Add mean tanh loss as hdr2

AstroDenoisePyGUI: update dep Kivy 2.1.0
AstroDenoisePyGUI: toolbar tweaks, add label for progress, rename Expand Low to Denoise Strength and move to Denoise section.
AstroDenoisePyGUI: processing improvement, implement cancellable processing and prevent multiple process calls
AstroDenoisePyGUI: add new denoise model as part of msi dist
AstroDenoisePyGUI: minor bug fixes
AstroDenoisePyGUI: bump version to 0.4.0

astrodenoisepy/dist-models/v0.4.0-01/config.json

@@ -0,0 +1 @@
+{"n_dim": 2, "axes": "YXC", "n_channel_in": 1, "n_channel_out": 1, "train_checkpoint": "weights_best.h5", "train_checkpoint_last": "weights_last.h5", "train_checkpoint_epoch": "weights_now.h5", "probabilistic": false, "unet_residual": true, "unet_n_depth": 4, "unet_kern_size": 3, "unet_n_first": 32, "unet_last_activation": "linear", "unet_input_shape": [null, null, 1], "train_loss": "hdr2", "train_epochs": 30, "train_steps_per_epoch": 75, "train_learning_rate": 0.0004, "train_batch_size": 128, "train_tensorboard": true, "train_reduce_lr": {"factor": 0.5, "patience": 10, "min_delta": 0}}

astrodenoisepygui-package-dist.py

@@ -6,7 +6,8 @@
     "packages": ["kivy"],
     "include_files": [
         "LICENSE.txt",
-        ["astrodenoisepy\\dist-models\\main", "models\\default"],
+        ["astrodenoisepy\\dist-models\\v0.4.0-01", "models\\default"],
+        ["astrodenoisepy\\dist-models\\v0.3.0-01", "models\\v0.3.0-01"],
         ["astrodenoisepy\\data", "astrodenoisepy\\data"],
         "astrodenoisepygui.kv",
         #angle
@@ -17,7 +18,7 @@
         ".venv\\share\\glew\\bin\\glew32.dll",
         #sdl2
         ".venv\\share\\sdl2\\bin\\libFLAC-8.dll",
-        ".venv\\share\\sdl2\\bin\\libfreetype-6.dll",
+        #".venv\\share\\sdl2\\bin\\libfreetype-6.dll",
         ".venv\\share\\sdl2\\bin\\libjpeg-9.dll",
         ".venv\\share\\sdl2\\bin\\libmodplug-1.dll",
         ".venv\\share\\sdl2\\bin\\libmpg123-0.dll",

astrodenoisepygui.kv

@@ -1,4 +1,4 @@
-#:kivy 2.0.0
+#:kivy 2.1.0
 #:import utils kivy.utils
 #:import np numpy
 #:import Path pathlib.Path
@@ -146,21 +146,6 @@ Root:
                             text: '{:.3f}'.format(s2.value)
                             on_text_validate: main.stfB = '{:.3f}'.format(np.clip(float(self.text),s2.min,s2.max))
                             input_filter: 'float'
-                SliderToolLayout:
-                    ToolLabel:
-                        text: 'Expand Low'
-                    BoxLayout:                    
-                        MySlider:
-                            id: s5
-                            min: 0
-                            max: 0.5
-                            value: main.expand_low
-                            step: 0.01
-                            on_slider_release: main.expand_low = self.value
-                        MySliderTextInput:
-                            text: '{:.3f}'.format(s5.value)
-                            on_text_validate: main.expand_low = '{:.3f}'.format(np.clip(float(self.text),s5.min,s5.max))
-                            input_filter: 'float'
                 SliderToolLayout1:
                     ToolLabel:
                         text: 'Denoise Model'
@@ -175,6 +160,21 @@ Root:
                         valign: 'middle'
                         padding_x: '5dp'
                         text_size: self.size
+                SliderToolLayout1:
+                    ToolLabel:
+                        text: 'Denoise Strength'
+                    BoxLayout:                    
+                        MySlider:
+                            id: s5
+                            min: 0
+                            max: 1
+                            value: main.expand_low
+                            step: 0.01
+                            on_slider_release: main.expand_low = self.value
+                        MySliderTextInput:
+                            text: '{:.3f}'.format(s5.value)
+                            on_text_validate: main.expand_low = '{:.3f}'.format(np.clip(float(self.text),s5.min,s5.max))
+                            input_filter: 'float'
                 SliderToolLayout1:
                     ToolLabel:
                         text: 'Denoise Tiling'
@@ -236,12 +236,15 @@ Root:
                         id: my_widget_process
                         text: "Process"
                         on_release: root.start_process()
-                ProgressBar:
-                    id: pb
-                    size_hint_x: .5
-                    size_hint_y: None
-                    height: '5dp'
-                    value_normalized: main.progress
+                SliderToolLayout1:
+                    ToolLabel:
+                        text: 'Denoise Progress'
+                    ProgressBar:
+                        id: pb
+                        size_hint_x: .5
+                        size_hint_y: None
+                        height: '5dp'
+                        value_normalized: main.progress
                 HSeparator:
                     rgba: 1, 1, 1, 0
                     height: dp(10)

astrodenoisepygui.py

@@ -1,5 +1,5 @@
 import kivy
-kivy.require('2.0.0')
+kivy.require('2.1.0')
 from kivy.app import App
 from kivy.uix.floatlayout import FloatLayout
 from kivy.uix.anchorlayout import AnchorLayout
@@ -17,11 +17,13 @@
 import os
 import sys
 import signal
+import time
 import traceback
 import threading
 from pathlib import Path
 
 import numpy as np
+import tqdm
 from tifffile import imread, imsave
 from astropy.io import fits
 import tensorflow as tf
@@ -112,14 +114,16 @@ def __init__(self, **kwargs):
         self.fits_headers = None
         self.process_trigger = Clock.create_trigger(self.process_now)
         self.bind(stfC=self.process_trigger, stfB=self.process_trigger,tilling=self.process_trigger,expand_low=self.process_trigger)
+        self.stop_event = threading.Event()
+        self.processthread = None
 
     imageout = ObjectProperty(None, allownone=True)
     filetoload = StringProperty()
     stfC = NumericProperty(-2.8)
     stfB = NumericProperty(0.25)
-    tilling  = NumericProperty(2)
+    tilling  = NumericProperty(3)
     sizing  = NumericProperty(1)
-    expand_low = NumericProperty(0)
+    expand_low = NumericProperty(0.9)
     denoise_enabled = BooleanProperty(False)
     normalize_enabled = BooleanProperty(True)
     autoupdate_enabled = BooleanProperty(True)
@@ -175,8 +179,7 @@ def load(self, path):
             if not isWindows():
                 self.dismiss_popup()
 
-        self.sizing = 1
-        self.denoise_enabled = False
+        self.sizing = 1        
         self.load_now()
 
     def load_now(self):
@@ -214,7 +217,8 @@ def load_result(self, result, headers):
         self.imageout = None
         self.processed = False
         self.preprocessed = False        
-        self.process_now()
+        self.denoise_enabled = False
+        self.process_trigger()
 
     def load_file_data(self, path):
 
@@ -322,19 +326,19 @@ def on_stfC(self, instance, value):
     def on_expand_low(self, instance, value):
         self.expand_low = value
         self.processed = False
-        self.preprocessed = False        
+        #self.preprocessed = False        
 
     def on_tilling(self, instance, value):
         self.tilling = value
         self.processed = False
-        self.preprocessed = False        
+        #self.preprocessed = False        
 
     def reset_sliders(self):
         self.processed = False
-        self.preprocessed = False        
+        #self.preprocessed = False
         self.stfC = -2.8
         self.stfB = 0.25
-        self.expand_low = 0
+        #self.expand_low = 0.9
 
     def denoise_check(self, instance, value):        
         self.denoise_enabled = value
@@ -390,12 +394,23 @@ def start_process(self):
         elif self.preprocessedimagedata is not None:
             self.process_result(self.preprocessedimagedata)
             return
+
+        if self.processthread is not None and self.processthread.is_alive():
+            self.stop_event.set()            
+            with tqdm.tqdm(desc='Waiting for thread to stop...') as waiter_loger:
+                wait_count = 0
+                while self.processthread.is_alive():
+                    wait_count += 1
+                    time.sleep(0.1)
+                    waiter_loger.update(wait_count)
+
+        self.stop_event.clear() 
 
         Window.set_system_cursor('wait')
 
-        processthread = threading.Thread(target=self.process_callback)
+        self.processthread = threading.Thread(target=self.process_callback)
         threading.excepthook = self.process_exception_callback
-        processthread.start()
+        self.processthread.start()
 
     def process_exception_callback(self,args):
         e = args[1]
@@ -407,8 +422,9 @@ def process_exception_callback(self,args):
 
     def process_callback(self):
         result = self.process(self.rawimagedata,self.stfC,self.stfB)
-        #(1417, 2073, 3)
-        self.process_result(result)
+
+        if result is not None:
+            self.process_result(result)
 
     @mainthread
     def process_result(self,result):
@@ -442,15 +458,17 @@ def update_progress(self,progress):
     def process(self, data, C=-2.8,B=0.25):
 
         self.update_progress(0)
-
-        normalizer = STFNormalizer(C=C,B=B,expand_low=self.expand_low,do_after=False) if self.normalize_enabled else NoNormalizer(expand_low=self.expand_low)
+        expand_low_actual = 0.5 - (self.expand_low/2)
+        normalizer = STFNormalizer(C=C,B=B,expand_low=expand_low_actual,do_after=False) if self.normalize_enabled else NoNormalizer(expand_low=expand_low_actual)
 
         if self.denoise_enabled:
             with tf.device(f"/{self.selected_device}:0"):
                 axes = 'YX'
                 model = CARE(config=None, name=self.selected_model, basedir=self.models_basedir)
                 output_denoised = []
                 for i, c in enumerate(data):
+                    if self.stop_event.is_set():
+                        return None
                     output_denoised.append(
                         model.predict(c, axes, normalizer=normalizer,resizer=PadAndCropResizer(), n_tiles = (self.tilling,self.tilling))
                         )

astrodenoisepyguiversion.py

@@ -1 +1 @@
-version = '0.3.3'
+version = '0.4.0'

csbdeep/internals/losses.py

@@ -59,6 +59,19 @@ def mse(y_true, y_pred):
             return R(K.square(y_pred[:,:n,...] - y_true))
         return mse
 
+def loss_hdr2(mean=True):
+    R = _mean_or_not(mean)
+    if backend_channels_last():
+        def hdr2(y_true, y_pred):
+            n = K.shape(y_true)[-1]
+            return R(K.abs(K.tanh(y_pred[...,:n])-K.tanh(y_true)))
+        return hdr2
+    else:
+        def hdr2(y_true, y_pred):
+            n = K.shape(y_true)[1]
+            return R(K.abs(K.tanh(y_pred[:,:n,...])-K.tanh(y_true)))
+        return hdr2
+
 def loss_hdr(mean=True):
     R = _mean_or_not(mean)
     if backend_channels_last():

csbdeep/internals/train.py

@@ -2,7 +2,7 @@
 from six.moves import range, zip, map, reduce, filter
 
 from ..utils import _raise, move_channel_for_backend, axes_dict, axes_check_and_normalize, backend_channels_last
-from ..internals.losses import loss_laplace, loss_mse, loss_mae, loss_hdr, loss_thresh_weighted_decay
+from ..internals.losses import loss_laplace, loss_mse, loss_mae, loss_hdr, loss_hdr2, loss_thresh_weighted_decay
 
 import numpy as np
 

csbdeep/models/config.py

@@ -220,7 +220,7 @@ def _is_int(v,low=None,high=None):
         )
         ok['train_loss'] = (
             (    self.probabilistic and self.train_loss == 'laplace'   ) or
-            (not self.probabilistic and self.train_loss in ('mse','mae','hdr'))
+            (not self.probabilistic and self.train_loss in ('mse','mae','hdr','hdr2'))
         )
         ok['train_epochs']          = _is_int(self.train_epochs,1)
         ok['train_steps_per_epoch'] = _is_int(self.train_steps_per_epoch,1)