while (True):
x_test, y_test, test_num = read_labels_batch_out(
labels_path, (224, 224), (224, 224),
root_images_folder,
batch_size=batch_size,
iteration_num=i,
random_horizontal_flip=False)
if (test_num == 0):
break
predicts = model.predict(x_test, batch_size=batch_size)
for j, value in enumerate(predicts):
predict = np.argmax(value)
truth_val = y_test[j]
confusion_mat.AddValueToConfusionMat(val=predict, label_val=truth_val)
file_writer.Write("[[" + str(j + (batch_size * i)) + "]] " +
str(truth_val) + " {" + str(predict) + "}")
print(
"====================================================================")
file_writer.Write(
"====================================================================")
print("result of iteration number : " + str(i))
file_writer.Write("result of iteration number : " + str(i))
print(" ")
file_writer.Write(" ")
print("---Show Current Score---\n")
file_writer.Write("---Show Current Score---\n")
else:
net.blobs['data'].reshape(batch_size, image_color_channel,
image_size_h, image_size_w)
net.blobs['data'].data[j_0, :, :, :] = transformer.preprocess(
'data', caffe_img)
#Run Model
out = net.forward()
for j_1, raw_predict_val in enumerate(out['score']):
predict_val = raw_predict_val.argmax()
#Storage Value
prop_dict = sub_list[j_1]
confusion_mat.AddValueToConfusionMat(
val=predict_val, label_val=prop_dict['truth_label'])
file_writer.Write("[[" + str(prop_dict['real_line_pos']) + "]] " +
" [" + str(prop_dict['iteration_id']) + "]" + " [" +
str(prop_dict['batch_id']) + "] " +
prop_dict['img_path'] + " " +
str(prop_dict['truth_label']) + " {" +
str(predict_val) + "}")
#Show confusion matrix every X iteration
if (i < iteration_count):
print(
"===================================================================="
)
file_writer.Write(
"===================================================================="
)