diff --git a/dnn/torch/osce/make_default_setup.py b/dnn/torch/osce/make_default_setup.py
index 06add8faef2f0d222e1c76dc4092f07b406f0d8a..2b2956625b6c843a9131f2bce134930135198d21 100644
--- a/dnn/torch/osce/make_default_setup.py
+++ b/dnn/torch/osce/make_default_setup.py
@@ -36,7 +36,7 @@ from utils.templates import setup_dict
parser = argparse.ArgumentParser()
parser.add_argument('name', type=str, help='name of default setup file')
-parser.add_argument('--model', choices=['lace'], help='model name', default='lace')
+parser.add_argument('--model', choices=['lace', 'nolace'], help='model name', default='lace')
parser.add_argument('--path2dataset', type=str, help='dataset path', default=None)
args = parser.parse_args()
diff --git a/dnn/torch/osce/models/__init__.py b/dnn/torch/osce/models/__init__.py
index c8dfc5d98e2190e65552a1e423f5774ad4224640..49a88ae27ed4b1b04df2f35c7181a362e0063d1d 100644
--- a/dnn/torch/osce/models/__init__.py
+++ b/dnn/torch/osce/models/__init__.py
@@ -28,9 +28,11 @@
"""
from .lace import LACE
+from .no_lace import NoLACE
model_dict = {
- 'lace': LACE
+ 'lace': LACE,
+ 'nolace': NoLACE
}
diff --git a/dnn/torch/osce/models/no_lace.py b/dnn/torch/osce/models/no_lace.py
index 8b2de87f03259f554c824b63c6c649ba77da84e9..4524906d3b5f88a0588ea9b0a1ef5f4f36ed0687 100644
--- a/dnn/torch/osce/models/no_lace.py
+++ b/dnn/torch/osce/models/no_lace.py
@@ -1,33 +1,4 @@
-"""
-/* Copyright (c) 2023 Amazon
- Written by Jan Buethe */
-/*
- Redistribution and use in source and binary forms, with or without
- modification, are permitted provided that the following conditions
- are met:
-
- - Redistributions of source code must retain the above copyright
- notice, this list of conditions and the following disclaimer.
-
- - Redistributions in binary form must reproduce the above copyright
- notice, this list of conditions and the following disclaimer in the
- documentation and/or other materials provided with the distribution.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
- ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
- LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
- A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
- OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
- EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
- PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
- PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
- LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
- NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-*/
-"""
-
import torch
from torch import nn
import torch.nn.functional as F
@@ -64,7 +35,8 @@ class NoLACE(NNSBase):
hidden_feature_dim=64,
partial_lookahead=True,
norm_p=2,
- avg_pool_k=4):
+ avg_pool_k=4,
+ pool_after=False):
super().__init__(skip=skip, preemph=preemph)
@@ -103,9 +75,9 @@ class NoLACE(NNSBase):
self.af1 = LimitedAdaptiveConv1d(1, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p)
# non-linear transforms
- self.tdshape1 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k)
- self.tdshape2 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k)
- self.tdshape3 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k)
+ self.tdshape1 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, pool_after=pool_after)
+ self.tdshape2 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, pool_after=pool_after)
+ self.tdshape3 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, pool_after=pool_after)
# combinators
self.af2 = LimitedAdaptiveConv1d(2, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p)
@@ -128,6 +100,7 @@ class NoLACE(NNSBase):
feature_net_flops = self.feature_net.flop_count(frame_rate)
comb_flops = self.cf1.flop_count(rate) + self.cf2.flop_count(rate)
af_flops = self.af1.flop_count(rate) + self.af2.flop_count(rate) + self.af3.flop_count(rate) + self.af4.flop_count(rate)
+ shape_flops = self.tdshape1.flop_count(rate) + self.tdshape2.flop_count(rate) + self.tdshape3.flop_count(rate)
feature_flops = (_conv1d_flop_count(self.post_cf1, frame_rate) + _conv1d_flop_count(self.post_cf2, frame_rate)
+ _conv1d_flop_count(self.post_af1, frame_rate) + _conv1d_flop_count(self.post_af2, frame_rate) + _conv1d_flop_count(self.post_af3, frame_rate))
@@ -137,7 +110,7 @@ class NoLACE(NNSBase):
print(f"adaptive conv: {af_flops / 1e6} MFLOPS")
print(f"feature transforms: {feature_flops / 1e6} MFLOPS")
- return feature_net_flops + comb_flops + af_flops + feature_flops
+ return feature_net_flops + comb_flops + af_flops + feature_flops + shape_flops
def feature_transform(self, f, layer):
f = f.permute(0, 2, 1)
@@ -180,4 +153,4 @@ class NoLACE(NNSBase):
y = torch.cat((y1, y2), dim=1)
y = self.af4(y, cf, debug=debug)
- return y
\ No newline at end of file
+ return y
diff --git a/dnn/torch/osce/utils/templates.py b/dnn/torch/osce/utils/templates.py
index 1232710f5908674f8cd7924e6f7b9fb1de10e006..c9648f440b8bf062e117a2dc4492cbb4881ba4b9 100644
--- a/dnn/torch/osce/utils/templates.py
+++ b/dnn/torch/osce/utils/templates.py
@@ -70,7 +70,6 @@ lace_setup = {
'lr': 5.e-4,
'lr_decay_factor': 2.5e-5,
'epochs': 50,
- 'frames_per_sample': 50,
'loss': {
'w_l1': 0,
'w_lm': 0,
@@ -86,7 +85,63 @@ lace_setup = {
}
+nolace_setup = {
+ 'dataset': '/local/datasets/silk_enhancement_v2_full_6to64kbps/training',
+ 'validation_dataset': '/local/datasets/silk_enhancement_v2_full_6to64kbps/validation',
+ 'model': {
+ 'name': 'nolace',
+ 'args': [],
+ 'kwargs': {
+ 'avg_pool_k': 4,
+ 'comb_gain_limit_db': 10,
+ 'cond_dim': 256,
+ 'conv_gain_limits_db': [-12, 12],
+ 'global_gain_limits_db': [-6, 6],
+ 'hidden_feature_dim': 96,
+ 'kernel_size': 15,
+ 'num_features': 93,
+ 'numbits_embedding_dim': 8,
+ 'numbits_range': [50, 650],
+ 'partial_lookahead': True,
+ 'pitch_embedding_dim': 64,
+ 'pitch_max': 300,
+ 'preemph': 0.85,
+ 'skip': 91
+ }
+ },
+ 'data': {
+ 'frames_per_sample': 100,
+ 'no_pitch_value': 7,
+ 'preemph': 0.85,
+ 'skip': 91,
+ 'pitch_hangover': 8,
+ 'acorr_radius': 2,
+ 'num_bands_clean_spec': 64,
+ 'num_bands_noisy_spec': 18,
+ 'noisy_spec_scale': 'opus',
+ 'pitch_hangover': 8,
+ },
+ 'training': {
+ 'batch_size': 256,
+ 'lr': 5.e-4,
+ 'lr_decay_factor': 2.5e-5,
+ 'epochs': 50,
+ 'loss': {
+ 'w_l1': 0,
+ 'w_lm': 0,
+ 'w_logmel': 0,
+ 'w_sc': 0,
+ 'w_wsc': 0,
+ 'w_xcorr': 0,
+ 'w_sxcorr': 1,
+ 'w_l2': 10,
+ 'w_slm': 2
+ }
+ }
+}
+
setup_dict = {
'lace': lace_setup,
+ 'nolace': nolace_setup
}