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quantized_conv1d_op.py
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quantized_conv1d_op.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
# Example script for exporting simple models to flatbuffer
import logging
import torch
from executorch.backends.cadence.aot.ops_registrations import * # noqa
from executorch.backends.cadence.aot.export_example import export_model
FORMAT = "[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s"
logging.basicConfig(level=logging.INFO, format=FORMAT)
if __name__ == "__main__":
(
shape,
in_channels,
out_channels,
kernel,
stride,
padding,
dilation,
depthwise,
bias,
channel_last,
) = [(1, 8, 33), 8, 16, 3, 2, 4, 3, False, True, False]
class QuantizedConv(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1d = torch.nn.Conv1d(
in_channels,
out_channels,
kernel,
stride=stride,
padding=padding,
dilation=dilation,
groups=in_channels if depthwise else 1,
bias=bias,
)
def forward(self, x: torch.Tensor):
return self.conv1d(x)
model = QuantizedConv()
model.eval()
example_inputs = (torch.randn(shape),)
export_model(model, example_inputs)