#码力全开·技术π对#Gemini模型多模态输入处理出现张量形状不匹配如何解决？

# 检查文本/图像预处理后的维度
print(f"Text tensor shape: {text_tensor.shape}")  # 预期 (batch, seq_len)
print(f"Image tensor shape: {image_tensor.shape}") # 预期 (batch, H, W, C)

# 标准化处理流程（TensorFlow示例）
def build_multimodal_pipeline():
    # 图像分支
    image_input = tf.keras.Input(shape=(224, 224, 3), name='image')
    image_processed = tf.keras.applications.resnet.preprocess_input(image_input)
    
    # 文本分支
    text_input = tf.keras.Input(shape=(None,), name='text')  # 动态序列长度
    text_embedded = tf.keras.layers.Embedding(vocab_size, 128)(text_input)
    text_processed = tf.keras.layers.LSTM(256)(text_embedded)
    
    # 模态融合
    merged = tf.keras.layers.concatenate([image_processed, text_processed], axis=-1)
    return tf.keras.Model(inputs=[image_input, text_input], outputs=merged)

# 在数据管道中添加强制校验
tf.debugging.assert_equal(tf.shape(image_tensor)[1:], [224, 224, 3])
tf.debugging.assert_equal(tf.rank(text_tensor), 2)

# 使用tf.data并行处理
dataset = tf.data.Dataset.zip(
    (image_dataset.map(preprocess_image),
     text_dataset.map(preprocess_text))
).batch(32).prefetch(tf.data.AUTOTUNE)

# 使用动态形状适配
image_input = tf.keras.layers.Input(shape=(None, None, 3), dtype=tf.float32)
resized_image = tf.keras.layers.Lambda(
    lambda x: tf.image.resize(x, (224,224)))(image_input)