AntoineH/smart_augmentation
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Ajout outil visualisation resultats TF

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Harle, Antoine (Contracteur) 2019-11-12 11:43:53 -05:00
parent a389920de3
commit b463ba1b06
4 changed files with 42 additions and 6 deletions
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34
higher/compare_res.py
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@ -1,5 +1,29 @@
from utils import *
tf_names = [
## Geometric TF ##
'Identity',
'FlipUD',
'FlipLR',
'Rotate',
'TranslateX',
'TranslateY',
'ShearX',
'ShearY',
## Color TF (Expect image in the range of [0, 1]) ##
'Contrast',
'Color',
'Brightness',
'Sharpness',
'Posterize',
'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
#Non fonctionnel
#'Auto_Contrast', #Pas opti pour des batch (Super lent)
#'Equalize',
]
if __name__ == "__main__":
#### Comparison ####
@ -21,9 +45,9 @@ if __name__ == "__main__":
## Acc, Time, Epochs = f(n_tf) ##
fig_name="res/TF_nb_tests_compare"
inner_its = [0, 10]
dataug_epoch_starts= [0, -1]
TF_nb = range(1,14+1)
inner_its = [10]
dataug_epoch_starts= [0]
TF_nb = [14]#range(1,14+1)
fig, ax = plt.subplots(ncols=3, figsize=(30, 8))
for in_it in inner_its:
@ -48,9 +72,11 @@ if __name__ == "__main__":
ax[1].plot(n_tf, time, label="{} in_it/{} dataug".format(in_it,dataug))
ax[2].plot(n_tf, epochs, label="{} in_it/{} dataug".format(in_it,dataug))
#for data in all_data:
#print(np.mean([x["param"] for x in data["Log"]], axis=0))
# print(len(data["Param_names"]), np.argsort(np.argsort(np.mean([x["param"] for x in data["Log"]], axis=0))))
#print(len(data["Param_names"]), np.argsort(np.argsort(np.mean([x["param"] for x in data["Log"]], axis=0))))
ax[0].set_title('Acc')
ax[1].set_title('Time')