modify license

app.py

@@ -13,6 +13,28 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+# MIT License
+
+# Copyright (c) 2022 Lorenzo Breschi
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
 import gradio as gr
 import numpy as np
 import time
@@ -25,7 +47,7 @@ from pathlib import Path
 from torch.utils.data import Dataset
 import datetime
 
-
+# below part is adapted from https://github.com/rnwzd/FSPBT-Image-Translation/blob/master/eval.py
 device = 'cpu' 
 dtype = torch.float32
 generator = torch.load("models/generator.pt")
@@ -45,11 +67,13 @@ class ImageDataset(Dataset):
         return len(self.imgs)
 
 
-# quantize model
+# below part is adapted from https://github.com/rnwzd/FSPBT-Image-Translation/blob/master/train.py
 data_path = Path('data/webcam')
 train_image_dd = prepare_data(data_path)
 dm = PatchDataModule(train_image_dd, patch_size=2**6,
                      batch_size=2**3, patch_num=2**6)
+
+# quantize model
 train_loader = dm.train_dataloader()
 train_loader_iter = iter(train_loader)
 quantized_model = Trainer.quantize(generator, accelerator=None,