Yolox improve with REPConv/ASFF/TOOD #154
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| @@ -35,7 +42,9 @@ | |||
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| # dataset settings | |||
| data_root = 'data/coco/' | |||
| # data_root = '/apsarapangu/disk2/xinyi.zxy/data/coco/' | |||
| data_root = '/apsara/xinyi.zxy/data/coco/' | |||
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delete commented lines and update data_root, so are the other files
easycv/apis/export.py
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| @@ -659,6 +746,7 @@ def forward(self, image): | |||
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| with torch.no_grad(): | |||
| if self.preprocess_fn is not None: | |||
| # print('before', image.shape) | |||
easycv/datasets/detection/raw.py
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| @@ -126,6 +126,9 @@ def visualize(self, results, vis_num=10, score_thr=0.3, **kwargs): | |||
| dict of image meta info, containing filename, img_shape, | |||
| origin_img_shape, scale_factor and so on. | |||
| """ | |||
| import copy | |||
| results = copy.deepcopy(results) | |||
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it is already fixed, please refer to: #67
remove deepcopy
convert_new.py
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| @@ -0,0 +1,78 @@ | |||
| # conver to new | |||
| @@ -0,0 +1,117 @@ | |||
| # from easycv.models.detection.detectors.yolox import YOLOX | |||
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move to easycv/test
| pass | ||
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| @torch.jit.script | ||
| def postprocess_fn(output): |
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why remove these export guides?
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we have changed a new design, and will discuss it.
docs/source/tutorials/yolox.md
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| - Provide an easy way to use PAI-Blade to accelerate the inference process. | ||
| - Provide a convenient way to train/evaluate/export YOLOX-PAI model and conduct end2end object detection. | ||
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| To learn more details of YOLOX-PAI, you can refer to our technical paper [??link][arxiv]. |
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sorry for the mistake, it is a chinese and have fixed it.
docs/source/tutorials/yolox.md
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| Put them in the following format: | ||
| ```shell | ||
| export_blade/ | ||
| ??? epoch_300_pre_notrt.pt.blade |
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sorry for the mistake, and have fixed it.
easycv/apis/export.py
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| else: | ||
| if hasattr(cfg, 'export'): | ||
| export_type = getattr(cfg.export, 'export_type', 'ori') |
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update export_type: 'ori' to 'raw'
| with io.open('/tmp/model.jit', 'wb') as f: | ||
| torch.jit.save(model_script, f) | ||
| """ | ||
| class ModelExportWrapper(torch.nn.Module): |
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why remove End2endModelExportWrapper and preprocess_fn, postprocess_fn
| return model_output | ||
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| class ProcessExportWrapper(torch.nn.Module): |
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named with ProcessExportWrapper, but it seems has nothing to do with export
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we use the wrapper to trace the model and export a preprocess jit model.
| @@ -183,6 +185,9 @@ def main(): | |||
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | |||
| print(f'use device {device}') | |||
| checkpoint = load_checkpoint(model, args.checkpoint, map_location=device) | |||
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| model = reparameterize_models(model) | |||
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add explanation for line189
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LGTM |
Motivation
Modification
Backward-Compatibility-Breaking (Optional)
Use cases (Optional)
Pull-Request Todo List