@@ -151,13 +151,9 @@ def main():
else:
scaler = None
- best_model_dict = load_model(
- config, model, model_type=config["Architecture"]["model_type"]
- )
- if len(best_model_dict):
- logger.info("metric in ckpt ***************")
- for k, v in best_model_dict.items():
- logger.info("{}:{}".format(k, v))
+ from ais_bench.infer.interface import InferSession
+ om_path = os.path.join(config["Global"].get("pretrained_model", "PP-OCRv5_server_det_infer/inference_linux_aarch64.om"))
+ model = InferSession(0, om_path)
# start eval
metric = program.eval(
@@ -662,55 +662,38 @@ def eval(
amp_custom_white_list=[],
amp_dtype="float16",
):
- model.eval()
- with paddle.no_grad():
- total_frame = 0.0
- total_time = 0.0
- pbar = tqdm(
- total=len(valid_dataloader), desc="eval model:", position=0, leave=True
- )
- max_iter = (
- len(valid_dataloader) - 1
- if platform.system() == "Windows"
- else len(valid_dataloader)
- )
- sum_images = 0
- for idx, batch in enumerate(valid_dataloader):
- if idx >= max_iter:
- break
- images = batch[0]
- start = time.time()
+
+ total_frame = 0.0
+ total_time = 0.0
+ pbar = tqdm(
+ total=len(valid_dataloader), desc="eval model:", position=0, leave=True
+ )
+ max_iter = (
+ len(valid_dataloader) - 1
+ if platform.system() == "Windows"
+ else len(valid_dataloader)
+ )
+ sum_images = 0
+ for idx, batch in enumerate(valid_dataloader):
+ if idx >= max_iter:
+ break
+ images = batch[0]
+ start = time.time()
- # use amp
- if scaler:
- with paddle.amp.auto_cast(
- level=amp_level,
- custom_black_list=amp_custom_black_list,
- dtype=amp_dtype,
- ):
- if model_type == "table" or extra_input:
- preds = model(images, data=batch[1:])
- elif model_type in ["kie"]:
- preds = model(batch)
- elif model_type in ["can"]:
- preds = model(batch[:3])
- elif model_type in ["latexocr"]:
- preds = model(batch)
- elif model_type in ["sr"]:
- preds = model(batch)
- sr_img = preds["sr_img"]
- lr_img = preds["lr_img"]
- else:
- preds = model(images)
- preds = to_float32(preds)
- else:
+ # use amp
+ if scaler:
+ with paddle.amp.auto_cast(
+ level=amp_level,
+ custom_black_list=amp_custom_black_list,
+ dtype=amp_dtype,
+ ):
if model_type == "table" or extra_input:
preds = model(images, data=batch[1:])
elif model_type in ["kie"]:
preds = model(batch)
elif model_type in ["can"]:
preds = model(batch[:3])
- elif model_type in ["latexocr", "unimernet", "pp_formulanet"]:
+ elif model_type in ["latexocr"]:
preds = model(batch)
elif model_type in ["sr"]:
preds = model(batch)
@@ -718,41 +701,58 @@ def eval(
lr_img = preds["lr_img"]
else:
preds = model(images)
-
- batch_numpy = []
- for item in batch:
- if isinstance(item, paddle.Tensor):
- batch_numpy.append(item.numpy())
- else:
- batch_numpy.append(item)
- # Obtain usable results from post-processing methods
- total_time += time.time() - start
- # Evaluate the results of the current batch
- if model_type in ["table", "kie"]:
- if post_process_class is None:
- eval_class(preds, batch_numpy)
- else:
- post_result = post_process_class(preds, batch_numpy)
- eval_class(post_result, batch_numpy)
- elif model_type in ["sr"]:
- eval_class(preds, batch_numpy)
+ preds = to_float32(preds)
+ else:
+ if model_type == "table" or extra_input:
+ preds = model.infer([images.numpy()], mode='dymshape', custom_sizes=1000000000)[0]
+ elif model_type in ["kie"]:
+ preds = model(batch)
elif model_type in ["can"]:
- eval_class(preds[0], batch_numpy[2:], epoch_reset=(idx == 0))
+ preds = model(batch[:3])
elif model_type in ["latexocr", "unimernet", "pp_formulanet"]:
- post_result = post_process_class(preds, batch[1], "eval")
- eval_class(post_result[0], post_result[1], epoch_reset=(idx == 0))
+ preds = model(batch)
+ elif model_type in ["sr"]:
+ preds = model(batch)
+ sr_img = preds["sr_img"]
+ lr_img = preds["lr_img"]
+ else:
+ outputs = model.infer([images.numpy()], mode='dymshape', custom_sizes=1000000000)
+ preds = {}
+ preds["maps"] = outputs[0]
+
+ batch_numpy = []
+ for item in batch:
+ if isinstance(item, paddle.Tensor):
+ batch_numpy.append(item.numpy())
else:
- post_result = post_process_class(preds, batch_numpy[1])
+ batch_numpy.append(item)
+ # Obtain usable results from post-processing methods
+ total_time += time.time() - start
+ # Evaluate the results of the current batch
+ if model_type in ["table", "kie"]:
+ if post_process_class is None:
+ eval_class(preds, batch_numpy)
+ else:
+ post_result = post_process_class(preds, batch_numpy)
eval_class(post_result, batch_numpy)
+ elif model_type in ["sr"]:
+ eval_class(preds, batch_numpy)
+ elif model_type in ["can"]:
+ eval_class(preds[0], batch_numpy[2:], epoch_reset=(idx == 0))
+ elif model_type in ["latexocr", "unimernet", "pp_formulanet"]:
+ post_result = post_process_class(preds, batch[1], "eval")
+ eval_class(post_result[0], post_result[1], epoch_reset=(idx == 0))
+ else:
+ post_result = post_process_class(preds, batch_numpy[1])
+ eval_class(post_result, batch_numpy)
- pbar.update(1)
- total_frame += len(images)
- sum_images += 1
- # Get final metric，eg. acc or hmean
- metric = eval_class.get_metric()
+ pbar.update(1)
+ total_frame += len(images)
+ sum_images += 1
+ # Get final metric，eg. acc or hmean
+ metric = eval_class.get_metric()
pbar.close()
- model.train()
# Avoid ZeroDivisionError
if total_time > 0:
metric["fps"] = total_frame / total_time
