diff --git a/AugmentTests.py b/AugmentTests.py
index c6f25ffbf240f31f5c7a04fc439d4528b08ee79c..b8dd7787d74b659d05913c4b7e3fe2e7dfc37e7b 100644
--- a/AugmentTests.py
+++ b/AugmentTests.py
@@ -15,19 +15,34 @@ if __name__ == "__main__":
# records.to_csv("output/perfs.csv",index = False)
- #Et enfin le rt-shift
- mean = 0
+ # #Ensuite le rt-shift
+ # mean = 0
+ # nTests = 5
+ # records = pd.read_csv("output/perfs.csv")
+ # for prob in [k/5 for k in range(1,6)]:
+ # args.augment_args[2] = prob
+ # args.augment_args[5] = mean
+ # for std in [k/2 for k in range(5,25,5)]:
+ # args.augment_args[6] = std
+ # losses = np.zeros(nTests); accs = np.zeros(nTests)
+ # for random in range(nTests):
+ # args.random_state = random
+ # losses[random], accs[random] = run_duo(args)
+ # records = pd.concat([records,pd.DataFrame([[f"rtShift prob{prob} mean{mean} std{std}",losses.mean().item(),losses.std().item(),accs.mean().item(),accs.std().item()]],columns = ["Augmentation","mu_loss","std_loss","mu_acc","std_acc"])])
+ # records.to_csv("output/perfs.csv",index = False)
+
+ #Et enfin le int_shift
+ nTests = 5
records = pd.read_csv("output/perfs.csv")
for prob in [k/5 for k in range(1,6)]:
- args.augment_args[2] = prob
- args.augment_args[5] = mean
- for std in [k/2 for k in range(5,25,5)]:
- args.augment_args[6] = std
- losses = np.zeros(5); accs = np.zeros(5)
- for random in range(5):
+ args.augment_args[1] = prob
+ for borne_max in [1.25,1.5,1.75,2.,2.25,2.5]:
+ args.augment_args[4] = borne_max
+ losses = np.zeros(nTests); accs = np.zeros(nTests)
+ for random in range(nTests):
args.random_state = random
losses[random], accs[random] = run_duo(args)
- records = pd.concat([records,pd.DataFrame([[f"rtShift prob{prob} mean{mean} std{std}",losses.mean().item(),losses.std().item(),accs.mean().item(),accs.std().item()]],columns = ["Augmentation","mu_loss","std_loss","mu_acc","std_acc"])])
+ records = pd.concat([records,pd.DataFrame([[f"intShift prob{prob} max{borne_max}",losses.mean().item(),losses.std().item(),accs.mean().item(),accs.std().item()]],columns = ["Augmentation","mu_loss","std_loss","mu_acc","std_acc"])])
records.to_csv("output/perfs.csv",index = False)
\ No newline at end of file
diff --git a/config/config.py b/config/config.py
index 63b5999c22eef360fb137a88c2320ce3282c589d..ae2861d93bbeea900fa59dfbbfb0381c937af0a2 100644
--- a/config/config.py
+++ b/config/config.py
@@ -5,7 +5,7 @@ def load_args():
parser = argparse.ArgumentParser()
parser.add_argument('--epoches', type=int, default=20)
parser.add_argument('--eval_inter', type=int, default=1)
- parser.add_argument('--augment_args', nargs = '+', type = float, default = [0,0,0.5,0.1,0.1,0.,7.5])
+ parser.add_argument('--augment_args', nargs = '+', type = float, default = [0,0,0,0.1,0.1,0.,7.5])
parser.add_argument('--noise_threshold', type=int, default=0)
parser.add_argument('--lr', type=float, default=0.001)
parser.add_argument('--optim', type = str, default = "Adam")
diff --git a/dataset/dataset.py b/dataset/dataset.py
index 7d881e266ef6c01089b33d8f2227947e1b35f438..1f1fcd664b991698179882306ab8a0d3b04c3614 100644
--- a/dataset/dataset.py
+++ b/dataset/dataset.py
@@ -220,8 +220,8 @@ class ImageFolderDuo(data.Dataset):
def load_data_duo(base_dir, batch_size, args, shuffle=True):
train_transform = transforms.Compose(
[#Random_erasing(args.augment_args[0], args.augment_args[3]),
- #Random_int_noise(args.augment_args[1], args.augment_args[4]),
- Random_shift_rt(args.augment_args[2], args.augment_args[5], args.augment_args[6]),
+ Random_int_noise(args.augment_args[1], args.augment_args[4]),
+ #Random_shift_rt(args.augment_args[2], args.augment_args[5], args.augment_args[6]),
transforms.Resize((224, 224)),
Threshold_noise(args.noise_threshold),
Log_normalisation(),
diff --git a/main.py b/main.py
index 2cd28d3050f8ab7ae463989b269f1b2bfc129394..7090c536d730452c7789e9bd9e338863838114bc 100644
--- a/main.py
+++ b/main.py
@@ -249,15 +249,17 @@ def run_duo(args):
best_loss = loss
best_acc = acc
# plot and save training figs
+ plt.figure(figsize=(14, 9))
plt.plot(train_loss)
plt.plot(val_loss)
plt.plot(train_loss)
plt.plot(train_loss)
-
- plt.savefig(f'output/Atraining_plot_model_{args.model}_noise_{args.noise_threshold}_lr_{args.lr}_optim_{args.optim + ("_momentum_"+str(args.momentum) if args.optim=="SGD" else "_betas_" + str(args.beta1)+ "_" +str(args.beta2))}.png')
+ plt.savefig(f'output/dataAugments/intShift prob {args.augment_args[1]} borne_max {args.augment_args[4]}.png')
+ #plt.savefig(f'output/Atraining_plot_model_{args.model}_noise_{args.noise_threshold}_lr_{args.lr}_optim_{args.optim + ("_momentum_"+str(args.momentum) if args.optim=="SGD" else "_betas_" + str(args.beta1)+ "_" +str(args.beta2))}.png')
#load and evaluate best model
load_model(model, args.save_path)
- make_prediction_duo(model,data_test, f'output/Amodel_{args.model}_noise_{args.noise_threshold}_lr_{args.lr}_optim_{args.optim + ("_momentum_"+str(args.momentum) if args.optim=="SGD" else "_betas_" + str(args.beta1)+ "_" +str(args.beta2))}.png')
+ make_prediction_duo(model,data_test, f'output/dataAugments/intShift prob {args.augment_args[1]} borne_max {args.augment_args[4]}.png')
+ #make_prediction_duo(model,data_test, f'output/Amodel_{args.model}_noise_{args.noise_threshold}_lr_{args.lr}_optim_{args.optim + ("_momentum_"+str(args.momentum) if args.optim=="SGD" else "_betas_" + str(args.beta1)+ "_" +str(args.beta2))}.png')
return best_loss,best_acc
def make_prediction_duo(model, data, f_name):
@@ -312,7 +314,7 @@ def make_prediction_duo(model, data, f_name):
confiance_df = pd.DataFrame(confiance, index=[i for i in classes],
columns=[i for i in classes])
plt.figure(figsize=(14, 9))
- sn.heatmap(confiance_df, annot=confiance.astype("<U4")+np.full(confiance.shape,"\u00B1")+variance.astype("<U4"),fmt='')
+ sn.heatmap(confiance_df, annot=confiance.astype("<U4")+np.full(confiance.shape,"\u00B1")+variance.astype("<U3"),fmt='')
confiName = f_name.split("/")
confiName[-1] = "confiance_matrix_"+confiName[-1]
plt.savefig('/'.join(confiName))
diff --git a/output/best_model.pt b/output/best_model.pt
index 9b11be368a33bf85823fdf9fd56de298944968a6..e5bd5ca6efe6ba8bbc977f68e482226458907780 100644
Binary files a/output/best_model.pt and b/output/best_model.pt differ