update

Author: li012589

FIR.py

@@ -18,7 +18,7 @@
 parser = argparse.ArgumentParser(description="")
 
 parser.add_argument("-folder", default=None, help="Path to load the trained model")
-parser.add_argument("-step", type=float, default=0.001, help="step of omega")
+parser.add_argument("-step", type=float, default=0.001, help="step of omega freq")
 parser.add_argument("-best", action='store_false', help="if load the best model")
 parser.add_argument('-target', type=str, default='original', choices=['original', 'CIFAR', 'ImageNet32', 'ImageNet64', 'MNIST'], metavar='DATASET', help='Dataset choice.')
 

encode.py

@@ -15,12 +15,12 @@
 
 parser.add_argument("-folder", default=None, help="Path to load the trained model")
 parser.add_argument("-cuda", type=int, default=-1, help="Which device to use with -1 standing for CPU, number bigger than -1 is N.O. of GPU.")
-parser.add_argument("-nbins", type=int, default=4096, help="bin number of ran")
+parser.add_argument("-nbins", type=int, default=4096, help="bin number of ran alg.")
 parser.add_argument("-batch", type=int, default=-1, help="batch size")
 parser.add_argument("-precision", type=int, default=24, help="precision of CDF")
-parser.add_argument("-earlyStop", type=int, default=-1, help="fewer batch of testing")
+parser.add_argument("-earlyStop", type=int, default=-1, help="epoches to run")
 parser.add_argument("-best", action='store_false', help="if load the best model")
-parser.add_argument("-valbest", action='store_true', help="if load the best model")
+parser.add_argument("-valbest", action='store_true', help="if load the best val. model")
 parser.add_argument('-target', type=str, default='original', choices=['original', 'CIFAR', 'ImageNet32', 'ImageNet64', 'MNIST'], metavar='DATASET', help='Dataset choice.')
 
 

jointman.py

@@ -15,11 +15,11 @@
 group.add_argument('-target', type=str, default='CIFAR', choices=['CIFAR', 'ImageNet32', 'ImageNet64', 'MNIST'], metavar='DATASET', help='Dataset choice.')
 
 group = parser.add_argument_group("Architecture Parameters")
-group.add_argument("-repeat", type=int, default=1, help="num of disentangler layers of each RG scale")
-group.add_argument("-hchnl", type=int, default=12, help="intermediate channel dimension of Conv2d inside NICE inside MERA")
-group.add_argument("-nhidden", type=int, default=1, help="num of intermediate channel of Conv2d inside NICE inside MERA")
+group.add_argument("-repeat", type=int, default=1, help="num of wavelet layers of each scale")
+group.add_argument("-hchnl", type=int, default=12, help="intermediate channel dimension of Conv2d inside NICE inside NeuralWavelet")
+group.add_argument("-nhidden", type=int, default=1, help="num of intermediate channel of Conv2d inside NICE inside NeuralWavelet")
 group.add_argument("-nMixing", type=int, default=5, help="num of mixing distributions of last sub-priors")
-group.add_argument("-simplePrior", action="store_true", help="if use simple version prior, no crossover")
+group.add_argument("-simplePrior", action="store_true", help="if use simple version prior, no crossover net")
 group.add_argument("-HUE", action="store_false", help="use YCbCr color scheme")
 group.add_argument("-clamp", type=float, default=-1, help="clamp of last prior's mean")
 
@@ -28,7 +28,7 @@
 group.add_argument("-batch", type=int, default=200, help="batch size")
 group.add_argument("-savePeriod", type=int, default=10, help="save after how many steps")
 group.add_argument("-lr", type=float, default=0.001, help="learning rate")
-group.add_argument("-decay", type=float, default=0.99, help="learning rate")
+group.add_argument("-decay", type=float, default=0.99, help="learning rate decay")
 
 group = parser.add_argument_group("Etc")
 group.add_argument("-folder", default=None, help="Path to save")

main.py

@@ -14,11 +14,11 @@
 group.add_argument('-target', type=str, default='CIFAR', choices=['CIFAR', 'ImageNet32', 'ImageNet64', 'MNIST'], metavar='DATASET', help='Dataset choice.')
 
 group = parser.add_argument_group("Architecture Parameters")
-group.add_argument("-repeat", type=int, default=1, help="num of disentangler layers of each RG scale")
-group.add_argument("-hchnl", type=int, default=12, help="intermediate channel dimension of Conv2d inside NICE inside MERA")
-group.add_argument("-nhidden", type=int, default=1, help="num of intermediate channel of Conv2d inside NICE inside MERA")
+group.add_argument("-repeat", type=int, default=1, help="num of wavelet layers of each scale")
+group.add_argument("-hchnl", type=int, default=12, help="intermediate channel dimension of Conv2d inside NICE inside NeuralWavelet")
+group.add_argument("-nhidden", type=int, default=1, help="num of intermediate channel of Conv2d inside NICE inside NeuralWavelet")
 group.add_argument("-nMixing", type=int, default=5, help="num of mixing distributions of last sub-priors")
-group.add_argument("-simplePrior", action="store_true", help="if use simple version prior, no crossover")
+group.add_argument("-simplePrior", action="store_true", help="if use simple version prior, no crossover net")
 group.add_argument("-diffDetail", action="store_false", help="if use same detail prior")
 group.add_argument("-HUE", action="store_false", help="use YCbCr color scheme")
 group.add_argument("-clamp", type=float, default=-1, help="clamp of last prior's mean")
@@ -29,7 +29,7 @@
 group.add_argument("-batch", type=int, default=200, help="batch size")
 group.add_argument("-savePeriod", type=int, default=10, help="save after how many steps")
 group.add_argument("-lr", type=float, default=0.001, help="learning rate")
-group.add_argument("-decay", type=float, default=0.99, help="learning rate")
+group.add_argument("-decay", type=float, default=0.99, help="learning rate decay")
 
 group = parser.add_argument_group("Etc")
 group.add_argument("-folder", default=None, help="Path to save")

oneTotwomain.py

@@ -15,18 +15,18 @@
 group.add_argument('-target', type=str, default='CIFAR', choices=['CIFAR', 'ImageNet32', 'ImageNet64', 'MNIST'], metavar='DATASET', help='Dataset choice.')
 
 group = parser.add_argument_group("Architecture Parameters")
-group.add_argument("-repeat", type=int, default=1, help="num of disentangler layers of each RG scale")
-group.add_argument("-hchnl", type=int, default=12, help="intermediate channel dimension of Conv1d inside NICE inside MERA")
-group.add_argument("-nhidden", type=int, default=1, help="num of intermediate channel of Conv1d inside NICE inside MERA")
+group.add_argument("-repeat", type=int, default=1, help="num of wavelet layers of each scale")
+group.add_argument("-hchnl", type=int, default=12, help="intermediate channel dimension of Conv1d inside NICE inside NeuralWavelet")
+group.add_argument("-nhidden", type=int, default=1, help="num of intermediate channel of Conv1d inside NICE inside NeuralWavelet")
 group.add_argument("-nMixing", type=int, default=5, help="num of mixing distributions of last sub-priors")
+group.add_argument("-simplePrior", action="store_true", help="if use simple version prior, no crossover net")
 
 group = parser.add_argument_group('Learning  parameters')
 group.add_argument('-init', type=str, default='harr', choices=['harr', 'legall'], metavar='initWavelet', help='which wavelet to init.')
 group.add_argument("-epoch", type=int, default=400, help="num of epoches to train")
 group.add_argument("-batch", type=int, default=200, help="batch size")
 group.add_argument("-savePeriod", type=int, default=10, help="save after how many steps")
 group.add_argument("-lr", type=float, default=0.001, help="learning rate")
-group.add_argument("-simplePrior", action="store_true", help="if use simple version prior, no crossover")
 
 group = parser.add_argument_group("Etc")
 group.add_argument("-folder", default=None, help="Path to save")

progressive.py

@@ -18,13 +18,13 @@
 
 parser.add_argument("-folder", default=None, help="Path to load the trained model")
 parser.add_argument("-cuda", type=int, default=-1, help="Which device to use with -1 standing for CPU, number bigger than -1 is N.O. of GPU.")
-parser.add_argument("-baseScale", type=float, default=-2.0, help="exp scaling of distribution's logscale to achieve better quality")
-parser.add_argument("-exBaseScale", type=float, default=-2.0, help="exp scaling of distribution's logscale to achieve better quality for exoplot")
+parser.add_argument("-baseScale", type=float, default=-2.0, help="base scaling of distribution's logscale to achieve better quality for progressice loading")
+parser.add_argument("-exBaseScale", type=float, default=-2.0, help="base scaling of distribution's logscale to achieve better quality for upsampling")
 parser.add_argument("-best", action='store_false', help="if load the best model")
 parser.add_argument("-epoch", type=int, default=-1, help="epoch to load")
-parser.add_argument("-exdepth", type=int, default=2, help="num of image to expand")
-parser.add_argument("-num", type=int, default=10, help="num of image to demo")
-parser.add_argument("-fix", action='store_true', help="fix color shift in exoplot")
+parser.add_argument("-exdepth", type=int, default=2, help="num iterations to upsampling")
+parser.add_argument("-num", type=int, default=10, help="num of image used")
+parser.add_argument("-fix", action='store_true', help="color shift to original image's scheme")
 parser.add_argument('-target', type=str, default='original', choices=['original', 'CIFAR', 'ImageNet32', 'ImageNet64', 'MNIST'], metavar='DATASET', help='Dataset choice.')
 
 args = parser.parse_args()

waveletPlot.py

@@ -18,7 +18,7 @@
 
 parser.add_argument("-folder", default=None, help="Path to load the trained model")
 parser.add_argument("-cuda", type=int, default=-1, help="Which device to use with -1 standing for CPU, number bigger than -1 is N.O. of GPU.")
-parser.add_argument("-depth", type=int, default=2, help="wavelet depth")
+parser.add_argument("-depth", type=int, default=2, help="how many iterations to perform")
 parser.add_argument("-best", action='store_false', help="if load the best model")
 parser.add_argument("-epoch", type=int, default=-1, help="epoch to load")
 parser.add_argument("-img", default=None, help="the img path")