【PPSCI Export&Infer No.21】tempoGAN

docs/zh/examples/tempoGAN.md

@@ -26,6 +26,22 @@
  python tempoGAN.py mode=eval EVAL.pretrained_model_path=https://paddle-org.bj.bcebos.com/paddlescience/models/tempoGAN/tempogan_pretrained.pdparams
  ```
 
+=== "模型导出命令"
+
+ ``` sh
+ python tempoGAN.py mode=export
+ ```
+
+=== "模型推理命令"
+
+ ``` sh
+ # linux
+ wget -nc https://paddle-org.bj.bcebos.com/paddlescience/datasets/tempoGAN/2d_valid.mat -P datasets/tempoGAN/
+ # windows
+ # curl https://paddle-org.bj.bcebos.com/paddlescience/datasets/tempoGAN/2d_valid.mat --output datasets/tempoGAN/2d_valid.mat
+ python tempoGAN.py mode=infer
+ ```
+
 | 预训练模型 | 指标 |
 |:--| :--|
 | [tempogan_pretrained.pdparams](https://paddle-org.bj.bcebos.com/paddlescience/models/tempoGAN/tempogan_pretrained.pdparams) | MSE: 4.21e-5<br>PSNR: 47.19<br>SSIM: 0.9974 |

examples/tempoGAN/conf/tempogan.yaml

@@ -11,6 +11,8 @@ hydra:
  - TRAIN.checkpoint_path
  - TRAIN.pretrained_model_path
  - EVAL.pretrained_model_path
+ - INFER.pretrained_model_path
+ - INFER.export_path
  - mode
  - output_dir
  - log_freq
@@ -92,3 +94,20 @@ TRAIN:
 EVAL:
  pretrained_model_path: null
  save_outs: true
+
+INFER:
+ pretrained_model_path: https://paddle-org.bj.bcebos.com/paddlescience/models/tempoGAN/tempogan_pretrained.pdparams
+ export_path: ./inference/tempoGAN
+ pdmodel_path: ${INFER.export_path}.pdmodel
+ pdpiparams_path: ${INFER.export_path}.pdiparams
+ device: gpu
+ engine: native
+ precision: fp32
+ onnx_path: ${INFER.export_path}.onnx
+ ir_optim: true
+ min_subgraph_size: 10
+ gpu_mem: 4000
+ gpu_id: 0
+ max_batch_size: 16
+ num_cpu_threads: 4
+ batch_size: 1

examples/tempoGAN/tempoGAN.py

@@ -406,14 +406,77 @@ def scale(data):
  )
 
 
+def export(cfg: DictConfig):
+ from paddle.static import InputSpec
+
+ # set models
+ gen_funcs = func_module.GenFuncs(cfg.WEIGHT_GEN, None)
+ model_gen = ppsci.arch.Generator(**cfg.MODEL.gen_net)
+ model_gen.register_input_transform(gen_funcs.transform_in)
+
+ # define model_list
+ model_list = ppsci.arch.ModelList((model_gen,))
+
+ # load pretrained model
+ solver = ppsci.solver.Solver(
+ model=model_list, pretrained_model_path=cfg.INFER.pretrained_model_path
+ )
+
+ # export models
+ input_spec = [
+ {"density_low": InputSpec([None, 1, 128, 128], "float32", name="density_low")},
+ ]
+ solver.export(input_spec, cfg.INFER.export_path, skip_prune_program=True)
+
+
+def inference(cfg: DictConfig):
+ from matplotlib import image as Img
+
+ from deploy.python_infer import pinn_predictor
+
+ # set model predictor
+ predictor = pinn_predictor.PINNPredictor(cfg)
+
+ # load dataset
+ dataset_infer = {
+ "density_low": hdf5storage.loadmat(cfg.DATASET_PATH_VALID)["density_low"]
+ }
+
+ output_dict = predictor.predict(dataset_infer, cfg.INFER.batch_size)
+
+ # mapping data to cfg.INFER.output_keys
+ output = [output_dict[key] for key in output_dict]
+
+ def scale(data):
+ smax = np.max(data)
+ smin = np.min(data)
+ return (data - smin) / (smax - smin)
+
+ for i, img in enumerate(output[0]):
+ img = scale(np.squeeze(img))
+ Img.imsave(
+ osp.join(cfg.output_dir, f"out_{i}.png"),
+ img,
+ vmin=0.0,
+ vmax=1.0,
+ cmap="gray",
+ )
+
+
 @hydra.main(version_base=None, config_path="./conf", config_name="tempogan.yaml")
 def main(cfg: DictConfig):
  if cfg.mode == "train":
  train(cfg)
  elif cfg.mode == "eval":
  evaluate(cfg)
+ elif cfg.mode == "export":
+ export(cfg)
+ elif cfg.mode == "infer":
+ inference(cfg)
  else:
- raise ValueError(f"cfg.mode should in ['train', 'eval'], but got '{cfg.mode}'")
+ raise ValueError(
+ f"cfg.mode should in ['train', 'eval', 'export', 'infer'], but got '{cfg.mode}'"
+ )
 
 
 if __name__ == "__main__":