我是一名计算机科学专业的本科生，目前正在研究一个实现 ConvMixerSwinV2 模型的项目。我尝试在代码中实现该模型并在 CIFAR100 数据集上对其进行训练，但出现了很多错误。
我收到的最新错误如下：
RuntimeError Traceback (most recent call last)
<ipython-input-95-3b522f5841c4> in <cell line: 189>()
199
200 optimizer.zero_grad()
--> 201 outputs = model(inputs)
202 loss = criterion(outputs, labels)
203
5 frames
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)
1499 or _global_backward_pre_hooks or _global_backward_hooks
1500 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501 return forward_call(*args, **kwargs)
1502 # Do not call functions when jit is used
1503 full_backward_hooks, non_full_backward_hooks = [], []
<ipython-input-95-3b522f5841c4> in forward(self, x)
136 x = x.permute(0, 1, 3, 2, 4, 5) # Trasponi gli assi per raggruppare i patch
137 x = x.reshape(-1, self.swin_transformer.patch_size[0] * self.swin_transformer.patch_size[1], C)
--> 138 x = self.swin_transformer(x)
139 x = x.mean(dim=[1, 2])
140 x = self.classifier(x)
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)
1499 or _global_backward_pre_hooks or _global_backward_hooks
1500 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501 return forward_call(*args, **kwargs)
1502 # Do not call functions when jit is used
1503 full_backward_hooks, non_full_backward_hooks = [], []
<ipython-input-95-3b522f5841c4> in forward(self, x)
104 x = x.reshape(-1, self.img_size[0] // self.patch_size[0], self.img_size[1] // self.patch_size[1], self.embed_dim)
105 for layer in self.layers:
--> 106 x = layer(x)
107 x = x.mean(dim=[1, 2])
108 x = self.linear(x)
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)
1499 or _global_backward_pre_hooks or _global_backward_hooks
1500 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501 return forward_call(*args, **kwargs)
1502 # Do not call functions when jit is used
1503 full_backward_hooks, non_full_backward_hooks = [], []
<ipython-input-95-3b522f5841c4> in forward(self, x)
66 B, C, H, W = x.shape # Get batch size, channels, height, and width
67 x = x.permute(0, 2, 3, 1) # Permute axes to get the correct format
---> 68 x = x.reshape(B, H // self.patch_size[0], W // self.patch_size[1], self.patch_size[0], self.patch_size[1], C)
69 x = x.permute(0, 1, 3, 2, 4, 5)
70 x = x.reshape(-1, self.patch_size[0] * self.patch_size[1], C)
RuntimeError: shape '[1600, 3, 16, 8, 8, 28]' is invalid for input of size 160563200
我尝试在网上寻找解决方案，但找不到任何对我有帮助的东西。如果您能帮助我了解我做错了什么以及如何修复此错误，我将不胜感激。
我已附上我正在使用的代码：
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
# Define the SwinTransformerAttention model
class SwinTransformerAttention(nn.Module):
def __init__(self, embed_dim, num_heads, qkv_bias, drop_path_rate):
super(SwinTransformerAttention, self).__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.qkv_bias = qkv_bias
self.drop_path_rate = drop_path_rate
# Initialize the layers for Q, K, and V transformations
self.q = nn.Linear(embed_dim, embed_dim * num_heads, bias=qkv_bias)
self.k = nn.Linear(embed_dim, embed_dim * num_heads, bias=qkv_bias)
self.v = nn.Linear(embed_dim, embed_dim * num_heads, bias=qkv_bias)
# Initialize the softmax and dropout layers
self.softmax = nn.Softmax(dim=-1)
self.dropout = nn.Dropout(drop_path_rate)
def forward(self, x):
# Apply linear transformations to input for Q, K, and V
q = self.q(x)
k = self.k(x)
v = self.v(x)
B, H, W, C = q.shape # Store the original shape
# Reshape Q, K, and V for attention calculation
q = q.reshape(B, H * W, self.num_heads, self.embed_dim // self.num_heads)
k = k.reshape(B, H * W, self.num_heads, self.embed_dim // self.num_heads)
v = v.reshape(B, H * W, self.num_heads, self.embed_dim // self.num_heads)
# Calculate attention scores and apply softmax
attn = self.softmax(q @ k.transpose(-1, -2))
attn = self.dropout(attn)
# Calculate the output using attention scores and V
out = attn @ v
out = out.reshape(B, H, W, -1)
return out
# Define the SwinTransformerBlock model
class SwinTransformerBlock(nn.Module):
def __init__(self, img_size, patch_size, embed_dim, num_heads, mlp_ratio, qkv_bias, drop_path_rate):
super(SwinTransformerBlock, self).__init__()
self.img_size = img_size
self.patch_size = patch_size
self.embed_dim = embed_dim
self.num_heads = num_heads
self.mlp_ratio = mlp_ratio
self.qkv_bias = qkv_bias
self.drop_path_rate = drop_path_rate
# Initialize Layer Normalization for the first normalization step
self.norm1 = nn.LayerNorm(embed_dim)
# Initialize the SwinTransformerAttention module
self.attn = SwinTransformerAttention(embed_dim, num_heads, qkv_bias, drop_path_rate)
# Initialize Layer Normalization for the second normalization step
self.norm2 = nn.LayerNorm(embed_dim)
# Initialize the Multi-Layer Perceptron (MLP)
self.mlp = nn.Sequential(
nn.Linear(embed_dim, int(embed_dim * mlp_ratio)),
nn.ReLU(),
nn.Linear(int(embed_dim * mlp_ratio), embed_dim),
)
def forward(self, x):
B, C, H, W = x.shape # Get batch size, channels, height, and width
x = x.permute(0, 2, 3, 1) # Permute axes for correct format
x = x.reshape(B, H // self.patch_size[0], W // self.patch_size[1], self.patch_size[0], self.patch_size[1], C) # Reshape into patches
x = x.permute(0, 1, 3, 2, 4, 5) # Permute axes to group patches
x = x.reshape(-1, self.patch_size[0] * self.patch_size[1], C) # Reshape for attention calculation
x = self.attn(x) # Apply the attention mechanism
x = x.reshape(B, H // self.patch_size[0], W // self.patch_size[1], -1) # Reshape back to patch grid
x = x.permute(0, 3, 1, 2) # Permute axes back to the original format
x = self.norm1(x) # Apply Layer Normalization
residual = x # Save the residual for later use
x = self.mlp(x) # Apply the MLP
x = x + residual # Add back the residual connection
x = self.norm2(x) # Apply Layer Normalization again
return x
# Define the SwinTransformerV2 model
class SwinTransformerV2(nn.Module):
def __init__(self, img_size, patch_size, embed_dim, depths, num_heads, mlp_ratio, qkv_bias, drop_path_rate):
super(SwinTransformerV2, self).__init__()
self.img_size = img_size
self.patch_size = patch_size
self.embed_dim = embed_dim
self.depths = depths
self.num_heads = num_heads
self.mlp_ratio = mlp_ratio
self.qkv_bias = qkv_bias
self.drop_path_rate = drop_path_rate
# Initialize a list of SwinTransformerBlock layers
self.layers = nn.ModuleList()
for i in range(self.depths):
self.layers.append(SwinTransformerBlock(img_size, patch_size, embed_dim, num_heads, mlp_ratio, qkv_bias, drop_path_rate))
# Initialize the final linear layer for classification
self.linear = nn.Linear(embed_dim, num_classes)
def forward(self, x):
x = x.reshape(-1, self.img_size[0] // self.patch_size[0], self.img_size[1] // self.patch_size[1], self.embed_dim)
for layer in self.layers:
x = layer(x) # Apply each SwinTransformerBlock layer
x = x.mean(dim=[1, 2]) # Global average pooling
x = self.linear(x) # Classification layer
return x
# Define the ConvMixerSwinV2 model
class ConvMixerSwinV2(nn.Module):
def __init__(self, patch_size, embed_dim, depths, num_heads, mlp_ratio, qkv_bias, drop_path_rate):
super(ConvMixerSwinV2, self).__init__()
# Initialize the first convolutional layer
self.conv1 = nn.Conv2d(3, 128, kernel_size=3, stride=1, padding=1)
# Initialize the SwinTransformerV2 model
self.swin_transformer = SwinTransformerV2(img_size, patch_size, embed_dim, depths, num_heads, mlp_ratio, qkv_bias, drop_path_rate)
# Initialize the final linear layer for classification
self.classifier = nn.Linear(128, 100) # Adjust the output dimension for your specific classification task
def forward(self, x):
x = self.conv1(x) # Apply the initial convolution
B, C, H, W = x.shape
x = x.permute(0, 2, 3, 1) # Transpose the axes
x = x.reshape(B, H // self.swin_transformer.patch_size[0], self.swin_transformer.patch_size[0], W // self.swin_transformer.patch_size[1], self.swin_transformer.patch_size[1], C)
x = x.permute(0, 1, 3, 2, 4, 5) # Transpose axes to group patches
x = x.reshape(-1, self.swin_transformer.patch_size[0] * self.swin_transformer.patch_size[1], C) # Reshape for attention calculation
x = self.swin_transformer(x) # Apply the SwinTransformerV2 model
x = x.mean(dim=[1, 2]) # Global average pooling
x = self.classifier(x) # Classification layer
return x
# Check if a GPU is available and set the correct device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Provide all the necessary values for creating SwinTransformerV2
img_size = (224, 224) # Input image size (upscale to 224x224 for compatibility with SwinTransformerV2)
patch_size = (8, 8) # Patch size
embed_dim = 128 # Embedding dimension
depths = 12 # Number of blocks
num_heads = 32 # Number of attention heads
mlp_ratio = 4 # Inner MLP dimension ratio
qkv_bias = True # Bias for QKV operations
drop_path_rate = 0.2 # Drop path rate for dropout
# Hyperparameters
batch_size = 32
learning_rate = 0.001
num_epochs = 5
num_classes = 100 # Number of classes in the classification task
# Hyperparameters
batch_size = 25 # Mini-batch size
accumulation_steps = 5 # Number of mini-batches to accumulate before performing an update
# Image transformations
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), # Pre-calculated mean and standard deviation values
])
# Load the CIFAR-100 dataset for training
train_dataset = datasets.CIFAR100(root='./data', train=True, transform=transform, download=True)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
# Load the CIFAR-100 dataset for testing
test_dataset = datasets.CIFAR100(root='./data', train=False, transform=transform, download=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
# Create the model and move the weights to the GPU
model = ConvMixerSwinV2(patch_size, embed_dim, depths, num_heads, mlp_ratio, qkv_bias, drop_path_rate).to(device)
# Define the optimizer and the loss function
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# Model training
for epoch in range(num_epochs):
model.train().to(device)
total_loss = 0.0
mini_batch_count = 0 # Counter for mini-batch accumulation
optimizer.zero_grad() # RESET GRADIENT AT THE BEGINNING OF EACH EPOCH
for inputs, labels in train_loader:
inputs = inputs.to(device) # Move data to the GPU if available
labels = labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward() # Accumulate gradient for subsequent updates
mini_batch_count += 1
if mini_batch_count == accumulation_steps:
# Perform weight update after accumulating gradients for the defined number of mini-batches
optimizer.step()
optimizer.zero_grad() # RESET GRADIENT AFTER UPDATE
mini_batch_count = 0
total_loss += loss.item() * inputs.size(0)
# Perform a final update if accumulation is not complete for the last batch
if mini_batch_count > 0:
optimizer.step()
average_loss = total_loss / len(train_loader.dataset)
print(f"Epoch [{epoch+1}/{num_epochs}], Average Loss: {average_loss:.4f}")
# Valutazione del modello sulla fase di validazione
model.eval()
total_correct = 0
total_samples = 0
with torch.no_grad():
for inputs, labels in test_loader:
outputs = model(inputs)
_, predicted = torch.max(outputs, 1)
total_correct += (predicted == labels).sum().item()
total_samples += labels.size(0)
accuracy = total_correct / total_samples
print(f"Epoch [{epoch+1}/{num_epochs}], Validation Accuracy: {accuracy:.4f}")
感谢您的宝贵时间。
真诚的，
洛伦佐
以下是我尝试纠正代码的尝试：
修改了SwinTransformerBlock块中x的尺寸，以确保尺寸与补丁数量一致。 在 SwinTransformerBlock 块内添加了排列和调整大小到正确的尺寸，以正确处理注意力计算步骤。 添加注释来解释对代码所做的每次修改的目的。 更正了训练部分的实现，包括处理梯度累积。 然而，实现中似乎仍然存在一些错误，因为“形状对于输入尺寸无效”错误不断出现。
此代码实现的期望是在 CIFAR-100 数据集上成功创建和训练用于图像分类的 ConvMixerSwinV2 模型。 ConvMixerSwinV2 模型将 ConvMixer 架构与 Swin Transformer 主干相结合，旨在提高图像理解任务的性能。
以下是代码的预期功能：
数据加载：加载CIFAR-100数据集并设置数据加载器以进行训练和测试。
模型定义：定义ConvMixerSwinV2模型，该模型由卷积层、Swin Transformer主干和线性分类头组成。
训练循环：使用适当的训练循环训练模型。该循环包括前向和后向传递、梯度累积、优化和损失计算。
评估：在测试数据集上评估训练好的模型，以衡量其准确性和泛化性能。
预期输出：在训练过程中，模型的平均损失应该下降，表明模型正在学习。训练后，模型在测试数据集上的准确性对于图像分类任务来说应该是合理的。
验证和调试：在整个过程中，调试任何运行时错误、确保正确的张量形状和维度以及验证转换和计算的正确性是关键期望。
感谢您的宝贵时间。
真诚的，
洛伦佐
根据我从您提供的错误和代码中推断出的信息，我猜输入
xSwinTransformerBlock.forward()我可以使用以下代码重现您的确切错误，该代码重新实现您的
SwinTransformerBlock.forward()ximport torch
x = torch.zeros((1600, 28, 28, 128))
patch_size = (8, 8)
B, C, H, W = x.shape # Get batch size, channels, height, and width
x = x.permute(0, 2, 3, 1) # Permute axes for correct format
x = x.reshape(B, H // patch_size[0], W // patch_size[1], patch_size[0], patch_size[1], C) # Reshape into patches
# >>> RuntimeError: shape '[1600, 3, 16, 8, 8, 28]' is invalid for input of size 160563200
我在这里注意到两件事：
x.reshape()建议： 检查
xSwinTransformerBlock.forward()print(x.shape)