diff --git a/CKA/.gitignore b/CKA/.gitignore
deleted file mode 100644
index 404cd4f20f412b74a2a10ed7c281c128992bf8c3..0000000000000000000000000000000000000000
--- a/CKA/.gitignore
+++ /dev/null
@@ -1,4 +0,0 @@
-.ipynb_checkpoints
-MNIST/*
-*.PNG.*
-*.PNG
diff --git a/CKA/Scratch.ipynb b/CKA/Scratch.ipynb
deleted file mode 100644
index 34853d04708788dcd256e4194da1575eeebac800..0000000000000000000000000000000000000000
--- a/CKA/Scratch.ipynb
+++ /dev/null
@@ -1,765 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "9843efe9-d80a-420c-bb5c-2d82cbd2efa1",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "device='cuda'\n",
- "tensor([3, 9, 4, 9, 9], device='cuda:0')\n",
- "79\n",
- "tensor([8, 2, 3, 3, 4], device='cuda:0')\n",
- "79\n"
- ]
- }
- ],
- "source": [
- "import sys\n",
- "import os\n",
- "# add the path to find colour_mnist\n",
- "sys.path.append(os.path.abspath('../ReferenceCode'))\n",
- "\n",
- "from torchvision.datasets import MNIST\n",
- "from torch.utils.data import DataLoader\n",
- "import torchvision.transforms as transforms\n",
- "\n",
- "import colour_mnist as cm\n",
- "import torch\n",
- "import random\n",
- "import numpy as np\n",
- "# fix random seed for reproducibility\n",
- "seed = 1\n",
- "torch.manual_seed(seed)\n",
- "torch.backends.cudnn.benchmark = False\n",
- "torch.backends.cudnn.deterministic = True\n",
- "random.seed(seed)\n",
- "torch.cuda.manual_seed(seed)\n",
- "np.random.seed(seed)\n",
- "\n",
- "device=(\"cuda\")\n",
- "print(f\"{device=}\")\n",
- "# biased means each digit has correct label and consistent colour - Expect network to learn the colours only\n",
- "#TDL = cm.get_biased_mnist_dataloader(root=\"./MNIST\", batch_size=128, data_label_correlation=0.1, train=False, bg_noise_level=0.1)\n",
- "transform_bw = transforms.Compose([\n",
- " transforms.Grayscale(num_output_channels=3), # Convert to 3 channels \n",
- " transforms.ToTensor(), # convert to tensor. We always do this one \n",
- " transforms.Normalize((0.1307,) * 3, (0.3081,) * 3) \n",
- "])\n",
- "#mnist_train = MNIST(\"./MNIST\", train=True, download=True, transform=transform_bw)\n",
- "mnist_test = MNIST(\"./MNIST\", train=False, download=True, transform=transform_bw)\n",
- "\n",
- "#bw_train_dataloader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
- "TDL = DataLoader(mnist_test, batch_size=128, shuffle=True, drop_last=False)\n",
- "\n",
- "count = 0\n",
- "for data in TDL:\n",
- " inputs, labels = data[0:2]\n",
- " inputs = inputs.to(device)\n",
- " labels = labels.to(device)\n",
- " if count == 0:\n",
- " print(labels[:5])\n",
- " count += 1\n",
- "print(count)\n",
- "\n",
- "\n",
- "count = 0\n",
- "for data in TDL:\n",
- " inputs, labels = data[0:2]\n",
- " inputs = inputs.to(device)\n",
- " labels = labels.to(device)\n",
- " if count == 0:\n",
- " print(labels[:5])\n",
- " count += 1\n",
- "print(count)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "fa5cec2d-c9ad-41c8-9060-fef4cb8086fe",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "ResNet(\n",
- " (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
- " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (relu): ReLU(inplace=True)\n",
- " (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
- " (layer1): Sequential(\n",
- " (0): BasicBlock(\n",
- " (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
- " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (relu): ReLU(inplace=True)\n",
- " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
- " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- " (1): BasicBlock(\n",
- " (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
- " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (relu): ReLU(inplace=True)\n",
- " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
- " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- " )\n",
- " (layer2): Sequential(\n",
- " (0): BasicBlock(\n",
- " (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
- " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (relu): ReLU(inplace=True)\n",
- " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
- " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (downsample): Sequential(\n",
- " (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
- " (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- " )\n",
- " (1): BasicBlock(\n",
- " (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
- " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (relu): ReLU(inplace=True)\n",
- " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
- " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- " )\n",
- " (layer3): Sequential(\n",
- " (0): BasicBlock(\n",
- " (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
- " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (relu): ReLU(inplace=True)\n",
- " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
- " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (downsample): Sequential(\n",
- " (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
- " (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- " )\n",
- " (1): BasicBlock(\n",
- " (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
- " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (relu): ReLU(inplace=True)\n",
- " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
- " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- " )\n",
- " (layer4): Sequential(\n",
- " (0): BasicBlock(\n",
- " (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
- " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (relu): ReLU(inplace=True)\n",
- " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
- " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (downsample): Sequential(\n",
- " (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
- " (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- " )\n",
- " (1): BasicBlock(\n",
- " (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
- " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (relu): ReLU(inplace=True)\n",
- " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
- " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- " )\n",
- " (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
- " (fc): Linear(in_features=512, out_features=10, bias=True)\n",
- ")\n"
- ]
- }
- ],
- "source": [
- "import torchvision\n",
- "\n",
- "model = torchvision.models.resnet18( num_classes=10) \n",
- "print(model)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "1bd9963e-007b-47cd-a9ca-b25f6e497fb5",
- "metadata": {},
- "outputs": [
- {
- "ename": "NameError",
- "evalue": "name 'CHEESE' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[11], line 33\u001b[0m\n\u001b[1;32m 31\u001b[0m sample \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata[idx], \u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtargets[idx])\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sample\n\u001b[0;32m---> 33\u001b[0m \u001b[43mSyntheticDataset\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrand\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.0\u001b[39;49m\u001b[43m)\u001b[49m\n",
- "Cell \u001b[0;32mIn[11], line 21\u001b[0m, in \u001b[0;36mSyntheticDataset.__init__\u001b[0;34m(self, train, activations, noise)\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(num_classes):\n\u001b[1;32m 17\u001b[0m \u001b[38;5;66;03m#activations = torch.randn(self.shape)\u001b[39;00m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;66;03m#print(activations.shape)\u001b[39;00m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata[c \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m num_classes : \n\u001b[1;32m 20\u001b[0m ((c\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m num_classes)] \u001b[38;5;241m=\u001b[39m activations[c]\n\u001b[0;32m---> 21\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtargets[c \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m num_classes : \u001b[43mCHEESE\u001b[49m\n\u001b[1;32m 22\u001b[0m ((c\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m num_classes)] \u001b[38;5;241m=\u001b[39m c\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m noise \u001b[38;5;241m*\u001b[39m torch\u001b[38;5;241m.\u001b[39mrandn(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;66;03m# add a configurable amount of noise\u001b[39;00m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mclamp(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n",
- "\u001b[0;31mNameError\u001b[0m: name 'CHEESE' is not defined"
- ]
- }
- ],
- "source": [
- "class SyntheticDataset(torch.utils.data.Dataset):\n",
- " def __init__(self, train, activations, noise=0.0):\n",
- " \"\"\"\n",
- " Args:\n",
- " train (bool): for train=True set, return 60_000 samples, otherwise 10_000\n",
- " activations: base set of activations: one per class. values in range [0,1]\n",
- " noise (float): factor by which to scale randn values before adding to the base activations. \n",
- " \"\"\"\n",
- " self.train = train\n",
- " self.size = 60_000 if train else 10_000\n",
- " \n",
- " self.data = torch.empty((self.size,) + activations[0].shape) # start off empty\n",
- " self.targets = torch.empty(self.size) #, dtype=torch.int) # Random targets\n",
- " num_classes = 10 # standard for MNIST and CIFAR-10\n",
- " \n",
- " for c in range(num_classes):\n",
- " #activations = torch.randn(self.shape)\n",
- " #print(activations.shape)\n",
- " self.data[c * self.size // num_classes : \n",
- " ((c+1) * self.size // num_classes)] = activations[c]\n",
- " self.targets[c * self.size // num_classes : \n",
- " ((c+1) * self.size // num_classes)] = c\n",
- "\n",
- " self.data += noise * torch.randn(self.data.shape) # add a configurable amount of noise\n",
- " self.data = torch.clamp(self.data, 0, 1) # and clamp to [0,1] range again so that it is batchnorm compatible\n",
- " \n",
- " def __len__(self):\n",
- " return self.size\n",
- "\n",
- " def __getitem__(self, idx):\n",
- " sample = self.data[idx], int(self.targets[idx])\n",
- " return sample\n",
- "SyntheticDataset(False, torch.rand(10,5), 0.0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8d67ba0d-6e96-4d1c-a60e-9b702c46f14f",
- "metadata": {},
- "outputs": [],
- "source": [
- "%matplotlib inline\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "b_train_iterator = iter(unbiased_train_dataloader)\n",
- "b_train_batch = next(b_train_iterator)\n",
- "#print(len(b_train_batch))\n",
- "#print(b_train_batch[0].shape)\n",
- "#print(b_train_batch[1].shape)\n",
- "#print(b_train_batch[2].shape)\n",
- "print(\"targets\")\n",
- "print(b_train_batch[1])\n",
- "print(\"bias_labels\")\n",
- "print(b_train_batch[2])\n",
- "print()\n",
- "\n",
- "for i in range(40):\n",
- " if b_train_batch[2][i] == 5:\n",
- " plt.imshow(b_train_batch[0][i].permute(1,2,0))\n",
- " print(b_train_batch[1][i])\n",
- " print(b_train_batch[2][i])\n",
- " plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "feb40aab-fd8c-425f-b462-99dcc8869862",
- "metadata": {},
- "outputs": [],
- "source": [
- "import torch\n",
- "import numpy as np\n",
- "data = torch.rand(5,3)\n",
- "print(data)\n",
- "\n",
- "indices = np.arange(len(data))\n",
- "targets = np.arange(100, 100+len(data))\n",
- "np.random.shuffle(indices)\n",
- "\n",
- "data = data[indices]\n",
- "targets = targets[indices]\n",
- "print(data)\n",
- "print(targets)\n",
- "\n",
- "n_labels = 6\n",
- "bias_indices = {str(label): int for label in range(n_labels)}\n",
- "print(bias_indices)\n",
- "\n",
- "targets = np.array([0, 1, 0, 2, 3, 0, 2])\n",
- "label = 0\n",
- "print(targets)\n",
- "indices = np.where((targets == label))[0]\n",
- "print(indices)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ef779d2c-4ad8-4ee6-8190-cce39319577f",
- "metadata": {},
- "outputs": [],
- "source": [
- "class MyClass:\n",
- " def __init__(self, name):\n",
- " super().__init__()\n",
- " self.name = name\n",
- " \n",
- " def print_hello(self, title):\n",
- " print(f\"Hello {title} {self.name}!\")\n",
- " \n",
- " def __call__(self, title):\n",
- " self.print_hello(title)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "05e5d91e-64c2-48a3-bf64-8d562262aa5a",
- "metadata": {},
- "outputs": [],
- "source": [
- "wibble = MyClass(\"Damian\")\n",
- "wibble.print_hello(\"Mx\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1dd165ae-0487-4e71-99ef-50e5d8c17a6e",
- "metadata": {},
- "outputs": [],
- "source": [
- "wibble(\"The\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ce098b94-82c1-4a84-b6c6-f5f063207e13",
- "metadata": {},
- "outputs": [],
- "source": [
- "import torch\n",
- "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
- "print(device)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "af722500-f4a1-43d4-aa57-615364afc69d",
- "metadata": {},
- "outputs": [],
- "source": [
- "t = torch.tensor([4,4,5])\n",
- "print(t)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "03cefaeb-3eb7-404d-abcc-f8eaea40fe76",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(t.device) #: returns the device that the tensor is on\n",
- "tcd = t.cuda() #: copies a tensor t to the first cuda device and returns the result (which we save in tc).\n",
- "tcp = t.cpu() #copies tensor t to the cpu and returns the result\n",
- "tvar = t.to(\"cuda:2\") #: copies tensor t to device dev. dev can be \"cpu\", \"cuda\" or, if the machine has multiple cuda devices \"cuda:n\" where n is replaced with the device number; a machine with 4 GPUs will have devices 0, 1, 2, 3.\n",
- "print(torch.cuda.is_available()) #: returns true if there is a cuda device available to use; false otherwise.\n",
- "\n",
- "tvar - tcp.to(\"cuda:2\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "09e61f06-e3bb-43fe-88e2-964574b2eff8",
- "metadata": {},
- "outputs": [],
- "source": [
- "import torch.nn as nn\n",
- "class MyModel(nn.Module):\n",
- " def __init__(self):\n",
- " super().__init__()\n",
- " # create all the layers with learnable parameters as instance variables:\n",
- " self.conv1 = nn.Conv2d(3, 3, kernel_size=3, padding=1)\n",
- " self.conv2 = nn.Conv2d(3, 128, kernel_size=3)\n",
- " self.linear = nn.Linear(128, 10)\n",
- " # we can also create other layers for later use too:\n",
- " self.relu = nn.ReLU()\n",
- " def forward(self, x):\n",
- " # pass the input through the first conv and relu:\n",
- " x1 = self.relu(self.conv1(x))\n",
- " # residual connection; storing the result in x\n",
- " x = x + x1\n",
- " # second conv and relu (note reuse of relu; it has no learnable parameters\n",
- " # so this is safe, but we could have used nn.functional.relu(x)\n",
- " # or even torch.max(0,x)!)\n",
- " x = self.relu(self.conv2(x))\n",
- " # adaptive pooling (no parameters so we can use the \"functional\" interface) \n",
- " x = nn.functional.adaptive_avg_pool2d(x, 1) \n",
- " # reshape (we could have used flatten, but view also works)\n",
- " x = x.view(x.shape[0], -1)\n",
- " # return the output of the linear layer\n",
- " return self.linear(x)\n",
- "model = MyModel()\n",
- "# works with 128x128 inputs:\n",
- "print(model(torch.rand(1,3,128,128)))\n",
- "# and 256x256 inputs, because of the global average pooling:\n",
- "print(model(torch.rand(1,3,256,256)))\n",
- "print()\n",
- "print(list(p.shape for p in model.parameters()))\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e146b858-45df-4bf6-a393-ccb288ddd89b",
- "metadata": {},
- "outputs": [],
- "source": [
- "device = \"cuda\"\n",
- "model = MyModel().to(device)\n",
- "result = model(torch.rand(1,3,128,128).to(device))\n",
- "result = result.cpu()\n",
- "print(result)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c7eb8a92-7a71-48aa-8a6d-31008644e3c9",
- "metadata": {},
- "outputs": [],
- "source": [
- "import torchvision\n",
- "from torch.utils.data import Subset\n",
- "import matplotlib.pyplot as plt\n",
- "from PIL import Image\n",
- "%matplotlib inline\n",
- "\n",
- "# Set up the data loader\n",
- "tf = torchvision.transforms.Compose([\n",
- " #torchvision.transforms.RandomRotation(5.0),\n",
- " torchvision.transforms.Grayscale(3),\n",
- " torchvision.transforms.ToTensor()\n",
- " ])\n",
- "\n",
- "mnist_dataset = torchvision.datasets.MNIST('.', download=True, transform=tf)\n",
- "print(len(mnist_dataset))\n",
- "print(mnist_dataset)\n",
- "\n",
- "print(mnist_dataset[0][1])\n",
- "print(mnist_dataset[1][1])\n",
- "print(mnist_dataset[2][1])\n",
- "\n",
- "mnist_third = Subset(mnist_dataset, range(0, len(mnist_dataset)//3))\n",
- "print(len(mnist_third))\n",
- "print(mnist_third)\n",
- "print(mnist_third[0][1])\n",
- "print(mnist_third[1][1])\n",
- "print(mnist_third[2][1])\n",
- "\n",
- "loader = torch.utils.data.DataLoader(mnist_third, batch_size=128, shuffle=True, num_workers=2)\n",
- "count = 0\n",
- "for batch in loader:\n",
- " count += len(batch[0])\n",
- " print(batch[1])\n",
- " assert False\n",
- "\n",
- "print(f\"{count=}\")\n",
- "\n",
- "assert False\n",
- "\n",
- "iterator = iter(loader)\n",
- "batch = next(iterator)\n",
- "\n",
- "#print(len(batch[0]))\n",
- "\n",
- "data = torch.ByteTensor()\n",
- "for i in range(len(batch[0])):\n",
- " data = torch.cat([data, batch[0][i]])\n",
- "\n",
- "#print(data.shape)\n",
- "\n",
- "def _binary_to_colour(data, colour, noise_level=0.0): \n",
- " fg_data = torch.zeros_like(data)\n",
- " fg_data[data != 0] = 255\n",
- " fg_data[data == 0] = 0\n",
- " fg_data = torch.stack([fg_data, fg_data, fg_data], dim=1)\n",
- "\n",
- " bg_data = torch.zeros_like(data)\n",
- " bg_data[data == 0] = 1\n",
- " bg_data[data != 0] = 0\n",
- " \n",
- " bg_data = bg_data.unsqueeze(3).expand(-1, -1, -1, 3)\n",
- " \n",
- " colour = torch.tensor(colour, dtype=torch.uint8)\n",
- " noise = noise_level * torch.randn(len(bg_data),3).clamp(-1,1)\n",
- " noisy_colour = (colour + noise * 255).clamp(0, 255).byte()\n",
- " #print(noisy_colour)\n",
- " bg_data = bg_data * noisy_colour.view(data.size(0), 1, 1, 3)\n",
- " \n",
- " bg_data = bg_data.permute(0, 3, 1, 2)\n",
- " print(bg_data.shape) \n",
- " \n",
- " data = fg_data + bg_data\n",
- " return data.permute(0, 2, 3, 1)\n",
- "\n",
- "new_img = _binary_to_colour(data, [255, 0, 0], 0.1)\n",
- "#print(new_img[0]) \n",
- "print(img)\n",
- "for i in range(4):\n",
- " img = Image.fromarray(new_img[i].numpy().astype(np.uint8), mode='RGB')\n",
- " plt.imshow(new_img[i])\n",
- " plt.show()\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8e1707ff-09e8-40b7-ac67-e42887627176",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "104e8cee-c86b-4403-985a-61a16ad264e4",
- "metadata": {},
- "outputs": [],
- "source": [
- "import torch\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline\n",
- "def _binary_to_colour(data, base_colour, noise_level=0.1):\n",
- " # Create a background tensor with the same shape as data\n",
- " bg_data = torch.zeros_like(data, dtype=torch.uint8)\n",
- " # Set background tensor values to 1 where data is 0 and to 0 where data is non-zero\n",
- " bg_data[data == 0] = 1\n",
- " bg_data[data != 0] = 0\n",
- " print(bg_data)\n",
- " \n",
- " # Stack the background tensor along a new dimension to create 3 channels (RGB)\n",
- " bg_data = bg_data.unsqueeze(3).expand(-1, -1, -1, 3)\n",
- " print(bg_data)\n",
- " \n",
- " # Generate noise for each image and each channel\n",
- " noise = (torch.randn(data.size(0), 3) * noise_level).clamp(-1, 1)\n",
- " \n",
- " # Add noise to the base colour and ensure it's within valid range\n",
- " noisy_colours = base_colour + (noise * 255)\n",
- " noisy_colours = noisy_colours.clamp(0, 255).byte()\n",
- " \n",
- " # Apply the noisy colours to the background data using broadcasting\n",
- " coloured_images = bg_data * noisy_colours.view(data.size(0), 1, 1, 3)\n",
- " \n",
- " # Permute dimensions to match (batch_size, channels, height, width)\n",
- " coloured_images = coloured_images.permute(0, 3, 1, 2)\n",
- " \n",
- " return coloured_images\n",
- "\n",
- "# Example usage\n",
- "# Create a batch of 3 binary images of shape (3, height, width)\n",
- "data = torch.tensor([\n",
- " [[1, 0, 1, 0], [0, 1, 0, 1], [1, 0, 1, 0]], # Image 1\n",
- " [[0, 1, 0, 1], [1, 0, 1, 0], [0, 1, 0, 1]], # Image 2\n",
- " #[[1, 1, 1], [0, 0, 0], [1, 1, 1]] # Image 3\n",
- "], dtype=torch.uint8)\n",
- "\n",
- "# Ensure the data tensor has shape (batch_size, height, width)\n",
- "print(\"Original data shape:\", data.shape)\n",
- "\n",
- "# Define the base RGB colour to apply to all images in the batch\n",
- "base_colour = torch.tensor([255, 0, 0], dtype=torch.uint8) # Red color\n",
- "\n",
- "# Apply the function to the batch of images with color noise\n",
- "result = _binary_to_colour(data, base_colour, noise_level=0.5)\n",
- "\n",
- "# Print the result and shape to verify\n",
- "#print(\"Result shape:\", result.shape)\n",
- "#print(result)\n",
- "\n",
- "for im in result:\n",
- " plt.imshow(im.permute(1,2,0))\n",
- " plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5968053e-31e5-4e2d-91a3-ac0127adc657",
- "metadata": {},
- "outputs": [],
- "source": [
- "bg_data = torch.tensor([\n",
- " [[1, 0, 1, 0], [0, 1, 0, 1], [1, 0, 1, 0]], # Image 1\n",
- " [[0, 1, 0, 1], [1, 0, 1, 0], [0, 1, 0, 1]], # Image 2\n",
- " \n",
- "], dtype=torch.uint8)\n",
- "\n",
- "bg_data = torch.tensor([ [1,2] ])\n",
- "print(bg_data)\n",
- "bg_data = bg_data.unsqueeze(1) #.expand(-1, -1, -1, 3)\n",
- "print(bg_data)\n",
- "bg_data = bg_data.expand(-1, 3, -1)\n",
- "print(bg_data)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "148b0d78-21b6-41aa-bc73-ad637629e248",
- "metadata": {},
- "outputs": [],
- "source": [
- "import torch\n",
- "import torch.nn as nn\n",
- "model = nn.Sequential(\n",
- " nn.Conv2d(3, 128, kernel_size=3, stride=1, padding=1),\n",
- " nn.ReLU(),\n",
- " nn.AdaptiveAvgPool2d(1),\n",
- " nn.Flatten(),\n",
- " nn.Linear(128, 10)\n",
- " )\n",
- "loss = nn.CrossEntropyLoss()\n",
- "image, target = torch.rand(1,3,128,128), torch.tensor([2])\n",
- "# gradients accumulate in the forward pass, so we must zero them first\n",
- "model.zero_grad()\n",
- "# The forward pass\n",
- "prediction = model(image)\n",
- "l = loss(prediction, target)\n",
- "# The backward pass\n",
- "l.backward()\n",
- "print(model[0].bias.grad)\n",
- "print(f\"{target=}\")\n",
- "print(image[0].shape)\n",
- "plt.imshow(image[0].permute(1,2,0))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f962054d-b8cd-47d6-88c7-7788dfdfb746",
- "metadata": {},
- "outputs": [],
- "source": [
- "import torchvision\n",
- "import torch\n",
- "import torch.nn\n",
- "# Create the model\n",
- "model = nn.Sequential(\n",
- "nn.Conv2d(1, 10, kernel_size=3, stride=1, padding=1),\n",
- "nn.ReLU(),\n",
- "nn.AdaptiveAvgPool2d(4),\n",
- "nn.Flatten(),\n",
- "nn.Linear(160, 10)\n",
- ")\n",
- "# Set up the data loader\n",
- "tf = torchvision.transforms.Compose([\n",
- "torchvision.transforms.RandomRotation(5.0),\n",
- "torchvision.transforms.ToTensor()\n",
- "])\n",
- "mnist = torchvision.datasets.MNIST('.', download=True, transform=tf)\n",
- "loader = torch.utils.data.DataLoader(mnist, batch_size=128,\n",
- "shuffle=True, num_workers=2)\n",
- "# Create the optimiser, passing it all the model's parameters to track\n",
- "opt = torch.optim.SGD(model.parameters(), lr=1e-2)\n",
- "# Create the loss function\n",
- "loss = nn.CrossEntropyLoss()\n",
- "# Set training mode on the model\n",
- "model.train = True\n",
- "# loop over the dataset 10 times\n",
- "for epoch in range(10):\n",
- " # loop over the batches\n",
- " for images, targets in loader:\n",
- " # gradients accumulate in the forward pass, so we must zero them first\n",
- " model.zero_grad()\n",
- " # The forward pass\n",
- " predictions = model(images)\n",
- " l = loss(predictions, targets)\n",
- " # The backward pass\n",
- " l.backward()\n",
- " # Update all the parameters\n",
- " opt.step()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2d746f06-feec-4265-bee5-c3795bb03da2",
- "metadata": {},
- "outputs": [],
- "source": [
- "import torch\n",
- "import torchvision\n",
- "import torchvision.transforms as transforms\n",
- "from torch.utils.data import DataLoader\n",
- "\n",
- "# Step 1: Define transformations for the MNIST dataset\n",
- "transform = transforms.Compose([\n",
- " transforms.Resize(32), # Resize MNIST images to 32x32 (optional, for VGG compatibility)\n",
- " transforms.Grayscale(num_output_channels=3), # Convert to 3 channels\n",
- " transforms.ToTensor(),\n",
- " transforms.Normalize((0.1307,) * 3, (0.3081,) * 3) # Normalize with mean and std for 3 channels\n",
- "])\n",
- "\n",
- "# Step 2: Download and load the MNIST training and test datasets\n",
- "trainset = torchvision.datasets.MNIST(root='.', train=True, download=True, transform=transform)\n",
- "trainloader = DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)\n",
- "\n",
- "testset = torchvision.datasets.MNIST(root='.', train=False, download=True, transform=transform)\n",
- "testloader = DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)\n",
- "\n",
- "# Verify that the transformation works by displaying the first batch of images\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "import numpy as np\n",
- "\n",
- "# Function to show an image\n",
- "def imshow(img):\n",
- " img = img / 2 + 0.5 # Unnormalize\n",
- " \n",
- " plt.imshow(img.permute(1,2,0))\n",
- " plt.show()\n",
- "\n",
- "for foo in trainloader:\n",
- " a, b = foo\n",
- " print(a[0].shape)\n",
- " imshow(a[0])\n",
- "\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": "3.10.14"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/CKA/investigate_basic_cnn.ipynb b/CKA/investigate_basic_cnn.ipynb
deleted file mode 100644
index 902e8b91f1e6e3bbae36e5a0d493dee155510724..0000000000000000000000000000000000000000
--- a/CKA/investigate_basic_cnn.ipynb
+++ /dev/null
@@ -1,8007 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "l_0yfh0aKE9k"
- },
- "source": [
- "# Investigate CNNs trained in various ways"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "9SrGBsUYKE9m"
- },
- "source": [
- "This code is based on example lab code provided in the University of Southampton Deep Learning course"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "id": "8Uf3sruTKE9o"
- },
- "outputs": [],
- "source": [
- "import torch\n",
- "import torch.nn.functional as F\n",
- "import torchvision\n",
- "import torchvision.transforms as transforms\n",
- "import torchbearer\n",
- "from torch import nn\n",
- "from torch import optim\n",
- "from torch.utils.data import DataLoader\n",
- "from torchvision.datasets import MNIST\n",
- "from torchbearer import Trial\n",
- "import matplotlib.pyplot as plt\n",
- "from PIL import Image\n",
- "from os.path import exists\n",
- "\n",
- "# https://pypi.org/project/torch-cka/\n",
- "import torch_cka\n",
- "from torch_cka import CKA\n",
- "\n",
- "%matplotlib inline\n",
- "\n",
- "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "gQl5tcalKE9o"
- },
- "source": [
- "Initialise the random number generator to a constant seed value for reproducibility of results."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "jWV9zBvFV11Z"
- },
- "source": [
- "<hr>The following imported from lab 5.2 to allow read-in of weights"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "transform = torchvision.transforms.ToTensor()\n",
- "\n",
- "if not exists(\"4.PNG\"):\n",
- " !wget https://ecs-vlc.github.io/COMP6258/labs/lab5/0.PNG\n",
- " !wget https://ecs-vlc.github.io/COMP6258/labs/lab5/1.PNG\n",
- " !wget https://ecs-vlc.github.io/COMP6258/labs/lab5/2.PNG\n",
- " !wget https://ecs-vlc.github.io/COMP6258/labs/lab5/3.PNG\n",
- " !wget https://ecs-vlc.github.io/COMP6258/labs/lab5/4.PNG\n",
- " !wget https://ecs-vlc.github.io/COMP6258/labs/lab5/5.PNG\n",
- " !wget https://ecs-vlc.github.io/COMP6258/labs/lab5/6.PNG\n",
- " !wget https://ecs-vlc.github.io/COMP6258/labs/lab5/7.PNG\n",
- " !wget https://ecs-vlc.github.io/COMP6258/labs/lab5/8.PNG\n",
- " !wget https://ecs-vlc.github.io/COMP6258/labs/lab5/9.PNG\n",
- " \n",
- " im = transform(Image.open(\"4.PNG\"))\n",
- " plt.imshow(im[0], cmap=plt.get_cmap('gray'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "class SimpleCNN(nn.Module):\n",
- " def __init__(self, dropout=True):\n",
- " super(SimpleCNN, self).__init__()\n",
- " self.conv1 = nn.Conv2d(1, 32, (5, 5), padding=0)\n",
- " self.fc1 = nn.Linear(32 * 12**2, 128)\n",
- " self.fc2 = nn.Linear(128, 10)\n",
- " self.dropout = dropout\n",
- "\n",
- " def forward(self, x):\n",
- " out = self.conv1(x)\n",
- " out = F.relu(out)\n",
- " out = F.max_pool2d(out, (2,2))\n",
- " if self.training and self.dropout:\n",
- " out = F.dropout(out, 0.2)\n",
- " out = out.view(out.shape[0], -1) # flattening but keeping batch dimension\n",
- " out = self.fc1(out)\n",
- " out = F.relu(out)\n",
- " out = self.fc2(out)\n",
- " return out\n",
- "\n",
- "# Model Definition\n",
- "class BetterCNN(nn.Module):\n",
- " def __init__(self, dropout=False):\n",
- " super(BetterCNN, self).__init__()\n",
- " self.conv1 = nn.Conv2d(1, 30, (5, 5), padding=0)\n",
- " self.conv2 = nn.Conv2d(30, 15, (3, 3), padding=0)\n",
- " self.fc1 = nn.Linear(15 * 5**2, 128)\n",
- " self.fc2 = nn.Linear(128, 50)\n",
- " self.fc3 = nn.Linear(50, 10)\n",
- " self.dropout = dropout\n",
- "\n",
- " def forward(self, x):\n",
- " #print(\"===============================\")\n",
- " #print(x.shape)\n",
- " # 1 Convolutional layer with 30 feature maps of size 5×5 and ReLU activation.\n",
- " out = self.conv1(x)\n",
- " out = F.relu(out)\n",
- " #print(out.shape)\n",
- " # 2 Pooling layer taking the max over 2×2 patches.\n",
- " out = F.max_pool2d(out, (2,2))\n",
- " #print(out.shape)\n",
- " # 3 Convolutional layer with 15 feature maps of size 3×3 and ReLU activation.\n",
- " out = self.conv2(out)\n",
- " out = F.relu(out)\n",
- " #print(out.shape)\n",
- " # 4 Pooling layer taking the max over 2×2 patches.\n",
- " out = F.max_pool2d(out, (2,2))\n",
- " #print(out.shape)\n",
- " # 5 Dropout layer with a probability of 20%.\n",
- " if self.training and self.dropout:\n",
- " out = F.dropout(out, 0.2)\n",
- " #print(out.shape)\n",
- " # 6 Flatten layer\n",
- " out = out.view(out.shape[0], -1)\n",
- " #print(out.shape)\n",
- " # 7 Fully connected layer with 128 neurons and ReLU activation\n",
- " out = self.fc1(out)\n",
- " out = F.relu(out)\n",
- " #print(out.shape)\n",
- " # 8 Fully connected layer with 50 neurons and ReLU activation.\n",
- " out = self.fc2(out)\n",
- " out = F.relu(out)\n",
- " #print(out.shape)\n",
- " # 9 Linear Output Layer\n",
- " out = self.fc3(out)\n",
- " #print(out.shape)\n",
- " return out"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "deletable": false,
- "id": "voZodMzHa4Q4",
- "nbgrader": {
- "checksum": "83517d0b5dd9912b14e753371891acda",
- "grade": false,
- "grade_id": "cell-25d7ce8447ab7c15",
- "locked": false,
- "schema_version": 1,
- "solution": true
- }
- },
- "outputs": [],
- "source": [
- "models = { \n",
- " \"simple_dropout_NOaugment\": SimpleCNN(dropout=True) ,\n",
- " \"simple_dropout_augment\": SimpleCNN(dropout=True), \n",
- " \"simple_NOdropout_NOaugment\": SimpleCNN(dropout=False), \n",
- " \"simple_NOdropout_augment\": SimpleCNN(dropout=False), \n",
- " \"better_dropout_NOaugment\": BetterCNN(dropout=True), \n",
- " \"better_dropout_augment\": BetterCNN(dropout=True), \n",
- " \"better_NOdropout_NOaugment\": BetterCNN(dropout=False), \n",
- " \"better_NOdropout_augment\": BetterCNN(dropout=False), \n",
- "}\n",
- "\n",
- "for m in models:\n",
- " models[m].load_state_dict(torch.load(\"./results/\" + m + '.weights', map_location=torch.device(device)))\n",
- " models[m].eval()\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "target_resolution = (28, 28)\n",
- "def get_transform(target_resolution, augment_data):\n",
- " # This is lifted from deep-feature reweighting. I should write my own version or credit #FIXME\n",
- " scale = 256.0 / 224.0\n",
- "\n",
- " if not augment_data:\n",
- " # Resizes the image to a slightly larger shape then crops the center.\n",
- " transform = transforms.Compose([\n",
- " transforms.Resize((int(target_resolution[0]*scale), int(target_resolution[1]*scale))),\n",
- " transforms.CenterCrop(target_resolution),\n",
- " transforms.ToTensor(),\n",
- " # TODO - what normalizing do I need, if any?\n",
- " # transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
- " ])\n",
- " else:\n",
- " transform = transforms.Compose([\n",
- " transforms.RandomResizedCrop(\n",
- " target_resolution,\n",
- " scale=(0.7, 1.0),\n",
- " ratio=(0.75, 1.3333333333333333),\n",
- " interpolation=2),\n",
- " transforms.RandomHorizontalFlip(),\n",
- " transforms.ToTensor(),\n",
- " # transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
- " ])\n",
- " return transform"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "X9v6g6n3cGEK",
- "outputId": "df10dffc-80fc-4725-a7fc-7d2f6c87d40a"
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "| Comparing features |: 100%|█████████████████████████████████████████████████████████| 468/468 [02:40<00:00, 2.91it/s]\n",
- "| Comparing features |: 100%|█████████████████████████████████████████████████████████| 468/468 [02:35<00:00, 3.02it/s]\n",
- "| Comparing features |: 100%|█████████████████████████████████████████████████████████| 468/468 [04:56<00:00, 1.58it/s]\n",
- "| Comparing features |: 100%|█████████████████████████████████████████████████████████| 468/468 [05:10<00:00, 1.51it/s]\n",
- "| Comparing features |: 100%|█████████████████████████████████████████████████████████| 468/468 [03:15<00:00, 2.40it/s]\n",
- "| Comparing features |: 100%|█████████████████████████████████████████████████████████| 468/468 [03:12<00:00, 2.43it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[<torch_cka.cka.CKA object at 0x7f09101888b0>, <torch_cka.cka.CKA object at 0x7f09fe719060>, <torch_cka.cka.CKA object at 0x7f091018a8f0>, <torch_cka.cka.CKA object at 0x7f0910111b40>, <torch_cka.cka.CKA object at 0x7f0910188ee0>, <torch_cka.cka.CKA object at 0x7f0910110460>]\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- }
- ],
- "source": [
- "# To just compare conv2 to conv2 define own list manually like:\n",
- "# just_conv2 = ['conv2']\n",
- "\n",
- "comparisons = [ \n",
- " [\"simple_dropout_NOaugment\", \"simple_dropout_augment\"],\n",
- " [\"simple_dropout_NOaugment\", \"simple_NOdropout_augment\"],\n",
- " [\"better_dropout_NOaugment\", \"better_dropout_augment\"],\n",
- " [\"better_dropout_NOaugment\", \"better_NOdropout_augment\"],\n",
- " [\"simple_dropout_augment\", \"better_dropout_augment\"],\n",
- " [\"simple_NOdropout_augment\", \"better_dropout_augment\"]\n",
- "]\n",
- "\n",
- "cka_list = []\n",
- "\n",
- "for comp in comparisons:\n",
- "\n",
- " model1_name = comp[0]\n",
- " model2_name = comp[1]\n",
- " model1 = models[model1_name] # from the list\n",
- " model2 = models[model2_name] \n",
- " \n",
- " model1_layers_list=[]\n",
- " for name, module in model1.named_children():\n",
- " if not name.startswith('params'):\n",
- " model1_layers_list.append(name)\n",
- " \n",
- " model2_layers_list=[]\n",
- " for name, module in model2.named_children():\n",
- " if not name.startswith('params'):\n",
- " model2_layers_list.append(name)\n",
- " \n",
- " cka_measure_set = MNIST(\".\", train=True, download=True, transform=get_transform(target_resolution, augment_data=False))\n",
- " \n",
- " torch.manual_seed(57)\n",
- " dataloader = DataLoader(cka_measure_set,\n",
- " batch_size=128, # according to your device memory\n",
- " shuffle=False, drop_last=True) # Don't forget to seed your dataloader\n",
- " \n",
- " cka = CKA(model1, model2,\n",
- " model1_name=model1_name, # good idea to provide names to avoid confusion\n",
- " model2_name=model2_name,\n",
- " model1_layers=model1_layers_list, # List of layers to extract features from\n",
- " model2_layers=model2_layers_list, # extracts all layer features by default\n",
- " device=device) # device will be cuda if available\n",
- " \n",
- " cka.compare(dataloader) # secondary dataloader is optional as second argument\n",
- " \n",
- " #results = cka.export() # returns a dict that contains model names, layer names\n",
- " # and the CKA matrix\n",
- " cka_list.append(cka)\n",
- "\n",
- "print(cka_list)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 595
- },
- "id": "uTdEPWsCgthV",
- "outputId": "84098320-bc18-4719-ef31-c8d3f8105249"
- },
- "outputs": [],
- "source": [
- "'''\n",
- "for cka in cka_list:\n",
- " results = cka.export() # returns a dict that contains model names, layer names\n",
- " # and the CKA matrix\n",
- " print(results)\n",
- " cka.plot_results()\n",
- "'''"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
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- "text/plain": [
- "<Figure size 640x480 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "for idx, comp in enumerate(comparisons):\n",
- " results = cka_list[idx].export()\n",
- " with open(\"./results/CKA_values.txt\", \"a\") as f: \n",
- " print(comp[0] + \"_VS_\" + comp[1], file=f)\n",
- " print(results['CKA'], file=f)\n",
- " plt.clf()\n",
- " plt.imshow(results['CKA'], cmap='magma')\n",
- " plt.ylabel(comp[0])\n",
- " plt.xlabel(comp[1])\n",
- " \n",
- " plt.yticks(range(results['CKA'].shape[0]))\n",
- " plt.xticks(range(results['CKA'].shape[1]))\n",
- " \n",
- " plt.gca().invert_yaxis()\n",
- " plt.colorbar()\n",
- " plt.savefig(\"results/\" + comp[0] + \"_VS_\" + comp[1] + \".png\")\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 435
- },
- "id": "kWNQd6TXg5H9",
- "outputId": "7c467755-6a49-44f1-e43d-980996a6fae8"
- },
- "outputs": [],
- "source": [
- "def visualise_maximum_activation(model, target, num=10, alpha = 1.0):\n",
- " for selected in range(num):\n",
- " input_img = torch.randn(1, 1, 28, 28, requires_grad=True)\n",
- "\n",
- " # we're interested in maximising outputs of the 3rd FC layer - ie the classifier logit:\n",
- " conv_output = None\n",
- "\n",
- " def hook_function(module, grad_in, grad_out):\n",
- " nonlocal conv_output\n",
- " # Gets the conv output of the selected filter/feature (from selected layer)\n",
- " conv_output = grad_out[0, selected]\n",
- "\n",
- " hook = target.register_forward_hook(hook_function)\n",
- " for i in range(30):\n",
- " model(input_img)\n",
- " #print(conv_output.shape)\n",
- " loss = torch.mean(conv_output) # optimisse for whole layer if not output logit\n",
- " #loss = conv_output[(selected%100)//10, selected%10]\n",
- " #print(f\"{loss.shape=}\")\n",
- " loss.backward()\n",
- "\n",
- " norm = input_img.grad.std() + 1e-5\n",
- " input_img.grad /= norm\n",
- " input_img.data = input_img + alpha * input_img.grad\n",
- "\n",
- " hook.remove()\n",
- "\n",
- " input_img = input_img.detach()\n",
- "\n",
- " plt.subplot(8,4,selected+1)\n",
- " plt.imshow(input_img[0,0], cmap=plt.get_cmap('gray'))\n",
- " #get current axes\n",
- " ax = plt.gca()\n",
- " #hide axes\n",
- " ax.get_xaxis().set_visible(False)\n",
- " ax.get_yaxis().set_visible(False)\n",
- " plt.show()\n",
- "\n",
- "if False: \n",
- " torch.manual_seed(seed)\n",
- " \n",
- " model = model_better\n",
- " visualise_maximum_activation(model.cpu(), model.conv1, 30)\n",
- " visualise_maximum_activation(model.cpu(), model.conv2, 15)\n",
- " visualise_maximum_activation(model.cpu(), model.fc1, 30)\n",
- " visualise_maximum_activation(model.cpu(), model.fc2, 30)\n",
- " visualise_maximum_activation(model.cpu(), model.fc3, 10)\n",
- "\n",
- "# Note that it's all high-frequency noise - see https://distill.pub/2017/feature-visualization/ for explanation\n"
- ]
- }
- ],
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- "value": " 469/469 [00:13<00:00, 39.89it/s, acc=0.8735, loss=0.4061, running_acc=0.9572, running_loss=0.1506]"
- }
- }
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- }
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diff --git a/CKA/results/20240620_cka_values.txt b/CKA/results/20240620_cka_values.txt
deleted file mode 100644
index c180b35def479e8ee2a373924e480cd74f62ec5b..0000000000000000000000000000000000000000
--- a/CKA/results/20240620_cka_values.txt
+++ /dev/null
@@ -1,16 +0,0 @@
-{'model1_name': 'BetterCNN', 'model2_name': 'SimpleCNN',
-'CKA': tensor([
- [0.9867, 0.6610, 0.4423],
- [0.9191, 0.7801, 0.5646],
- [0.6281, 0.9158, 0.7864],
- [0.4664, 0.8383, 0.8708],
- [0.4383, 0.7840, 0.8741]])
-
- , 'model1_layers': ['conv1', 'conv2', 'fc1', 'fc2', 'fc3'],
- 'model2_layers': ['conv1', 'fc1', 'fc2'], 'dataset1_name': 'Dataset MNIST', 'dataset2_name': 'Dataset MNIST'}
-
-{'model1_name': 'BetterCNN', 'model2_name': 'BetterCNN_no_dropout', 'CKA': tensor([[0.9978, 0.9478, 0.6513, 0.4629, 0.4117],
- [0.9355, 0.9891, 0.7902, 0.5761, 0.5297],
- [0.6247, 0.7293, 0.9578, 0.8131, 0.7348],
- [0.4682, 0.5615, 0.8131, 0.9138, 0.8599],
- [0.4380, 0.5257, 0.7401, 0.8632, 0.9353]]), 'model1_layers': ['conv1', 'conv2', 'fc1', 'fc2', 'fc3'], 'model2_layers': ['conv1', 'conv2', 'fc1', 'fc2', 'fc3'], 'dataset1_name': 'Dataset MNIST', 'dataset2_name': 'Dataset MNIST'}
\ No newline at end of file
diff --git a/CKA/results/CKA_values.txt b/CKA/results/CKA_values.txt
deleted file mode 100644
index c035c5f1526386af412ac31e94f26eb319ff0160..0000000000000000000000000000000000000000
--- a/CKA/results/CKA_values.txt
+++ /dev/null
@@ -1,56 +0,0 @@
-simple_dropout_NOaugment_VS_simple_dropout_augment
-tensor([[0.9745, 0.8928, 0.5040],
- [0.6511, 0.6861, 0.4116],
- [0.4324, 0.4613, 0.2697]])
-simple_NOdropout_NOaugment_VS_simple_NOdropout_augment
-tensor([[0.9799, 0.9144, 0.4900],
- [0.8961, 0.8515, 0.4447],
- [0.5191, 0.4954, 0.2352]])
-better_dropout_NOaugment_VS_better_dropout_augment
-tensor([[0.9981, 0.9482, 0.8197, 0.7361, 0.5619],
- [0.9586, 0.9740, 0.9025, 0.8273, 0.6336],
- [0.8140, 0.8792, 0.9039, 0.8235, 0.6341],
- [0.7199, 0.7659, 0.8067, 0.7586, 0.6172],
- [0.4610, 0.4955, 0.5106, 0.4828, 0.4507]])
-better_NOdropout_NOaugment_VS_better_NOdropout_augment
-tensor([[0.9874, 0.9562, 0.8382, 0.7171, 0.5378],
- [0.9415, 0.9345, 0.8716, 0.7609, 0.5597],
- [0.7703, 0.7795, 0.8042, 0.7306, 0.5147],
- [0.6582, 0.6721, 0.7075, 0.6481, 0.4224],
- [0.5217, 0.5432, 0.5830, 0.5254, 0.3145]])
-simple_dropout_augment_VS_better_dropout_augment
-tensor([[0.9928, 0.9635, 0.8496, 0.7687, 0.5850],
- [0.9019, 0.8740, 0.7889, 0.7053, 0.5242],
- [0.5075, 0.4746, 0.4235, 0.3611, 0.2007]])
-simple_NOdropout_augment_VS_better_dropout_augment
-tensor([[0.9891, 0.9321, 0.8099, 0.7301, 0.5569],
- [0.9138, 0.8944, 0.8150, 0.7406, 0.5789],
- [0.4780, 0.4825, 0.4684, 0.4185, 0.3570]])
-simple_dropout_NOaugment_VS_simple_dropout_augment
-tensor([[0.9937, 0.7063, 0.3905],
- [0.6679, 0.8717, 0.6766],
- [0.4441, 0.7035, 0.6964]])
-simple_dropout_NOaugment_VS_simple_NOdropout_augment
-tensor([[0.9954, 0.6955, 0.3899],
- [0.6554, 0.8835, 0.6585],
- [0.4365, 0.7423, 0.6789]])
-better_dropout_NOaugment_VS_better_dropout_augment
-tensor([[0.9973, 0.8950, 0.5863, 0.4477, 0.4113],
- [0.9348, 0.9519, 0.6882, 0.5378, 0.5062],
- [0.6463, 0.8140, 0.9132, 0.7718, 0.7080],
- [0.4816, 0.6226, 0.7541, 0.7713, 0.7211],
- [0.4465, 0.5940, 0.7188, 0.7699, 0.7942]])
-better_dropout_NOaugment_VS_better_NOdropout_augment
-tensor([[0.9884, 0.8743, 0.6390, 0.4578, 0.4329],
- [0.9247, 0.9243, 0.7341, 0.5337, 0.5236],
- [0.6759, 0.8324, 0.9157, 0.7652, 0.7326],
- [0.4953, 0.6296, 0.7335, 0.7204, 0.7155],
- [0.4591, 0.6024, 0.7180, 0.6970, 0.7629]])
-simple_dropout_augment_VS_better_dropout_augment
-tensor([[0.9748, 0.8876, 0.6274, 0.4705, 0.4304],
- [0.6711, 0.8077, 0.8871, 0.7797, 0.7037],
- [0.3756, 0.5147, 0.7088, 0.8665, 0.8761]])
-simple_NOdropout_augment_VS_better_dropout_augment
-tensor([[0.9879, 0.8931, 0.6082, 0.4584, 0.4220],
- [0.6628, 0.8166, 0.9036, 0.7895, 0.7176],
- [0.3724, 0.5170, 0.7077, 0.8486, 0.8742]])
diff --git a/CKA/results/better_NOdropout_NOaugment.weights b/CKA/results/better_NOdropout_NOaugment.weights
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diff --git a/CKA/train_basic_cnn.ipynb b/CKA/train_basic_cnn.ipynb
deleted file mode 100644
index b2ae3ee73d760f33928ab10d19535b37f6f881b5..0000000000000000000000000000000000000000
--- a/CKA/train_basic_cnn.ipynb
+++ /dev/null
@@ -1,9171 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "l_0yfh0aKE9k"
- },
- "source": [
- "# Train a simple and slightly better CNN with MNIST both with and without augmentation"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "9SrGBsUYKE9m"
- },
- "source": [
- "__Before starting, we recommend you enable GPU acceleration if you're running on Colab.__\n",
- "This code is based on example lab code provided in the University of Southampton Deep Learning course"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "id": "8Uf3sruTKE9o"
- },
- "outputs": [],
- "source": [
- "import torch\n",
- "import torch.nn.functional as F\n",
- "import torchvision\n",
- "import torchvision.transforms as transforms\n",
- "import torchbearer\n",
- "from torch import nn\n",
- "from torch import optim\n",
- "from torch.utils.data import DataLoader\n",
- "from torchvision.datasets import MNIST\n",
- "from torchbearer import Trial\n",
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "gQl5tcalKE9o"
- },
- "source": [
- "Initialise the random number generator to a constant seed value for reproducibility of results."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "id": "0hShF6S_KE9o"
- },
- "outputs": [],
- "source": [
- "# fix random seed for reproducibility\n",
- "seed = 7\n",
- "torch.manual_seed(seed)\n",
- "torch.backends.cudnn.deterministic = True"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "BdFhb_qRKE9p"
- },
- "source": [
- "Next we need to load the MNIST dataset. Unlike with the MLP example we looked at previously, we don't want to flatten the images into vectors, although we do still want to convert the PIL images to tensors.\n",
- "\n",
- "In PyTorch, images are represented as tensors with dimensions `[pixels][height][width]`. In the case of RGB, the first dimension, pixels, would be 3 for the red, green and blue components. In the case of MNIST where the pixel values are greyscale, the pixel dimension is set to 1."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "target_resolution = (28, 28)\n",
- "def get_transform(target_resolution, augment_data):\n",
- " # This is lifted from deep-feature reweighting. I should write my own version or credit #FIXME\n",
- " scale = 256.0 / 224.0\n",
- "\n",
- " if not augment_data:\n",
- " # Resizes the image to a slightly larger shape then crops the center.\n",
- " transform = transforms.Compose([\n",
- " transforms.Resize((int(target_resolution[0]*scale), int(target_resolution[1]*scale))),\n",
- " transforms.CenterCrop(target_resolution),\n",
- " transforms.ToTensor(),\n",
- " # TODO - what normalizing do I need, if any?\n",
- " # transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
- " ])\n",
- " else:\n",
- " transform = transforms.Compose([\n",
- " transforms.RandomResizedCrop(\n",
- " target_resolution,\n",
- " scale=(0.7, 1.0),\n",
- " ratio=(0.75, 1.3333333333333333),\n",
- " interpolation=2),\n",
- " transforms.RandomHorizontalFlip(),\n",
- " transforms.ToTensor(),\n",
- " # transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
- " ])\n",
- " return transform"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "w2U7IxAxKE9p",
- "outputId": "ef3dc98f-a9de-4023-fe0a-76e34a872eb2"
- },
- "outputs": [],
- "source": [
- "# convert each image to tensor format\n",
- "#transform = transforms.Compose([\n",
- "# transforms.ToTensor() # convert to tensor\n",
- "#])\n",
- "\n",
- "# load data\n",
- "#trainset = MNIST(\".\", train=True, download=True, transform=transform)\n",
- "#testset = MNIST(\".\", train=False, download=True, transform=transform)\n",
- "dl = False # set to choose download/local\n",
- "trainset_normal = MNIST(\".\", train=True, download=dl, transform=get_transform(target_resolution, augment_data=False))\n",
- "trainset_augmented = MNIST(\".\", train=True, download=dl, transform=get_transform(target_resolution, augment_data=True))\n",
- "testset = MNIST(\".\", train=False, download=dl, transform=get_transform(target_resolution, augment_data=False))\n",
- "\n",
- "# create data loaders\n",
- "#trainloader_normal = DataLoader(trainset_normal, batch_size=128, shuffle=True)\n",
- "#trainloader_augmented = DataLoader(trainset_augmented, batch_size=128, shuffle=True)\n",
- "#testloader = DataLoader(testset, batch_size=128, shuffle=True)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "QzautJfYKE9p"
- },
- "source": [
- "## A Simple CNN\n",
- "\n",
- "Next we define our neural network model.\n",
- "\n",
- "Convolutional neural networks are more complex than standard multi-layer perceptrons, so we will start by using a simple structure to begin with that uses all of the elements required for **near** state of the art results. The network architecture is summarised below:\n",
- "\n",
- "1. The first hidden layer is a convolutional layer called a `Convolution2D`. The layer has 32 feature maps, which with the size of 5×5 and a rectified linear unit activation function.\n",
- "2. Next we define a pooling layer that takes the max called `MaxPooling2D`. It is configured with a pool size of 2×2.\n",
- "3. The next layer is a regularization layer using dropout called `Dropout`. It is configured to randomly exclude 20% of neurons in the layer in order to reduce overfitting.\n",
- "4. Next is an operation that flattens or reshapes the tensor to a vector. It allows the output to be processed by standard fully connected layers.\n",
- "5. Next a fully connected layer with 128 neurons and rectifier linear unit activation function.\n",
- "6. Finally, the output layer has 10 neurons for the 10 classes.\n",
- "\n",
- "Just like with the MLP we create a PyTorch `nn.Module` subclass to define our network model:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "id": "ix-Y0Ee7KE9q"
- },
- "outputs": [],
- "source": [
- "class SimpleCNN(nn.Module):\n",
- " def __init__(self, dropout=True):\n",
- " super(SimpleCNN, self).__init__()\n",
- " self.conv1 = nn.Conv2d(1, 32, (5, 5), padding=0)\n",
- " self.fc1 = nn.Linear(32 * 12**2, 128)\n",
- " self.fc2 = nn.Linear(128, 10)\n",
- " self.dropout = dropout\n",
- "\n",
- " def forward(self, x):\n",
- " out = self.conv1(x)\n",
- " out = F.relu(out)\n",
- " out = F.max_pool2d(out, (2,2))\n",
- " if self.training and self.dropout:\n",
- " out = F.dropout(out, 0.2)\n",
- " out = out.view(out.shape[0], -1) # flattening but keeping batch dimension\n",
- " out = self.fc1(out)\n",
- " out = F.relu(out)\n",
- " out = self.fc2(out)\n",
- " return out"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "W8X-NoEyKE9q"
- },
- "source": [
- "## An improved CNN\n",
- "\n",
- "Now that we have seen how to create a simple CNN, let’s take a look at a model capable of ***close to*** state of the art results. This time you will implement a large CNN architecture with additional convolutional, max pooling layers and fully connected layers. The network topology of the model is summarised as follows:\n",
- "\n",
- "1. Convolutional layer with 30 feature maps of size 5×5 and ReLU activation.\n",
- "2. Pooling layer taking the max over 2×2 patches.\n",
- "3. Convolutional layer with 15 feature maps of size 3×3 and ReLU activation.\n",
- "4. Pooling layer taking the max over 2×2 patches.\n",
- "5. Dropout layer with a probability of 20%.\n",
- "6. Flatten layer.\n",
- "7. Fully connected layer with 128 neurons and ReLU activation.\n",
- "8. Fully connected layer with 50 neurons and ReLU activation.\n",
- "9. Linear output layer.\n",
- "\n",
- "__Complete the implementation of the BetterCNN module which implements the above network in the following code block.__"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "id": "a4kNrE0pKE9r"
- },
- "outputs": [],
- "source": [
- "# Model Definition\n",
- "class BetterCNN(nn.Module):\n",
- " def __init__(self, dropout=False):\n",
- " super(BetterCNN, self).__init__()\n",
- " self.conv1 = nn.Conv2d(1, 30, (5, 5), padding=0)\n",
- " self.conv2 = nn.Conv2d(30, 15, (3, 3), padding=0)\n",
- " self.fc1 = nn.Linear(15 * 5**2, 128)\n",
- " self.fc2 = nn.Linear(128, 50)\n",
- " self.fc3 = nn.Linear(50, 10)\n",
- " self.dropout = dropout\n",
- "\n",
- " def forward(self, x):\n",
- " #print(\"===============================\")\n",
- " #print(x.shape)\n",
- " # 1 Convolutional layer with 30 feature maps of size 5×5 and ReLU activation.\n",
- " out = self.conv1(x)\n",
- " out = F.relu(out)\n",
- " #print(out.shape)\n",
- " # 2 Pooling layer taking the max over 2×2 patches.\n",
- " out = F.max_pool2d(out, (2,2))\n",
- " #print(out.shape)\n",
- " # 3 Convolutional layer with 15 feature maps of size 3×3 and ReLU activation.\n",
- " out = self.conv2(out)\n",
- " out = F.relu(out)\n",
- " #print(out.shape)\n",
- " # 4 Pooling layer taking the max over 2×2 patches.\n",
- " out = F.max_pool2d(out, (2,2))\n",
- " #print(out.shape)\n",
- " # 5 Dropout layer with a probability of 20%.\n",
- " if self.training and self.dropout:\n",
- " out = F.dropout(out, 0.2)\n",
- " #print(out.shape)\n",
- " # 6 Flatten layer\n",
- " out = out.view(out.shape[0], -1)\n",
- " #print(out.shape)\n",
- " # 7 Fully connected layer with 128 neurons and ReLU activation\n",
- " out = self.fc1(out)\n",
- " out = F.relu(out)\n",
- " #print(out.shape)\n",
- " # 8 Fully connected layer with 50 neurons and ReLU activation.\n",
- " out = self.fc2(out)\n",
- " out = F.relu(out)\n",
- " #print(out.shape)\n",
- " # 9 Linear Output Layer\n",
- " out = self.fc3(out)\n",
- " #print(out.shape)\n",
- " return out"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Train in a loop\n",
- "Note this training uses the torchbearer module\n"
- ]
- },
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- "execution_count": 8,
- "metadata": {},
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- "output_type": "stream",
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- "cuda:0\n",
- "Training for simple_dropout_NOaugment\n"
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- "models_to_train = [ \n",
- " [\"simple_dropout_NOaugment\", SimpleCNN(dropout=True), trainset_normal] ,\n",
- " [\"simple_dropout_augment\", SimpleCNN(dropout=True), trainset_augmented], \n",
- " [\"simple_NOdropout_NOaugment\", SimpleCNN(dropout=False), trainset_normal] ,\n",
- " [\"simple_NOdropout_augment\", SimpleCNN(dropout=False), trainset_augmented] ,\n",
- "\n",
- " [\"better_dropout_NOaugment\", BetterCNN(dropout=True), trainset_normal] ,\n",
- " [\"better_dropout_augment\", BetterCNN(dropout=True), trainset_augmented] ,\n",
- " [\"better_NOdropout_NOaugment\", BetterCNN(dropout=False), trainset_normal] ,\n",
- " [\"better_NOdropout_augment\", BetterCNN(dropout=False), trainset_augmented] ,\n",
- "]\n",
- "\n",
- "# define the loss function and the optimiser\n",
- "loss_function = nn.CrossEntropyLoss()\n",
- "\n",
- "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
- "print(device)\n",
- "\n",
- "for el in models_to_train:\n",
- " print(f\"Training for {el[0]}\")\n",
- " #reset the data loaders\n",
- " torch.manual_seed(seed)\n",
- "\n",
- " # create data loaders\n",
- " trainloader = DataLoader(el[2], batch_size=128, shuffle=True)\n",
- " testloader = DataLoader(testset, batch_size=128, shuffle=True)\n",
- "\n",
- " model = el[1]\n",
- " optimiser = optim.Adam(model.parameters())\n",
- " \n",
- " trial = Trial(model, optimiser, loss_function, metrics=['loss', 'accuracy']).to(device)\n",
- " trial.with_generators(trainloader, test_generator=testloader)\n",
- " trial.run(epochs=10)\n",
- " results = trial.evaluate(data_key=torchbearer.TEST_DATA)\n",
- " \n",
- " #save the trained model weights\n",
- " torch.save(model.state_dict(), \"./results/\" + el[0] + \".weights\")"
- ]
- }
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- "value": " 469/469 [00:13<00:00, 39.89it/s, acc=0.8735, loss=0.4061, running_acc=0.9572, running_loss=0.1506]"
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diff --git a/README.md b/README.md
index 1c51b8a7201abb16ca1c0a922f024793e170d20c..7272d63e955e857bf5066283d8beffa38fcc66aa 100644
--- a/README.md
+++ b/README.md
@@ -26,4 +26,9 @@ rank and accuracy.
books.
- conda_env_file.yml YAML file to recreate conda environment used for the ex-
periments. To set up equivalent environment use:
-`$ conda env create --name tempenv --file ./conda_env_file.yml`
\ No newline at end of file
+`$ conda env create --name tempenv --file ./conda_env_file.yml`
+
+
+# License
+Most of this project is made available under the BSD license (see ./LICENSE).
+However ./ReferenceCode/colour_mnist.py is derived from https://github.com/DHLSmith/clovaai-rebias/blob/master/datasets/colour_mnist.py and is covered by the MIT License.
\ No newline at end of file
diff --git a/ReferenceCode/MIT_LICENSE_FOR_colour_mnist b/ReferenceCode/MIT_LICENSE_FOR_colour_mnist
new file mode 100644
index 0000000000000000000000000000000000000000..6261de78133f3d5583983490c1b65a152ee6dec9
--- /dev/null
+++ b/ReferenceCode/MIT_LICENSE_FOR_colour_mnist
@@ -0,0 +1,20 @@
+Copyright (c) 2024-present Damian Smith
+Copyright (c) 2020-2024 NAVER Corp.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
\ No newline at end of file
diff --git a/ReferenceCode/colour_mnist.py b/ReferenceCode/colour_mnist.py
index 6a6d463fd3558dc1e352c32121b06346f9a127ef..c377c3559d856fd853d96648308b1c158657df26 100644
--- a/ReferenceCode/colour_mnist.py
+++ b/ReferenceCode/colour_mnist.py
@@ -6,6 +6,9 @@ MIT license
Python implementation of Biased-MNIST.
Modified to add background only and mixed datasets
+Original at https://github.com/clovaai/rebias/blob/master/datasets/colour_mnist.py
+Forked to https://github.com/DHLSmith/clovaai-rebias/blob/master/datasets/colour_mnist.py
+
Cite Original As:
@inproceedings{bahng2019rebias,
title={Learning De-biased Representations with Biased Representations},
diff --git a/ReferenceCode/examples_anatome.ipynb b/ReferenceCode/examples_anatome.ipynb
deleted file mode 100644
index eccb421d71172d4c24e62cfe457d8d7e5361fbf8..0000000000000000000000000000000000000000
--- a/ReferenceCode/examples_anatome.ipynb
+++ /dev/null
@@ -1,297 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "import torch\n",
- "from torch.nn import functional as F\n",
- "from torchvision.models import resnet18\n",
- "from torchvision import transforms\n",
- "from torchvision.datasets import ImageFolder\n",
- "from torch.utils.data import DataLoader\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "batch_size = 128\n",
- "\n",
- "model = resnet18(pretrained=True)\n",
- "imagenet = ImageFolder('~/.torch/data/imagenet/val', \n",
- " transforms.Compose([transforms.CenterCrop(224),transforms.ToTensor(),\n",
- " transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))]))\n",
- "data = next(iter(DataLoader(imagenet, batch_size=batch_size, num_workers=8)))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "model.eval()\n",
- "model.cuda()\n",
- "data = data[0].cuda(), data[1].cuda()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## CCA\n",
- "\n",
- "Measure CCA distance between trained and not trained models."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": "tensor(0.3290, device='cuda:0')"
- },
- "metadata": {},
- "execution_count": 3
- }
- ],
- "source": [
- "from anatome import CCAHook\n",
- "random_model = resnet18().cuda()\n",
- "\n",
- "hook1 = CCAHook(model, \"layer1.0.conv1\")\n",
- "hook2 = CCAHook(random_model, \"layer1.0.conv1\")\n",
- "\n",
- "with torch.no_grad():\n",
- " model(data[0])\n",
- " random_model(data[0])\n",
- "hook1.distance(hook2, size=8)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "# clear hooked tensors and caches\n",
- "hook1.clear()\n",
- "hook2.clear()\n",
- "torch.cuda.empty_cache()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Loss Landscape\n",
- "\n",
- "Show loss landscape in 1D space."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stderr",
- "text": "100%|███████████████████████████████████████████| 21/21 [00:01<00:00, 13.02it/s]\n"
- }
- ],
- "source": [
- "from anatome import landscape1d\n",
- "x, y = landscape1d(model, data, F.cross_entropy, x_range=(-1, 1), step_size=0.1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": "[<matplotlib.lines.Line2D at 0x7f383b1da5b0>]"
- },
- "metadata": {},
- "execution_count": 6
- },
- {
- "output_type": "display_data",
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\n"
- },
- "metadata": {
- "needs_background": "light"
- }
- }
- ],
- "source": [
- "plt.plot(x, y)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "torch.cuda.empty_cache()"
- ]
- },
- {
- "cell_type": "markdown",
- "source": [
- "## Fourier Analysis\n",
- "\n",
- "Show a model's robustness."
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "outputs": [],
- "source": [
- "from anatome import fourier_map\n",
- "map = fourier_map(model, data, F.cross_entropy, 20, (6, 6), mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))"
- ],
- "metadata": {
- "collapsed": false,
- "pycharm": {
- "name": "#%%\n"
- }
- }
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "outputs": [],
- "source": [
- "plt.imshow(map, interpolation='nearest')"
- ],
- "metadata": {
- "collapsed": false,
- "pycharm": {
- "name": "#%%\n"
- }
- }
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "outputs": [],
- "source": [
- "torch.cuda.empty_cache()"
- ],
- "metadata": {
- "collapsed": false,
- "pycharm": {
- "name": "#%%\n"
- }
- }
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stderr",
- "text": "100%|███████████████████████████████████████████| 21/21 [00:03<00:00, 6.08it/s]\n"
- }
- ],
- "source": [
- "from anatome import fourier_map\n",
- "map = fourier_map(model, data, F.cross_entropy, 20, (6, 6), mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": "<matplotlib.image.AxesImage at 0x7f4f6fb70a30>"
- },
- "metadata": {},
- "execution_count": 9
- },
- {
- "output_type": "display_data",
- "data": {
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- },
- "metadata": {
- "needs_background": "light"
- }
- }
- ],
- "source": [
- "plt.imshow(map, interpolation='nearest')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
- "source": [
- "torch.cuda.empty_cache()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3.8.3 64-bit ('jupyter': conda)",
- "language": "python",
- "name": "python38364bitjupyterconda644e0e65b1f74b2f90dbbacb7386a62c"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 2
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "3.8.3-final"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/ReferenceCode/lab5ex.ipynb b/ReferenceCode/lab5ex.ipynb
deleted file mode 100644
index 1c67a9ebed8ed937db86f22b5e93ff61148bde71..0000000000000000000000000000000000000000
--- a/ReferenceCode/lab5ex.ipynb
+++ /dev/null
@@ -1,69879 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "77d4e562-2c27-42ba-84ab-fc5736a4f9dc",
- "metadata": {
- "id": "77d4e562-2c27-42ba-84ab-fc5736a4f9dc"
- },
- "source": [
- "Section 1 - Import code (next section) as supplied."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "573ebfc8-04e4-4f96-bd40-3129d4b0a568",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "573ebfc8-04e4-4f96-bd40-3129d4b0a568",
- "outputId": "7902582d-f6f4-4bf7-c758-5fc96f9681fc"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Collecting torchbearer\n",
- " Downloading torchbearer-0.5.5-py3-none-any.whl (139 kB)\n",
- "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/139.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.4/139.4 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25hRequirement already satisfied: torch>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from torchbearer) (2.3.0+cu121)\n",
- "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchbearer) (1.25.2)\n",
- "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from torchbearer) (4.66.4)\n",
- "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->torchbearer) (3.15.3)\n",
- "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->torchbearer) (4.12.2)\n",
- "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->torchbearer) (1.12.1)\n",
- "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->torchbearer) (3.3)\n",
- "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->torchbearer) (3.1.4)\n",
- "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->torchbearer) (2023.6.0)\n",
- "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.0.0->torchbearer)\n",
- " Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
- "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.0.0->torchbearer)\n",
- " Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
- "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.0.0->torchbearer)\n",
- " Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
- "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.0.0->torchbearer)\n",
- " Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
- "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.0.0->torchbearer)\n",
- " Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
- "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.0.0->torchbearer)\n",
- " Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
- "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.0.0->torchbearer)\n",
- " Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
- "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.0.0->torchbearer)\n",
- " Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
- "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.0.0->torchbearer)\n",
- " Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
- "Collecting nvidia-nccl-cu12==2.20.5 (from torch>=1.0.0->torchbearer)\n",
- " Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
- "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.0.0->torchbearer)\n",
- " Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
- "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->torchbearer) (2.3.0)\n",
- "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.0.0->torchbearer)\n",
- " Downloading nvidia_nvjitlink_cu12-12.5.40-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m67.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25hRequirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.0.0->torchbearer) (2.1.5)\n",
- "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.0.0->torchbearer) (1.3.0)\n",
- "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, torchbearer\n",
- "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 torchbearer-0.5.5\n"
- ]
- }
- ],
- "source": [
- "# Execute this code block to install dependencies when running on colab\n",
- "try:\n",
- " import torch\n",
- "except:\n",
- " from os.path import exists\n",
- " from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag\n",
- " platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag())\n",
- " cuda_output = !ldconfig -p|grep cudart.so|sed -e 's/.*\\.\\([0-9]*\\)\\.\\([0-9]*\\)$/cu\\1\\2/'\n",
- " accelerator = cuda_output[0] if exists('/dev/nvidia0') else 'cpu'\n",
- "\n",
- " !pip install -q http://download.pytorch.org/whl/{accelerator}/torch-1.0.0-{platform}-linux_x86_64.whl torchvision\n",
- "\n",
- "try:\n",
- " import torchbearer\n",
- "except:\n",
- " !pip install torchbearer\n",
- "\n",
- "\n",
- "#!pip install torch\n",
- "#!pip install torchbearer\n",
- "#!pip install torchvision\n",
- "%matplotlib inline\n",
- "import matplotlib.pyplot as plt\n",
- "from PIL import Image\n",
- "import torch\n",
- "from torchvision import transforms\n",
- "from torch.utils.data import Dataset\n",
- "import torchvision\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "db18ac41-d234-4d14-84d7-924df4b78f21",
- "metadata": {
- "id": "db18ac41-d234-4d14-84d7-924df4b78f21"
- },
- "outputs": [],
- "source": [
- "\n",
- "class MyDataset(Dataset):\n",
- " def __init__(self, size=5000, dim=40, random_offset=0):\n",
- " super(MyDataset, self).__init__()\n",
- " self.size = size\n",
- " self.dim = dim\n",
- " self.random_offset = random_offset\n",
- "\n",
- " def __getitem__(self, index):\n",
- " if index >= len(self):\n",
- " raise IndexError(\"{} index out of range\".format(self.__class__.__name__))\n",
- "\n",
- " rng_state = torch.get_rng_state()\n",
- " torch.manual_seed(index + self.random_offset)\n",
- "\n",
- " while True:\n",
- " img = torch.zeros(self.dim, self.dim)\n",
- " dx = torch.randint(-10,10,(1,),dtype=torch.float)\n",
- " dy = torch.randint(-10,10,(1,),dtype=torch.float)\n",
- " c = torch.randint(-20,20,(1,), dtype=torch.float)\n",
- "\n",
- " params = torch.cat((dy/dx, c))\n",
- " # randomly chooses x values and then sets the y values\n",
- " # based on the randomly chosen gradient and intercept\n",
- " xy = torch.randint(0,img.shape[1], (20, 2), dtype=torch.float)\n",
- " xy[:,1] = xy[:,0] * params[0] + params[1]\n",
- "\n",
- " xy.round_()\n",
- " xy = xy[ xy[:,1] > 0 ]\n",
- " xy = xy[ xy[:,1] < self.dim ]\n",
- " xy = xy[ xy[:,0] < self.dim ]\n",
- "\n",
- " for i in range(xy.shape[0]):\n",
- " x, y = xy[i][0], self.dim - xy[i][1]\n",
- " img[int(y), int(x)]=1\n",
- " if img.sum() > 2:\n",
- " break\n",
- "\n",
- " torch.set_rng_state(rng_state)\n",
- " return img.unsqueeze(0), params\n",
- "\n",
- " def __len__(self):\n",
- " return self.size\n",
- "\n",
- "train_data = MyDataset()\n",
- "val_data = MyDataset(size=500, random_offset=33333)\n",
- "test_data = MyDataset(size=500, random_offset=99999)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "39539784-702d-4e94-9f67-e16414e7fa6d",
- "metadata": {
- "id": "39539784-702d-4e94-9f67-e16414e7fa6d"
- },
- "source": [
- "Show some of the test data\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "89704ab8-d704-413f-8df2-3906fcf41ab7",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 681
- },
- "id": "89704ab8-d704-413f-8df2-3906fcf41ab7",
- "outputId": "030a6864-3f20-4a32-afd3-e9aae43c87f7"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<class 'tuple'>\n",
- "2\n",
- "<class 'torch.Tensor'>\n",
- "<class 'torch.Tensor'>\n",
- "torch.Size([1, 40, 40])\n",
- "torch.Size([2])\n",
- "tensor([[[0., 0., 0., ..., 0., 0., 0.],\n",
- " [0., 0., 0., ..., 0., 0., 0.],\n",
- " [0., 0., 0., ..., 0., 0., 0.],\n",
- " ...,\n",
- " [0., 0., 0., ..., 0., 0., 0.],\n",
- " [0., 0., 0., ..., 0., 0., 0.],\n",
- " [0., 0., 0., ..., 0., 0., 0.]]])\n",
- "tensor([ 1., -17.])\n"
- ]
- },
- {
- "data": {
- "image/png": "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\n",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "print(type(test_data[0]))\n",
- "print(len(test_data[0]))\n",
- "print(type(test_data[0][0]))\n",
- "print(type(test_data[0][1]))\n",
- "\n",
- "print(test_data[0][0].shape)\n",
- "print(test_data[0][1].shape)\n",
- "\n",
- "print(test_data[0][0])\n",
- "\n",
- "transform = torchvision.transforms.ToTensor()\n",
- "#im = transform(Image.open(\"4.PNG\"))\n",
- "#plt.imshow(im[0], cmap=plt.get_cmap('gray'))\n",
- "plt.imshow(test_data[18][0][0], cmap=plt.get_cmap('gray'))\n",
- "\n",
- "print(test_data[18][1])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "508bf03b-7926-4c2e-921a-661e2f82e62f",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 449
- },
- "id": "508bf03b-7926-4c2e-921a-661e2f82e62f",
- "outputId": "f306eeef-9d7c-4207-dcd6-57dbceb9b11c"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "tensor([-0.6667, 11.0000])\n"
- ]
- },
- {
- "data": {
- "image/png": "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\n",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.imshow(test_data[6][0][0], cmap=plt.get_cmap('gray'))\n",
- "\n",
- "print(test_data[6][1])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a73fd3ab-4d23-433c-a285-2b6013d7d6fa",
- "metadata": {
- "id": "a73fd3ab-4d23-433c-a285-2b6013d7d6fa"
- },
- "source": [
- "NOTE: Each item of the dataset is a tuple of a 40x40 tensor (zeroth dimension exists but must be [0]) and its parameters"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a48aac38-1884-419e-9f47-926b7d4dbbe1",
- "metadata": {
- "id": "a48aac38-1884-419e-9f47-926b7d4dbbe1"
- },
- "outputs": [],
- "source": [
- "import torch.nn.functional as F\n",
- "import matplotlib.pyplot as plt\n",
- "from torch import nn\n",
- "\n",
- "indim = test_data[0][0].shape[1]\n",
- "\n",
- "# Model Definition\n",
- "class BaselineCNN(nn.Module):\n",
- " def __init__(self):\n",
- " super(BaselineCNN, self).__init__()\n",
- " self.conv1 = nn.Conv2d(1, 48, (3, 3), padding=1, stride=1)\n",
- " self.fc1 = nn.Linear(48 * indim**2, 128)\n",
- " self.fc2 = nn.Linear(128, 2)\n",
- "\n",
- " def forward(self, x):\n",
- " # 1 Convolution2D , channe l s=48, s i z e=3x3 , s t r i d e =1, padding=1\n",
- " out = self.conv1(x)\n",
- " # 2 ReLU\n",
- " out = F.relu(out)\n",
- " # 3 Flatten layer\n",
- " out = out.view(out.shape[0], -1)\n",
- " # 4 Fully connected layer with 128 neurons and ReLU activation\n",
- " out = self.fc1(out)\n",
- " out = F.relu(out)\n",
- " # 5 Fully connected layer with 2 neurons\n",
- " out = self.fc2(out)\n",
- "\n",
- " return out\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "03d2f968-ebc1-438f-b9a3-9a6ce9dce200",
- "metadata": {
- "id": "03d2f968-ebc1-438f-b9a3-9a6ce9dce200"
- },
- "source": [
- "Train it using Adam (default parameters) for 100 epochs (use\n",
- "a GPU and be prepared to wait 6 or 7 minutes!). Use shuffled batches of 128 items. State the loss\n",
- "function you’re using. Comment on the performance of the model.."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "29200da2-8f96-478d-af67-a71c48baae0f",
- "metadata": {
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- "base_uri": "https://localhost:8080/",
- "height": 1000,
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- "text": [
- "{'test_loss': 17.829017639160156}\n"
- ]
- }
- ],
- "source": [
- "import torchbearer\n",
- "from torch import nn\n",
- "from torch import optim\n",
- "from torch.utils.data import DataLoader\n",
- "from torchbearer import Trial\n",
- "\n",
- "# fix random seed for reproducibility\n",
- "seed = 100 # 7\n",
- "torch.manual_seed(seed)\n",
- "torch.backends.cudnn.deterministic = True\n",
- "\n",
- "# convert each image to tensor format\n",
- "transform = transforms.Compose([\n",
- " transforms.ToTensor() # convert to tensor\n",
- "])\n",
- "\n",
- "# load data\n",
- "#trainset = MNIST(\".\", train=True, download=True, transform=transform)\n",
- "#testset = MNIST(\".\", train=False, download=True, transform=transform)\n",
- "\n",
- "##\n",
- "#train_data = MyDataset()\n",
- "#val_data = MyDataset(size=500, random_offset=33333)\n",
- "#test_data = MyDataset(size=500, random_offset=99999)\n",
- "\n",
- "# create data loaders\n",
- "traingen = DataLoader(train_data, batch_size=128, shuffle=True)\n",
- "valgen = DataLoader(val_data, batch_size=128, shuffle=True)\n",
- "testgen = DataLoader(test_data, batch_size=128, shuffle=True)\n",
- "\n",
- "# build the model\n",
- "model = BaselineCNN()\n",
- "\n",
- "# define the loss function and the optimiser\n",
- "loss_function = nn.MSELoss()\n",
- "optimiser = optim.Adam(model.parameters())\n",
- "\n",
- "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
- "trial = Trial(model, optimiser, loss_function, metrics=['loss']).to(device)\n",
- "#trial.with_generators(trainloader, test_generator=testloader)\n",
- "trial.with_generators(train_generator=traingen, val_generator=valgen, test_generator=testgen)\n",
- "trial.run(epochs=100)\n",
- "results = trial.evaluate(data_key=torchbearer.TEST_DATA)\n",
- "print(results)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "j1ISyoOomlkU",
- "metadata": {
- "id": "j1ISyoOomlkU"
- },
- "source": [
- "99/100(t): 100% 40/40 [00:04<00:00, 8.79it/s, loss=0.1645, running_loss=0.1715]\n",
- "99/100(v): 100% 4/4 [00:00<00:00, 8.05it/s, val_loss=8.4951]\n",
- "0/1(e): 100% 4/4 [00:00<00:00, 8.35it/s, test_loss=10.5415]\n",
- "{'test_loss': 10.541460990905762}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7455e543-b4d3-4b89-b22b-70ec31eaac1f",
- "metadata": {
- "colab": {
- "background_save": true
- },
- "id": "7455e543-b4d3-4b89-b22b-70ec31eaac1f",
- "outputId": "4e10055a-d25d-4a95-ce23-66619f63e562"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "tensor([-0., 14.])\n",
- "tensor([[-0.4504, 11.0795]], device='cuda:0', grad_fn=<AddmmBackward0>)\n"
- ]
- }
- ],
- "source": [
- "\n",
- "#plt.imshow(test_data[6][0][0], cmap=plt.get_cmap('gray'))\n",
- "#print(test_data[6][0][0])\n",
- "print(test_data[2][1])\n",
- "model = model.eval()\n",
- "#batch = test_data[6][0][0].unsqueeze(0).to(device)\n",
- "batch = test_data[2][0].unsqueeze(0).to(device)\n",
- "predictions = model(batch)\n",
- "print(predictions)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9PiCBvUUWP3w",
- "metadata": {
- "id": "9PiCBvUUWP3w"
- },
- "source": [
- "# 2. Simple CNN with global Pooling\n",
- "Implement the following CNN model, and train it using Adam (default parameters) for 100 epochs. Use\n",
- "shuffled batches of 128 items. Comment on the model performance.\n",
- "1. Convolution2D , channels=48, size=3x3 , stride=1, padding=1\n",
- "2. ReLU\n",
- "3. Convolution2D , channels=48, size=3x3 , stride=1, padding=1\n",
- "4. ReLU\n",
- "5. Global Max Pool (AdaptiveMaxPool2d)\n",
- "1. Linear , 128 outputs\n",
- "1. ReLU\n",
- "1. Linear , 2 outputs"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "E8wkmLMiWhwq",
- "metadata": {
- "colab": {
- "background_save": true
- },
- "id": "E8wkmLMiWhwq"
- },
- "outputs": [],
- "source": [
- "\n",
- "\n",
- "# Model Definition\n",
- "class PoolingCNN(nn.Module):\n",
- " def __init__(self):\n",
- " super(PoolingCNN, self).__init__()\n",
- " self.conv1 = nn.Conv2d(1, 48, (3, 3), padding=1, stride=1)\n",
- " self.conv2 = nn.Conv2d(1, 48, (3, 3), padding=1, stride=1)\n",
- " self.mp2d = nn.AdaptiveMaxPool2d(1)\n",
- " self.fc1 = nn.Linear(48 , 128)\n",
- " self.fc2 = nn.Linear(128, 2)\n",
- " # out = F.max_pool2d(out, (2,2))\n",
- "\n",
- " def forward(self, x):\n",
- " # 1 Convolution2D , channels=48, size=3x3 , stride=1, padding=1\n",
- " out = self.conv1(x)\n",
- " # 2 ReLU\n",
- " out = F.relu(out)\n",
- "\n",
- " # 2 Convolution2D , channels=48, size=3x3 , stride=1, padding=1\n",
- " out = self.conv1(x)\n",
- " # 3 ReLU\n",
- " out = F.relu(out)\n",
- " #print(f\"after ReLu: {out.shape=}\")\n",
- " # Global Max Pool then flatten\n",
- " out = self.mp2d(out)\n",
- " out = out.view(out.shape[0], -1)\n",
- " #print(f\"after MaxPool: {out.shape=}\")\n",
- "\n",
- " # 4 Fully connected layer with 128 neurons and ReLU activation\n",
- " out = self.fc1(out)\n",
- " out = F.relu(out)\n",
- " # 5 Fully connected layer with 2 neurons\n",
- " out = self.fc2(out)\n",
- "\n",
- " return out\n"
- ]
- },
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- "output_type": "stream",
- "text": [
- "{'test_loss': 46.98672866821289}\n"
- ]
- }
- ],
- "source": [
- "# fix random seed for reproducibility\n",
- "seed = 7\n",
- "torch.manual_seed(seed)\n",
- "torch.backends.cudnn.deterministic = True\n",
- "\n",
- "# convert each image to tensor format\n",
- "transform = transforms.Compose([\n",
- " transforms.ToTensor() # convert to tensor\n",
- "])\n",
- "\n",
- "# create data loaders\n",
- "traingen = DataLoader(train_data, batch_size=128, shuffle=True)\n",
- "valgen = DataLoader(val_data, batch_size=128, shuffle=True)\n",
- "testgen = DataLoader(test_data, batch_size=128, shuffle=True)\n",
- "\n",
- "# build the model\n",
- "model = PoolingCNN()\n",
- "\n",
- "# define the loss function and the optimiser\n",
- "loss_function = nn.MSELoss()\n",
- "optimiser = optim.Adam(model.parameters())\n",
- "\n",
- "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
- "trial = Trial(model, optimiser, loss_function, metrics=['loss']).to(device)\n",
- "#trial.with_generators(trainloader, test_generator=testloader)\n",
- "trial.with_generators(train_generator=traingen, val_generator=valgen, test_generator=testgen)\n",
- "trial.run(epochs=100)\n",
- "results = trial.evaluate(data_key=torchbearer.TEST_DATA)\n",
- "print(results)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9ZLr9f-amuTk",
- "metadata": {
- "id": "9ZLr9f-amuTk"
- },
- "source": [
- "99/100(t): 100% 40/40 [00:05<00:00, 3.40it/s, loss=45.3086, running_loss=46.4704]\n",
- "99/100(v): 100% 4/4 [00:00<00:00, 6.35it/s, val_loss=44.8113]\n",
- "0/1(e): 100% 4/4 [00:00<00:00, 8.46it/s, test_loss=46.9867]\n",
- "{'test_loss': 46.98672866821289}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "SoEggorBb4BC",
- "metadata": {
- "colab": {
- "background_save": true
- },
- "id": "SoEggorBb4BC",
- "outputId": "7045c672-6fae-446d-edaf-10c27ca66592"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "string"
- },
- "text/plain": [
- "'m = nn.AdaptiveMaxPool2d(1)\\ninput = torch.randn(2, 4, 3, 3)\\noutput = m(input)\\nprint(input.shape)\\nprint(input)\\nprint(output.shape)\\nprint(output)\\noutput = output.view(output.shape[0], -1)\\nprint(output)\\n'"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "'''m = nn.AdaptiveMaxPool2d(1)\n",
- "input = torch.randn(2, 4, 3, 3)\n",
- "output = m(input)\n",
- "print(input.shape)\n",
- "print(input)\n",
- "print(output.shape)\n",
- "print(output)\n",
- "output = output.view(output.shape[0], -1)\n",
- "print(output)\n",
- "'''"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "AGJ36UV0hPaz",
- "metadata": {
- "id": "AGJ36UV0hPaz"
- },
- "source": [
- "# 3. Regress\n",
- "Use 3 input channels in first conv layer\n",
- "\n",
- "before the first convolution, modify the input"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "gRwh_EmBhrwg",
- "metadata": {
- "colab": {
- "background_save": true
- },
- "id": "gRwh_EmBhrwg"
- },
- "outputs": [],
- "source": [
- "# Model Definition\n",
- "class RegressiveCNN(nn.Module):\n",
- " def __init__(self):\n",
- " super(RegressiveCNN, self).__init__()\n",
- " self.conv1 = nn.Conv2d(3, 48, (3, 3), padding=1, stride=1)\n",
- " self.conv2 = nn.Conv2d(1, 48, (3, 3), padding=1, stride=1)\n",
- " self.mp2d = nn.AdaptiveMaxPool2d(1)\n",
- " self.fc1 = nn.Linear(48 , 128)\n",
- " self.fc2 = nn.Linear(128, 2)\n",
- " # out = F.max_pool2d(out, (2,2))\n",
- "\n",
- " def forward(self, x):\n",
- " # 0 Prepare the Data\n",
- " idxx=torch.repeat_interleave(\n",
- " torch.arange(-20 , 20, dtype=torch.float).unsqueeze(0)/40.0,\n",
- " repeats=40,dim=0).to(x.device)\n",
- " print(idxx)\n",
- " idxy=idxx.clone().t()\n",
- " idx=torch.stack([idxx,idxy]).unsqueeze(0)\n",
- " idx=torch.repeatinterleave(idx,repeats=x.shape[0],dim=0)\n",
- " x=torch.cat([x,idx],dim=1)\n",
- " # 1 Convolution2D , channels=48, size=3x3 , stride=1, padding=1\n",
- " out = self.conv1(x)\n",
- " # 2 ReLU\n",
- " out = F.relu(out)\n",
- "\n",
- " # 2 Convolution2D , channels=48, size=3x3 , stride=1, padding=1\n",
- " out = self.conv1(x)\n",
- " # 3 ReLU\n",
- " out = F.relu(out)\n",
- " #print(f\"after ReLu: {out.shape=}\")\n",
- " # Global Max Pool then flatten\n",
- " out = self.mp2d(out)\n",
- " out = out.view(out.shape[0], -1)\n",
- " #print(f\"after MaxPool: {out.shape=}\")\n",
- "\n",
- " # 4 Fully connected layer with 128 neurons and ReLU activation\n",
- " out = self.fc1(out)\n",
- " out = F.relu(out)\n",
- " # 5 Fully connected layer with 2 neurons\n",
- " out = self.fc2(out)\n",
- "\n",
- " return out"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "z4xLys1GjAIj",
- "metadata": {
- "colab": {
- "background_save": true,
- "referenced_widgets": [
- "2ffc76edac6d430fa8261f89e7b1a86c"
- ]
- },
- "id": "z4xLys1GjAIj",
- "outputId": "1c7833ca-9395-4ca6-b2cd-0b06021f3b70"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "2ffc76edac6d430fa8261f89e7b1a86c",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "0/100(t): 0%| | 0/40 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "tensor([[-0.5000, -0.4750, -0.4500, ..., 0.4250, 0.4500, 0.4750],\n",
- " [-0.5000, -0.4750, -0.4500, ..., 0.4250, 0.4500, 0.4750],\n",
- " [-0.5000, -0.4750, -0.4500, ..., 0.4250, 0.4500, 0.4750],\n",
- " ...,\n",
- " [-0.5000, -0.4750, -0.4500, ..., 0.4250, 0.4500, 0.4750],\n",
- " [-0.5000, -0.4750, -0.4500, ..., 0.4250, 0.4500, 0.4750],\n",
- " [-0.5000, -0.4750, -0.4500, ..., 0.4250, 0.4500, 0.4750]],\n",
- " device='cuda:0')\n"
- ]
- },
- {
- "ename": "Exception",
- "evalue": "[AttributeError(\"module 'torch' has no attribute 'repeatinterleave'\")]",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchbearer/bases.py\u001b[0m in \u001b[0;36m_forward_with_exceptions\u001b[0;34m(x, model, y_pred, state)\u001b[0m\n\u001b[1;32m 341\u001b[0m \u001b[0mexc_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 342\u001b[0;31m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0m_get_param_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 343\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1531\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1532\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1533\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1540\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mTypeError\u001b[0m: RegressiveCNN.forward() got an unexpected keyword argument 'state'",
- "\nDuring handling of the above exception, another exception occurred:\n",
- "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchbearer/bases.py\u001b[0m in \u001b[0;36m_forward_with_exceptions\u001b[0;34m(x, model, y_pred, state)\u001b[0m\n\u001b[1;32m 345\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 346\u001b[0;31m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0m_get_param_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 347\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1531\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1532\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1533\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1540\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m<ipython-input-11-d714fea9c87e>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midxx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0midxy\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0midx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepeatinterleave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrepeats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 21\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/__init__.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m 2002\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2003\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"module '{__name__}' has no attribute '{name}'\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mAttributeError\u001b[0m: module 'torch' has no attribute 'repeatinterleave'",
- "\nDuring handling of the above exception, another exception occurred:\n",
- "\u001b[0;31mException\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-12-63226f92edd8>\u001b[0m in \u001b[0;36m<cell line: 27>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;31m#trial.with_generators(trainloader, test_generator=testloader)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_generators\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_generator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraingen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_generator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalgen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_generator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtestgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 28\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_key\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorchbearer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTEST_DATA\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchbearer/trial.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 131\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorchbearer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCALLBACK_LIST\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCallbackListInjection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprinter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback_list_old\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 133\u001b[0;31m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorchbearer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCALLBACK_LIST\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcallback_list_old\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchbearer/trial.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, epochs, verbose)\u001b[0m\n\u001b[1;32m 988\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorchbearer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCALLBACK_LIST\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_start_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 989\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 990\u001b[0;31m \u001b[0mfinal_metrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit_pass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorchbearer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMETRICS\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 991\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 992\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorchbearer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSTOP_TRAINING\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchbearer/trial.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 296\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorchbearer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSTEPS\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 298\u001b[0;31m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 299\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchbearer/trial.py\u001b[0m in \u001b[0;36m_fit_pass\u001b[0;34m(self, state)\u001b[0m\n\u001b[1;32m 1033\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1034\u001b[0m \u001b[0;31m# Update parameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1035\u001b[0;31m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorchbearer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOPTIMIZER\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclosure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1036\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1037\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorchbearer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMETRICS\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorchbearer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMETRIC_LIST\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/optim/optimizer.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 389\u001b[0m )\n\u001b[1;32m 390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 391\u001b[0;31m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 392\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_optimizer_step_code\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 393\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/optim/optimizer.py\u001b[0m in \u001b[0;36m_use_grad\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_grad_enabled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefaults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'differentiable'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dynamo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_break\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 76\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 77\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 78\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dynamo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_break\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/optim/adam.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mclosure\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 147\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 148\u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclosure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 150\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mgroup\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_groups\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchbearer/trial.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1033\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1034\u001b[0m \u001b[0;31m# Update parameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1035\u001b[0;31m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorchbearer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOPTIMIZER\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclosure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1036\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1037\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorchbearer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMETRICS\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorchbearer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMETRIC_LIST\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchbearer/bases.py\u001b[0m in \u001b[0;36mclosure\u001b[0;34m(state)\u001b[0m\n\u001b[1;32m 384\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 385\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 386\u001b[0;31m \u001b[0m_forward_with_exceptions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 387\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 388\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorchbearer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCALLBACK_LIST\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchbearer/bases.py\u001b[0m in \u001b[0;36m_forward_with_exceptions\u001b[0;34m(x, model, y_pred, state)\u001b[0m\n\u001b[1;32m 354\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 355\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 356\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 357\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 358\u001b[0m \u001b[0mprint_trace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mException\u001b[0m: [AttributeError(\"module 'torch' has no attribute 'repeatinterleave'\")]"
- ]
- }
- ],
- "source": [
- "# fix random seed for reproducibility\n",
- "seed = 7\n",
- "torch.manual_seed(seed)\n",
- "torch.backends.cudnn.deterministic = True\n",
- "\n",
- "# convert each image to tensor format\n",
- "transform = transforms.Compose([\n",
- " transforms.ToTensor() # convert to tensor\n",
- "])\n",
- "\n",
- "# create data loaders\n",
- "traingen = DataLoader(train_data, batch_size=128, shuffle=True)\n",
- "valgen = DataLoader(val_data, batch_size=128, shuffle=True)\n",
- "testgen = DataLoader(test_data, batch_size=128, shuffle=True)\n",
- "\n",
- "# build the model\n",
- "model = RegressiveCNN()\n",
- "\n",
- "# define the loss function and the optimiser\n",
- "loss_function = nn.MSELoss()\n",
- "optimiser = optim.Adam(model.parameters())\n",
- "\n",
- "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
- "trial = Trial(model, optimiser, loss_function, metrics=['loss']).to(device)\n",
- "#trial.with_generators(trainloader, test_generator=testloader)\n",
- "trial.with_generators(train_generator=traingen, val_generator=valgen, test_generator=testgen)\n",
- "trial.run(epochs=100)\n",
- "results = trial.evaluate(data_key=torchbearer.TEST_DATA)\n",
- "print(results)"
- ]
- }
- ],
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