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import sys
import glob
import h5py
import copy
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
# Part of the code is referred from: http://nlp.seas.harvard.edu/2018/04/03/attention.html#positional-encoding
def clones(module, N):
return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])
def attention(query, key, value, mask=None, dropout=None):
d_k = query.size(-1)
scores = torch.matmul(query, key.transpose(-2, -1).contiguous()) / math.sqrt(d_k)
if mask is not None:
scores = scores.masked_fill(mask == 0, -1e9)
p_attn = F.softmax(scores, dim=-1)
return torch.matmul(p_attn, value), p_attn
def nearest_neighbor(src, dst):
inner = -2 * torch.matmul(src.transpose(1, 0).contiguous(), dst) # src, dst (num_dims, num_points)
distances = -torch.sum(src ** 2, dim=0, keepdim=True).transpose(1, 0).contiguous() - inner - torch.sum(dst ** 2,
dim=0,
keepdim=True)
distances, indices = distances.topk(k=1, dim=-1)
return distances, indices
class EncoderDecoder(nn.Module):
"""
A standard Encoder-Decoder architecture. Base for this and many
other models.
"""
def __init__(self, encoder, decoder, src_embed, tgt_embed, generator):
super(EncoderDecoder, self).__init__()
self.encoder = encoder
self.decoder = decoder
self.src_embed = src_embed
self.tgt_embed = tgt_embed
self.generator = generator
def forward(self, src, tgt, src_mask, tgt_mask):
"Take in and process masked src and target sequences."
return self.decode(self.encode(src, src_mask), src_mask,
tgt, tgt_mask)
def encode(self, src, src_mask):
return self.encoder(self.src_embed(src), src_mask)
def decode(self, memory, src_mask, tgt, tgt_mask):
return self.generator(self.decoder(self.tgt_embed(tgt), memory, src_mask, tgt_mask))
class Generator(nn.Module):
def __init__(self, emb_dims):
super(Generator, self).__init__()
self.nn = nn.Sequential(nn.Linear(emb_dims, emb_dims // 2),
nn.BatchNorm1d(emb_dims // 2),
nn.ReLU(),
nn.Linear(emb_dims // 2, emb_dims // 4),
nn.BatchNorm1d(emb_dims // 4),
nn.ReLU(),
nn.Linear(emb_dims // 4, emb_dims // 8),
nn.BatchNorm1d(emb_dims // 8),
nn.ReLU())
self.proj_rot = nn.Linear(emb_dims // 8, 4)
self.proj_trans = nn.Linear(emb_dims // 8, 3)
def forward(self, x):
x = self.nn(x.max(dim=1)[0])
rotation = self.proj_rot(x)
translation = self.proj_trans(x)
rotation = rotation / torch.norm(rotation, p=2, dim=1, keepdim=True)
return rotation, translation
class Encoder(nn.Module):
def __init__(self, layer, N):
super(Encoder, self).__init__()
self.layers = clones(layer, N)
self.norm = LayerNorm(layer.size)
def forward(self, x, mask):
for layer in self.layers:
x = layer(x, mask)
return self.norm(x)
class Decoder(nn.Module):
"Generic N layer decoder with masking."
def __init__(self, layer, N):
super(Decoder, self).__init__()
self.layers = clones(layer, N)
self.norm = LayerNorm(layer.size)
def forward(self, x, memory, src_mask, tgt_mask):
for layer in self.layers:
x = layer(x, memory, src_mask, tgt_mask)
return self.norm(x)
class LayerNorm(nn.Module):
def __init__(self, features, eps=1e-6):
super(LayerNorm, self).__init__()
self.a_2 = nn.Parameter(torch.ones(features))
self.b_2 = nn.Parameter(torch.zeros(features))
self.eps = eps
def forward(self, x):
mean = x.mean(-1, keepdim=True)
std = x.std(-1, keepdim=True)
return self.a_2 * (x - mean) / (std + self.eps) + self.b_2
class SublayerConnection(nn.Module):
def __init__(self, size, dropout=None):
super(SublayerConnection, self).__init__()
self.norm = LayerNorm(size)
def forward(self, x, sublayer):
return x + sublayer(self.norm(x))
class EncoderLayer(nn.Module):
def __init__(self, size, self_attn, feed_forward, dropout):
super(EncoderLayer, self).__init__()
self.self_attn = self_attn
self.feed_forward = feed_forward
self.sublayer = clones(SublayerConnection(size, dropout), 2)
self.size = size
def forward(self, x, mask):
x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))
return self.sublayer[1](x, self.feed_forward)
class DecoderLayer(nn.Module):
"Decoder is made of self-attn, src-attn, and feed forward (defined below)"
def __init__(self, size, self_attn, src_attn, feed_forward, dropout):
super(DecoderLayer, self).__init__()
self.size = size
self.self_attn = self_attn
self.src_attn = src_attn
self.feed_forward = feed_forward
self.sublayer = clones(SublayerConnection(size, dropout), 3)
def forward(self, x, memory, src_mask, tgt_mask):
"Follow Figure 1 (right) for connections."
m = memory
x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, tgt_mask))
x = self.sublayer[1](x, lambda x: self.src_attn(x, m, m, src_mask))
return self.sublayer[2](x, self.feed_forward)
class MultiHeadedAttention(nn.Module):
def __init__(self, h, d_model, dropout=0.1):
"Take in model size and number of heads."
super(MultiHeadedAttention, self).__init__()
assert d_model % h == 0
# We assume d_v always equals d_k
self.d_k = d_model // h
self.h = h
self.linears = clones(nn.Linear(d_model, d_model), 4)
self.attn = None
self.dropout = None
def forward(self, query, key, value, mask=None):
"Implements Figure 2"
if mask is not None:
# Same mask applied to all h heads.
mask = mask.unsqueeze(1)
nbatches = query.size(0)
# 1) Do all the linear projections in batch from d_model => h x d_k
query, key, value = \
[l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2).contiguous()
for l, x in zip(self.linears, (query, key, value))]
# 2) Apply attention on all the projected vectors in batch.
x, self.attn = attention(query, key, value, mask=mask,
dropout=self.dropout)
# 3) "Concat" using a view and apply a final linear.
x = x.transpose(1, 2).contiguous() \
.view(nbatches, -1, self.h * self.d_k)
return self.linears[-1](x)
class PositionwiseFeedForward(nn.Module):
"Implements FFN equation."
def __init__(self, d_model, d_ff, dropout=0.1):
super(PositionwiseFeedForward, self).__init__()
self.w_1 = nn.Linear(d_model, d_ff)
self.norm = nn.Sequential() # nn.BatchNorm1d(d_ff)
self.w_2 = nn.Linear(d_ff, d_model)
self.dropout = None
def forward(self, x):
return self.w_2(self.norm(F.relu(self.w_1(x)).transpose(2, 1).contiguous()).transpose(2, 1).contiguous())
class Identity(nn.Module):
def __init__(self):
super(Identity, self).__init__()
def forward(self, *input):
return input
class Transformer(nn.Module):
def __init__(self, emb_dims, n_blocks, dropout, ff_dims, n_heads):
super(Transformer, self).__init__()
self.emb_dims = emb_dims
self.N = n_blocks
self.dropout = dropout
self.ff_dims = ff_dims
self.n_heads = n_heads
c = copy.deepcopy
attn = MultiHeadedAttention(self.n_heads, self.emb_dims)
ff = PositionwiseFeedForward(self.emb_dims, self.ff_dims, self.dropout)
self.model = EncoderDecoder(Encoder(EncoderLayer(self.emb_dims, c(attn), c(ff), self.dropout), self.N),
Decoder(DecoderLayer(self.emb_dims, c(attn), c(attn), c(ff), self.dropout), self.N),
nn.Sequential(),
nn.Sequential(),
nn.Sequential())
def forward(self, *input):
src = input[0]
tgt = input[1]
src = src.transpose(2, 1).contiguous()
tgt = tgt.transpose(2, 1).contiguous()
tgt_embedding = self.model(src, tgt, None, None).transpose(2, 1).contiguous()
src_embedding = self.model(tgt, src, None, None).transpose(2, 1).contiguous()
return src_embedding, tgt_embedding |