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accTable.py
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from os import listdir
from pathlib import Path
from prettytable import PrettyTable
import os
import parameters
import handshape_datasets as hd
import numpy as np
default_folder = Path.home() / 'handshape-classification' / 'Results'
folders = ["MobileNet", "DenseNet", "EfficientNet", "ganDiscriminator","ganDiscriminator_neutral"]
epochs = 15
table=PrettyTable(["Dataset", "MobileNet", "DenseNet", "EfficientNet", "ganDiscriminator", "PugeaultASLA_GanDiscriminator"])
table_test=PrettyTable(["Dataset", "MobileNet", "DenseNet", "EfficientNet", "ganDiscriminator","PugeaultASLA_GanDiscriminator"])
table_noTL=PrettyTable(["Dataset", "MobileNet", "DenseNet", "EfficientNet"])
table_test_noTL=PrettyTable(["Dataset", "MobileNet", "DenseNet", "EfficientNet"])
for dataset_id in hd.ids():
acc_value=np.zeros((len(folders)))
val_acc_value = np.zeros((len(folders)))
acc_value_gan = np.zeros((len(folders)))
val_acc_value_gan = np.zeros((len(folders)))
acc_value_noTL = np.zeros((len(folders)))
val_acc_value_noTL = np.zeros((len(folders)))
for i,model in enumerate(folders):
model_path = os.path.join(default_folder, model)
if (model=="MobileNet"):
batch_size=parameters.get_batch_mobile(dataset_id)
else:
if(model=="DenseNet"):
batch_size = parameters.get_batch_dense(dataset_id)
else:
if (model=="EfficientNet"):
batch_size = parameters.get_batch_eff(dataset_id)
else:
if(model=="ganDiscriminator"):
ganfolders= list(filter(lambda x: f"_epochs50" in x, listdir(model_path)))
ganfolders_act= list(filter(lambda x: f"{dataset_id}_" in x, ganfolders))
else:
ganfolders_neutral = list(filter(lambda x: f"_epochs50" in x, listdir(model_path)))
ganfolders_act_neutral = list(filter(lambda x: f"{dataset_id}_" in x, ganfolders_neutral))
if(model=="ganDiscriminator"):
ganfolder= os.path.join(model_path,ganfolders_act[0])
acc_history_path_gan = os.path.join(ganfolder, "acc_history.txt")
val_acc_history_path_gan = os.path.join(ganfolder, "val_acc_history.txt")
with open(acc_history_path_gan) as f:
lines = f.readlines()
acc_value_gan = lines[len(lines) - 1]
with open(val_acc_history_path_gan) as f:
lines = f.readlines()
val_acc_value_gan = lines[len(lines) - 1]
else:
if(model=="ganDiscriminator_neutral"):
ganfolder_neutral = os.path.join(model_path, ganfolders_act_neutral[0])
acc_history_path_gan_neutral = os.path.join(ganfolder_neutral, "acc_history.txt")
val_acc_history_path_gan_neutral = os.path.join(ganfolder_neutral, "val_acc_history.txt")
with open(acc_history_path_gan_neutral) as f:
lines = f.readlines()
acc_value_gan_neutral = lines[len(lines) - 1]
with open(val_acc_history_path_gan_neutral) as f:
lines = f.readlines()
val_acc_value_gan_neutral = lines[len(lines) - 1]
else:
subsets_folders = list(
filter(lambda x: f"{dataset_id}_{model}_batch{batch_size}_epochs{epochs}" in x,
listdir(model_path)))
subsets_folders_TL= list(
filter(lambda x: f"_noTL" not in x,
subsets_folders))
subsets_folders_noTL = list(
filter(lambda x: f"{dataset_id}_{model}_batch{batch_size}_epochs{epochs}_noTL" in x,
listdir(model_path)))
folder_act_noTL = os.path.join(model_path, subsets_folders_noTL[0])
acc_history_path_noTL = os.path.join(folder_act_noTL, "acc_history.txt")
val_acc_history_path_noTL = os.path.join(folder_act_noTL, "val_acc_history.txt")
with open(acc_history_path_noTL) as f:
lines = f.readlines()
acc_value_noTL[i] = lines[len(lines) - 1]
with open(val_acc_history_path_noTL) as f:
lines = f.readlines()
val_acc_value_noTL[i] = lines[len(lines) - 1]
folder_act = os.path.join(model_path, subsets_folders_TL[0])
acc_history_path = os.path.join(folder_act, "acc_history.txt")
val_acc_history_path = os.path.join(folder_act, "val_acc_history.txt")
with open(acc_history_path) as f:
lines = f.readlines()
acc_value[i]=lines[len(lines)-1]
with open(val_acc_history_path) as f:
lines = f.readlines()
val_acc_value[i]=lines[len(lines)-1]
table_test_noTL.add_row([dataset_id, val_acc_value_noTL[0], val_acc_value_noTL[1], val_acc_value_noTL[2]])
table_noTL.add_row([dataset_id, acc_value_noTL[0], acc_value_noTL[1], acc_value_noTL[2]])
table_test.add_row([dataset_id, val_acc_value[0], val_acc_value[1], val_acc_value[2], val_acc_value_gan, val_acc_value_gan_neutral])
table.add_row([dataset_id, acc_value[0], acc_value[1], acc_value[2],acc_value_gan, acc_value_gan_neutral])
print (table)
print (table_test)
print (table_noTL)
print (table_test_noTL)
data = table.get_string()
data_test= table_test.get_string()
data_noTL = table_noTL.get_string()
data_test_noTL= table_test_noTL.get_string()
file_noTL = os.path.join(default_folder, 'Accuracy_table_noTL.txt')
with open(file_noTL, 'w') as f:
f.write(data_noTL)
file_test_noTL = os.path.join(default_folder, 'val_test_Accuracy_table_noTL.txt')
with open(file_test_noTL, 'w') as f:
f.write(data_test_noTL)
file = os.path.join(default_folder, 'Accuracy_table.txt')
with open(file, 'w') as f:
f.write(data)
file_test = os.path.join(default_folder, 'val_test_Accuracy_table.txt')
with open(file_test, 'w') as f:
f.write(data_test)