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diff --git a/data/processed_data/png_image/data_training/Klebsiella oxytoca/KLEOXY8_ANA.png b/data/processed_data/png_image/data_training/Klebsiella oxytoca/KLEOXY8_ANA.png
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diff --git a/image_processing/build_dataset.py b/image_processing/build_dataset.py
index 6f7c4f71cbd15ed21771b20a99dfcf8d8bde6ad6..1123acdadc24df556cb0b7d65c3f3ae3d68bec34 100644
--- a/image_processing/build_dataset.py
+++ b/image_processing/build_dataset.py
@@ -38,7 +38,7 @@ def create_antibio_dataset(path='../data/label_raw/230804_strain_peptides_antibi
l = split_before_number(s)
species = l[0]
nb = l[1]
- return '{}-{}-{}_100vW_100SPD.mzML'.format(species,nb,analyse)
+ return '{}-{}-{}-d200.mzML'.format(species,nb,analyse)
df['path_ana'] = df['sample_name'].map(lambda x: create_fname(x,analyse='ANA'))
df['path_aer'] = df['sample_name'].map(lambda x: create_fname(x, analyse='AER'))
@@ -64,14 +64,16 @@ def create_dataset():
name = label[label['path_aer'] == path.split("/")[-1]]['sample_name'].values[0]
analyse = 'AER'
if species is not None:
- directory_path = '../data/processed_data/{}'.format(species)
- if not os.path.isdir(directory_path):
- os.makedirs(directory_path)
+ directory_path_png = '../data/processed_data/png_image/{}'.format(species)
+ directory_path_npy = '../data/processed_data/npy_image/{}'.format(species)
+ if not os.path.isdir(directory_path_png):
+ os.makedirs(directory_path_png)
+ if not os.path.isdir(directory_path_npy):
+ os.makedirs(directory_path_npy)
mat = build_image_ms1(path, 1)
- mpimg.imsave(directory_path + "/" + name + '_' + analyse + '.png', mat)
- np.save(directory_path + "/" + name + '_' + analyse + '.npy', mat)
+ mpimg.imsave(directory_path_png + "/" + name + '_' + analyse + '.png', mat)
+ np.save(directory_path_npy + "/" + name + '_' + analyse + '.npy', mat)
-#TODO : train val test split
if __name__ =='__main__' :
create_dataset()
\ No newline at end of file
diff --git a/main.py b/main.py
index cd8556e94654b87dc3fbba68d36479e7e4ff12a8..ba90ffaf5e1bf17edab389546101ad77cc31b7ad 100644
--- a/main.py
+++ b/main.py
@@ -59,7 +59,10 @@ def test(model, data_test, loss_function, epoch):
return losses,acc
def run(args):
- model = Classification_model(n_class=9)
+ data_train, data_test = load_data(base_dir=args.dataset_dir, batch_size=args.batch_size)
+ model = Classification_model(n_class=len(data_train.dataset.dataset.classes))
+ if args.pretrain_path is not None :
+ load_model(model,args.pretrain_path)
if torch.cuda.is_available():
model = model.cuda()
best_acc = 0
@@ -67,9 +70,6 @@ def run(args):
train_loss=[]
val_acc=[]
val_loss=[]
- if args.pretrain_path is not None :
- load_model(model,args.pretrain_path)
- data_train, data_test = load_data(base_dir=args.dataset_dir, batch_size=args.batch_size)
loss_function = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
@@ -121,7 +121,7 @@ def make_prediction(model, data):
columns=[i for i in classes])
plt.figure(figsize=(12, 7))
sn.heatmap(df_cm, annot=True)
- plt.savefig('output.png')
+ plt.savefig('confusion_matrix.png')
def save_model(model, path):