diff --git a/ppocr/modeling/backbones/multiheadAttention.py b/ppocr/modeling/backbones/multiheadAttention.py index f18e9957..6aba81de 100755 --- a/ppocr/modeling/backbones/multiheadAttention.py +++ b/ppocr/modeling/backbones/multiheadAttention.py @@ -9,214 +9,6 @@ from paddle.nn.initializer import XavierNormal as xavier_normal_ zeros_ = constant_(value=0.) ones_ = constant_(value=1.) -class MultiheadAttention(nn.Layer): - r"""Allows the model to jointly attend to information - from different representation subspaces. - See reference: Attention Is All You Need - - .. math:: - \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O - \text{where} head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V) - - Args: - embed_dim: total dimension of the model - num_heads: parallel attention layers, or heads - - Examples:: - - >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads) - >>> attn_output, attn_output_weights = multihead_attn(query, key, value) - """ - - def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bias_kv=False, add_zero_attn=False): - super(MultiheadAttention, self).__init__() - self.embed_dim = embed_dim - self.num_heads = num_heads - self.dropout = dropout - self.head_dim = embed_dim // num_heads - assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads" - self.scaling = self.head_dim ** -0.5 - - self.out_proj = Linear(embed_dim, embed_dim, bias_attr=bias) - - if add_bias_kv: - self.bias_k = self.create_parameter( - shape=(1, 1, embed_dim), default_initializer=zeros_) - self.add_parameter("bias_k", self.bias_k) - self.bias_v = self.create_parameter( - shape=(1, 1, embed_dim), default_initializer=zeros_) - self.add_parameter("bias_v", self.bias_v) - else: - self.bias_k = self.bias_v = None - - self.add_zero_attn = add_zero_attn - - self._reset_parameters() - - self.conv1 = paddle.nn.Conv2D(in_channels=embed_dim, out_channels=embed_dim, kernel_size=(1, 1)) - self.conv2 = paddle.nn.Conv2D(in_channels=embed_dim, out_channels=embed_dim * 2, kernel_size=(1, 1)) - self.conv3 = paddle.nn.Conv2D(in_channels=embed_dim, out_channels=embed_dim * 3, kernel_size=(1, 1)) - - def _reset_parameters(self): - - - xavier_uniform_(self.out_proj.weight) - if self.bias_k is not None: - xavier_normal_(self.bias_k) - if self.bias_v is not None: - xavier_normal_(self.bias_v) - - def forward(self, query, key, value, key_padding_mask=None, incremental_state=None, - need_weights=True, static_kv=False, attn_mask=None, qkv_ = [False,False,False]): - """ - Inputs of forward function - query: [target length, batch size, embed dim] - key: [sequence length, batch size, embed dim] - value: [sequence length, batch size, embed dim] - key_padding_mask: if True, mask padding based on batch size - incremental_state: if provided, previous time steps are cashed - need_weights: output attn_output_weights - static_kv: key and value are static - - Outputs of forward function - attn_output: [target length, batch size, embed dim] - attn_output_weights: [batch size, target length, sequence length] - """ - qkv_same = qkv_[0] - kv_same = qkv_[1] - - tgt_len, bsz, embed_dim = query.shape - assert embed_dim == self.embed_dim - assert list(query.shape) == [tgt_len, bsz, embed_dim] - assert key.shape == value.shape - - if qkv_same: - # self-attention - q, k, v = self._in_proj_qkv(query) - elif kv_same: - # encoder-decoder attention - q = self._in_proj_q(query) - if key is None: - assert value is None - k = v = None - else: - k, v = self._in_proj_kv(key) - else: - q = self._in_proj_q(query) - k = self._in_proj_k(key) - v = self._in_proj_v(value) - q *= self.scaling - - if self.bias_k is not None: - assert self.bias_v is not None - self.bias_k = paddle.concat([self.bias_k for i in range(bsz)],axis=1) - self.bias_v = paddle.concat([self.bias_v for i in range(bsz)],axis=1) - k = paddle.concat([k, self.bias_k]) - v = paddle.concat([v, self.bias_v]) - if attn_mask is not None: - attn_mask = paddle.concat([attn_mask, paddle.zeros([attn_mask.shape[0], 1],dtype=attn_mask.dtype)], axis=1) - if key_padding_mask is not None: - key_padding_mask = paddle.concat( - [key_padding_mask,paddle.zeros([key_padding_mask.shape[0], 1],dtype=key_padding_mask.dtype)], axis=1) - - q = q.reshape([tgt_len, bsz * self.num_heads, self.head_dim]).transpose([1, 0, 2]) - if k is not None: - k = k.reshape([-1, bsz * self.num_heads, self.head_dim]).transpose([1, 0, 2]) - if v is not None: - v = v.reshape([-1, bsz * self.num_heads, self.head_dim]).transpose([1, 0, 2]) - - - - src_len = k.shape[1] - - if key_padding_mask is not None: - assert key_padding_mask.shape[0] == bsz - assert key_padding_mask.shape[1] == src_len - - if self.add_zero_attn: - src_len += 1 - k = paddle.concat([k, paddle.zeros((k.shape[0], 1) + k.shape[2:],dtype=k.dtype)], axis=1) - v = paddle.concat([v, paddle.zeros((v.shape[0], 1) + v.shape[2:],dtype=v.dtype)], axis=1) - if attn_mask is not None: - attn_mask = paddle.concat([attn_mask, paddle.zeros([attn_mask.shape[0], 1],dtype=attn_mask.dtype)], axis=1) - if key_padding_mask is not None: - key_padding_mask = paddle.concat( - [key_padding_mask, paddle.zeros([key_padding_mask.shape[0], 1],dtype=key_padding_mask.dtype)], axis=1) - attn_output_weights = paddle.bmm(q, k.transpose([0,2,1])) - assert list(attn_output_weights.shape) == [bsz * self.num_heads, tgt_len, src_len] - - if attn_mask is not None: - attn_mask = attn_mask.unsqueeze(0) - attn_output_weights += attn_mask - if key_padding_mask is not None: - attn_output_weights = attn_output_weights.reshape([bsz, self.num_heads, tgt_len, src_len]) - key = key_padding_mask.unsqueeze(1).unsqueeze(2).astype('float32') - y = paddle.full(shape=key.shape, dtype='float32', fill_value='-inf') - y = paddle.where(key==0.,key, y) - attn_output_weights += y - attn_output_weights = attn_output_weights.reshape([bsz*self.num_heads, tgt_len, src_len]) - - attn_output_weights = F.softmax( - attn_output_weights.astype('float32'), axis=-1, - dtype=paddle.float32 if attn_output_weights.dtype == paddle.float16 else attn_output_weights.dtype) - attn_output_weights = F.dropout(attn_output_weights, p=self.dropout, training=self.training) - - attn_output = paddle.bmm(attn_output_weights, v) - assert list(attn_output.shape) == [bsz * self.num_heads, tgt_len, self.head_dim] - attn_output = attn_output.transpose([1, 0,2]).reshape([tgt_len, bsz, embed_dim]) - attn_output = self.out_proj(attn_output) - if need_weights: - # average attention weights over heads - attn_output_weights = attn_output_weights.reshape([bsz, self.num_heads, tgt_len, src_len]) - attn_output_weights = attn_output_weights.sum(axis=1) / self.num_heads - else: - attn_output_weights = None - - return attn_output, attn_output_weights - - def _in_proj_qkv(self, query): - query = query.transpose([1, 2, 0]) - query = paddle.unsqueeze(query, axis=2) - res = self.conv3(query) - res = paddle.squeeze(res, axis=2) - res = res.transpose([2, 0, 1]) - return res.chunk(3, axis=-1) - - def _in_proj_kv(self, key): - key = key.transpose([1, 2, 0]) - key = paddle.unsqueeze(key, axis=2) - res = self.conv2(key) - res = paddle.squeeze(res, axis=2) - res = res.transpose([2, 0, 1]) - return res.chunk(2, axis=-1) - - def _in_proj_q(self, query): - query = query.transpose([1, 2, 0]) - query = paddle.unsqueeze(query, axis=2) - res = self.conv1(query) - res = paddle.squeeze(res, axis=2) - res = res.transpose([2, 0, 1]) - return res - - def _in_proj_k(self, key): - - key = key.transpose([1, 2, 0]) - key = paddle.unsqueeze(key, axis=2) - res = self.conv1(key) - res = paddle.squeeze(res, axis=2) - res = res.transpose([2, 0, 1]) - return res - - def _in_proj_v(self, value): - - value = value.transpose([1,2,0])#(1, 2, 0) - value = paddle.unsqueeze(value, axis=2) - res = self.conv1(value) - res = paddle.squeeze(res, axis=2) - res = res.transpose([2, 0, 1]) - return res - - class MultiheadAttentionOptim(nn.Layer): r"""Allows the model to jointly attend to information @@ -362,4 +154,4 @@ class MultiheadAttentionOptim(nn.Layer): res = self.conv3(value) res = paddle.squeeze(res, axis=2) res = res.transpose([2, 0, 1]) - return res \ No newline at end of file + return res