From 488003fc9e082bb6a2a11d320e942cc671d72533 Mon Sep 17 00:00:00 2001 From: Alexander Mordvintsev Date: Wed, 30 Oct 2024 03:29:21 -0700 Subject: [PATCH] No public description PiperOrigin-RevId: 691354263 --- notebooks/texture_nca_pytorch.ipynb | 441 ++-------------------------- 1 file changed, 29 insertions(+), 412 deletions(-) diff --git a/notebooks/texture_nca_pytorch.ipynb b/notebooks/texture_nca_pytorch.ipynb index a46bf22..ac42c94 100644 --- a/notebooks/texture_nca_pytorch.ipynb +++ b/notebooks/texture_nca_pytorch.ipynb @@ -33,21 +33,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "FR8YNR-g9JXA", - "outputId": "a7ee746d-9d87-4f3b-91ad-15bbc16a4d00" + "outputId": "1348f95f-4d70-4708-8665-efd317cce977" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "GPU 0: Tesla V100-SXM2-16GB (UUID: GPU-2e58775e-5421-78a5-a720-d4b7f2f85f29)\n" + "GPU 0: NVIDIA A100-SXM4-40GB (UUID: GPU-56825252-6146-1748-e1e0-a9a421998875)\n" ] } ], @@ -84,7 +84,7 @@ " f = url\n", " img = PIL.Image.open(f)\n", " if max_size is not None:\n", - " img.thumbnail((max_size, max_size), PIL.Image.ANTIALIAS)\n", + " img.thumbnail((max_size, max_size))\n", " if mode is not None:\n", " img = img.convert(mode)\n", " img = np.float32(img)/255.0\n", @@ -196,7 +196,7 @@ " self._outro.append(img)\n", " if len(self._outro) > self.cross_len:\n", " super().add(self._outro.pop(0))\n", - " \n", + "\n", " def close(self):\n", " for t in np.linspace(0, 1, len(self._intro)):\n", " img = self._intro.pop(0)*t + self._outro.pop(0)*(1.0-t)\n", @@ -208,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "id": "C1hkDT38hSaP" }, @@ -217,63 +217,24 @@ "import torch\n", "import torchvision.models as models\n", "import torch.nn.functional as F\n", + "from torch import nn\n", "\n", - "torch.set_default_tensor_type('torch.cuda.FloatTensor')" + "torch.set_default_device('cuda')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { - "id": "vFqmv9w5tPQB", - "outputId": "9a3a5119-d722-4e43-f650-f1fd33fa238c", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 66, - "referenced_widgets": [ - "bbe0021d1ba84649adc94efbc8c2c7ce", - "8c57ddbe3f7b4e3ab6d74aff5d4fa7ad", - "89ed0da2942041958dc79ff52a4ce554", - "fcce833eeee6496f99faa182ba4ea26a", - "295dd5b2b86541eab668d869e6cb9ea9", - "9f5e7e6564be40d0874f76a9c43a25b7", - "9b2cf45c60274be1a171c23868a3dc1d", - "473d40c0cba049e185587972d3b643f8", - "194701ad8e18440aaed712abde0b01c6", - "b8d1236246534455b14acf1cade5814d", - "221e737c59224ed7a63f591f52dd78e7" - ] - } + "id": "vFqmv9w5tPQB" }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - " 0%| | 0.00/528M [00:00" - ], - "image/jpeg": 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+ ] }, "metadata": {} } @@ -407,14 +368,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "id": "9Cd1uMD3cZw3" }, "outputs": [], "source": [ "#@title setup training\n", - "ca = CA() \n", + "ca = CA()\n", "opt = torch.optim.Adam(ca.parameters(), 1e-3, capturable=True)\n", "lr_sched = torch.optim.lr_scheduler.MultiStepLR(opt, [1000, 2000], 0.3)\n", "loss_log = []\n", @@ -489,12 +450,12 @@ " with torch.no_grad():\n", " loss.backward()\n", " for p in ca.parameters():\n", - " p.grad /= (p.grad.norm()+1e-8) # normalize gradients \n", + " p.grad /= (p.grad.norm()+1e-8) # normalize gradients\n", " opt.step()\n", " opt.zero_grad()\n", " lr_sched.step()\n", " pool[batch_idx] = x # update pool\n", - " \n", + "\n", " loss_log.append(loss.item())\n", " if i%5 == 0:\n", " display(Markdown(f'''\n", @@ -577,9 +538,7 @@ }, { "cell_type": "code", - "source": [ - "" - ], + "source": [], "metadata": { "id": "rFI2pzXNrbXq" }, @@ -590,10 +549,10 @@ "metadata": 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