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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "929a1d7b-51a8-44ed-ab3b-8b2a2baa31dc",
"metadata": {},
"outputs": [],
"source": [
"import arc_json_model as ajm\n",
"from visualize import ajm_image_show\n",
"\n",
"ajm.Image.show = ajm_image_show"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "bcc018d5-5c3e-4acc-a9fd-e670d8a21c27",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import numpy as np\n",
"from torchvision import datasets\n",
"import torchvision.transforms as transforms\n",
"from torchsummary import summary\n",
"from tqdm import tqdm\n",
"\n",
"\n",
"device = torch.device(\"mps\")\n",
"#device = torch.device(\"cuda\")\n",
"#device = torch.device(\"cpu\")\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "fbaa81fe-7682-4cb3-9f57-fe94d5dd4fa4",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 500x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"filename = 'testdata/af902bf9.json'\n",
"#filename = 'testdata/62c24649.json'\n",
"#filename = 'testdata/a699fb00.json'\n",
"#filename = 'testdata/f76d97a5.json'\n",
"#filename = 'testdata/f5b8619d.json'\n",
"task = ajm.Task.load(filename)\n",
"image = task.pairs[0].input\n",
"image.show()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8e2d17ed-0262-4915-a6e6-a5459b5a09ab",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"len(train_input_output) 21\n",
"len(test_input_output) 7\n"
]
}
],
"source": [
"from color_xy import color_to_xy\n",
"\n",
"# TODO: get it to work with size 30x30\n",
"IMAGE_SIZE = 28\n",
"\n",
"def obfuscate_image(image: ajm.Image, offset: float) -> np.ndarray:\n",
" # empty image with size: IMAGE_SIZE x IMAGE_SIZE\n",
" # each pixel is an array with the 2 obfuscated [x,y] values\n",
" rows2 = []\n",
" for y in range(0,IMAGE_SIZE):\n",
" columns2 = [[0.0, 0.0]] * IMAGE_SIZE\n",
" rows2.append(columns2)\n",
" # assign obfuscated values\n",
" for row_index, rows in enumerate(image.pixels):\n",
" for column_index, pixel in enumerate(rows):\n",
" xy = color_to_xy(pixel, 10, offset)\n",
" rows2[row_index][column_index] = xy\n",
" obfuscated_image = np.array(rows2)\n",
" return obfuscated_image\n",
"\n",
"def numpy_image_to_tensors(obfuscated_image: np.ndarray, device: torch.device) -> torch.Tensor:\n",
" tensors = torch.from_numpy(obfuscated_image)\n",
" #print(tensors)\n",
" # cast it to 32-bit floating point tensor\n",
" tensors = tensors.type('torch.FloatTensor')\n",
" #print(tensors)\n",
" #print(\"before\", tensors.shape)\n",
" # change layout from: rows -> columns -> obfuscated_pixel_array\n",
" # change layout to: obfuscated_pixel_array -> rows -> columns\n",
" tensors = tensors.permute(2, 0, 1)\n",
" #print(\"after\", tensors.shape)\n",
" tensors_on_device = tensors.to(device)\n",
" return tensors_on_device\n",
" \n",
"all_images = []\n",
"for pair in task.pairs:\n",
" all_images.append(pair.input)\n",
" all_images.append(pair.output)\n",
"\n",
"train_input_output = []\n",
"test_input_output = []\n",
"number_of_offsets = 7\n",
"for i in range(0, number_of_offsets):\n",
" offset = float(i) / float(number_of_offsets)\n",
" if i == 0:\n",
" offset = 0.0\n",
" if i == 1:\n",
" offset = 0.25\n",
" if i == 2:\n",
" offset = 0.5\n",
" if i == 3:\n",
" offset = 0.75\n",
" if i == 4:\n",
" offset = 0.125\n",
" if i == 5:\n",
" offset = 0.375\n",
" if i == 6:\n",
" offset = 0.625\n",
" if i == 7:\n",
" offset = 0.330823\n",
" if i == 8:\n",
" offset = 0.23\n",
" if i == 9:\n",
" offset = 0.893\n",
" if i == 10:\n",
" offset = 0.01111\n",
" if i == 11:\n",
" offset = 0.71313\n",
" if i == 12:\n",
" offset = 0.91555\n",
" if i == 13:\n",
" offset = 0.51717\n",
" \n",
" for pair in task.pairs:\n",
" input_image = obfuscate_image(pair.input, offset)\n",
" output_image = obfuscate_image(pair.output, offset)\n",
" input_tensors = numpy_image_to_tensors(input_image, device)\n",
" output_tensors = numpy_image_to_tensors(output_image, device)\n",
" input_output_offset = (input_tensors, output_tensors, offset)\n",
" if pair.pair_type == ajm.PairType.TRAIN:\n",
" train_input_output.append(input_output_offset)\n",
" else:\n",
" test_input_output.append(input_output_offset)\n",
"print(\"len(train_input_output)\", len(train_input_output))\n",
"print(\"len(test_input_output)\", len(test_input_output))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "45e2653e-3539-4b61-ade5-18a193c93616",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ConvAutoencoder(\n",
" (conv1): Conv2d(2, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (conv2): Conv2d(16, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
" (t_conv1): ConvTranspose2d(8, 16, kernel_size=(2, 2), stride=(2, 2))\n",
" (t_conv2): ConvTranspose2d(16, 2, kernel_size=(2, 2), stride=(2, 2))\n",
")\n",
"----------------------------------------------------------------\n",
" Layer (type) Output Shape Param #\n",
"================================================================\n",
" Conv2d-1 [-1, 16, 30, 30] 304\n",
" MaxPool2d-2 [-1, 16, 15, 15] 0\n",
" Conv2d-3 [-1, 8, 15, 15] 1,160\n",
" MaxPool2d-4 [-1, 8, 7, 7] 0\n",
" ConvTranspose2d-5 [-1, 16, 14, 14] 528\n",
" ConvTranspose2d-6 [-1, 2, 28, 28] 130\n",
"================================================================\n",
"Total params: 2,122\n",
"Trainable params: 2,122\n",
"Non-trainable params: 0\n",
"----------------------------------------------------------------\n",
"Input size (MB): 0.01\n",
"Forward/backward pass size (MB): 0.19\n",
"Params size (MB): 0.01\n",
"Estimated Total Size (MB): 0.20\n",
"----------------------------------------------------------------\n"
]
}
],
"source": [
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"\n",
"# define the NN architecture\n",
"class ConvAutoencoder(nn.Module):\n",
" def __init__(self):\n",
" super(ConvAutoencoder, self).__init__()\n",
" ## encoder layers ##\n",
" # conv layer (depth from 2 --> 16), 3x3 kernels\n",
" self.conv1 = nn.Conv2d(2, 16, 3, padding=1) \n",
" # conv layer (depth from 16 --> 4), 3x3 kernels\n",
" self.conv2 = nn.Conv2d(16, 8, 3, padding=1)\n",
" # pooling layer to reduce x-y dims by two; kernel and stride of 2\n",
" self.pool = nn.MaxPool2d(2, 2)\n",
" \n",
" ## decoder layers ##\n",
" ## a kernel of 2 and a stride of 2 will increase the spatial dims by 2\n",
" self.t_conv1 = nn.ConvTranspose2d(8, 16, 2, stride=2)\n",
" self.t_conv2 = nn.ConvTranspose2d(16, 2, 2, stride=2)\n",
"\n",
"\n",
" def forward(self, x):\n",
" ## encode ##\n",
" # add hidden layers with relu activation function\n",
" # and maxpooling after\n",
" x = F.relu(self.conv1(x))\n",
" x = self.pool(x)\n",
" # add second hidden layer\n",
" x = F.relu(self.conv2(x))\n",
" x = self.pool(x) # compressed representation\n",
" \n",
" ## decode ##\n",
" # add transpose conv layers, with relu activation function\n",
" x = F.relu(self.t_conv1(x))\n",
" # output layer (with sigmoid for scaling from 0 to 1)\n",
" x = F.sigmoid(self.t_conv2(x))\n",
" \n",
" return x\n",
"\n",
"# initialize the NN\n",
"model = ConvAutoencoder()\n",
"print(model)\n",
"summary(model,input_size=(2,30,30))\n",
"model = model.to(device)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "32898b91-a8f1-47f0-9a75-ea7917f05f72",
"metadata": {},
"outputs": [],
"source": [
"# specify loss function\n",
"criterion = nn.MSELoss()\n",
"#criterion = nn.L1Loss()\n",
"#criterion = nn.CrossEntropyLoss()\n",
"#criterion = nn.KLDivLoss()\n",
"#criterion = nn.BCELoss()\n",
"#criterion = nn.BCEWithLogitsLoss()\n",
"\n",
"# specify loss function\n",
"optimizer = torch.optim.Adam(model.parameters(), lr=0.001)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "24fa3733-aaed-49a8-883b-408aac441ebf",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 21/21 [00:00<00:00, 74.27it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 1 \tTraining Loss: 0.568792\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 21/21 [00:00<00:00, 203.56it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 2 \tTraining Loss: 0.524046\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 21/21 [00:00<00:00, 204.70it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 3 \tTraining Loss: 0.448499\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 21/21 [00:00<00:00, 209.98it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 4 \tTraining Loss: 0.334821\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 21/21 [00:00<00:00, 208.83it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 5 \tTraining Loss: 0.211844\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 21/21 [00:00<00:00, 206.77it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 6 \tTraining Loss: 0.142588\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 21/21 [00:00<00:00, 213.06it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 7 \tTraining Loss: 0.121466\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 21/21 [00:00<00:00, 206.44it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 8 \tTraining Loss: 0.114849\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 21/21 [00:00<00:00, 207.66it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 9 \tTraining Loss: 0.111831\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 21/21 [00:00<00:00, 210.06it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 10 \tTraining Loss: 0.109630\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"import random\n",
"#random.Random(42).shuffle(train_input_output)\n",
"\n",
"# number of epochs to train the model\n",
"n_epochs = 10\n",
"\n",
"for epoch in range(1, n_epochs+1):\n",
" # monitor training loss\n",
" train_loss = 0.0\n",
"\n",
" #random.Random(epoch).shuffle(train_input_output)\n",
" \n",
" ###################\n",
" # train the model #\n",
" ###################\n",
" for input_output_offset in tqdm(train_input_output):\n",
" input, expected_output, _ = input_output_offset\n",
" \n",
" # clear the gradients of all optimized variables\n",
" optimizer.zero_grad()\n",
" # forward pass: compute predicted outputs by passing inputs to the model\n",
"\n",
" outputs = model(input)\n",
" # calculate the loss\n",
" loss = criterion(outputs, expected_output)\n",
" # backward pass: compute gradient of the loss with respect to model parameters\n",
" loss.backward()\n",
" # perform a single optimization step (parameter update)\n",
" optimizer.step()\n",
" # update running training loss\n",
" train_loss += loss.item()*input.size(0)\n",
" \n",
" # print avg training statistics \n",
" train_loss = train_loss/len(train_input_output)\n",
" print('Epoch: {} \\tTraining Loss: {:.6f}'.format(\n",
" epoch, \n",
" train_loss\n",
" ))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2ed1d86b-8cce-4ce6-be89-7a2e7a325d23",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 500x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# obtain one batch of test images\n",
"dataiter = iter(test_input_output)\n",
"input, expected_output, offset = next(dataiter)\n",
"\n",
"# get sample outputs\n",
"output = model(input)\n",
"\n",
"output_tensor = output.cpu().detach()\n",
"#print(\"output_tensor.shape\", output_tensor.shape)\n",
"output_tensor = output_tensor.permute(1, 2, 0)\n",
"#print(\"output_tensor.shape\", output_tensor.shape)\n",
"output_numpy = output_tensor.numpy()\n",
"#print(\"output_numpy.shape\", output_numpy.shape)\n",
"\n",
"from color_xy import xy_to_color\n",
"\n",
"deobfuscated = []\n",
"COLOR_UNDEFINED = 10\n",
"for y in range(0,IMAGE_SIZE):\n",
" columns2 = [COLOR_UNDEFINED] * IMAGE_SIZE\n",
" deobfuscated.append(columns2)\n",
"\n",
"for row_index, row in enumerate(output_numpy):\n",
" #print(\"row_index\", row_index)\n",
" for column_index, xy in enumerate(row):\n",
" x, y = xy\n",
" distance = x * x + y * y\n",
" if distance > 0.35 and distance < 1.5:\n",
" color = xy_to_color((x, y), 10, offset)\n",
" deobfuscated[row_index][column_index] = color\n",
"\n",
"pixels = np.array(deobfuscated, np.int32)\n",
"result = ajm.Image(pixels, \"predicted\")\n",
"result.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "789a56c4-e809-462c-8a02-ad6fff0922c0",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|