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""" |
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DETR Transformer class. |
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Copy-paste from torch.nn.Transformer with modifications: |
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* positional encodings are passed in MHattention |
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* extra LN at the end of encoder is removed |
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* decoder returns a stack of activations from all decoding layers |
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""" |
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import copy |
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from typing import Optional |
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import torch |
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import torch.nn.functional as F |
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from torch import nn, Tensor |
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from .position_encoding import * |
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class lang_tf_enc(nn.Module): |
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def __init__(self, input_1, input_2, hidden_dim, head_num, dropout=0.1): |
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super(lang_tf_enc, self).__init__() |
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self.pos_embedding_1 = PositionEmbeddingSine(input_2, normalize=True) |
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self.pos_embedding_2 = PositionEmbeddingSine(input_1, normalize=True) |
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self.dense_q = nn.Linear(input_1, hidden_dim) |
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self.dense_k = nn.Linear(input_2, hidden_dim) |
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self.dense_v = nn.Linear(input_2, hidden_dim) |
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self.self_attn = nn.MultiheadAttention(hidden_dim, head_num, dropout=dropout) |
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self.forward_dim = 2048 |
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self.norm1 = nn.LayerNorm(hidden_dim) |
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self.norm2 = nn.LayerNorm(hidden_dim) |
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self.linear1 = nn.Linear(hidden_dim, self.forward_dim) |
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self.linear2 = nn.Linear(self.forward_dim, hidden_dim) |
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self.activation = _get_activation("relu") |
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self.dropout = nn.Dropout(dropout) |
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def forward(self, vision_input, lang_input): |
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decoder_embed_lang = lang_input |
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decoder_embed_vis = vision_input |
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q_inp = F.relu(self.dense_q(decoder_embed_vis).permute(1, 0, 2)) |
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k_inp = F.relu(self.dense_k(decoder_embed_lang).permute(1, 0, 2)) |
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v_inp = F.relu(self.dense_v(decoder_embed_lang).permute(1, 0, 2)) |
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lang_input = lang_input.permute(1, 0, 2) |
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decoded_layer, weights = self.self_attn(q_inp, k_inp, v_inp) |
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decoded_layer = decoded_layer.permute(1, 0, 2) |
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add_layer = decoded_layer + vision_input |
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add_layer = self.norm1(add_layer) |
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add_layer2 = self.linear2(self.dropout(self.activation(self.linear1(add_layer)))) |
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add_layer = add_layer + self.dropout(add_layer2) |
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add_layer = self.norm2(add_layer) |
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return add_layer |
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def _get_clones(module, N): |
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return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) |
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def _get_activation(activation): |
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if activation == "relu": |
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return F.relu |
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if activation == "gelu": |
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return F.gelu |
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if activation == "glu": |
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return F.glu |
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raise RuntimeError(F"activation shuld be relu/gelu, not {activation}.") |
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class TransformerEncoder(nn.Module): |
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def __init__(self, encoder_layer, num_layers, norm=None): |
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super().__init__() |
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self.layers = _get_clones(encoder_layer, num_layers) |
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self.num_layers = num_layers |
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self.norm = norm |
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def forward(self, src, pos: Optional[Tensor] = None): |
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output = src |
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for layer in self.layers: |
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output = layer(output, pos=pos) |
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if self.norm is not None: |
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output = self.norm(output) |
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return output |
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class TransformerDecoder(nn.Module): |
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def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False): |
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super().__init__() |
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self.layers = _get_clones(decoder_layer, num_layers) |
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self.num_layers = num_layers |
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self.norm = norm |
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self.return_intermediate = return_intermediate |
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def forward(self, tgt, memory, pos: Optional[Tensor] = None, query_pos: Optional[Tensor] = None): |
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output = tgt |
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intermediate = [] |
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for layer in self.layers: |
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output = layer(output, memory, pos=pos, query_pos=query_pos) |
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if self.return_intermediate: |
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intermediate.append(self.norm(output)) |
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if self.norm is not None: |
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output = self.norm(output) |
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if self.return_intermediate: |
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intermediate.pop() |
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intermediate.append(output) |
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if self.return_intermediate: |
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return torch.stack(intermediate) |
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return output |
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class TransformerEncoderLayer(nn.Module): |
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def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, |
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activation="relu", normalize_before=False): |
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super().__init__() |
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self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) |
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self.linear1 = nn.Linear(d_model, dim_feedforward) |
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self.dropout = nn.Dropout(dropout) |
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self.linear2 = nn.Linear(dim_feedforward, d_model) |
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self.norm1 = nn.LayerNorm(d_model) |
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self.norm2 = nn.LayerNorm(d_model) |
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self.dropout1 = nn.Dropout(dropout) |
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self.dropout2 = nn.Dropout(dropout) |
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self.activation = _get_activation_fn(activation) |
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self.normalize_before = normalize_before |
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def with_pos_embed(self, tensor, pos: Optional[Tensor]): |
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return tensor if pos is None else tensor + pos |
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def forward_post(self, src, pos: Optional[Tensor] = None): |
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q = k = self.with_pos_embed(src, pos) |
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src2, weights = self.self_attn(q, k, value=src, need_weights=False) |
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src = src + self.dropout1(src2) |
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src = self.norm1(src) |
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src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) |
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src = src + self.dropout2(src2) |
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src = self.norm2(src) |
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return src |
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def forward_pre(self, src, pos: Optional[Tensor] = None): |
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src2 = self.norm1(src) |
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q = k = self.with_pos_embed(src2, pos) |
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src2, weights = self.self_attn(q, k, value=src2) |
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src = src + self.dropout1(src2) |
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src2 = self.norm2(src) |
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src2 = self.linear2(self.dropout(self.activation(self.linear1(src2)))) |
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src = src + self.dropout2(src2) |
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return src |
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def forward(self, src, pos: Optional[Tensor] = None): |
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if self.normalize_before: |
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return self.forward_pre(src, pos) |
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return self.forward_post(src, pos) |
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class TransformerDecoderLayer(nn.Module): |
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def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, |
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activation="relu", normalize_before=False): |
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super().__init__() |
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self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) |
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self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) |
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self.linear1 = nn.Linear(d_model, dim_feedforward) |
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self.dropout = nn.Dropout(dropout) |
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self.linear2 = nn.Linear(dim_feedforward, d_model) |
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self.norm1 = nn.LayerNorm(d_model) |
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self.norm2 = nn.LayerNorm(d_model) |
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self.norm3 = nn.LayerNorm(d_model) |
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self.dropout1 = nn.Dropout(dropout) |
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self.dropout2 = nn.Dropout(dropout) |
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self.dropout3 = nn.Dropout(dropout) |
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self.activation = _get_activation_fn(activation) |
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self.normalize_before = normalize_before |
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def with_pos_embed(self, tensor, pos: Optional[Tensor]): |
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return tensor if pos is None else tensor + pos |
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def forward_post(self, tgt, memory, pos: Optional[Tensor] = None, query_pos: Optional[Tensor] = None): |
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q = k = self.with_pos_embed(tgt, query_pos) |
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tgt2, weights = self.self_attn(q, k, value=tgt) |
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tgt = tgt + self.dropout1(tgt2) |
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tgt = self.norm1(tgt) |
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tgt2, weights = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos), |
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key=self.with_pos_embed(memory, pos), |
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value=memory) |
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tgt = tgt + self.dropout2(tgt2) |
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tgt = self.norm2(tgt) |
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tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) |
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tgt = tgt + self.dropout3(tgt2) |
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tgt = self.norm3(tgt) |
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return tgt |
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def forward_pre(self, tgt, memory, pos: Optional[Tensor] = None, |
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query_pos: Optional[Tensor] = None): |
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tgt2 = self.norm1(tgt) |
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q = k = self.with_pos_embed(tgt2, query_pos) |
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tgt2, weights = self.self_attn(q, k, value=tgt2) |
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tgt = tgt + self.dropout1(tgt2) |
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tgt2 = self.norm2(tgt) |
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tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos), |
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key=self.with_pos_embed(memory, pos), |
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value=memory) |
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tgt = tgt + self.dropout2(tgt2) |
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tgt2 = self.norm3(tgt) |
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tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) |
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tgt = tgt + self.dropout3(tgt2) |
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return tgt |
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def forward(self, tgt, memory, pos: Optional[Tensor] = None, |
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query_pos: Optional[Tensor] = None): |
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if self.normalize_before: |
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return self.forward_pre(tgt, memory, pos, query_pos) |
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return self.forward_post(tgt, memory, pos, query_pos) |
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def _get_clones(module, N): |
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return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) |
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def _get_activation_fn(activation): |
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"""Return an activation function given a string""" |
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if activation == "relu": |
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return F.relu |
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if activation == "gelu": |
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return F.gelu |
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if activation == "glu": |
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return F.glu |
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raise RuntimeError(F"activation should be relu/gelu, not {activation}.") |
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