基于transformer的表情识别代码

基于Transformer的表情识别代码可以使用自然语言处理模型来实现。下面是一个简单的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torchtext.data import Field, TabularDataset, BucketIterator
from torch.nn import TransformerEncoder, TransformerEncoderLayer

# 定义模型
class TransformerModel(nn.Module):
    def __init__(self, input_dim, output_dim, nhead, nhid, nlayers, dropout):
        super(TransformerModel, self).__init__()
        self.model_type = 'Transformer'
        self.embedding = nn.Embedding(input_dim, nhid)
        self.pos_encoder = PositionalEncoding(nhid, dropout)
        encoder_layers = TransformerEncoderLayer(nhid, nhead, nhid, dropout)
        self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)
        self.fc = nn.Linear(nhid, output_dim)
        self.softmax = nn.Softmax(dim=1)

    def forward(self, src):
        src = self.embedding(src) * math.sqrt(self.nhid)
        src = self.pos_encoder(src)
        output = self.transformer_encoder(src)
        output = self.fc(output[:, 0, :])  # 取第一个位置的输出
        output = self.softmax(output)
        return output

# 数据预处理
TEXT = Field(tokenize='spacy', lower=True)
LABEL = Field(sequential=False, use_vocab=False)

fields = [('text', TEXT), ('label', LABEL)]
train_data, test_data = TabularDataset.splits(
    path='data_path',
    train='train.csv',
    test='test.csv',
    format='csv',
    fields=fields,
)

TEXT.build_vocab(train_data)
LABEL.build_vocab(train_data)

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# 创建迭代器
train_iterator, test_iterator = BucketIterator.splits(
    (train_data, test_data),
    batch_size=64,
    device=device,
)

# 初始化模型
INPUT_DIM = len(TEXT.vocab)
OUTPUT_DIM = 7  # 表情类别数量
NHEAD = 8
NHID = 256
NLAYERS = 4
DROPOUT = 0.2

model = TransformerModel(INPUT_DIM, OUTPUT_DIM, NHEAD, NHID, NLAYERS, DROPOUT).to(device)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters())

# 训练模型
def train(model, iterator, optimizer, criterion):
    model.train()
    epoch_loss = 0
    for batch in iterator:
        optimizer.zero_grad()
        text = batch.text
        label = batch.label
        predictions = model(text)
        loss = criterion(predictions, label)
        loss.backward()
        optimizer.step()
        epoch_loss += loss.item()
    return epoch_loss / len(iterator)

# 测试模型
def evaluate(model, iterator, criterion):
    model.eval()
    epoch_loss = 0
    with torch.no_grad():
        for batch in iterator:
            text = batch.text
            label = batch.label
            predictions = model(text)
            loss = criterion(predictions, label)
            epoch_loss += loss.item()
    return epoch_loss / len(iterator)

# 训练和测试模型
N_EPOCHS = 10

for epoch in range(N_EPOCHS):
    train_loss = train(model, train_iterator, optimizer, criterion)
    test_loss = evaluate(model, test_iterator, criterion)
    print(f'Epoch: {epoch+1:02} | Train Loss: {train_loss:.3f} | Test Loss: {test_loss:.3f}')

# 使用模型进行预测
def predict_sentiment(model, sentence):
    model.eval()
    tokenized = [tok.text for tok in nlp.tokenizer(sentence)]
    indexed = [TEXT.vocab.stoi[t] for t in tokenized]
    tensor = torch.LongTensor(indexed).to(device)
    tensor = tensor.unsqueeze(1)
    prediction = model(tensor)
    _, pred_label = torch.max(prediction, 1)
    return pred_label.item()

# 示例预测
example_sentence = "I am happy"
predicted_label = predict_sentiment(model, example_sentence)
print(f'Predicted Label: {predicted_label}')

这是一个基于Transformer的表情识别代码示例，它使用了PyTorch和torchtext库。代码中包括了数据预处理、模型定义、训练和测试过程以及使用模型进行预测的部分。你可以根据自己的数据集和需求进行相应的修改和调整。