132 lines
4.2 KiB
Python
132 lines
4.2 KiB
Python
import torch
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import torch.nn as nn
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from einops import rearrange, repeat
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from layers.SelfAttention_Family import TwoStageAttentionLayer
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class SegMerging(nn.Module):
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def __init__(self, d_model, win_size, norm_layer=nn.LayerNorm):
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super().__init__()
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self.d_model = d_model
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self.win_size = win_size
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self.linear_trans = nn.Linear(win_size * d_model, d_model)
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self.norm = norm_layer(win_size * d_model)
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def forward(self, x):
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batch_size, ts_d, seg_num, d_model = x.shape
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pad_num = seg_num % self.win_size
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if pad_num != 0:
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pad_num = self.win_size - pad_num
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x = torch.cat((x, x[:, :, -pad_num:, :]), dim=-2)
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seg_to_merge = []
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for i in range(self.win_size):
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seg_to_merge.append(x[:, :, i::self.win_size, :])
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x = torch.cat(seg_to_merge, -1)
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x = self.norm(x)
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x = self.linear_trans(x)
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return x
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class scale_block(nn.Module):
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def __init__(self, configs, win_size, d_model, n_heads, d_ff, depth, dropout, \
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seg_num=10, factor=10):
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super(scale_block, self).__init__()
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if win_size > 1:
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self.merge_layer = SegMerging(d_model, win_size, nn.LayerNorm)
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else:
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self.merge_layer = None
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self.encode_layers = nn.ModuleList()
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for i in range(depth):
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self.encode_layers.append(TwoStageAttentionLayer(configs, seg_num, factor, d_model, n_heads, \
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d_ff, dropout))
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def forward(self, x, attn_mask=None, tau=None, delta=None):
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_, ts_dim, _, _ = x.shape
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if self.merge_layer is not None:
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x = self.merge_layer(x)
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for layer in self.encode_layers:
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x = layer(x)
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return x, None
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class Encoder(nn.Module):
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def __init__(self, attn_layers):
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super(Encoder, self).__init__()
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self.encode_blocks = nn.ModuleList(attn_layers)
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def forward(self, x):
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encode_x = []
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encode_x.append(x)
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for block in self.encode_blocks:
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x, attns = block(x)
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encode_x.append(x)
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return encode_x, None
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class DecoderLayer(nn.Module):
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def __init__(self, self_attention, cross_attention, seg_len, d_model, d_ff=None, dropout=0.1):
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super(DecoderLayer, self).__init__()
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self.self_attention = self_attention
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self.cross_attention = cross_attention
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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.dropout = nn.Dropout(dropout)
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self.MLP1 = nn.Sequential(nn.Linear(d_model, d_model),
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nn.GELU(),
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nn.Linear(d_model, d_model))
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self.linear_pred = nn.Linear(d_model, seg_len)
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def forward(self, x, cross):
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batch = x.shape[0]
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x = self.self_attention(x)
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x = rearrange(x, 'b ts_d out_seg_num d_model -> (b ts_d) out_seg_num d_model')
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cross = rearrange(cross, 'b ts_d in_seg_num d_model -> (b ts_d) in_seg_num d_model')
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tmp, attn = self.cross_attention(x, cross, cross, None, None, None,)
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x = x + self.dropout(tmp)
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y = x = self.norm1(x)
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y = self.MLP1(y)
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dec_output = self.norm2(x + y)
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dec_output = rearrange(dec_output, '(b ts_d) seg_dec_num d_model -> b ts_d seg_dec_num d_model', b=batch)
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layer_predict = self.linear_pred(dec_output)
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layer_predict = rearrange(layer_predict, 'b out_d seg_num seg_len -> b (out_d seg_num) seg_len')
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return dec_output, layer_predict
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class Decoder(nn.Module):
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def __init__(self, layers):
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super(Decoder, self).__init__()
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self.decode_layers = nn.ModuleList(layers)
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def forward(self, x, cross):
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final_predict = None
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i = 0
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ts_d = x.shape[1]
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for layer in self.decode_layers:
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cross_enc = cross[i]
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x, layer_predict = layer(x, cross_enc)
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if final_predict is None:
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final_predict = layer_predict
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else:
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final_predict = final_predict + layer_predict
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i += 1
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final_predict = rearrange(final_predict, 'b (out_d seg_num) seg_len -> b (seg_num seg_len) out_d', out_d=ts_d)
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return final_predict
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