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tcn_loupe.py
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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from loupe_pytorch import NetVLAD
from TCN import TemporalConvNet
class TCNModel(nn.Module):
def __init__(
self,
input_dim,
hidden_dim,
levels,
batch_size,
output_dim,
kernel_size,
dropout,
cluster_size,
cluster_output_dim,
gating,
bnorm,
device,
):
super(TCNModel, self).__init__()
self.hidden_dim = hidden_dim
self.levels = levels
self.batch_size = batch_size
self.num_channels = [self.hidden_dim] * levels
self.kernel_size = kernel_size
self.dropout = dropout
self.cluster_size = cluster_size
self.cluster_output_dim = cluster_output_dim
self.gating = gating
self.bnorm = bnorm
self.tcn_subject_acc = TemporalConvNet(
input_dim,
self.num_channels,
kernel_size=self.kernel_size,
dropout=self.dropout,
)
self.tcn_neighbors_acc = TemporalConvNet(
input_dim,
self.num_channels,
kernel_size=self.kernel_size,
dropout=self.dropout,
)
self.tcn_subject_mic = TemporalConvNet(
input_dim,
self.num_channels,
kernel_size=self.kernel_size,
dropout=self.dropout,
)
self.tcn_neighbors_mic = TemporalConvNet(
input_dim,
self.num_channels,
kernel_size=self.kernel_size,
dropout=self.dropout,
)
self.vlad_acc = NetVLAD(
feature_size=self.hidden_dim,
max_samples=5,
cluster_size=self.cluster_size,
output_dim=self.cluster_output_dim,
gating=self.gating,
add_batch_norm=self.bnorm,
)
self.vlad_mic = NetVLAD(
feature_size=self.hidden_dim,
max_samples=5,
cluster_size=self.cluster_size,
output_dim=self.cluster_output_dim,
gating=self.gating,
add_batch_norm=self.bnorm,
)
self.fc1 = nn.Linear(int(self.cluster_output_dim * 2), self.cluster_output_dim)
self.dp = nn.Dropout(p=self.dropout)
self.fcout = nn.Linear(self.cluster_output_dim, output_dim)
self.device = device
def forward(self, x_acc, x_mic, acc_lens, mic_lens):
# (int(np.shape(x_acc)[0] / 5) is the current batch size
acc_s_batch = x_acc[0 : np.shape(x_acc)[0] : 5, :, :]
mic_s_batch = x_mic[0 : np.shape(x_mic)[0] : 5, :, :]
acc_s_lengths = torch.tensor(acc_lens[0 : np.shape(x_acc)[0] : 5])
mic_s_lengths = torch.tensor(mic_lens[0 : np.shape(x_acc)[0] : 5])
acc_s_batch = acc_s_batch.permute(0, 2, 1)
mic_s_batch = mic_s_batch.permute(0, 2, 1)
acc_s_batch = acc_s_batch.to(self.device)
mic_s_batch = mic_s_batch.to(self.device)
out_acc_s = self.tcn_subject_acc(acc_s_batch.float())
out_mic_s = self.tcn_subject_mic(mic_s_batch.float())
ending_acc_s_outputs = torch.zeros(
(int(np.shape(x_acc)[0] / 5), self.hidden_dim)
)
ending_mic_s_outputs = torch.zeros(
(int(np.shape(x_acc)[0] / 5), self.hidden_dim)
)
for b_num, cur_size in zip(
np.arange(int(np.shape(x_acc)[0] / 5)), acc_s_lengths
):
if cur_size == 0:
continue
else:
ending_acc_s_outputs[b_num, :] = out_acc_s[b_num, :, cur_size - 1]
for b_num, cur_size in zip(
np.arange(int(np.shape(x_acc)[0] / 5)), mic_s_lengths
):
if cur_size == 0:
continue
else:
ending_mic_s_outputs[b_num, :] = out_mic_s[b_num, :, cur_size - 1]
# Get last time step
out_acc_s_last_timestep = ending_acc_s_outputs.to(self.device)
out_mic_s_last_timestep = ending_mic_s_outputs.to(self.device)
out_neighbors_acc = []
out_neighbors_mic = []
# Neighbor's data
for i in np.arange(1, 5):
cur_acc_n_batch = x_acc[i : np.shape(x_acc)[0] : 5, :, :]
cur_mic_n_batch = x_mic[i : np.shape(x_mic)[0] : 5, :, :]
cur_acc_n_lengths = torch.tensor(acc_lens[i : np.shape(x_acc)[0] : 5])
cur_mic_n_lengths = torch.tensor(mic_lens[i : np.shape(x_mic)[0] : 5])
cur_acc_n_batch = cur_acc_n_batch.permute(0, 2, 1)
cur_mic_n_batch = cur_mic_n_batch.permute(0, 2, 1)
cur_acc_n_batch = cur_acc_n_batch.to(self.device)
cur_mic_n_batch = cur_mic_n_batch.to(self.device)
out_neighbor_acc = self.tcn_neighbors_acc(cur_acc_n_batch.float())
out_neighbor_mic = self.tcn_neighbors_mic(cur_mic_n_batch.float())
ending_acc_n_outputs = torch.zeros(
(int(np.shape(x_acc)[0] / 5), self.hidden_dim)
)
ending_mic_n_outputs = torch.zeros(
(int(np.shape(x_mic)[0] / 5), self.hidden_dim)
)
for b_num, cur_size in zip(
np.arange(int(np.shape(x_acc)[0] / 5)), cur_acc_n_lengths
):
if cur_size == 0:
continue
else:
ending_acc_n_outputs[b_num, :] = out_neighbor_acc[
b_num, :, cur_size - 1
]
for b_num, cur_size in zip(
np.arange(int(np.shape(x_acc)[0] / 5)), cur_mic_n_lengths
):
if cur_size == 0:
continue
else:
ending_mic_n_outputs[b_num, :] = out_neighbor_mic[
b_num, :, cur_size - 1
]
ending_acc_n_outputs = ending_acc_n_outputs.to(self.device)
ending_mic_n_outputs = ending_mic_n_outputs.to(self.device)
out_neighbors_acc.append(ending_acc_n_outputs)
out_neighbors_mic.append(ending_mic_n_outputs)
out_neighbors_acc.insert(0, out_acc_s_last_timestep)
out_neighbors_mic.insert(0, out_mic_s_last_timestep)
all_out_acc = torch.zeros((int(np.shape(x_acc)[0] / 5), 5, self.hidden_dim)).to(
self.device
)
all_out_mic = torch.zeros((int(np.shape(x_acc)[0] / 5), 5, self.hidden_dim)).to(
self.device
)
for bc in np.arange(5):
all_out_acc[:, bc, :] = out_neighbors_acc[bc]
all_out_mic[:, bc, :] = out_neighbors_mic[bc]
all_out_acc = self.vlad_acc(all_out_acc)
all_out_mic = self.vlad_mic(all_out_mic)
all_out = torch.cat((all_out_acc, all_out_mic), 1)
all_out = self.dp(F.leaky_relu(self.fc1(all_out)))
out = self.fcout(all_out)
return out