add license

app.py

@@ -1,3 +1,18 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
 import gradio as gr
 import numpy as np
 import time

data.py

@@ -1,4 +1,4 @@
-# This file is adapted from https://github.com/rnwzd/FSPBT-Image-Translation/blob/master/data.py
+# This file is copied from https://github.com/rnwzd/FSPBT-Image-Translation/blob/master/data.py
 
 # MIT License
 
@@ -22,14 +22,19 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
-
 from typing import Callable, Dict
+
 import torch
+
 from torch.utils.data import Dataset
+
 import torchvision.transforms.functional as F
-from bigdl.nano.pytorch.vision.transforms import transforms
+from torchvision import transforms
 import pytorch_lightning as pl
+
 from collections.abc import Iterable
+
+
 # image reader writer
 from pathlib import Path
 from PIL import Image
@@ -92,7 +97,6 @@ def prepare_data(data_path: Path, read_func: Callable = read_image_tensor) -> Di
     """
     Takes a data_path of a folder which contains subfolders with input, target, etc.
     lablelled by the same names.
-
     :param data_path: Path of the folder containing data
     :param read_func: function that reads data and returns a tensor
     """
@@ -103,7 +107,8 @@ def prepare_data(data_path: Path, read_func: Callable = read_image_tensor) -> Di
     # checks only files for which there is an target
     # TODO check for images
     name_ls = [file.name for file in (
-        data_path / "target").iterdir() if file.is_file()]  # 数据集大小=3
+        data_path / "target").iterdir() if file.is_file()]
+
     subdirs = [data_path / sdn for sdn in subdir_names]
     for sd in subdirs:
         if sd.is_dir():
@@ -130,7 +135,6 @@ class DataDictLoader():
                  max_length: int = 128,
                  shuffle: bool = False):
         """
-
         """
 
         self.batch_size = batch_size
@@ -139,7 +143,7 @@ class DataDictLoader():
         self.batch_size = batch_size
 
         self.data_dict = data_dict
-        self.dataset_len = data_dict['len']  # train: 93
+        self.dataset_len = data_dict['len']
         self.len = self.dataset_len if max_length is None else min(
             self.dataset_len, max_length)
         # Calculate # batches
@@ -147,7 +151,6 @@ class DataDictLoader():
         if remainder > 0:
             num_batches += 1
         self.num_batches = num_batches
-    
 
     def __iter__(self):
         if self.shuffle:
@@ -182,8 +185,8 @@ class PatchDataModule(pl.LightningDataModule):
         self.len = data_dict['len']
 
         self.batch_size = batch_size
-        self.patch_size = patch_size  # 32
-        self.patch_num = patch_num  # 64
+        self.patch_size = patch_size
+        self.patch_num = patch_num
 
     def dataloader(self, data_dict,  **kwargs):
         return DataDictLoader(data_dict, **kwargs)
@@ -191,7 +194,7 @@ class PatchDataModule(pl.LightningDataModule):
     def train_dataloader(self):
         patches = self.cut_patches()
         return self.dataloader(patches, batch_size=self.batch_size, shuffle=True,
-                               max_length=self.patch_num)  # patch num = 64
+                               max_length=self.patch_num)
 
     def val_dataloader(self):
         return self.dataloader(self.data_dict, batch_size=1)
@@ -227,4 +230,4 @@ class ImageDataset(Dataset):
         return read_image_tensor(file), file.name
 
     def __len__(self) -> int:
-        return len(self.file_paths)
+        return len(self.file_paths)

original_models.py

@@ -1,3 +1,28 @@
+# This file is copied from https://github.com/rnwzd/FSPBT-Image-Translation/blob/master/original_models.py
+
+# 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 torch
 import torch.nn as nn
 from torch.autograd import Variable

requirements.txt

@@ -2,13 +2,11 @@
 bigdl-nano[pytorch]==2.1.0b20220606
 setuptools==58.0.4
 protobuf==3.20.1
-# numpy==1.21.6
+
 # inference
 neural_compressor
 onnx==1.9.0
 onnxruntime==1.10.0
 onnxruntime-extensions
 
-opencv-python
-pathlib
-gif
+pathlib