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authorMarcin Chrzanowski <m@m-chrzan.xyz>2021-05-27 19:54:24 +0200
committerMarcin Chrzanowski <m@m-chrzan.xyz>2021-05-27 19:54:24 +0200
commitbb6a6004bfb5398b5cdf8f8a973b47e5659aec79 (patch)
treef770a9cdb856d25e731289c86583ac3c71937f3a /model
parent22cd84cf78a114d75a93f75f66f6ea61c02c94ef (diff)
Fix indents
Diffstat (limited to 'model')
-rw-r--r--model/encoder.py90
1 files changed, 49 insertions, 41 deletions
diff --git a/model/encoder.py b/model/encoder.py
index 63a5149..85b3141 100644
--- a/model/encoder.py
+++ b/model/encoder.py
@@ -4,45 +4,53 @@ from torch import nn
from util.util import get_positional_encoding
from model.encoder_layer import EncoderLayer
+def log(string, verbose):
+ if verbose:
+ print(string)
+
class EncoderModel(nn.Module):
- def __init__(self, input_dim, hidden_dim, d_ff, output_dim, n_layers,
- num_heads, use_attention=True, use_feedforward=True,
- use_positional=True, device='cpu'):
- super(EncoderModel, self).__init__()
- self._device = device
- self._use_positional = use_positional
- self.embedding_layer = nn.Embedding(input_dim, hidden_dim)
- self.layers = nn.ModuleList([
- EncoderLayer(hidden_dim, d_ff, num_heads, use_attention,
- use_feedforward) for i in range(n_layers)])
- self.output_layer = nn.Linear(hidden_dim, output_dim)
-
- def forward(self, x, return_att_weights=False):
- # x shape: (seqlen, batch)
- hidden = self.embedding_layer(x)
- # hidden shape: (seqlen, batch, hiddendim)
-
- if self._use_positional:
- positional_encoding = get_positional_encoding(
- n_positions=hidden.shape[0],
- n_dimensions=hidden.shape[-1],
- device=self._device
- )
- # reshaping to (seqlen, 1, hiddendim)
- positional_encoding = torch.reshape(
- positional_encoding,
- (hidden.shape[0], 1, hidden.shape[-1])
- )
- hidden = hidden + positional_encoding
-
- list_att_weights = []
- for layer in self.layers:
- hidden, att_weights = layer(hidden)
- list_att_weights.append(att_weights)
-
- result = self.output_layer(hidden)
-
- if return_att_weights:
- return result, list_att_weights
- else:
- return result
+ def __init__(self, input_dim, hidden_dim, d_ff, output_dim, n_layers,
+ num_heads, use_attention=True, use_feedforward=True,
+ use_positional=True, device='cpu'):
+ super(EncoderModel, self).__init__()
+ self._device = device
+ self._use_positional = use_positional
+ self.embedding_layer = nn.Embedding(input_dim, hidden_dim)
+ self.layers = nn.ModuleList([
+ EncoderLayer(hidden_dim, d_ff, num_heads, use_attention,
+ use_feedforward) for i in range(n_layers)
+ ])
+ self.output_layer = nn.Linear(hidden_dim, output_dim)
+
+ def forward(self, x, return_att_weights=False, verbose=False):
+ log(f'Handling {x}', verbose)
+ # x shape: (seqlen, batch)
+ hidden = self.embedding_layer(x)
+ # hidden shape: (seqlen, batch, hiddendim)
+
+ if self._use_positional:
+ positional_encoding = get_positional_encoding(
+ n_positions=hidden.shape[0],
+ n_dimensions=hidden.shape[-1],
+ device=self._device
+ )
+ # reshaping to (seqlen, 1, hiddendim)
+ positional_encoding = torch.reshape(
+ positional_encoding,
+ (hidden.shape[0], 1, hidden.shape[-1])
+ )
+ hidden = hidden + positional_encoding
+
+ list_att_weights = []
+ for layer in self.layers:
+ hidden, att_weights = layer(hidden)
+ list_att_weights.append(att_weights)
+
+ result = self.output_layer(hidden)
+
+ log('Result: {result}', verbose)
+
+ if return_att_weights:
+ return result, list_att_weights
+ else:
+ return result