def main():
# histories = Customdash(ModelName = 'SLVC06_evaluation_process',
# email = '*****@*****.**',
# password = '******')
if args.phase == 'test':
print(args)
if not os.path.exists('./results/'):
os.makedirs('./results/')
args.results_dir = './results/' + args.results_dir + '/'
args.checkpoint_dir = './checkpoints/' + args.checkpoint_dir + '/'
if args.dataset == 'Amazon_RO':
args.dataset = 'Amazonia_Legal/'
dataset = AMAZON_RO(args)
if args.dataset == 'Amazon_MT':
args.dataset = 'Amazonia_Legal/'
dataset = AMAZON_MT(args)
if args.dataset == 'Amazon_PA':
args.dataset = 'Amazonia_Legal/'
dataset = AMAZON_PA(args)
if args.dataset == 'Cerrado_MA':
args.dataset = 'Cerrado_Biome/'
dataset = CERRADO_MA(args)
dataset.Tiles_Configuration(args, 0)
dataset.Coordinates_Creator(args, 0)
checkpoint_files = os.listdir(args.checkpoint_dir)
for i in range(len(checkpoint_files)):
model_folder = checkpoint_files[i]
args.trained_model_path = args.checkpoint_dir + '/' + model_folder + '/'
model_folder_fields = model_folder.split('_')
now = datetime.now()
dt_string = now.strftime("%d_%m_%Y_%H_%M_%S")
args.save_results_dir = args.results_dir + args.method_type + '_' + 'Model_Results_' + 'Trained_' + model_folder_fields[
3] + '_' + model_folder_fields[4] + '_' + model_folder[
-19:] + '_Tested_' + args.data_t1_year + '_' + args.data_t2_year + '_' + dt_string + '/'
#args.save_results_dir = args.results_dir + '\\model_' + str(i) + '\\'
if not os.path.exists(args.save_results_dir):
os.makedirs(args.save_results_dir)
print('[*]Initializing the model...')
model = Models(args, dataset)
model.Test()
def main():
print(args)
#histories = Customdash(ModelName = 'SLVC06_train_process', email = '*****@*****.**', password = '******')
if not os.path.exists('./checkpoints/'):
os.makedirs('./checkpoints/')
args.checkpoint_dir = './checkpoints/' + args.checkpoint_dir
if args.source_dataset == 'Amazon_RO':
args.dataset = 'Amazonia_Legal/'
args.buffer_dimension_in = args.source_buffer_dimension_in
args.buffer_dimension_out = args.source_buffer_dimension_out
args.data_t1_name = args.source_data_t1_name
args.data_t2_name = args.source_data_t2_name
args.reference_t1_name = args.source_reference_t1_name
args.reference_t2_name = args.source_reference_t2_name
dataset_s = AMAZON_RO(args)
if args.source_dataset == 'Amazon_PA':
args.dataset = 'Amazonia_Legal/'
args.buffer_dimension_in = args.source_buffer_dimension_in
args.buffer_dimension_out = args.source_buffer_dimension_out
args.data_t1_name = args.source_data_t1_name
args.data_t2_name = args.source_data_t2_name
args.reference_t1_name = args.source_reference_t1_name
args.reference_t2_name = args.source_reference_t2_name
dataset_s = AMAZON_PA(args)
if args.source_dataset == 'Cerrado_MA':
args.dataset = 'Cerrado_Biome/'
args.buffer_dimension_in = args.source_buffer_dimension_in
args.buffer_dimension_out = args.source_buffer_dimension_out
args.data_t1_name = args.source_data_t1_name
args.data_t2_name = args.source_data_t2_name
args.reference_t1_name = args.source_reference_t1_name
args.reference_t2_name = args.source_reference_t2_name
dataset_s = CERRADO_MA(args)
if args.target_dataset == 'Amazon_RO':
args.dataset = 'Amazonia_Legal/'
args.buffer_dimension_in = args.target_buffer_dimension_in
args.buffer_dimension_out = args.target_buffer_dimension_out
args.data_t1_name = args.target_data_t1_name
args.data_t2_name = args.target_data_t2_name
args.reference_t1_name = args.target_reference_t1_name
args.reference_t2_name = args.target_reference_t2_name if args.target_reference_t2_name is not None else 'None'
dataset_t = AMAZON_RO(args)
if args.target_dataset == 'Amazon_PA':
args.dataset = 'Amazonia_Legal/'
args.buffer_dimension_in = args.target_buffer_dimension_in
args.buffer_dimension_out = args.target_buffer_dimension_out
args.data_t1_name = args.target_data_t1_name
args.data_t2_name = args.target_data_t2_name
args.reference_t1_name = args.target_reference_t1_name
args.reference_t2_name = args.target_reference_t2_name if args.target_reference_t2_name is not None else 'None'
dataset_t = AMAZON_PA(args)
if args.target_dataset == 'Cerrado_MA':
args.dataset = 'Cerrado_Biome/'
args.buffer_dimension_in = args.target_buffer_dimension_in
args.buffer_dimension_out = args.target_buffer_dimension_out
args.data_t1_name = args.target_data_t1_name
args.data_t2_name = args.target_data_t2_name
args.reference_t1_name = args.target_reference_t1_name
args.reference_t2_name = args.target_reference_t2_name if args.target_reference_t2_name is not None else 'None'
dataset_t = CERRADO_MA(args)
print(np.shape(dataset_s.images_norm))
print(np.shape(dataset_t.images_norm))
#print(np.shape(dataset_t.images_norm))
for i in range(args.runs):
dataset = []
print(i)
now = datetime.now()
dt_string = now.strftime("%d_%m_%Y_%H_%M_%S")
print(dt_string)
if args.training_type == 'classification':
args.save_checkpoint_path = args.checkpoint_dir + '/' + args.method_type + '_' + dt_string + '/'
if args.training_type == 'domain_adaptation' or args.training_type == 'domain_adaptation_balance':
args.save_checkpoint_path = args.checkpoint_dir + '/' + 'Tr_M_' + dt_string + '/'
if not os.path.exists(args.save_checkpoint_path):
os.makedirs(args.save_checkpoint_path)
#Writing the args into a file
with open(args.save_checkpoint_path + 'commandline_args.txt', 'w') as f:
json.dump(args.__dict__, f, indent=2)
args.vertical_blocks = args.source_vertical_blocks
args.horizontal_blocks = args.source_horizontal_blocks
args.overlap = args.overlap_s
args.porcent_of_positive_pixels_in_actual_reference = args.porcent_of_positive_pixels_in_actual_reference_s
dataset_s.Tiles_Configuration(args, i)
dataset_s.Coordinates_Creator(args, i)
args.vertical_blocks = args.target_vertical_blocks
args.horizontal_blocks = args.target_horizontal_blocks
args.overlap = args.overlap_t
args.porcent_of_positive_pixels_in_actual_reference = args.porcent_of_positive_pixels_in_actual_reference_t
dataset_t.Tiles_Configuration(args, i)
dataset_t.Coordinates_Creator(args, i)
dataset.append(dataset_s)
dataset.append(dataset_t)
print('[*]Initializing the model...')
model = Models(args, dataset)
model.Train()
def Main():
if args.dataset == 'Amazon_RO':
args.dataset = 'Amazonia_Legal/'
dataset = AMAZON_RO(args)
# if args.dataset == 'Amazon_MT':
# args.dataset = 'Amazonia_Legal/'
# dataset = AMAZON_MT(args)
if args.dataset == 'Amazon_PA':
args.dataset = 'Amazonia_Legal/'
dataset = AMAZON_PA(args)
if args.dataset == 'Cerrado_MA':
args.dataset = 'Cerrado_Biome/'
dataset = CERRADO_MA(args)
if not os.path.exists('./results_avg/'):
os.makedirs('./results_avg/')
args.average_results_dir = './results_avg/' + args.results_dir + '/'
if not os.path.exists(args.average_results_dir):
os.makedirs(args.average_results_dir)
args.results_dir = './results/' + args.results_dir + '/'
args.checkpoint_dir = './checkpoints/' + args.checkpoint_dir + '/'
counter = 0
files = os.listdir(args.results_dir)
initial_flag = True
for i in range(0, len(files)):
if files[i] == 'Results.txt':
print('Results file')
else:
Hit_map_path = args.results_dir + files[i] + '/hit_map.npy'
if os.path.exists(Hit_map_path):
hit_map = np.load(Hit_map_path)
counter += 1
if initial_flag:
HIT_MAP = np.zeros_like(hit_map)
initial_flag = False
HIT_MAP += hit_map
dataset.Tiles_Configuration(args, 0)
Avg_hit_map = HIT_MAP/counter
args.file = 'Avg_Scores'
args.results_dir = args.average_results_dir
if not os.path.exists(args.results_dir + args.file + '/'):
os.makedirs(args.results_dir + args.file + '/')
if args.save_result_text:
# Open a file in order to save the training history
f = open(args.results_dir + "Results.txt","a")
if counter == 0:
ACCURACY_ = []
FSCORE_ = []
RECALL_ = []
PRECISION_ = []
ALERT_AREA_ = []
ACCURACY, FSCORE, RECALL, PRECISION, CONFUSION_MATRIX, ALERT_AREA = Metrics_For_Test_M(Avg_hit_map,
dataset.references[0], dataset.references[1],
dataset.Train_tiles, dataset.Valid_tiles, dataset.Undesired_tiles,
args)
if args.save_result_text:
ACCURACY_.append(ACCURACY[0,0])
FSCORE_.append(FSCORE[0,0])
RECALL_.append(RECALL[0,0])
PRECISION_.append(PRECISION[0,0])
ALERT_AREA_.append(ALERT_AREA[0,0])
#histories.sendLoss(loss = FSCORE[0 , 0], epoch = i, total_epochs = len(files))
f.write("Run: %d Accuracy: %.2f%% F1-Score: %.2f%% Recall: %.2f%% Precision: %.2f%% Area: %.2f%% File Name: %s\n" % (counter, ACCURACY, FSCORE, RECALL, PRECISION, ALERT_AREA, args.file))
f.close()
print(ACCURACY_)
else:
print('Coming up!')
#histories.sendLoss(loss = 0.0, epoch = i, total_epochs = len(files))
if args.save_result_text:
f = open(args.results_dir + "Results.txt","a")
ACCURACY_m = np.mean(ACCURACY_)
FSCORE_m = np.mean(FSCORE_)
RECALL_m = np.mean(RECALL_)
PRECISION_m = np.mean(PRECISION_)
ALERT_AREA_m = np.mean(ALERT_AREA_)
ACCURACY_s = np.std(ACCURACY_)
FSCORE_s = np.std(FSCORE_)
RECALL_s = np.std(RECALL_)
PRECISION_s = np.std(PRECISION_)
ALERT_AREA_s = np.std(ALERT_AREA_)
#histories.sendLoss(loss = FSCORE_m, epoch = i + 1, total_epochs = len(files) + 1)
f.write("Mean: %d Accuracy: %f%% F1-Score: %f%% Recall: %f%% Precision: %f%% Area: %f%%\n" % ( 0, ACCURACY_m, FSCORE_m, RECALL_m, PRECISION_m, ALERT_AREA_m))
f.write("Std: %d Accuracy: %.2f%% F1-Score: %.2f%% Recall: %.2f%% Precision: %.2f%% Area: %.2f%%\n" % ( 0, ACCURACY_s, FSCORE_s, RECALL_s, PRECISION_s, ALERT_AREA_s))
f.close()
def Main():
# histories = Customdash(ModelName = 'SLVC06_Metrics_compute',
# email = '*****@*****.**',
# password = '******')
#Thresholds = np.linspace(1 , 0.01, 50)
if args.dataset == 'Amazon_RO':
args.dataset = 'Amazonia_Legal/'
dataset = AMAZON_RO(args)
# if args.dataset == 'Amazon_MT':
# args.dataset = 'Amazonia_Legal/'
# dataset = AMAZON_MT(args)
if args.dataset == 'Amazon_PA':
args.dataset = 'Amazonia_Legal/'
dataset = AMAZON_PA(args)
if args.dataset == 'Cerrado_MA':
args.dataset = 'Cerrado_Biome/'
dataset = CERRADO_MA(args)
args.results_dir = './results/' + args.results_dir + '/'
args.checkpoint_dir = './checkpoints/' + args.checkpoint_dir + '/'
counter = 0
files = os.listdir(args.results_dir)
for i in range(0, len(files)):
if files[i] == 'Results.txt':
print('Results file')
else:
Hit_map_path = args.results_dir + files[i] + '/hit_map.npy'
args.file = files[i]
if os.path.exists(Hit_map_path):
hit_map = np.load(Hit_map_path)
fields_file = files[i].split('_')
checkpoint_name = fields_file[0] + '_' + fields_file[
3] + '_' + fields_file[1] + '_' + fields_file[
4] + '_' + fields_file[5] + '_' + fields_file[
6] + '_' + fields_file[7] + '_' + fields_file[
8] + '_' + fields_file[9] + '_' + fields_file[
10] + '_' + fields_file[11]
args.save_checkpoint_path = args.checkpoint_dir + checkpoint_name + '/'
#need to put the path of the checkpoint to recover if needed the original train, validation, and test tiles.
dataset.Tiles_Configuration(args, i)
if args.save_result_text:
# Open a file in order to save the training history
f = open(args.results_dir + "Results.txt", "a")
if counter == 0:
ACCURACY_ = []
FSCORE_ = []
RECALL_ = []
PRECISION_ = []
ALERT_AREA_ = []
ACCURACY, FSCORE, RECALL, PRECISION, CONFUSION_MATRIX, ALERT_AREA = Metrics_For_Test_M(
hit_map, dataset.references[0], dataset.references[1],
dataset.Train_tiles, dataset.Valid_tiles,
dataset.Undesired_tiles, args)
if args.save_result_text:
ACCURACY_.append(ACCURACY[0, 0])
FSCORE_.append(FSCORE[0, 0])
RECALL_.append(RECALL[0, 0])
PRECISION_.append(PRECISION[0, 0])
ALERT_AREA_.append(ALERT_AREA[0, 0])
#histories.sendLoss(loss = FSCORE[0 , 0], epoch = i, total_epochs = len(files))
f.write(
"Run: %d Accuracy: %.2f%% F1-Score: %.2f%% Recall: %.2f%% Precision: %.2f%% Area: %.2f%% File Name: %s\n"
% (counter, ACCURACY, FSCORE, RECALL, PRECISION,
ALERT_AREA, args.file))
f.close()
print(ACCURACY_)
else:
print('Coming up!')
#histories.sendLoss(loss = 0.0, epoch = i, total_epochs = len(files))
counter += 1
if args.save_result_text:
f = open(args.results_dir + "Results.txt", "a")
ACCURACY_m = np.mean(ACCURACY_)
FSCORE_m = np.mean(FSCORE_)
RECALL_m = np.mean(RECALL_)
PRECISION_m = np.mean(PRECISION_)
ALERT_AREA_m = np.mean(ALERT_AREA_)
ACCURACY_s = np.std(ACCURACY_)
FSCORE_s = np.std(FSCORE_)
RECALL_s = np.std(RECALL_)
PRECISION_s = np.std(PRECISION_)
ALERT_AREA_s = np.std(ALERT_AREA_)
#histories.sendLoss(loss = FSCORE_m, epoch = i + 1, total_epochs = len(files) + 1)
f.write(
"Mean: %d Accuracy: %f%% F1-Score: %f%% Recall: %f%% Precision: %f%% Area: %f%%\n"
% (0, ACCURACY_m, FSCORE_m, RECALL_m, PRECISION_m, ALERT_AREA_m))
f.write(
"Std: %d Accuracy: %.2f%% F1-Score: %.2f%% Recall: %.2f%% Precision: %.2f%% Area: %.2f%%\n"
% (0, ACCURACY_s, FSCORE_s, RECALL_s, PRECISION_s, ALERT_AREA_s))
f.close()