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Aadi/batch patches #2

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8c48ac8
add '.to' function to PPSeqmodel
aaprasad Feb 24, 2025
4f98f4a
fix potential shape error (B=1,N,T) (eg for torch dataset)
aaprasad Feb 24, 2025
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9 changes: 7 additions & 2 deletions ppseq/batch_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,8 @@ def __init__(self,
beta_a0: float=0.,
alpha_b0: float=0.,
beta_b0: float=0.,
alpha_t0: float=0.,
beta_t0:float=0.,
device=None):
super().__init__(num_templates,
num_neurons,
Expand All @@ -26,6 +28,8 @@ def __init__(self,
beta_a0,
alpha_b0,
beta_b0,
alpha_t0,
beta_t0,
device)

def fit(self,
Expand All @@ -40,7 +44,7 @@ def fit(self,


init_method = dict(random=self.initialize_random)[initialization.lower()]
amplitude_batches =[init_method(data) for data in data_batches]
amplitude_batches =[init_method(data.squeeze()) for data in data_batches]

# TODO: Initialize amplitudes more intelligently?
# amplitudes = torch.rand(K, T, device=self.device) + 1e-4
Expand All @@ -50,12 +54,13 @@ def fit(self,
for _ in progress_bar(range(num_iter)):
ll = 0
for i, data in enumerate(data_batches):
data = data.squeeze() # prevents indexing error when data_shape = (1, N, T) (e.g in a torch dataloader)
amplitude_batches[i] = self._update_amplitudes(data,
amplitude_batches[i])
self._update_base_rates(data, amplitude_batches[i])
self._update_templates(data, amplitude_batches[i])
ll += self.log_likelihood(data, amplitude_batches[i])
lps.append(ll)
lps.append(ll) #return the sum or avg log likelihood?

lps = torch.stack(lps) if num_iter > 0 else torch.tensor([])
return lps, amplitude_batches
Expand Down
13 changes: 13 additions & 0 deletions ppseq/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,6 +47,7 @@ def __init__(self,


self.base_rates = torch.ones(num_neurons, device=device)

self.template_scales = torch.ones(num_templates, num_neurons, device=device) / num_neurons
self.template_offsets = template_duration * torch.rand(num_templates, num_neurons, device=device)
self.template_widths = torch.ones(self.num_templates, self.num_neurons, device=device)
Expand All @@ -59,6 +60,17 @@ def __init__(self,
self.alpha_t0 = alpha_t0
self.beta_t0 = beta_t0

def to(self, map_location: str | torch.DeviceObjType | torch.dtype):

self.base_rates = self.base_rates.to(map_location)
self.template_scales = self.template_scales.to(map_location)
self.template_offsets = self.template_offsets.to(map_location)
self.template_widths = self.template_widths.to(map_location)

if not isinstance(map_location, torch.dtype):
self.device=map_location


@property
def templates(self) -> Float[Tensor, "num_templates num_neurons duration"]:
"""Compute the templates from the mean, std, and amplitude of the Gaussian kernel.
Expand All @@ -68,6 +80,7 @@ def templates(self) -> Float[Tensor, "num_templates num_neurons duration"]:
ds = torch.arange(D, device=self.device)[:, None, None]
p = dist.Normal(mu, sigma)
W = p.log_prob(ds).exp().permute(1,2,0)

return W / W.sum(dim=2, keepdim=True) * amp[:, :, None]

def reconstruct(self,
Expand Down