rain-1 · March 25, 2023 03:41
diff --git a/0-MNIST.md b/0-MNIST.md
diff --git a/1-try.py b/1-try.py
 from __future__ import print_function
 import argparse
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 from torchvision import datasets, transforms
 from torch.optim.lr_scheduler import StepLR

 from torchvision.io import read_image
 from torchvision.io import ImageReadMode
 import torchvision.transforms.functional as TF
 from PIL import Image, ImageOps

 class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout(0.25)
        self.dropout2 = nn.Dropout(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        
        x = self.conv1(x)
        
        x = F.relu(x)
        
        x = self.conv2(x)
        
        x = F.relu(x)
        
        x = F.max_pool2d(x, 2)
        
        x = self.dropout1(x)
        
        x = torch.flatten(x, 1)
        
        x = self.fc1(x)
        
        x = F.relu(x)
        
        x = self.dropout2(x)
        
        x = self.fc2(x)
        
        output = F.log_softmax(x, dim=1)
        return output


 def main():
    # Training settings
    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='disables CUDA training')
    parser.add_argument('--no-mps', action='store_true', default=False,
                        help='disables macOS GPU training')
    parser.add_argument('--load-model', action='store_true', default='mnist_cnn.pt',
                        help='For Loading the current Model')
    parser.add_argument('--input', required=True, action='store', default=False,
                        help='32x32 png')
    args = parser.parse_args()
    use_cuda = not args.no_cuda and torch.cuda.is_available()
    use_mps = not args.no_mps and torch.backends.mps.is_available()

    #torch.manual_seed(args.seed)

    if use_cuda:
        device = torch.device("cuda")
    elif use_mps:
        device = torch.device("mps")
    else:
        device = torch.device("cpu")

    if use_cuda:
        cuda_kwargs = {'num_workers': 1,
                       'pin_memory': True,
                       'shuffle': True}
        #train_kwargs.update(cuda_kwargs)
        #test_kwargs.update(cuda_kwargs)

    transform=transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))
        ])

    model = Net().to(device)
    if args.load_model:
        res = torch.load("mnist_cnn.pt")
        model.load_state_dict(res)
    else:
        return
    
    #img = read_image(args.input, ImageReadMode.GRAY)
    #img = TF.convert_image_dtype(img, dtype=torch.float)
    img = Image.open(args.input)
    img = ImageOps.fit(img, (28, 28))
    img = ImageOps.grayscale(img)
    img = transform(img)
    img.to(device)

    torch.set_printoptions(precision=2, sci_mode=False)
    print(torch.exp(model.forward(img.unsqueeze(1))) * 100)


 if __name__ == '__main__':
    main()


diff --git a/2-viz.py b/2-viz.py
 from PIL import Image, ImageDraw

 def create_image_with_dots(input_image_path, brightness_array, output_image_path):
    # Load the input image
    img = Image.open(input_image_path)
    width, height = img.size

    # Create a new image with extra space for the dots
    new_height = height + 16
    new_img = Image.new('RGB', (width, new_height), color=(0, 0, 0))
    new_img.paste(img, (0, 0))

    # Draw the dots with brightness controlled by the brightness_array
    draw = ImageDraw.Draw(new_img)
    for i, brightness in enumerate(brightness_array):
        x = int(width / 6 * (i % 5 + 1))
        y = height + i // 5 * 10
        radius = 2
        color = int(255 * brightness/100.0)
        draw.ellipse((x - radius, y - radius, x + radius, y + radius), fill=(255, 255-color, 255))

    # Save the new image
    new_img.save(output_image_path)

 # Example usage
 input_image_path = 'my-ex-3/noise3.png'
 brightness_array = [10.68, 10.36,  8.21,  7.92,  9.92, 11.62, 10.18,  9.50, 10.65, 10.97]
 output_image_path = input_image_path + ".out.png"

 create_image_with_dots(input_image_path, brightness_array, output_image_path)

diff --git a/z-disclosure.md b/z-disclosure.md
	from __future__ import print_function
	import argparse
	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	import torch.optim as optim
	from torchvision import datasets, transforms
	from torch.optim.lr_scheduler import StepLR

	from torchvision.io import read_image
	from torchvision.io import ImageReadMode
	import torchvision.transforms.functional as TF
	from PIL import Image, ImageOps

	class Net(nn.Module):
	def __init__(self):
	super(Net, self).__init__()
	self.conv1 = nn.Conv2d(1, 32, 3, 1)
	self.conv2 = nn.Conv2d(32, 64, 3, 1)
	self.dropout1 = nn.Dropout(0.25)
	self.dropout2 = nn.Dropout(0.5)
	self.fc1 = nn.Linear(9216, 128)
	self.fc2 = nn.Linear(128, 10)

	def forward(self, x):

	x = self.conv1(x)

	x = F.relu(x)

	x = self.conv2(x)

	x = F.relu(x)

	x = F.max_pool2d(x, 2)

	x = self.dropout1(x)

	x = torch.flatten(x, 1)

	x = self.fc1(x)

	x = F.relu(x)

	x = self.dropout2(x)

	x = self.fc2(x)

	output = F.log_softmax(x, dim=1)
	return output


	def main():
	# Training settings
	parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
	parser.add_argument('--no-cuda', action='store_true', default=False,
	help='disables CUDA training')
	parser.add_argument('--no-mps', action='store_true', default=False,
	help='disables macOS GPU training')
	parser.add_argument('--load-model', action='store_true', default='mnist_cnn.pt',
	help='For Loading the current Model')
	parser.add_argument('--input', required=True, action='store', default=False,
	help='32x32 png')
	args = parser.parse_args()
	use_cuda = not args.no_cuda and torch.cuda.is_available()
	use_mps = not args.no_mps and torch.backends.mps.is_available()

	#torch.manual_seed(args.seed)

	if use_cuda:
	device = torch.device("cuda")
	elif use_mps:
	device = torch.device("mps")
	else:
	device = torch.device("cpu")

	if use_cuda:
	cuda_kwargs = {'num_workers': 1,
	'pin_memory': True,
	'shuffle': True}
	#train_kwargs.update(cuda_kwargs)
	#test_kwargs.update(cuda_kwargs)

	transform=transforms.Compose([
	transforms.ToTensor(),
	transforms.Normalize((0.1307,), (0.3081,))
	])

	model = Net().to(device)
	if args.load_model:
	res = torch.load("mnist_cnn.pt")
	model.load_state_dict(res)
	else:
	return

	#img = read_image(args.input, ImageReadMode.GRAY)
	#img = TF.convert_image_dtype(img, dtype=torch.float)
	img = Image.open(args.input)
	img = ImageOps.fit(img, (28, 28))
	img = ImageOps.grayscale(img)
	img = transform(img)
	img.to(device)

	torch.set_printoptions(precision=2, sci_mode=False)
	print(torch.exp(model.forward(img.unsqueeze(1))) * 100)


	if __name__ == '__main__':
	main()
	from PIL import Image, ImageDraw

	def create_image_with_dots(input_image_path, brightness_array, output_image_path):
	# Load the input image
	img = Image.open(input_image_path)
	width, height = img.size

	# Create a new image with extra space for the dots
	new_height = height + 16
	new_img = Image.new('RGB', (width, new_height), color=(0, 0, 0))
	new_img.paste(img, (0, 0))

	# Draw the dots with brightness controlled by the brightness_array
	draw = ImageDraw.Draw(new_img)
	for i, brightness in enumerate(brightness_array):
	x = int(width / 6 * (i % 5 + 1))
	y = height + i // 5 * 10
	radius = 2
	color = int(255 * brightness/100.0)
	draw.ellipse((x - radius, y - radius, x + radius, y + radius), fill=(255, 255-color, 255))

	# Save the new image
	new_img.save(output_image_path)

	# Example usage
	input_image_path = 'my-ex-3/noise3.png'
	brightness_array = [10.68, 10.36, 8.21, 7.92, 9.92, 11.62, 10.18, 9.50, 10.65, 10.97]
	output_image_path = input_image_path + ".out.png"

	create_image_with_dots(input_image_path, brightness_array, output_image_path)