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import numpy as np
def aggregate_loss_dict(agg_loss_dict):
mean_vals = {}
for output in agg_loss_dict:
for key in output:
mean_vals[key] = mean_vals.setdefault(key, []) + [output[key]]
for key in mean_vals:
if len(mean_vals[key]) > 0:
mean_vals[key] = sum(mean_vals[key]) / len(mean_vals[key])
else:
print(f'{key} has no value')
mean_vals[key] = 0
return mean_vals
def compute_cosine_weights(x):
""" Computes weights to be used in the id loss function with minimum value of 0.5 and maximum value of 1. """
values = np.abs(x.cpu().detach().numpy())
assert np.min(values) >= 0. and np.max(values) <= 1., "Input values should be between 0. and 1!"
weights = 0.25 * (np.cos(np.pi * values)) + 0.75
return weights
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