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Refactor demucs get_module to return an nn.Module #229

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Merged
jansel merged 2 commits into from
Feb 7, 2021
Merged

Refactor demucs get_module to return an nn.Module #229

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6b28757
Refactor demucs get_module to return an nn.Module
jansel Feb 2, 2021
d9903e6
Merge branch 'master' of github.com:pytorch/benchmark into demucs202102
jansel Feb 7, 2021
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44 changes: 21 additions & 23 deletions torchbenchmark/models/demucs/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,20 @@
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False


class DemucsWrapper(torch.nn.Module):
def __init__(self, model, augment):
super(DemucsWrapper, self).__init__()
self.model = model
self.augment = augment

def forward(self, streams):
sources = streams[:, 1:]
sources = self.augment(sources)
mix = sources.sum(dim=1)
return sources, self.model(mix)


class Model(BenchmarkModel):
def __init__(self, device=None, jit=False):
super().__init__()
Expand All @@ -33,7 +47,7 @@ def __init__(self, device=None, jit=False):
if 1:
samples = 80000
# TODO: calculate the right shape
self.example_inputs = (torch.rand([4, 5, 2, 135576]), )
self.example_inputs = (torch.rand([4, 5, 2, 135576], device=device),)

self.duration = Fraction(samples + args.data_stride, args.samplerate)
self.stride = Fraction(args.data_stride, args.samplerate)
Expand All @@ -49,41 +63,25 @@ def __init__(self, device=None, jit=False):
else:
self.augment = Shift(args.data_stride)

self.model = DemucsWrapper(self.model, self.augment)

def _set_mode(self, train):
self.model.train(train)

def get_module(self):
# TODO: merge this with train and eval
def helper(streams):
streams = streams.to(self.device)
sources = streams[:, 1:]
sources = self.augment(sources)
mix = sources.sum(dim=1)
return self.model(mix)
return helper, self.example_inputs
self.model.eval()
return self.model, self.example_inputs

def eval(self, niter=1):
# TODO: implement the eval version
for _ in range(niter):
streams, = self.example_inputs
streams = streams.to(self.device)
sources = streams[:, 1:]
sources = self.augment(sources)
mix = sources.sum(dim=1)

estimates = self.model(mix)
sources, estimates = self.model(*self.example_inputs)
sources = center_trim(sources, estimates)
loss = self.criterion(estimates, sources)

def train(self, niter=1):
for _ in range(niter):
streams, = self.example_inputs
streams = streams.to(self.device)
sources = streams[:, 1:]
sources = self.augment(sources)
mix = sources.sum(dim=1)

estimates = self.model(mix)
sources, estimates = self.model(*self.example_inputs)
sources = center_trim(sources, estimates)
loss = self.criterion(estimates, sources)
loss.backward()
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2 changes: 0 additions & 2 deletions torchbenchmark/models/maml/meta.py
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Expand Up @@ -161,8 +161,6 @@ def finetunning(self, x_spt, y_spt, x_qry, y_qry):
:param y_qry: [querysz]
:return:
"""
assert len(x_spt.shape) == 4
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@jansel jansel Feb 2, 2021

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Note this is an unrelated change to make this module fx.symbolic_trace


querysz = x_qry.size(0)

corrects = [0 for _ in range(self.update_step_test + 1)]
Expand Down