nom57
commented
2 years ago https://github.com/autonomousvision/projected_gan/issues/87#issuecomment-1180581191 someone else facing the same issue , they just deleted act1 from projector.py but this is not recommended
Should wait for @xl-sr 's reply on this issue in my opinion, or perhaps @woctezuma 's since he is also very active here.
netram2
I'm getting this error message when running in colab, didn't happen before today. Any ideas?
Constructing networks... Downloading: "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite0-0aa007d2.pth" to /root/.cache/torch/hub/checkpoints/tf_efficientnet_lite0-0aa007d2.pth
AttributeError Traceback (most recent call last)()
21 seed=0,
22 workers=0,
---> 23 restart_every=999999,
24 )
9 frames
/content/projected_gan/training/training_loop.py in training_loop(run_dir, training_set_kwargs, data_loader_kwargs, G_kwargs, D_kwargs, G_opt_kwargs, D_opt_kwargs, loss_kwargs, metrics, random_seed, num_gpus, rank, batch_size, batch_gpu, ema_kimg, ema_rampup, G_reg_interval, D_reg_interval, total_kimg, kimg_per_tick, image_snapshot_ticks, network_snapshot_ticks, resume_pkl, resume_kimg, cudnn_benchmark, abort_fn, progress_fn, restart_every) 159 common_kwargs = dict(c_dim=training_set.label_dim, img_resolution=training_set.resolution, img_channels=training_set.num_channels) 160 G = dnnlib.util.construct_class_by_name(G_kwargs, common_kwargs).train().requiresgrad(False).to(device) # subclass of torch.nn.Module --> 161 D = dnnlib.util.construct_class_by_name(D_kwargs, common_kwargs).train().requiresgrad(False).to(device) # subclass of torch.nn.Module 162 G_ema = copy.deepcopy(G).eval() 163
/content/projected_gan/dnnlib/util.py in construct_class_by_name(class_name, *args, kwargs) 301 def construct_class_by_name(*args, class_name: str = None, *kwargs) -> Any: 302 """Finds the python class with the given name and constructs it with the given arguments.""" --> 303 return call_func_by_name(args, func_name=class_name, kwargs) 304 305
/content/projected_gan/dnnlib/util.py in call_func_by_name(func_name, *args, *kwargs) 296 func_obj = get_obj_by_name(func_name) 297 assert callable(func_obj) --> 298 return func_obj(args, **kwargs) 299 300
/content/projected_gan/pg_modules/discriminator.py in init(self, diffaug, interp224, backbone_kwargs, kwargs) 159 self.diffaug = diffaug 160 self.interp224 = interp224 --> 161 self.feature_network = F_RandomProj(backbone_kwargs) 162 self.discriminator = MultiScaleD( 163 channels=self.feature_network.CHANNELS,
/content/projected_gan/pg_modules/projector.py in init(self, im_res, cout, expand, proj_type, **kwargs) 106 107 # build pretrained feature network and random decoder (scratch) --> 108 self.pretrained, self.scratch = _make_projector(im_res=im_res, cout=self.cout, proj_type=self.proj_type, expand=self.expand) 109 self.CHANNELS = self.pretrained.CHANNELS 110 self.RESOLUTIONS = self.pretrained.RESOLUTIONS
/content/projected_gan/pg_modules/projector.py in _make_projector(im_res, cout, proj_type, expand) 62 ### Build pretrained feature network 63 model = timm.create_model('tf_efficientnet_lite0', pretrained=True) ---> 64 pretrained = _make_efficientnet(model) 65 66 # determine resolution of feature maps, this is later used to calculate the number
/content/projected_gan/pg_modules/projector.py in _make_efficientnet(model) 33 def _make_efficientnet(model): 34 pretrained = nn.Module() ---> 35 pretrained.layer0 = nn.Sequential(model.conv_stem, model.bn1, model.act1, model.blocks[0:2]) 36 pretrained.layer1 = nn.Sequential(model.blocks[2:3]) 37 pretrained.layer2 = nn.Sequential(*model.blocks[3:5])
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in getattr(self, name) 1184 return modules[name] 1185 raise AttributeError("'{}' object has no attribute '{}'".format( -> 1186 type(self).name, name)) 1187 1188 def setattr(self, name: str, value: Union[Tensor, 'Module']) -> None:
AttributeError: 'EfficientNet' object has no attribute 'act1'