submit

BiSTNet-NTIRE2023/.gitignore

@@ -0,0 +1,14 @@
+data
+sample_videos
+runs
+checkpoints
+*.mp4
+*.pyc
+tmp
+tmp_train
+tmp_test
+tmp_seg
+log
+result
+pretrain
+mmediting

BiSTNet-NTIRE2023/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020 zhangmozhe
+
+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.

BiSTNet-NTIRE2023/environment.yaml

@@ -0,0 +1,114 @@
+name: bistnet
+channels:
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=main
+  - _openmp_mutex=5.1=1_gnu
+  - ca-certificates=2023.01.10=h06a4308_0
+  - certifi=2021.5.30=py36h06a4308_0
+  - ld_impl_linux-64=2.38=h1181459_1
+  - libffi=3.3=he6710b0_2
+  - libgcc-ng=11.2.0=h1234567_1
+  - libgomp=11.2.0=h1234567_1
+  - libstdcxx-ng=11.2.0=h1234567_1
+  - ncurses=6.4=h6a678d5_0
+  - openssl=1.1.1t=h7f8727e_0
+  - python=3.6.13=h12debd9_1
+  - readline=8.2=h5eee18b_0
+  - sqlite=3.40.1=h5082296_0
+  - tk=8.6.12=h1ccaba5_0
+  - wheel=0.37.1=pyhd3eb1b0_0
+  - xz=5.2.10=h5eee18b_1
+  - zlib=1.2.13=h5eee18b_0
+  - pip:
+      - absl-py==1.0.0
+      - addict==2.4.0
+      - av==8.0.3
+      - cachetools==4.2.4
+      - charset-normalizer==2.0.12
+      - click==8.0.4
+      - colorama==0.4.5
+      - commonmark==0.9.1
+      - cycler==0.11.0
+      - dataclasses==0.8
+      - decorator==4.4.2
+      - easydict==1.9
+      - einops==0.4.1
+      - facexlib==0.2.5
+      - filterpy==1.4.5
+      - future==0.18.2
+      - google-auth==2.6.0
+      - google-auth-oauthlib==0.4.6
+      - grpcio==1.44.0
+      - idna==3.3
+      - imageio==2.15.0
+      - imageio-ffmpeg==0.4.7
+      - importlib-metadata==4.8.3
+      - importlib-resources==5.4.0
+      - joblib==1.1.1
+      - kiwisolver==1.3.1
+      - llvmlite==0.36.0
+      - lmdb==1.3.0
+      - logger==1.4
+      - lpips==0.1.4
+      - markdown==3.3.6
+      - matplotlib==3.3.4
+      - mmcv-full==1.7.1
+      - model-index==0.1.11
+      - moviepy==1.0.3
+      - msgpack==1.0.4
+      - networkx==2.5.1
+      - numba==0.53.1
+      - numpy==1.19.5
+      - oauthlib==3.2.0
+      - opencv-contrib-python==4.5.3.56
+      - openmim==0.3.6
+      - ordered-set==4.0.2
+      - packaging==21.3
+      - pandas==1.1.5
+      - pillow==8.4.0
+      - pip==21.3.1
+      - prefetch-generator==1.0.1
+      - proglog==0.1.10
+      - protobuf==3.19.4
+      - pyarrow==6.0.1
+      - pyasn1==0.4.8
+      - pyasn1-modules==0.2.8
+      - pygments==2.14.0
+      - pyparsing==3.0.7
+      - pypng==0.0.21
+      - python-dateutil==2.8.2
+      - python-graphviz==0.19.1
+      - pytz==2022.1
+      - pywavelets==1.1.1
+      - pyyaml==6.0
+      - requests==2.27.1
+      - requests-oauthlib==1.3.1
+      - rich==12.6.0
+      - rsa==4.8
+      - scikit-image==0.17.2
+      - scikit-learn==0.24.2
+      - scipy==1.5.4
+      - setuptools==59.6.0
+      - six==1.16.0
+      - sklearn==0.0.post1
+      - tabulate==0.8.10
+      - tensorboard==2.8.0
+      - tensorboard-data-server==0.6.1
+      - tensorboard-plugin-wit==1.8.1
+      - threadpoolctl==3.1.0
+      - tifffile==2020.9.3
+      - timm==0.6.7
+      - torch==1.10.0+cu113
+      - torchaudio==0.10.0+cu113
+      - torchcontrib==0.0.2
+      - torchvision==0.11.1+cu113
+      - tqdm==4.63.0
+      - typing-extensions==4.1.1
+      - urllib3==1.26.8
+      - werkzeug==2.0.3
+      - wget==3.2
+      - yacs==0.1.8
+      - yapf==0.32.0
+      - zipp==3.6.0
+prefix: /data2/yangyixin/anaconda3/envs/bistnet

BiSTNet-NTIRE2023/lib/.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "python.formatting.provider": "yapf"
+}

BiSTNet-NTIRE2023/lib/FeatVGG.py

@@ -0,0 +1,65 @@
+import torch.nn as nn
+import utils.vgg_util as vgg_util
+from torchvision import models
+
+
+def conv_to_relu(layer_names):
+    out_layernames = []
+    for name in layer_names:
+        if name.startswith("conv"):
+            out_layernames.append("relu" + name[4:])
+        else:
+            out_layernames.append(name)
+    return out_layernames
+
+
+class FeatVGG(nn.Module):
+    def __init__(self, content_layers=["relu3_1"]):
+        super(FeatVGG, self).__init__()
+        self.content_layers = conv_to_relu(content_layers)
+        self.vgg19 = vgg_util.get_renamed_vgg()
+        self.last_c_layer = self.content_layers[-1]
+        is_last_content = False
+        replace_layers, del_layers = [], []
+        for name, mod in self.vgg19.named_children():
+            if is_last_content:
+                del_layers.append(name)
+            else:
+                if name == self.last_c_layer:
+                    is_last_content = True
+
+        for name in del_layers:
+            delattr(self.vgg19, name)
+
+        # no need for gradweight vgg19
+        for param in self.vgg19.parameters():
+            param.requires_grad = False
+
+    def forward(self, input_img):
+        # input image is BGR image
+        # each channel ranges in [0,255]
+        # should be normalized with mean = [0.406*255, 0.456*255, 0.485*255] = [103,116,123]
+        # out = {}
+        return self.vgg19(input_img)
+
+
+class VGGNet_multilayer(nn.Module):
+    def __init__(self):
+        """Select conv1_1 ~ conv5_1 activation maps."""
+        super(VGGNet_multilayer, self).__init__()
+        self.select = ["0", "5", "10", "19", "28"]
+        self.vgg = models.vgg19(pretrained=True).features
+
+    def forward(self, x):
+        """Extract multiple convolutional feature maps.
+        x: rgb image
+        ranges in [0,1]
+        should be normalzied with mean = [0.485, 0.456, 0.406]
+        and variance = [0.229, 0.224, 0.225]
+        """
+        features = []
+        for name, layer in self.vgg._modules.items():
+            x = layer(x)
+            if name in self.select:
+                features.append(x)
+        return features