add whole_chain test
dongshuilong
parent
4af9f51088
commit
7d42f2dfe6
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@ -41,6 +41,26 @@ class ClsPredictor(Predictor):
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if "PostProcess" in config:
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self.postprocess = build_postprocess(config["PostProcess"])
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# for whole_chain project to test each repo of paddle
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self.benchmark = config.get(["benchmark"], False)
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if self.benchmark:
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import auto_log
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import os
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pid = os.getpid()
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self.auto_log = auto_log.AutoLogger(
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model_name='cls',
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model_precision='fp16'
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if config["Global"]["use_fp16"] else 'fp32',
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batch_size=1,
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data_shape=[3, 224, 224],
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save_path="../output/auto_log.lpg",
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inference_config=None,
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pids=pid,
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process_name=None,
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gpu_ids=None,
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time_keys=['preprocess_time', 'inference_time'],
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warmup=10)
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def predict(self, images):
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input_names = self.paddle_predictor.get_input_names()
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input_tensor = self.paddle_predictor.get_input_handle(input_names[0])
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@ -49,16 +69,22 @@ class ClsPredictor(Predictor):
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output_tensor = self.paddle_predictor.get_output_handle(output_names[
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0])
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if self.benchmark:
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self.auto_log.times.start()
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if not isinstance(images, (list, )):
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images = [images]
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for idx in range(len(images)):
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for ops in self.preprocess_ops:
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images[idx] = ops(images[idx])
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image = np.array(images)
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if self.benchmark:
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self.auto_log.times.stamp()
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input_tensor.copy_from_cpu(image)
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self.paddle_predictor.run()
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batch_output = output_tensor.copy_to_cpu()
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if self.benchmark:
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self.auto_log.times.stamp()
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return batch_output
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@ -71,6 +97,9 @@ def main(config):
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img = cv2.imread(image_file)[:, :, ::-1]
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output = cls_predictor.predict(img)
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output = cls_predictor.postprocess(output, [image_file])
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if cls_predictor.benchmark:
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cls_predictor.auto_log.times.end(stamp=True)
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cls_predictor.auto_log.report()
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print(output)
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return
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@ -0,0 +1,132 @@
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# global configs
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Global:
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checkpoints: null
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pretrained_model: null
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output_dir: ./output/
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device: gpu
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save_interval: 1
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eval_during_train: True
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eval_interval: 1
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epochs: 10
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print_batch_step: 10
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use_visualdl: False
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# used for static mode and model export
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image_shape: [3, 224, 224]
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save_inference_dir: ./inference
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# training model under @to_static
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to_static: False
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# model architecture
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Arch:
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name: ResNet50
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class_num: 1000
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# loss function config for traing/eval process
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Loss:
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Train:
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- CELoss:
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weight: 1.0
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Eval:
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- CELoss:
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weight: 1.0
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Optimizer:
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name: Momentum
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momentum: 0.9
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lr:
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name: Piecewise
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learning_rate: 0.1
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decay_epochs: [30, 60, 90]
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values: [0.1, 0.01, 0.001, 0.0001]
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regularizer:
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name: 'L2'
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coeff: 0.0001
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# data loader for train and eval
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DataLoader:
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Train:
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dataset:
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name: ImageNetDataset
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image_root: ./dataset/chain_dataset/
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cls_label_path: ./dataset/chain_dataset/train.txt
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transform_ops:
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- DecodeImage:
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to_rgb: True
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channel_first: False
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- RandCropImage:
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size: 224
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- RandFlipImage:
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flip_code: 1
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- NormalizeImage:
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scale: 1.0/255.0
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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order: ''
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sampler:
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name: DistributedBatchSampler
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batch_size: 64
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drop_last: False
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shuffle: True
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loader:
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num_workers: 4
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use_shared_memory: True
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Eval:
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dataset:
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name: ImageNetDataset
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image_root: ./dataset/chain_dataset/
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cls_label_path: ./dataset/chain_dataset/val.txt
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transform_ops:
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- DecodeImage:
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to_rgb: True
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channel_first: False
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- ResizeImage:
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resize_short: 256
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- CropImage:
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size: 224
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- NormalizeImage:
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scale: 1.0/255.0
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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order: ''
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sampler:
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name: DistributedBatchSampler
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batch_size: 64
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drop_last: False
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shuffle: False
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loader:
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num_workers: 4
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use_shared_memory: True
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Infer:
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infer_imgs: docs/images/whl/demo.jpg
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batch_size: 10
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transforms:
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- DecodeImage:
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to_rgb: True
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channel_first: False
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- ResizeImage:
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resize_short: 256
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- CropImage:
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size: 224
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- NormalizeImage:
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scale: 1.0/255.0
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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order: ''
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- ToCHWImage:
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PostProcess:
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name: Topk
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topk: 5
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class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
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Metric:
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Train:
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- TopkAcc:
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topk: [1, 5]
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Eval:
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- TopkAcc:
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topk: [1, 5]
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@ -0,0 +1,57 @@
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===========================train_params===========================
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model_name:ResNet50_vd|ResNeXt101_vd_64x4d|HRNet_W18_C|MobileNetV3_large_x1_0|DarkNet53|MobileNetV1|MobileNetV2|ShuffleNetV2_x1_0
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model_name_pact:ResNet50_vd|MobileNetV3_large_x1_0
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model_name_fpgm:ResNet50_vd|MobileNetV3_large_x1_0
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model_name_kl:ResNet50_vd|MobileNetV3_large_x1_0
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python:python3.7
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gpu_list:0|0,1|-1
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Global.epoch_num:10
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Global.save_model_dir:./output/
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Global.pretrained_model:null
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Global.save_inference_dir:null
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===========================scripts===========================
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train:tools/train.py -c test_ci/benchmark.yaml
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eval:tools/eval.py -c test_ci/benchmark.yaml
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norm_export:tools/export_model.py -c test_ci/benchmark.yaml
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inference:python/predict_cls.py -c configs/inference_cls.yaml
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===========================infer_params===========================
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Global.save_log_path:./test/output/
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Global.use_gpu:True|False
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Global.enable_mkldnn:True|False
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Global.cpu_num_threads:1|6
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Global.batch_size:1
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Global.use_tensorrt:True|False
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Global.use_fp16:True|False
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Global.inference_model_dir:./inference
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Global.infer_imgs:./dataset/chain_dataset/val
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===========================pretrained_model===========================
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ResNet50_vd:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_pretrained.pdparams
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ResNeXt101_vd_64x4d:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_vd_64x4d_pretrained.pdparams
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HRNet_W18_C:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_pretrained.pdparams
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MobileNetV3_large_x1_0:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_pretrained.pdparams
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DarkNet53:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DarkNet53_pretrained.pdparams
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MobileNetV1:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_pretrained.pdparams
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MobileNetV2:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_pretrained.pdparams
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ShuffleNetV2_x1_0:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_0_pretrained.pdparams
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#!/bin/bash
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FILENAME=$1
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# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer']
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MODE=$2
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dataline=$(cat ${FILENAME})
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# parser params
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IFS=$'\n'
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lines=(${dataline})
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function func_parser_value(){
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strs=$1
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ifs=":"
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array=(${strs})
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tmp=${array[1]}
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echo ${tmp}
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}
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ResNet50_vd=$(func_parser_value "${lines[49]}")
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ResNeXt101_vd_64x4d=$(func_parser_value "${lines[50]}")
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HRNet_W18_C=$(func_parser_value "${lines[51]}")
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MobileNetV3_large_x1_0=$(func_parser_value "${lines[52]}")
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DarkNet53=$(func_parser_value "${lines[53]}")
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MobileNetV1=$(func_parser_value "${lines[54]}")
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MobileNetV2=$(func_parser_value "${lines[55]}")
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ShuffleNetV2_x1_0=$(func_parser_value "${lines[56]}")
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model_name_list=$(func_parser_value "${lines[1]}")
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if [ ${MODE} = "lite_train_infer" ] || [ ${MODE} = "whole_infer" ];then
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# pretrain lite train data
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cd dataset
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wget -nc http://10.21.226.181:8011/dataset/quanliantiao/little_train.tar
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tar xf little_train.tar
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ln -s little_train chain_dataset
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cd ../
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elif [ ${MODE} = "infer" ];then
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# download data
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cd dataset
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wget -nc http://10.21.226.181:8011/dataset/quanliantiao/infer.tar
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tar xf little.tar
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ln -s infer chain_dataset
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cd ../
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# download pretrained model
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mkdir -p pretrained_models
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cd pretrained_models
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eval "wget -nc $ResNet50_vd"
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eval "wget -nc $ResNeXt101_vd_64x4d"
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eval "wget -nc $HRNet_W18_C"
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eval "wget -nc $MobileNetV3_large_x1_0"
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eval "wget -nc $DarkNet53"
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eval "wget -nc $MobileNetV1"
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eval "wget -nc $MobileNetV2"
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eval "wget -nc $ShuffleNetV2_x1_0"
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elif [ ${MODE} = "whole_train_infer" ];then
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cd dataset
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wget -nc http://10.21.226.181:8011/dataset/quanliantiao/CIFAR100.tar
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tar xf CIFAR100.tar
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ln -s CIFAR100 chain_dataset
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fi
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@ -0,0 +1,209 @@
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#!/bin/bash
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FILENAME=$1
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# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer']
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MODE=$2
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dataline=$(cat ${FILENAME})
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# parser params
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IFS=$'\n'
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lines=(${dataline})
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function func_parser_key(){
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strs=$1
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IFS=":"
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array=(${strs})
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tmp=${array[0]}
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echo ${tmp}
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}
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function func_parser_value(){
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strs=$1
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IFS=":"
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array=(${strs})
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tmp=${array[1]}
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echo ${tmp}
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}
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function status_check(){
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last_status=$1 # the exit code
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run_command=$2
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run_log=$3
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if [ $last_status -eq 0 ]; then
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echo -e "\033[33m Run successfully with command - ${run_command}! \033[0m" | tee -a ${run_log}
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else
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echo -e "\033[33m Run failed with command - ${run_command}! \033[0m" | tee -a ${run_log}
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fi
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}
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IFS=$'\n'
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# The training params
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model_name_list=$(func_parser_value "${lines[1]}")
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model_name_pact_list=$(func_parser_value "${lines[2]}")
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model_name_fpgm_list=$(func_parser_value "${lines[3]}")
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model_name_kl_list=$(func_parser_value "${lines[4]}")
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python=$(func_parser_value "${lines[5]}")
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gpu_list=$(func_parser_value "${lines[6]}")
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epoch_key=$(func_parser_key "${lines[7]}")
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epoch_value=$(func_parser_value "${lines[7]}")
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save_model_key=$(func_parser_key "${lines[8]}")
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save_model_value=$(func_parser_value "${lines[8]}")
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pretrain_model_key=$(func_parser_key "${lines[9]}")
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save_infer_key=$(func_parser_key "${lines[10]}")
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#scripts
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train_py=$(func_parser_value "${lines[20]}")
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eval_py=$(func_parser_value "${lines[21]}")
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norm_export=$(func_parser_value "${lines[22]}")
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inference_py=$(func_parser_value "${lines[23]}")
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#The inference params
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use_gpu_key=$(func_parser_key "${lines[33]}")
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use_gpu_list=$(func_parser_value "${lines[33]}")
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use_mkldnn_key=$(func_parser_key "${lines[34]}")
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use_mkldnn_list=$(func_parser_value "${lines[34]}")
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cpu_threads_key=$(func_parser_key "${lines[35]}")
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cpu_threads_list=$(func_parser_value "${lines[35]}")
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batch_size_key=$(func_parser_key "${lines[36]}")
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batch_size_list=$(func_parser_value "${lines[36]}")
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use_trt_key=$(func_parser_key "${lines[37]}")
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use_trt_list=$(func_parser_value "${lines[37]}")
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precision_key=$(func_parser_key "${lines[38]}")
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precision_list=$(func_parser_value "${lines[38]}")
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infer_model_key=$(func_parser_key "${lines[39]}")
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infer_model=$(func_parser_value "${lines[39]}")
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image_dir_key=$(func_parser_key "${lines[40]}")
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infer_img_dir=$(func_parser_value "${lines[40]}")
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save_log_key=$(func_parser_key "${lines[32]}")
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LOG_PATH="./test/output"
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mkdir -p ${LOG_PATH}
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status_log="${LOG_PATH}/results.log"
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function func_inference(){
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IFS='|'
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_python=$1
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_script=$2
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_model_dir=$3
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_log_path=$4
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_img_dir=$5
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_model_name=$6
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# inference
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for use_gpu in ${use_gpu_list[*]}; do
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if [ ${use_gpu} = "False" ]; then
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for use_mkldnn in ${use_mkldnn_list[*]}; do
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for threads in ${cpu_threads_list[*]}; do
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for batch_size in ${batch_size_list[*]}; do
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_save_log_path="${_log_path}/${_model_name}_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log"
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command="${_python} ${_script} -o ${use_gpu_key}=${use_gpu} -o ${use_mkldnn_key}=${use_mkldnn} -o ${cpu_threads_key}=${threads} -o ${infer_model_key}=${_model_dir} -o ${batch_size_key}=${batch_size} -o ${image_dir_key}=${_img_dir} -o ${save_log_key}=${_save_log_path} -o benchmark=True"
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eval $command
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status_check $? "${command}" "${status_log}"
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done
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done
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done
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else
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for use_trt in ${use_trt_list[*]}; do
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for precision in ${precision_list[*]}; do
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if [ ${use_trt} = "False" ] && [ ${precision} != "fp32" ]; then
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continue
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fi
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for batch_size in ${batch_size_list[*]}; do
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_save_log_path="${_log_path}/${_model_name}_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
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command="${_python} ${_script} -o ${use_gpu_key}=${use_gpu} -o ${use_trt_key}=${use_trt} -o ${precision_key}=${precision} -o ${infer_model_key}=${_model_dir} -o ${batch_size_key}=${batch_size} -o ${image_dir_key}=${_img_dir} -o ${save_log_key}=${_save_log_path} -o benchmark=True"
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eval $command
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status_check $? "${command}" "${status_log}"
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done
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done
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done
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fi
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done
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}
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if [ ${MODE} != "infer" ]; then
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IFS="|"
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for gpu in ${gpu_list[*]}; do
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use_gpu=True
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if [ ${gpu} = "-1" ];then
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use_gpu=False
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env=""
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elif [ ${#gpu} -le 1 ];then
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env="export CUDA_VISIBLE_DEVICES=${gpu}"
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eval ${env}
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elif [ ${#gpu} -le 15 ];then
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IFS=","
|
||||
array=(${gpu})
|
||||
env="export CUDA_VISIBLE_DEVICES=${array[0]}"
|
||||
IFS="|"
|
||||
else
|
||||
IFS=";"
|
||||
array=(${gpu})
|
||||
ips=${array[0]}
|
||||
gpu=${array[1]}
|
||||
IFS="|"
|
||||
env=" "
|
||||
fi
|
||||
for model_name in ${model_name_list[*]}; do
|
||||
# not set epoch when whole_train_infer
|
||||
if [ ${MODE} != "whole_train_infer" ]; then
|
||||
set_epoch="-o ${epoch_key}=${epoch_num}"
|
||||
else
|
||||
set_epoch=" "
|
||||
fi
|
||||
save_log="${LOG_PATH}/${model_name}_gpus_${gpu}"
|
||||
# train with cpu
|
||||
if [ ${gpu} = "-1" ];then
|
||||
cmd="${python} ${train_py} -o Arch.name=${model_name} -o Global.device=cpu -o ${save_model_key}=${save_log} ${set_epoch}"
|
||||
# train with single gpu
|
||||
elif [ ${#gpu} -le 2 ];then # train with single gpu
|
||||
cmd="${python} ${train_py} -o Arch.name=${model_name} -o ${save_model_key}=${save_log} ${set_epoch}"
|
||||
elif [ ${#gpu} -le 15 ];then # train with multi-gpu
|
||||
cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${train_py} -o Arch.name=${model_name} -o ${save_model_key}=${save_log} ${set_epoch}"
|
||||
else # train with multi-machine
|
||||
cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${train_py} -o Arch.name=${model_name} -c ${save_model_key}=${save_log} ${set_epoch}"
|
||||
fi
|
||||
# run train
|
||||
eval $cmd
|
||||
status_check $? "${cmd}" "${status_log}"
|
||||
|
||||
# run eval
|
||||
eval_cmd="${python} ${eval_py} -o Arch.name=${model_name} -o ${pretrain_model_key}=${save_log}/${model_name}/best_model"
|
||||
eval $eval_cmd
|
||||
status_check $? "${eval_cmd}" "${status_log}"
|
||||
|
||||
# run export model
|
||||
save_infer_path="${save_log}"
|
||||
export_cmd="${python} ${norm_export} -o Arch.name=${model_name} -o ${pretrain_model_key}=${save_log}/${model_name}/best_model -o ${save_infer_key}=${save_infer_path}"
|
||||
eval $export_cmd
|
||||
status_check $? "${export_cmd}" "${status_log}"
|
||||
|
||||
#run inference
|
||||
eval $env
|
||||
save_infer_path="${save_log}"
|
||||
cd deploy
|
||||
func_inference "${python}" "${inference_py}" "../${save_log}" "../${LOG_PATH}" "../${infer_img_dir}" model_name
|
||||
eval "unset CUDA_VISIBLE_DEVICES"
|
||||
cd ..
|
||||
done
|
||||
done
|
||||
|
||||
else
|
||||
GPUID=$3
|
||||
if [ ${#GPUID} -le 0 ];then
|
||||
env=" "
|
||||
else
|
||||
env="export CUDA_VISIBLE_DEVICES=${GPUID}"
|
||||
fi
|
||||
echo $env
|
||||
# export inference model
|
||||
mkdir -p inference_models
|
||||
for model_name in ${model_name_list[*]}; do
|
||||
export_cmd="${python} ${norm_export} -o Arch.name=${model_name} -o ${pretrain_model_key}=pretrained_models/${model_name}_pretrained -o ${save_infer_key}=./inference_models/${model_name}"
|
||||
eval $export_cmd
|
||||
done
|
||||
#run inference
|
||||
cd deploy
|
||||
for model_name in ${model_name_list[*]}; do
|
||||
func_inference "${python}" "${inference_py}" "../inference_models/${model_name}" "../${LOG_PATH}" "../${infer_img_dir}"
|
||||
done
|
||||
cd ..
|
||||
fi
|
||||
Loading…
Reference in New Issue