""" /* 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 from torch.nn.utils import remove_weight_norm def count_parameters(model, verbose=False): total = 0 for name, p in model.named_parameters(): count = torch.ones_like(p).sum().item() if verbose: print(f"{name}: {count} parameters") total += count return total def count_nonzero_parameters(model, verbose=False): total = 0 for name, p in model.named_parameters(): count = torch.count_nonzero(p).item() if verbose: print(f"{name}: {count} non-zero parameters") total += count return total def retain_grads(module): for p in module.parameters(): if p.requires_grad: p.retain_grad() def get_grad_norm(module, p=2): norm = 0 for param in module.parameters(): if param.requires_grad: norm = norm + (torch.abs(param.grad) ** p).sum() return norm ** (1/p) def create_weights(s_real, s_gen, alpha): weights = [] with torch.no_grad(): for sr, sg in zip(s_real, s_gen): weight = torch.exp(alpha * (sr[-1] - sg[-1])) weights.append(weight) return weights def _get_candidates(module: torch.nn.Module): candidates = [] for key in module.__dict__.keys(): if hasattr(module, key + '_v'): candidates.append(key) return candidates def remove_all_weight_norm(model : torch.nn.Module, verbose=False): for name, m in model.named_modules(): candidates = _get_candidates(m) for candidate in candidates: try: remove_weight_norm(m, name=candidate) if verbose: print(f'removed weight norm on weight {name}.{candidate}') except: pass