add prune demo

deploy/slim/prune/sensitivity_anal.py

@@ -139,31 +139,6 @@ def main(config, device, logger, vdl_writer):
     program.train(config, train_dataloader, valid_dataloader, device, model,
                   loss_class, optimizer, lr_scheduler, post_process_class,
                   eval_class, pre_best_model_dict, logger, vdl_writer)
-    mode = 'infer'
-    if mode == 'infer':
-        from paddle.jit import to_static
-
-        infer_shape = [3, -1, -1]
-        if config['Architecture']['model_type'] == "rec":
-            infer_shape = [3, 32, -1]  # for rec model, H must be 32
-            if 'Transform' in config['Architecture'] and config['Architecture'][
-                    'Transform'] is not None and config['Architecture'][
-                        'Transform']['name'] == 'TPS':
-                logger.info(
-                    'When there is tps in the network, variable length input is not supported, and the input size needs to be the same as during training'
-                )
-                infer_shape[-1] = 100
-        model = to_static(
-            model,
-            input_spec=[
-                paddle.static.InputSpec(
-                    shape=[None] + infer_shape, dtype='float32')
-            ])
-
-        save_path = '{}/inference'.format(config['Global'][
-            'save_inference_dir'])
-        paddle.jit.save(model, save_path)
-        logger.info('inference model is saved to {}'.format(save_path))
 
 
 if __name__ == '__main__':