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tcn_mtl_avgpool.py
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tcn_mtl_avgpool.py
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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from TCN import TemporalConvNet
class TCNModel(nn.Module):
def __init__(
self,
input_dim,
hidden_dim,
levels,
batch_size,
output_dim,
kernel_size,
dropout,
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.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.fc_eff = nn.Linear(
int(self.hidden_dim * 2), self.hidden_dim
)
self.fc_fst = nn.Linear(
int(self.hidden_dim * 2), self.hidden_dim
)
self.fc_sts = nn.Linear(
int(self.hidden_dim * 2), self.hidden_dim
)
self.drp_eff = nn.Dropout(p=self.dropout)
self.drp_fst = nn.Dropout(p=self.dropout)
self.drp_sts = nn.Dropout(p=self.dropout)
self.out_eff = nn.Linear(self.hidden_dim , output_dim)
self.out_fst = nn.Linear(self.hidden_dim , output_dim)
self.out_sts = nn.Linear(self.hidden_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)
pooled_outputs_acc = []
pooled_outputs_mic = []
for bn in np.arange(int(np.shape(x_acc)[0] / 5)):
# Get the data of the subject entered the ESM
sbj_acc = out_neighbors_acc[0][bn, :].view(1, self.hidden_dim)
sbj_mic = out_neighbors_mic[0][bn, :].view(1, self.hidden_dim)
# Get the neighbours
n1_acc = out_neighbors_acc[1][bn, :].view(1, self.hidden_dim)
n2_acc = out_neighbors_acc[2][bn, :].view(1, self.hidden_dim)
n3_acc = out_neighbors_acc[3][bn, :].view(1, self.hidden_dim)
n4_acc = out_neighbors_acc[4][bn, :].view(1, self.hidden_dim)
n1_mic = out_neighbors_mic[1][bn, :].view(1, self.hidden_dim)
n2_mic = out_neighbors_mic[2][bn, :].view(1, self.hidden_dim)
n3_mic = out_neighbors_mic[3][bn, :].view(1, self.hidden_dim)
n4_mic = out_neighbors_mic[4][bn, :].view(1, self.hidden_dim)
if len(torch.nonzero(n1_acc)) != 0:
sbj_acc = torch.cat((sbj_acc, n1_acc), dim=0)
if len(torch.nonzero(n2_acc)) != 0:
sbj_acc = torch.cat((sbj_acc, n2_acc), dim=0)
if len(torch.nonzero(n3_acc)) != 0:
sbj_acc = torch.cat((sbj_acc, n3_acc), dim=0)
if len(torch.nonzero(n4_acc)) != 0:
sbj_acc = torch.cat((sbj_acc, n4_acc), dim=0)
if len(torch.nonzero(n1_mic)) != 0:
sbj_mic = torch.cat((sbj_mic, n1_mic), dim=0)
if len(torch.nonzero(n2_mic)) != 0:
sbj_mic = torch.cat((sbj_mic, n2_mic), dim=0)
if len(torch.nonzero(n3_mic)) != 0:
sbj_mic = torch.cat((sbj_mic, n3_mic), dim=0)
if len(torch.nonzero(n4_mic)) != 0:
sbj_mic = torch.cat((sbj_mic, n4_mic), dim=0)
avg_pooled_acc = torch.mean(sbj_acc, dim=0)
pooled_outputs_acc.append(avg_pooled_acc)
avg_pooled_mic = torch.mean(sbj_mic, dim=0)
pooled_outputs_mic.append(avg_pooled_mic)
pooled_outputs_acc = torch.stack(pooled_outputs_acc)
pooled_outputs_mic = torch.stack(pooled_outputs_mic)
all_out = torch.cat((pooled_outputs_acc, pooled_outputs_mic), 1)
all_out_eff = self.drp_eff(F.leaky_relu(self.fc_eff(all_out)))
all_out_fst = self.drp_fst(F.leaky_relu(self.fc_fst(all_out)))
all_out_sts = self.drp_sts(F.leaky_relu(self.fc_sts(all_out)))
out_eff = self.out_eff(all_out_eff)
out_fst = self.out_fst(all_out_fst)
out_sts = self.out_sts(all_out_sts)
return out_eff, out_fst, out_sts