Kaggle/Petals-to-the-Metal/main.py

# %%
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!")
# %%