190 KiB
190 KiB
In [1]:
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
In [2]:
from transformers import ViTForImageClassification, ViTImageProcessor
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_name = "google/vit-base-patch16-224-in21k" # 在ImageNet21k上预训练
model = ViTForImageClassification.from_pretrained(model_name, num_labels=104)
model.to(device)
feature_extractor = ViTImageProcessor.from_pretrained(model_name)
print(model,feature_extractor)Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.
config.json: 0%| | 0.00/502 [00:00<?, ?B/s]
model.safetensors: 0%| | 0.00/346M [00:00<?, ?B/s]
Loading weights: 0%| | 0/198 [00:00<?, ?it/s]
ViTForImageClassification LOAD REPORT from: google/vit-base-patch16-224-in21k Key | Status | --------------------+------------+- pooler.dense.weight | UNEXPECTED | pooler.dense.bias | UNEXPECTED | classifier.bias | MISSING | classifier.weight | MISSING | Notes: - UNEXPECTED :can be ignored when loading from different task/architecture; not ok if you expect identical arch. - MISSING :those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.
preprocessor_config.json: 0%| | 0.00/160 [00:00<?, ?B/s]
ViTForImageClassification(
(vit): ViTModel(
(embeddings): ViTEmbeddings(
(patch_embeddings): ViTPatchEmbeddings(
(projection): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))
)
(dropout): Dropout(p=0.0, inplace=False)
)
(encoder): ViTEncoder(
(layer): ModuleList(
(0-11): 12 x ViTLayer(
(attention): ViTAttention(
(attention): ViTSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
)
(output): ViTSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.0, inplace=False)
)
)
(intermediate): ViTIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): ViTOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(dropout): Dropout(p=0.0, inplace=False)
)
(layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
)
)
)
(layernorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
)
(classifier): Linear(in_features=768, out_features=104, bias=True)
) ViTImageProcessor {
"do_convert_rgb": null,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "ViTImageProcessor",
"image_std": [
0.5,
0.5,
0.5
],
"resample": 2,
"rescale_factor": 0.00392156862745098,
"size": {
"height": 224,
"width": 224
}
}
In [3]:
transform = torchvision.transforms.Compose([
torchvision.transforms.Resize((224, 224)), # 调整尺寸为224x224
torchvision.transforms.ToTensor(), # 转换为张量
# 使用特征提取器的参数进行标准化
torchvision.transforms.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
])
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()WARNING: All log messages before absl::InitializeLog() is called are written to STDERR I0000 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 I0000 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 Clipping 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].
In [4]:
!pip install torchao==0.16.0
from peft import LoraConfig, get_peft_model
# 加载预训练模型
model = ViTForImageClassification.from_pretrained("google/vit-base-patch16-224")
# 修改分类器的输出维度
model.classifier = torch.nn.Linear(model.classifier.in_features, 104)
target_layers = [5,7,9,11] # 指定要应用 LoRA 的层索引
target_modules = []
for layer in target_layers:
target_modules.append(f"encoder.layer.{layer}.attention.attention.query")
target_modules.append(f"encoder.layer.{layer}.attention.attention.value")
# 配置 LoRA
config = LoraConfig(
r=8, # LoRA 的秩
lora_alpha=16, # LoRA 的缩放因子
target_modules=target_modules, # 目标模块
lora_dropout=0.1, # Dropout 概率
bias="none", # 是否更新偏置
modules_to_save=["classifier"], # 指定分类器需要被微调
)
# 封装为 LoRA 模型
model = get_peft_model(model, config)
# 验证分类器是否被微调
print("验证分类器参数是否被训练:")
for name, param in model.named_parameters():
if param.requires_grad:
print(f"{name}: requires_grad = {param.requires_grad}")
Collecting torchao==0.16.0 Downloading torchao-0.16.0-cp310-abi3-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl.metadata (20 kB) Downloading torchao-0.16.0-cp310-abi3-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl (3.2 MB) [2K [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m3.2/3.2 MB[0m [31m4.6 MB/s[0m eta [36m0:00:00[0m00:01[0m00:01[0m [?25hInstalling collected packages: torchao Attempting uninstall: torchao Found existing installation: torchao 0.10.0 Uninstalling torchao-0.10.0: Successfully uninstalled torchao-0.10.0 Successfully installed torchao-0.16.0
config.json: 0.00B [00:00, ?B/s]
model.safetensors: 0%| | 0.00/346M [00:00<?, ?B/s]
Loading weights: 0%| | 0/200 [00:00<?, ?it/s]
验证分类器参数是否被训练: base_model.model.vit.encoder.layer.5.attention.attention.query.lora_A.default.weight: requires_grad = True base_model.model.vit.encoder.layer.5.attention.attention.query.lora_B.default.weight: requires_grad = True base_model.model.vit.encoder.layer.5.attention.attention.value.lora_A.default.weight: requires_grad = True base_model.model.vit.encoder.layer.5.attention.attention.value.lora_B.default.weight: requires_grad = True base_model.model.vit.encoder.layer.7.attention.attention.query.lora_A.default.weight: requires_grad = True base_model.model.vit.encoder.layer.7.attention.attention.query.lora_B.default.weight: requires_grad = True base_model.model.vit.encoder.layer.7.attention.attention.value.lora_A.default.weight: requires_grad = True base_model.model.vit.encoder.layer.7.attention.attention.value.lora_B.default.weight: requires_grad = True base_model.model.vit.encoder.layer.9.attention.attention.query.lora_A.default.weight: requires_grad = True base_model.model.vit.encoder.layer.9.attention.attention.query.lora_B.default.weight: requires_grad = True base_model.model.vit.encoder.layer.9.attention.attention.value.lora_A.default.weight: requires_grad = True base_model.model.vit.encoder.layer.9.attention.attention.value.lora_B.default.weight: requires_grad = True base_model.model.vit.encoder.layer.11.attention.attention.query.lora_A.default.weight: requires_grad = True base_model.model.vit.encoder.layer.11.attention.attention.query.lora_B.default.weight: requires_grad = True base_model.model.vit.encoder.layer.11.attention.attention.value.lora_A.default.weight: requires_grad = True base_model.model.vit.encoder.layer.11.attention.attention.value.lora_B.default.weight: requires_grad = True base_model.model.classifier.modules_to_save.default.weight: requires_grad = True base_model.model.classifier.modules_to_save.default.bias: requires_grad = True
In [5]:
@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).logits,dim=1)
acc+=pred.eq(labels).sum()
total+=labels.size(0)
print(f"acc:{acc/total}")
return acc/total
from tqdm import tqdm
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model=model.to(device)
loss_func = nn.CrossEntropyLoss()
optimizer = torch.optim.AdamW(model.parameters(), lr=2e-4)
scheduler=torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)
epochs = 30
best_acc=0.0
for epoch in range(epochs):
model.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 = model(X).logits
loss = loss_func(outputs, labels)
loss.backward()
optimizer.step()
training_loss += loss.item()
lens+=1
# 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()
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）
current_acc=validate(model, val_dataloader)
if current_acc > best_acc :
torch.save(model.state_dict(), 'model.pth')
print(f"best model save,acc:{current_acc}")
best_acc=current_accEpoch 1/10: 399it [04:33, 1.46it/s, loss=0.6496, avg_loss=1.2149]
Epoch 1 train_loss: 1.2149 acc:0.9571659564971924 best model save,acc:0.9571659564971924
Epoch 2/10: 23it [00:16, 1.40it/s, loss=0.3227, avg_loss=0.2330]
[0;31m---------------------------------------------------------------------------[0m [0;31mKeyboardInterrupt[0m Traceback (most recent call last) [0;32m/tmp/ipykernel_58/32597610.py[0m in [0;36m<cell line: 0>[0;34m()[0m [1;32m 39[0m [0moptimizer[0m[0;34m.[0m[0mstep[0m[0;34m([0m[0;34m)[0m[0;34m[0m[0;34m[0m[0m [1;32m 40[0m [0;34m[0m[0m [0;32m---> 41[0;31m [0mtraining_loss[0m [0;34m+=[0m [0mloss[0m[0;34m.[0m[0mitem[0m[0;34m([0m[0;34m)[0m[0;34m[0m[0;34m[0m[0m [0m[1;32m 42[0m [0mlens[0m[0;34m+=[0m[0;36m1[0m[0;34m[0m[0;34m[0m[0m [1;32m 43[0m [0;31m# 更新进度条显示当前 batch 的损失[0m[0;34m[0m[0;34m[0m[0m [0;31mKeyboardInterrupt[0m:
In [ ]:
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=[]
model.load_state_dict(torch.load('model.pth'))
model.eval()
with torch.no_grad():
for batch in test_dataloader:
input_ids = batch[0].to(device)
idd = batch[1]
outputs = model(input_ids).logits
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
})
In [ ]:
submission['id'] = submission['id'].apply(lambda x: x.decode('utf-8'))
print(submission)
submission.to_csv('submission.csv', index=False)
print("Submission saved!")In [ ]: