Module openpack_torch.models.imu.deep_conv_lstm
Implementation of DeepConvLSTM Reference: - https://www.mdpi.com/1424-8220/16/1/115
Expand source code
""" Implementation of DeepConvLSTM
Reference:
- https://www.mdpi.com/1424-8220/16/1/115
"""
import torch
from torch import nn
class DeepConvLSTM(nn.Module):
"""Imprementation of DeepConvLSTM [Sensors 2016].
Note:
https://www.mdpi.com/1424-8220/16/1/115 (Sensors, 2016)
"""
def __init__(self, in_ch: int = 6, num_classes: int = None):
super().__init__()
# NOTE: The first block is input layer.
# -- [L2-5] Convolutions --
blocks = []
for i in range(4):
in_ch_ = in_ch if i == 0 else 64
blocks.append(
nn.Sequential(
nn.Conv2d(in_ch_, 64, kernel_size=(5, 1), padding=(2, 0)),
nn.BatchNorm2d(64),
nn.ReLU(),
)
)
self.conv2to5 = nn.ModuleList(blocks)
# -- [L6-7] LSTM --
hidden_units = 128
self.lstm6 = nn.LSTM(64, hidden_units, batch_first=True)
self.lstm7 = nn.LSTM(hidden_units, hidden_units, batch_first=True)
self.dropout6 = nn.Dropout(p=0.5)
self.dropout7 = nn.Dropout(p=0.5)
# -- [L8] Softmax Layer (Output Layer) --
self.out8 = nn.Conv2d(
hidden_units,
num_classes,
1,
stride=1,
padding=0,
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Args:
x (torch.Tensor): shape = (B, CH, T, 1)
Returns:
torch.Tensor: shape = (B, N_CLASSES, T, 1)
"""
# -- Conv --
for i in range(4):
x = self.conv2to5[i](x)
# -- LSTM --
# Reshape: (B, CH, 1, T) -> (B, T, CH)
x = x.squeeze(3).transpose(1, 2)
x, _ = self.lstm6(x)
x = self.dropout6(x)
x, _ = self.lstm7(x)
x = self.dropout7(x)
# -- [L8] Softmax Layer (Output Layer) --
# Reshape: (B, T, CH) -> (B, CH, T, 1)
x = x.transpose(1, 2).unsqueeze(3)
x = self.out8(x)
return x
class DeepConvLSTMSelfAttn(nn.Module):
"""Imprementation of a DeepConvLSTM with Self-Attention used in ''Deep ConvLSTM with
self-attention for human activity decoding using wearable sensors'' (Sensors 2020).
Note:
https://ieeexplore.ieee.org/document/9296308 (Sensors 2020)
"""
def __init__(
self,
in_ch: int = 6,
num_classes: int = None,
cnn_filters=3,
lstm_units=32,
num_attn_heads: int = 1,
):
super().__init__()
# NOTE: The first block is input layer.
# -- [1] Embedding Layer --
self.conv = nn.Sequential(
nn.Conv2d(in_ch, cnn_filters, kernel_size=1, padding=0),
nn.BatchNorm2d(cnn_filters),
nn.ReLU(),
)
# -- [2] LSTM Encoder --
self.lstm = nn.LSTM(cnn_filters, lstm_units, batch_first=True)
self.dropout = nn.Dropout(p=0.5)
# -- [3] Self-Attention --
self.attention = nn.MultiheadAttention(
lstm_units,
num_attn_heads,
batch_first=True,
)
# -- [4] Softmax Layer (Output Layer) --
self.out = nn.Conv2d(
lstm_units,
num_classes,
1,
stride=1,
padding=0,
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Args:
x (torch.Tensor): shape = (B, CH, T, 1)
Returns:
torch.Tensor: shape = (B, N_CLASSES, T, 1)
"""
# -- [1] Embedding Layer --
x = self.conv(x)
# -- [2] LSTM Encoder --
# Reshape: (B, CH, 1, T) -> (B, T, CH)
x = x.squeeze(3).transpose(1, 2)
x, _ = self.lstm(x)
x = self.dropout(x)
# -- [3] Self-Attention --
x, w = self.attention(x.clone(), x.clone(), x.clone())
# -- [4] Softmax Layer (Output Layer) --
# Reshape: (B, T, CH) -> (B, CH, T, 1)
x = x.transpose(1, 2).unsqueeze(3)
x = self.out(x)
return xClasses
class DeepConvLSTM (in_ch: int = 6, num_classes: int = None)-
Imprementation of DeepConvLSTM [Sensors 2016].
Note
https://www.mdpi.com/1424-8220/16/1/115 (Sensors, 2016)
Initialize internal Module state, shared by both nn.Module and ScriptModule.
Expand source code
class DeepConvLSTM(nn.Module): """Imprementation of DeepConvLSTM [Sensors 2016]. Note: https://www.mdpi.com/1424-8220/16/1/115 (Sensors, 2016) """ def __init__(self, in_ch: int = 6, num_classes: int = None): super().__init__() # NOTE: The first block is input layer. # -- [L2-5] Convolutions -- blocks = [] for i in range(4): in_ch_ = in_ch if i == 0 else 64 blocks.append( nn.Sequential( nn.Conv2d(in_ch_, 64, kernel_size=(5, 1), padding=(2, 0)), nn.BatchNorm2d(64), nn.ReLU(), ) ) self.conv2to5 = nn.ModuleList(blocks) # -- [L6-7] LSTM -- hidden_units = 128 self.lstm6 = nn.LSTM(64, hidden_units, batch_first=True) self.lstm7 = nn.LSTM(hidden_units, hidden_units, batch_first=True) self.dropout6 = nn.Dropout(p=0.5) self.dropout7 = nn.Dropout(p=0.5) # -- [L8] Softmax Layer (Output Layer) -- self.out8 = nn.Conv2d( hidden_units, num_classes, 1, stride=1, padding=0, ) def forward(self, x: torch.Tensor) -> torch.Tensor: """ Args: x (torch.Tensor): shape = (B, CH, T, 1) Returns: torch.Tensor: shape = (B, N_CLASSES, T, 1) """ # -- Conv -- for i in range(4): x = self.conv2to5[i](x) # -- LSTM -- # Reshape: (B, CH, 1, T) -> (B, T, CH) x = x.squeeze(3).transpose(1, 2) x, _ = self.lstm6(x) x = self.dropout6(x) x, _ = self.lstm7(x) x = self.dropout7(x) # -- [L8] Softmax Layer (Output Layer) -- # Reshape: (B, T, CH) -> (B, CH, T, 1) x = x.transpose(1, 2).unsqueeze(3) x = self.out8(x) return xAncestors
- torch.nn.modules.module.Module
Methods
def forward(self, x: torch.Tensor) ‑> torch.Tensor-
Args
x:torch.Tensor- shape = (B, CH, T, 1)
Returns
torch.Tensor- shape = (B, N_CLASSES, T, 1)
Expand source code
def forward(self, x: torch.Tensor) -> torch.Tensor: """ Args: x (torch.Tensor): shape = (B, CH, T, 1) Returns: torch.Tensor: shape = (B, N_CLASSES, T, 1) """ # -- Conv -- for i in range(4): x = self.conv2to5[i](x) # -- LSTM -- # Reshape: (B, CH, 1, T) -> (B, T, CH) x = x.squeeze(3).transpose(1, 2) x, _ = self.lstm6(x) x = self.dropout6(x) x, _ = self.lstm7(x) x = self.dropout7(x) # -- [L8] Softmax Layer (Output Layer) -- # Reshape: (B, T, CH) -> (B, CH, T, 1) x = x.transpose(1, 2).unsqueeze(3) x = self.out8(x) return x
class DeepConvLSTMSelfAttn (in_ch: int = 6, num_classes: int = None, cnn_filters=3, lstm_units=32, num_attn_heads: int = 1)-
Imprementation of a DeepConvLSTM with Self-Attention used in ''Deep ConvLSTM with self-attention for human activity decoding using wearable sensors'' (Sensors 2020).
Note
https://ieeexplore.ieee.org/document/9296308 (Sensors 2020)
Initialize internal Module state, shared by both nn.Module and ScriptModule.
Expand source code
class DeepConvLSTMSelfAttn(nn.Module): """Imprementation of a DeepConvLSTM with Self-Attention used in ''Deep ConvLSTM with self-attention for human activity decoding using wearable sensors'' (Sensors 2020). Note: https://ieeexplore.ieee.org/document/9296308 (Sensors 2020) """ def __init__( self, in_ch: int = 6, num_classes: int = None, cnn_filters=3, lstm_units=32, num_attn_heads: int = 1, ): super().__init__() # NOTE: The first block is input layer. # -- [1] Embedding Layer -- self.conv = nn.Sequential( nn.Conv2d(in_ch, cnn_filters, kernel_size=1, padding=0), nn.BatchNorm2d(cnn_filters), nn.ReLU(), ) # -- [2] LSTM Encoder -- self.lstm = nn.LSTM(cnn_filters, lstm_units, batch_first=True) self.dropout = nn.Dropout(p=0.5) # -- [3] Self-Attention -- self.attention = nn.MultiheadAttention( lstm_units, num_attn_heads, batch_first=True, ) # -- [4] Softmax Layer (Output Layer) -- self.out = nn.Conv2d( lstm_units, num_classes, 1, stride=1, padding=0, ) def forward(self, x: torch.Tensor) -> torch.Tensor: """ Args: x (torch.Tensor): shape = (B, CH, T, 1) Returns: torch.Tensor: shape = (B, N_CLASSES, T, 1) """ # -- [1] Embedding Layer -- x = self.conv(x) # -- [2] LSTM Encoder -- # Reshape: (B, CH, 1, T) -> (B, T, CH) x = x.squeeze(3).transpose(1, 2) x, _ = self.lstm(x) x = self.dropout(x) # -- [3] Self-Attention -- x, w = self.attention(x.clone(), x.clone(), x.clone()) # -- [4] Softmax Layer (Output Layer) -- # Reshape: (B, T, CH) -> (B, CH, T, 1) x = x.transpose(1, 2).unsqueeze(3) x = self.out(x) return xAncestors
- torch.nn.modules.module.Module
Methods
def forward(self, x: torch.Tensor) ‑> torch.Tensor-
Args
x:torch.Tensor- shape = (B, CH, T, 1)
Returns
torch.Tensor- shape = (B, N_CLASSES, T, 1)
Expand source code
def forward(self, x: torch.Tensor) -> torch.Tensor: """ Args: x (torch.Tensor): shape = (B, CH, T, 1) Returns: torch.Tensor: shape = (B, N_CLASSES, T, 1) """ # -- [1] Embedding Layer -- x = self.conv(x) # -- [2] LSTM Encoder -- # Reshape: (B, CH, 1, T) -> (B, T, CH) x = x.squeeze(3).transpose(1, 2) x, _ = self.lstm(x) x = self.dropout(x) # -- [3] Self-Attention -- x, w = self.attention(x.clone(), x.clone(), x.clone()) # -- [4] Softmax Layer (Output Layer) -- # Reshape: (B, T, CH) -> (B, CH, T, 1) x = x.transpose(1, 2).unsqueeze(3) x = self.out(x) return x