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feat: add safetensors export for RT-DETR models #9481

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feat: add safetensors export for RT-DETR models #9481

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36 changes: 35 additions & 1 deletion ppdet/engine/trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1410,7 +1410,8 @@ def _get_infer_cfg_and_input_spec(self,

return static_model, pruned_input_spec, input_spec

def export(self, output_dir='output_inference', for_fd=False):
def export(self, output_dir='output_inference', for_fd=False,
export_safetensors=False):
if hasattr(self.model, 'aux_neck'):
self.model.__delattr__('aux_neck')
if hasattr(self.model, 'aux_head'):
Expand All @@ -1437,6 +1438,19 @@ def export(self, output_dir='output_inference', for_fd=False):

if not os.path.exists(save_dir):
os.makedirs(save_dir)

# Export safetensors (before jit.to_static which destroys dygraph state)
if export_safetensors:
from ppdet.utils.safetensors_export import export_safetensors as _export_st
safetensors_dir = os.path.join(save_dir, 'safetensors')
_export_st(model, self.cfg, safetensors_dir)
# Generate inference.yml into safetensors dir
infer_yaml_name = yaml_name or 'inference.yml'
self._dump_safetensors_infer_config(
model, safetensors_dir, infer_yaml_name)
logger.info(
"Safetensors export saved in {}".format(safetensors_dir))

static_model, pruned_input_spec, input_spec = self._get_infer_cfg_and_input_spec(
save_dir, yaml_name=yaml_name, model=model)

Expand Down Expand Up @@ -1467,6 +1481,26 @@ def export(self, output_dir='output_inference', for_fd=False):
input_spec=pruned_input_spec)
logger.info("Export model and saved in {}".format(save_dir))

def _dump_safetensors_infer_config(self, model, save_dir, yaml_name):
"""Generate inference.yml for safetensors export directory."""
from ppdet.engine.export_utils import _dump_infer_config
reader_cfg = self.cfg.get('TestReader', {})
image_shape = reader_cfg.get('inputs_def', {}).get(
'image_shape', [3, 640, 640])
image_shape = [None] + image_shape
# Build minimal input_spec for _dump_infer_config
input_spec = [{
'image': InputSpec(
shape=image_shape, name='image'),
'im_shape': InputSpec(
shape=[None, 2], dtype='float32', name='im_shape'),
'scale_factor': InputSpec(
shape=[None, 2], dtype='float32', name='scale_factor'),
}]
_dump_infer_config(
self.cfg, os.path.join(save_dir, yaml_name),
image_shape, model, input_spec)

def post_quant(self, output_dir='output_inference'):
model_name = os.path.splitext(os.path.split(self.cfg.filename)[-1])[0]
save_dir = os.path.join(output_dir, model_name)
Expand Down
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