That ingredient.capture is from Sacred, a tool for managing experiments. I removed it from the network and still get the same error. dir(model.module) gives the output below. I’m storing the model on the GPU model = torch.nn.DataParallel(model).cuda()

[‘call’, ‘class’, ‘delattr’, ‘dict’, ‘dir’, ‘doc’, ‘eq’, ‘format’, ‘ge’, ‘getattr’, ‘getattribute’, ‘gt’, ‘hash’, ‘init’, ‘le’, ‘lt’, ‘module’, ‘ne’, ‘new’, ‘reduce’, ‘reduce_ex’, ‘repr’, ‘setattr’, ‘setstate’, ‘sizeof’, ‘str’, ‘subclasshook’, ‘weakref’, ‘_all_buffers’, ‘_apply’, ‘_backend’, ‘_backward_hooks’, ‘_buffers’, ‘_forward_hooks’, ‘_forward_pre_hooks’, ‘_modules’, ‘_parameters’, ‘add_module’, ‘apply’, ‘children’, ‘cpu’, ‘cuda’, ‘device_ids’, ‘dim’, ‘double’, ‘dump_patches’, ‘eval’, ‘float’, ‘forward’, ‘gather’, ‘half’, ‘load_state_dict’, ‘module’, ‘modules’, ‘named_children’, ‘named_modules’, ‘named_parameters’, ‘output_device’, ‘parallel_apply’, ‘parameters’, ‘register_backward_hook’, ‘register_buffer’, ‘register_forward_hook’, ‘register_forward_pre_hook’, ‘register_parameter’, ‘replicate’, ‘scatter’, ‘share_memory’, ‘state_dict’, ‘train’, ‘training’, ‘type’, ‘zero_grad’]