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from typing import Optional |
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import torch |
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from torch import nn, Tensor |
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from torch.nn import functional as F |
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from timm.models.layers import trunc_normal_ |
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from detectron2.layers import Conv2d |
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import fvcore.nn.weight_init as weight_init |
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from ..utils import MultiheadAttention |
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class SelfAttentionLayer(nn.Module): |
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def __init__(self, d_model, nhead, dropout=0.0, |
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activation="relu", normalize_before=False): |
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super().__init__() |
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self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout) |
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self.norm = nn.LayerNorm(d_model) |
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self.dropout = 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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self._reset_parameters() |
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def _reset_parameters(self): |
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for p in self.parameters(): |
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if p.dim() > 1: |
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nn.init.xavier_uniform_(p) |
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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, |
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tgt_mask: Optional[Tensor] = None, |
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tgt_key_padding_mask: Optional[Tensor] = None, |
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query_pos: Optional[Tensor] = None): |
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q = k = self.with_pos_embed(tgt, query_pos) |
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tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, |
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key_padding_mask=tgt_key_padding_mask)[0] |
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tgt = tgt + self.dropout(tgt2) |
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tgt = self.norm(tgt) |
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return tgt |
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def forward_pre(self, tgt, |
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tgt_mask: Optional[Tensor] = None, |
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tgt_key_padding_mask: Optional[Tensor] = None, |
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query_pos: Optional[Tensor] = None): |
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tgt2 = self.norm(tgt) |
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q = k = self.with_pos_embed(tgt2, query_pos) |
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tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, |
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key_padding_mask=tgt_key_padding_mask)[0] |
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tgt = tgt + self.dropout(tgt2) |
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return tgt |
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def forward(self, tgt, |
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tgt_mask: Optional[Tensor] = None, |
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tgt_key_padding_mask: 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, tgt_mask, |
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tgt_key_padding_mask, query_pos) |
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return self.forward_post(tgt, tgt_mask, |
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tgt_key_padding_mask, query_pos) |
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class CrossAttentionLayer(nn.Module): |
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def __init__(self, d_model, nhead, dropout=0.0, |
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activation="relu", normalize_before=False): |
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super().__init__() |
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self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) |
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self.norm = nn.LayerNorm(d_model) |
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self.dropout = 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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self._reset_parameters() |
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def _reset_parameters(self): |
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for p in self.parameters(): |
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if p.dim() > 1: |
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nn.init.xavier_uniform_(p) |
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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, |
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memory_mask: Optional[Tensor] = None, |
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memory_key_padding_mask: Optional[Tensor] = None, |
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pos: Optional[Tensor] = None, |
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query_pos: Optional[Tensor] = None): |
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tgt2, avg_attn = 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, attn_mask=memory_mask, |
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key_padding_mask=memory_key_padding_mask) |
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tgt = tgt + self.dropout(tgt2) |
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tgt = self.norm(tgt) |
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return tgt, avg_attn |
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def forward_pre(self, tgt, memory, |
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memory_mask: Optional[Tensor] = None, |
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memory_key_padding_mask: Optional[Tensor] = None, |
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pos: Optional[Tensor] = None, |
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query_pos: Optional[Tensor] = None): |
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tgt2 = self.norm(tgt) |
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tgt2, avg_attn = 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, attn_mask=memory_mask, |
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key_padding_mask=memory_key_padding_mask) |
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tgt = tgt + self.dropout(tgt2) |
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return tgt, avg_attn |
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def forward(self, tgt, memory, |
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memory_mask: Optional[Tensor] = None, |
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memory_key_padding_mask: Optional[Tensor] = None, |
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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, memory_mask, |
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memory_key_padding_mask, pos, query_pos) |
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return self.forward_post(tgt, memory, memory_mask, |
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memory_key_padding_mask, pos, query_pos) |
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class FFNLayer(nn.Module): |
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def __init__(self, d_model, dim_feedforward=2048, dropout=0.0, |
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activation="relu", normalize_before=False): |
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super().__init__() |
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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.norm = nn.LayerNorm(d_model) |
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self.activation = _get_activation_fn(activation) |
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self.normalize_before = normalize_before |
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self._reset_parameters() |
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def _reset_parameters(self): |
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for p in self.parameters(): |
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if p.dim() > 1: |
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nn.init.xavier_uniform_(p) |
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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): |
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tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) |
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tgt = tgt + self.dropout(tgt2) |
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tgt = self.norm(tgt) |
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return tgt |
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def forward_pre(self, tgt): |
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tgt2 = self.norm(tgt) |
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tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) |
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tgt = tgt + self.dropout(tgt2) |
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return tgt |
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def forward(self, tgt): |
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if self.normalize_before: |
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return self.forward_pre(tgt) |
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return self.forward_post(tgt) |
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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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class MLP(nn.Module): |
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""" Very simple multi-layer perceptron (also called FFN)""" |
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def __init__(self, input_dim, hidden_dim, output_dim, num_layers): |
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super().__init__() |
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self.num_layers = num_layers |
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h = [hidden_dim] * (num_layers - 1) |
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self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) |
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def forward(self, x): |
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for i, layer in enumerate(self.layers): |
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x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) |
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return x |
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