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Kaggle/Petals-to-the-Metal-ViT-Lora/main.ipynb
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2026-07-09 21:42:42 +08:00

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{"metadata":{"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"},"kaggle":{"accelerator":"none","dataSources":[],"dockerImageVersionId":28755,"isGpuEnabled":false,"isInternetEnabled":false,"language":"python","sourceType":"notebook"},"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_minor":5,"nbformat":4,"cells":[{"id":"befdc3e8","cell_type":"code","source":"import tensorflow as tf\nimport torch\nimport torchvision\nfrom torch.utils.data import IterableDataset, DataLoader\nfrom matplotlib import pyplot as plt\nimport numpy as np\nimport torch.nn as nn\nfrom PIL import Image\ndef 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\ndef 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\nclass 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\n","metadata":{"execution":{"iopub.status.busy":"2026-07-09T12:49:34.283479Z","iopub.execute_input":"2026-07-09T12:49:34.283693Z","iopub.status.idle":"2026-07-09T12:49:58.875160Z","shell.execute_reply.started":"2026-07-09T12:49:34.283674Z","shell.execute_reply":"2026-07-09T12:49:58.874468Z"},"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":[],"trusted":true},"outputs":[],"execution_count":1},{"id":"92b8d6c3-f964-4e11-97ed-2482ad60772b","cell_type":"code","source":"from transformers import ViTForImageClassification, ViTImageProcessor\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n \n\nmodel_name = \"google/vit-base-patch16-224-in21k\" # 在ImageNet21k上预训练\n\nmodel = ViTForImageClassification.from_pretrained(model_name, num_labels=104)\n \nmodel.to(device)\n \nfeature_extractor = ViTImageProcessor.from_pretrained(model_name)\nprint(model,feature_extractor)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-09T12:49:58.877138Z","iopub.execute_input":"2026-07-09T12:49:58.877537Z","iopub.status.idle":"2026-07-09T12:50:19.278743Z","shell.execute_reply.started":"2026-07-09T12:49:58.877513Z","shell.execute_reply":"2026-07-09T12:50:19.277780Z"}},"outputs":[{"name":"stderr","text":"Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"config.json: 0%| | 0.00/502 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"27297653b6704804a3b6d4d5c4448f94"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model.safetensors: 0%| | 0.00/346M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f1c73edf82ca4597a45a1291ef96ff71"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Loading weights: 0%| | 0/198 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"62f1cc361f674386928ae4d61b57aa54"}},"metadata":{}},{"name":"stderr","text":"ViTForImageClassification LOAD REPORT from: google/vit-base-patch16-224-in21k\nKey | Status | \n--------------------+------------+-\npooler.dense.weight | UNEXPECTED | \npooler.dense.bias | UNEXPECTED | \nclassifier.bias | MISSING | \nclassifier.weight | MISSING | \n\nNotes:\n- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"preprocessor_config.json: 0%| | 0.00/160 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f4dac62b1d78412682a5ec8dc6b7d057"}},"metadata":{}},{"name":"stdout","text":"ViTForImageClassification(\n (vit): ViTModel(\n (embeddings): ViTEmbeddings(\n (patch_embeddings): ViTPatchEmbeddings(\n (projection): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n )\n (dropout): Dropout(p=0.0, inplace=False)\n )\n (encoder): ViTEncoder(\n (layer): ModuleList(\n (0-11): 12 x ViTLayer(\n (attention): ViTAttention(\n (attention): ViTSelfAttention(\n (query): Linear(in_features=768, out_features=768, bias=True)\n (key): Linear(in_features=768, out_features=768, bias=True)\n (value): Linear(in_features=768, out_features=768, bias=True)\n )\n (output): ViTSelfOutput(\n (dense): Linear(in_features=768, out_features=768, bias=True)\n (dropout): Dropout(p=0.0, inplace=False)\n )\n )\n (intermediate): ViTIntermediate(\n (dense): Linear(in_features=768, out_features=3072, bias=True)\n (intermediate_act_fn): GELUActivation()\n )\n (output): ViTOutput(\n (dense): Linear(in_features=3072, out_features=768, bias=True)\n (dropout): Dropout(p=0.0, inplace=False)\n )\n (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n )\n )\n )\n (layernorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n )\n (classifier): Linear(in_features=768, out_features=104, bias=True)\n) ViTImageProcessor {\n \"do_convert_rgb\": null,\n \"do_normalize\": true,\n \"do_rescale\": true,\n \"do_resize\": true,\n \"image_mean\": [\n 0.5,\n 0.5,\n 0.5\n ],\n \"image_processor_type\": \"ViTImageProcessor\",\n \"image_std\": [\n 0.5,\n 0.5,\n 0.5\n ],\n \"resample\": 2,\n \"rescale_factor\": 0.00392156862745098,\n \"size\": {\n \"height\": 224,\n \"width\": 224\n }\n}\n\n","output_type":"stream"}],"execution_count":2},{"id":"c9de877a-1005-46b9-990d-7751bfc65989","cell_type":"code","source":"transform = torchvision.transforms.Compose([\n torchvision.transforms.Resize((224, 224)), # 调整尺寸为224x224\n torchvision.transforms.ToTensor(), # 转换为张量\n # 使用特征提取器的参数进行标准化\n torchvision.transforms.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)\n])\ntfrecord_path = '/kaggle/input/competitions/tpu-getting-started/tfrecords-jpeg-224x224/train/*'\ndataset = TFRecordToPyTorch(tfrecord_path,transform)\ntfrecord_path = '/kaggle/input/competitions/tpu-getting-started/tfrecords-jpeg-224x224/val/*'\ndataset2 = TFRecordToPyTorch(tfrecord_path,transform)\n# 可以配合 DataLoader 使用\ntrain_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\nval_dataloader = DataLoader(dataset2, batch_size=32, num_workers=0)\nfor batch in train_dataloader:\n plt.imshow(batch[0][1].permute(1,2,0).numpy())\n break\n plt.axis('off')\n plt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-09T12:50:19.279954Z","iopub.execute_input":"2026-07-09T12:50:19.280840Z","iopub.status.idle":"2026-07-09T12:50:20.327355Z","shell.execute_reply.started":"2026-07-09T12:50:19.280811Z","shell.execute_reply":"2026-07-09T12:50:20.326553Z"}},"outputs":[{"name":"stderr","text":"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1783601419.561283 58 gpu_device.cc:2020] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13374 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\nI0000 00:00:1783601419.563618 58 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\nClipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..1.0].\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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\n"},"metadata":{}}],"execution_count":3},{"id":"eba3034c-36a1-46d9-aac8-c02488afd023","cell_type":"code","source":"!pip install torchao==0.16.0\nfrom peft import LoraConfig, get_peft_model\n\n# 加载预训练模型\nmodel = ViTForImageClassification.from_pretrained(\"google/vit-base-patch16-224\")\n\n# 修改分类器的输出维度\nmodel.classifier = torch.nn.Linear(model.classifier.in_features, 104)\ntarget_layers = [5,7,9,11] # 指定要应用 LoRA 的层索引\ntarget_modules = []\nfor layer in target_layers:\n target_modules.append(f\"encoder.layer.{layer}.attention.attention.query\")\n target_modules.append(f\"encoder.layer.{layer}.attention.attention.value\")\n# 配置 LoRA\nconfig = LoraConfig(\n r=8, # LoRA 的秩\n lora_alpha=16, # LoRA 的缩放因子\n target_modules=target_modules, # 目标模块\n lora_dropout=0.1, # Dropout 概率\n bias=\"none\", # 是否更新偏置\n modules_to_save=[\"classifier\"], # 指定分类器需要被微调\n)\n\n# 封装为 LoRA 模型\nmodel = get_peft_model(model, config)\n\n# 验证分类器是否被微调\nprint(\"验证分类器参数是否被训练:\")\nfor name, param in model.named_parameters():\n if param.requires_grad:\n print(f\"{name}: requires_grad = {param.requires_grad}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-09T12:50:20.328354Z","iopub.execute_input":"2026-07-09T12:50:20.328771Z","iopub.status.idle":"2026-07-09T12:50:31.964839Z","shell.execute_reply.started":"2026-07-09T12:50:20.328736Z","shell.execute_reply":"2026-07-09T12:50:31.963581Z"}},"outputs":[{"name":"stdout","text":"Collecting torchao==0.16.0\n Downloading torchao-0.16.0-cp310-abi3-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl.metadata (20 kB)\nDownloading torchao-0.16.0-cp310-abi3-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl (3.2 MB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.2/3.2 MB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hInstalling collected packages: torchao\n Attempting uninstall: torchao\n Found existing installation: torchao 0.10.0\n Uninstalling torchao-0.10.0:\n Successfully uninstalled torchao-0.10.0\nSuccessfully installed torchao-0.16.0\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"config.json: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8054eb6fbd7041f2bf2249741c1a5e8a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model.safetensors: 0%| | 0.00/346M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"864a88c0ae3d4e7982503187675eb38f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Loading weights: 0%| | 0/200 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8424ce3589e84fa5b3a896230aa5517f"}},"metadata":{}},{"name":"stdout","text":"验证分类器参数是否被训练:\nbase_model.model.vit.encoder.layer.5.attention.attention.query.lora_A.default.weight: requires_grad = True\nbase_model.model.vit.encoder.layer.5.attention.attention.query.lora_B.default.weight: requires_grad = True\nbase_model.model.vit.encoder.layer.5.attention.attention.value.lora_A.default.weight: requires_grad = True\nbase_model.model.vit.encoder.layer.5.attention.attention.value.lora_B.default.weight: requires_grad = True\nbase_model.model.vit.encoder.layer.7.attention.attention.query.lora_A.default.weight: requires_grad = True\nbase_model.model.vit.encoder.layer.7.attention.attention.query.lora_B.default.weight: requires_grad = True\nbase_model.model.vit.encoder.layer.7.attention.attention.value.lora_A.default.weight: requires_grad = True\nbase_model.model.vit.encoder.layer.7.attention.attention.value.lora_B.default.weight: requires_grad = True\nbase_model.model.vit.encoder.layer.9.attention.attention.query.lora_A.default.weight: requires_grad = True\nbase_model.model.vit.encoder.layer.9.attention.attention.query.lora_B.default.weight: requires_grad = True\nbase_model.model.vit.encoder.layer.9.attention.attention.value.lora_A.default.weight: requires_grad = True\nbase_model.model.vit.encoder.layer.9.attention.attention.value.lora_B.default.weight: requires_grad = True\nbase_model.model.vit.encoder.layer.11.attention.attention.query.lora_A.default.weight: requires_grad = True\nbase_model.model.vit.encoder.layer.11.attention.attention.query.lora_B.default.weight: requires_grad = True\nbase_model.model.vit.encoder.layer.11.attention.attention.value.lora_A.default.weight: requires_grad = True\nbase_model.model.vit.encoder.layer.11.attention.attention.value.lora_B.default.weight: requires_grad = True\nbase_model.model.classifier.modules_to_save.default.weight: requires_grad = True\nbase_model.model.classifier.modules_to_save.default.bias: requires_grad = True\n","output_type":"stream"}],"execution_count":4},{"id":"b4e04690-82e1-436a-bf51-fe1f3405ca42","cell_type":"code","source":"@torch.no_grad()\ndef 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).logits,dim=1)\n acc+=pred.eq(labels).sum()\n total+=labels.size(0)\n print(f\"acc:{acc/total}\")\n return acc/total\nfrom tqdm import tqdm \ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nmodel=model.to(device)\nloss_func = nn.CrossEntropyLoss()\noptimizer = torch.optim.AdamW(model.parameters(), lr=2e-4)\nscheduler=torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)\nepochs = 30\nbest_acc=0.0\nfor epoch in range(epochs):\n model.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 = model(X).logits\n loss = loss_func(outputs, labels)\n loss.backward()\n optimizer.step()\n \n training_loss += loss.item()\n lens+=1\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 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 current_acc=validate(model, val_dataloader)\n if current_acc > best_acc :\n torch.save(model.state_dict(), 'model.pth')\n print(f\"best model save,acc:{current_acc}\")\n best_acc=current_acc","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-07-09T12:50:31.966937Z","iopub.execute_input":"2026-07-09T12:50:31.968278Z","iopub.status.idle":"2026-07-09T12:56:08.208570Z","shell.execute_reply.started":"2026-07-09T12:50:31.968225Z","shell.execute_reply":"2026-07-09T12:56:08.207161Z"}},"outputs":[{"name":"stderr","text":"Epoch 1/10: 399it [04:33, 1.46it/s, loss=0.6496, avg_loss=1.2149]\n","output_type":"stream"},{"name":"stdout","text":"Epoch 1 train_loss: 1.2149\nacc:0.9571659564971924\nbest model save,acc:0.9571659564971924\n","output_type":"stream"},{"name":"stderr","text":"Epoch 2/10: 23it [00:16, 1.40it/s, loss=0.3227, avg_loss=0.2330]\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_58/32597610.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\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 40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0mtraining_loss\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\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 42\u001b[0m \u001b[0mlens\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;31m# 更新进度条显示当前 batch 的损失\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"}],"execution_count":5},{"id":"981cd43a","cell_type":"code","source":"import pandas as pd\ndef 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\ndef 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\nclass 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\ntfrecord_path = '/kaggle/input/competitions/tpu-getting-started/tfrecords-jpeg-224x224/test/*'\ndataset3 = TFRecordToPyTorchTest(tfrecord_path,transform)\ntest_dataloader = DataLoader(dataset3, batch_size=32, num_workers=0)\nid_array=[]\nall_preds=[]\nmodel.load_state_dict(torch.load('model.pth'))\nmodel.eval()\nwith torch.no_grad():\n for batch in test_dataloader:\n input_ids = batch[0].to(device)\n idd = batch[1]\n outputs = model(input_ids).logits\n preds = torch.argmax(outputs, dim=1)\n all_preds.extend(preds.cpu().numpy())\n id_array.extend(idd)\nsubmission = pd.DataFrame({\n 'id':id_array,\n 'label': all_preds\n})\n","metadata":{"execution":{"iopub.status.busy":"2026-07-09T12:56:08.209751Z","iopub.status.idle":"2026-07-09T12:56:08.210131Z","shell.execute_reply.started":"2026-07-09T12:56:08.209978Z","shell.execute_reply":"2026-07-09T12:56:08.209998Z"},"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":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"1e950456","cell_type":"code","source":"submission['id'] = submission['id'].apply(lambda x: x.decode('utf-8'))\nprint(submission)\nsubmission.to_csv('submission.csv', index=False)\nprint(\"Submission saved!\")","metadata":{"execution":{"iopub.status.busy":"2026-07-09T12:56:08.211519Z","iopub.status.idle":"2026-07-09T12:56:08.211970Z","shell.execute_reply.started":"2026-07-09T12:56:08.211729Z","shell.execute_reply":"2026-07-09T12:56:08.211767Z"},"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":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"79c03587","cell_type":"code","source":"","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":[],"trusted":true},"outputs":[],"execution_count":null}]}