1""" 2/* Copyright (c) 2023 Amazon 3 Written by Jan Buethe */ 4/* 5 Redistribution and use in source and binary forms, with or without 6 modification, are permitted provided that the following conditions 7 are met: 8 9 - Redistributions of source code must retain the above copyright 10 notice, this list of conditions and the following disclaimer. 11 12 - Redistributions in binary form must reproduce the above copyright 13 notice, this list of conditions and the following disclaimer in the 14 documentation and/or other materials provided with the distribution. 15 16 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 17 ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 18 LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 19 A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER 20 OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 21 EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 22 PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 23 PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF 24 LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING 25 NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 26 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 27*/ 28""" 29 30import torch 31from torch.nn.utils import remove_weight_norm 32 33def count_parameters(model, verbose=False): 34 total = 0 35 for name, p in model.named_parameters(): 36 count = torch.ones_like(p).sum().item() 37 38 if verbose: 39 print(f"{name}: {count} parameters") 40 41 total += count 42 43 return total 44 45def count_nonzero_parameters(model, verbose=False): 46 total = 0 47 for name, p in model.named_parameters(): 48 count = torch.count_nonzero(p).item() 49 50 if verbose: 51 print(f"{name}: {count} non-zero parameters") 52 53 total += count 54 55 return total 56def retain_grads(module): 57 for p in module.parameters(): 58 if p.requires_grad: 59 p.retain_grad() 60 61def get_grad_norm(module, p=2): 62 norm = 0 63 for param in module.parameters(): 64 if param.requires_grad: 65 norm = norm + (torch.abs(param.grad) ** p).sum() 66 67 return norm ** (1/p) 68 69def create_weights(s_real, s_gen, alpha): 70 weights = [] 71 with torch.no_grad(): 72 for sr, sg in zip(s_real, s_gen): 73 weight = torch.exp(alpha * (sr[-1] - sg[-1])) 74 weights.append(weight) 75 76 return weights 77 78 79def _get_candidates(module: torch.nn.Module): 80 candidates = [] 81 for key in module.__dict__.keys(): 82 if hasattr(module, key + '_v'): 83 candidates.append(key) 84 return candidates 85 86def remove_all_weight_norm(model : torch.nn.Module, verbose=False): 87 for name, m in model.named_modules(): 88 candidates = _get_candidates(m) 89 90 for candidate in candidates: 91 try: 92 remove_weight_norm(m, name=candidate) 93 if verbose: print(f'removed weight norm on weight {name}.{candidate}') 94 except: 95 pass 96