Changes for the Masters Thesis

Author: HedgehogCode

.devcontainer/Dockerfile

@@ -19,6 +19,7 @@ RUN apt-get -y install --no-install-recommends libgl1-mesa-glx
 
 # Runtime dependencies
 COPY ./requirements/requirements_docker.txt /root/requirements_docker.txt
+RUN pip install Cython
 RUN pip install -r /root/requirements_docker.txt
 
 # Develop dependencies

.devcontainer/devcontainer.json

@@ -6,11 +6,18 @@
 	},
 	// Set *default* container specific settings.json values on container create.
 	"settings": {
-		"terminal.integrated.shell.linux": "/usr/bin/zsh",
+		"terminal.integrated.defaultProfile.linux": "zsh",
+		"terminal.integrated.profiles.linux": {
+			"zsh": {
+			"path": "/usr/bin/zsh",
+			"args": []
+			}
+		},
 		"editor.rulers": [100],
 		// ---- Python specific settings
+		"python.experiments.enabled": false,
 		"python.pythonPath": "/usr/local/bin/python",
-		"python.formatting.provider": "autopep8",
+		"python.formatting.provider": "black",
 		"python.linting.pylintEnabled": false,
 		"jupyter.alwaysTrustNotebooks": true,
 		"python.languageServer": "Pylance",
@@ -21,7 +28,8 @@
 		// ---- Python specific extensions
 		"ms-python.python",
 		"kevinrose.vsc-python-indent",
-		"ms-python.vscode-pylance"
+		"ms-python.vscode-pylance",
+		"njpwerner.autodocstring"
 	],
 	// ---- Run with GPU driver
 	"runArgs": [

README.md

@@ -3,13 +3,14 @@
 This repository implements two algorithms for image reconstruction using a Gaussian denoiser in TensorFlow 2.
 
 Algorithms:
-* **DMSP:** S. A. Bigdeli, P. Favaro, M. Jin, and M. Zwicker, [“Deep Mean-Shift Priors for Image Restoration,”](http://papers.nips.cc/paper/6678-deep-mean-shift-priors-for-image-restoration.pdf) in Advances in Neural Information Processing Systems 30, 2017, pp. 763–772.
-* **HQS:** K. Zhang, Y. Li, W. Zuo, L. Zhang, L. Van Gool, and R. Timofte, [“Plug-and-Play Image Restoration with Deep Denoiser Prior,”](http://arxiv.org/abs/2008.13751) arXiv:2008.13751 [cs, eess], Aug. 2020
 
+- **DMSP:** S. A. Bigdeli, P. Favaro, M. Jin, and M. Zwicker, [“Deep Mean-Shift Priors for Image Restoration,”](http://papers.nips.cc/paper/6678-deep-mean-shift-priors-for-image-restoration.pdf) in Advances in Neural Information Processing Systems 30, 2017, pp. 763–772.
+- **HQS:** K. Zhang, Y. Li, W. Zuo, L. Zhang, L. Van Gool, and R. Timofte, [“Plug-and-Play Image Restoration with Deep Denoiser Prior,”](http://arxiv.org/abs/2008.13751) IEEE Trans. Pattern Anal. Mach. Intell., pp. 1–1, 2021
 
 ## Getting Started
 
 Install using pip
+
 ```
 $ pip install git+https://github.com/HedgehogCode/deep-plug-and-play-prior
 ```
@@ -21,10 +22,12 @@ See the example [notebooks](notebooks/) for usage examples.
 ## Related Repositories
 
 DMSP Implementations (significantly slower):
-* Matlab: https://github.com/siavashBigdeli/DMSP (only deblur)
-* TensorFlow 1: https://github.com/siavashBigdeli/DMSP-tensorflow (only deblur)
-* TensorFlow 2: https://github.com/siavashBigdeli/DMSP-TF2 (deblur and sr (but not bicubic))
+
+- Matlab: https://github.com/siavashBigdeli/DMSP (only deblur)
+- TensorFlow 1: https://github.com/siavashBigdeli/DMSP-tensorflow (only deblur)
+- TensorFlow 2: https://github.com/siavashBigdeli/DMSP-TF2 (deblur and sr (but not bicubic))
 
 HQS:
-* Matlab: https://github.com/cszn/IRCNN
-* Pytorch: https://github.com/cszn/DPIR
+
+- Matlab: https://github.com/cszn/IRCNN
+- Pytorch: https://github.com/cszn/DPIR

dppp/__init__.py

@@ -1,5 +1,16 @@
 from .utils import *
 from .callback import *
 from .metrics import *
-from .dmsp import dmsp_deblur_nb, dmsp_super_resolve, dmsp_inpaint
-from .hqs import hqs_deblur_nb, hqs_super_resolve, hqs_inpaint
+from .dmsp import (
+    dmsp_deblur_nb,
+    dmsp_deblur_blind,
+    dmsp_super_resolve,
+    dmsp_inpaint,
+)
+from .hqs import (
+    hqs_deblur_nb,
+    hqs_super_resolve,
+    hqs_inpaint,
+    hqs_video_super_resolve,
+    hqs_lf_super_resolve,
+)

dppp/callback.py

@@ -11,10 +11,8 @@
 # TENSORBOARD
 ###############################################################################
 
-def callback_tb_image(tb_log_dir: str,
-                      summary_name: str,
-                      frequency: int
-                      ) -> Callback:
+
+def callback_tb_image(tb_log_dir: str, summary_name: str, frequency: int) -> Callback:
     tb_writer = tf.summary.create_file_writer(tb_log_dir)
 
     def call(x: TensorLike, step: TensorLike):
@@ -25,10 +23,9 @@ def call(x: TensorLike, step: TensorLike):
     return call
 
 
-def callback_tb_scalar(tb_log_dir: str,
-                       summary_name: str,
-                       summary_fn: Callable[[TensorLike], TensorLike]
-                       ) -> Callback:
+def callback_tb_scalar(
+    tb_log_dir: str, summary_name: str, summary_fn: Callable[[TensorLike], TensorLike]
+) -> Callback:
     tb_writer = tf.summary.create_file_writer(tb_log_dir)
 
     def call(x: TensorLike, step: TensorLike):
@@ -38,67 +35,87 @@ def call(x: TensorLike, step: TensorLike):
     return call
 
 
-def callback_tb_psnr(tb_log_dir: str, summary_name: str, gt_image: TensorLike) -> Callback:
-    return callback_tb_scalar(tb_log_dir=tb_log_dir, summary_name=summary_name,
-                              summary_fn=lambda x: tf.reduce_mean(dppp.psnr(x, gt_image)))
+def callback_tb_psnr(
+    tb_log_dir: str, summary_name: str, gt_image: TensorLike
+) -> Callback:
+    return callback_tb_scalar(
+        tb_log_dir=tb_log_dir,
+        summary_name=summary_name,
+        summary_fn=lambda x: tf.reduce_mean(dppp.psnr(x, gt_image)),
+    )
 
 
-def callback_tb_ssim(tb_log_dir: str, summary_name: str, gt_image: TensorLike) -> Callback:
-    return callback_tb_scalar(tb_log_dir=tb_log_dir, summary_name=summary_name,
-                              summary_fn=lambda x: tf.reduce_mean(dppp.ssim(x, gt_image)))
+def callback_tb_ssim(
+    tb_log_dir: str, summary_name: str, gt_image: TensorLike
+) -> Callback:
+    return callback_tb_scalar(
+        tb_log_dir=tb_log_dir,
+        summary_name=summary_name,
+        summary_fn=lambda x: tf.reduce_mean(dppp.ssim(x, gt_image)),
+    )
 
 
 ###############################################################################
 # TF.PRINT
 ###############################################################################
 
-def callback_print_scalar(name: str,
-                          frequency: int,
-                          summary_fn: Callable[[TensorLike], TensorLike]
-                          ) -> Callback:
+
+def callback_print_scalar(
+    name: str, frequency: int, summary_fn: Callable[[TensorLike], TensorLike]
+) -> Callback:
     def call(x: TensorLike, step: TensorLike):
         if step % frequency == 0:  # type: ignore
-            tf.print('Step:', step, ', ', name, ':', summary_fn(x))
+            tf.print("Step:", step, ", ", name, ":", summary_fn(x))
 
     return call
 
 
 def callback_print_psnr(name: str, frequency: int, gt_image: TensorLike) -> Callback:
-    return callback_print_scalar(name=name, frequency=frequency,
-                                 summary_fn=lambda x: tf.reduce_mean(dppp.psnr(x, gt_image)))
+    return callback_print_scalar(
+        name=name,
+        frequency=frequency,
+        summary_fn=lambda x: tf.reduce_mean(dppp.psnr(x, gt_image)),
+    )
 
 
 def callback_print_ssim(name: str, frequency: int, gt_image: TensorLike) -> Callback:
-    return callback_print_scalar(name=name, frequency=frequency,
-                                 summary_fn=lambda x: tf.reduce_mean(dppp.ssim(x, gt_image)))
+    return callback_print_scalar(
+        name=name,
+        frequency=frequency,
+        summary_fn=lambda x: tf.reduce_mean(dppp.ssim(x, gt_image)),
+    )
 
 
 ###############################################################################
 # CSV
 ###############################################################################
 
-def callback_csv_metric(metrics: Dict[str, Tuple[MetricFnType, int]],
-                        num_steps: int):  # TODO typing -> Tuple[Callable[[tf.Tensor, tf.Tensor], None], tf.Variable]:
+
+def callback_csv_metric(metrics: Dict[str, Tuple[MetricFnType, int]], num_steps: int):
     metric_names = list(metrics.keys())
-    csv = tf.Variable('Step,Image_Id,' + ','.join(metric_names) + '\n')
+    csv = tf.Variable("Step,Image_Id," + ",".join(metric_names) + "\n")
 
     def for_image(image_id: str, gt_image: TensorLike):
-
-        @tf.function(input_signature=(tf.TensorSpec(shape=[None, None, None, None], dtype=tf.float32),
-                                      tf.TensorSpec(shape=[], dtype=tf.int64)))
+        @tf.function(
+            input_signature=(
+                tf.TensorSpec(shape=[None, None, None, None], dtype=tf.float32),
+                tf.TensorSpec(shape=[], dtype=tf.int64),
+            )
+        )
         def call(x: TensorLike, step: TensorLike):
             def val_for_metric(m):
                 metric_fn, per_step = metrics[m]
                 if step == num_steps - 1 or (step + 1) % per_step == 0:  # type: ignore
                     val = metric_fn(x, gt_image)[0]  # type: ignore
                 else:
-                    val = tf.constant(float('nan'))
+                    val = tf.constant(float("nan"))
                 return tf.strings.as_string(val)
 
             values = [val_for_metric(m) for m in metric_names]
-            line = tf.strings.join([tf.strings.as_string(
-                step), image_id, *values], separator=',')
-            csv.assign(tf.strings.join([csv, line, '\n']))
+            line = tf.strings.join(
+                [tf.strings.as_string(step), image_id, *values], separator=","
+            )
+            csv.assign(tf.strings.join([csv, line, "\n"]))
 
         return call