, input_zero_point = input_details[
"quantization"]
test_image = test_image / input_scale + input_zero_point
test_image = np.expand_dims(test_image, axis=0).astype(
input_details["dtype"])
interpreter.set_tensor(input_details["index"], test_image)
interpreter.invoke()
output = interpreter.get_tensor(output_details["index"])[0]
results = np.squeeze(output)
prediction_top_3 = results.argsort()[-3:][::-1]
total += 1
if prediction_top_3[0] == label:
top1_correct += 1
if label in prediction_top_3:
top3_correct += 1
if show_wrong:
if prediction_top_3[0] != label:
print("True", label, "Pred", prediction_top_3,
"Image Name:", imgname, "Top1 acc:",
top1_correct / total)
img = Image.open(os.path.join(path, imgname))
plt.figure(imgname)
plt.imshow(img)
plt.axis('on')
plt.xlabel(imgname)
plt.title("True" + str(label + 1) +
CLASSES[label] + " Pred" +
str(prediction_top_3[0] + 1) +
CLASSES[prediction_top_3[0]]) # 图像题目
plt.show()
else:
print("True", label, "Pred", prediction_top_3,
"Top1 acc:", top1_correct / total)
if save:
copyfile(
os.path.join(path, imgname),
"/home/share/data/extra_data/" + CLASSES[label])
print("Top1 Acc:", top1_correct / total, "Top3 Acc:", top3_correct / total)
def test():
'''Evaluate TFLite'''
evaluate_tflite = False
if evaluate_tflite:
save_wrong = True
CLASSES = [
'Portrait', 'Group Portrait', 'Kids / Infants', 'Dog', 'Cat',
'Macro / Close-up', 'Food / Gourmet', 'Beach', 'Mountains',
'Waterfall', 'Snow', 'Landscape', 'Underwater', 'Architecture',
'Sunrise / Sunset', 'Blue Sky', 'Overcast / Cloudy Sky',
'Greenery / Green Plants / Grass', 'Autumn Plants', 'Flower',
'Night Shot', 'Stage / Concert', 'Fireworks', 'Candle light',
'Neon Lights / Neon Signs', 'Indoor', 'Backlight / Contre-jour',
'Text / Document', 'QR Code', 'Monitor Screen'
]
for i in range(len(CLASSES)):
CLASSES[i] = "".join("".join(CLASSES[i].split()).split('/'))
model_file = "save/" + train_name + "/" + train_name + ".tflite"
print("Model Name:", model_file, "Create Time:",
get_FileCreateTime(model_file))
interpreter = interpreter_wrapper.Interpreter(model_path=model_file)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
floating_model = False
if input_details[0]['dtype'] == type(np.float32(1.0)):
floating_model = True
# Get the size of the input / output tensors
height = input_details[0]['shape'][1]
width = input_details[0]['shape'][2]
# Process test_model images and display the results
total = 0
top1_correct = 0
top3_correct = 0
top1_error = 0
top3_error = 0
correct = {}
total_dict = {}
predict_dict = {}
top1_wrong_label = {}
top3_wrong_label = {}
if save_wrong:
if not os.path.exists("save/" + train_name + "/wrong/"):
mkdir("save/" + train_name + "/wrong")
for dirpath, dirnames, filenames in os.walk(val_path):
for dirname in dirnames:
for path, _, imgnames in os.walk(os.path.join(
dirpath, dirname)):
for imgname in imgnames:
label = int(dirname.split('_')[0]) - 1
total += 1
image = load_image(os.path.join(path, imgname))
image = img_preprocess(image, IMAGE_SIZE,
NORMALIZATION, False)
image = np.reshape(image,
(1, IMAGE_SIZE, IMAGE_SIZE, 3))
input_data = image
if floating_model:
input_data = np.float32(input_data)
else:
input_data = np.uint8(input_data)
interpreter.set_tensor(input_details[0]['index'],
input_data)
interpreter.invoke()
output_data = interpreter.get_tensor(
output_details[0]['index'])
results = np.squeeze(output_data)
prediction = np.argmax(results)
prediction_top_3 = results.argsort()[-3:][::-1]
if CLASSES[label] not in total_dict:
total_dict[CLASSES[label]] = 1
correct[CLASSES[label]] = 0
else:
total_dict[CLASSES[label]] += 1
if CLASSES[prediction_top_3[0]] not in predict_dict:
predict_dict[CLASSES[prediction_top_3[0]]] = 1
else:
predict_dict[CLASSES[prediction_top_3[0]]] += 1
if prediction_top_3[0] == label:
top1_correct += 1
correct[CLASSES[label]] += 1
else:
if save_wrong:
if not os.path.exists("save/" + train_name +
"/wrong/" +
CLASSES[label]):
mkdir("save/" + train_name + "/wrong/" +
CLASSES[label])
copyfile(
os.path.join(path, imgname),
os.path.join(
"save/" + train_name + "/wrong/" +
CLASSES[label],
CLASSES[prediction_top_3[0]] + "_" +
CLASSES[prediction_top_3[1]] + "_" +
CLASSES[prediction_top_3[2]] + "_" +
".jpg"))
if dirname in top1_wrong_label:
top1_wrong_label[dirname] += 1
else:
top1_wrong_label[dirname] = 1
top1_error += 1
if label in prediction_top_3:
top3_correct += 1
else:
top3_error += 1
if dirname in top3_wrong_label:
top3_wrong_label[dirname] += 1
else:
top3_wrong_label[dirname] = 1
print(os.path.join(path, imgname), "True", label,
"Pred", prediction_top_3)
accuracy = {}
recall = {}
for k in correct.keys():
accuracy[k] = correct[k] / total_dict[k]
recall[k] = correct[k] / (predict_dict[k])
with open("save/" + train_name + "/tflite_test_log.txt", 'w') as f:
f.write("Top 1 Acc " + str(top1_correct / total))
f.write('\n')
f.write("Top 1 Wrong " + str(
sorted(top1_wrong_label.items(),
key=lambda kv: (kv[1], kv[0]),
reverse=True)))
f.write('\n')
f.write("Top 3 Acc " + str(top3_correct / total))
f.write('\n')
f.write("Top 3 Wrong " + str(
sorted(top3_wrong_label.items(),
key=lambda kv: (kv[1], kv[0]),
reverse=True)))
f.write('\n')
f.write("Accuracy in category:" + str(accuracy))
f.write('\n')
f.write("Recall in category:" + str(recall))
f.write('\n')
print("Top 1 Acc ", top1_correct / total)
print(
"Top 1 Wrong ",
sorted(top1_wrong_label.items(),
key=lambda kv: (kv[1], kv[0]),
reverse=True))
print("Top 3 Acc ", top3_correct / total)
print(
"Top 3 Wrong ",
sorted(top3_wrong_label.items(),
key=lambda kv: (kv[1], kv[0]),
reverse=True))
print("Accuracy in category:", accuracy)
print("Recall in category:", recall)