两道新题

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": [],
+   "outputs": [
-   "execution_count": null
+    {
+     "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": [],
+   "outputs": [
-   "execution_count": null
+    {
+     "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": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>premise</th>\n",
+       "      <th>hypothesis</th>\n",
+       "      <th>lang_abv</th>\n",
+       "      <th>language</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>9989</th>\n",
+       "      <td>6417481c8d</td>\n",
+       "      <td>यद्यपि हम आज के समय में अल कायदा के साथ केएसएए...</td>\n",
+       "      <td>हर व्यक्ति केएसएम को हमेशा अल कायदा के बराबर म...</td>\n",
+       "      <td>hi</td>\n",
+       "      <td>Hindi</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3880</th>\n",
+       "      <td>5c5ca34cf6</td>\n",
+       "      <td>'Upload him into his body? What body?'</td>\n",
+       "      <td>I don't think he has a body at all.</td>\n",
+       "      <td>en</td>\n",
+       "      <td>English</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8559</th>\n",
+       "      <td>1d3c28ecff</td>\n",
+       "      <td>yeah and then about every five years you have ...</td>\n",
+       "      <td>You have to dig them up every five years, thro...</td>\n",
+       "      <td>en</td>\n",
+       "      <td>English</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6316</th>\n",
+       "      <td>22e2a4903d</td>\n",
+       "      <td>θέλω να πω ότι υπήρχε είχα, είχα το ρολόι μου ...</td>\n",
+       "      <td>Ήταν ενοχλητικό όταν κάλυψε τα παπούτσια μου.</td>\n",
+       "      <td>el</td>\n",
+       "      <td>Greek</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>762</th>\n",
+       "      <td>307016c21f</td>\n",
+       "      <td>Yet, in the mouths of the white townsfolk of S...</td>\n",
+       "      <td>White townsfolk in Salisbury, N.C. are easily ...</td>\n",
+       "      <td>en</td>\n",
+       "      <td>English</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 4
   },
   {
    "metadata": {

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!")
+# %%