update DARTS-V1
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		| @@ -20,7 +20,7 @@ from models       import get_cell_based_tiny_net, get_search_spaces | ||||
| from nas_201_api  import NASBench201API as API | ||||
|  | ||||
|  | ||||
| def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, epoch_str, print_freq, logger): | ||||
| def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, epoch_str, print_freq, logger, gradient_clip): | ||||
|   data_time, batch_time = AverageMeter(), AverageMeter() | ||||
|   base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter() | ||||
|   arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter() | ||||
| @@ -38,7 +38,7 @@ def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer | ||||
|     _, logits = network(base_inputs) | ||||
|     base_loss = criterion(logits, base_targets) | ||||
|     base_loss.backward() | ||||
|     torch.nn.utils.clip_grad_norm_(network.parameters(), 5) | ||||
|     if gradient_clip > 0: torch.nn.utils.clip_grad_norm_(network.parameters(), gradient_clip) | ||||
|     w_optimizer.step() | ||||
|     # record | ||||
|     base_prec1, base_prec5 = obtain_accuracy(logits.data, base_targets.data, topk=(1, 5)) | ||||
| @@ -165,7 +165,7 @@ def main(xargs): | ||||
|     epoch_str = '{:03d}-{:03d}'.format(epoch, total_epoch) | ||||
|     logger.log('\n[Search the {:}-th epoch] {:}, LR={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr()))) | ||||
|  | ||||
|     search_w_loss, search_w_top1, search_w_top5 = search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, logger) | ||||
|     search_w_loss, search_w_top1, search_w_top5 = search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, logger, xargs.gradient_clip) | ||||
|     search_time.update(time.time() - start_time) | ||||
|     logger.log('[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s'.format(epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum)) | ||||
|     valid_a_loss , valid_a_top1 , valid_a_top5  = valid_func(valid_loader, network, criterion) | ||||
| @@ -225,6 +225,7 @@ if __name__ == '__main__': | ||||
|   parser.add_argument('--track_running_stats',type=int,   choices=[0,1],help='Whether use track_running_stats or not in the BN layer.') | ||||
|   parser.add_argument('--config_path',        type=str,   help='The config path.') | ||||
|   parser.add_argument('--model_config',       type=str,   help='The path of the model configuration. When this arg is set, it will cover max_nodes / channels / num_cells.') | ||||
|   parser.add_argument('--gradient_clip',      type=float, default=5, help='') | ||||
|   # architecture leraning rate | ||||
|   parser.add_argument('--arch_learning_rate', type=float, default=3e-4, help='learning rate for arch encoding') | ||||
|   parser.add_argument('--arch_weight_decay',  type=float, default=1e-3, help='weight decay for arch encoding') | ||||
|   | ||||
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