Add files using upload-large-folder tool

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
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.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+test.ipynb
+output.png
+style.png
+content.png
+output_roll.png
+
+
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# PyPI configuration file
+.pypirc

.gradio/certificate.pem

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.idea/.gitignore

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+# 默认忽略的文件
+/shelf/
+/workspace.xml
+# 基于编辑器的 HTTP 客户端请求
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/AttentionDistillation-main.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="attention_distillation" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/deployment.xml

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+<project version="4">
+  <component name="PublishConfigData" remoteFilesAllowedToDisappearOnAutoupload="false">
+    <serverData>
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+        <serverdata>
+          <mappings>
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+          </mappings>
+        </serverdata>
+      </paths>
+      <paths name="root@connect.yza1.seetacloud.com:44585 password (2)">
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+      <paths name="root@connect.yza1.seetacloud.com:44585 password (3)">
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+          </mappings>
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.idea/inspectionProfiles/Project_Default.xml

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+  <profile version="1.0">
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+            <item index="10" class="java.lang.String" itemvalue="PyYAML" />
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+            <item index="13" class="java.lang.String" itemvalue="numpy" />
+            <item index="14" class="java.lang.String" itemvalue="opencv-python-headless" />
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+            <item index="22" class="java.lang.String" itemvalue="safetensors" />
+            <item index="23" class="java.lang.String" itemvalue="accelerate" />
+            <item index="24" class="java.lang.String" itemvalue="bson" />
+            <item index="25" class="java.lang.String" itemvalue="notebook" />
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.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
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+  <component name="Black">
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+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="attention_distillation" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<project version="4">
+  <component name="ProjectModuleManager">
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+      <module fileurl="file://$PROJECT_DIR$/.idea/AttentionDistillation-main.iml" filepath="$PROJECT_DIR$/.idea/AttentionDistillation-main.iml" />
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.idea/workspace.xml

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+  <component name="ChangeListManager">
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+  <component name="FileTemplateManagerImpl">
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+    "Python.train_vae.executor": "Run",
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+    "RunOnceActivity.ShowReadmeOnStart": "true",
+    "last_opened_file_path": "D:/learn_torch/Exploration_Platform",
+    "node.js.detected.package.eslint": "true",
+    "node.js.detected.package.tslint": "true",
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+}]]></component>
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+      <recent name="D:\learn_torch\AttentionDistillation-main\cyclegan_model\data" />
+      <recent name="D:\learn_torch\AttentionDistillation-main\checkpoints\ostracoda_cyclegan" />
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LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 gaoxu
+
+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.

README.md

@@ -0,0 +1,61 @@
+---
+title: Exploration_Platform
+app_file: app.py
+sdk: gradio
+sdk_version: 5.32.1
+---
+# Attention Distillation: A Unified Approach to Visual Characteristics Transfer
+
+
+### [Project Page](https://xugao97.github.io/AttentionDistillation/)   [Paper](https://arxiv.org/abs/2502.20235)
+![alt text](assets/1.jpg)
+
+## 🔥🔥  News
+* **2025/03/08**: We provide a  new notebook with `Style-specific T2I Generation with Flux.1-dev`. See [Issue 1](https://github.com/xugao97/AttentionDistillation/issues/1) for more details.
+
+* **2025/03/05**: We add `tiling` to enable seamless textures generation. See [Issue 3](https://github.com/xugao97/AttentionDistillation/issues/3) for more details.
+
+* **2025/03/01**: We provide a simple HuggingFace🤗 demo. Check it out [here](https://huggingface.co/spaces/ccchenzc/AttentionDistillation) !
+
+
+## Setup
+
+This code was tested with Python 3.10, Pytorch 2.5 and Diffusers 0.32.
+
+## Examples
+### Texture Synthesis
+- See [**Texture Synthesis**] part of [ad] notebook for generating texture images using SD1.5.
+
+![alt text](assets/2.jpg)
+
+### Style/Appearance Transfer
+- See [**Style/Appearance Transfer**] part of [ad] notebook for style/appearance transfer using SD1.5.
+
+![alt text](assets/3.jpg)
+
+### Style-specific T2I Generation
+- See [**Style-specific T2I Generation**] part of [ad] notebook for style-specific T2I generation using SD1.5 or SDXL.
+
+![alt text](assets/4.jpg)
+
+[ad]: ad.ipynb
+
+
+### VAE Finetuning
+
+```bash
+python train_vae.py \
+    --image_path=/path/to/image \
+    --vae_model_path=/path/to/vae
+```
+
+
+### Web UI
+Run the following command to start the Web UI:
+```bash
+python app.py
+```
+The Web UI will be available at [http://localhost:7860](http://localhost:7860).
+
+### ComfyUI
+We also provide an implementation of Attention Distillation for ComfyUI. For more details, see [here](https://github.com/zichongc/ComfyUI-Attention-Distillation).

app.py

@@ -0,0 +1,48 @@
+import gradio as gr
+from webui import (
+    create_interface_sddfrcnn,
+    create_interface_cyclegan,
+    create_interactive_generative_inpainting,
+    create_interface_style_transfer,
+    create_interface_yolov8
+)
+from webui.runner import AttentionRunner,InpaintingRunner,CycleGANRunner,SDDFRCNNRunner,YOLORunner
+import os
+os.environ["no_proxy"] = "localhost,127.0.0.1,::1"
+
+
+def main():
+    attention_runner = AttentionRunner()
+    inpainting_runner = InpaintingRunner()
+    cyclegan_runner = CycleGANRunner()
+    sddfrcnn_runner = SDDFRCNNRunner()
+    yolo_runner = YOLORunner()
+
+    with gr.Blocks(analytics_enabled=False,
+                   title='Mars Life Exploration Platform',
+                   ) as demo:
+        md_txt = "# 火星生命探索平台" \
+                "\n一个探索火星生命的综合平台，在这里你可以在样本中发现可能的生物体，修复它们，并尝试还原它们生前的样貌."
+        gr.Markdown(md_txt)
+        with gr.Tabs(selected='tab_sdd&frcnn'):
+            with gr.TabItem("SDD & FRCNN",id='tab_sdd&frcnn'):
+                create_interface_sddfrcnn(sddfrcnn_runner)
+
+            with gr.TabItem("YOLOv8",id='tab_yolov8'):
+                create_interface_yolov8(yolo_runner)
+
+            with gr.TabItem("Generative Inpainting", id='tab_generative_inpainting'):
+                create_interactive_generative_inpainting(inpainting_runner)
+
+            with gr.TabItem("Style Transfer", id='tab_style_transfer'):
+                create_interface_style_transfer(runner= attention_runner)
+
+            with gr.TabItem("CycleGAN", id='tab_cyclegan'):
+                create_interface_cyclegan(runner= cyclegan_runner)
+
+        # demo.queue().launch()
+        demo.launch(share=True, debug=False)
+
+
+if __name__ == '__main__':
+   main()

checkpoints/imagenet/hole_benchmark/20250208113201369767.log

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+2025-02-08 11:32:01,370 INFO Arguments: Namespace(config='configs/config.yaml', seed=None)
+2025-02-08 11:32:01,370 INFO Random seed: 3449
+2025-02-08 11:32:01,371 INFO Configuration: {'dataset_name': 'imagenet', 'data_with_subfolder': 'ture', 'train_data_path': '/media/ouc/4T_A/datasets/ImageNet/ILSVRC2012_img_train/', 'val_data_path': None, 'resume': None, 'batch_size': 48, 'image_shape': [256, 256, 3], 'mask_shape': [128, 128], 'mask_batch_same': True, 'max_delta_shape': [32, 32], 'margin': [0, 0], 'discounted_mask': True, 'spatial_discounting_gamma': 0.9, 'random_crop': True, 'mask_type': 'hole', 'mosaic_unit_size': 12, 'expname': 'benchmark', 'cuda': True, 'gpu_ids': [0], 'num_workers': 4, 'lr': 0.0001, 'beta1': 0.5, 'beta2': 0.9, 'n_critic': 5, 'niter': 500000, 'print_iter': 100, 'viz_iter': 1000, 'viz_max_out': 16, 'snapshot_save_iter': 5000, 'coarse_l1_alpha': 1.2, 'l1_loss_alpha': 1.2, 'ae_loss_alpha': 1.2, 'global_wgan_loss_alpha': 1.0, 'gan_loss_alpha': 0.001, 'wgan_gp_lambda': 10, 'netG': {'input_dim': 3, 'ngf': 32}, 'netD': {'input_dim': 3, 'ndf': 64}}
+2025-02-08 11:32:01,371 INFO Training on dataset: imagenet
+2025-02-08 11:32:01,373 ERROR [WinError 3] ϵͳ�Ҳ���ָ����·����: '/media/ouc/4T_A/datasets/ImageNet/ILSVRC2012_img_train/'

checkpoints/imagenet/hole_benchmark/20250208141825018139.log

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+2025-02-08 14:18:25,018 INFO Arguments: Namespace(config='configs/config.yaml', seed=None)
+2025-02-08 14:18:25,019 INFO Random seed: 4990
+2025-02-08 14:18:25,020 INFO Configuration: {'dataset_name': 'imagenet', 'data_with_subfolder': 'ture', 'train_data_path': 'G:/generative-inpainting-pytorch-master/traindata/train', 'val_data_path': None, 'resume': 'D:\\generative-inpainting-pytorch-master\\checkpoints\\imagenet\\hole_benchmark', 'batch_size': 4, 'image_shape': [256, 256, 3], 'mask_shape': [128, 128], 'mask_batch_same': True, 'max_delta_shape': [32, 32], 'margin': [0, 0], 'discounted_mask': True, 'spatial_discounting_gamma': 0.9, 'random_crop': True, 'mask_type': 'hole', 'mosaic_unit_size': 12, 'expname': 'benchmark', 'cuda': True, 'gpu_ids': [0], 'num_workers': 4, 'lr': 0.0001, 'beta1': 0.5, 'beta2': 0.9, 'n_critic': 5, 'niter': 500000, 'print_iter': 100, 'viz_iter': 1000, 'viz_max_out': 16, 'snapshot_save_iter': 5000, 'coarse_l1_alpha': 1.2, 'l1_loss_alpha': 1.2, 'ae_loss_alpha': 1.2, 'global_wgan_loss_alpha': 1.0, 'gan_loss_alpha': 0.001, 'wgan_gp_lambda': 10, 'netG': {'input_dim': 3, 'ngf': 32}, 'netD': {'input_dim': 3, 'ndf': 64}}
+2025-02-08 14:18:25,020 INFO Training on dataset: imagenet
+2025-02-08 14:18:25,021 ERROR num_samples should be a positive integer value, but got num_samples=0

checkpoints/imagenet/hole_benchmark/20250208141954613001.log

@@ -0,0 +1,5 @@
+2025-02-08 14:19:54,614 INFO Arguments: Namespace(config='configs/config.yaml', seed=None)
+2025-02-08 14:19:54,614 INFO Random seed: 7436
+2025-02-08 14:19:54,615 INFO Configuration: {'dataset_name': 'imagenet', 'data_with_subfolder': 'ture', 'train_data_path': 'G:/generative-inpainting-pytorch-master/traindata/train', 'val_data_path': 'G:/generative-inpainting-pytorch-master/traindata/val', 'resume': 'D:\\generative-inpainting-pytorch-master\\checkpoints\\imagenet\\hole_benchmark', 'batch_size': 4, 'image_shape': [256, 256, 3], 'mask_shape': [128, 128], 'mask_batch_same': True, 'max_delta_shape': [32, 32], 'margin': [0, 0], 'discounted_mask': True, 'spatial_discounting_gamma': 0.9, 'random_crop': True, 'mask_type': 'hole', 'mosaic_unit_size': 12, 'expname': 'benchmark', 'cuda': True, 'gpu_ids': [0], 'num_workers': 4, 'lr': 3e-05, 'beta1': 0.5, 'beta2': 0.9, 'n_critic': 5, 'niter': 430500, 'print_iter': 100, 'viz_iter': 1000, 'viz_max_out': 16, 'snapshot_save_iter': 5000, 'coarse_l1_alpha': 1.2, 'l1_loss_alpha': 1.2, 'ae_loss_alpha': 1.2, 'global_wgan_loss_alpha': 1.0, 'gan_loss_alpha': 0.001, 'wgan_gp_lambda': 10, 'netG': {'input_dim': 3, 'ngf': 32}, 'netD': {'input_dim': 3, 'ndf': 64}}
+2025-02-08 14:19:54,616 INFO Training on dataset: imagenet
+2025-02-08 14:19:54,618 ERROR num_samples should be a positive integer value, but got num_samples=0

checkpoints/imagenet/hole_benchmark/20250208142058422720.log

@@ -0,0 +1,274 @@
+2025-02-08 14:20:58,423 INFO Arguments: Namespace(config='configs/config.yaml', seed=None)
+2025-02-08 14:20:58,423 INFO Random seed: 2437
+2025-02-08 14:20:58,424 INFO Configuration: {'dataset_name': 'imagenet', 'data_with_subfolder': False, 'train_data_path': 'G:/generative-inpainting-pytorch-master/traindata/train', 'val_data_path': 'G:/generative-inpainting-pytorch-master/traindata/val', 'resume': 'D:\\generative-inpainting-pytorch-master\\checkpoints\\imagenet\\hole_benchmark', 'batch_size': 4, 'image_shape': [256, 256, 3], 'mask_shape': [128, 128], 'mask_batch_same': True, 'max_delta_shape': [32, 32], 'margin': [0, 0], 'discounted_mask': True, 'spatial_discounting_gamma': 0.9, 'random_crop': True, 'mask_type': 'hole', 'mosaic_unit_size': 12, 'expname': 'benchmark', 'cuda': True, 'gpu_ids': [0], 'num_workers': 4, 'lr': 3e-05, 'beta1': 0.5, 'beta2': 0.9, 'n_critic': 5, 'niter': 430500, 'print_iter': 100, 'viz_iter': 1000, 'viz_max_out': 16, 'snapshot_save_iter': 5000, 'coarse_l1_alpha': 1.2, 'l1_loss_alpha': 1.2, 'ae_loss_alpha': 1.2, 'global_wgan_loss_alpha': 1.0, 'gan_loss_alpha': 0.001, 'wgan_gp_lambda': 10, 'netG': {'input_dim': 3, 'ngf': 32}, 'netD': {'input_dim': 3, 'ndf': 64}}
+2025-02-08 14:20:58,424 INFO Training on dataset: imagenet
+2025-02-08 14:20:59,771 INFO 
+Generator(
+  (coarse_generator): CoarseGenerator(
+    (conv1): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(5, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
+    )
+    (conv2_downsample): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+    )
+    (conv3): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv4_downsample): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+    )
+    (conv5): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv6): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv7_atrous): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2))
+    )
+    (conv8_atrous): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4))
+    )
+    (conv9_atrous): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(8, 8), dilation=(8, 8))
+    )
+    (conv10_atrous): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(16, 16), dilation=(16, 16))
+    )
+    (conv11): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv12): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv13): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv14): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv15): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv16): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv17): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (conv): Conv2d(16, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+  )
+  (fine_generator): FineGenerator(
+    (conv1): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(5, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
+    )
+    (conv2_downsample): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+    )
+    (conv3): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv4_downsample): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+    )
+    (conv5): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv6): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv7_atrous): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2))
+    )
+    (conv8_atrous): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4))
+    )
+    (conv9_atrous): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(8, 8), dilation=(8, 8))
+    )
+    (conv10_atrous): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(16, 16), dilation=(16, 16))
+    )
+    (pmconv1): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(5, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
+    )
+    (pmconv2_downsample): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+    )
+    (pmconv3): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (pmconv4_downsample): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+    )
+    (pmconv5): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (pmconv6): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ReLU(inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (contextul_attention): ContextualAttention()
+    (pmconv9): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (pmconv10): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (allconv11): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (allconv12): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (allconv13): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (allconv14): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (allconv15): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (allconv16): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (allconv17): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (conv): Conv2d(16, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+  )
+)
+2025-02-08 14:20:59,773 INFO 
+LocalDis(
+  (dis_conv_module): DisConvModule(
+    (conv1): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): LeakyReLU(negative_slope=0.2, inplace=True)
+      (conv): Conv2d(3, 64, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2))
+    )
+    (conv2): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): LeakyReLU(negative_slope=0.2, inplace=True)
+      (conv): Conv2d(64, 128, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2))
+    )
+    (conv3): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): LeakyReLU(negative_slope=0.2, inplace=True)
+      (conv): Conv2d(128, 256, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2))
+    )
+    (conv4): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): LeakyReLU(negative_slope=0.2, inplace=True)
+      (conv): Conv2d(256, 256, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2))
+    )
+  )
+  (linear): Linear(in_features=16384, out_features=1, bias=True)
+)
+2025-02-08 14:20:59,774 INFO 
+GlobalDis(
+  (dis_conv_module): DisConvModule(
+    (conv1): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): LeakyReLU(negative_slope=0.2, inplace=True)
+      (conv): Conv2d(3, 64, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2))
+    )
+    (conv2): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): LeakyReLU(negative_slope=0.2, inplace=True)
+      (conv): Conv2d(64, 128, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2))
+    )
+    (conv3): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): LeakyReLU(negative_slope=0.2, inplace=True)
+      (conv): Conv2d(128, 256, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2))
+    )
+    (conv4): Conv2dBlock(
+      (pad): ZeroPad2d(padding=(0, 0, 0, 0), value=0.0)
+      (activation): LeakyReLU(negative_slope=0.2, inplace=True)
+      (conv): Conv2d(256, 256, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2))
+    )
+  )
+  (linear): Linear(in_features=65536, out_features=1, bias=True)
+)
+2025-02-08 14:20:59,918 INFO Resume from D:\generative-inpainting-pytorch-master\checkpoints\imagenet\hole_benchmark at iteration 430000
+2025-02-08 14:21:13,818 ERROR one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [65536, 1]], which is output 0 of TBackward, is at version 3; expected version 2 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).

checkpoints/imagenet/hole_benchmark/20250427163138491612.log

@@ -0,0 +1,273 @@
+2025-04-27 16:31:38,491 INFO Arguments: Namespace(config='configs/config.yaml', seed=None)
+2025-04-27 16:31:38,492 INFO Random seed: 6593
+2025-04-27 16:31:38,493 INFO Configuration: {'dataset_name': 'imagenet', 'data_with_subfolder': True, 'train_data_path': 'traindata/train', 'val_data_path': 'traindata/val', 'resume': 'checkpoints\\imagenet\\hole_benchmark', 'batch_size': 4, 'image_shape': [256, 256, 3], 'mask_shape': [128, 128], 'mask_batch_same': True, 'max_delta_shape': [32, 32], 'margin': [0, 0], 'discounted_mask': True, 'spatial_discounting_gamma': 0.9, 'random_crop': True, 'mask_type': 'hole', 'mosaic_unit_size': 12, 'expname': 'benchmark', 'cuda': 'Ture', 'gpu_ids': [0], 'num_workers': 4, 'lr': 0.0001, 'beta1': 0.5, 'beta2': 0.9, 'n_critic': 5, 'niter': 480000, 'print_iter': 100, 'viz_iter': 1000, 'viz_max_out': 16, 'snapshot_save_iter': 5000, 'coarse_l1_alpha': 1.2, 'l1_loss_alpha': 1.2, 'ae_loss_alpha': 1.2, 'global_wgan_loss_alpha': 1.0, 'gan_loss_alpha': 0.001, 'wgan_gp_lambda': 10, 'netG': {'input_dim': 3, 'ngf': 32}, 'netD': {'input_dim': 3, 'ndf': 64}}
+2025-04-27 16:31:38,493 INFO Training on dataset: imagenet
+2025-04-27 16:31:38,953 INFO 
+Generator(
+  (coarse_generator): CoarseGenerator(
+    (conv1): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(5, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
+    )
+    (conv2_downsample): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+    )
+    (conv3): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv4_downsample): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+    )
+    (conv5): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv6): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv7_atrous): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2))
+    )
+    (conv8_atrous): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4))
+    )
+    (conv9_atrous): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(8, 8), dilation=(8, 8))
+    )
+    (conv10_atrous): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(16, 16), dilation=(16, 16))
+    )
+    (conv11): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv12): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv13): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv14): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv15): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv16): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv17): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (conv): Conv2d(16, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+  )
+  (fine_generator): FineGenerator(
+    (conv1): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(5, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
+    )
+    (conv2_downsample): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+    )
+    (conv3): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv4_downsample): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+    )
+    (conv5): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv6): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (conv7_atrous): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2))
+    )
+    (conv8_atrous): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4))
+    )
+    (conv9_atrous): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(8, 8), dilation=(8, 8))
+    )
+    (conv10_atrous): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(16, 16), dilation=(16, 16))
+    )
+    (pmconv1): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(5, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
+    )
+    (pmconv2_downsample): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+    )
+    (pmconv3): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (pmconv4_downsample): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+    )
+    (pmconv5): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (pmconv6): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ReLU(inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (contextul_attention): ContextualAttention()
+    (pmconv9): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (pmconv10): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (allconv11): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (allconv12): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (allconv13): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (allconv14): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (allconv15): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (allconv16): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): ELU(alpha=1.0, inplace=True)
+      (conv): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+    (allconv17): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (conv): Conv2d(16, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    )
+  )
+)
+2025-04-27 16:31:38,953 INFO 
+LocalDis(
+  (dis_conv_module): DisConvModule(
+    (conv1): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): LeakyReLU(negative_slope=0.2, inplace=True)
+      (conv): Conv2d(3, 64, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2))
+    )
+    (conv2): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): LeakyReLU(negative_slope=0.2, inplace=True)
+      (conv): Conv2d(64, 128, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2))
+    )
+    (conv3): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): LeakyReLU(negative_slope=0.2, inplace=True)
+      (conv): Conv2d(128, 256, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2))
+    )
+    (conv4): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): LeakyReLU(negative_slope=0.2, inplace=True)
+      (conv): Conv2d(256, 256, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2))
+    )
+  )
+  (linear): Linear(in_features=16384, out_features=1, bias=True)
+)
+2025-04-27 16:31:38,953 INFO 
+GlobalDis(
+  (dis_conv_module): DisConvModule(
+    (conv1): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): LeakyReLU(negative_slope=0.2, inplace=True)
+      (conv): Conv2d(3, 64, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2))
+    )
+    (conv2): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): LeakyReLU(negative_slope=0.2, inplace=True)
+      (conv): Conv2d(64, 128, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2))
+    )
+    (conv3): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): LeakyReLU(negative_slope=0.2, inplace=True)
+      (conv): Conv2d(128, 256, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2))
+    )
+    (conv4): Conv2dBlock(
+      (pad): ZeroPad2d((0, 0, 0, 0))
+      (activation): LeakyReLU(negative_slope=0.2, inplace=True)
+      (conv): Conv2d(256, 256, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2))
+    )
+  )
+  (linear): Linear(in_features=65536, out_features=1, bias=True)
+)
+2025-04-27 16:31:38,955 ERROR 'NoneType' object has no attribute 'seek'. You can only torch.load from a file that is seekable. Please pre-load the data into a buffer like io.BytesIO and try to load from it instead.

checkpoints/imagenet/hole_benchmark/config.yaml

@@ -0,0 +1,52 @@
+# data parameters
+dataset_name: imagenet
+data_with_subfolder: True
+
+train_data_path: traindata/train
+val_data_path: traindata/val
+resume: checkpoints/imagenet/hole_benchmark
+
+
+batch_size: 4
+image_shape: [256, 256, 3]
+mask_shape: [128, 128]
+mask_batch_same: True
+max_delta_shape: [32, 32]
+margin: [0, 0]
+discounted_mask: True
+spatial_discounting_gamma: 0.9
+random_crop: True
+mask_type: hole     # hole | mosaic
+mosaic_unit_size: 12
+
+# training parameters
+expname: benchmark
+cuda: Ture
+gpu_ids: [0]    # set the GPU ids to use, e.g. [0] or [1, 2]
+num_workers: 4
+lr: 0.0001
+beta1: 0.5
+beta2: 0.9
+n_critic: 5
+niter: 480000
+print_iter: 100
+viz_iter: 1000
+viz_max_out: 16
+snapshot_save_iter: 5000
+
+# loss weight
+coarse_l1_alpha: 1.2
+l1_loss_alpha: 1.2
+ae_loss_alpha: 1.2
+global_wgan_loss_alpha: 1.
+gan_loss_alpha: 0.001
+wgan_gp_lambda: 10
+
+# network parameters
+netG:
+  input_dim: 3
+  ngf: 32
+
+netD:
+  input_dim: 3
+  ndf: 64