def forward(self, scores, features, image_size):
num_targets = scores.shape[0]
num_fmaps = features[next(iter(self.ft_channels))].shape[0]
if num_targets > num_fmaps:
multi_targets = True
else:
multi_targets = False
x = None
for i, L in enumerate(self.ft_channels):
ft = features[L]
s = interpolate(scores, ft.shape[-2:]) # Resample scores to match features size
if multi_targets:
h, hpool = self.TSE[L](ft.repeat(num_targets, 1, 1, 1), s, x)
else:
h, hpool = self.TSE[L](ft, s, x)
h = self.RRB1[L](h)
h = self.CAB[L](hpool, h)
x = self.RRB2[L](h)
x = self.project(x, image_size)
return x
def forward(self, deeper, shallower):
shallow_pool = F.adaptive_avg_pool2d(shallower, (1, 1))
deeper_pool = deeper if self.deepest else F.adaptive_avg_pool2d(deeper, (1, 1))
global_pool = torch.cat((shallow_pool, deeper_pool), dim=1)
conv_1x1 = self.convreluconv(global_pool)
inputs = shallower * torch.sigmoid(conv_1x1)
out = inputs + interpolate(deeper, inputs.shape[-2:])
return out
def _align_time(self):
attr_list = ['ppg', 'acc', 'hr', 'activity']
self._get_global_timestamps().astype('datetime64[ms]').astype(int)
for attr_name in attr_list:
attr = getattr(self, attr_name)
try:
time_attr = getattr(self, f'time_{attr_name}').astype('datetime64[ms]').astype(int)
except AttributeError:
time_attr = getattr(self, 'time_sensors').astype('datetime64[ms]').astype(int)
if attr is not None:
time_for_interp = self.time.astype(int)
attr_interp = interpolate(time_for_interp, time_attr, attr)
setattr(self, attr_name, attr_interp)
def forward(self, scores, features, image_size):
x = None
for i, L in enumerate(self.ft_channels):
ft = features[L]
s = interpolate(scores, ft.shape[-2:]) # Resample scores to match features size
h, hpool = self.TSE[L](ft, s, x)
h = self.RRB1[L](h)
h = self.CAB[L](hpool, h)
x = self.RRB2[L](h)
x = self.project(x, image_size)
return x
def _forward(self, image):
batch_size = image.shape[0]
features = self.feature_extractor(image)
scores = []
ft = features[self.tmodels[0].discriminator.layer]
for i, tmdl in zip(range(batch_size), self.tmodels):
x = ft[i, None]
s = tmdl.classify(x)
scores.append(s)
scores = torch.cat(scores, dim=0)
y = self.refiner(scores, features, image.shape)
y = interpolate(y, image.shape[-2:])
return torch.sigmoid(y)
