def change_context_window(self, context_window: int):
"""
Update the context window of the SqueezeExcitation module, in-place if possible.
Will update the pooling layer to either nn.AdaptiveAvgPool1d() (for global SE) or nn.AvgPool1d()
(for limited context SE).
If only the context window is changing but still a limited SE context block - then
the earlier instance of nn.AvgPool1d() will be updated.
Args:
context_window: An integer representing the number of input timeframes that will be used
to compute the context. Each timeframe corresponds to a single window stride of the
STFT features.
Say the window_stride = 0.01s, then a context window of 128 represents 128 * 0.01 s
of context to compute the Squeeze step.
"""
if hasattr(self, 'context_window'):
logging.info(
f"Changing Squeeze-Excitation context window from {self.context_window} to {context_window}"
)
self.context_window = int(context_window)
if self.context_window <= 0:
if PYTORCH_QUANTIZATION_AVAILABLE and self._quantize:
if not isinstance(self.pool,
quant_nn.QuantAdaptiveAvgPool1d(1)):
self.pool = quant_nn.QuantAdaptiveAvgPool1d(
1) # context window = T
elif not PYTORCH_QUANTIZATION_AVAILABLE and self._quantize:
raise ImportError(
"pytorch-quantization is not installed. Install from "
"https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization."
)
else:
if not isinstance(self.pool, nn.AdaptiveAvgPool1d):
self.pool = nn.AdaptiveAvgPool1d(1) # context window = T
else:
if PYTORCH_QUANTIZATION_AVAILABLE and self._quantize:
if not isinstance(self.pool, quant_nn.QuantAvgPool1d):
self.pool = quant_nn.QuantAvgPool1d(self.context_window,
stride=1)
elif not PYTORCH_QUANTIZATION_AVAILABLE and self._quantize:
raise ImportError(
"pytorch-quantization is not installed. Install from "
"https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization."
)
else:
if not isinstance(self.pool, nn.AvgPool1d):
self.pool = nn.AvgPool1d(self.context_window, stride=1)
else:
# update the context window
self.pool.kernel_size = _single(self.context_window)
def __init__(
self,
channels: int,
reduction_ratio: int,
context_window: int = -1,
interpolation_mode: str = 'nearest',
activation: Optional[Callable] = None,
quantize: bool = False,
):
"""
Squeeze-and-Excitation sub-module.
Args:
channels: Input number of channels.
reduction_ratio: Reduction ratio for "squeeze" layer.
context_window: Integer number of timesteps that the context
should be computed over, using stride 1 average pooling.
If value < 1, then global context is computed.
interpolation_mode: Interpolation mode of timestep dimension.
Used only if context window is > 1.
The modes available for resizing are: `nearest`, `linear` (3D-only),
`bilinear`, `area`
activation: Intermediate activation function used. Must be a
callable activation function.
"""
super(SqueezeExcite, self).__init__()
self.context_window = int(context_window)
self.interpolation_mode = interpolation_mode
if self.context_window <= 0:
if PYTORCH_QUANTIZATION_AVAILABLE and quantize:
self.pool = quant_nn.QuantAdaptiveAvgPool1d(
1) # context window = T
elif not PYTORCH_QUANTIZATION_AVAILABLE and quantize:
raise ImportError(
"pytorch-quantization is not installed. Install from "
"https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization."
)
else:
self.pool = nn.AdaptiveAvgPool1d(1) # context window = T
else:
if PYTORCH_QUANTIZATION_AVAILABLE and quantize:
self.pool = quant_nn.QuantAvgPool1d(self.context_window,
stride=1)
elif not PYTORCH_QUANTIZATION_AVAILABLE and quantize:
raise ImportError(
"pytorch-quantization is not installed. Install from "
"https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization."
)
else:
self.pool = nn.AvgPool1d(self.context_window, stride=1)
if activation is None:
activation = nn.ReLU(inplace=True)
if PYTORCH_QUANTIZATION_AVAILABLE and quantize:
self.fc = nn.Sequential(
quant_nn.QuantLinear(channels,
channels // reduction_ratio,
bias=False),
activation,
quant_nn.QuantLinear(channels // reduction_ratio,
channels,
bias=False),
)
elif not PYTORCH_QUANTIZATION_AVAILABLE and quantize:
raise ImportError(
"pytorch-quantization is not installed. Install from "
"https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization."
)
else:
self.fc = nn.Sequential(
nn.Linear(channels, channels // reduction_ratio, bias=False),
activation,
nn.Linear(channels // reduction_ratio, channels, bias=False),
)