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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"something\n"
]
}
],
"source": [
"print(\"something\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[0.8833, 0.1793, 0.9218],\n",
" [0.8408, 0.2123, 0.5323],\n",
" [0.5581, 0.2310, 0.7946],\n",
" [0.8700, 0.1769, 0.7497],\n",
" [0.1971, 0.3898, 0.8916]])\n"
]
}
],
"source": [
"import torch\n",
"x = torch.rand(5, 3)\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"ename": "ImportError",
"evalue": "cannot import name 'batched_dot_mul_sum' from '__main__' (unknown location)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/var/folders/4k/y4ljh2217c57vl68z1zkl0440000gn/T/ipykernel_49379/927675702.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m globals={'x': x})\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeit\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeit\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[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/pytorch_m1/lib/python3.8/site-packages/torch/utils/benchmark/utils/timer.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, number)\u001b[0m\n\u001b[1;32m 259\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mcommon\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_torch_threads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_task_spec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_threads\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 260\u001b[0m \u001b[0;31m# Warmup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 261\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\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 262\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 263\u001b[0m return common.Measurement(\n",
"\u001b[0;32m~/miniforge3/envs/pytorch_m1/lib/python3.8/timeit.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, number)\u001b[0m\n\u001b[1;32m 175\u001b[0m \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable\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 176\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--> 177\u001b[0;31m \u001b[0mtiming\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimer\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 178\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 179\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgcold\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<timeit-src>\u001b[0m in \u001b[0;36minner\u001b[0;34m(_it, _timer)\u001b[0m\n",
"\u001b[0;31mImportError\u001b[0m: cannot import name 'batched_dot_mul_sum' from '__main__' (unknown location)"
]
}
],
"source": [
"import torch.utils.benchmark as benchmark\n",
"\n",
"t0 = benchmark.Timer(\n",
" stmt='batched_dot_mul_sum(x, x)',\n",
" setup='from __main__ import batched_dot_mul_sum',\n",
" globals={'x': x})\n",
"\n",
"t1 = benchmark.Timer(\n",
" stmt='batched_dot_bmm(x, x)',\n",
" setup='from __main__ import batched_dot_bmm',\n",
" globals={'x': x})\n",
"\n",
"print(t0.timeit(100))\n",
"print(t1.timeit(100))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# packages in environment at /Users/johnnydevriese/miniconda3:\n",
"#\n",
"# Name Version Build Channel\n",
"aom 3.2.0 he49afe7_2 conda-forge\n",
"appnope 0.1.2 py39h6e9494a_2 conda-forge\n",
"backcall 0.2.0 pyh9f0ad1d_0 conda-forge\n",
"backports 1.0 py_2 conda-forge\n",
"backports.functools_lru_cache 1.6.4 pyhd8ed1ab_0 conda-forge\n",
"blas 1.0 mkl \n",
"brotlipy 0.7.0 py39h89e85a6_1003 conda-forge\n",
"bzip2 1.0.8 h0d85af4_4 conda-forge\n",
"ca-certificates 2021.10.8 h033912b_0 conda-forge\n",
"certifi 2021.10.8 py39h6e9494a_1 conda-forge\n",
"cffi 1.15.0 py39he338e87_0 conda-forge\n",
"chardet 4.0.0 py39h6e9494a_2 conda-forge\n",
"charset-normalizer 2.0.0 pyhd8ed1ab_0 conda-forge\n",
"colorama 0.4.4 pyh9f0ad1d_0 conda-forge\n",
"conda 4.10.3 py39h6e9494a_3 conda-forge\n",
"conda-package-handling 1.7.3 py39h89e85a6_1 conda-forge\n",
"cryptography 35.0.0 py39h209aa08_2 conda-forge\n",
"debugpy 1.5.1 py39h9fcab8e_0 conda-forge\n",
"decorator 5.1.0 pyhd8ed1ab_0 conda-forge\n",
"entrypoints 0.3 pyhd8ed1ab_1003 conda-forge\n",
"ffmpeg 4.4.1 h79e7b16_0 conda-forge\n",
"freetype 2.10.4 h4cff582_1 conda-forge\n",
"gettext 0.19.8.1 hd1a6beb_1008 conda-forge\n",
"gmp 6.2.1 h2e338ed_0 conda-forge\n",
"gnutls 3.6.13 h756fd2b_1 conda-forge\n",
"icu 69.1 he49afe7_0 conda-forge\n",
"idna 3.1 pyhd3deb0d_0 conda-forge\n",
"ipykernel 6.5.0 py39h71a6800_1 conda-forge\n",
"ipython 7.29.0 py39h71a6800_2 conda-forge\n",
"jbig 2.1 h0d85af4_2003 conda-forge\n",
"jedi 0.18.1 py39h6e9494a_0 conda-forge\n",
"jpeg 9d hbcb3906_0 conda-forge\n",
"jupyter_client 7.0.6 pyhd8ed1ab_0 conda-forge\n",
"jupyter_core 4.9.1 py39h6e9494a_1 conda-forge\n",
"lame 3.100 h35c211d_1001 conda-forge\n",
"lcms2 2.12 h577c468_0 conda-forge\n",
"lerc 3.0 he49afe7_0 conda-forge\n",
"libcxx 12.0.1 habf9029_0 conda-forge\n",
"libdeflate 1.8 h0d85af4_0 conda-forge\n",
"libffi 3.4.2 h0d85af4_5 conda-forge\n",
"libiconv 1.16 haf1e3a3_0 conda-forge\n",
"libpng 1.6.37 h7cec526_2 conda-forge\n",
"libsodium 1.0.18 hbcb3906_1 conda-forge\n",
"libtiff 4.3.0 hd146c10_2 conda-forge\n",
"libuv 1.42.0 h0d85af4_0 conda-forge\n",
"libvpx 1.11.0 he49afe7_3 conda-forge\n",
"libwebp-base 1.2.1 h0d85af4_0 conda-forge\n",
"libxml2 2.9.12 h7e28ab6_1 conda-forge\n",
"libzlib 1.2.11 h9173be1_1013 conda-forge\n",
"llvm-openmp 12.0.1 hda6cdc1_1 conda-forge\n",
"lz4-c 1.9.3 he49afe7_1 conda-forge\n",
"matplotlib-inline 0.1.3 pyhd8ed1ab_0 conda-forge\n",
"mkl 2021.4.0 h89fa619_689 conda-forge\n",
"mkl-service 2.4.0 py39h89e85a6_0 conda-forge\n",
"mkl_fft 1.3.1 py39h7ae3660_1 conda-forge\n",
"mkl_random 1.2.2 py39h4d6be9b_0 conda-forge\n",
"ncurses 6.2 h2e338ed_4 conda-forge\n",
"nest-asyncio 1.5.1 pyhd8ed1ab_0 conda-forge\n",
"nettle 3.6 hedd7734_0 conda-forge\n",
"numpy 1.21.2 py39h4b4dc7a_0 \n",
"numpy-base 1.21.2 py39he0bd621_0 \n",
"olefile 0.46 pyh9f0ad1d_1 conda-forge\n",
"openh264 2.1.1 hfd3ada9_0 conda-forge\n",
"openjpeg 2.4.0 h6e7aa92_1 conda-forge\n",
"openssl 1.1.1l h0d85af4_0 conda-forge\n",
"parso 0.8.2 pyhd8ed1ab_0 conda-forge\n",
"pexpect 4.8.0 pyh9f0ad1d_2 conda-forge\n",
"pickleshare 0.7.5 py_1003 conda-forge\n",
"pillow 8.4.0 py39he9bb72f_0 conda-forge\n",
"pip 21.3.1 pyhd8ed1ab_0 conda-forge\n",
"prompt-toolkit 3.0.22 pyha770c72_0 conda-forge\n",
"ptyprocess 0.7.0 pyhd3deb0d_0 conda-forge\n",
"pycosat 0.6.3 py39h89e85a6_1009 conda-forge\n",
"pycparser 2.21 pyhd8ed1ab_0 conda-forge\n",
"pygments 2.10.0 pyhd8ed1ab_0 conda-forge\n",
"pyopenssl 21.0.0 pyhd8ed1ab_0 conda-forge\n",
"pysocks 1.7.1 py39h6e9494a_4 conda-forge\n",
"python 3.9.7 h1248fe1_3_cpython conda-forge\n",
"python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge\n",
"python.app 3 py39h9ed2024_0 \n",
"python_abi 3.9 2_cp39 conda-forge\n",
"pytorch 1.10.0 py3.9_0 pytorch\n",
"pyzmq 22.3.0 py39h7fec2f1_1 conda-forge\n",
"readline 8.1 h05e3726_0 conda-forge\n",
"requests 2.26.0 pyhd8ed1ab_0 conda-forge\n",
"ruamel_yaml 0.15.80 py39h89e85a6_1006 conda-forge\n",
"setuptools 59.1.1 py39h6e9494a_0 conda-forge\n",
"six 1.16.0 pyh6c4a22f_0 conda-forge\n",
"sqlite 3.36.0 h23a322b_2 conda-forge\n",
"svt-av1 0.8.7 he49afe7_1 conda-forge\n",
"tbb 2021.4.0 h940c156_1 conda-forge\n",
"tk 8.6.11 h5dbffcc_1 conda-forge\n",
"torchvision 0.11.1 py39_cpu pytorch\n",
"tornado 6.1 py39h89e85a6_2 conda-forge\n",
"tqdm 4.62.3 pyhd8ed1ab_0 conda-forge\n",
"traitlets 5.1.1 pyhd8ed1ab_0 conda-forge\n",
"typing_extensions 4.0.0 pyha770c72_0 conda-forge\n",
"tzdata 2021e he74cb21_0 conda-forge\n",
"urllib3 1.26.7 pyhd8ed1ab_0 conda-forge\n",
"wcwidth 0.2.5 pyh9f0ad1d_2 conda-forge\n",
"wheel 0.37.0 pyhd8ed1ab_1 conda-forge\n",
"x264 1!161.3030 h0d85af4_1 conda-forge\n",
"x265 3.5 h940c156_1 conda-forge\n",
"xz 5.2.5 haf1e3a3_1 conda-forge\n",
"yaml 0.2.5 haf1e3a3_0 conda-forge\n",
"zeromq 4.3.4 he49afe7_1 conda-forge\n",
"zlib 1.2.11 h9173be1_1013 conda-forge\n",
"zstd 1.5.0 h582d3a0_0 conda-forge\n"
]
}
],
"source": [
"! conda list"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"usage: ipykernel_launcher.py [-h] [--batch-size N] [--test-batch-size N]\n",
" [--epochs N] [--lr LR] [--gamma M] [--no-cuda]\n",
" [--dry-run] [--seed S] [--log-interval N]\n",
" [--save-model]\n",
"ipykernel_launcher.py: error: unrecognized arguments: -f /Users/johnnydevriese/Library/Jupyter/runtime/kernel-59506205-59ae-4a59-8704-3ff419da213d.json\n"
]
},
{
"ename": "SystemExit",
"evalue": "2",
"output_type": "error",
"traceback": [
"An exception has occurred, use %tb to see the full traceback.\n",
"\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m 2\n"
]
}
],
"source": [
"from __future__ import print_function\n",
"import argparse\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"import torch.optim as optim\n",
"from torchvision import datasets, transforms\n",
"from torch.optim.lr_scheduler import StepLR\n",
"\n",
"\n",
"class Net(nn.Module):\n",
" def __init__(self):\n",
" super(Net, self).__init__()\n",
" self.conv1 = nn.Conv2d(1, 32, 3, 1)\n",
" self.conv2 = nn.Conv2d(32, 64, 3, 1)\n",
" self.dropout1 = nn.Dropout(0.25)\n",
" self.dropout2 = nn.Dropout(0.5)\n",
" self.fc1 = nn.Linear(9216, 128)\n",
" self.fc2 = nn.Linear(128, 10)\n",
"\n",
" def forward(self, x):\n",
" x = self.conv1(x)\n",
" x = F.relu(x)\n",
" x = self.conv2(x)\n",
" x = F.relu(x)\n",
" x = F.max_pool2d(x, 2)\n",
" x = self.dropout1(x)\n",
" x = torch.flatten(x, 1)\n",
" x = self.fc1(x)\n",
" x = F.relu(x)\n",
" x = self.dropout2(x)\n",
" x = self.fc2(x)\n",
" output = F.log_softmax(x, dim=1)\n",
" return output\n",
"\n",
"\n",
"def train(args, model, device, train_loader, optimizer, epoch):\n",
" model.train()\n",
" for batch_idx, (data, target) in enumerate(train_loader):\n",
" data, target = data.to(device), target.to(device)\n",
" optimizer.zero_grad()\n",
" output = model(data)\n",
" loss = F.nll_loss(output, target)\n",
" loss.backward()\n",
" optimizer.step()\n",
" if batch_idx % args.log_interval == 0:\n",
" print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
" epoch, batch_idx * len(data), len(train_loader.dataset),\n",
" 100. * batch_idx / len(train_loader), loss.item()))\n",
" if args.dry_run:\n",
" break\n",
"\n",
"\n",
"def test(model, device, test_loader):\n",
" model.eval()\n",
" test_loss = 0\n",
" correct = 0\n",
" with torch.no_grad():\n",
" for data, target in test_loader:\n",
" data, target = data.to(device), target.to(device)\n",
" output = model(data)\n",
" test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss\n",
" pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability\n",
" correct += pred.eq(target.view_as(pred)).sum().item()\n",
"\n",
" test_loss /= len(test_loader.dataset)\n",
"\n",
" print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
" test_loss, correct, len(test_loader.dataset),\n",
" 100. * correct / len(test_loader.dataset)))\n",
"\n",
"\n",
"def main():\n",
" # Training settings\n",
" parser = argparse.ArgumentParser(description='PyTorch MNIST Example')\n",
" parser.add_argument('--batch-size', type=int, default=64, metavar='N',\n",
" help='input batch size for training (default: 64)')\n",
" parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',\n",
" help='input batch size for testing (default: 1000)')\n",
" parser.add_argument('--epochs', type=int, default=14, metavar='N',\n",
" help='number of epochs to train (default: 14)')\n",
" parser.add_argument('--lr', type=float, default=1.0, metavar='LR',\n",
" help='learning rate (default: 1.0)')\n",
" parser.add_argument('--gamma', type=float, default=0.7, metavar='M',\n",
" help='Learning rate step gamma (default: 0.7)')\n",
" parser.add_argument('--no-cuda', action='store_true', default=False,\n",
" help='disables CUDA training')\n",
" parser.add_argument('--dry-run', action='store_true', default=False,\n",
" help='quickly check a single pass')\n",
" parser.add_argument('--seed', type=int, default=1, metavar='S',\n",
" help='random seed (default: 1)')\n",
" parser.add_argument('--log-interval', type=int, default=10, metavar='N',\n",
" help='how many batches to wait before logging training status')\n",
" parser.add_argument('--save-model', action='store_true', default=False,\n",
" help='For Saving the current Model')\n",
" args = parser.parse_args()\n",
" use_cuda = not args.no_cuda and torch.cuda.is_available()\n",
"\n",
" torch.manual_seed(args.seed)\n",
"\n",
" device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
"\n",
" train_kwargs = {'batch_size': args.batch_size}\n",
" test_kwargs = {'batch_size': args.test_batch_size}\n",
" if use_cuda:\n",
" cuda_kwargs = {'num_workers': 1,\n",
" 'pin_memory': True,\n",
" 'shuffle': True}\n",
" train_kwargs.update(cuda_kwargs)\n",
" test_kwargs.update(cuda_kwargs)\n",
"\n",
" transform=transforms.Compose([\n",
" transforms.ToTensor(),\n",
" transforms.Normalize((0.1307,), (0.3081,))\n",
" ])\n",
" dataset1 = datasets.MNIST('../data', train=True, download=True,\n",
" transform=transform)\n",
" dataset2 = datasets.MNIST('../data', train=False,\n",
" transform=transform)\n",
" train_loader = torch.utils.data.DataLoader(dataset1,**train_kwargs)\n",
" test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)\n",
"\n",
" model = Net().to(device)\n",
" optimizer = optim.Adadelta(model.parameters(), lr=args.lr)\n",
"\n",
" scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)\n",
" for epoch in range(1, args.epochs + 1):\n",
" train(args, model, device, train_loader, optimizer, epoch)\n",
" test(model, device, test_loader)\n",
" scheduler.step()\n",
"\n",
" if args.save_model:\n",
" torch.save(model.state_dict(), \"mnist_cnn.pt\")\n",
"\n",
"\n",
"main()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loss: 0.000393 after 405 batches\n",
"==> Learned function:\ty = -9.48 x^1 +4.39 x^2 -1.07 x^3 -3.53 x^4 +3.16\n",
"==> Actual function:\ty = -9.52 x^1 +4.48 x^2 -1.06 x^3 -3.55 x^4 +3.13\n"
]
}
],
"source": [
"#!/usr/bin/env python\n",
"from __future__ import print_function\n",
"from itertools import count\n",
"\n",
"import torch\n",
"import torch.nn.functional as F\n",
"\n",
"POLY_DEGREE = 4\n",
"W_target = torch.randn(POLY_DEGREE, 1) * 5\n",
"b_target = torch.randn(1) * 5\n",
"\n",
"\n",
"def make_features(x):\n",
" \"\"\"Builds features i.e. a matrix with columns [x, x^2, x^3, x^4].\"\"\"\n",
" x = x.unsqueeze(1)\n",
" return torch.cat([x ** i for i in range(1, POLY_DEGREE+1)], 1)\n",
"\n",
"\n",
"def f(x):\n",
" \"\"\"Approximated function.\"\"\"\n",
" return x.mm(W_target) + b_target.item()\n",
"\n",
"\n",
"def poly_desc(W, b):\n",
" \"\"\"Creates a string description of a polynomial.\"\"\"\n",
" result = 'y = '\n",
" for i, w in enumerate(W):\n",
" result += '{:+.2f} x^{} '.format(w, i + 1)\n",
" result += '{:+.2f}'.format(b[0])\n",
" return result\n",
"\n",
"\n",
"def get_batch(batch_size=32):\n",
" \"\"\"Builds a batch i.e. (x, f(x)) pair.\"\"\"\n",
" random = torch.randn(batch_size)\n",
" x = make_features(random)\n",
" y = f(x)\n",
" return x, y\n",
"\n",
"\n",
"# Define model\n",
"fc = torch.nn.Linear(W_target.size(0), 1)\n",
"\n",
"for batch_idx in count(1):\n",
" # Get data\n",
" batch_x, batch_y = get_batch()\n",
"\n",
" # Reset gradients\n",
" fc.zero_grad()\n",
"\n",
" # Forward pass\n",
" output = F.smooth_l1_loss(fc(batch_x), batch_y)\n",
" loss = output.item()\n",
"\n",
" # Backward pass\n",
" output.backward()\n",
"\n",
" # Apply gradients\n",
" for param in fc.parameters():\n",
" param.data.add_(-0.1 * param.grad)\n",
"\n",
" # Stop criterion\n",
" if loss < 1e-3:\n",
" break\n",
"\n",
"print('Loss: {:.6f} after {} batches'.format(loss, batch_idx))\n",
"print('==> Learned function:\\t' + poly_desc(fc.weight.view(-1), fc.bias))\n",
"print('==> Actual function:\\t' + poly_desc(W_target.view(-1), b_target))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[0.9464, 0.6891],\n",
" [0.1501, 0.2989],\n",
" [0.6019, 0.5568],\n",
" [0.8334, 0.2827],\n",
" [0.1098, 0.2141],\n",
" [0.0985, 0.8353],\n",
" [0.1616, 0.3116],\n",
" [0.2264, 0.0013],\n",
" [0.3426, 0.7077],\n",
" [0.1323, 0.4294]])\n"
]
}
],
"source": [
"import torch\n",
"x = torch.rand(10, 2)\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5.95 µs ± 11.5 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"# Calculate the projection matrix of x on the CPU\n",
"H = x.mm( (x.t().mm(x)).inverse() ).mm(x.t())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"import math\n",
"\n",
"WGS84A = 6378137.0\n",
"WGS84F = 1.0 / 298.257223563\n",
"WGS84B = WGS84A - WGS84F * WGS84A\n",
"\n",
"x = 652954.1006\n",
"y = 4774619.7919\n",
"z = -2217647.7937\n",
"\n",
"\n",
"\n",
"def ecef2GeodeticJohnny(x, y, z, a, b):\n",
" e2 = (a*a - b*b) / (a*a) # first eccentricity squared\n",
" d = (a*a - b*b) / b\n",
" \n",
" # p2 = np.square(x) + np.square(y)\n",
" p2 = x * x + y * y\n",
" p = p2 * p2 \n",
" r = math.sqrt(p2 + z*z)\n",
" tu = b*z*(1 + d/r)/(a*p)\n",
" tu2 = tu*tu\n",
" cu3 = (1/math.sqrt(1 + tu2))**3\n",
" su3 = cu3*tu2*tu\n",
" tp = (z + d*su3)/(p - e2*a*cu3)\n",
" lat = math.atan(tp)\n",
" \n",
" lon = math.atan2(y,x)\n",
" \n",
" return lat, lon"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"662 ns ± 4.45 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"ecef2GeodeticJohnny(x, y, z, WGS84A, WGS84B)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# running pytorch on m1 gpu \n",
"\n",
"https://pytorch.org/docs/stable/notes/mps.html"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/johnnydevriese/miniforge3/envs/pytorch-nightly/lib/python3.10/site-packages/torch/_tensor_str.py:103: UserWarning: The operator 'aten::bitwise_and.Tensor_out' is not currently supported on the MPS backend and will fall back to run on the CPU. This may have performance implications. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/mps/MPSFallback.mm:11.)\n",
" nonzero_finite_vals = torch.masked_select(tensor_view, torch.isfinite(tensor_view) & tensor_view.ne(0))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([1., 1., 1., 1., 1.], device='mps:0')\n"
]
}
],
"source": [
"import torch\n",
"\n",
"\n",
"# Check that MPS is available\n",
"if not torch.backends.mps.is_available():\n",
" if not torch.backends.mps.is_built():\n",
" print(\"MPS not available because the current PyTorch install was not \"\n",
" \"built with MPS enabled.\")\n",
" else:\n",
" print(\"MPS not available because the current MacOS version is not 12.3+ \"\n",
" \"and/or you do not have an MPS-enabled device on this machine.\")\n",
"\n",
"else:\n",
" mps_device = torch.device(\"mps\")\n",
"\n",
" # Create a Tensor directly on the mps device\n",
" x = torch.ones(5, device=mps_device)\n",
" # Or\n",
" # x = torch.ones(5, device=\"mps\")\n",
" print(x)\n",
"\n",
" # # Any operation happens on the GPU\n",
" # y = x * 2\n",
"\n",
" # # Move your model to mps just like any other device\n",
" # model = YourFavoriteNet()\n",
" # model.to(mps_device)\n",
"\n",
" # # Now every call runs on the GPU\n",
" # pred = model(x)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.10.5 ('pytorch-nightly')",
"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": "3.10.5"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "8a8bcccfb183d1298694efece6cf41240378bc61621e95c864629a40c5876542"
}
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|