Domain_generalization_experiments.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4e46114d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1, 3, 5, 7])\n",
      "tensor([1, 3, 5, 7])\n",
      "tensor([[1, 3, 5, 7],\n",
      "        [2, 4, 6, 8]])\n",
      "tensor([1, 2, 3, 4, 5, 6, 7, 8])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6239/2054669105.py:4: UserWarning: The use of `x.T` on tensors of dimension other than 2 to reverse their shape is deprecated and it will throw an error in a future release. Consider `x.mT` to transpose batches of matrices or `x.permute(*torch.arange(x.ndim - 1, -1, -1))` to reverse the dimensions of a tensor. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3614.)\n",
      "  print(a.T)\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "a = torch.tensor([1, 3, 5, 7])\n",
    "b = torch.tensor([2, 4, 6, 8])\n",
    "print(a.T)\n",
    "print(a)\n",
    "# Merge tensors using torch.cat\n",
    "result_tensor = torch.cat((a, b), dim=0)\n",
    "print(torch.stack((a,b)))\n",
    "stack_ed = torch.flatten(torch.stack((a, b), dim=1))\n",
    "print(stack_ed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "785f6d1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original tensor:\n",
      "tensor([[1, 2, 3],\n",
      "        [4, 5, 6],\n",
      "        [7, 8, 9]])\n",
      "\n",
      "Shifted tensor along dimension 1: \n",
      "tensor([[2, 3, 1],\n",
      "        [5, 6, 4],\n",
      "        [8, 9, 7]])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "# Example tensor\n",
    "tensor = torch.tensor([[1, 2, 3],\n",
    "                       [4, 5, 6],\n",
    "                       [7, 8, 9]])\n",
    "\n",
    "# Dimension to shift along\n",
    "dim = 1\n",
    "\n",
    "# Shift all values along dimension 'dim' by 1\n",
    "shifted_tensor = torch.cat([tensor[:, 1:], tensor[:, :1]], dim=dim)\n",
    "shifted_tensor1 = torch.cat([tensor[-1:, :], tensor[:-1, :]], dim=0) # 1 and -1 shift achieve shift to left and right\n",
    "\n",
    "\n",
    "print(\"Original tensor:\")\n",
    "print(tensor)\n",
    "\n",
    "print(\"\\nShifted tensor along dimension {}: \".format(dim))\n",
    "print(shifted_tensor)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "01d35609",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1, 3])\n",
      "tensor([1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7])\n"
     ]
    }
   ],
   "source": [
    "expansion = torch.tile(a,(3,))\n",
    "print(a[:2])\n",
    "print(expansion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e57d121",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "95613eae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision.transforms as transforms\n",
    "\n",
    "transform = transforms.Compose([\n",
    "    # Resize to have the shortest edge be 224, maintaining aspect ratio\n",
    "    transforms.Resize(size=(224, 224)),\n",
    "\n",
    "    # Randomly crop a 224x224 region (87.5% of the image)\n",
    "    transforms.RandomResizedCrop(size=224, scale=(0.875, 1.0), ratio=(1.0, 1.0)),\n",
    "\n",
    "    # Rescale pixel values to [0, 1]\n",
    "    transforms.ToTensor(),\n",
    "\n",
    "    # Normalize using the specified mean and standard deviation\n",
    "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb56db6d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b19f5c47",
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'ConvNeXTV2Config' from 'transformers' (/home/niklas/anaconda3/lib/python3.11/site-packages/transformers/__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[8], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AutoImageProcessor, ConvNextV2ForImageClassification\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ConvNeXTV2Config, ConvNextV2Model\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# Initializing a ConvNeXTV2 convnextv2-tiny-1k-224 style configuration\u001b[39;00m\n\u001b[1;32m      6\u001b[0m configuration \u001b[38;5;241m=\u001b[39m ConvNeXTV2Config()\n",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'ConvNeXTV2Config' from 'transformers' (/home/niklas/anaconda3/lib/python3.11/site-packages/transformers/__init__.py)"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0884bf40",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/niklas/anaconda3/lib/python3.11/site-packages/datasets/load.py:1454: FutureWarning: The repository for huggingface/cats-image contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/huggingface/cats-image\n",
      "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
      "Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'torch.Tensor'>\n",
      "torch.Size([1, 3, 224, 224])\n",
      "281\n",
      "tabby, tabby cat\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoImageProcessor, ConvNextV2ForImageClassification,ConvNextV2Config\n",
    "import tensorboard\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "import torchvision\n",
    "import transformers\n",
    "\n",
    "\n",
    "import torch\n",
    "from datasets import load_dataset\n",
    "temp = SummaryWriter()\n",
    "dataset = load_dataset(\"huggingface/cats-image\")\n",
    "image = dataset[\"test\"][\"image\"][0]\n",
    "#trainloader = torch.utils.data.DataLoader(images, batch_size = 2, shuffle=True)\n",
    "#image, labels =next(iter(trainloader))\n",
    "image_processor = AutoImageProcessor.from_pretrained(\"facebook/convnextv2-tiny-1k-224\")\n",
    "model = ConvNextV2ForImageClassification.from_pretrained(\"facebook/convnextv2-tiny-1k-224\")\n",
    "dummy_input = image\n",
    "#writer.add_graph(image_processor, image)\n",
    "\n",
    "#model.layernorm = nn.LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
    "\n",
    "inputs = image_processor(image, return_tensors=\"pt\")\n",
    "print(type(inputs[\"pixel_values\"]))\n",
    "print(inputs[\"pixel_values\"].shape)\n",
    "\n",
    "\n",
    "\n",
    "with torch.no_grad():\n",
    "\n",
    "    logits = model(**inputs).logits\n",
    "\n",
    "# model predicts one of the 1000 ImageNet classes\n",
    "\n",
    "predicted_label = logits.argmax(-1).item()\n",
    "    \n",
    "\n",
    "    #temp.add_graph(model, inputs[\"pixel_values\"], use_strict_trace=False)\n",
    "    #temp.add_graph(model, inputs[\"input_values\"])\n",
    "\n",
    "# model predicts one of the 1000 ImageNet classes\n",
    "predicted_label = logits.argmax(-1).item()\n",
    "print(predicted_label)\n",
    "#grid = torchvision.utils.make_grid(image)\n",
    "#writer.add_image('images', grid, 0)\n",
    "\n",
    "temp.close()\n",
    "print(model.config.id2label[predicted_label])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7f2dbcbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch \n",
    "from torch import nn\n",
    "class Wrapp(nn.Module):\n",
    "    \n",
    "    def __init__(self, in_channels=3, num_classes=1000):\n",
    "        super(Wrapp, self).__\n",
    "        init__()\n",
    "        self.tokenizer = AutoImageProcessor.from_pretrained(\"facebook/convnextv2-tiny-1k-224\")\n",
    "        self.convnext2 = ConvNextV2ForImageClassification.from_pretrained(\"facebook/convnextv2-tiny-1k-224\")\n",
    "    def forward(self, x):\n",
    "        x = self.tokenizer(x, return_tensors=\"pt\")\n",
    "        x = self.convnext2(**x)\n",
    "        x = x.logits\n",
    "        print(type(x))\n",
    "        print(dir(x))\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ae994e1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch \n",
    "from torch import nn\n",
    "class WrappMnist(nn.Module):\n",
    "    \n",
    "    def __init__(self, in_channels=1, num_classes=10):\n",
    "        super(WrappMnist, self).__init__()\n",
    "        self.tokenizer = AutoImageProcessor.from_pretrained(\"facebook/convnextv2-tiny-1k-224\", do_normalize=False)\n",
    "        self.convnext2 = ConvNextV2ForImageClassification.from_pretrained(\"facebook/convnextv2-tiny-1k-224\")\n",
    "        config = ConvNextV2Config.from_pretrained(\"facebook/convnextv2-tiny-1k-224\")\n",
    "        config.num_channels = 1 \n",
    "        convnext2 = ConvNextV2ForImageClassification(config)\n",
    "        #convnext2.classifier = nn.Linear(in_channels=3072, out_channels=10)\n",
    "        self.convnext2 = convnext2\n",
    "        \n",
    "        \n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.tokenizer(x, return_tensors=\"pt\")\n",
    "       \n",
    "        x = self.convnext2(**x)\n",
    "        x = x.logits\n",
    "     \n",
    "        return x\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e1c5705f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "convnext2.convnextv2.embeddings.patch_embeddings.weight\n",
      "convnext2.convnextv2.embeddings.patch_embeddings.bias\n",
      "convnext2.convnextv2.embeddings.layernorm.weight\n",
      "convnext2.convnextv2.embeddings.layernorm.bias\n",
      "convnext2.convnextv2.encoder.stages.0.layers.0.dwconv.weight\n",
      "convnext2.convnextv2.encoder.stages.0.layers.0.dwconv.bias\n",
      "convnext2.convnextv2.encoder.stages.0.layers.0.layernorm.weight\n",
      "convnext2.convnextv2.encoder.stages.0.layers.0.layernorm.bias\n",
      "convnext2.convnextv2.encoder.stages.0.layers.0.pwconv1.weight\n",
      "convnext2.convnextv2.encoder.stages.0.layers.0.pwconv1.bias\n",
      "convnext2.convnextv2.encoder.stages.0.layers.0.grn.weight\n",
      "convnext2.convnextv2.encoder.stages.0.layers.0.grn.bias\n",
      "convnext2.convnextv2.encoder.stages.0.layers.0.pwconv2.weight\n",
      "convnext2.convnextv2.encoder.stages.0.layers.0.pwconv2.bias\n",
      "convnext2.convnextv2.encoder.stages.0.layers.1.dwconv.weight\n",
      "convnext2.convnextv2.encoder.stages.0.layers.1.dwconv.bias\n",
      "convnext2.convnextv2.encoder.stages.0.layers.1.layernorm.weight\n",
      "convnext2.convnextv2.encoder.stages.0.layers.1.layernorm.bias\n",
      "convnext2.convnextv2.encoder.stages.0.layers.1.pwconv1.weight\n",
      "convnext2.convnextv2.encoder.stages.0.layers.1.pwconv1.bias\n",
      "convnext2.convnextv2.encoder.stages.0.layers.1.grn.weight\n",
      "convnext2.convnextv2.encoder.stages.0.layers.1.grn.bias\n",
      "convnext2.convnextv2.encoder.stages.0.layers.1.pwconv2.weight\n",
      "convnext2.convnextv2.encoder.stages.0.layers.1.pwconv2.bias\n",
      "convnext2.convnextv2.encoder.stages.0.layers.2.dwconv.weight\n",
      "convnext2.convnextv2.encoder.stages.0.layers.2.dwconv.bias\n",
      "convnext2.convnextv2.encoder.stages.0.layers.2.layernorm.weight\n",
      "convnext2.convnextv2.encoder.stages.0.layers.2.layernorm.bias\n",
      "convnext2.convnextv2.encoder.stages.0.layers.2.pwconv1.weight\n",
      "convnext2.convnextv2.encoder.stages.0.layers.2.pwconv1.bias\n",
      "convnext2.convnextv2.encoder.stages.0.layers.2.grn.weight\n",
      "convnext2.convnextv2.encoder.stages.0.layers.2.grn.bias\n",
      "convnext2.convnextv2.encoder.stages.0.layers.2.pwconv2.weight\n",
      "convnext2.convnextv2.encoder.stages.0.layers.2.pwconv2.bias\n",
      "convnext2.convnextv2.encoder.stages.1.downsampling_layer.0.weight\n",
      "convnext2.convnextv2.encoder.stages.1.downsampling_layer.0.bias\n",
      "convnext2.convnextv2.encoder.stages.1.downsampling_layer.1.weight\n",
      "convnext2.convnextv2.encoder.stages.1.downsampling_layer.1.bias\n",
      "convnext2.convnextv2.encoder.stages.1.layers.0.dwconv.weight\n",
      "convnext2.convnextv2.encoder.stages.1.layers.0.dwconv.bias\n",
      "convnext2.convnextv2.encoder.stages.1.layers.0.layernorm.weight\n",
      "convnext2.convnextv2.encoder.stages.1.layers.0.layernorm.bias\n",
      "convnext2.convnextv2.encoder.stages.1.layers.0.pwconv1.weight\n",
      "convnext2.convnextv2.encoder.stages.1.layers.0.pwconv1.bias\n",
      "convnext2.convnextv2.encoder.stages.1.layers.0.grn.weight\n",
      "convnext2.convnextv2.encoder.stages.1.layers.0.grn.bias\n",
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      "convnext2.convnextv2.encoder.stages.1.layers.1.dwconv.weight\n",
      "convnext2.convnextv2.encoder.stages.1.layers.1.dwconv.bias\n",
      "convnext2.convnextv2.encoder.stages.1.layers.1.layernorm.weight\n",
      "convnext2.convnextv2.encoder.stages.1.layers.1.layernorm.bias\n",
      "convnext2.convnextv2.encoder.stages.1.layers.1.pwconv1.weight\n",
      "convnext2.convnextv2.encoder.stages.1.layers.1.pwconv1.bias\n",
      "convnext2.convnextv2.encoder.stages.1.layers.1.grn.weight\n",
      "convnext2.convnextv2.encoder.stages.1.layers.1.grn.bias\n",
      "convnext2.convnextv2.encoder.stages.1.layers.1.pwconv2.weight\n",
      "convnext2.convnextv2.encoder.stages.1.layers.1.pwconv2.bias\n",
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      "convnext2.convnextv2.encoder.stages.1.layers.2.dwconv.bias\n",
      "convnext2.convnextv2.encoder.stages.1.layers.2.layernorm.weight\n",
      "convnext2.convnextv2.encoder.stages.1.layers.2.layernorm.bias\n",
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      "convnext2.convnextv2.encoder.stages.1.layers.2.pwconv2.weight\n",
      "convnext2.convnextv2.encoder.stages.1.layers.2.pwconv2.bias\n",
      "convnext2.convnextv2.encoder.stages.2.downsampling_layer.0.weight\n",
      "convnext2.convnextv2.encoder.stages.2.downsampling_layer.0.bias\n",
      "convnext2.convnextv2.encoder.stages.2.downsampling_layer.1.weight\n",
      "convnext2.convnextv2.encoder.stages.2.downsampling_layer.1.bias\n",
      "convnext2.convnextv2.encoder.stages.2.layers.0.dwconv.weight\n",
      "convnext2.convnextv2.encoder.stages.2.layers.0.dwconv.bias\n",
      "convnext2.convnextv2.encoder.stages.2.layers.0.layernorm.weight\n",
      "convnext2.convnextv2.encoder.stages.2.layers.0.layernorm.bias\n",
      "convnext2.convnextv2.encoder.stages.2.layers.0.pwconv1.weight\n",
      "convnext2.convnextv2.encoder.stages.2.layers.0.pwconv1.bias\n",
      "convnext2.convnextv2.encoder.stages.2.layers.0.grn.weight\n",
      "convnext2.convnextv2.encoder.stages.2.layers.0.grn.bias\n",
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      "convnext2.convnextv2.encoder.stages.2.layers.1.dwconv.weight\n",
      "convnext2.convnextv2.encoder.stages.2.layers.1.dwconv.bias\n",
      "convnext2.convnextv2.encoder.stages.2.layers.1.layernorm.weight\n",
      "convnext2.convnextv2.encoder.stages.2.layers.1.layernorm.bias\n",
      "convnext2.convnextv2.encoder.stages.2.layers.1.pwconv1.weight\n",
      "convnext2.convnextv2.encoder.stages.2.layers.1.pwconv1.bias\n",
      "convnext2.convnextv2.encoder.stages.2.layers.1.grn.weight\n",
      "convnext2.convnextv2.encoder.stages.2.layers.1.grn.bias\n",
      "convnext2.convnextv2.encoder.stages.2.layers.1.pwconv2.weight\n",
      "convnext2.convnextv2.encoder.stages.2.layers.1.pwconv2.bias\n",
      "convnext2.convnextv2.encoder.stages.2.layers.2.dwconv.weight\n",
      "convnext2.convnextv2.encoder.stages.2.layers.2.dwconv.bias\n",
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      "convnext2.convnextv2.encoder.stages.2.layers.3.dwconv.weight\n",
      "convnext2.convnextv2.encoder.stages.2.layers.3.dwconv.bias\n",
      "convnext2.convnextv2.encoder.stages.2.layers.3.layernorm.weight\n",
      "convnext2.convnextv2.encoder.stages.2.layers.3.layernorm.bias\n",
      "convnext2.convnextv2.encoder.stages.2.layers.3.pwconv1.weight\n",
      "convnext2.convnextv2.encoder.stages.2.layers.3.pwconv1.bias\n",
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      "convnext2.convnextv2.encoder.stages.2.layers.4.dwconv.weight\n",
      "convnext2.convnextv2.encoder.stages.2.layers.4.dwconv.bias\n",
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      "convnext2.convnextv2.encoder.stages.2.layers.4.pwconv1.weight\n",
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      "convnext2.convnextv2.encoder.stages.2.layers.4.grn.bias\n",
      "convnext2.convnextv2.encoder.stages.2.layers.4.pwconv2.weight\n",
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      "convnext2.convnextv2.encoder.stages.2.layers.8.pwconv1.weight\n",
      "convnext2.convnextv2.encoder.stages.2.layers.8.pwconv1.bias\n",
      "convnext2.convnextv2.encoder.stages.2.layers.8.grn.weight\n",
      "convnext2.convnextv2.encoder.stages.2.layers.8.grn.bias\n",
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      "convnext2.convnextv2.encoder.stages.2.layers.8.pwconv2.bias\n",
      "convnext2.convnextv2.encoder.stages.3.downsampling_layer.0.weight\n",
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      "convnext2.convnextv2.encoder.stages.3.downsampling_layer.1.weight\n",
      "convnext2.convnextv2.encoder.stages.3.downsampling_layer.1.bias\n",
      "convnext2.convnextv2.encoder.stages.3.layers.0.dwconv.weight\n",
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      "convnext2.convnextv2.encoder.stages.3.layers.0.layernorm.bias\n",
      "convnext2.convnextv2.encoder.stages.3.layers.0.pwconv1.weight\n",
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      "convnext2.convnextv2.encoder.stages.3.layers.0.grn.weight\n",
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      "convnext2.convnextv2.encoder.stages.3.layers.0.pwconv2.weight\n",
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      "convnext2.convnextv2.encoder.stages.3.layers.1.dwconv.weight\n",
      "convnext2.convnextv2.encoder.stages.3.layers.1.dwconv.bias\n",
      "convnext2.convnextv2.encoder.stages.3.layers.1.layernorm.weight\n",
      "convnext2.convnextv2.encoder.stages.3.layers.1.layernorm.bias\n",
      "convnext2.convnextv2.encoder.stages.3.layers.1.pwconv1.weight\n",
      "convnext2.convnextv2.encoder.stages.3.layers.1.pwconv1.bias\n",
      "convnext2.convnextv2.encoder.stages.3.layers.1.grn.weight\n",
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      "convnext2.convnextv2.encoder.stages.3.layers.1.pwconv2.weight\n",
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      "convnext2.convnextv2.encoder.stages.3.layers.2.dwconv.weight\n",
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      "convnext2.convnextv2.encoder.stages.3.layers.2.layernorm.weight\n",
      "convnext2.convnextv2.encoder.stages.3.layers.2.layernorm.bias\n",
      "convnext2.convnextv2.encoder.stages.3.layers.2.pwconv1.weight\n",
      "convnext2.convnextv2.encoder.stages.3.layers.2.pwconv1.bias\n",
      "convnext2.convnextv2.encoder.stages.3.layers.2.grn.weight\n",
      "convnext2.convnextv2.encoder.stages.3.layers.2.grn.bias\n",
      "convnext2.convnextv2.encoder.stages.3.layers.2.pwconv2.weight\n",
      "convnext2.convnextv2.encoder.stages.3.layers.2.pwconv2.bias\n",
      "convnext2.convnextv2.layernorm.weight\n",
      "convnext2.convnextv2.layernorm.bias\n",
      "convnext2.classifier.weight\n",
      "convnext2.classifier.bias\n",
      "cpu\n",
      "torch.Size([1, 3, 480, 640])\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Make sure that the channel dimension of the pixel values match with the one set in the configuration.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[35], line 88\u001b[0m\n\u001b[1;32m     86\u001b[0m targets \u001b[38;5;241m=\u001b[39m targets\u001b[38;5;241m.\u001b[39mlong()\n\u001b[1;32m     87\u001b[0m targets \u001b[38;5;241m=\u001b[39m targets\u001b[38;5;241m.\u001b[39mto(device\u001b[38;5;241m=\u001b[39mdevice)\n\u001b[0;32m---> 88\u001b[0m scores \u001b[38;5;241m=\u001b[39m model(data)\n\u001b[1;32m     89\u001b[0m scores \u001b[38;5;241m=\u001b[39m scores\u001b[38;5;241m.\u001b[39mto(device\u001b[38;5;241m=\u001b[39mdevice)\n\u001b[1;32m     90\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(scores, targets)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "Cell \u001b[0;32mIn[24], line 20\u001b[0m, in \u001b[0;36mWrappMnist.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[1;32m     18\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtokenizer(x, return_tensors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 20\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvnext2(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mx)\n\u001b[1;32m     21\u001b[0m     x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mlogits\n\u001b[1;32m     23\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m x\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/transformers/models/convnextv2/modeling_convnextv2.py:453\u001b[0m, in \u001b[0;36mConvNextV2ForImageClassification.forward\u001b[0;34m(self, pixel_values, labels, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    445\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    446\u001b[0m \u001b[38;5;124;03mlabels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\u001b[39;00m\n\u001b[1;32m    447\u001b[0m \u001b[38;5;124;03m    Labels for computing the image classification/regression loss. Indices should be in `[0, ...,\u001b[39;00m\n\u001b[1;32m    448\u001b[0m \u001b[38;5;124;03m    config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If\u001b[39;00m\n\u001b[1;32m    449\u001b[0m \u001b[38;5;124;03m    `config.num_labels > 1` a classification loss is computed (Cross-Entropy).\u001b[39;00m\n\u001b[1;32m    450\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    451\u001b[0m return_dict \u001b[38;5;241m=\u001b[39m return_dict \u001b[38;5;28;01mif\u001b[39;00m return_dict \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39muse_return_dict\n\u001b[0;32m--> 453\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvnextv2(pixel_values, output_hidden_states\u001b[38;5;241m=\u001b[39moutput_hidden_states, return_dict\u001b[38;5;241m=\u001b[39mreturn_dict)\n\u001b[1;32m    455\u001b[0m pooled_output \u001b[38;5;241m=\u001b[39m outputs\u001b[38;5;241m.\u001b[39mpooler_output \u001b[38;5;28;01mif\u001b[39;00m return_dict \u001b[38;5;28;01melse\u001b[39;00m outputs[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m    457\u001b[0m logits \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclassifier(pooled_output)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/transformers/models/convnextv2/modeling_convnextv2.py:385\u001b[0m, in \u001b[0;36mConvNextV2Model.forward\u001b[0;34m(self, pixel_values, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    382\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pixel_values \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    383\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou have to specify pixel_values\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 385\u001b[0m embedding_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membeddings(pixel_values)\n\u001b[1;32m    387\u001b[0m encoder_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mencoder(\n\u001b[1;32m    388\u001b[0m     embedding_output,\n\u001b[1;32m    389\u001b[0m     output_hidden_states\u001b[38;5;241m=\u001b[39moutput_hidden_states,\n\u001b[1;32m    390\u001b[0m     return_dict\u001b[38;5;241m=\u001b[39mreturn_dict,\n\u001b[1;32m    391\u001b[0m )\n\u001b[1;32m    393\u001b[0m last_hidden_state \u001b[38;5;241m=\u001b[39m encoder_outputs[\u001b[38;5;241m0\u001b[39m]\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/transformers/models/convnextv2/modeling_convnextv2.py:164\u001b[0m, in \u001b[0;36mConvNextV2Embeddings.forward\u001b[0;34m(self, pixel_values)\u001b[0m\n\u001b[1;32m    162\u001b[0m num_channels \u001b[38;5;241m=\u001b[39m pixel_values\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m    163\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_channels \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_channels:\n\u001b[0;32m--> 164\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    165\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMake sure that the channel dimension of the pixel values match with the one set in the configuration.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    166\u001b[0m     )\n\u001b[1;32m    167\u001b[0m embeddings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch_embeddings(pixel_values)\n\u001b[1;32m    168\u001b[0m embeddings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlayernorm(embeddings)\n",
      "\u001b[0;31mValueError\u001b[0m: Make sure that the channel dimension of the pixel values match with the one set in the configuration."
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "A simple walkthrough of how to code a convolutional neural network (CNN)\n",
    "using the PyTorch library. For demonstration we train it on the very\n",
    "common MNIST dataset of handwritten digits. In this code we go through\n",
    "how to create the network as well as initialize a loss function, optimizer,\n",
    "check accuracy and more.\n",
    "\n",
    "Programmed by Aladdin Persson\n",
    "* 2020-04-08: Initial coding\n",
    "* 2021-03-24: More detailed comments and small revision of the code\n",
    "* 2022-12-19: Small revision of code, checked that it works with latest PyTorch version\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "# Imports\n",
    "import torch\n",
    "\n",
    "from torchvision.transforms import v2 \n",
    "import torch.nn.functional as F  # Parameterless functions, like (some) activation functions\n",
    "import torchvision.datasets as datasets  # Standard datasets\n",
    "import torchvision.transforms as transforms  # Transformations we can perform on our dataset for augmentation\n",
    "from torch import optim  # For optimizers like SGD, Adam, etc.\n",
    "from torch import nn  # All neural network modules\n",
    "from torch.utils.data import (\n",
    "    DataLoader,\n",
    ")  # Gives easier dataset managment by creating mini batches etc.\n",
    "from tqdm import tqdm  # For nice progress bar!\n",
    "import tensorboard\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "# Simple CNN\n",
    "\n",
    "\n",
    "\n",
    "# Set device\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "writer = SummaryWriter()\n",
    "# Hyperparameters\n",
    "in_channels = 1\n",
    "num_classes = 10\n",
    "learning_rate = 3e-4 # karpathy's constant\n",
    "batch_size = 4\n",
    "num_epochs = 3\n",
    "\n",
    "# Load Data\n",
    "train_dataset = datasets.MNIST(\n",
    "    root=\"dataset/\", train=True, transform=transforms.ToTensor(), download=True\n",
    ")\n",
    "test_dataset = datasets.MNIST(\n",
    "    root=\"dataset/\", train=False, transform=transforms.ToTensor(), download=True\n",
    ")\n",
    "\n",
    "train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "# Initialize network\n",
    "model = WrappMnist()\n",
    "for name, param in model.named_parameters():\n",
    "    print(name)\n",
    "param_device = next(model.parameters()).device\n",
    "print(param_device)\n",
    "# Loss and optimizer\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
    "# visualizationdataset = load_dataset(\"huggingface/cats-image\")\n",
    "image = dataset[\"test\"][\"image\"][0]\n",
    "\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "])\n",
    "\n",
    "# Apply the transformation to the image\n",
    "tensor_image = transform(image)\n",
    "\n",
    "# If you need to add a batch dimension (1 for single image)\n",
    "tensor_image = tensor_image.unsqueeze(0)\n",
    "\n",
    "# Now, tensor_image is a PyTorch tensor representing the JPEG image\n",
    "print(tensor_image.shape)\n",
    "\n",
    "dummy_input = tensor_image\n",
    "data = dummy_input\n",
    "#writer.add_graph(model,dummy_input)\n",
    "data = data.to(device=device)\n",
    "targets = torch.zeros(1)\n",
    "targets[0] = 2\n",
    "targets = targets.long()\n",
    "targets = targets.to(device=device)\n",
    "scores = model(data)\n",
    "scores = scores.to(device=device)\n",
    "loss = criterion(scores, targets)\n",
    "\n",
    "        # backward\n",
    "optimizer.zero_grad()\n",
    "loss.backward()\n",
    "\n",
    "        # gradient descent or adam step\n",
    "optimizer.step()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a6be1866",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/15000 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([4, 1, 28, 28])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same or input should be a MKLDNN tensor and weight is a dense tensor",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[28], line 52\u001b[0m\n\u001b[1;32m     48\u001b[0m     model\u001b[38;5;241m.\u001b[39mtrain()\n\u001b[1;32m     49\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m num_correct \u001b[38;5;241m/\u001b[39m num_samples\n\u001b[0;32m---> 52\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAccuracy on training set: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcheck_accuracy(train_loader,\u001b[38;5;250m \u001b[39mmodel)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     53\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAccuracy on test set: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcheck_accuracy(test_loader,\u001b[38;5;250m \u001b[39mmodel)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
      "Cell \u001b[0;32mIn[28], line 43\u001b[0m, in \u001b[0;36mcheck_accuracy\u001b[0;34m(loader, model)\u001b[0m\n\u001b[1;32m     39\u001b[0m x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mto(device\u001b[38;5;241m=\u001b[39mdevice)\n\u001b[1;32m     40\u001b[0m y \u001b[38;5;241m=\u001b[39m y\u001b[38;5;241m.\u001b[39mto(device\u001b[38;5;241m=\u001b[39mdevice)\n\u001b[0;32m---> 43\u001b[0m scores \u001b[38;5;241m=\u001b[39m model(x)\n\u001b[1;32m     44\u001b[0m _, predictions \u001b[38;5;241m=\u001b[39m scores\u001b[38;5;241m.\u001b[39mmax(\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m     45\u001b[0m num_correct \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (predictions \u001b[38;5;241m==\u001b[39m y)\u001b[38;5;241m.\u001b[39msum()\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "Cell \u001b[0;32mIn[24], line 20\u001b[0m, in \u001b[0;36mWrappMnist.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[1;32m     18\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtokenizer(x, return_tensors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 20\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvnext2(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mx)\n\u001b[1;32m     21\u001b[0m     x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mlogits\n\u001b[1;32m     23\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m x\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/transformers/models/convnextv2/modeling_convnextv2.py:453\u001b[0m, in \u001b[0;36mConvNextV2ForImageClassification.forward\u001b[0;34m(self, pixel_values, labels, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    445\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    446\u001b[0m \u001b[38;5;124;03mlabels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\u001b[39;00m\n\u001b[1;32m    447\u001b[0m \u001b[38;5;124;03m    Labels for computing the image classification/regression loss. Indices should be in `[0, ...,\u001b[39;00m\n\u001b[1;32m    448\u001b[0m \u001b[38;5;124;03m    config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If\u001b[39;00m\n\u001b[1;32m    449\u001b[0m \u001b[38;5;124;03m    `config.num_labels > 1` a classification loss is computed (Cross-Entropy).\u001b[39;00m\n\u001b[1;32m    450\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    451\u001b[0m return_dict \u001b[38;5;241m=\u001b[39m return_dict \u001b[38;5;28;01mif\u001b[39;00m return_dict \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39muse_return_dict\n\u001b[0;32m--> 453\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvnextv2(pixel_values, output_hidden_states\u001b[38;5;241m=\u001b[39moutput_hidden_states, return_dict\u001b[38;5;241m=\u001b[39mreturn_dict)\n\u001b[1;32m    455\u001b[0m pooled_output \u001b[38;5;241m=\u001b[39m outputs\u001b[38;5;241m.\u001b[39mpooler_output \u001b[38;5;28;01mif\u001b[39;00m return_dict \u001b[38;5;28;01melse\u001b[39;00m outputs[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m    457\u001b[0m logits \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclassifier(pooled_output)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/transformers/models/convnextv2/modeling_convnextv2.py:385\u001b[0m, in \u001b[0;36mConvNextV2Model.forward\u001b[0;34m(self, pixel_values, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    382\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pixel_values \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    383\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou have to specify pixel_values\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 385\u001b[0m embedding_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membeddings(pixel_values)\n\u001b[1;32m    387\u001b[0m encoder_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mencoder(\n\u001b[1;32m    388\u001b[0m     embedding_output,\n\u001b[1;32m    389\u001b[0m     output_hidden_states\u001b[38;5;241m=\u001b[39moutput_hidden_states,\n\u001b[1;32m    390\u001b[0m     return_dict\u001b[38;5;241m=\u001b[39mreturn_dict,\n\u001b[1;32m    391\u001b[0m )\n\u001b[1;32m    393\u001b[0m last_hidden_state \u001b[38;5;241m=\u001b[39m encoder_outputs[\u001b[38;5;241m0\u001b[39m]\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/transformers/models/convnextv2/modeling_convnextv2.py:167\u001b[0m, in \u001b[0;36mConvNextV2Embeddings.forward\u001b[0;34m(self, pixel_values)\u001b[0m\n\u001b[1;32m    163\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_channels \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_channels:\n\u001b[1;32m    164\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    165\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMake sure that the channel dimension of the pixel values match with the one set in the configuration.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    166\u001b[0m     )\n\u001b[0;32m--> 167\u001b[0m embeddings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch_embeddings(pixel_values)\n\u001b[1;32m    168\u001b[0m embeddings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlayernorm(embeddings)\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m embeddings\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/nn/modules/conv.py:460\u001b[0m, in \u001b[0;36mConv2d.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    459\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 460\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_conv_forward(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweight, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbias)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/torch/nn/modules/conv.py:456\u001b[0m, in \u001b[0;36mConv2d._conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m    452\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzeros\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    453\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m F\u001b[38;5;241m.\u001b[39mconv2d(F\u001b[38;5;241m.\u001b[39mpad(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode),\n\u001b[1;32m    454\u001b[0m                     weight, bias, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstride,\n\u001b[1;32m    455\u001b[0m                     _pair(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdilation, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroups)\n\u001b[0;32m--> 456\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m F\u001b[38;5;241m.\u001b[39mconv2d(\u001b[38;5;28minput\u001b[39m, weight, bias, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstride,\n\u001b[1;32m    457\u001b[0m                 \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdilation, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroups)\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same or input should be a MKLDNN tensor and weight is a dense tensor"
     ]
    }
   ],
   "source": [
    "# Train Network\n",
    "for epoch in range(num_epochs):\n",
    "    for batch_idx, (data, targets) in enumerate(tqdm(train_loader)):\n",
    "        # Get data to cuda if possible\n",
    "        print(data.shape)\n",
    "        break\n",
    "       \n",
    "        if(data.shape[1] == 1):\n",
    "            data = torch.tile(data, (1,1,1,1))\n",
    "        \n",
    "        \n",
    "\n",
    "        # forward\n",
    "       \n",
    "        data = data.to(device=device)\n",
    "        targets = targets.to(device=device)\n",
    "        \n",
    "        scores = model(data)\n",
    "        loss = criterion(scores, targets)\n",
    "\n",
    "        # backward\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "\n",
    "        # gradient descent or adam step\n",
    "        optimizer.step()\n",
    "        print(\"worked\")\n",
    "    break\n",
    "\n",
    "\n",
    "# Check accuracy on training & test to see how good our model\n",
    "def check_accuracy(loader, model):\n",
    "    num_correct = 0\n",
    "    num_samples = 0\n",
    "    model.eval()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for x, y in loader:\n",
    "            x = x.to(device=device)\n",
    "            y = y.to(device=device)\n",
    "            \n",
    "\n",
    "            scores = model(x)\n",
    "            _, predictions = scores.max(1)\n",
    "            num_correct += (predictions == y).sum()\n",
    "            num_samples += predictions.size(0)\n",
    "\n",
    "    model.train()\n",
    "    return num_correct / num_samples\n",
    "\n",
    "\n",
    "print(f\"Accuracy on training set: {check_accuracy(train_loader, model)*100:.2f}\")\n",
    "print(f\"Accuracy on test set: {check_accuracy(test_loader, model)*100:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "510f9f56",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'super' object has no attribute '__'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[23], line 18\u001b[0m\n\u001b[1;32m     16\u001b[0m dataset \u001b[38;5;241m=\u001b[39m load_dataset(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhuggingface/cats-image\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     17\u001b[0m image \u001b[38;5;241m=\u001b[39m dataset[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimage\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m---> 18\u001b[0m model \u001b[38;5;241m=\u001b[39m Wrapp()\n\u001b[1;32m     19\u001b[0m output \u001b[38;5;241m=\u001b[39m model(image)\n\u001b[1;32m     21\u001b[0m \u001b[38;5;66;03m# model predicts one of the 1000 ImageNet classes\u001b[39;00m\n",
      "Cell \u001b[0;32mIn[8], line 6\u001b[0m, in \u001b[0;36mWrapp.__init__\u001b[0;34m(self, in_channels, num_classes)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, in_channels\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m, num_classes\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1000\u001b[39m):\n\u001b[0;32m----> 6\u001b[0m     \u001b[38;5;28msuper\u001b[39m(Wrapp, \u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m__\n\u001b[1;32m      7\u001b[0m     init__()\n\u001b[1;32m      8\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtokenizer \u001b[38;5;241m=\u001b[39m AutoImageProcessor\u001b[38;5;241m.\u001b[39mfrom_pretrained(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfacebook/convnextv2-tiny-1k-224\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'super' object has no attribute '__'"
     ]
    }
   ],
   "source": [
    "from transformers import AutoImageProcessor, ConvNextV2ForImageClassification,ConvNextV2Config\n",
    "import tensorboard\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "import torchvision\n",
    "import transformers\n",
    "\n",
    "\n",
    "import torch\n",
    "from datasets import load_dataset\n",
    "temp = SummaryWriter()\n",
    "t = ConvNextV2ForImageClassification.from_pretrained(\"facebook/convnextv2-tiny-1k-224\")\n",
    "config = ConvNextV2Config.from_pretrained(\"facebook/convnextv2-tiny-1k-224\")\n",
    "config.num_channels = 1 \n",
    "model= ConvNextV2ForImageClassification(config)\n",
    "\n",
    "dataset = load_dataset(\"huggingface/cats-image\")\n",
    "image = dataset[\"test\"][\"image\"][0]\n",
    "model = Wrapp()\n",
    "output = model(image)\n",
    "\n",
    "# model predicts one of the 1000 ImageNet classes\n",
    "\n",
    "predicted_label = output.argmax(-1).item()\n",
    "\n",
    "print(predicted_label)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9b761f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(image_processor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18fa2dc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "image_processor = AutoImageProcessor.from_pretrained(\"facebook/convnextv2-tiny-1k-224\")\n",
    "image_processor.normalize?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1658f20a",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(dir(ConvNextV2ForImageClassification))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6812fb6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#subprocess.run([\"ls\", \"-a\"], capture_output=True, std_out=PIPE)\n",
    "def make_executable(script_path):\n",
    "    try:\n",
    "        # Add execute permissions to the script\n",
    "        os.chmod(script_path, os.stat(script_path).st_mode | 0o111)\n",
    "        print(f\"Successfully made '{script_path}' executable.\")\n",
    "    except OSError as e:\n",
    "        print(f\"Error making '{script_path}' executable: {e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "efdeee6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'Domain_generalization_experiments.ipynb\\n.ipynb_checkpoints\\n'\n",
      "Successfully made 'TDANN0resnet50.sh' executable.\n",
      "Successfully made 'TDANN0ConvNext.sh' executable.\n",
      "Successfully made 'TDANN1resnet50.sh' executable.\n",
      "Successfully made 'TDANN1ConvNext.sh' executable.\n",
      "Successfully made 'TDANN2resnet50.sh' executable.\n",
      "Successfully made 'TDANN2ConvNext.sh' executable.\n",
      "Successfully made 'TDANN3resnet50.sh' executable.\n",
      "Successfully made 'TDANN3ConvNext.sh' executable.\n"
     ]
    }
   ],
   "source": [
    "import subprocess\n",
    "from subprocess import PIPE\n",
    "import os\n",
    "p1 = subprocess.Popen([\"ls\",\"-a\"], stdout=PIPE)\n",
    "p2 = subprocess.Popen([\"grep\", \"ipynb\"], stdin=p1.stdout, stdout=PIPE)\n",
    "p1.stdout.close()  # Allow p1 to receive a SIGPIPE if p2 exits.\n",
    "output = p2.communicate()[0]\n",
    "print(output)\n",
    "script_lines = [\n",
    "    \"#!/bin/bash\",\n",
    "    \"#SBATCH --nodes=1\",\n",
    "    \"#SBATCH --cpus-per-task=2\",\n",
    "    \"#SBATCH --time=2:00\",\n",
    "    \"#SBATCH --mem=20000mb\",\n",
    "    \"\",\n",
    "    \"module load devel/cuda/11.8\",\n",
    "    \"module load devel/miniconda/23.9.0-py3.9.15\",\n",
    "    \"module load devel/python/3.11.7_intel_2021.4.0\",\n",
    "    \"module load devel/cudnn/9.2\",\n",
    "    \"# cd foldername von venv\",\n",
    "    \"cd UniDG\",\n",
    "    \"# source venv/bin/activate\"\n",
    "]\n",
    "algorithms = [\"CORAL\",\"Mixup\",\"ERM\",\"Inter_domain_adaptation\"]\n",
    "test_environments = [0,1,2,3]\n",
    "backbones = [\"resnet50\", \"ConvNext\"]\n",
    "adapt_algorithm =\"T3A\"\n",
    "\n",
    "command_firstpart = \"python -m domainbed.scripts.train       --data_dir=./data     --dataset TerraIncognita\"\n",
    "\n",
    "adapt = False\n",
    "\n",
    "\n",
    "for algorithm in algorithms:\n",
    "    for test_env in test_environments:\n",
    "        for backbone in backbones:\n",
    "            if adapt== True:\n",
    "                path = \"TAdapt_{algorithm}{test_env}{backbone}.sh\".format(algorithm=algorithm,backbone=backbone,test_env=test_env)\n",
    "                \n",
    "            else:\n",
    "                path = \"T{algorithm}{test_env}{backbone}.sh\".format(algorithm=algorithm,backbone=backbone,test_env=test_env)\n",
    "                \n",
    "            \n",
    "            \n",
    "            try:\n",
    "                with open(path,'a') as file:\n",
    "                    for line in script_lines:\n",
    "                        file.write(line + '\\n')\n",
    "\n",
    "                    if adapt == True:\n",
    "                        unsupervised_adaptation = \"python -m domainbed.scripts.unsupervised_adaptation        --input_dir my/pretrain/{algorithm}/TerraIncognita/{backbone}/{test_env}       --adapt_algorithm={adapt_algorithm}\".format(algorithm=algorithm,backbone=backbone,test_env=test_env,adapt_algorithm=adapt_algorithm)\n",
    "                        command = unsupervised_adaptation\n",
    "                    else:\n",
    "                        command_secondpart = '  --algorithm {algorithm}        --test_env {test_env} --hparams \"{{\\\\\"backbone\\\\\": \\\\\"{backbone}\\\\\"}}\"     --output_dir my/pretrain/{algorithm}/TerraIncognita/{backbone}/{test_env}'.format(algorithm=algorithm, backbone=backbone, test_env = test_env)\n",
    "                        command = command_firstpart+command_secondpart\n",
    "                        \n",
    "                        \n",
    "                    \n",
    "                    file.write(command)\n",
    "                    os.utime(path)\n",
    "            except Exception as e:\n",
    "                print(f\"Error touching file '{filename}': {e}\")\n",
    "            make_executable(path)\n",
    "\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f135eecc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env0_in_acc\n",
      "env0_in_ent\n",
      "env0_out_acc\n",
      "env0_out_ent\n",
      "env1_in_acc\n",
      "env1_in_ent\n",
      "env1_out_acc\n",
      "env1_out_ent\n",
      "env2_in_acc\n",
      "env2_in_ent\n",
      "env2_out_acc\n",
      "env2_out_ent\n",
      "env3_in_acc\n",
      "env3_in_ent\n",
      "env3_out_acc\n",
      "env3_out_ent\n",
      "['test_env', 'backbone', 'dataset', 'h1', 'env0_in_acc', 'env0_in_ent', 'env0_out_acc', 'env0_out_ent', 'env1_in_acc', 'env1_in_ent', 'env1_out_acc', 'env1_out_ent', 'env2_in_acc', 'env2_in_ent', 'env2_out_acc', 'env2_out_ent', 'env3_in_acc', 'env3_in_ent', 'env3_out_acc', 'env3_out_ent', 'filter_K', 'gamma', 'lambd']\n",
      "   test_env  backbone          dataset  env0_in_acc   env0_in_ent  \\\n",
      "0         1  ConvNext  TerraIncongnita     0.898761  1.449553e-02   \n",
      "1         1  resnet50   TerraIncognita     0.603744  2.884490e-02   \n",
      "2         2  resnet50   TerraIncognita     0.672291  2.700609e-02   \n",
      "3         3  resnet50   TerraIncognita     0.354073  3.222888e-02   \n",
      "4         0  resnet50   TerraIncognita     0.202742  2.459363e-09   \n",
      "5         3  ConvNext   TerraIncognita     0.621408  7.235391e-03   \n",
      "6         2  ConvNext   TerraIncognita     0.848141  1.458260e-02   \n",
      "7         0  ConvNext  TerraIncongnita     0.202742  9.882219e-11   \n",
      "\n",
      "   env0_out_acc  env0_out_ent  env1_in_acc   env1_in_ent  env1_out_acc  \\\n",
      "0      0.200422  1.298103e-06     0.457440  6.786958e-12      0.473549   \n",
      "1      0.584388  2.300208e-03     0.099884  1.479731e-03      0.090909   \n",
      "2      0.521097  4.468328e-09     0.020670  1.688295e-08      0.017463   \n",
      "3      0.200422  1.870765e-08     0.488766  1.898810e-03      0.473549   \n",
      "4      0.200422  5.184532e-09     0.492618  7.489278e-03      0.473549   \n",
      "5      0.521097  7.355305e-15     0.025292  2.862083e-03      0.017463   \n",
      "6      0.521097  8.643014e-14     0.027475  1.081547e-03      0.017463   \n",
      "7      0.200422  2.603313e-11     0.647965  9.414867e-03      0.473549   \n",
      "\n",
      "   env1_out_ent  env2_in_acc   env2_in_ent  env2_out_acc  env2_out_ent  \\\n",
      "0  8.902836e-14     0.600126  3.249991e-02      0.185139  6.705441e-11   \n",
      "1  3.928968e-04     0.103589  4.730969e-03      0.085642  2.518118e-03   \n",
      "2  4.635573e-09     0.100756  1.573988e-09      0.084383  1.552304e-09   \n",
      "3  2.023755e-08     0.195529  2.298086e-08      0.185139  2.167281e-08   \n",
      "4  1.343200e-08     0.195529  2.459504e-08      0.185139  1.978551e-08   \n",
      "5  2.043718e-14     0.100756  8.214751e-15      0.084383  2.167896e-14   \n",
      "6  1.411363e-13     0.100756  2.019138e-13      0.084383  1.183115e-13   \n",
      "7  4.582123e-11     0.195529  2.406315e-12      0.185139  5.238949e-11   \n",
      "\n",
      "   env3_in_acc   env3_in_ent  env3_out_acc  env3_out_ent  \n",
      "0     0.326747  3.986515e-03      0.270408  3.476567e-03  \n",
      "1     0.217548  8.622175e-03      0.205782  1.061230e-02  \n",
      "2     0.234332  1.668240e-04      0.252551  7.309579e-09  \n",
      "3     0.224134  1.075734e-09      0.202381  3.130245e-09  \n",
      "4     0.224134  2.021984e-08      0.202381  2.356063e-08  \n",
      "5     0.234332  2.029327e-14      0.252551  2.842973e-14  \n",
      "6     0.234332  7.353088e-14      0.252551  2.167148e-13  \n",
      "7     0.230720  1.394671e-03      0.202381  5.917441e-11  \n",
      "0    0.457440\n",
      "1    0.099884\n",
      "2    0.100756\n",
      "3    0.224134\n",
      "4    0.202742\n",
      "5    0.234332\n",
      "6    0.100756\n",
      "7    0.202742\n",
      "Name: test_in_acc, dtype: float64\n",
      "0    0.473549\n",
      "1    0.090909\n",
      "2    0.084383\n",
      "3    0.202381\n",
      "4    0.200422\n",
      "5    0.252551\n",
      "6    0.084383\n",
      "7    0.200422\n",
      "Name: test_out_acc, dtype: float64\n",
      "backbone  dataset        \n",
      "ConvNext  TerraIncognita     0.167544\n",
      "          TerraIncongnita    0.330091\n",
      "resnet50  TerraIncognita     0.156879\n",
      "Name: test_in_acc, dtype: float64\n",
      "backbone  dataset        \n",
      "ConvNext  TerraIncognita     0.168467\n",
      "          TerraIncongnita    0.336985\n",
      "resnet50  TerraIncognita     0.144524\n",
      "Name: test_out_acc, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# calculation of the unsupervised adaptation results\n",
    "import pandas as pd\n",
    "filepath = \"/home/niklas/Desktop/results.csv\"\n",
    "types = [\"in_acc\",\"in_ent\", \"out_acc\", \"out_ent\" ]\n",
    "columns = []\n",
    "for i in range(4):\n",
    "    for t in types:\n",
    "        column_name = \"env{env}_{t}\".format(env=i, t=t)\n",
    "        print(column_name)\n",
    "        columns.append(column_name)\n",
    "\n",
    "names = [\"test_env\", \"backbone\", \"dataset\", \"h1\", *columns, \"filter_K\", \"gamma\", \"lambd\"]\n",
    "print(names)\n",
    "df = pd.read_csv(filepath, names = names)\n",
    "data = df.drop([\"h1\", \"filter_K\", \"gamma\", \"lambd\"], axis=1)\n",
    "print(data)\n",
    "\n",
    "data[\"test_in_acc\"] = data.apply(lambda row: row[f'env{row[\"test_env\"]}_in_acc'], axis=1)\n",
    "data[\"test_out_acc\"] =data.apply(lambda row: row[f'env{row[\"test_env\"]}_out_acc'], axis=1)\n",
    "print(data[\"test_in_acc\"])\n",
    "print(data[\"test_out_acc\"])\n",
    "\n",
    "grouped = data.groupby([\"backbone\", \"dataset\"])\n",
    "\n",
    "print(grouped[\"test_in_acc\"].mean())\n",
    "print(grouped[\"test_out_acc\"].mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5b10444b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env0_in_acc\n",
      "env0_in_ent\n",
      "env0_out_acc\n",
      "env0_out_ent\n",
      "env1_in_acc\n",
      "env1_in_ent\n",
      "env1_out_acc\n",
      "env1_out_ent\n",
      "env2_in_acc\n",
      "env2_in_ent\n",
      "env2_out_acc\n",
      "env2_out_ent\n",
      "env3_in_acc\n",
      "env3_in_ent\n",
      "env3_out_acc\n",
      "env3_out_ent\n",
      "['test_env', 'backbone', 'dataset', 'env0_in_acc', 'env0_in_ent', 'env0_out_acc', 'env0_out_ent', 'env1_in_acc', 'env1_in_ent', 'env1_out_acc', 'env1_out_ent', 'env2_in_acc', 'env2_in_ent', 'env2_out_acc', 'env2_out_ent', 'env3_in_acc', 'env3_in_ent', 'env3_out_acc', 'env3_out_ent']\n",
      "   test_env  backbone          dataset  env0_in_acc  env0_in_ent  \\\n",
      "0         1  resnet50   TerraIncognita     0.979699     0.075036   \n",
      "1         2  ConvNext   TerraIncognita     0.988136     0.024928   \n",
      "2         3  ConvNext   TerraIncognita     0.986554     0.030286   \n",
      "3         0  ConvNext  TerraIncongnita     0.631426     0.543387   \n",
      "4         2  resnet50   TerraIncognita     0.971526     0.105339   \n",
      "5         3  resnet50   TerraIncognita     0.976008     0.082380   \n",
      "6         0  resnet50   TerraIncognita     0.536515     0.662211   \n",
      "\n",
      "   env0_out_acc  env0_out_ent  env1_in_acc  env1_in_ent  env1_out_acc  \\\n",
      "0      0.983122      0.077283     0.391321     0.518993      0.406780   \n",
      "1      0.991561      0.028141     0.976249     0.055441      0.979969   \n",
      "2      0.994726      0.026401     0.974708     0.054258      0.974833   \n",
      "3      0.644515      0.514426     0.924124     0.191052      0.933744   \n",
      "4      0.971519      0.109776     0.932084     0.205509      0.944530   \n",
      "5      0.986287      0.092283     0.944666     0.207458      0.941448   \n",
      "6      0.533755      0.686946     0.931570     0.248831      0.940935   \n",
      "\n",
      "   env1_out_ent  env2_in_acc  env2_in_ent  env2_out_acc  env2_out_ent  \\\n",
      "0      0.510084     0.948678     0.142730      0.945844      0.146100   \n",
      "1      0.051403     0.643892     0.366815      0.622166      0.392034   \n",
      "2      0.052782     0.973866     0.061550      0.968514      0.066843   \n",
      "3      0.174069     0.888224     0.277635      0.903023      0.270086   \n",
      "4      0.187675     0.589421     0.569826      0.528967      0.564929   \n",
      "5      0.194312     0.940806     0.152265      0.950882      0.133108   \n",
      "6      0.225815     0.939861     0.222539      0.942065      0.213112   \n",
      "\n",
      "   env3_in_acc  env3_in_ent  env3_out_acc  env3_out_ent  \n",
      "0     0.922668     0.214226      0.920068      0.201017  \n",
      "1     0.960484     0.072896      0.964286      0.063844  \n",
      "2     0.527725     0.498752      0.546769      0.493976  \n",
      "3     0.873380     0.330059      0.877551      0.324352  \n",
      "4     0.910346     0.262541      0.904762      0.263363  \n",
      "5     0.399618     0.555390      0.389456      0.541330  \n",
      "6     0.910771     0.270714      0.905612      0.252048  \n",
      "0    0.391321\n",
      "1    0.643892\n",
      "2    0.527725\n",
      "3    0.631426\n",
      "4    0.589421\n",
      "5    0.399618\n",
      "6    0.536515\n",
      "Name: test_in_acc, dtype: float64\n",
      "0    0.406780\n",
      "1    0.622166\n",
      "2    0.546769\n",
      "3    0.644515\n",
      "4    0.528967\n",
      "5    0.389456\n",
      "6    0.533755\n",
      "Name: test_out_acc, dtype: float64\n",
      "backbone  dataset        \n",
      "ConvNext  TerraIncognita     0.585808\n",
      "          TerraIncongnita    0.631426\n",
      "resnet50  TerraIncognita     0.479218\n",
      "Name: test_in_acc, dtype: float64\n",
      "backbone  dataset        \n",
      "ConvNext  TerraIncognita     0.584467\n",
      "          TerraIncongnita    0.644515\n",
      "resnet50  TerraIncognita     0.464739\n",
      "Name: test_out_acc, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#testing of base results\n",
    "filepath = \"/home/niklas/Desktop/base_results.csv\"\n",
    "types = [\"in_acc\",\"in_ent\", \"out_acc\", \"out_ent\" ]\n",
    "columns = []\n",
    "for i in range(4):\n",
    "    for t in types:\n",
    "        column_name = \"env{env}_{t}\".format(env=i, t=t)\n",
    "        print(column_name)\n",
    "        columns.append(column_name)\n",
    "\n",
    "names = [\"test_env\", \"backbone\", \"dataset\", *columns]\n",
    "print(names)\n",
    "df = pd.read_csv(filepath, names = names)\n",
    "data = df\n",
    "print(data)\n",
    "\n",
    "data[\"test_in_acc\"] = data.apply(lambda row: row[f'env{row[\"test_env\"]}_in_acc'], axis=1)\n",
    "data[\"test_out_acc\"] =data.apply(lambda row: row[f'env{row[\"test_env\"]}_out_acc'], axis=1)\n",
    "print(data[\"test_in_acc\"])\n",
    "print(data[\"test_out_acc\"])\n",
    "\n",
    "grouped = data.groupby([\"backbone\", \"dataset\"])\n",
    "\n",
    "print(grouped[\"test_in_acc\"].mean())\n",
    "print(grouped[\"test_out_acc\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8f8700b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         ERM-Reference    SWAD SIMPLE  UniDG\n",
      "resnet50         42.25  50.786   None   55.0\n",
      "ConvNext          58.3    None   None  64.52\n",
      "Ensemble          None    None  46.49   None\n"
     ]
    }
   ],
   "source": [
    "#create tables for latex \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "algorithms = [\"CORAL\",\"Mixup\",\"ERM\",\"Inter_domain_adaptation\"]\n",
    "algorithms_positive = [\"ERM-Reference\", \"SWAD\", \"SIMPLE\", \"UniDG\"]\n",
    "backbones = [\"resnet50\", \"ConvNext\",\"Ensemble\"]\n",
    "data_ERM = [42.25,58.3,None]\n",
    "data_SWAD = [50.786,None,None]\n",
    "data_SIMPLE = [None,None,46.49]\n",
    "data_UniDG = [55.00,64.52,None]\n",
    "data_comparison = data_ERM,data_SWAD, data_SIMPLE, data_UniDG\n",
    "data_comparison = np.array(data_comparison)\n",
    "\n",
    "base_values = pd.DataFrame(data= data_comparison.T,columns=algorithms_positive)\n",
    "base_values.index = backbones\n",
    "print(base_values)\n",
    "base_values.to_csv('out.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e99fdc0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            ERM  CORAL  Mixup   DANN\n",
      "resnet50  42.25  46.03  47.04  20.48\n",
      "ConvNext  58.30  59.04  60.06  56.36\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "algorithms = [\"ERM\",\"CORAL\",\"Mixup\",\"DANN\"]\n",
    "backbones = [\"resnet50\", \"ConvNext\"]\n",
    "data_ERM = [42.25,58.3]\n",
    "data_CORAL = [46.03,59.04]\n",
    "data_Mixup = [47.04,60.06]\n",
    "data_DANN = [20.48,56.36]\n",
    "data_base = data_ERM,data_CORAL,data_Mixup, data_DANN\n",
    "data_base = np.array(data_base)\n",
    "base_values = pd.DataFrame(data= data_base.T, columns=algorithms)\n",
    "base_values.index = backbones\n",
    "print(base_values)\n",
    "base_values.to_csv('baseResultsDomainBed.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "589f1882",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Adapt_Algorithm', 'backbone', 'algorithm', 'testenv', 'results'], dtype='object')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar diagrams of the results\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "columns = [\"adaptation_algorithm\",\"base_algorithm\", \"backbone\",\"testenv\"]\n",
    "excel_file = \"results_combined.xlsx\"\n",
    "\n",
    "# Load the Excel file into a Pandas DataFrame\n",
    "df = pd.read_excel(excel_file,sheet_name=1)\n",
    "print(df.columns)\n",
    "locations = {0:100, 1:38, 2:43,3:46}# 38 43 46 100 \n",
    "df[\"Location\"]= df[\"testenv\"].map(locations)\n",
    "\n",
    "options=[\"SIMPLE\",\"SWAD\",\"Inter\",\"Promptstyler optimized\", \"Car-FT\",\"Coral+UniDG\",\"ERM+UniDG\"]\n",
    "\n",
    "Implemented_algorithm = df.loc[df[\"algorithm\"].isin(options)]\n",
    "\n",
    "grouped_df = Implemented_algorithm.groupby([\"backbone\", \"algorithm\", \"Adapt_Algorithm\", \"Location\"]).mean().reset_index()\n",
    "sns.set_style(\"whitegrid\")\n",
    "# Plotting grouped bar chart\n",
    "grouped_df.pivot_table(index=['backbone', 'algorithm'], columns='Location', values='results').plot(kind='barh')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "be4fe231",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Adapt_Algorithm', 'backbone', 'algorithm', 'testenv', 'results'], dtype='object')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar diagrams of the results\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "columns = [\"adaptation_algorithm\",\"base_algorithm\", \"backbone\",\"testenv\"]\n",
    "excel_file = \"results_combined.xlsx\"\n",
    "\n",
    "# Load the Excel file into a Pandas DataFrame\n",
    "df = pd.read_excel(excel_file,sheet_name=1)\n",
    "print(df.columns)\n",
    "locations = {0:100, 1:38, 2:43,3:46}# 38 43 46 100 \n",
    "df[\"Location\"]= df[\"testenv\"].map(locations)\n",
    "\n",
    "options=[\"Car-FT\",\"ERM\"]\n",
    "backbones = [\"resnet\", \"ViT-L/14\", \"ViT-B/16\"]\n",
    "Robins_algorithm = df.loc[df[\"algorithm\"].isin(options)]\n",
    "Implemented_algorithm = Robins_algorithm.loc[Robins_algorithm[\"backbone\"].isin(backbones)]\n",
    "\n",
    "grouped_df = Implemented_algorithm.groupby([\"backbone\", \"algorithm\", \"Adapt_Algorithm\", \"Location\"]).mean().reset_index()\n",
    "sns.set_style(\"whitegrid\")\n",
    "# Plotting grouped bar chart\n",
    "grouped_df.pivot_table(index=['backbone', 'algorithm'], columns='Location', values='results').plot(kind='bar')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fe726001",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Adapt_Algorithm backbone algorithm  testenv  results  Location\n",
      "4               NO   resnet       ERM        0  0.45000       100\n",
      "5               NO   resnet       ERM        1  0.33100        38\n",
      "6               NO   resnet       ERM        2  0.57200        43\n",
      "7               NO   resnet       ERM        3  0.33700        46\n",
      "68              NO   resnet      SWAD        0  0.58450       100\n",
      "69              NO   resnet      SWAD        1  0.46120        38\n",
      "70              NO   resnet      SWAD        2  0.59446        43\n",
      "71              NO   resnet      SWAD        3  0.38836        46\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='backbone,algorithm'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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55pu1evVq3X///QarBwCg7BBGDLn++uv10UcfubQ9+eSTat68ueLi4mS1Wos8zuFwKC8vryJKBACgQhBGDKldu7ZCQ0Nd2mrWrKmAgACFhoYqJydHr732mqKjoxUcHKwTJ07onXfe0e+//65bbrnFUNUAAJS9qh1GgkIvvY+H9mO1WrV//36tXbtWWVlZCggIUEREhBISEtSyZcsy7w8AAFOqbhjJt1/Wg8hK1Z9X0ZdWSmr58uXO//fz89P8+fMvtyoAADxe1b2b5jKDgcf3BwBAFVF1wwgAAKgUCCMAAMCoUoWRhIQERUdHKyIiQgMHDlRSUlKx+27evFlhYWGFfu3bt6/URQMAgKrD7QmsGzZsUHx8vJ599llFRUVp5cqViouL0/r169WgQYNij/v4449Vu3Zt52seaQ4AAKRSjIwsXbpUgwYN0uDBgxUSEqJJkyapfv36WrFixUWPq1u3roKDg52/inuoFwAAqF7cGhnJy8vTzp07NWrUKJf2bt26KTk5+aLH9u/fX3l5eQoJCdHo0aN1/fXXu12s3W4vss3hcDh/4eIKvie73V7k9/lnpgNjfn6+0f6Bqqy6nt+mP/el/tytakr6ed0KI1lZWbLb7apbt65Le1BQkDIyMoo8Jjg4WFOnTlWbNm2Ul5enDz74QPfcc4+WL1+uTp06udO9duzYUWS7t7e3cnNz+curBM6ePatz584pLS3tovv5+/urdevWFVRV0fbs2aPc3FyjNQBVUXU9vz3hc+/evZs/14pQqoeeWSwWl9cOh6NQW4HmzZurefPmzteRkZH6/fff9eabb7odRiIiIgql2jNnzujXX3+Vv7+/atSo4db7VUdeXl7y8fFRixYtPP77atmypby8uOELqIqq6/kdFhZmuoQKZbfbix1I+DO3wkhgYKCsVquOHTvm0p6ZmamgoKASv0+7du304YcfutO1pAvDa38NI1arVRaLxfkLF1fwPRX1XXoaLy8vj68RQOlU1/O7On7mknArlvr6+qpNmzbauHGjS/umTZsUGRlZ4vdJTU1VcHCwO127zZ5fsdflLre/RYsWKSwsTNOnT3dp37dvnx544AF16NBBkZGRuuOOO3TkyJHL6gsAAE/i9mWaESNGaPz48QoPD1dkZKRWrVolm82m2NhYSdKcOXN09OhRzZo1S5L01ltvqVGjRmrRooXOnTunDz/8UJ988oleeeWVsv0kf2H1smrifyZq/8n95dqPJDW/orlm9JhR6uN//PFHrVq1qtDw3cGDB3XXXXdp0KBBevjhh1WnTh3t27dPfn5+l1syAAAew+0wEhMTo6ysLC1YsEDp6ekKDQ3V4sWL1bBhQ0lSRkaGbDabc/9z585p5syZOnr0qGrUqKEWLVpo8eLF6tmzZ9l9imLsP7lfqcdTy72fy5Gdna0nnnhC06ZN08KFC122zZ07Vz169ND48eOdbY0bN67oEgEAKFelmsA6dOhQDR06tMhtM2a4jhDExcUpLi6uNN1UC1OmTFHPnj3VtWtXlzCSn5+vr7/+WiNHjtR9992nXbt2qVGjRrr//vvVq1cvgxUDAFC2qt9UZg+yfv167dq1S2PHji20LTMzUzk5OXr99dd1ww03aMmSJerdu7ceeughbdmyxUC1AACUj1KNjODy2Ww2TZ8+XUuWLClyDkjBM1Nuuukm3XPPPZKka6+9Vtu3b9fKlSt13XXXVWS5AACUG8KIITt37lRmZqYGDhzobLPb7dq6dasSEhKUkpIib29vhYSEuBwXEhKibdu2VXS5AACUG8KIIddff70++ugjl7Ynn3xSzZs3V1xcnHx9fRUREaEDBw647PPLL784JwsDAFAVEEYMqV27tkJDQ13aatasqYCAAGf7fffdp8cee0ydOnVS586d9e233+qrr77SsmXLTJQMAEC5qNJhpPkVzS+9kwf307t3bz333HNavHixpk2bpmuuuUbz5s1Tx44dy6U/AABMqLJhxJ5vv6wHkZWmP6vX5T3md/ny5YXabr/9dt1+++2X9b4AAHiyKntr7+UGA0/vDwCAqqLKhhEAAFA5EEYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQBD7PkO0yUAHqHKPg7eYbfLYq24p6KWpr+tW7fqzTff1E8//aSMjAy9+uqr6tWr1///ng6H5s+fr1WrVumPP/5Qu3bt9Mwzz6hly5bOffLy8jRz5kytW7dOZ8+e1fXXX6/nnntO9evXL7PPBqB8WL0semRlsvamn67Qfm8MC9YTfVpVaJ/AxVTZMGKxWvXbuCeUt39/uffl27y5Gr442+3jcnJyFBYWpoEDB2rMmDGFtr/++utaunSpZsyYoWbNmmnhwoUaMWKEPv74Y9WuXVuSNH36dH311VeaO3euAgICNGPGDN1///16//33Za3AMAagdPamn9bOI39UaJ8hwbUqtD/gUqpsGJGkvP37dWbXLtNlFKtnz57q2bNnkdscDoeWLVumBx54QDfffLMkaebMmeratavWrVun2NhYnTp1SmvWrNGsWbPUtWtXSdLs2bN14403atOmTbrhhhsq7LMAAFBazBnxUIcPH1ZGRoa6d+/ubPP19VWnTp2UnJwsSfrpp5907tw5devWzbnPVVddpZYtWzr3AQDA0xFGPFRGRoYkqW7dui7tQUFBOnbsmCTp2LFj8vHx0RVXXFHsPgAAeDrCiIezWCwurx2OS8++L8k+AAB4CsKIhwoODpakQiMcmZmZCgoKknRhBOTcuXM6efJksfvAfQ67vVr2DQCmVOkJrJVZo0aNFBwcrI0bN6p169aSLtzGu3XrVo0bN06SFB4eLh8fH23cuFExMTGSpPT0dO3Zs0dPPPGEsdoru4q8E+vPSntXFgBUdoQRg7Kzs3Xw4EHn68OHDys1NVVXXHGFGjRooLvvvluLFi1Ss2bN1LRpUy1atEg1atRQ3759JUl16tTRoEGDNHPmTAUGBuqKK67QzJkzFRoa6ry7BqXj6XdiAUBVUqXDiG/z5h7dz08//aS7777b+To+Pl6SNGDAAM2YMUNxcXE6e/asnn/+eZ08eVLt2rXTkiVLnM8YkaSnnnpK3t7eevTRR3XmzBl16dJFM2bM4BkjAIBKo8qGEYfdXqFD3qV5Amvnzp21e/fuYrdbLBaNGTOmyAeiFfDz89PkyZM1efJkt/oGAMBTVNkJrBX5KHgT/QEAUFVU2TACAAAqB8IIAAAwijACAACMIowAAACjqkwYyc/PN11CpcCj4gEAnqbS39rr6+srLy8vHTlyRMHBwfL19S20ngsucDgcysjIkMVikY+Pj+lyAACQVAXCiJeXl6655hrZbDYdOXLEdDkez2KxqFGjRjwUDQDgMSp9GJEujI40adJE58+fl52Fxi7Kx8eHIAIA8ChVIoxIcl564PIDAACVS5WZwAoAAConwggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwqlRhJCEhQdHR0YqIiNDAgQOVlJRUouO2bdum1q1b67bbbitNtwAAoApyO4xs2LBB8fHxGj16tBITE9WhQwfFxcVdcsXcU6dOacKECerSpUupiwUAAFWP22Fk6dKlGjRokAYPHqyQkBBNmjRJ9evX14oVKy563DPPPKO+ffuqffv2pa0VAABUQW6t2puXl6edO3dq1KhRLu3dunVTcnJyscetWbNGBw8e1OzZs7Vw4cLSVSrJbreX+li4z2q1Gu0/Pz/fSL+mPzc/59WH6Z81kzi/q4eSfl63wkhWVpbsdrvq1q3r0h4UFKSMjIwij/nll180Z84cJSQkyNvbre4K2bFjx2Udj5Lz9/dX69atjdawZ88e5ebmVmifnvC5d+/eXeGfGxXPE37WTOL8xp+VKh1YLBaX1w6Ho1CbdCERjR07VmPGjNE111xTugr/JCIiwniqRcVp2bKlvLyq3w1fYWFhpksAyh3nd/Vgt9tLNJDgVhgJDAyU1WrVsWPHXNozMzMVFBRUaP/s7Gz99NNPSk1N1dSpUyVdGJpzOBxq3bq13nzzTbcmtFqtVsJINeLl5VUtf7+r42dG9cP5jT9zK4z4+vqqTZs22rhxo3r37u1s37Rpk2666aZC+9euXVsfffSRS9s777yj77//XvPmzVOjRo1KWTYAAKgq3L5MM2LECI0fP17h4eGKjIzUqlWrZLPZFBsbK0maM2eOjh49qlmzZsnLy0uhoaEux9etW1d+fn6F2gEAQPXkdhiJiYlRVlaWFixYoPT0dIWGhmrx4sVq2LChJCkjI0M2m63MCwUAAFVTqSawDh06VEOHDi1y24wZMy567JgxYzRmzJjSdAsAAKqg6jeVGQBgTu16sufbmcgJF5f34A8AANxRI0BWL6sm/mei9p/cX6Fdd2/YXQ9HPVyhfaJkCCMAgAq3/+R+pR5PrdA+r7ni8p93hfLBZRoAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEXie2vVkz7fLarWargQAUAG8TRcAFFIjQFYvqyb+Z6L2n9xfoV13b9hdD0c9XKF9AkB1RxiBx9p/cr9Sj6dWaJ/XXHFNhfYHAOAyjcez5ztMlwAAQLliZMTDWb0semRlsvamn67Qfm8MC9YTfVpVaJ8AgOqJMFIJ7E0/rZ1H/qjQPkOCa1VofwCA6ovLNAAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwqVRhJSEhQdHS0IiIiNHDgQCUlJRW7b1JSkmJjY9W5c2e1bdtWt9xyi956663S1gsAAKoYtxfK27Bhg+Lj4/Xss88qKipKK1euVFxcnNavX68GDRoU2r9mzZoaNmyYwsLC5O/vr23btunZZ5+Vv7+/hgwZUiYfAgAAVF5uj4wsXbpUgwYN0uDBgxUSEqJJkyapfv36WrFiRZH7t27dWn379lXLli3VqFEj3XbbberevftFR1MAAED14VYYycvL086dO9W9e3eX9m7duik5OblE77Fr1y4lJyfruuuuc6drAABQRbl1mSYrK0t2u11169Z1aQ8KClJGRsZFj+3Ro4eOHz8uu92uhx56SIMHD3a7WLvd7vYxlZ3VajVdAipYdfw5r644v6uf6nZ+l/Tzuj1nRJIsFovLa4fDUajtrxISEpSTk6MffvhBc+bMUdOmTdW3b1+3+t2xY4fbtVZm/v7+at26tekyUMF2796t3Nxc02WgnHF+V0+c30VzK4wEBgbKarXq2LFjLu2ZmZkKCgq66LGNGzeWJIWFhenYsWN65ZVX3A4jERER/EsCVV5YWJjpEgCUk+p2ftvt9hINJLgVRnx9fdWmTRtt3LhRvXv3drZv2rRJN910U4nfx+Fw6Ny5c+50LenCkCZhBFUdP+NA1cX5XTS3L9OMGDFC48ePV3h4uCIjI7Vq1SrZbDbFxsZKkubMmaOjR49q1qxZki5cnrn66qvVvHlzSdK2bdu0ZMkSDRs2rAw/BgAAqKzcDiMxMTHKysrSggULlJ6ertDQUC1evFgNGzaUJGVkZMhmszn3z8/P10svvaTDhw/LarWqSZMmGjt2rDO8AACA6q1UE1iHDh2qoUOHFrltxowZLq+HDx+u4cOHl6YbAABQDbA2DQAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMKpUYSQhIUHR0dGKiIjQwIEDlZSUVOy+n376qUaMGKHrr79eUVFRGjJkiL799ttSFwwAAKoWt8PIhg0bFB8fr9GjRysxMVEdOnRQXFycjhw5UuT+W7duVdeuXbV48WK9//776ty5s0aPHq1du3ZddvEAAKDyczuMLF26VIMGDdLgwYMVEhKiSZMmqX79+lqxYkWR+0+aNElxcXFq27atmjVrpscff1xNmzbVl19+ednFAwCAys/bnZ3z8vK0c+dOjRo1yqW9W7duSk5OLtF75OfnKzs7WwEBAe50LUmy2+1uH1PZWa1W0yWgglXHn/PqivO7+qlu53dJP69bYSQrK0t2u11169Z1aQ8KClJGRkaJ3mPJkiXKzc3Vrbfe6k7XkqQdO3a4fUxl5u/vr9atW5suAxVs9+7dys3NNV0Gyhnnd/XE+V00t8JIAYvF4vLa4XAUaivKunXrNH/+fC1YsKBQoCmJiIgI/iWBKi8sLMx0CQDKSXU7v+12e4kGEtwKI4GBgbJarTp27JhLe2ZmpoKCgi567IYNGzRp0iS9/PLL6tq1qzvdOlmtVsIIqjx+xoGqi/O7aG5NYPX19VWbNm20ceNGl/ZNmzYpMjKy2OPWrVuniRMnas6cObrxxhtLVSgAAKia3L5MM2LECI0fP17h4eGKjIzUqlWrZLPZFBsbK0maM2eOjh49qlmzZkm6EEQmTJigp556Su3atXPOLalRo4bq1KlThh8FAABURm6HkZiYGGVlZWnBggVKT09XaGioFi9erIYNG0qSMjIyZLPZnPuvWrVK58+f15QpUzRlyhRn+4ABAzRjxowy+AgAAKAyK9UE1qFDh2ro0KFFbvtrwFi+fHlpugAAANUEa9MAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMKlUYSUhIUHR0tCIiIjRw4EAlJSUVu296errGjh2rPn36qFWrVpo+fXqpiwUAAFWP22Fkw4YNio+P1+jRo5WYmKgOHTooLi5OR44cKXL/vLw8BQYGavTo0WrVqtVlFwwAAKoWt8PI0qVLNWjQIA0ePFghISGaNGmS6tevrxUrVhS5f6NGjfT000+rf//+qlOnzmUXDAAAqhZvd3bOy8vTzp07NWrUKJf2bt26KTk5uUwLK4rdbi/3PjyN1Wo1XQIqWHX8Oa+uOL+rn+p2fpf087oVRrKysmS321W3bl2X9qCgIGVkZLjzVqWyY8eOcu/Dk/j7+6t169amy0AF2717t3Jzc02XgXLG+V09cX4Xza0wUsBisbi8djgchdrKQ0REBP+SQJUXFhZmugQA5aS6nd92u71EAwluhZHAwEBZrVYdO3bMpT0zM1NBQUHuVVgKVquVMIIqj59xoOri/C6aWxNYfX191aZNG23cuNGlfdOmTYqMjCzTwgAAQPXg9mWaESNGaPz48QoPD1dkZKRWrVolm82m2NhYSdKcOXN09OhRzZo1y3lMamqqJCk7O1vHjx9XamqqfHx81KJFizL6GAAAoLJyO4zExMQoKytLCxYsUHp6ukJDQ7V48WI1bNhQkpSRkSGbzeZyTP/+/Z3/v3PnTq1bt04NGzbUl19+eXnVAwCASq9UE1iHDh2qoUOHFrltxowZhdp2795dmm4AAEA1wNo0AADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAo0oVRhISEhQdHa2IiAgNHDhQSUlJF91/y5YtGjhwoCIiInTTTTdpxYoVpSoWAABUPW6HkQ0bNig+Pl6jR49WYmKiOnTooLi4OB05cqTI/Q8dOqRRo0apQ4cOSkxM1AMPPKDp06frk08+ueziAQBA5ed2GFm6dKkGDRqkwYMHKyQkRJMmTVL9+vWLHe1YuXKlrr76ak2aNEkhISEaPHiwBg4cqCVLllx28QAAoPLzdmfnvLw87dy5U6NGjXJp79atm5KTk4s8JiUlRd26dXNpu+GGG7RmzRqdO3dOPj4+l+zX4XA4+7dare6UXOlZrVZdW7+W/Cr4Yzer6y+73S4Ft5G8/Cq284Dmkt2u0CtC5WvxrdCum9RqIrvdLu/QUPn5Vmzf3s2ayW63X/jeUS1wfnN+V3UFn7fg7/HiuBVGsrKyZLfbVbduXZf2oKAgZWRkFHnMsWPHFBQU5NJWt25dnT9/XllZWapXr94l+83Pz5ck7dq1y51yq4w7QySF1KzgXnOUkpIiNR0pNa3griUpJUUDag2QapnoOkUafHvFdywpPSXFSL8wh/O7ortO4fw2oODv8eK4FUYKWCwWl9cOh6NQ26X2L6q9ON7e3oqIiJCXl1eJjwEAAGY5HA7l5+fL2/viccOtMBIYGCir1apjx465tGdmZhYa/ShQ1KjJ8ePH5e3trYCAgBL16+XlJd8KHlIDAAAVw60JrL6+vmrTpo02btzo0r5p0yZFRkYWeUz79u21adMml7bvvvtO4eHhJZovAgAAqja376YZMWKEVq9erdWrV2vfvn164YUXZLPZFBsbK0maM2eOxo8f79w/NjZWR44cUXx8vPbt26fVq1drzZo1uvfee8vuUwAAgErL7TkjMTExysrK0oIFC5Senq7Q0FAtXrxYDRs2lCRlZGTIZrM592/cuLEWL16s+Ph4JSQkqF69epo0aZL69OlTdp8CAABUWhbHpe63AQAAKEesTQMAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCrV4+CBsnLq1Cl99tlnSkpK0m+//aYzZ84oMDBQrVu3Vvfu3RUVFWW6RACXyWaz6bffflNubq6uvPJKtWzZkqdqwwW39sKI9PR0zZs3Tx999JGCgoLUtm1bXXXVVfLz89PJkye1Z88e7dy5Uw0aNNBDDz2kmJgY0yUDcMNvv/2mlStXav369bLZbC6rtvr4+Khjx46644471KdPH3l5MUhf3RFGYESXLl3Uv39/DRgwQKGhoUXuc+bMGX3++ed6++23dcstt+i+++6r4CoBlMa0adP0/vvvq3v37oqOjlZERISuuuoq1ahRQydPntTPP/+sbdu2ad26dfL29tYLL7ygtm3bmi4bBhFGYMTx48d15ZVXltv+AMyZOXOm4uLiSnTOfvPNN8rNzdUtt9xSAZXBUxFGAACAUUxghTFbt24t0X6dOnUq50oAlKfjx4/rt99+k8ViUcOGDRUYGGi6JHgYRkZgTKtWrWSxWCRJxf0YWiwWpaamVmRZAMrInj179Nxzz2n79u0u7Z06ddJzzz2n5s2bG6oMnoYwAmM6d+6sWrVqacCAAbrtttuK/ddSnTp1KrgyAJcrIyNDffv21ZVXXqnY2Fg1b95cDodD+/bt07vvvqsTJ05o3bp1qlu3rulS4QEIIzAmLy9Pn3/+udasWaOkpCT17NlTgwYNUo8ePZwjJgAqp9mzZ+u///2vVqxYIT8/P5dtZ86c0V133aVu3bpp7NixhiqEJyGMwCPYbDatWbNGiYmJysvL04ABAzRmzBh5ezOtCaiMBgwYoLi4uGKfEbR+/Xq98cYbWrt2bQVXBk/Ek2bgEa6++mo99NBDWrp0qZo1a6bFixfr9OnTpssCUEqHDh1SmzZtit0eHh6uQ4cOVWBF8GT8sxPG5eXl6ZNPPtGaNWuUkpKinj17atGiRQoICDBdGoBSys7OVu3atYvdXqtWLeXk5FRgRfBkhBEY8+OPP2rNmjXasGGDGjVqpAEDBuif//wnIQSoIrKzswvNFylw+vTpYu+iQ/XDnBEY06pVKzVo0ED9+/e/6HDuTTfdVIFVASgLf751vygOh4Nb9+FEGIExrVq1uuQ+/GEFVE5btmwp0X7XXXddOVeCyoAwAgAwgjWnUIC7aeDRcnNzTZcAoAw5HA598803euihh9SjRw/T5cBDEEbgkc6ePaslS5aoV69epksBUAYOHTqkuXPn6sYbb9S4ceNUo0YNzZ0713RZ8BDcTQNj8vLy9Oqrr+q7776Tj4+PRo4cqV69emnNmjWaO3euLBaL7r77btNlAiils2fP6uOPP9bq1auVkpKibt26KSMjQ4mJiQoNDTVdHjwIc0ZgzEsvvaSEhAR169ZN27dvV1ZWlgYNGqTNmzfrgQceUN++feXj42O6TACl8Nxzz2nDhg265ppr9Pe//10xMTEKDAxUmzZt9MEHH6hFixamS4QHYWQExvz73//WjBkz1Lt3b6Wlpal///46ffq01q9fz2PggUru3XffVVxcnOLi4i768DNAYs4IDLLZbIqIiJB04TZfHx8fxcXFEUSAKmDmzJn68ccfdcMNN+jRRx/VV199pfPnz5suCx6KP/VhzPnz510uw3h7e/MvKKCK6Nevn/r166fDhw/r/fff15QpU5Sbm6v8/Hzt3buXyzRwwZwRGNOqVSvdcccd8vf3lyQlJCTo73//u+rUqeOy35NPPmmiPABlyOFw6Ntvv9WaNWv05ZdfKjAwUDfffLOefvpp06XBAxBGYMzw4cMvuY/FYtGyZcsqoBoAZSk3N9f5D42/OnHihBITE/X+++/rww8/rODK4IkIIwCAMhceHq62bduqc+fO6ty5s6KiouTr62u6LHgowggAoMwlJiZq69at2rJliw4dOiQ/Pz+1a9fOGU7atWvHrftwIozAmJiYGL3zzjsKCAiQJE2ePFmPPfaYc62KzMxMRUdH64cffjBYJYDL9fvvv+v777/Xli1btHnzZh05ckQ1atRQVFSU3nzzTdPlwQNway+M2b9/v+x2u/P1+vXrlZ2d7XztcDh09uxZE6UBKEP169dX//799cILL+jNN9/U/fffLy8vL23atMl0afAQ3NoLj1HUIJ3FYjFQCYCycujQIeeoyJYtW3Tq1ClFRkYqLi5OnTp1Ml0ePARhBABQ5iZMmKDNmzcrOztbUVFR6tixo4YNG6bw8HBZrVbT5cHDEEZgjMViYeQDqKI++OADNWjQQPfff7+6dOmi1q1bc76jWExghTGtWrVSy5YtnY9/3717t5o3b+6cYX/+/Hnt3btXqampJssEUAr79u1zXprZunWrzp49qw4dOqhTp0667rrr1KZNG3l5MW0RFxBGYMz8+fNLtN9DDz1UzpUAKG979+513k2zbds2nTlzRh06dNCiRYtMlwYPQBgBAFSIjIwMZyBZv369cnJyGPmEJOaMAADKSWZmpjZv3qzNmzdry5Yt+uWXX+Tj46O2bdvqnnvuUefOnU2XCA/ByAgAoMzFxMTowIEDslqtioiI0HXXXafrr79eUVFR8vPzM10ePAwjIwCAMnfTTTepc+fO6tChQ7EL5gEFGBkBAFSY8+fP6+zZs6pVq5bpUuBBuK8KAFDmvvnmGyUmJrq0LVy4UJGRkerUqZPuvfdenTx50kxx8DiEEXi0xMREHTx40HQZANz0xhtvuKw1tX37ds2bN0//+Mc/9M9//lM2m00LFiwwWCE8CWEEHm3ixImKiYnR1KlTTZcCwA179+5VZGSk8/Unn3yirl27avTo0br55ps1ceJEffXVVwYrhCdhAis8Wlpamg4fPqzvvvvOdCkA3JCdna2AgADn623btqlPnz7O1y1atFB6erqByuCJGBmBx2vUqJFiY2NNlwHADfXq1dO+ffskXQgmaWlpioqKcm4/ceKEatSoYao8eBjCCIy79tprlZmZWag9KytL1157rYGKAFyuPn366IUXXlBiYqImT56s4OBgtW/f3rn9p59+0jXXXGOuQHgULtPAuOLuLs/Ly3MumgegcnnooYeUnp6u6dOnKygoSLNnz5bVanVuX7dunf72t78ZrBCehDACY5YtWyZJslgseu+991SzZk3ntvz8fG3dulXNmzc3VR6Ay+Dv76/Zs2cXu3358uUVWA08HQ89gzHR0dGSpCNHjqh+/fouy4n7+PioUaNGevjhh9WuXTtTJQIAKgBhBMYNHz5c8+fP1xVXXGG6FACAAYQReIy8vDwdPnxYTZo0kbc3VxABoLrgbhoYd+bMGT311FNq3769+vbtK5vNJkmaNm2aFi9ebLg6AEB5I4zAuBdffFFpaWlatmyZy9LiXbp00YYNGwxWBgCoCIQRGPfFF1/omWeeUceOHV3aW7Rowbo0QBXG2lMoQBiBccePH1fdunULtefm5spisRioCEBFYO0pFCCMwLiIiAh9/fXXhdrfffddlyc2Aqha0tLS9PHHH6tly5amS4Fh3E0D47Zv366RI0eqX79+Wrt2rYYMGaK9e/cqJSVFy5cvV3h4uOkSAQDliJERGBcVFaUVK1bozJkzatKkiTZu3Ki6detq5cqVBBGgkmPtKZQED3OARwgLC9PMmTNNlwGgjLH2FEqCMAKPkJ+fr19//VWZmZmF/vDq1KmToaoAlBZrT8EdzBmBcSkpKRo7dqyOHDlSKIhYLBalpqYaqgxAabH2FNxBGIFxt912m5o1a6aHH35YwcHBhW7nrVOnjqHKAFwu1p5CSRBGYFz79u31wQcfqGnTpqZLAVBOWHsKF8PdNDCubdu2+vXXX02XAaAcsPYUSoIwAuOGDx+umTNn6v3339dPP/2ktLQ0l18AKi/WnkJJMFYG48aMGSNJeuqpp5xtFotFDoeDCaxAJffFF19o7ty5hZ6mzNpT+DPCCIz74osvTJcAoJyw9hRKgjAC4xo2bGi6BADlpGDtqeHDh7u0s/YU/owwAuPWrl2rwMBA3XjjjZKkWbNm6d1331WLFi00Z84cwgpQiT3++OMaOXKk9u7dK7vdrmXLlrmsPQVITGCFB3jttdecE9uSk5OVkJCgJ554QgEBAYqPjzdcHYDLwdpTKAlGRmDc77//7nzGyOeff64+ffpoyJAhioqKKjS0C6DyYe0pXAphBMbVrFlTJ06cUIMGDbRx40bdc889kiQ/Pz+dPXvWbHEALhtrT+FSCCMwrmvXrnr66ad17bXX6pdfflHPnj0lSXv27GG+CFDJsfYUSoLHwcO4P/74Q//85z9ls9l05513qkePHpKkefPmycfHR6NHjzZcIYDSYu0plARhBABQblh7CiXB3TTwCElJSRo3bpxiY2N19OhRSVJiYqKSkpIMVwbgcrD2FEqCOSMw7pNPPtH48ePVr18/7dy5U3l5eZKk7OxsLVq0SB07djRcIYDSKlh76tixYwoNDS20Ym+rVq0MVQZPwmUaGNe/f3/dc8896t+/vyIjI/Xhhx+qcePGSk1N1ciRI7Vx40bTJQIopaLCBmtP4a8YGYFxBw4cKHL0o3bt2vrjjz8MVASgrLD2FEqCMALjgoODdfDgQTVq1Milfdu2bWrcuLGhqgCUBW7PR0kwgRXGDRkyRNOnT9cPP/wgi8Wio0eP6sMPP9TMmTN11113mS4PwGVYu3atvv76a+frWbNmqWPHjoqNjdVvv/1mrjB4FOaMwCPMnTtXb731lvOJq76+vrr33nv16KOPmi0MwGXp06ePnnvuOXXp0kXJycm655579NRTT+mrr76St7e35s+fb7pEeAAu08Aou92ubdu2acSIEXrggQe0d+9eORwOhYSEqFatWqbLA3CZWHsKJcFlGhhltVp133336dSpU/L391dERITatm1LEAGqiIK1pyRp48aN6tq1qyTWnoIrRkZgXGhoqA4fPsxkVaAKYu0plAQjIzDuscce08yZM/XVV18pPT1dp0+fdvkFoPJ69tln1b59ex0/flzz5s1TYGCgJGnnzp363//9X8PVwVMwgRXG/fmhSH9eRIuHIgFA9cBlGhi3bNky0yUAKEdJSUlauXKlDh8+rJdffllXXXWVEhMT1ahRI5Z7gCTCCDzAddddZ7oEAOWEtadQEswZgRFHjhxxa/+ClXwBVC4LFy7U888/r2nTprkskhcVFaVdu3YZrAyehDACI26//XY9/fTT+vHHH4vd59SpU3r33XfVt29fffrppxVYHYCywtpTKAku08CIDRs2aNGiRRo5cqS8vb0VHh6uevXqyc/PTydPntS+ffu0Z88ehYeH64knnnDeDgigcmHtKZQEd9PAqLNnz+qbb75RUlKSfvvtN505c0aBgYFq3bq1unfvrtDQUNMlArgMr7/+uhITE/XCCy9oxIgRWrx4sY4cOaL4+Hg9+OCDGjZsmOkS4QEIIwCAcsXaU7gUwggAoFwUrD0VGhoqPz8/1p5CsQgjAIByExERoQ0bNjA/BBfF3TQAgHJTsPYUcDGMjAAAys13332nF198UY888ojatGmjmjVrumyvXbu2ocrgSQgjAIByw9pTKAmeMwIAKDesPYWSYGQEAAAYxQRWAECZYu0puIswAgAoU6w9BXdxmQYAUKZOnDihRYsWac2aNZdce2r06NGsPQXCCACgfLD2FEqKMAIAAIxizggAADCKMAIAAIwijAAAAKMIIwAAwCjCCFDBhg8frunTp5fb+0+cOFH/+Mc/it3+yiuv6Lbbbiu3/stbdHS03nrrLY/pq7J/n4AnIIwAQDFWr16tIUOGOF+HhYXp888/N1gRUDWxUB4A/EVeXp58fX115ZVXmi4FqBYYGQEMsNvtmjJlijp27KjOnTtr7ty5KnjkzwcffKCBAwcqMjJS3bp109ixY5WZmely/J49ezRq1ChFRUUpMjJSd911lw4ePFhkXz/99JO6dOmihQsXurSvXLlSPXv2VLt27fTwww/rjz/+cG7Lz8/X/Pnz1aNHD4WHh+u2227Tf/7zH+f2w4cPKywsTJ9++qmGDx+udu3a6e9//7uSk5Nd+ti+fbuGDh2qtm3bqmfPnpo2bZpycnKK/V4OHjyo0aNHq2vXroqMjNSgQYO0adOmi36X+/bt05133qmIiAjFxMRo06ZNhUYwdu/erbvvvltt27ZV586dNXnyZGVnZzu3F1zaWrRokbp3765bbrlFkutlmujoaEnSgw8+qLCwMOfrAomJiYqOjlaHDh302GOP6fTp085tw4cP19SpUzV9+nR16tRJXbt21apVq5STk6Mnn3xSkZGR6tWrl7755puLflagqiKMAAasXbtWVqtV7777riZNmqS3335b7733niTp3LlzeuSRR/Thhx/q1Vdf1eHDhzVx4kTnsUePHtWwYcPk5+ent99+W++//74GDRqk8+fPF+pn8+bNuueeezRmzBiNHj3a2X7w4EH9+9//1muvvaY33nhDaWlpev75553bly1bpqVLl2rChAn68MMP1b17d/3jH//QL7/84vL+c+fO1X333afExEQ1a9ZMY8eOddaxe/du3Xffferdu7c+/PBDzZ07V9u2bdPUqVOL/V5ycnLUs2dPLV26VGvXrlX37t31wAMPFLvwWn5+vh588EH5+/vrvffe05QpUzR37lyXfXJzczVy5EhdccUVWr16tf75z39q06ZNher473//q3379mnp0qV67bXXCvW1evVqSVJ8fLy+++475+uC7/OLL77Qa6+9pkWLFmnr1q16/fXXXY5fu3atAgMD9d5772nYsGF67rnn9MgjjygyMtL5WcePH6/c3Nxivx+gynIAqFDDhg1z3HrrrY78/Hxn2+zZsx233nprkfv/8MMPjtDQUMfp06cdDofDMWfOHEd0dLQjLy+vyP0nTJjgGD16tOOzzz5zREZGOj766COX7fPmzXNce+21DpvN5mz75ptvHK1atXKkp6c7HA6Ho3v37o6FCxe6HDdo0CDHc88953A4HI5Dhw45QkNDHe+++65z+549exyhoaGOvXv3OhwOh+OJJ55wTJ482eU9tm7d6mjVqpXjzJkzxX9BfxETE+NYvny58/Xf/vY3x9KlS511t27d2lm3w+FwbNy40REaGur47LPPHA6Hw7Fq1SpHp06dHNnZ2c59vv76a0erVq0cGRkZzu+sa9eujrNnz7r0/ee+HA6Hy/sWmDdvnqNdu3aOU6dOOdtmzpzpGDx4sPP1sGHDHHfeeafz9fnz5x3t27d3PPHEE8629PR0R2hoqCM5ObmkXw1QZTBnBDCgXbt2slgsztft27fX0qVLZbfbtXv3br3yyitKS0vTiRMnnJdvbDabWrRoodTUVHXs2FE+Pj7Fvv+PP/6or7/+Wi+//LJ69+5daPvVV1+t+vXrO19HRkYqPz9fBw4ckL+/v9LT0xUVFeVyTFRUlNLS0lzawsLCnP8fHBwsSTp+/LhCQkK0c+dO/frrr/roo4+c+zgcDuXn5+vw4cMKCQkpVFdOTo7mz5+vr7/+Wunp6bLb7Tpz5kyxIyMHDhxQ/fr1nX1LUtu2bV322bdvn8LCwlSzZk2Xz1LweYOCgiRJoaGh8vX1LbKfS2nYsKFq167tfF2vXr1Cl9b+/F1ZrVYFBAS4rM1SUMdfjwOqA8II4EHOnj2re++9V926ddPs2bMVGBgom82m++67T+fOnZMk1ahR45Lv07hxYwUEBGjNmjXq2bPnJf+SLQhGfw5If/5/6UKQ+GvbnwNRwbb8/Hznf2NjYzV8+PBC/V199dVF1jFr1ix99913mjBhgpo0aaIaNWro4Ycfdn72vyqqJnf2+XO7v7//Rd/nYry9C/9R6vjLsl9/3cdisbi0FdTy1+OA6oA5I4ABP/zwQ6HXTZs21f79+5WVlaVx48apY8eOCgkJKfJf2ElJScX+BS1JgYGBevvtt3Xw4EE99thjhfa12Ww6evSo83VycrK8vLzUrFkz1a5dW/Xq1dO2bdtcjklOTi5yNKM4rVu31p49e9S0adNCv4oLR9u2bdOAAQPUu3dvhYWFKSgoSL/99luxfTRv3lw2m03Hjh1ztu3YscNlnxYtWigtLc1l4uz27dudn9cdPj4+stvtbh0D4NIII4ABNptN8fHx2r9/v9atW6d//etfuvvuu9WgQQP5+Pho+fLlOnTokL744gstWLDA5dihQ4fq9OnTevzxx7Vjxw798ssvSkxM1P79+132q1u3rt5++23t37/fZWKpJPn5+WnixIlKS0tTUlKSpk2bpltvvdV5ueO+++7T66+/rg0bNmj//v168cUXlZaWprvvvrvEnzEuLk4pKSl6/vnnlZqaql9++UVffPHFRSewNmnSRJ999plSU1OVlpamsWPHOkdaitKtWzc1btxYEyZMUFpamrZt21ZoAmu/fv3k6+uriRMn6ueff9b333+vqVOn6rbbbnNeGimphg0b6r///a8yMjJ08uRJt44FUDzCCGBA//79debMGQ0ePFhTpkzRsGHDNGTIEF155ZWaMWOGPv74Y8XExOj111/XhAkTXI4tGPXIycnR8OHDNXDgQL333ntFziEJDg7W22+/rd27d2vcuHHOf9U3adJEvXv3VlxcnO69916Fhobq2WefdR539913a8SIEZoxY4b+/ve/69tvv9WCBQvcGklo1aqVli9frl9//VV33XWXBgwYoJdfftllfscrr7zicovsk08+qf/5n/9RbGysHnjgAd1www1q06ZNsX1YrVa9+uqrysnJ0e23366nn37aedeQn5+fpAuXX958802dOHFCt99+ux555BF16dJFkydPLvFnKTBhwgRt2rRJN954owYMGOD28QCKZnFwgRKAIQW3LM+YMaPM3nPbtm2666679Nlnn6lJkyZl9r4Ayg8TWAEYs2XLFiUkJFzWe3z22WeqWbOmmjZtqoMHD2r69OmKiooiiACVCGEEgDFffvnlZb9Hdna2Zs+eLZvNpsDAQHXt2rXQpS0Ano3LNAAAwCgmsAIAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACj/h8i3q4I+c39tAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options = [\"ERM\", \"SWAD\"]\n",
    "backbones = ['resnet']\n",
    "unsupervised_adaptations = [\"NO\"]\n",
    "df_unsupervised = df.loc[df.Adapt_Algorithm.isin(unsupervised_adaptations)]\n",
    "ERM_SWAD_Algorithms = df_unsupervised.loc[df_unsupervised.algorithm.isin(options)]\n",
    "ERM_SWAD_Comparison = ERM_SWAD_Algorithms.loc[ERM_SWAD_Algorithms.backbone.isin(backbones)]\n",
    "\n",
    "print(ERM_SWAD_Comparison)\n",
    "grouped_df = ERM_SWAD_Comparison.groupby([\"backbone\", \"algorithm\", \"Adapt_Algorithm\", \"Location\"]).mean().reset_index()\n",
    "sns.set_style(\"whitegrid\")\n",
    "# Plotting grouped bar chart\n",
    "grouped_df.pivot_table(index=['backbone', 'algorithm'], columns='Location', values='results').plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bbaf06f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Adapt_Algorithm  backbone  algorithm  testenv  results  Location\n",
      "4               NO    resnet        ERM        0   0.4500       100\n",
      "5               NO    resnet        ERM        1   0.3310        38\n",
      "6               NO    resnet        ERM        2   0.5720        43\n",
      "7               NO    resnet        ERM        3   0.3370        46\n",
      "16              NO  ConvNext        ERM        0   0.6269       100\n",
      "17              NO  ConvNext        ERM        1   0.5040        38\n",
      "18              NO  ConvNext        ERM        2   0.6751        43\n",
      "19              NO  ConvNext        ERM        3   0.5260        46\n",
      "44           UniDG    resnet  ERM+UniDG        0   0.6150       100\n",
      "45           UniDG    resnet  ERM+UniDG        1   0.4982        38\n",
      "46           UniDG    resnet  ERM+UniDG        2   0.6044        43\n",
      "47           UniDG    resnet  ERM+UniDG        3   0.4823        46\n",
      "48           UniDG  ConvNext  ERM+UniDG        0   0.7361       100\n",
      "49           UniDG  ConvNext  ERM+UniDG        1   0.6008        38\n",
      "50           UniDG  ConvNext  ERM+UniDG        2   0.6996        43\n",
      "51           UniDG  ConvNext  ERM+UniDG        3   0.5751        46\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='backbone,algorithm'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "options = [\"ERM\",\"ERM+UniDG\" ]\n",
    "backbones = ['resnet','ConvNext']\n",
    "\n",
    "df_unsupervised = df.loc[df.Adapt_Algorithm.isin(unsupervised_adaptations)]\n",
    "ERM_SWAD_Algorithms = df_unsupervised.loc[df_unsupervised.algorithm.isin(options)]\n",
    "ERM_SWAD_Comparison = ERM_SWAD_Algorithms.loc[ERM_SWAD_Algorithms.backbone.isin(backbones)]\n",
    "\n",
    "print(ERM_SWAD_Comparison)\n",
    "grouped_df = ERM_SWAD_Comparison.groupby([\"backbone\", \"algorithm\", \"Adapt_Algorithm\", \"Location\"]).mean().reset_index()\n",
    "sns.set_style(\"whitegrid\")\n",
    "# Plotting grouped bar chart\n",
    "grouped_df.pivot_table(index=['backbone', 'algorithm'], columns='Location', values='results').plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8536eca3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.646600\n",
      "1    0.652900\n",
      "2    0.549975\n",
      "Name: results, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Adapt_Algorithm,backbone,algorithm'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options = [\"SWAD\",\"SIMPLE\",\"Inter\"]\n",
    "Implemented_algorithm = df.loc[df[\"algorithm\"].isin(options)]\n",
    "adapted = [\"T3A\",\"UniDG\"]\n",
    "condition = df[\"Adapt_Algorithm\"] == (\"UniDG\" or \"T3A\") \n",
    "adapted_part = df[condition]\n",
    "non_adapted_part = df[~condition]\n",
    "non_adapted_part.drop([\"Adapt_Algorithm\"], axis = 1)\n",
    "accuracies_adapted = adapted_part.groupby([\"backbone\", \"algorithm\", \"Adapt_Algorithm\"]).mean().reset_index()\n",
    "print(accuracies_adapted['results'])\n",
    "adapted_df = adapted_part.groupby([\"backbone\", \"algorithm\", \"Adapt_Algorithm\", \"Location\"]).mean().reset_index()\n",
    "\n",
    "adapted_df.pivot_table(index=['Adapt_Algorithm','backbone', 'algorithm'], columns='Location', values='results').plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fd46e543",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Adapt_Algorithm  backbone algorithm  testenv  results  Location\n",
      "0              NO  ConvNext     Inter        0   0.4250       100\n",
      "1              NO  ConvNext     Inter        3   0.5435        46\n",
      "2              NO  ConvNext     Inter        2   0.5056        43\n",
      "3              NO  ConvNext     Inter        1   0.4200        38\n",
      "4              NO    resnet       ERM        0   0.4500       100\n",
      "5              NO    resnet       ERM        1   0.3310        38\n",
      "6              NO    resnet       ERM        2   0.5720        43\n",
      "7              NO    resnet       ERM        3   0.3370        46\n",
      "8              NO    resnet     mixup        0   0.5637       100\n",
      "9              NO    resnet     mixup        1   0.4363        38\n"
     ]
    }
   ],
   "source": [
    "print(non_adapted_part.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c9a59e1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 9912422/9912422 [00:04<00:00, 2153273.87it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 28881/28881 [00:00<00:00, 112412.90it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1648877/1648877 [00:01<00:00, 1430661.06it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4542/4542 [00:00<00:00, 20331407.44it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Load Colored MNIST dataset\n",
    "colored_mnist_train = torchvision.datasets.MNIST(root='./data', train=True, transform=torchvision.transforms.Compose([\n",
    "                                torchvision.transforms.ToTensor(),\n",
    "                                torchvision.transforms.Lambda(lambda x: x.repeat(3, 1, 1))]), download=True)\n",
    "\n",
    "# Choose two random images\n",
    "idx1 = np.random.randint(0, len(colored_mnist_train))\n",
    "idx2 = np.random.randint(0, len(colored_mnist_train))\n",
    "\n",
    "# Get the images and labels\n",
    "img1, label1 = colored_mnist_train[idx1]\n",
    "img2, label2 = colored_mnist_train[idx2]\n",
    "\n",
    "# Convert images to numpy arrays\n",
    "img1_np = img1.numpy().transpose((1, 2, 0))  # Convert from (C, H, W) to (H, W, C)\n",
    "img2_np = img2.numpy().transpose((1, 2, 0))\n",
    "\n",
    "# Mixup parameter (lambda)\n",
    "lambda_val = 0.5  # You can adjust this value\n",
    "\n",
    "# Overlay the images using Mixup\n",
    "mixed_img_np = lambda_val * img1_np + (1 - lambda_val) * img2_np\n",
    "\n",
    "# Visualize the original images\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.imshow(img1_np)\n",
    "plt.title(f\"Image 1 (Label: {label1})\")\n",
    "plt.axis('off')\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.imshow(img2_np)\n",
    "plt.title(f\"Image 2 (Label: {label2})\")\n",
    "plt.axis('off')\n",
    "\n",
    "# Visualize the mixed image\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.imshow(mixed_img_np)\n",
    "plt.title(\"Mixed Image\")\n",
    "plt.axis('off')\n",
    "\n",
    "plt.show()\n"
   ]
  }
 ],
 "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}