Pix2Seq 算法阅读记录

2024-01-02 20:17:48

前向传播过程

batch_preds--> tgt-->tgt=cat(tgt, padding)-->tgt_embedding
                                          -->tgt_mask,tgt_padding_mask

以NLP的角度，tgt 代表了?词汇表的长度，encoder部分直接对图像进行处理，输出序列的patch，16倍下采样，最终输出的序列长度为576。

decoder中，根据句子的最大长度生成了掩码mask，下三角矩阵全为0.还有paddding mask，第一个为False，其余全为填充的，第一个是开始标志。

decoder的输入序列初始化全为填充的token，为406，外加一个开始标志，因此输入的词向量嵌入都根据它初始化。

decoder的输入包括?encoder的输出序列+位置编码，初始化的词向量嵌入，掩码mask， padding掩码。

因为只检测一张图片，而NLP任务中需要预测一句话，可能包含多个单词。所以，输出只采用了

return self.output(preds)[:, length-1, :]

来进行预测。?

注意

NLP中的语句生成，贪心所搜，与top_k_top_p_filtering相关见这里

采用自回归方式生成预测，前向过程后生成的预测结果可视化如下

其中的404由

num_bins + class

?得出。实际离散化后包含406个标记，因为加入了开始(404)和结束(405)标记。

得到上述的网络的输出预测后，开始对这些进行处理。?

1、 得到第一个结束标志 EOS 的索引 index
2、 判断 index-1 是否是 5 的倍数，若不是，则本次的预测不进行处理，默认没有检测到任何目标
3、 去掉额外填充的噪声
4、 迭代的每次拿出5个token
5、 前4维 为 box的信息，第5维为类别信息
6、 预测的表示类别的离散化token需要减去 num_bins，才是最后的类别
7、 box 反离散化， box / (num_bins - 1), 这个是输出特征尺度下的归一化的box的坐标
8、 将box的尺度返回输入图片的尺度， box的信息为 (Xmin,Ymin,Xmax,Ymax)

训练过程：

至于文中的各种训练的设置，包括序列增强技术，没有看到，没有仔细的看。

损失函数，文章中说用的极大似然估计，最终的形式是交叉熵损失函数。

网络结构

EncoderDecoder(
  (encoder): Encoder(
    (model): VisionTransformer(
      (patch_embed): PatchEmbed(
        (proj): Conv2d(3, 384, kernel_size=(16, 16), stride=(16, 16))
        (norm): Identity()
      )
      (pos_drop): Dropout(p=0.0, inplace=False)
      (blocks): Sequential(
        (0): Block(
          (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (attn): Attention(
            (qkv): Linear(in_features=384, out_features=1152, bias=True)
            (attn_drop): Dropout(p=0.0, inplace=False)
            (proj): Linear(in_features=384, out_features=384, bias=True)
            (proj_drop): Dropout(p=0.0, inplace=False)
          )
          (ls1): LayerScale()
          (drop_path1): Identity()
          (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=384, out_features=1536, bias=True)
            (act): GELU()
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=1536, out_features=384, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (ls2): LayerScale()
          (drop_path2): Identity()
        )
        (1): Block(
          (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (attn): Attention(
            (qkv): Linear(in_features=384, out_features=1152, bias=True)
            (attn_drop): Dropout(p=0.0, inplace=False)
            (proj): Linear(in_features=384, out_features=384, bias=True)
            (proj_drop): Dropout(p=0.0, inplace=False)
          )
          (ls1): LayerScale()
          (drop_path1): Identity()
          (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=384, out_features=1536, bias=True)
            (act): GELU()
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=1536, out_features=384, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (ls2): LayerScale()
          (drop_path2): Identity()
        )
        (2): Block(
          (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (attn): Attention(
            (qkv): Linear(in_features=384, out_features=1152, bias=True)
            (attn_drop): Dropout(p=0.0, inplace=False)
            (proj): Linear(in_features=384, out_features=384, bias=True)
            (proj_drop): Dropout(p=0.0, inplace=False)
          )
          (ls1): LayerScale()
          (drop_path1): Identity()
          (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=384, out_features=1536, bias=True)
            (act): GELU()
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=1536, out_features=384, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (ls2): LayerScale()
          (drop_path2): Identity()
        )
        (3): Block(
          (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (attn): Attention(
            (qkv): Linear(in_features=384, out_features=1152, bias=True)
            (attn_drop): Dropout(p=0.0, inplace=False)
            (proj): Linear(in_features=384, out_features=384, bias=True)
            (proj_drop): Dropout(p=0.0, inplace=False)
          )
          (ls1): LayerScale()
          (drop_path1): Identity()
          (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=384, out_features=1536, bias=True)
            (act): GELU()
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=1536, out_features=384, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (ls2): LayerScale()
          (drop_path2): Identity()
        )
        (4): Block(
          (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (attn): Attention(
            (qkv): Linear(in_features=384, out_features=1152, bias=True)
            (attn_drop): Dropout(p=0.0, inplace=False)
            (proj): Linear(in_features=384, out_features=384, bias=True)
            (proj_drop): Dropout(p=0.0, inplace=False)
          )
          (ls1): LayerScale()
          (drop_path1): Identity()
          (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=384, out_features=1536, bias=True)
            (act): GELU()
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=1536, out_features=384, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (ls2): LayerScale()
          (drop_path2): Identity()
        )
        (5): Block(
          (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (attn): Attention(
            (qkv): Linear(in_features=384, out_features=1152, bias=True)
            (attn_drop): Dropout(p=0.0, inplace=False)
            (proj): Linear(in_features=384, out_features=384, bias=True)
            (proj_drop): Dropout(p=0.0, inplace=False)
          )
          (ls1): LayerScale()
          (drop_path1): Identity()
          (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=384, out_features=1536, bias=True)
            (act): GELU()
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=1536, out_features=384, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (ls2): LayerScale()
          (drop_path2): Identity()
        )
        (6): Block(
          (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (attn): Attention(
            (qkv): Linear(in_features=384, out_features=1152, bias=True)
            (attn_drop): Dropout(p=0.0, inplace=False)
            (proj): Linear(in_features=384, out_features=384, bias=True)
            (proj_drop): Dropout(p=0.0, inplace=False)
          )
          (ls1): LayerScale()
          (drop_path1): Identity()
          (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=384, out_features=1536, bias=True)
            (act): GELU()
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=1536, out_features=384, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (ls2): LayerScale()
          (drop_path2): Identity()
        )
        (7): Block(
          (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (attn): Attention(
            (qkv): Linear(in_features=384, out_features=1152, bias=True)
            (attn_drop): Dropout(p=0.0, inplace=False)
            (proj): Linear(in_features=384, out_features=384, bias=True)
            (proj_drop): Dropout(p=0.0, inplace=False)
          )
          (ls1): LayerScale()
          (drop_path1): Identity()
          (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=384, out_features=1536, bias=True)
            (act): GELU()
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=1536, out_features=384, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (ls2): LayerScale()
          (drop_path2): Identity()
        )
        (8): Block(
          (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (attn): Attention(
            (qkv): Linear(in_features=384, out_features=1152, bias=True)
            (attn_drop): Dropout(p=0.0, inplace=False)
            (proj): Linear(in_features=384, out_features=384, bias=True)
            (proj_drop): Dropout(p=0.0, inplace=False)
          )
          (ls1): LayerScale()
          (drop_path1): Identity()
          (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=384, out_features=1536, bias=True)
            (act): GELU()
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=1536, out_features=384, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (ls2): LayerScale()
          (drop_path2): Identity()
        )
        (9): Block(
          (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (attn): Attention(
            (qkv): Linear(in_features=384, out_features=1152, bias=True)
            (attn_drop): Dropout(p=0.0, inplace=False)
            (proj): Linear(in_features=384, out_features=384, bias=True)
            (proj_drop): Dropout(p=0.0, inplace=False)
          )
          (ls1): LayerScale()
          (drop_path1): Identity()
          (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=384, out_features=1536, bias=True)
            (act): GELU()
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=1536, out_features=384, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (ls2): LayerScale()
          (drop_path2): Identity()
        )
        (10): Block(
          (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (attn): Attention(
            (qkv): Linear(in_features=384, out_features=1152, bias=True)
            (attn_drop): Dropout(p=0.0, inplace=False)
            (proj): Linear(in_features=384, out_features=384, bias=True)
            (proj_drop): Dropout(p=0.0, inplace=False)
          )
          (ls1): LayerScale()
          (drop_path1): Identity()
          (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=384, out_features=1536, bias=True)
            (act): GELU()
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=1536, out_features=384, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (ls2): LayerScale()
          (drop_path2): Identity()
        )
        (11): Block(
          (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (attn): Attention(
            (qkv): Linear(in_features=384, out_features=1152, bias=True)
            (attn_drop): Dropout(p=0.0, inplace=False)
            (proj): Linear(in_features=384, out_features=384, bias=True)
            (proj_drop): Dropout(p=0.0, inplace=False)
          )
          (ls1): LayerScale()
          (drop_path1): Identity()
          (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
          (mlp): Mlp(
            (fc1): Linear(in_features=384, out_features=1536, bias=True)
            (act): GELU()
            (drop1): Dropout(p=0.0, inplace=False)
            (fc2): Linear(in_features=1536, out_features=384, bias=True)
            (drop2): Dropout(p=0.0, inplace=False)
          )
          (ls2): LayerScale()
          (drop_path2): Identity()
        )
      )
      (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
      (fc_norm): Identity()
      (head): Identity()
    )
    (bottleneck): AdaptiveAvgPool1d(output_size=256)
  )
  (decoder): Decoder(
    (embedding): Embedding(407, 256)
    (decoder_pos_drop): Dropout(p=0.05, inplace=False)
    (decoder): TransformerDecoder(
      (layers): ModuleList(
        (0): TransformerDecoderLayer(
          (self_attn): MultiheadAttention(
            (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
          )
          (multihead_attn): MultiheadAttention(
            (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
          )
          (linear1): Linear(in_features=256, out_features=2048, bias=True)
          (dropout): Dropout(p=0.1, inplace=False)
          (linear2): Linear(in_features=2048, out_features=256, bias=True)
          (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
          (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
          (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
          (dropout1): Dropout(p=0.1, inplace=False)
          (dropout2): Dropout(p=0.1, inplace=False)
          (dropout3): Dropout(p=0.1, inplace=False)
        )
        (1): TransformerDecoderLayer(
          (self_attn): MultiheadAttention(
            (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
          )
          (multihead_attn): MultiheadAttention(
            (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
          )
          (linear1): Linear(in_features=256, out_features=2048, bias=True)
          (dropout): Dropout(p=0.1, inplace=False)
          (linear2): Linear(in_features=2048, out_features=256, bias=True)
          (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
          (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
          (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
          (dropout1): Dropout(p=0.1, inplace=False)
          (dropout2): Dropout(p=0.1, inplace=False)
          (dropout3): Dropout(p=0.1, inplace=False)
        )
        (2): TransformerDecoderLayer(
          (self_attn): MultiheadAttention(
            (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
          )
          (multihead_attn): MultiheadAttention(
            (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
          )
          (linear1): Linear(in_features=256, out_features=2048, bias=True)
          (dropout): Dropout(p=0.1, inplace=False)
          (linear2): Linear(in_features=2048, out_features=256, bias=True)
          (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
          (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
          (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
          (dropout1): Dropout(p=0.1, inplace=False)
          (dropout2): Dropout(p=0.1, inplace=False)
          (dropout3): Dropout(p=0.1, inplace=False)
        )
        (3): TransformerDecoderLayer(
          (self_attn): MultiheadAttention(
            (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
          )
          (multihead_attn): MultiheadAttention(
            (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
          )
          (linear1): Linear(in_features=256, out_features=2048, bias=True)
          (dropout): Dropout(p=0.1, inplace=False)
          (linear2): Linear(in_features=2048, out_features=256, bias=True)
          (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
          (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
          (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
          (dropout1): Dropout(p=0.1, inplace=False)
          (dropout2): Dropout(p=0.1, inplace=False)
          (dropout3): Dropout(p=0.1, inplace=False)
        )
        (4): TransformerDecoderLayer(
          (self_attn): MultiheadAttention(
            (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
          )
          (multihead_attn): MultiheadAttention(
            (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
          )
          (linear1): Linear(in_features=256, out_features=2048, bias=True)
          (dropout): Dropout(p=0.1, inplace=False)
          (linear2): Linear(in_features=2048, out_features=256, bias=True)
          (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
          (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
          (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
          (dropout1): Dropout(p=0.1, inplace=False)
          (dropout2): Dropout(p=0.1, inplace=False)
          (dropout3): Dropout(p=0.1, inplace=False)
        )
        (5): TransformerDecoderLayer(
          (self_attn): MultiheadAttention(
            (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
          )
          (multihead_attn): MultiheadAttention(
            (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)
          )
          (linear1): Linear(in_features=256, out_features=2048, bias=True)
          (dropout): Dropout(p=0.1, inplace=False)
          (linear2): Linear(in_features=2048, out_features=256, bias=True)
          (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
          (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
          (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
          (dropout1): Dropout(p=0.1, inplace=False)
          (dropout2): Dropout(p=0.1, inplace=False)
          (dropout3): Dropout(p=0.1, inplace=False)
        )
      )
    )
    (output): Linear(in_features=256, out_features=407, bias=True)
    (encoder_pos_drop): Dropout(p=0.05, inplace=False)
  )
)

文章来源:https://blog.csdn.net/allrubots/article/details/135339356
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