diff --git a/README_ch.md b/README_ch.md index 74b71bbeec06c9a207291a19961077ef141841a0..b3a0011e5481a296985be60a52968d99e28d996e 100644 --- a/README_ch.md +++ b/README_ch.md @@ -7,7 +7,7 @@ 飞桨图像识别套件PaddleClas是飞桨为工业界和学术界所准备的一个图像识别任务的工具集，助力使用者训练出更好的视觉模型和应用落地。 **近期更新** - +- 2022.4.21 新增 CVPR2022 oral论文 [MixFormmer](https://arxiv.org/pdf/2204.02557.pdf) 相关[代码](https://github.com/PaddlePaddle/PaddleClas/pull/1820/files)。 - 2022.1.27 全面升级文档；新增[PaddleServing C++ pipeline部署方式](./deploy/paddleserving)和[18M图像识别安卓部署Demo](./deploy/lite_shitu)。 - 2021.11.1 发布[PP-ShiTu技术报告](https://arxiv.org/pdf/2111.00775.pdf)，新增饮料识别demo - 2021.10.23 发布轻量级图像识别系统PP-ShiTu，CPU上0.2s即可完成在10w+库的图像识别。 @@ -35,10 +35,11 @@ Res2Net200_vd预训练模型Top-1精度高达85.1%。 ## 欢迎加入技术交流群 -* 您可以扫描下面的微信群二维码， 加入PaddleClas 微信交流群。获得更高效的问题答疑，与各行各业开发者充分交流，期待您的加入。 +* 您可以扫描下面的QQ/微信二维码（添加小助手微信并回复“C”），加入PaddleClas微信交流群，获得更高效的问题答疑，与各行各业开发者充分交流，期待您的加入。
- + +
## 快速体验 diff --git a/README_en.md b/README_en.md index 57b809d6be08d102d5f678d53a630023b21f758d..7ea40c63ddd84882079c36ad59ff4f67682df414 100644 --- a/README_en.md +++ b/README_en.md @@ -8,6 +8,8 @@ PaddleClas is an image recognition toolset for industry and academia, helping us **Recent updates** +- 2022.4.21 Added the related [code](https://github.com/PaddlePaddle/PaddleClas/pull/1820/files) of the CVPR2022 oral paper [MixFormmer](https://arxiv.org/pdf/2204.02557.pdf). + - 2021.09.17 Add PP-LCNet series model developed by PaddleClas, these models show strong competitiveness on Intel CPUs. For the introduction of PP-LCNet, please refer to [paper](https://arxiv.org/pdf/2109.15099.pdf) or [PP-LCNet model introduction](docs/en/models/PP-LCNet_en.md). The metrics and pretrained model are available [here](docs/en/ImageNet_models_en.md). @@ -38,10 +40,11 @@ Four sample solutions are provided, including product recognition, vehicle recog ## Welcome to Join the Technical Exchange Group -* You can also scan the QR code below to join the PaddleClas WeChat group to get more efficient answers to your questions and to communicate with developers from all walks of life. We look forward to hearing from you. +* You can also scan the QR code below to join the PaddleClas QQ group and WeChat group (add and replay "C") to get more efficient answers to your questions and to communicate with developers from all walks of life. We look forward to hearing from you.
- + +
## Quick Start diff --git a/docs/en/quick_start/quick_start_classification_professional_en.md b/docs/en/quick_start/quick_start_classification_professional_en.md index 10c2174063c9914ac2f97c03ce5bb789a116a9d9..840643aafe4e9d432331910fabbb42978105e645 100644 --- a/docs/en/quick_start/quick_start_classification_professional_en.md +++ b/docs/en/quick_start/quick_start_classification_professional_en.md @@ -75,9 +75,23 @@ python3 -m paddle.distributed.launch \ The highest accuracy of the validation set is around 0.415. -* ** Note** +Here, multiple GPUs are used for training. If only one GPU is used, please specify the GPU with the `CUDA_VISIBLE_DEVICES` setting, and specify the GPU with the `--gpus` setting, the same below. For example, to train with only GPU 0: + +```shell +export CUDA_VISIBLE_DEVICES=0 +python3 -m paddle.distributed.launch \ + --gpus="0" \ + tools/train.py \ + -c ./ppcls/configs/quick_start/professional/ResNet50_vd_CIFAR100.yaml \ + -o Global.output_dir="output_CIFAR" \ + -o Optimizer.lr.learning_rate=0.01 +``` + +* **Notice**: + +* The GPUs specified in `--gpus` can be a subset of the GPUs specified in `CUDA_VISIBLE_DEVICES`. +* Since the initial learning rate and batch-size need to maintain a linear relationship, when training is switched from 4 GPUs to 1 GPU, the total batch-size is reduced to 1/4 of the original, and the learning rate also needs to be reduced to 1/4 of the original, so changed the default learning rate from 0.04 to 0.01. - * If the number of GPU cards is not 4, the accuracy of the validation set may be different from 0.415. To maintain a comparable accuracy, you need to change the learning rate in the configuration file to `the current learning rate / 4 \* current card number`. The same below. diff --git a/docs/zh_CN/quick_start/quick_start_classification_professional.md b/docs/zh_CN/quick_start/quick_start_classification_professional.md index 4564b059f54034db346c9ae03d4bc2cbb8a8a1e4..9a44d939ce999e291120c006801b374d302ab8ba 100644 --- a/docs/zh_CN/quick_start/quick_start_classification_professional.md +++ b/docs/zh_CN/quick_start/quick_start_classification_professional.md @@ -75,9 +75,22 @@ python3 -m paddle.distributed.launch \ 验证集的最高准确率为 0.415 左右。 -* **注意** +此处使用了多个 GPU 训练，如果只使用一个 GPU，请将 `CUDA_VISIBLE_DEVICES` 设置指定 GPU，`--gpus`设置指定 GPU，下同。例如，只使用 0 号 GPU 训练： + +```shell +export CUDA_VISIBLE_DEVICES=0 +python3 -m paddle.distributed.launch \ + --gpus="0" \ + tools/train.py \ + -c ./ppcls/configs/quick_start/professional/ResNet50_vd_CIFAR100.yaml \ + -o Global.output_dir="output_CIFAR" \ + -o Optimizer.lr.learning_rate=0.01 +``` + +* **注意**: - * 如果 GPU 卡数不是 4，验证集的准确率可能与 0.415 有差异，若需保持相当的准确率，需要将配置文件中的学习率改为`当前学习率 / 4 \* 当前卡数`。下同。 +* `--gpus`中指定的 GPU 可以是 `CUDA_VISIBLE_DEVICES` 指定的 GPU 的子集。 +* 由于初始学习率和 batch-size 需要保持线性关系，所以训练从 4 个 GPU 切换到 1 个 GPU 训练时，总 batch-size 缩减为原来的 1/4，学习率也需要缩减为原来的 1/4，所以改变了默认的学习率从 0.04 到 0.01。 @@ -157,7 +170,7 @@ python3 -m paddle.distributed.launch \ * **注意** * 其他数据增广的配置文件可以参考 `ppcls/configs/ImageNet/DataAugment/` 中的配置文件。 - * 训练 CIFAR100 的迭代轮数较少，因此进行训练时，验证集的精度指标可能会有 1% 左右的波动。 +* 训练 CIFAR100 的迭代轮数较少，因此进行训练时，验证集的精度指标可能会有 1% 左右的波动。 diff --git a/ppcls/data/dataloader/imagenet_dataset.py b/ppcls/data/dataloader/imagenet_dataset.py index 1166ab3851b0c594469f135c1c4b3c7bc921ac5f..0e2b1c8a0b4cbc7e2f251ed2363fca71ec3239e7 100644 --- a/ppcls/data/dataloader/imagenet_dataset.py +++ b/ppcls/data/dataloader/imagenet_dataset.py @@ -21,6 +21,15 @@ from .common_dataset import CommonDataset class ImageNetDataset(CommonDataset): + def __init__( + self, + image_root, + cls_label_path, + transform_ops=None, + delimiter=None): + self.delimiter = delimiter if delimiter is not None else " " + super(ImageNetDataset, self).__init__(image_root, cls_label_path, transform_ops) + def _load_anno(self, seed=None): assert os.path.exists(self._cls_path) assert os.path.exists(self._img_root) @@ -32,7 +41,7 @@ class ImageNetDataset(CommonDataset): if seed is not None: np.random.RandomState(seed).shuffle(lines) for l in lines: - l = l.strip().split(" ") + l = l.strip().split(self.delimiter) self.images.append(os.path.join(self._img_root, l[0])) self.labels.append(np.int64(l[1])) assert os.path.exists(self.images[-1]) diff --git a/ppcls/data/postprocess/topk.py b/ppcls/data/postprocess/topk.py index 0dde72aa11274996087156b2e026447c340f421a..df02719471300ea8e2b7c1db286d104adabe116f 100644 --- a/ppcls/data/postprocess/topk.py +++ b/ppcls/data/postprocess/topk.py @@ -19,10 +19,11 @@ import paddle.nn.functional as F class Topk(object): - def __init__(self, topk=1, class_id_map_file=None): + def __init__(self, topk=1, class_id_map_file=None, delimiter=None): assert isinstance(topk, (int, )) self.class_id_map = self.parse_class_id_map(class_id_map_file) self.topk = topk + self.delimiter = delimiter if delimiter is not None else " " def parse_class_id_map(self, class_id_map_file): if class_id_map_file is None: @@ -38,7 +39,7 @@ class Topk(object): with open(class_id_map_file, "r") as fin: lines = fin.readlines() for line in lines: - partition = line.split("\n")[0].partition(" ") + partition = line.split("\n")[0].partition(self.delimiter) class_id_map[int(partition[0])] = str(partition[-1]) except Exception as ex: print(ex) diff --git a/ppcls/engine/engine.py b/ppcls/engine/engine.py index 20bdb75571a00fe4b0bc37c0e13de1df7ab011eb..607bfb9d68c77f4ffc2ee732314cd3e0ced364cc 100644 --- a/ppcls/engine/engine.py +++ b/ppcls/engine/engine.py @@ -224,7 +224,7 @@ class Engine(object): # build optimizer if self.mode == 'train': self.optimizer, self.lr_sch = build_optimizer( - self.config, self.config["Global"]["epochs"], + self.config["Optimizer"], self.config["Global"]["epochs"], len(self.train_dataloader), [self.model, self.train_loss_func]) diff --git a/ppcls/engine/train/utils.py b/ppcls/engine/train/utils.py index 7f64104da9b745e020c70b5804329ca96a6f35df..ca211ff932f19ca63804a5a1ff52def5eb89477f 100644 --- a/ppcls/engine/train/utils.py +++ b/ppcls/engine/train/utils.py @@ -39,7 +39,7 @@ def update_loss(trainer, loss_dict, batch_size): def log_info(trainer, batch_size, epoch_id, iter_id): lr_msg = ", ".join([ - "lr_{}: {:.8f}".format(i + 1, lr.get_lr()) + "lr({}): {:.8f}".format(lr.__class__.__name__, lr.get_lr()) for i, lr in enumerate(trainer.lr_sch) ]) metric_msg = ", ".join([ @@ -64,7 +64,7 @@ def log_info(trainer, batch_size, epoch_id, iter_id): for i, lr in enumerate(trainer.lr_sch): logger.scaler( - name="lr_{}".format(i + 1), + name="lr({})".format(lr.__class__.__name__), value=lr.get_lr(), step=trainer.global_step, writer=trainer.vdl_writer) diff --git a/ppcls/optimizer/__init__.py b/ppcls/optimizer/__init__.py index b0a6e620ce40a74ef531613846183a5d18ba4bee..67b090fe1eac509928d95666f4c5bc900b342e1d 100644 --- a/ppcls/optimizer/__init__.py +++ b/ppcls/optimizer/__init__.py @@ -44,8 +44,7 @@ def build_lr_scheduler(lr_config, epochs, step_each_epoch): # model_list is None in static graph def build_optimizer(config, epochs, step_each_epoch, model_list=None): - config = copy.deepcopy(config) - optim_config = config["Optimizer"] + optim_config = copy.deepcopy(config) if isinstance(optim_config, dict): # convert {'name': xxx, **optim_cfg} to [{'name': {'scope': xxx, **optim_cfg}}] optim_name = optim_config.pop("name") @@ -61,19 +60,20 @@ def build_optimizer(config, epochs, step_each_epoch, model_list=None): """NOTE: Currently only support optim objets below. 1. single optimizer config. - 2. next level uner Arch, such as Arch.backbone, Arch.neck, Arch.head. - 3. loss which has parameters, such as CenterLoss. + 2. model(entire Arch), backbone, neck, head. + 3. loss(entire Loss), specific loss listed in ppcls/loss/__init__.py. """ for optim_item in optim_config: # optim_cfg = {optim_name: {'scope': xxx, **optim_cfg}} # step1 build lr optim_name = list(optim_item.keys())[0] # get optim_name - optim_scope = optim_item[optim_name].pop('scope') # get optim_scope + optim_scope_list = optim_item[optim_name].pop('scope').split( + ' ') # get optim_scope list optim_cfg = optim_item[optim_name] # get optim_cfg lr = build_lr_scheduler(optim_cfg.pop('lr'), epochs, step_each_epoch) logger.info("build lr ({}) for scope ({}) success..".format( - lr.__class__.__name__, optim_scope)) + lr.__class__.__name__, optim_scope_list)) # step2 build regularization if 'regularizer' in optim_cfg and optim_cfg['regularizer'] is not None: if 'weight_decay' in optim_cfg: @@ -85,46 +85,53 @@ def build_optimizer(config, epochs, step_each_epoch, model_list=None): reg = getattr(paddle.regularizer, reg_name)(**reg_config) optim_cfg["weight_decay"] = reg logger.info("build regularizer ({}) for scope ({}) success..". - format(reg.__class__.__name__, optim_scope)) + format(reg.__class__.__name__, optim_scope_list)) # step3 build optimizer if 'clip_norm' in optim_cfg: clip_norm = optim_cfg.pop('clip_norm') grad_clip = paddle.nn.ClipGradByNorm(clip_norm=clip_norm) + logger.info("build gradclip ({}) for scope ({}) success..".format( + grad_clip.__class__.__name__, optim_scope_list)) else: grad_clip = None optim_model = [] - for i in range(len(model_list)): - if len(model_list[i].parameters()) == 0: - continue - if optim_scope == "all": - # optimizer for all - optim_model.append(model_list[i]) - else: - if "Loss" in optim_scope: - # optimizer for loss - if hasattr(model_list[i], 'loss_func'): - for j in range(len(model_list[i].loss_func)): - if model_list[i].loss_func[ - j].__class__.__name__ == optim_scope: - optim_model.append(model_list[i].loss_func[j]) - elif optim_scope == "model": - # opmizer for entire model - if not model_list[i].__class__.__name__.lower().endswith( - "loss"): - optim_model.append(model_list[i]) - else: - # opmizer for module in model, such as backbone, neck, head... - if hasattr(model_list[i], optim_scope): - optim_model.append(getattr(model_list[i], optim_scope)) - assert len(optim_model) == 1, \ - "Invalid optim model for optim scope({}), number of optim_model={}".\ - format(optim_scope, [m.__class__.__name__ for m in optim_model]) + # for static graph + if model_list is None: + optim = getattr(optimizer, optim_name)( + learning_rate=lr, grad_clip=grad_clip, + **optim_cfg)(model_list=optim_model) + return optim, lr + + # for dynamic graph + for scope in optim_scope_list: + if scope == "all": + optim_model += model_list + elif scope == "model": + optim_model += [model_list[0], ] + elif scope in ["backbone", "neck", "head"]: + optim_model += [getattr(model_list[0], scope, None), ] + elif scope == "loss": + optim_model += [model_list[1], ] + else: + optim_model += [ + model_list[1].loss_func[i] + for i in range(len(model_list[1].loss_func)) + if model_list[1].loss_func[i].__class__.__name__ == scope + ] + # remove invalid items + optim_model = [ + optim_model[i] for i in range(len(optim_model)) + if (optim_model[i] is not None + ) and (len(optim_model[i].parameters()) > 0) + ] + assert len(optim_model) > 0, \ + f"optim_model is empty for optim_scope({optim_scope_list})" optim = getattr(optimizer, optim_name)( learning_rate=lr, grad_clip=grad_clip, **optim_cfg)(model_list=optim_model) logger.info("build optimizer ({}) for scope ({}) success..".format( - optim.__class__.__name__, optim_scope)) + optim.__class__.__name__, optim_scope_list)) optim_list.append(optim) lr_list.append(lr) return optim_list, lr_list diff --git a/test_tipc/config/CSWinTransformer/CSWinTransformer_base_224_train_infer_python.txt b/test_tipc/config/CSWinTransformer/CSWinTransformer_base_224_train_infer_python.txt index f290c63b7775743f56df7ad8bc71e43aabcfc2c2..3de6add85078c13a323e0f62a8d805880f189c6c 100644 --- a/test_tipc/config/CSWinTransformer/CSWinTransformer_base_224_train_infer_python.txt +++ b/test_tipc/config/CSWinTransformer/CSWinTransformer_base_224_train_infer_python.txt @@ -50,9 +50,5 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran -o Global.benchmark:True null:null null:null -===========================train_benchmark_params========================== -batch_size:32 -fp_items:fp32 -epoch:1 ---profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile -flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 +===========================infer_benchmark_params========================== +random_infer_input:[{float32,[3,224,224]}] diff --git a/test_tipc/config/CSWinTransformer/CSWinTransformer_base_384_train_infer_python.txt b/test_tipc/config/CSWinTransformer/CSWinTransformer_base_384_train_infer_python.txt index 8b689e04db0ffbc2eb935bb95cd80870d21c86ac..3a913e253ee31e04d5c3d86b3be34aeb0e777949 100644 --- a/test_tipc/config/CSWinTransformer/CSWinTransformer_base_384_train_infer_python.txt +++ b/test_tipc/config/CSWinTransformer/CSWinTransformer_base_384_train_infer_python.txt @@ -50,9 +50,5 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran -o Global.benchmark:True null:null null:null -===========================train_benchmark_params========================== -batch_size:32 -fp_items:fp32 -epoch:1 ---profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile -flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 +===========================infer_benchmark_params========================== +random_infer_input:[{float32,[3,384,384]}] diff --git a/test_tipc/config/CSWinTransformer/CSWinTransformer_large_224_train_infer_python.txt b/test_tipc/config/CSWinTransformer/CSWinTransformer_large_224_train_infer_python.txt index 2088b195656ba2c52a1379ddc63a1c315d3fc21c..d376d4cba38f52522fb7ecc9a93e38bef1b7b710 100644 --- a/test_tipc/config/CSWinTransformer/CSWinTransformer_large_224_train_infer_python.txt +++ b/test_tipc/config/CSWinTransformer/CSWinTransformer_large_224_train_infer_python.txt @@ -50,9 +50,5 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran -o Global.benchmark:True null:null null:null -===========================train_benchmark_params========================== -batch_size:32 -fp_items:fp32 -epoch:1 ---profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile -flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 +===========================infer_benchmark_params========================== +random_infer_input:[{float32,[3,224,224]}] diff --git a/test_tipc/config/CSWinTransformer/CSWinTransformer_large_384_train_infer_python.txt b/test_tipc/config/CSWinTransformer/CSWinTransformer_large_384_train_infer_python.txt index 5355648b6dae6d3d5f62b8bc27a1d8bcb55947db..781fefe58dbe18b1bbf89026b59b95c995ecf0c5 100644 --- a/test_tipc/config/CSWinTransformer/CSWinTransformer_large_384_train_infer_python.txt +++ b/test_tipc/config/CSWinTransformer/CSWinTransformer_large_384_train_infer_python.txt @@ -50,9 +50,5 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran -o Global.benchmark:True null:null null:null -===========================train_benchmark_params========================== -batch_size:8 -fp_items:fp32 -epoch:1 ---profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile -flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 +===========================infer_benchmark_params========================== +random_infer_input:[{float32,[3,384,384]}] diff --git a/test_tipc/config/CSWinTransformer/CSWinTransformer_small_224_train_infer_python.txt b/test_tipc/config/CSWinTransformer/CSWinTransformer_small_224_train_infer_python.txt index cf2996a8f93587c127d26a83809edc3d2aa073c2..8057bb84b766e0f618b93a85f3f69f82fc8fa712 100644 --- a/test_tipc/config/CSWinTransformer/CSWinTransformer_small_224_train_infer_python.txt +++ b/test_tipc/config/CSWinTransformer/CSWinTransformer_small_224_train_infer_python.txt @@ -50,9 +50,5 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran -o Global.benchmark:True null:null null:null -===========================train_benchmark_params========================== -batch_size:64 -fp_items:fp32 -epoch:1 ---profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile -flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 +===========================infer_benchmark_params========================== +random_infer_input:[{float32,[3,224,224]}] diff --git a/test_tipc/config/CSWinTransformer/CSWinTransformer_tiny_224_train_infer_python.txt b/test_tipc/config/CSWinTransformer/CSWinTransformer_tiny_224_train_infer_python.txt index a6bacbd3fa0e5b0c79a4887af0959c3db30a35a9..11b2f9dd9eb100c43a87f08192606310c7b555de 100644 --- a/test_tipc/config/CSWinTransformer/CSWinTransformer_tiny_224_train_infer_python.txt +++ b/test_tipc/config/CSWinTransformer/CSWinTransformer_tiny_224_train_infer_python.txt @@ -56,3 +56,5 @@ fp_items:fp32 epoch:1 --profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 +===========================infer_benchmark_params========================== +random_infer_input:[{float32,[3,224,224]}] diff --git a/test_tipc/config/MobileViT/MobileViT_S_train_infer_python.txt b/test_tipc/config/MobileViT/MobileViT_S_train_infer_python.txt index d45bbb9960325cf9745bda3e63fa3655527d1c9d..d69f26412fbd82c89bdef6095aca27d20df33903 100644 --- a/test_tipc/config/MobileViT/MobileViT_S_train_infer_python.txt +++ b/test_tipc/config/MobileViT/MobileViT_S_train_infer_python.txt @@ -56,3 +56,5 @@ fp_items:fp32 epoch:1 --profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 +===========================infer_benchmark_params========================== +random_infer_input:[{float32,[3,256,256]}] diff --git a/test_tipc/config/MobileViT/MobileViT_XS_train_infer_python.txt b/test_tipc/config/MobileViT/MobileViT_XS_train_infer_python.txt index a366d42c44832ab30a1c5f9f1b2b7eea36be63fe..b7436adaaac2a94df9692845b92bc5c46887ba62 100644 --- a/test_tipc/config/MobileViT/MobileViT_XS_train_infer_python.txt +++ b/test_tipc/config/MobileViT/MobileViT_XS_train_infer_python.txt @@ -50,9 +50,5 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran -o Global.benchmark:True null:null null:null -===========================train_benchmark_params========================== -batch_size:128 -fp_items:fp32 -epoch:1 ---profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile -flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 +===========================infer_benchmark_params========================== +random_infer_input:[{float32,[3,256,256]}] diff --git a/test_tipc/config/MobileViT/MobileViT_XXS_train_infer_python.txt b/test_tipc/config/MobileViT/MobileViT_XXS_train_infer_python.txt index cc7169416d858dc8b525ad29d4ff1fc8683dc981..55cb442148ece745be4e2de6d95e2bcaf5fb0074 100644 --- a/test_tipc/config/MobileViT/MobileViT_XXS_train_infer_python.txt +++ b/test_tipc/config/MobileViT/MobileViT_XXS_train_infer_python.txt @@ -50,9 +50,5 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran -o Global.benchmark:True null:null null:null -===========================train_benchmark_params========================== -batch_size:128 -fp_items:fp32 -epoch:1 ---profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile -flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 +===========================infer_benchmark_params========================== +random_infer_input:[{float32,[3,256,256]}] diff --git a/test_tipc/config/PVTV2/PVT_V2_B0.txt b/test_tipc/config/PVTV2/PVT_V2_B0_train_infer_python.txt similarity index 85% rename from test_tipc/config/PVTV2/PVT_V2_B0.txt rename to test_tipc/config/PVTV2/PVT_V2_B0_train_infer_python.txt index 522634a5a34a961abd061d4dd8b2c7736406f7a6..a4229d854a9706cfa9bd0baeb18c6c59803197ce 100644 --- a/test_tipc/config/PVTV2/PVT_V2_B0.txt +++ b/test_tipc/config/PVTV2/PVT_V2_B0_train_infer_python.txt @@ -50,9 +50,5 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran -o Global.benchmark:True null:null null:null -===========================train_benchmark_params========================== -batch_size:128 -fp_items:fp32 -epoch:1 ---profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile -flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 +===========================infer_benchmark_params========================== +random_infer_input:[{float32,[3,224,224]}] \ No newline at end of file diff --git a/test_tipc/config/PVTV2/PVT_V2_B1.txt b/test_tipc/config/PVTV2/PVT_V2_B1_train_infer_python.txt similarity index 85% rename from test_tipc/config/PVTV2/PVT_V2_B1.txt rename to test_tipc/config/PVTV2/PVT_V2_B1_train_infer_python.txt index 4039d51e12e7356a4d92908a840fcabc837f59c9..5394790a03b68366a83b4ea2fba3e67c988d7f1e 100644 --- a/test_tipc/config/PVTV2/PVT_V2_B1.txt +++ b/test_tipc/config/PVTV2/PVT_V2_B1_train_infer_python.txt @@ -50,9 +50,5 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran -o Global.benchmark:True null:null null:null -===========================train_benchmark_params========================== -batch_size:128 -fp_items:fp32 -epoch:1 ---profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile -flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 +===========================infer_benchmark_params========================== +random_infer_input:[{float32,[3,224,224]}] \ No newline at end of file diff --git a/test_tipc/config/PVTV2/PVT_V2_B2_Linear.txt b/test_tipc/config/PVTV2/PVT_V2_B2_Linear_train_infer_python.txt similarity index 94% rename from test_tipc/config/PVTV2/PVT_V2_B2_Linear.txt rename to test_tipc/config/PVTV2/PVT_V2_B2_Linear_train_infer_python.txt index bf825ddfee4b436999773042f0350d68dce91521..b2aa7df69f36f797f26ab985f379634952fa45d3 100644 --- a/test_tipc/config/PVTV2/PVT_V2_B2_Linear.txt +++ b/test_tipc/config/PVTV2/PVT_V2_B2_Linear_train_infer_python.txt @@ -56,3 +56,5 @@ fp_items:fp32 epoch:1 --profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 +===========================infer_benchmark_params========================== +random_infer_input:[{float32,[3,224,224]}] diff --git a/test_tipc/config/PVTV2/PVT_V2_B2.txt b/test_tipc/config/PVTV2/PVT_V2_B2_train_infer_python.txt similarity index 85% rename from test_tipc/config/PVTV2/PVT_V2_B2.txt rename to test_tipc/config/PVTV2/PVT_V2_B2_train_infer_python.txt index 6a5d36a22684eab302ae30902bc4ab5a6f989296..d2de07223364b1eb78597c7de97e8a10725bbc18 100644 --- a/test_tipc/config/PVTV2/PVT_V2_B2.txt +++ b/test_tipc/config/PVTV2/PVT_V2_B2_train_infer_python.txt @@ -50,9 +50,5 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran -o Global.benchmark:True null:null null:null -===========================train_benchmark_params========================== -batch_size:128 -fp_items:fp32 -epoch:1 ---profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile -flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 +===========================infer_benchmark_params========================== +random_infer_input:[{float32,[3,224,224]}] diff --git a/test_tipc/config/PVTV2/PVT_V2_B3.txt b/test_tipc/config/PVTV2/PVT_V2_B3_train_infer_python.txt similarity index 85% rename from test_tipc/config/PVTV2/PVT_V2_B3.txt rename to test_tipc/config/PVTV2/PVT_V2_B3_train_infer_python.txt index dc46216d637cc50ab9925546b4385d149b374847..8f71e148fdf61147d6b4c5acdd818e8051222409 100644 --- a/test_tipc/config/PVTV2/PVT_V2_B3.txt +++ b/test_tipc/config/PVTV2/PVT_V2_B3_train_infer_python.txt @@ -50,9 +50,5 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran -o Global.benchmark:True null:null null:null -===========================train_benchmark_params========================== -batch_size:128 -fp_items:fp32 -epoch:1 ---profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile -flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 +===========================infer_benchmark_params========================== +random_infer_input:[{float32,[3,224,224]}] diff --git a/test_tipc/config/PVTV2/PVT_V2_B4.txt b/test_tipc/config/PVTV2/PVT_V2_B4_train_infer_python.txt similarity index 85% rename from test_tipc/config/PVTV2/PVT_V2_B4.txt rename to test_tipc/config/PVTV2/PVT_V2_B4_train_infer_python.txt index 92793a43d154765ea1be88c66a7fc1fc2ddf166f..304deb6872641c7cb5ae51ccc1aa241fc621e660 100644 --- a/test_tipc/config/PVTV2/PVT_V2_B4.txt +++ b/test_tipc/config/PVTV2/PVT_V2_B4_train_infer_python.txt @@ -50,9 +50,5 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran -o Global.benchmark:True null:null null:null -===========================train_benchmark_params========================== -batch_size:128 -fp_items:fp32 -epoch:1 ---profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile -flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 +===========================infer_benchmark_params========================== +random_infer_input:[{float32,[3,224,224]}] diff --git a/test_tipc/config/PVTV2/PVT_V2_B5.txt b/test_tipc/config/PVTV2/PVT_V2_B5_train_infer_python.txt similarity index 85% rename from test_tipc/config/PVTV2/PVT_V2_B5.txt rename to test_tipc/config/PVTV2/PVT_V2_B5_train_infer_python.txt index 63f168b3f5260cbc225a4d9e17954dd5a795447d..9527cb2d385ed217e3212fd535629b2407ff8dee 100644 --- a/test_tipc/config/PVTV2/PVT_V2_B5.txt +++ b/test_tipc/config/PVTV2/PVT_V2_B5_train_infer_python.txt @@ -50,9 +50,5 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran -o Global.benchmark:True null:null null:null -===========================train_benchmark_params========================== -batch_size:128 -fp_items:fp32 -epoch:1 ---profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile -flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 +===========================infer_benchmark_params========================== +random_infer_input:[{float32,[3,224,224]}] diff --git a/test_tipc/config/VAN/VAN_tiny.txt b/test_tipc/config/VAN/VAN_tiny_train_infer_python.txt similarity index 85% rename from test_tipc/config/VAN/VAN_tiny.txt rename to test_tipc/config/VAN/VAN_tiny_train_infer_python.txt index 4ad311b58a653c38e9ae2d656024023102c46737..7d884d27ea666fa89fddd9487909d1371436e760 100644 --- a/test_tipc/config/VAN/VAN_tiny.txt +++ b/test_tipc/config/VAN/VAN_tiny_train_infer_python.txt @@ -50,9 +50,5 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran -o Global.benchmark:True null:null null:null -===========================train_benchmark_params========================== -batch_size:128 -fp_items:fp32 -epoch:1 ---profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile -flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 +===========================infer_benchmark_params========================== +random_infer_input:[{float32,[3,224,224]}]