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models_t1000_att.py
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"""
This file contains the pytorch model definitions for the dataset using
the top 1000 select tags.
"""
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import Sequential, Linear, Dropout, ReLU, Sigmoid, Conv2d, ConvTranspose2d, BatchNorm1d, BatchNorm2d, LeakyReLU
class Flatten(nn.Module):
def forward(self, input):
return input.view(input.size(0), -1)
class UnFlatten(nn.Module):
def forward(self, input, size=128):
return input.view(input.size(0), size, 3, 3)
class ScaledDotProductAttention(nn.Module):
''' Scaled Dot-Product Attention '''
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
def forward(self, q, k, v, mask=None):
attn = torch.matmul(q / self.temperature, k.transpose(2, 3))
if mask is not None:
attn = attn.masked_fill(mask == 0, -1e9)
attn = self.dropout(F.softmax(attn, dim=-1))
output = torch.matmul(attn, v)
return output, attn
class AudioEncoder(nn.Module):
def __init__(self, size_w_rep=1152):
super(AudioEncoder, self).__init__()
self.audio_encoder = Sequential(
Conv2d(1, 128, kernel_size=4, stride=2, padding=1, padding_mode='zeros'),
BatchNorm2d(128),
ReLU(), # 128x48x48
Dropout(.25),
Conv2d(128, 128, kernel_size=4, stride=2, padding=1, padding_mode='zeros'),
BatchNorm2d(128),
ReLU(), # 128x24x24
Dropout(.25),
Conv2d(128, 128, kernel_size=4, stride=2, padding=1, padding_mode='zeros'),
BatchNorm2d(128),
ReLU(), # 128x12x12
Dropout(.25),
Conv2d(128, 128, kernel_size=4, stride=2, padding=1, padding_mode='zeros'),
BatchNorm2d(128),
ReLU(), # 128x6x6
Dropout(.25),
Conv2d(128, 128, kernel_size=4, stride=2, padding=1, padding_mode='zeros'),
BatchNorm2d(128),
ReLU(), # 128x3x3
Dropout(.25),
Flatten(),
nn.LayerNorm(1152, eps=1e-6),
)
self.fc_audio = Sequential(
Linear(1152, size_w_rep, bias=False),
Dropout(0.25),
)
def forward(self, x):
z = self.audio_encoder(x)
z_d = self.fc_audio(z)
return z, z_d
class AudioDecoder(nn.Module):
def __init__(self):
super(AudioDecoder, self).__init__()
self.audio_decoder = Sequential(
UnFlatten(),
Dropout(.25),
ConvTranspose2d(128, 128, kernel_size=4, stride=2, padding=1, padding_mode='zeros'),
BatchNorm2d(128),
ReLU(),
Dropout(.25),
ConvTranspose2d(128, 128, kernel_size=4, stride=2, padding=1, padding_mode='zeros'),
BatchNorm2d(128),
ReLU(),
Dropout(.25),
ConvTranspose2d(128, 128, kernel_size=4, stride=2, padding=1, padding_mode='zeros'),
BatchNorm2d(128),
ReLU(),
Dropout(.25),
ConvTranspose2d(128, 128, kernel_size=4, stride=2, padding=1, padding_mode='zeros'),
BatchNorm2d(128),
ReLU(),
ConvTranspose2d(128, 1, kernel_size=4, stride=2, padding=1, padding_mode='zeros'),
BatchNorm2d(1),
Sigmoid(),
)
def forward(self, z):
return self.audio_decoder(z)
class TagMeanEncoder(nn.Module):
def __init__(self, vocab_size, word_embedding_dim, d_model, dropout=0.1):
super(TagMeanEncoder, self).__init__()
self.d_model = d_model
if d_model == 128:
self.embeddings = nn.Embedding.from_pretrained(torch.Tensor(np.load('embedding_matrix_128.npy')))
else:
self.embeddings = nn.Embedding.from_pretrained(torch.Tensor(np.load('embedding_matrix.npy')))
self.fc = nn.Linear(word_embedding_dim, d_model, bias=False)
self.dropout = nn.Dropout(dropout)
self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
def forward(self, tags, z_audio, mask=None):
tag_embeddings = self.embeddings(tags)
q = self.fc(tag_embeddings)
# get mean average over non-0 embeddings
q = torch.mul(q.sum(1), torch.repeat_interleave(1/((mask !=0).sum(-1).float()), repeats=self.d_model, dim=-1))
# q = torch.mean(q, dim=1)
q = self.dropout(q)
q = self.layer_norm(q)
return q, None
# Inspired from
# https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/fec78a687210851f055f792d45300d27cc60ae41/transformer/SubLayers.py#L9
class TagSelfAttentionEncoder(nn.Module):
def __init__(self, vocab_size, word_embedding_dim, n_head, d_model, d_k, d_v, dropout=0.1, max_num_tags=10):
super(TagSelfAttentionEncoder, self).__init__()
if d_model == 128:
self.embeddings = nn.Embedding.from_pretrained(torch.Tensor(np.load('embedding_matrix_128.npy')))
else:
self.embeddings = nn.Embedding.from_pretrained(torch.Tensor(np.load('embedding_matrix.npy')))
self.max_num_tags = max_num_tags
self.n_head = n_head
self.d_k = d_k
self.d_v = d_v
self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False)
self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False)
self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False)
self.fc = nn.Linear(n_head * d_v, d_model, bias=False)
self.attention = ScaledDotProductAttention(temperature=d_k ** 0.5)
self.dropout = nn.Dropout(dropout)
self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
def forward(self, tags, q=None, mask=None):
# in self attention q comes from the same source
tag_embeddings = self.embeddings(tags)
k = tag_embeddings
v = tag_embeddings
q = tag_embeddings
d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1)
# No residual for now
# residual = q
# Pass through the pre-attention projection: b x lq x (n*dv)
# Separate different heads: b x lq x n x dv
q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)
# Transpose for attention dot product: b x n x lq x dv
q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
if mask is not None:
mask = mask.unsqueeze(1) # For head axis broadcasting.
q, attn = self.attention(q, k, v, mask=mask)
# Transpose to move the head dimension back: b x lq x n x dv
# Combine the last two dimensions to concatenate all the heads together: b x lq x (n*dv)
q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1)
q = self.dropout(self.fc(q))
# q += residual
# we sum all the values that were multiplied with attention weights
q = q.sum(1)
q = self.layer_norm(q)
q = q.view(-1, q.shape[-1])
return q, attn
# From original COALA method
class TagEncoder(nn.Module):
def __init__(self):
super(TagEncoder, self).__init__()
self.tag_encoder = Sequential(
Linear(1000, 512),
BatchNorm1d(512),
ReLU(),
Dropout(.25),
Linear(512, 512),
BatchNorm1d(512),
ReLU(),
Dropout(.25),
Linear(512, 1152),
BatchNorm1d(1152),
ReLU(),
Dropout(.25),
)
self.fc_tag = Sequential(
Linear(1152, 1152, bias=False),
Dropout(.25),
)
def forward(self, tags):
z = self.tag_encoder(tags)
z_d = self.fc_tag(z)
return z, z_d
class TagDecoder(nn.Module):
def __init__(self, size_w_rep=1152):
super(TagDecoder, self).__init__()
self.tag_decoder = Sequential(
Linear(size_w_rep, 512),
BatchNorm1d(512),
ReLU(),
Dropout(.25),
Linear(512, 512),
BatchNorm1d(512),
ReLU(),
Linear(512, 1000),
BatchNorm1d(1000),
Sigmoid(),
)
def forward(self, z):
return self.tag_decoder(z)