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encoder.py
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encoder.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from transformer_layers import TransformerEncoderLayer, LabelWiseTransformerEncoderLayer
from embedding import Point_Embeddings, Fuse_Point_Embeddings, Sentence_Embeddings
class Encoder(nn.Module):
"""
Base encoder class
"""
@property
def output_size(self):
"""
Return the output size
:return:
"""
return self._output_size
class TransformerEncoder(Encoder):
"""
Transformer Encoder
"""
#pylint: disable=unused-argument
def __init__(self,
hidden_size: int = 512,
ff_size: int = 2048,
num_layers: int = 6,
num_heads: int = 8,
dropout: float = 0.1,
emb_dropout: float = 0.1,
freeze: bool = False,
**kwargs):
"""
Initializes the Transformer.
:param hidden_size: hidden size and size of embeddings
:param ff_size: position-wise feed-forward layer size.
(Typically this is 2*hidden_size.)
:param num_layers: number of layers
:param num_heads: number of heads for multi-headed attention
:param dropout: dropout probability for Transformer layers
:param emb_dropout: Is applied to the input (word embeddings).
:param freeze: freeze the parameters of the encoder during training
:param kwargs:
"""
super(TransformerEncoder, self).__init__()
# build all (num_layers) layers
self.layers = nn.ModuleList([
TransformerEncoderLayer(size=hidden_size, ff_size=ff_size,
num_heads=num_heads, dropout=dropout)
for _ in range(num_layers)])
self.layer_norm = nn.LayerNorm(hidden_size, eps=1e-6, elementwise_affine=False)
self.emb_dropout = nn.Dropout(p=emb_dropout)
self._output_size = hidden_size
self._hidden_size = hidden_size
#pylint: disable=arguments-differ
def forward(self,
embed_src: Tensor,
mask: Tensor) -> (Tensor, Tensor):
"""
Pass the input (and mask) through each layer in turn.
Applies a Transformer encoder to sequence of embeddings x.
The input mini-batch x needs to be sorted by src length.
x and mask should have the same dimensions [batch, time, dim].
:param embed_src: embedded src inputs,
shape (batch_size, src_len, embed_size)
:param mask: indicates padding areas (zeros where padding), shape
(batch_size, src_len, embed_size)
:return:
- output: hidden states with
shape (batch_size, max_length, directions*hidden),
- hidden_concat: last hidden state with
shape (batch_size, directions*hidden)
"""
x = embed_src
for layer in self.layers:
x = F.relu(layer(x, mask), inplace= False)
return self.layer_norm(x)
def __repr__(self):
return "%s(num_layers=%r, num_heads=%r)" % (
self.__class__.__name__, len(self.layers),
self.layers[0].src_src_att.num_heads)
class LabelWiseTransformerEncoder(Encoder):
"""
Transformer Encoder
"""
#pylint: disable=unused-argument
def __init__(self,
hidden_size: int = 512,
ff_size: int = 2048,
num_layers: int = 6,
num_heads: int = 8,
dropout: float = 0.1,
max_label: int = 4,
emb_dropout: float = 0.1,
freeze: bool = False,
**kwargs):
"""
Initializes the Transformer.
:param hidden_size: hidden size and size of embeddings
:param ff_size: position-wise feed-forward layer size.
(Typically this is 2*hidden_size.)
:param num_layers: number of layers
:param num_heads: number of heads for multi-headed attention
:param dropout: dropout probability for Transformer layers
:param emb_dropout: Is applied to the input (word embeddings).
:param freeze: freeze the parameters of the encoder during training
:param kwargs:
"""
super(LabelWiseTransformerEncoder, self).__init__()
# build all (num_layers) layers
self.layers = nn.ModuleList([
TransformerEncoderLayer(size=hidden_size, ff_size=ff_size,
num_heads=num_heads, dropout=dropout)
for _ in range(num_layers)])
self.layers.append(LabelWiseTransformerEncoderLayer(size=hidden_size, ff_size=ff_size,
num_heads=num_heads, dropout=dropout, max_label = max_label))
self.layer_norm = nn.LayerNorm(hidden_size, eps=1e-6, elementwise_affine=False)
self.emb_dropout = nn.Dropout(p=emb_dropout)
self._output_size = hidden_size
self._hidden_size = hidden_size
#pylint: disable=arguments-differ
def forward(self,
embed_src: Tensor,
label: Tensor,
mask: Tensor) -> (Tensor, Tensor):
"""
Pass the input (and mask) through each layer in turn.
Applies a Transformer encoder to sequence of embeddings x.
The input mini-batch x needs to be sorted by src length.
x and mask should have the same dimensions [batch, time, dim].
:param embed_src: embedded src inputs,
shape (batch_size, src_len, embed_size)
:param mask: indicates padding areas (zeros where padding), shape
(batch_size, src_len, embed_size)
:return:
- output: hidden states with
shape (batch_size, max_length, directions*hidden),
- hidden_concat: last hidden state with
shape (batch_size, directions*hidden)
"""
x = embed_src
for layer in self.layers[:-1]:
x = F.relu(layer(x, mask), inplace= False)
x = F.relu(self.layers[-1](x, label, mask), inplace= False)
return self.layer_norm(x)
def __repr__(self):
return "%s(num_layers=%r, num_heads=%r)" % (
self.__class__.__name__, len(self.layers),
self.layers[0].src_src_att.num_heads)
class LabelWiseCrossTransformerEncoder(Encoder):
"""
Transformer Encoder
"""
#pylint: disable=unused-argument
def __init__(self,
hidden_size: int = 512,
ff_size: int = 2048,
num_layers: int = 6,
num_heads: int = 8,
dropout: float = 0.1,
max_label: int = 4,
emb_dropout: float = 0.1,
freeze: bool = False,
**kwargs):
"""
Initializes the Transformer.
:param hidden_size: hidden size and size of embeddings
:param ff_size: position-wise feed-forward layer size.
(Typically this is 2*hidden_size.)
:param num_layers: number of layers
:param num_heads: number of heads for multi-headed attention
:param dropout: dropout probability for Transformer layers
:param emb_dropout: Is applied to the input (word embeddings).
:param freeze: freeze the parameters of the encoder during training
:param kwargs:
"""
super(LabelWiseCrossTransformerEncoder, self).__init__()
# build all (num_layers) layers
self.layers = nn.ModuleList([
TransformerEncoderLayer(size=hidden_size, ff_size=ff_size,
num_heads=num_heads, dropout=dropout)
for _ in range(num_layers)])
self.layers.append(LabelWiseTransformerEncoderLayer(size=hidden_size, ff_size=ff_size,
num_heads=num_heads, dropout=dropout, max_label = max_label))
self.layer_norm = nn.LayerNorm(hidden_size, eps=1e-6, elementwise_affine=False)
self.emb_dropout = nn.Dropout(p=emb_dropout)
self._output_size = hidden_size
self._hidden_size = hidden_size
#pylint: disable=arguments-differ
def forward(self,
embed_src: Tensor,
label: Tensor,
self_mask: Tensor,
cross_embed:Tensor,
cross_mask:Tensor,) -> (Tensor, Tensor):
"""
Pass the input (and mask) through each layer in turn.
Applies a Transformer encoder to sequence of embeddings x.
The input mini-batch x needs to be sorted by src length.
x and mask should have the same dimensions [batch, time, dim].
:param embed_src: embedded src inputs,
shape (batch_size, src_len, embed_size)
:param mask: indicates padding areas (zeros where padding), shape
(batch_size, src_len, embed_size)
:return:
- output: hidden states with
shape (batch_size, max_length, directions*hidden),
- hidden_concat: last hidden state with
shape (batch_size, directions*hidden)
"""
x = embed_src
cnt = 0
for layer in self.layers[:-1]:
if cnt == 1:
x = F.relu(layer(x,self_mask,cross_embed,cross_mask))
else:
x = F.relu(layer(x, self_mask), inplace= False)
cnt+=1
x = F.relu(self.layers[-1](x, label, self_mask), inplace= False)
return self.layer_norm(x)
def __repr__(self):
return "%s(num_layers=%r, num_heads=%r)" % (
self.__class__.__name__, len(self.layers),
self.layers[0].src_src_att.num_heads)
class RelEncoder(nn.Module):
"""
BERT model : Bidirectional Encoder Representations from Transformers.
"""
def __init__(self, vocab_size=204, obj_classes_size=154, hidden_size=512, num_layers=6, attn_heads=8, dropout=0.1, cfg=None):
super(RelEncoder, self).__init__()
self.input_embeddings = Sentence_Embeddings(vocab_size, obj_classes_size, hidden_size, max_rel_pair= 33)
self.encoder = TransformerEncoder(hidden_size=hidden_size, ff_size=hidden_size * 4,
num_layers=num_layers, \
num_heads=attn_heads, dropout=dropout, emb_dropout=dropout)
self.hidden_size = hidden_size
self.vocab_classifier = nn.Linear(hidden_size, vocab_size)
self.obj_id_classifier = nn.Linear(hidden_size, obj_classes_size)
self.token_type_classifier = nn.Linear(hidden_size, 4)
def forward(self, input_token, input_obj_id, segment_label, token_type, src_mask):
batch_size = input_token.shape[0]
src, class_embeds = self.input_embeddings(input_token,
input_obj_id, segment_label, token_type)
encoder_output = self.encoder(src, src_mask)
vocab_logits = self.vocab_classifier(encoder_output)
obj_id_logits = self.obj_id_classifier(encoder_output)
token_type_logits = self.token_type_classifier(encoder_output)
return encoder_output, vocab_logits, obj_id_logits, token_type_logits, src, class_embeds
class PointEncoder(nn.Module):
"""
BERT model : Bidirectional Encoder Representations from Transformers.
"""
def __init__(self, point_dim=3, max_token=6, hidden_size=256, num_layers=6, attn_heads=8, dropout=0.3, cfg=None):
super(PointEncoder, self).__init__()
self.input_embeddings = Fuse_Point_Embeddings(point_dim, hidden_size, max_token= 6, hidden_dropout_prob=dropout)
self.encoder = LabelWiseTransformerEncoder(hidden_size=hidden_size, ff_size=hidden_size * 4,
num_layers=num_layers, \
num_heads=attn_heads, dropout=dropout, max_label = max_token-2, emb_dropout=dropout)
self.hidden_size = hidden_size
self.label_classifier = nn.Linear(hidden_size, max_token-2)
self.ratio_regressor = Ratio_Regressor(hidden_size)
self.max_token = max_token
def forward(self, input_points, token_type, label, src_mask, pretrain = False, input_ratio = None):
batch_size = input_points.shape[0]
src, class_embeds = self.input_embeddings(input_points, token_type)
encoder_output = self.encoder(src, label ,src_mask)
if pretrain:
label_logits = self.label_classifier(encoder_output)
ratio_logits = self.ratio_regressor(encoder_output[:, :self.max_token-2], input_ratio)
return encoder_output, label_logits, ratio_logits, None, None
else:
ratio_logits = self.ratio_regressor(encoder_output[:, :self.max_token-2], input_ratio)
return encoder_output, None, ratio_logits, None, None
class Ratio_Regressor(nn.Module):
def __init__(self, input_dim, num_parts = 4):
super(Ratio_Regressor, self).__init__()
self.num_parts = num_parts
self.ratio_embed = nn.Linear(num_parts, input_dim)
self.regressor = nn.Sequential(
nn.Linear(input_dim, 64),
nn.ReLU(inplace=False),
nn.LayerNorm(64, elementwise_affine=False),
nn.Dropout(0.5),
nn.Linear(64,1)
)
def forward(self, encoder_output, input_ratio):
batch_size = encoder_output.shape[0]
x = encoder_output + self.ratio_embed(input_ratio).unsqueeze(1).repeat(1,self.num_parts,1)
x = F.softmax(self.regressor(x).squeeze(), -1)
return x
class TVAEPointEncoder(nn.Module):
"""
BERT model : Bidirectional Encoder Representations from Transformers.
"""
def __init__(self, point_dim=3, max_token=6, hidden_size=256, num_layers=6, attn_heads=8, dropout=0.3, cfg=None):
super(TVAEPointEncoder, self).__init__()
self.input_embeddings = Fuse_Point_Embeddings(point_dim, hidden_size, max_token= max_token, hidden_dropout_prob=dropout)
self.encoder = LabelWiseTransformerEncoder(hidden_size=hidden_size, ff_size=hidden_size * 4,
num_layers=num_layers, \
num_heads=attn_heads, dropout=dropout, max_label = max_token-2, emb_dropout=dropout)
self.hidden_size = hidden_size
self.label_classifier = nn.Linear(hidden_size, max_token-2)
self.ratio_regressor = Ratio_Regressor(hidden_size, num_parts=max_token-2)
self.max_token = max_token
self.mu_mlp = nn.ModuleList(
[
nn.Linear(hidden_size, hidden_size) for _ in range(self.max_token-2)
]
)
self.std_mlp = nn.ModuleList(
[
nn.Linear(hidden_size, hidden_size) for _ in range(self.max_token-2)
]
)
self.l_norm = nn.LayerNorm(self.hidden_size, elementwise_affine=False)
self.hidden_size = hidden_size
def forward(self, input_points, token_type, label, src_mask, pretrain = False, input_ratio = None):
batch_size = input_points.shape[0]
src, class_embeds = self.input_embeddings(input_points, token_type)
encoder_output = self.encoder(src, label ,src_mask)
mu = torch.zeros_like(encoder_output)
log_std = torch.zeros_like(encoder_output)
for i in range(self.max_token-2):
### mu_i only consider output related to i-th part
mu_i = self.mu_mlp[i](encoder_output[:, self.max_token-2:])*((label==i).int().unsqueeze(-1).repeat(1,1,self.hidden_size)[:, self.max_token-2:].to(encoder_output.device))
log_std_i = self.std_mlp[i](encoder_output[:, self.max_token-2:])*((label==i).int().unsqueeze(-1).repeat(1,1,self.hidden_size)[:, self.max_token-2:].to(encoder_output.device))
mu[:, self.max_token-2:] = mu[:, self.max_token-2:] + mu_i
log_std[:, self.max_token-2:] = log_std[:, self.max_token-2:] + log_std_i
if pretrain:
label_logits = self.label_classifier(encoder_output)
ratio_logits = self.ratio_regressor(encoder_output[:, :self.max_token-2], input_ratio)
return encoder_output, label_logits, ratio_logits, None, None
else:
input_ratio = torch.zeros_like(input_ratio)
ratio_logits = self.ratio_regressor(encoder_output[:, :self.max_token-2], input_ratio)
return encoder_output, None, ratio_logits, mu, log_std
def normalize(self, labels, samples, prototypes):
point_num = samples.size(1)
normalized_samples = torch.zeros_like(samples)
for i in range(self.max_token-2):
d_samples = ((samples-prototypes[:,i].unsqueeze(1).repeat(1,point_num,1))/(torch.abs(prototypes[:,i]).reshape(-1,1,samples.size(-1)).repeat(1,point_num,1)))*(labels==i).int().to(samples.device).unsqueeze(-1).repeat(1,1,samples.size(-1))
safe_tensor = torch.where(torch.isnan(d_samples), torch.zeros_like(d_samples), d_samples)
d_samples = d_samples * (torch.isnan(d_samples).int())
print(d_samples)
normalized_samples += d_samples
return normalized_samples
def normalize(labels, samples, prototypes):
normalized_samples = torch.zeros_like(samples)
for i in range(4):
d_samples = ((samples-prototypes[:,i].unsqueeze(1).repeat(1,10,1))/(torch.norm(prototypes[:,i], dim=1)+1e-8))*(labels==i).int().to(samples.device).unsqueeze(-1).repeat(1,1,samples.size(-1))
normalized_samples += d_samples
print(torch.norm(prototypes[:,i], dim=1))
return normalized_samples
if __name__ == '__main__':
labels = torch.Tensor([[0,0,0,1,1,1,2,2,3,3]])
samples = torch.Tensor([[
[1,1,1],
[1,1,1],
[1,1,1],
[2,2,2],
[2,2,2],
[2,2,2],
[3,3,3],
[3,3,3],
[4,4,4],
[4,4,4]
]])+1
prototypes = torch.Tensor(
[[
[1,1,1],
[2,2,2],
[3,3,3],
[4,4,4]
]]
)
print(labels.size(), samples.size(),prototypes.size())
print(normalize(labels, samples, prototypes))