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ConvGRU.py
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ConvGRU.py
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import torch
import torch.nn.functional as F
from torch.nn.utils.parametrizations import spectral_norm
class ConvGRUCell(torch.nn.Module):
"""A ConvGRU implementation."""
def __init__(self, input_channels: int, output_channels: int, kernel_size=3, sn_eps=0.0001):
"""Constructor.
Args:
kernel_size: kernel size of the convolutions. Default: 3.
sn_eps: constant for spectral normalization. Default: 1e-4.
"""
super().__init__()
self._kernel_size = kernel_size
self._sn_eps = sn_eps
self.read_gate_conv = spectral_norm(
torch.nn.Conv2d(
in_channels=input_channels,
out_channels=output_channels,
kernel_size=(kernel_size, kernel_size),
padding=1,
),
eps=sn_eps,
)
self.update_gate_conv = spectral_norm(
torch.nn.Conv2d(
in_channels=input_channels,
out_channels=output_channels,
kernel_size=(kernel_size, kernel_size),
padding=1,
),
eps=sn_eps,
)
self.output_conv = spectral_norm(
torch.nn.Conv2d(
in_channels=input_channels,
out_channels=output_channels,
kernel_size=(kernel_size, kernel_size),
padding=1,
),
eps=sn_eps,
)
def forward(self, x, prev_state):
"""
ConvGRU forward, returning the current+new state
Args:
x: Input tensor
prev_state: Previous state
Returns:
New tensor plus the new state
"""
# Concatenate the inputs and previous state along the channel axis.
xh = torch.cat([x, prev_state], dim=1)
# Read gate of the GRU.
read_gate = F.sigmoid(self.read_gate_conv(xh))
# Update gate of the GRU.
update_gate = F.sigmoid(self.update_gate_conv(xh))
# Gate the inputs.
gated_input = torch.cat([x, read_gate * prev_state], dim=1)
# Gate the cell and state / outputs.
c = F.relu(self.output_conv(gated_input))
out = update_gate * prev_state + (1.0 - update_gate) * c
new_state = out
return out, new_state
class ConvGRU(torch.nn.Module):
"""ConvGRU Cell wrapper to replace tf.static_rnn in TF implementation"""
def __init__(
self,
input_channels: int,
output_channels: int,
kernel_size: int = 3,
sn_eps=0.0001,
):
super().__init__()
self.cell = ConvGRUCell(input_channels, output_channels, kernel_size, sn_eps)
def forward(self, x: torch.Tensor, hidden_state=None) -> torch.Tensor:
outputs = []
for step in range(len(x)):
# Compute current timestep
output, hidden_state = self.cell(x[step], hidden_state)
outputs.append(output)
# Stack outputs to return as tensor
outputs = torch.stack(outputs, dim=0)
return outputs