Merge pull request #16 from Rexicon226/more-fixes

reorganize and delete a lot of bloat

CompositeEnvironment.py

@@ -5,6 +5,7 @@
 from Terrain import generator
 import matplotlib.pyplot as plt
 from DNoise.dnoise import EncoderDecoder as ed
+from Terrain.timing import FunctionTimer
 import torch
 from torch import nn
 
@@ -81,7 +82,14 @@ def dNoiseVis(self, inputpic):
         inputpic = torch.tensor(inputpic, dtype=torch.float32).view(1, 1, 256, 256)
         inputpic = inputpic.type(torch.cuda.FloatTensor)
 
+        de_noise_timer = FunctionTimer("De-Noising")
+        de_noise_timer.start()
+
         de_noised_image = model(inputpic)
+
+        de_noise_timer.stop()
+        de_noise_timer.print()
+
         loss = loss_fn(de_noised_image, inputpic)
 
         loss = (1 - loss.item()) * 100
@@ -154,5 +162,4 @@ def thresholdDNoise(input, x):
     masked = ev.generate()
 
     vi = Visualizer("./DNoise/models/synthnav-model-0.pth", pic)
-
     vi.dNoiseVis(masked)

DNoise/array2img.py

@@ -7,30 +7,30 @@
 import threading
 from Terrain import generator
 import math
-from Terrain import timers
+from Terrain import timing
 import os
 
 
 def array2image(x, y, octaves, weight, seed: int = 0, iD: int = 0):
-    timer = timers.FunctionTimer("Clean - Generate")
+    timer = timer.FunctionTimer("Clean - Generate")
     array = generator.generateClean(x, y, octaves, seed)
     timer.stop()
     bool_array = np.array(array, dtype=bool)
-    timer = timers.FunctionTimer("Clean - To Image")
+    timer = timer.FunctionTimer("Clean - To Image")
     img = PIL.Image.fromarray(bool_array)
     print(img)
     timer.stop()
-    timer = timers.FunctionTimer("Clean - Image Save")
+    timer = timer.FunctionTimer("Clean - Image Save")
     img.save("./val_images/clean/" + str(iD) + "_clean.jpeg", bits=1, optimize=True)
     timer.stop()
-    timer = timers.FunctionTimer("Noisy - Generate")
+    timer = timer.FunctionTimer("Noisy - Generate")
     array = generator.generateNoise(x, y, octaves, weight, seed)
     timer.stop()
     bool_array = np.array(array, dtype=bool)
-    timer = timers.FunctionTimer("Noisy - To Image")
+    timer = timer.FunctionTimer("Noisy - To Image")
     img = PIL.Image.fromarray(bool_array)
     timer.stop()
-    timer = timers.FunctionTimer("Noisy - Image Save")
+    timer = timer.FunctionTimer("Noisy - Image Save")
     img.save("./val_images/noisy/" + str(iD) + "_noisy.jpeg", bits=1, optimize=True)
     timer.stop()
 

DNoise/dnoise.py

@@ -66,7 +66,6 @@ def main():
     criterion = MAELoss()
     optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
 
-    # noinspection PyTypeChecker
     def create_synced_dictionary(folder_name):
         sync_dir_inner = {}
         for folder in os.walk(folder_name):