From 3efdca98e6311e70f8587b52f46099369029642e Mon Sep 17 00:00:00 2001 From: yukun-hh Date: Wed, 8 Jul 2026 21:58:01 +0800 Subject: [PATCH] =?UTF-8?q?=E4=B8=A4=E9=81=93=E6=96=B0=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Contradictory-My-Dear-Watson/main.ipynb | 145 +++- Petals-to-the-Metal /main.ipynb | 908 ++++++++++++++++++++ Petals-to-the-Metal /main.py | 221 +++++ digit-recognizer-convolution-ver/main.ipynb | 45 +- 4 files changed, 1299 insertions(+), 20 deletions(-) create mode 100644 Petals-to-the-Metal /main.ipynb create mode 100644 Petals-to-the-Metal /main.py diff --git a/Contradictory-My-Dear-Watson/main.ipynb b/Contradictory-My-Dear-Watson/main.ipynb index 518d482..0258c76 100644 --- a/Contradictory-My-Dear-Watson/main.ipynb +++ b/Contradictory-My-Dear-Watson/main.ipynb @@ -4,7 +4,11 @@ "cell_type": "code", "id": "initial_id", "metadata": { - "collapsed": true + "collapsed": true, + "ExecuteTime": { + "end_time": "2026-07-05T15:05:59.049967188Z", + "start_time": "2026-07-05T15:05:56.833387637Z" + } }, "source": [ "import os\n", @@ -17,10 +21,15 @@ "from tqdm import tqdm" ], "outputs": [], - "execution_count": null + "execution_count": 2 }, { - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-07-05T15:05:59.150289671Z", + "start_time": "2026-07-05T15:05:59.051997256Z" + } + }, "cell_type": "code", "source": [ "train_df = pd.read_csv('./train.csv')\n", @@ -30,16 +39,136 @@ "print(f\"Train: {len(train_df)}, Val: {len(val_df)}, Test: {len(test_df)}\")" ], "id": "f32065752f1597fb", - "outputs": [], - "execution_count": null + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train: 10908, Val: 1212, Test: 5195\n" + ] + } + ], + "execution_count": 3 }, { - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-07-05T15:05:59.184794043Z", + "start_time": "2026-07-05T15:05:59.156968452Z" + } + }, "cell_type": "code", "source": "train_df.head()", "id": "460c6fffc81814df", - "outputs": [], - "execution_count": null + "outputs": [ + { + "data": { + "text/plain": [ + " id premise \\\n", + "9989 6417481c8d यद्यपि हम आज के समय में अल कायदा के साथ केएसएए... \n", + "3880 5c5ca34cf6 'Upload him into his body? What body?' \n", + "8559 1d3c28ecff yeah and then about every five years you have ... \n", + "6316 22e2a4903d θέλω να πω ότι υπήρχε είχα, είχα το ρολόι μου ... \n", + "762 307016c21f Yet, in the mouths of the white townsfolk of S... \n", + "\n", + " hypothesis lang_abv language \\\n", + "9989 हर व्यक्ति केएसएम को हमेशा अल कायदा के बराबर म... hi Hindi \n", + "3880 I don't think he has a body at all. en English \n", + "8559 You have to dig them up every five years, thro... en English \n", + "6316 Ήταν ενοχλητικό όταν κάλυψε τα παπούτσια μου. el Greek \n", + "762 White townsfolk in Salisbury, N.C. are easily ... en English \n", + "\n", + " label \n", + "9989 2 \n", + "3880 1 \n", + "8559 1 \n", + "6316 1 \n", + "762 1 " + ], + "text/html": [ + "
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idpremisehypothesislang_abvlanguagelabel
99896417481c8dयद्यपि हम आज के समय में अल कायदा के साथ केएसएए...हर व्यक्ति केएसएम को हमेशा अल कायदा के बराबर म...hiHindi2
38805c5ca34cf6'Upload him into his body? What body?'I don't think he has a body at all.enEnglish1
85591d3c28ecffyeah and then about every five years you have ...You have to dig them up every five years, thro...enEnglish1
631622e2a4903dθέλω να πω ότι υπήρχε είχα, είχα το ρολόι μου ...Ήταν ενοχλητικό όταν κάλυψε τα παπούτσια μου.elGreek1
762307016c21fYet, in the mouths of the white townsfolk of S...White townsfolk in Salisbury, N.C. are easily ...enEnglish1
\n", + "
" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 4 }, { "metadata": { diff --git a/Petals-to-the-Metal /main.ipynb b/Petals-to-the-Metal /main.ipynb new file mode 100644 index 0000000..dd07e9e --- /dev/null +++ b/Petals-to-the-Metal /main.ipynb @@ -0,0 +1,908 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "befdc3e8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-08T12:24:31.746883Z", + "iopub.status.busy": "2026-07-08T12:24:31.746615Z", + "iopub.status.idle": "2026-07-08T12:25:02.131639Z", + "shell.execute_reply": "2026-07-08T12:25:02.130586Z" + }, + "papermill": { + "duration": 30.393653, + "end_time": "2026-07-08T12:25:02.135962+00:00", + "exception": false, + "start_time": "2026-07-08T12:24:31.742309+00:00", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1783513501.576520 23 gpu_device.cc:2020] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\n", + "I0000 00:00:1783513501.579453 23 gpu_device.cc:2020] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13756 MB memory: -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-2.0836544..2.64].\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import tensorflow as tf\n", + "import torch\n", + "import torchvision\n", + "from torch.utils.data import IterableDataset, DataLoader\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import torch.nn as nn\n", + "from PIL import Image\n", + "def parse_tfrecord(example_proto):\n", + " feature_description = {\n", + " 'image': tf.io.FixedLenFeature([], tf.string),\n", + " 'class': tf.io.FixedLenFeature([], tf.int64),\n", + " 'id' : tf.io.FixedLenFeature([], tf.string),\n", + " }\n", + " parsed = tf.io.parse_single_example(example_proto, feature_description)\n", + " image = tf.image.decode_jpeg(parsed['image'], channels=3)\n", + " image = tf.image.resize(image, [224, 224])\n", + " #image = tf.image.convert_image_dtype(image, tf.float32)\n", + " label = parsed['class']\n", + " idd = parsed['id']\n", + " return image, label,idd\n", + "\n", + "def load_tfrecord_dataset(pattern):\n", + " files = tf.io.gfile.glob(pattern)\n", + " if not files:\n", + " raise ValueError(f\"No files found for pattern {pattern}\")\n", + " dataset = tf.data.TFRecordDataset(files)\n", + " dataset = dataset.map(parse_tfrecord)\n", + " # 可选：打乱、批处理等，但此处我们只返回样本级别的数据集\n", + " return dataset\n", + "\n", + "class TFRecordToPyTorch(IterableDataset):\n", + " def __init__(self, tfrecord_pattern,transform=None):\n", + " self.tfrecord_pattern = tfrecord_pattern\n", + " self.transform=transform\n", + "\n", + " def __iter__(self):\n", + " # 每次迭代创建新的数据集，保证可重复使用\n", + " dataset = load_tfrecord_dataset(self.tfrecord_pattern)\n", + " # 使用 as_numpy_iterator() 获取 NumPy 数组，便于转换为 PyTorch 张量\n", + " for image_np, label_np,idd in dataset.as_numpy_iterator():\n", + " # image_np shape: (224,224,3), dtype float32, label_np scalar int64\n", + " # 转为 PyTorch 张量，并调整为 CxHxW\n", + " image_pil = Image.fromarray((image_np).astype('uint8')) \n", + " if self.transform:\n", + " image_tensor = self.transform(image_pil)\n", + " else:\n", + " # 如果不需要 transform，至少转为 tensor\n", + " image_tensor = torch.from_numpy(image_np).permute(2,0,1)\n", + " #image_torch = torch.from_numpy(image_np).permute(2, 0, 1) # (3,224,224)\n", + " label_torch = torch.tensor(label_np, dtype=torch.long)\n", + " id_torch = idd\n", + " yield image_tensor, label_torch,id_torch\n", + "\n", + "# 使用\n", + "transform = torchvision.transforms.Compose([\n", + " torchvision.transforms.ToTensor(),\n", + " torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406],\n", + " std=[0.229, 0.224, 0.225])\n", + "])\n", + "tfrecord_path = '/kaggle/input/competitions/tpu-getting-started/tfrecords-jpeg-224x224/train/*'\n", + "dataset = TFRecordToPyTorch(tfrecord_path,transform)\n", + "tfrecord_path = '/kaggle/input/competitions/tpu-getting-started/tfrecords-jpeg-224x224/val/*'\n", + "dataset2 = TFRecordToPyTorch(tfrecord_path,transform)\n", + "# 可以配合 DataLoader 使用\n", + "train_dataloader = DataLoader(dataset, batch_size=32, num_workers=0) # num_workers 设为0，因为 TF 数据集内部已并行\n", + "val_dataloader = DataLoader(dataset2, batch_size=32, num_workers=0)\n", + "for batch in train_dataloader:\n", + " plt.imshow(batch[0][1].permute(1,2,0).numpy())\n", + " break\n", + " plt.axis('off')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7e062233", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-08T12:25:02.145618Z", + "iopub.status.busy": "2026-07-08T12:25:02.145357Z", + "iopub.status.idle": "2026-07-08T12:25:03.234393Z", + "shell.execute_reply": "2026-07-08T12:25:03.233285Z" + }, + "papermill": { + "duration": 1.09574, + "end_time": "2026-07-08T12:25:03.236312+00:00", + "exception": false, + "start_time": "2026-07-08T12:25:02.140572+00:00", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.12/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", + " warnings.warn(\n", + "/usr/local/lib/python3.12/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 97.8M/97.8M [00:00<00:00, 182MB/s]\n" + ] + } + ], + "source": [ + "pretrained_net = torchvision.models.resnet50(pretrained=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "606beb4f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-08T12:25:03.246016Z", + "iopub.status.busy": "2026-07-08T12:25:03.245773Z", + "iopub.status.idle": "2026-07-08T12:25:03.279527Z", + "shell.execute_reply": "2026-07-08T12:25:03.278755Z" + }, + "papermill": { + "duration": 0.040261, + "end_time": "2026-07-08T12:25:03.280979+00:00", + "exception": false, + "start_time": "2026-07-08T12:25:03.240718+00:00", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Parameter containing:\n", + "tensor([[ 0.0391, -0.0359, 0.0388, ..., -0.0067, -0.0466, 0.0074],\n", + " [ 0.0360, -0.0080, 0.0314, ..., 0.0155, 0.0126, -0.0466],\n", + " [ 0.0393, 0.0281, -0.0347, ..., 0.0169, -0.0164, 0.0289],\n", + " ...,\n", + " [-0.0022, -0.0320, -0.0400, ..., -0.0068, 0.0455, -0.0202],\n", + " [-0.0064, 0.0433, 0.0035, ..., 0.0013, -0.0382, 0.0487],\n", + " [ 0.0182, 0.0425, 0.0161, ..., 0.0343, -0.0364, 0.0126]],\n", + " requires_grad=True)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pretrained_net.fc=nn.Linear(pretrained_net.fc.in_features,104)\n", + "nn.init.xavier_uniform_(pretrained_net.fc.weight)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3caa2b59", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-08T12:25:03.290688Z", + "iopub.status.busy": "2026-07-08T12:25:03.290007Z", + "iopub.status.idle": "2026-07-08T12:25:03.295201Z", + "shell.execute_reply": "2026-07-08T12:25:03.294433Z" + }, + "papermill": { + "duration": 0.011388, + "end_time": "2026-07-08T12:25:03.296615+00:00", + "exception": false, + "start_time": "2026-07-08T12:25:03.285227+00:00", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "for parm in pretrained_net.conv1.parameters():\n", + " parm.requires_grad=False\n", + "for parm in pretrained_net.bn1.parameters():\n", + " parm.requires_grad=False\n", + "for parm in pretrained_net.layer1.parameters():\n", + " parm.requires_grad=False\n", + "for parm in pretrained_net.layer2.parameters():\n", + " parm.requires_grad=False\n", + "for parm in pretrained_net.layer3.parameters():\n", + " parm.requires_grad=False" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "799bad35", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-08T12:25:03.310453Z", + "iopub.status.busy": "2026-07-08T12:25:03.309534Z", + "iopub.status.idle": "2026-07-08T12:25:03.314616Z", + "shell.execute_reply": "2026-07-08T12:25:03.313963Z" + }, + "papermill": { + "duration": 0.014735, + "end_time": "2026-07-08T12:25:03.316029+00:00", + "exception": false, + "start_time": "2026-07-08T12:25:03.301294+00:00", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + " def print_trainable_info(model):\n", + " frozen = sum(p.numel() for p in model.parameters() if not p.requires_grad)\n", + " trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", + " total = frozen + trainable\n", + " print(f\" 冻结参数: {frozen:,} 可训练参数: {trainable:,} ({100.*trainable/total:.1f}%)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5e78f3a1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-08T12:25:03.325175Z", + "iopub.status.busy": "2026-07-08T12:25:03.324965Z", + "iopub.status.idle": "2026-07-08T12:25:03.330029Z", + "shell.execute_reply": "2026-07-08T12:25:03.329021Z" + }, + "papermill": { + "duration": 0.011349, + "end_time": "2026-07-08T12:25:03.331384+00:00", + "exception": false, + "start_time": "2026-07-08T12:25:03.320035+00:00", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 冻结参数: 8,543,296 可训练参数: 15,177,832 (64.0%)\n" + ] + } + ], + "source": [ + "print_trainable_info(pretrained_net)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3eac7215", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-08T12:25:03.340381Z", + "iopub.status.busy": "2026-07-08T12:25:03.339863Z", + "iopub.status.idle": "2026-07-08T12:25:03.344586Z", + "shell.execute_reply": "2026-07-08T12:25:03.344009Z" + }, + "papermill": { + "duration": 0.010776, + "end_time": "2026-07-08T12:25:03.345906+00:00", + "exception": false, + "start_time": "2026-07-08T12:25:03.335130+00:00", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "@torch.no_grad()\n", + "def validate(model,loader):\n", + " model.eval()\n", + " acc=0\n", + " total=0\n", + " for batch in loader:\n", + " X = batch[0]\n", + " labels = batch[1]\n", + " X = X.to(device)\n", + " labels = labels.to(device)\n", + " pred=torch.argmax(model(X),dim=1)\n", + " acc+=pred.eq(labels).sum()\n", + " total+=labels.size(0)\n", + " print(f\"acc:{acc/total}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "19aaeb28", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-08T12:25:03.355217Z", + "iopub.status.busy": "2026-07-08T12:25:03.355008Z", + "iopub.status.idle": "2026-07-08T12:55:31.896478Z", + "shell.execute_reply": "2026-07-08T12:55:31.895418Z" + }, + "papermill": { + "duration": 1828.548066, + "end_time": "2026-07-08T12:55:31.898198+00:00", + "exception": false, + "start_time": "2026-07-08T12:25:03.350132+00:00", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/20: 399it [01:18, 5.06it/s, loss=1.0582, avg_loss=1.2128]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1 train_loss: 0.0379\n", + "acc:0.8380926847457886\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 2/20: 399it [01:18, 5.07it/s, loss=0.2247, avg_loss=0.2473]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2 train_loss: 0.0077\n", + "acc:0.860722005367279\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 3/20: 399it [01:18, 5.06it/s, loss=0.0718, avg_loss=0.0589]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 3 train_loss: 0.0018\n", + "acc:0.8809267282485962\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 4/20: 399it [01:18, 5.07it/s, loss=0.0066, avg_loss=0.0290]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 4 train_loss: 0.0009\n", + "acc:0.8741918206214905\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 5/20: 399it [01:18, 5.06it/s, loss=0.0016, avg_loss=0.0107]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 5 train_loss: 0.0003\n", + "acc:0.904633641242981\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 6/20: 399it [01:18, 5.07it/s, loss=0.0014, avg_loss=0.0027]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 6 train_loss: 0.0001\n", + "acc:0.9043642282485962\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 7/20: 399it [01:18, 5.07it/s, loss=0.0009, avg_loss=0.0011]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 7 train_loss: 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loss=0.0007, avg_loss=0.0006]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 11 train_loss: 0.0000\n", + "acc:0.9073275923728943\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 12/20: 399it [01:18, 5.06it/s, loss=0.0007, avg_loss=0.0006]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 12 train_loss: 0.0000\n", + "acc:0.9073275923728943\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 13/20: 399it [01:18, 5.08it/s, loss=0.0006, avg_loss=0.0006]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 13 train_loss: 0.0000\n", + "acc:0.9073275923728943\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 14/20: 399it [01:18, 5.08it/s, loss=0.0005, avg_loss=0.0005]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 14 train_loss: 0.0000\n", + "acc:0.9073275923728943\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 15/20: 399it [01:18, 5.09it/s, loss=0.0003, avg_loss=0.0004]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 15 train_loss: 0.0000\n", + "acc:0.9089439511299133\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 16/20: 399it [01:18, 5.07it/s, loss=0.0002, avg_loss=0.0002]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 16 train_loss: 0.0000\n", + "acc:0.9105603694915771\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 17/20: 399it [01:19, 5.04it/s, loss=0.0002, avg_loss=0.0002]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 17 train_loss: 0.0000\n", + "acc:0.9110991358757019\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 18/20: 399it [01:18, 5.07it/s, loss=0.0001, avg_loss=0.0001]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 18 train_loss: 0.0000\n", + "acc:0.9119073152542114\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 19/20: 399it [01:19, 5.05it/s, loss=0.0001, avg_loss=0.0001]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 19 train_loss: 0.0000\n", + "acc:0.912446141242981\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 20/20: 399it [01:18, 5.08it/s, loss=0.0000, avg_loss=0.0000]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 20 train_loss: 0.0000\n", + "acc:0.9135236740112305\n" + ] + } + ], + "source": [ + "from tqdm import tqdm \n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "pretrained_net=pretrained_net.to(device)\n", + "loss_func = nn.CrossEntropyLoss()\n", + "optimizer = torch.optim.AdamW(pretrained_net.parameters(), lr=2e-4)\n", + "scheduler=torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)\n", + "epochs = 20\n", + "for epoch in range(epochs):\n", + " pretrained_net.train()\n", + " training_loss = 0\n", + " \n", + " # 使用 tqdm 包装 dataloader，并设置描述信息\n", + " progress_bar = tqdm(train_dataloader, desc=f\"Epoch {epoch+1}/{epochs}\")\n", + " lens=0\n", + " for batch in progress_bar:\n", + " optimizer.zero_grad()\n", + " X = batch[0].to(device)\n", + " labels = batch[1].to(device)\n", + " \n", + " outputs = pretrained_net(X)\n", + " loss = loss_func(outputs, labels)\n", + " loss.backward()\n", + " optimizer.step()\n", + " \n", + " training_loss += loss.item()\n", + " lens+=labels.size(0)\n", + " # 更新进度条显示当前 batch 的损失\n", + " progress_bar.set_postfix({\n", + " 'loss': f'{loss.item():.4f}',\n", + " 'avg_loss': f'{training_loss / (progress_bar.n+1):.4f}' # progress_bar.n 是已处理 batch 数\n", + " })\n", + " \n", + " scheduler.step()\n", + " \n", + " # 计算平均训练损失（注意：len(train_dataloader) 才是 batch 总数）\n", + " avg_train_loss = training_loss / lens\n", + " print(f\"Epoch {epoch+1} train_loss: {avg_train_loss:.4f}\")\n", + " \n", + " # 验证（你也可以为验证添加进度条，见下方建议）\n", + " validate(pretrained_net, val_dataloader)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "981cd43a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-08T12:55:33.032330Z", + "iopub.status.busy": "2026-07-08T12:55:33.031985Z", + "iopub.status.idle": "2026-07-08T12:56:06.103071Z", + "shell.execute_reply": "2026-07-08T12:56:06.102141Z" + }, + "papermill": { + "duration": 33.636176, + "end_time": "2026-07-08T12:56:06.104939+00:00", + "exception": false, + "start_time": "2026-07-08T12:55:32.468763+00:00", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "def parse_tfrecord_test(example_proto):\n", + " feature_description = {\n", + " 'image': tf.io.FixedLenFeature([], tf.string),\n", + " 'id' : tf.io.FixedLenFeature([], tf.string)\n", + " }\n", + " parsed = tf.io.parse_single_example(example_proto, feature_description)\n", + " image = tf.image.decode_jpeg(parsed['image'], channels=3)\n", + " image = tf.image.resize(image, [224, 224])\n", + " #image = tf.image.convert_image_dtype(image, tf.float32)\n", + " idd = parsed['id']\n", + " return image,idd\n", + "def load_tfrecord_dataset_test(pattern):\n", + " files = tf.io.gfile.glob(pattern)\n", + " if not files:\n", + " raise ValueError(f\"No files found for pattern {pattern}\")\n", + " dataset = tf.data.TFRecordDataset(files)\n", + " dataset = dataset.map(parse_tfrecord_test)\n", + " # 可选：打乱、批处理等，但此处我们只返回样本级别的数据集\n", + " return dataset\n", + "class TFRecordToPyTorchTest(IterableDataset):\n", + " def __init__(self, tfrecord_pattern,transform=None):\n", + " self.tfrecord_pattern = tfrecord_pattern\n", + " self.transform=transform\n", + "\n", + " def __iter__(self):\n", + " # 每次迭代创建新的数据集，保证可重复使用\n", + " dataset = load_tfrecord_dataset_test(self.tfrecord_pattern)\n", + " # 使用 as_numpy_iterator() 获取 NumPy 数组，便于转换为 PyTorch 张量\n", + " for image_np,idd in dataset.as_numpy_iterator():\n", + " # image_np shape: (224,224,3), dtype float32, label_np scalar int64\n", + " # 转为 PyTorch 张量，并调整为 CxHxW\n", + " image_pil = Image.fromarray((image_np).astype('uint8')) \n", + " if self.transform:\n", + " image_tensor = self.transform(image_pil)\n", + " else:\n", + " # 如果不需要 transform，至少转为 tensor\n", + " image_tensor = torch.from_numpy(image_np).permute(2,0,1)\n", + " #image_torch = torch.from_numpy(image_np).permute(2, 0, 1) # (3,224,224)\n", + " #label_torch = torch.tensor(label_np, dtype=torch.long)\n", + " id_torch = idd\n", + " yield image_tensor,id_torch\n", + "tfrecord_path = '/kaggle/input/competitions/tpu-getting-started/tfrecords-jpeg-224x224/test/*'\n", + "dataset3 = TFRecordToPyTorchTest(tfrecord_path,transform)\n", + "test_dataloader = DataLoader(dataset3, batch_size=32, num_workers=0)\n", + "id_array=[]\n", + "all_preds=[]\n", + "with torch.no_grad():\n", + " for batch in test_dataloader:\n", + " input_ids = batch[0].to(device)\n", + " idd = batch[1]\n", + " outputs = pretrained_net(input_ids)\n", + " preds = torch.argmax(outputs, dim=1)\n", + " all_preds.extend(preds.cpu().numpy())\n", + " id_array.extend(idd)\n", + "submission = pd.DataFrame({\n", + " 'id':id_array,\n", + " 'label': all_preds\n", + "})\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1e950456", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-08T12:56:07.318686Z", + "iopub.status.busy": "2026-07-08T12:56:07.318282Z", + "iopub.status.idle": "2026-07-08T12:56:07.361026Z", + "shell.execute_reply": "2026-07-08T12:56:07.360031Z" + }, + "papermill": { + "duration": 0.611337, + "end_time": "2026-07-08T12:56:07.362666+00:00", + "exception": false, + "start_time": "2026-07-08T12:56:06.751329+00:00", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " id label\n", + "0 59d1b6146 46\n", + "1 48c96bd6b 15\n", + "2 7b437ba4e 9\n", + "3 1b7aef8e8 79\n", + "4 d6143b4d4 4\n", + "... ... ...\n", + "7377 2a608c0db 103\n", + "7378 d82a21bbd 93\n", + "7379 f9c931893 53\n", + "7380 18c7b92b8 41\n", + "7381 523df966b 102\n", + "\n", + "[7382 rows x 2 columns]\n", + "Submission saved!\n" + ] + } + ], + "source": [ + "submission['id'] = submission['id'].apply(lambda x: x.decode('utf-8'))\n", + "print(submission)\n", + "submission.to_csv('submission.csv', index=False)\n", + "print(\"Submission saved!\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "79c03587", + "metadata": { + "papermill": { + "duration": 0.645772, + "end_time": "2026-07-08T12:56:08.568042+00:00", + "exception": false, + "start_time": "2026-07-08T12:56:07.922270+00:00", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kaggle": { + "accelerator": "none", + "dataSources": [], + "dockerImageVersionId": 28755, + "isGpuEnabled": false, + "isInternetEnabled": false, + "language": "python", + "sourceType": "notebook" + }, + "kernelspec": { + "display_name": "Python 3", + "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": "3.12.13" + }, + "papermill": { + "default_parameters": {}, + "duration": 1903.252205, + "end_time": "2026-07-08T12:56:12.294916+00:00", + "environment_variables": {}, + "exception": null, + "input_path": "__notebook__.ipynb", + "output_path": "__notebook__.ipynb", + "parameters": {}, + "start_time": "2026-07-08T12:24:29.042711+00:00", + "version": "2.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Petals-to-the-Metal /main.py b/Petals-to-the-Metal /main.py new file mode 100644 index 0000000..e826ab6 --- /dev/null +++ b/Petals-to-the-Metal /main.py @@ -0,0 +1,221 @@ +# %% +import tensorflow as tf +import torch +import torchvision +from torch.utils.data import IterableDataset, DataLoader +from matplotlib import pyplot as plt +import numpy as np +import torch.nn as nn +from PIL import Image +def parse_tfrecord(example_proto): + feature_description = { + 'image': tf.io.FixedLenFeature([], tf.string), + 'class': tf.io.FixedLenFeature([], tf.int64), + 'id' : tf.io.FixedLenFeature([], tf.string), + } + parsed = tf.io.parse_single_example(example_proto, feature_description) + image = tf.image.decode_jpeg(parsed['image'], channels=3) + image = tf.image.resize(image, [224, 224]) + #image = tf.image.convert_image_dtype(image, tf.float32) + label = parsed['class'] + idd = parsed['id'] + return image, label,idd + +def load_tfrecord_dataset(pattern): + files = tf.io.gfile.glob(pattern) + if not files: + raise ValueError(f"No files found for pattern {pattern}") + dataset = tf.data.TFRecordDataset(files) + dataset = dataset.map(parse_tfrecord) + # 可选：打乱、批处理等，但此处我们只返回样本级别的数据集 + return dataset + +class TFRecordToPyTorch(IterableDataset): + def __init__(self, tfrecord_pattern,transform=None): + self.tfrecord_pattern = tfrecord_pattern + self.transform=transform + + def __iter__(self): + # 每次迭代创建新的数据集，保证可重复使用 + dataset = load_tfrecord_dataset(self.tfrecord_pattern) + # 使用 as_numpy_iterator() 获取 NumPy 数组，便于转换为 PyTorch 张量 + for image_np, label_np,idd in dataset.as_numpy_iterator(): + # image_np shape: (224,224,3), dtype float32, label_np scalar int64 + # 转为 PyTorch 张量，并调整为 CxHxW + image_pil = Image.fromarray((image_np).astype('uint8')) + if self.transform: + image_tensor = self.transform(image_pil) + else: + # 如果不需要 transform，至少转为 tensor + image_tensor = torch.from_numpy(image_np).permute(2,0,1) + #image_torch = torch.from_numpy(image_np).permute(2, 0, 1) # (3,224,224) + label_torch = torch.tensor(label_np, dtype=torch.long) + id_torch = idd + yield image_tensor, label_torch,id_torch + +# 使用 +transform = torchvision.transforms.Compose([ + torchvision.transforms.ToTensor(), + torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) +]) +tfrecord_path = '/kaggle/input/competitions/tpu-getting-started/tfrecords-jpeg-224x224/train/*' +dataset = TFRecordToPyTorch(tfrecord_path,transform) +tfrecord_path = '/kaggle/input/competitions/tpu-getting-started/tfrecords-jpeg-224x224/val/*' +dataset2 = TFRecordToPyTorch(tfrecord_path,transform) +# 可以配合 DataLoader 使用 +train_dataloader = DataLoader(dataset, batch_size=32, num_workers=0) # num_workers 设为0，因为 TF 数据集内部已并行 +val_dataloader = DataLoader(dataset2, batch_size=32, num_workers=0) +for batch in train_dataloader: + plt.imshow(batch[0][1].permute(1,2,0).numpy()) + break + plt.axis('off') + plt.show() +# %% +pretrained_net = torchvision.models.resnet50(pretrained=True) +# %% +pretrained_net.fc=nn.Linear(pretrained_net.fc.in_features,104) +nn.init.xavier_uniform_(pretrained_net.fc.weight) +# %% +for parm in pretrained_net.conv1.parameters(): + parm.requires_grad=False +for parm in pretrained_net.bn1.parameters(): + parm.requires_grad=False +for parm in pretrained_net.layer1.parameters(): + parm.requires_grad=False +for parm in pretrained_net.layer2.parameters(): + parm.requires_grad=False +for parm in pretrained_net.layer3.parameters(): + parm.requires_grad=False +# %% + def print_trainable_info(model): + frozen = sum(p.numel() for p in model.parameters() if not p.requires_grad) + trainable = sum(p.numel() for p in model.parameters() if p.requires_grad) + total = frozen + trainable + print(f" 冻结参数: {frozen:,} 可训练参数: {trainable:,} ({100.*trainable/total:.1f}%)") +# %% +print_trainable_info(pretrained_net) +# %% +@torch.no_grad() +def validate(model,loader): + model.eval() + acc=0 + total=0 + for batch in loader: + X = batch[0] + labels = batch[1] + X = X.to(device) + labels = labels.to(device) + pred=torch.argmax(model(X),dim=1) + acc+=pred.eq(labels).sum() + total+=labels.size(0) + print(f"acc:{acc/total}") +# %% +from tqdm import tqdm +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +pretrained_net=pretrained_net.to(device) +loss_func = nn.CrossEntropyLoss() +optimizer = torch.optim.AdamW(pretrained_net.parameters(), lr=2e-4) +scheduler=torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10) +epochs = 20 +for epoch in range(epochs): + pretrained_net.train() + training_loss = 0 + + # 使用 tqdm 包装 dataloader，并设置描述信息 + progress_bar = tqdm(train_dataloader, desc=f"Epoch {epoch+1}/{epochs}") + lens=0 + for batch in progress_bar: + optimizer.zero_grad() + X = batch[0].to(device) + labels = batch[1].to(device) + + outputs = pretrained_net(X) + loss = loss_func(outputs, labels) + loss.backward() + optimizer.step() + + training_loss += loss.item() + lens+=labels.size(0) + # 更新进度条显示当前 batch 的损失 + progress_bar.set_postfix({ + 'loss': f'{loss.item():.4f}', + 'avg_loss': f'{training_loss / (progress_bar.n+1):.4f}' # progress_bar.n 是已处理 batch 数 + }) + + scheduler.step() + + # 计算平均训练损失（注意：len(train_dataloader) 才是 batch 总数） + avg_train_loss = training_loss / lens + print(f"Epoch {epoch+1} train_loss: {avg_train_loss:.4f}") + + # 验证（你也可以为验证添加进度条，见下方建议） + validate(pretrained_net, val_dataloader) + +# %% +import pandas as pd +def parse_tfrecord_test(example_proto): + feature_description = { + 'image': tf.io.FixedLenFeature([], tf.string), + 'id' : tf.io.FixedLenFeature([], tf.string) + } + parsed = tf.io.parse_single_example(example_proto, feature_description) + image = tf.image.decode_jpeg(parsed['image'], channels=3) + image = tf.image.resize(image, [224, 224]) + #image = tf.image.convert_image_dtype(image, tf.float32) + idd = parsed['id'] + return image,idd +def load_tfrecord_dataset_test(pattern): + files = tf.io.gfile.glob(pattern) + if not files: + raise ValueError(f"No files found for pattern {pattern}") + dataset = tf.data.TFRecordDataset(files) + dataset = dataset.map(parse_tfrecord_test) + # 可选：打乱、批处理等，但此处我们只返回样本级别的数据集 + return dataset +class TFRecordToPyTorchTest(IterableDataset): + def __init__(self, tfrecord_pattern,transform=None): + self.tfrecord_pattern = tfrecord_pattern + self.transform=transform + + def __iter__(self): + # 每次迭代创建新的数据集，保证可重复使用 + dataset = load_tfrecord_dataset_test(self.tfrecord_pattern) + # 使用 as_numpy_iterator() 获取 NumPy 数组，便于转换为 PyTorch 张量 + for image_np,idd in dataset.as_numpy_iterator(): + # image_np shape: (224,224,3), dtype float32, label_np scalar int64 + # 转为 PyTorch 张量，并调整为 CxHxW + image_pil = Image.fromarray((image_np).astype('uint8')) + if self.transform: + image_tensor = self.transform(image_pil) + else: + # 如果不需要 transform，至少转为 tensor + image_tensor = torch.from_numpy(image_np).permute(2,0,1) + #image_torch = torch.from_numpy(image_np).permute(2, 0, 1) # (3,224,224) + #label_torch = torch.tensor(label_np, dtype=torch.long) + id_torch = idd + yield image_tensor,id_torch +tfrecord_path = '/kaggle/input/competitions/tpu-getting-started/tfrecords-jpeg-224x224/test/*' +dataset3 = TFRecordToPyTorchTest(tfrecord_path,transform) +test_dataloader = DataLoader(dataset3, batch_size=32, num_workers=0) +id_array=[] +all_preds=[] +with torch.no_grad(): + for batch in test_dataloader: + input_ids = batch[0].to(device) + idd = batch[1] + outputs = pretrained_net(input_ids) + preds = torch.argmax(outputs, dim=1) + all_preds.extend(preds.cpu().numpy()) + id_array.extend(idd) +submission = pd.DataFrame({ + 'id':id_array, + 'label': all_preds +}) + +# %% +submission['id'] = submission['id'].apply(lambda x: x.decode('utf-8')) +print(submission) +submission.to_csv('submission.csv', index=False) +print("Submission saved!") +# %% diff --git a/digit-recognizer-convolution-ver/main.ipynb b/digit-recognizer-convolution-ver/main.ipynb index 1dd4901..1442322 100644 --- a/digit-recognizer-convolution-ver/main.ipynb +++ b/digit-recognizer-convolution-ver/main.ipynb @@ -135,14 +135,15 @@ " self.cov3=nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5)\n", " self.cov4=nn.Conv2d(in_channels=64, out_channels=128, kernel_size=5)\n", " self.cov5=nn.Conv2d(in_channels=128, out_channels=256, kernel_size=5)\n", - " self.linear1 = nn.Linear(in_features=4*4*256, out_features=128)\n", + " self.cov6=nn.Conv2d(in_channels=256, out_channels=512, kernel_size=4)\n", + " self.linear1 = nn.Linear(in_features=1*1*512, out_features=128)\n", " self.linear2 = nn.Linear(in_features=128, out_features=10)\n", " self.relu = nn.ReLU()\n", "\n", " def forward(self,X):\n", " X=X.view(-1,1,28,28)\n", - " X=self.cov5(self.cov4(self.cov3(self.cov2(self.cov1(X)))))\n", - " X=X.view(-1,256*4*4)\n", + " X=self.cov6(self.cov5(self.cov4(self.cov3(self.cov2(self.cov1(X))))))\n", + " X=X.view(-1,512)\n", " return self.linear2(self.relu(self.linear1(X)))\n" ], "metadata": { @@ -155,12 +156,12 @@ "shell.execute_reply": "2026-07-04T16:16:16.388703Z" }, "ExecuteTime": { - "end_time": "2026-07-04T17:01:40.912905906Z", - "start_time": "2026-07-04T17:01:40.865475423Z" + "end_time": "2026-07-04T17:05:35.534234524Z", + "start_time": "2026-07-04T17:05:35.497304936Z" } }, "outputs": [], - "execution_count": 50 + "execution_count": 56 }, { "cell_type": "code", @@ -181,12 +182,12 @@ "shell.execute_reply": "2026-07-04T16:16:18.580038Z" }, "ExecuteTime": { - "end_time": "2026-07-04T17:01:40.964552679Z", - "start_time": "2026-07-04T17:01:40.913934010Z" + "end_time": "2026-07-04T17:05:36.050933953Z", + "start_time": "2026-07-04T17:05:36.028103516Z" } }, "outputs": [], - "execution_count": 51 + "execution_count": 57 }, { "cell_type": "code", @@ -201,11 +202,31 @@ "shell.execute_reply": "2026-07-04T16:18:53.081057Z" }, "ExecuteTime": { - "start_time": "2026-07-04T17:01:40.968982093Z" + "end_time": "2026-07-04T17:05:48.843650166Z", + "start_time": "2026-07-04T17:05:36.590087546Z" } }, - "outputs": [], - "execution_count": null + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001B[31m---------------------------------------------------------------------------\u001B[39m", + "\u001B[31mKeyboardInterrupt\u001B[39m Traceback (most recent call last)", + "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[58]\u001B[39m\u001B[32m, line 14\u001B[39m\n\u001B[32m 12\u001B[39m loss = loss_func(model(X),labels)\n\u001B[32m 13\u001B[39m loss.backward()\n\u001B[32m---> \u001B[39m\u001B[32m14\u001B[39m \u001B[43moptimizer\u001B[49m\u001B[43m.\u001B[49m\u001B[43mstep\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 15\u001B[39m training_loss+=loss.item()\n\u001B[32m 16\u001B[39m scheduler.step()\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/.conda/envs/nn/lib/python3.11/site-packages/torch/optim/lr_scheduler.py:166\u001B[39m, in \u001B[36mLRScheduler.__init__..patch_track_step_called..wrap_step..wrapper\u001B[39m\u001B[34m(*args, **kwargs)\u001B[39m\n\u001B[32m 164\u001B[39m opt = opt_ref()\n\u001B[32m 165\u001B[39m opt._opt_called = \u001B[38;5;28;01mTrue\u001B[39;00m \u001B[38;5;66;03m# type: ignore[union-attr]\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m166\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m.\u001B[49m\u001B[34;43m__get__\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mopt\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mopt\u001B[49m\u001B[43m.\u001B[49m\u001B[34;43m__class__\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/.conda/envs/nn/lib/python3.11/site-packages/torch/optim/optimizer.py:526\u001B[39m, in \u001B[36mOptimizer.profile_hook_step..wrapper\u001B[39m\u001B[34m(*args, **kwargs)\u001B[39m\n\u001B[32m 521\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mRuntimeError\u001B[39;00m(\n\u001B[32m 522\u001B[39m \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mfunc\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m must return None or a tuple of (new_args, new_kwargs), but got \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mresult\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m.\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 523\u001B[39m )\n\u001B[32m 525\u001B[39m \u001B[38;5;66;03m# pyrefly: ignore [invalid-param-spec]\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m526\u001B[39m out = \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 527\u001B[39m \u001B[38;5;28mself\u001B[39m._optimizer_step_code()\n\u001B[32m 529\u001B[39m \u001B[38;5;66;03m# call optimizer step post hooks\u001B[39;00m\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/.conda/envs/nn/lib/python3.11/site-packages/torch/optim/optimizer.py:81\u001B[39m, in \u001B[36m_use_grad_for_differentiable.._use_grad\u001B[39m\u001B[34m(*args, **kwargs)\u001B[39m\n\u001B[32m 79\u001B[39m torch.set_grad_enabled(\u001B[38;5;28mself\u001B[39m.defaults[\u001B[33m\"\u001B[39m\u001B[33mdifferentiable\u001B[39m\u001B[33m\"\u001B[39m])\n\u001B[32m 80\u001B[39m torch._dynamo.graph_break()\n\u001B[32m---> \u001B[39m\u001B[32m81\u001B[39m ret = \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 82\u001B[39m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[32m 83\u001B[39m torch._dynamo.graph_break()\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/.conda/envs/nn/lib/python3.11/site-packages/torch/optim/adam.py:248\u001B[39m, in \u001B[36mAdam.step\u001B[39m\u001B[34m(self, closure)\u001B[39m\n\u001B[32m 236\u001B[39m beta1, beta2 = group[\u001B[33m\"\u001B[39m\u001B[33mbetas\u001B[39m\u001B[33m\"\u001B[39m]\n\u001B[32m 238\u001B[39m has_complex = \u001B[38;5;28mself\u001B[39m._init_group(\n\u001B[32m 239\u001B[39m group,\n\u001B[32m 240\u001B[39m params_with_grad,\n\u001B[32m (...)\u001B[39m\u001B[32m 245\u001B[39m state_steps,\n\u001B[32m 246\u001B[39m )\n\u001B[32m--> \u001B[39m\u001B[32m248\u001B[39m \u001B[43madam\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 249\u001B[39m \u001B[43m \u001B[49m\u001B[43mparams_with_grad\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 250\u001B[39m \u001B[43m \u001B[49m\u001B[43mgrads\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 251\u001B[39m \u001B[43m \u001B[49m\u001B[43mexp_avgs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 252\u001B[39m \u001B[43m 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\u001B[36m_disable_dynamo_if_unsupported..wrapper..maybe_fallback\u001B[39m\u001B[34m(*args, **kwargs)\u001B[39m\n\u001B[32m 149\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m disabled_func(*args, **kwargs)\n\u001B[32m 150\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m151\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/.conda/envs/nn/lib/python3.11/site-packages/torch/optim/adam.py:970\u001B[39m, in \u001B[36madam\u001B[39m\u001B[34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, foreach, capturable, differentiable, fused, grad_scale, found_inf, has_complex, decoupled_weight_decay, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize)\u001B[39m\n\u001B[32m 967\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m 968\u001B[39m func = _single_tensor_adam\n\u001B[32m--> \u001B[39m\u001B[32m970\u001B[39m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 971\u001B[39m \u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 972\u001B[39m \u001B[43m \u001B[49m\u001B[43mgrads\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 973\u001B[39m \u001B[43m \u001B[49m\u001B[43mexp_avgs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 974\u001B[39m \u001B[43m \u001B[49m\u001B[43mexp_avg_sqs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 975\u001B[39m \u001B[43m \u001B[49m\u001B[43mmax_exp_avg_sqs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 976\u001B[39m \u001B[43m \u001B[49m\u001B[43mstate_steps\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 977\u001B[39m \u001B[43m \u001B[49m\u001B[43mamsgrad\u001B[49m\u001B[43m=\u001B[49m\u001B[43mamsgrad\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 978\u001B[39m \u001B[43m \u001B[49m\u001B[43mhas_complex\u001B[49m\u001B[43m=\u001B[49m\u001B[43mhas_complex\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 979\u001B[39m \u001B[43m \u001B[49m\u001B[43mbeta1\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbeta1\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 980\u001B[39m \u001B[43m \u001B[49m\u001B[43mbeta2\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbeta2\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 981\u001B[39m \u001B[43m \u001B[49m\u001B[43mlr\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlr\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 982\u001B[39m \u001B[43m \u001B[49m\u001B[43mweight_decay\u001B[49m\u001B[43m=\u001B[49m\u001B[43mweight_decay\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 983\u001B[39m \u001B[43m \u001B[49m\u001B[43meps\u001B[49m\u001B[43m=\u001B[49m\u001B[43meps\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 984\u001B[39m \u001B[43m \u001B[49m\u001B[43mmaximize\u001B[49m\u001B[43m=\u001B[49m\u001B[43mmaximize\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 985\u001B[39m \u001B[43m \u001B[49m\u001B[43mcapturable\u001B[49m\u001B[43m=\u001B[49m\u001B[43mcapturable\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 986\u001B[39m \u001B[43m \u001B[49m\u001B[43mdifferentiable\u001B[49m\u001B[43m=\u001B[49m\u001B[43mdifferentiable\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 987\u001B[39m \u001B[43m \u001B[49m\u001B[43mgrad_scale\u001B[49m\u001B[43m=\u001B[49m\u001B[43mgrad_scale\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 988\u001B[39m \u001B[43m \u001B[49m\u001B[43mfound_inf\u001B[49m\u001B[43m=\u001B[49m\u001B[43mfound_inf\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 989\u001B[39m \u001B[43m \u001B[49m\u001B[43mdecoupled_weight_decay\u001B[49m\u001B[43m=\u001B[49m\u001B[43mdecoupled_weight_decay\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 990\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/.conda/envs/nn/lib/python3.11/site-packages/torch/optim/adam.py:545\u001B[39m, in \u001B[36m_single_tensor_adam\u001B[39m\u001B[34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, grad_scale, found_inf, amsgrad, has_complex, beta1, beta2, lr, weight_decay, eps, maximize, capturable, differentiable, decoupled_weight_decay)\u001B[39m\n\u001B[32m 543\u001B[39m denom = (max_exp_avg_sqs[i].sqrt() / bias_correction2_sqrt).add_(eps)\n\u001B[32m 544\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m545\u001B[39m denom = (\u001B[43mexp_avg_sq\u001B[49m\u001B[43m.\u001B[49m\u001B[43msqrt\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m / bias_correction2_sqrt).add_(eps)\n\u001B[32m 547\u001B[39m param.addcdiv_(exp_avg, denom, value=-step_size) \u001B[38;5;66;03m# type: ignore[arg-type]\u001B[39;00m\n\u001B[32m 549\u001B[39m \u001B[38;5;66;03m# Lastly, switch back to complex view\u001B[39;00m\n", + "\u001B[31mKeyboardInterrupt\u001B[39m: " + ] + } + ], + "execution_count": 58 }, { "cell_type": "code",