AttributeError: type object 'Reduction' has no attribute 'AUTO

这个错误通常是由于 PyTorch 版本不兼容导致的。在 PyTorch 1.6 及以上版本中，`torch.nn.functional` 中的 `reduction` 参数已经被移除，取而代之的是 `torch.nn.Reduction` 中的 `enum` 类型。因此，如果你使用的是 PyTorch 1.6 及以上版本，可以将 `reduction` 参数替换为 `torch.nn.Reduction` 中的枚举类型，例如：

import torch.nn as nn

loss_fn = nn.CrossEntropyLoss(reduction=nn.Reduction.SUM)

如果你使用的是 PyTorch 1.5 及以下版本，可以将 `reduction` 参数设置为字符串类型，例如：

import torch.nn as nn

loss_fn = nn.CrossEntropyLoss(reduction='sum')

相关问题

mmdetection AttributeError: 'SSDHead' object has no attribute 'loss_cls'

这个错误通常是由于代码中的某些变量或方法未正确定义或导入而引起的。在这种情况下，错误信息表明在SSDHead对象中找不到loss_cls属性。这可能是由于以下原因之一导致的：

1.代码中确实没有定义loss_cls属性或方法。
2.代码中定义了loss_cls属性或方法，但是由于某些原因未正确导入或初始化。
3.代码中定义了loss_cls属性或方法，但是在SSDHead对象中未正确调用。

为了解决这个问题，你可以尝试以下几个步骤：

1.检查代码中是否正确定义了loss_cls属性或方法，并确保它们被正确导入和初始化。
2.检查代码中是否正确调用了loss_cls属性或方法，并确保它们被正确传递和使用。
3.检查代码中是否存在拼写错误或语法错误，并进行必要的更正。
4.检查代码中是否存在其他与此错误相关的警告或错误，并进行必要的更正。

以下是一个可能的解决方案：

class SSDHead(nn.Module):
    def __init__(self, num_classes, in_channels, feat_channels=256, stacked_convs=2, **kwargs):
        super(SSDHead, self).__init__(**kwargs)
        self.num_classes = num_classes
        self.in_channels = in_channels
        self.feat_channels = feat_channels
        self.stacked_convs = stacked_convs
        self.loss_cls = nn.CrossEntropyLoss() # 定义loss_cls属性
        self.loss_bbox = nn.L1Loss(reduction='none')
        self.conv1x1 = nn.ModuleList()
        self.conv3x3 = nn.ModuleList()
        for i in range(self.stacked_convs):
            self.conv1x1.append(nn.Conv2d(self.in_channels, self.feat_channels, kernel_size=1))
            self.conv3x3.append(nn.Conv2d(self.feat_channels, self.feat_channels, kernel_size=3, padding=1))
        self.cls_convs = nn.ModuleList()
        self.reg_convs = nn.ModuleList()
        for i in range(4):
            self.cls_convs.append(nn.Conv2d(self.feat_channels, self.feat_channels, kernel_size=3, padding=1))
            self.reg_convs.append(nn.Conv2d(self.feat_channels, self.feat_channels, kernel_size=3, padding=1))
        self.cls_out = nn.Conv2d(self.feat_channels, self.num_classes, kernel_size=3, padding=1)
        self.reg_out = nn.Conv2d(self.feat_channels, 4, kernel_size=3, padding=1)

    def forward(self, x):
        cls_scores = []
        bbox_preds = []
        for feat in x:
            cls_feat = feat
            reg_feat = feat
            for i in range(self.stacked_convs):
                cls_feat = F.relu(self.conv1x1[i](cls_feat))
                cls_feat = F.relu(self.conv3x3[i](cls_feat))
                reg_feat = F.relu(self.conv1x1[i](reg_feat))
                reg_feat = F.relu(self.conv3x3[i](reg_feat))
            cls_feat = cls_feat + feat
            reg_feat = reg_feat + feat
            cls_feat = self.cls_convs[0](cls_feat)
            reg_feat = self.reg_convs[0](reg_feat)
            for i in range(1, 4):
                cls_feat = F.relu(cls_feat)
                reg_feat = F.relu(reg_feat)
                cls_feat = self.cls_convs[i](cls_feat)
                reg_feat = self.reg_convs[i](reg_feat)
            cls_score = self.cls_out(cls_feat)
            bbox_pred = self.reg_out(reg_feat)
            cls_scores.append(cls_score)
            bbox_preds.append(bbox_pred)
        return cls_scores, bbox_preds

    def loss(self, cls_scores, bbox_preds, gt_bboxes, gt_labels, img_metas):
        losses = dict()
        batch_size = cls_scores[0].size(0)
        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
        device = cls_scores[0].device
        gt_bboxes = [gt_bbox.to(device) for gt_bbox in gt_bboxes]
        gt_labels = [gt_label.to(device) for gt_label in gt_labels]
        anchor_list, valid_flag_list = self.get_anchors(featmap_sizes, img_metas, device=device)
        cls_reg_targets = self.anchor_target(anchor_list, valid_flag_list, gt_bboxes, img_metas, gt_labels)
        if cls_reg_targets is None:
            return None
        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets
        num_total_samples = reduce_mean([labels.size(0) for labels in labels_list])
        losses['loss_cls'] = self.loss_cls(cls_scores, labels_list, label_weights_list) # 调用loss_cls属性
        losses['loss_bbox'] = self.loss_bbox(bbox_preds, bbox_targets_list, bbox_weights_list, avg_factor=num_total_samples)
        return losses

解决AttributeError: module 'tensorflow._api.v2.compat.v1.nn.rnn_cell' has no attribute 'seq2seq'

在 TensorFlow 2.0 中，`tf.nn.rnn_cell` 模块已经被移除了，因此 `tf.nn.rnn_cell.seq2seq` 也不再可用。相反，您可以使用 `tf.keras.layers` 中的相应函数来构建您的模型。下面是一个使用 `tf.keras` 实现 seq2seq 模型的示例：

import tensorflow as tf

# 定义编码器
class Encoder(tf.keras.Model):
    def __init__(self, vocab_size, embedding_dim, enc_units, batch_sz):
        super(Encoder, self).__init__()
        self.batch_sz = batch_sz
        self.enc_units = enc_units
        self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
        self.gru = tf.keras.layers.GRU(self.enc_units, return_sequences=True, return_state=True, recurrent_initializer='glorot_uniform')

    def call(self, x, hidden):
        x = self.embedding(x)
        output, state = self.gru(x, initial_state = hidden)
        return output, state

    def initialize_hidden_state(self):
        return tf.zeros((self.batch_sz, self.enc_units))

# 定义注意力层
class BahdanauAttention(tf.keras.layers.Layer):
    def __init__(self, units):
        super(BahdanauAttention, self).__init__()
        self.W1 = tf.keras.layers.Dense(units)
        self.W2 = tf.keras.layers.Dense(units)
        self.V = tf.keras.layers.Dense(1)

    def call(self, query, values):
        # query: 上一时间步的隐藏状态，shape=(batch_size, hidden_size)
        # values: 编码器的输出，shape=(batch_size, max_length, hidden_size)
        hidden_with_time_axis = tf.expand_dims(query, 1)

        score = self.V(tf.nn.tanh(
            self.W1(values) + self.W2(hidden_with_time_axis)))

        # attention_weights shape == (batch_size, max_length, 1)
        attention_weights = tf.nn.softmax(score, axis=1)

        # context_vector shape after sum == (batch_size, hidden_size)
        context_vector = attention_weights * values
        context_vector = tf.reduce_sum(context_vector, axis=1)

        return context_vector, attention_weights

# 定义解码器
class Decoder(tf.keras.Model):
    def __init__(self, vocab_size, embedding_dim, dec_units, batch_sz):
        super(Decoder, self).__init__()
        self.batch_sz = batch_sz
        self.dec_units = dec_units
        self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
        self.gru = tf.keras.layers.GRU(self.dec_units, return_sequences=True, return_state=True, recurrent_initializer='glorot_uniform')
        self.fc = tf.keras.layers.Dense(vocab_size)

        # 用于注意力
        self.attention = BahdanauAttention(self.dec_units)

    def call(self, x, hidden, enc_output):
        # enc_output shape == (batch_size, max_length, hidden_size)
        context_vector, attention_weights = self.attention(hidden, enc_output)

        # x shape after passing through embedding == (batch_size, 1, embedding_dim)
        x = self.embedding(x)

        # 将上一时间步的隐藏状态和注意力向量拼接起来作为输入传给 GRU
        x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)

        # 将拼接后的向量传给 GRU
        output, state = self.gru(x)

        # output shape == (batch_size * 1, hidden_size)
        output = tf.reshape(output, (-1, output.shape[2]))

        # output shape == (batch_size, vocab)
        x = self.fc(output)

        return x, state, attention_weights

# 定义损失函数和优化器
optimizer = tf.keras.optimizers.Adam()
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction='none')

def loss_function(real, pred):
    mask = tf.math.logical_not(tf.math.equal(real, 0))
    loss_ = loss_object(real, pred)

    mask = tf.cast(mask, dtype=loss_.dtype)
    loss_ *= mask

    return tf.reduce_mean(loss_)

# 定义训练步骤
@tf.function
def train_step(inp, targ, enc_hidden):
    loss = 0

    with tf.GradientTape() as tape:
        enc_output, enc_hidden = encoder(inp, enc_hidden)

        dec_hidden = enc_hidden

        dec_input = tf.expand_dims([tokenizer.word_index['<start>']] * BATCH_SIZE, 1)

        # teacher forcing - 将目标词作为下一个输入传给解码器
        for t in range(1, targ.shape[1]):
            # 将编码器的输出和上一时间步的隐藏状态传给解码器
            predictions, dec_hidden, _ = decoder(dec_input, dec_hidden, enc_output)

            loss += loss_function(targ[:, t], predictions)

            # 使用 teacher forcing
            dec_input = tf.expand_dims(targ[:, t], 1)

    batch_loss = (loss / int(targ.shape[1]))

    variables = encoder.trainable_variables + decoder.trainable_variables

    gradients = tape.gradient(loss, variables)

    optimizer.apply_gradients(zip(gradients, variables))

    return batch_loss

# 定义预测函数
def evaluate(sentence):
    attention_plot = np.zeros((max_length_targ, max_length_inp))

    sentence = preprocess_sentence(sentence)

    inputs = [tokenizer.word_index[i] for i in sentence.split(' ')]
    inputs = tf.keras.preprocessing.sequence.pad_sequences([inputs], maxlen=max_length_inp, padding='post')
    inputs = tf.convert_to_tensor(inputs)

    result = ''

    hidden = [tf.zeros((1, units))]
    enc_out, enc_hidden = encoder(inputs, hidden)

    dec_hidden = enc_hidden
    dec_input = tf.expand_dims([tokenizer.word_index['<start>']], 0)

    for t in range(max_length_targ):
        predictions, dec_hidden, attention_weights = decoder(dec_input, dec_hidden, enc_out)

        # 存储注意力权重以便后面制图
        attention_weights = tf.reshape(attention_weights, (-1, ))
        attention_plot[t] = attention_weights.numpy()

        predicted_id = tf.argmax(predictions[0]).numpy()

        result += tokenizer.index_word[predicted_id] + ' '

        if tokenizer.index_word[predicted_id] == '<end>':
            return result, sentence, attention_plot

        # 将预测的 ID 作为下一个解码器输入的 ID
        dec_input = tf.expand_dims([predicted_id], 0)

    return result, sentence, attention_plot

在上面的代码中，我们使用了 `tf.keras.layers` 中的 `Embedding`、`GRU` 和 `Dense` 层来构建编码器和解码器，使用 `tf.keras.optimizers.Adam` 作为优化器，使用 `tf.keras.losses.SparseCategoricalCrossentropy` 作为损失函数。同时，我们还定义了一个 `BahdanauAttention` 层来实现注意力机制。