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Diffstat (limited to 'dnn/torch/dnntools/dnntools/relegance/meta_critic.py')
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1 files changed, 85 insertions, 0 deletions
diff --git a/dnn/torch/dnntools/dnntools/relegance/meta_critic.py b/dnn/torch/dnntools/dnntools/relegance/meta_critic.py new file mode 100644 index 00000000..1af0f8ff --- /dev/null +++ b/dnn/torch/dnntools/dnntools/relegance/meta_critic.py @@ -0,0 +1,85 @@ +""" +/* 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 + +class MetaCritic(): + def __init__(self, normalize=False, gamma=0.9, beta=0.0, joint_stats=False): + """ Class for assessing relevance of discriminator scores + + Args: + gamma (float, optional): update rate for tracking discriminator stats. Defaults to 0.9. + beta (float, optional): Miminum confidence related threshold. Defaults to 0.0. + """ + self.normalize = normalize + self.gamma = gamma + self.beta = beta + self.joint_stats = joint_stats + + self.disc_stats = dict() + + def __call__(self, disc_id, real_scores, generated_scores): + """ calculates relevance from normalized scores + + Args: + disc_id (any valid key): id for tracking discriminator statistics + real_scores (torch.tensor): scores for real data + generated_scores (torch.tensor): scores for generated data; expecting device to match real_scores.device + + Returns: + torch.tensor: output-domain relevance + """ + + if self.normalize: + real_std = torch.std(real_scores.detach()).cpu().item() + gen_std = torch.std(generated_scores.detach()).cpu().item() + std = (real_std**2 + gen_std**2) ** .5 + mean = torch.mean(real_scores.detach()).cpu().item() - torch.mean(generated_scores.detach()).cpu().item() + + key = 0 if self.joint_stats else disc_id + + if key in self.disc_stats: + self.disc_stats[key]['std'] = self.gamma * self.disc_stats[key]['std'] + (1 - self.gamma) * std + self.disc_stats[key]['mean'] = self.gamma * self.disc_stats[key]['mean'] + (1 - self.gamma) * mean + else: + self.disc_stats[key] = { + 'std': std + 1e-5, + 'mean': mean + } + + std = self.disc_stats[key]['std'] + mean = self.disc_stats[key]['mean'] + else: + mean, std = 0, 1 + + relevance = torch.relu((real_scores - generated_scores - mean) / std + mean - self.beta) + + if False: print(f"relevance({disc_id}): {relevance.min()=} {relevance.max()=} {relevance.mean()=}") + + return relevance
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