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HuguesTHOMAS 2021-07-30 17:43:02 +00:00
parent a223db4d65
commit e13cc3874d
2 changed files with 6 additions and 6 deletions
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8
plot_convergence.py
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@ -221,7 +221,7 @@ def compare_trainings(list_of_paths, list_of_labels=None):
print(path) print(path)
if ('val_IoUs.txt' in [f for f in listdir(path)]) or ('val_confs.txt' in [f for f in listdir(path)]): if ('val_IoUs.txt' in [f.decode('ascii') for f in listdir(path)]) or ('val_confs.txt' in [f.decode('ascii') for f in listdir(path)]):
config = Config() config = Config()
config.load(path) config.load(path)
else: else:
@ -698,7 +698,7 @@ def experiment_name_1():
# Using the dates of the logs, you can easily gather consecutive ones. All logs should be of the same dataset. # Using the dates of the logs, you can easily gather consecutive ones. All logs should be of the same dataset.
start = 'Log_2020-04-22_11-52-58' start = 'Log_2020-04-22_11-52-58'
end = 'Log_2020-05-22_11-52-58' end = 'Log_2023-07-29_12-40-27'
# Name of the result path # Name of the result path
res_path = 'results' res_path = 'results'
@ -707,9 +707,7 @@ def experiment_name_1():
logs = np.sort([join(res_path, l) for l in listdir(res_path) if start <= l <= end]) logs = np.sort([join(res_path, l) for l in listdir(res_path) if start <= l <= end])
# Give names to the logs (for plot legends) # Give names to the logs (for plot legends)
logs_names = ['name_log_1', logs_names = ['name_log_1']
'name_log_2',
'name_log_3']
# safe check log names # safe check log names
logs_names = np.array(logs_names[:len(logs)]) logs_names = np.array(logs_names[:len(logs)])

4
train_S3DIS.py
View file

@ -94,8 +94,10 @@ class S3DISConfig(Config):
'resnetb', 'resnetb',
'resnetb_strided', 'resnetb_strided',
'resnetb', 'resnetb',
'resnetb',
'resnetb_strided', 'resnetb_strided',
'resnetb', 'resnetb',
'resnetb',
'resnetb_strided', 'resnetb_strided',
'resnetb', 'resnetb',
'resnetb', 'resnetb',
@ -119,7 +121,7 @@ class S3DISConfig(Config):
num_kernel_points = 15 num_kernel_points = 15
# Radius of the input sphere (decrease value to reduce memory cost) # Radius of the input sphere (decrease value to reduce memory cost)
in_radius = 1.2 in_radius = 1.8
# Size of the first subsampling grid in meter (increase value to reduce memory cost) # Size of the first subsampling grid in meter (increase value to reduce memory cost)
first_subsampling_dl = 0.03 first_subsampling_dl = 0.03