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IDEA-Research / detrex

detrex is a research platform for DETR-based object detection, segmentation, pose estimation and other visual recognition tasks.
https://detrex.readthedocs.io/en/latest/
Apache License 2.0
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[Docs] Provide the converters for converting official weights into detrex format #209

Closed rentainhe closed 1 year ago

rentainhe commented 1 year ago

Convert tools

Here we list our convert tools for converting official pretrained weights into detrex format, all the converters can be run and evaluation as:

We will also add this tutorials in our documentation.

rentainhe commented 1 year ago

DETR

convert DETR-R50 & DETR-R101 ```python # coding=utf-8 # Copyright 2022 The IDEA Authors. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ------------------------------------------------------------------------------------------------ # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved # ------------------------------------------------------------------------------------------------ # Modified from: # https://github.com/facebookresearch/detr/blob/main/d2/converter.py # ------------------------------------------------------------------------------------------------ """ Helper script to convert models trained with the main version of DETR to be used in detrex. """ import argparse import numpy as np import torch def parse_args(): parser = argparse.ArgumentParser("detrex model converter") parser.add_argument( "--source_model", default="", type=str, help="Path or url to the DETR model to convert" ) parser.add_argument( "--output_model", default="", type=str, help="Path where to save the converted model" ) return parser.parse_args() def main(): args = parse_args() # D2 expects contiguous classes, so we need to remap the 92 classes from DETR # fmt: off coco_idx = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, # noqa 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, # noqa 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, # noqa 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91] # fmt: on coco_idx = np.array(coco_idx) if args.source_model.startswith("https"): checkpoint = torch.hub.load_state_dict_from_url( args.source_model, map_location="cpu", check_hash=True ) else: checkpoint = torch.load(args.source_model, map_location="cpu") model_to_convert = checkpoint["model"] model_converted = {} for k in model_to_convert.keys(): old_k = k if "backbone" in k: k = k.replace("backbone.0.body.", "") if "layer" not in k: k = "stem." + k for t in [1, 2, 3, 4]: k = k.replace(f"layer{t}", f"res{t + 1}") for t in [1, 2, 3]: k = k.replace(f"bn{t}", f"conv{t}.norm") k = k.replace("downsample.0", "shortcut") k = k.replace("downsample.1", "shortcut.norm") k = "backbone." + k # add new convert content if "encoder.layers" in k: if "self_attn" in k: k = k.replace("self_attn", "attentions.0.attn") elif "linear1" in k: k = k.replace("linear1", "ffns.0.layers.0.0") elif "linear2" in k: k = k.replace("linear2", "ffns.0.layers.1") elif "norm1" in k: k = k.replace("norm1", "norms.0") elif "norm2" in k: k = k.replace("norm2", "norms.1") if "decoder" in k: if "decoder.norm" in k: k = k.replace("decoder.norm", "decoder.post_norm_layer") elif "linear1" in k: k = k.replace("linear1", "ffns.0.layers.0.0") elif "linear2" in k: k = k.replace("linear2", "ffns.0.layers.1") elif "norm1" in k: k = k.replace("norm1", "norms.0") elif "norm2" in k: k = k.replace("norm2", "norms.1") elif "norm3" in k: k = k.replace("norm3", "norms.2") elif "self_attn" in k: k = k.replace("self_attn", "attentions.0.attn") elif "multihead_attn" in k: k = k.replace("multihead_attn", "attentions.1.attn") # old fashion of detr convert function print(old_k, "->", k) if "class_embed" in old_k: v = model_to_convert[old_k].detach() if v.shape[0] == 92: shape_old = v.shape model_converted[k] = v[coco_idx] print( "Head conversion: changing shape from {} to {}".format( shape_old, model_converted[k].shape ) ) continue model_converted[k] = model_to_convert[old_k].detach() model_to_save = {"model": model_converted} torch.save(model_to_save, args.output_model) if __name__ == "__main__": main() ```
convert DETR-R50-DC5 & DETR-R101-DC5 ```python # coding=utf-8 # Copyright 2022 The IDEA Authors. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ------------------------------------------------------------------------------------------------ # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved # ------------------------------------------------------------------------------------------------ # Modified from: # https://github.com/facebookresearch/detr/blob/main/d2/converter.py # ------------------------------------------------------------------------------------------------ """ Helper script to convert models trained with the main version of DETR to be used in detrex. """ import argparse import numpy as np import torch def parse_args(): parser = argparse.ArgumentParser("detrex model converter") parser.add_argument( "--source_model", default="", type=str, help="Path or url to the DETR model to convert" ) parser.add_argument( "--output_model", default="", type=str, help="Path where to save the converted model" ) return parser.parse_args() def main(): args = parse_args() # D2 expects contiguous classes, so we need to remap the 92 classes from DETR # fmt: off coco_idx = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, # noqa 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, # noqa 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, # noqa 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91] # fmt: on coco_idx = np.array(coco_idx) if args.source_model.startswith("https"): checkpoint = torch.hub.load_state_dict_from_url( args.source_model, map_location="cpu", check_hash=True ) else: checkpoint = torch.load(args.source_model, map_location="cpu") model_to_convert = checkpoint["model"] model_converted = {} for k in model_to_convert.keys(): old_k = k if "backbone" in k: k = k.replace("backbone.0.body.", "") k = "backbone.body." + k # add new convert content if "encoder.layers" in k: if "self_attn" in k: k = k.replace("self_attn", "attentions.0.attn") elif "linear1" in k: k = k.replace("linear1", "ffns.0.layers.0.0") elif "linear2" in k: k = k.replace("linear2", "ffns.0.layers.1") elif "norm1" in k: k = k.replace("norm1", "norms.0") elif "norm2" in k: k = k.replace("norm2", "norms.1") if "decoder" in k: if "decoder.norm" in k: k = k.replace("decoder.norm", "decoder.post_norm_layer") elif "linear1" in k: k = k.replace("linear1", "ffns.0.layers.0.0") elif "linear2" in k: k = k.replace("linear2", "ffns.0.layers.1") elif "norm1" in k: k = k.replace("norm1", "norms.0") elif "norm2" in k: k = k.replace("norm2", "norms.1") elif "norm3" in k: k = k.replace("norm3", "norms.2") elif "self_attn" in k: k = k.replace("self_attn", "attentions.0.attn") elif "multihead_attn" in k: k = k.replace("multihead_attn", "attentions.1.attn") # old fashion of detr convert function print(old_k, "->", k) if "class_embed" in old_k: v = model_to_convert[old_k].detach() if v.shape[0] == 92: shape_old = v.shape model_converted[k] = v[coco_idx] print( "Head conversion: changing shape from {} to {}".format( shape_old, model_converted[k].shape ) ) continue model_converted[k] = model_to_convert[old_k].detach() model_to_save = {"model": model_converted} torch.save(model_to_save, args.output_model) if __name__ == "__main__": main() ```
rentainhe commented 1 year ago

DAB-DETR

convert DAB-DETR-R50-DC5 & DAB-DETR-R101-DC5 ```python # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved """ Helper script to convert models trained with the main version of DETR to be used with the Detectron2 version. """ import argparse import numpy as np import torch def parse_args(): parser = argparse.ArgumentParser("D2 model converter") parser.add_argument( "--source_model", default="", type=str, help="Path or url to the DETR model to convert" ) parser.add_argument( "--output_model", default="", type=str, help="Path where to save the converted model" ) return parser.parse_args() def main(): args = parse_args() # D2 expects contiguous classes, so we need to remap the 92 classes from DETR # fmt: off coco_idx = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90] # fmt: on coco_idx = np.array(coco_idx) if args.source_model.startswith("https"): checkpoint = torch.hub.load_state_dict_from_url( args.source_model, map_location="cpu", check_hash=True ) else: checkpoint = torch.load(args.source_model, map_location="cpu") model_to_convert = checkpoint["model"] model_converted = {} for k in model_to_convert.keys(): old_k = k if "backbone" in k: k = k.replace("backbone.0.body.", "") k = "backbone.body." + k # k = "detr." + k # add new convert content if "decoder" in k: if "decoder.norm" in k: k = k.replace("decoder.norm", "decoder.post_norm_layer") if "ca_kcontent_proj" in k: k = k.replace("ca_kcontent_proj", "attentions.1.key_content_proj") elif "ca_kpos_proj" in k: k = k.replace("ca_kpos_proj", "attentions.1.key_pos_proj") elif "ca_qcontent_proj" in k: k = k.replace("ca_qcontent_proj", "attentions.1.query_content_proj") elif "ca_qpos_proj" in k: k = k.replace("ca_qpos_proj", "attentions.1.query_pos_proj") elif "ca_qpos_sine_proj" in k: k = k.replace("ca_qpos_sine_proj", "attentions.1.query_pos_sine_proj") elif "ca_v_proj" in k: k = k.replace("ca_v_proj", "attentions.1.value_proj") elif "sa_kcontent_proj" in k: k = k.replace("sa_kcontent_proj", "attentions.0.key_content_proj") elif "sa_kpos_proj" in k: k = k.replace("sa_kpos_proj", "attentions.0.key_pos_proj") elif "sa_qcontent_proj" in k: k = k.replace("sa_qcontent_proj", "attentions.0.query_content_proj") elif "sa_qpos_proj" in k: k = k.replace("sa_qpos_proj", "attentions.0.query_pos_proj") elif "sa_v_proj" in k: k = k.replace("sa_v_proj", "attentions.0.value_proj") elif "self_attn.out_proj" in k: k = k.replace("self_attn.out_proj", "attentions.0.out_proj") elif "cross_attn.out_proj" in k: k = k.replace("cross_attn.out_proj", "attentions.1.out_proj") elif "linear1" in k: k = k.replace("linear1", "ffns.0.layers.0.0") elif "linear2" in k: k = k.replace("linear2", "ffns.0.layers.1") elif "norm1" in k: k = k.replace("norm1", "norms.0") elif "norm2" in k: k = k.replace("norm2", "norms.1") elif "norm3" in k: k = k.replace("norm3", "norms.2") elif "activation" in k: k = k.replace("activation", "ffns.0.layers.0.1") if "encoder" in k: if "self_attn" in k: k = k.replace("self_attn", "attentions.0.attn") if "linear1" in k: k = k.replace("linear1", "ffns.0.layers.0.0") elif "linear2" in k: k = k.replace("linear2", "ffns.0.layers.1") elif "norm1" in k: k = k.replace("norm1", "norms.0") elif "norm2" in k: k = k.replace("norm2", "norms.1") elif "activation" in k: k = k.replace("activation", "ffns.0.layers.0.1") if "refpoint_embed" in k: k = k.replace("refpoint_embed", "anchor_box_embed") print(old_k, "->", k) if "class_embed" in old_k: v = model_to_convert[old_k].detach() if v.shape[0] == 91: shape_old = v.shape model_converted[k] = v[coco_idx] print( "Head conversion: changing shape from {} to {}".format( shape_old, model_converted[k].shape ) ) continue model_converted[k] = model_to_convert[old_k].detach() model_to_save = {"model": model_converted} torch.save(model_to_save, args.output_model) if __name__ == "__main__": main() ```
convert DAB-DETR-R50 & DAB-DETR-R101 & DAB-DETR-3patterns ```python # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved """ Helper script to convert models trained with the main version of DETR to be used with the Detectron2 version. """ import argparse import numpy as np import torch def parse_args(): parser = argparse.ArgumentParser("D2 model converter") parser.add_argument( "--source_model", default="", type=str, help="Path or url to the DETR model to convert" ) parser.add_argument( "--output_model", default="", type=str, help="Path where to save the converted model" ) return parser.parse_args() def main(): args = parse_args() # D2 expects contiguous classes, so we need to remap the 92 classes from DETR # fmt: off coco_idx = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90] # fmt: on coco_idx = np.array(coco_idx) if args.source_model.startswith("https"): checkpoint = torch.hub.load_state_dict_from_url( args.source_model, map_location="cpu", check_hash=True ) else: checkpoint = torch.load(args.source_model, map_location="cpu") model_to_convert = checkpoint["model"] model_converted = {} for k in model_to_convert.keys(): old_k = k if "backbone" in k: k = k.replace("backbone.0.body.", "") if "layer" not in k: k = "stem." + k for t in [1, 2, 3, 4]: k = k.replace(f"layer{t}", f"res{t + 1}") for t in [1, 2, 3]: k = k.replace(f"bn{t}", f"conv{t}.norm") k = k.replace("downsample.0", "shortcut") k = k.replace("downsample.1", "shortcut.norm") k = "backbone." + k # k = "detr." + k # add new convert content if "decoder" in k: if "decoder.norm" in k: k = k.replace("decoder.norm", "decoder.post_norm_layer") if "ca_kcontent_proj" in k: k = k.replace("ca_kcontent_proj", "attentions.1.key_content_proj") elif "ca_kpos_proj" in k: k = k.replace("ca_kpos_proj", "attentions.1.key_pos_proj") elif "ca_qcontent_proj" in k: k = k.replace("ca_qcontent_proj", "attentions.1.query_content_proj") elif "ca_qpos_proj" in k: k = k.replace("ca_qpos_proj", "attentions.1.query_pos_proj") elif "ca_qpos_sine_proj" in k: k = k.replace("ca_qpos_sine_proj", "attentions.1.query_pos_sine_proj") elif "ca_v_proj" in k: k = k.replace("ca_v_proj", "attentions.1.value_proj") elif "sa_kcontent_proj" in k: k = k.replace("sa_kcontent_proj", "attentions.0.key_content_proj") elif "sa_kpos_proj" in k: k = k.replace("sa_kpos_proj", "attentions.0.key_pos_proj") elif "sa_qcontent_proj" in k: k = k.replace("sa_qcontent_proj", "attentions.0.query_content_proj") elif "sa_qpos_proj" in k: k = k.replace("sa_qpos_proj", "attentions.0.query_pos_proj") elif "sa_v_proj" in k: k = k.replace("sa_v_proj", "attentions.0.value_proj") elif "self_attn.out_proj" in k: k = k.replace("self_attn.out_proj", "attentions.0.out_proj") elif "cross_attn.out_proj" in k: k = k.replace("cross_attn.out_proj", "attentions.1.out_proj") elif "linear1" in k: k = k.replace("linear1", "ffns.0.layers.0.0") elif "linear2" in k: k = k.replace("linear2", "ffns.0.layers.1") elif "norm1" in k: k = k.replace("norm1", "norms.0") elif "norm2" in k: k = k.replace("norm2", "norms.1") elif "norm3" in k: k = k.replace("norm3", "norms.2") elif "activation" in k: k = k.replace("activation", "ffns.0.layers.0.1") if "encoder" in k: if "self_attn" in k: k = k.replace("self_attn", "attentions.0.attn") if "linear1" in k: k = k.replace("linear1", "ffns.0.layers.0.0") elif "linear2" in k: k = k.replace("linear2", "ffns.0.layers.1") elif "norm1" in k: k = k.replace("norm1", "norms.0") elif "norm2" in k: k = k.replace("norm2", "norms.1") elif "activation" in k: k = k.replace("activation", "ffns.0.layers.0.1") if "refpoint_embed" in k: k = k.replace("refpoint_embed", "anchor_box_embed") print(old_k, "->", k) if "class_embed" in old_k: v = model_to_convert[old_k].detach() if v.shape[0] == 91: shape_old = v.shape model_converted[k] = v[coco_idx] print( "Head conversion: changing shape from {} to {}".format( shape_old, model_converted[k].shape ) ) continue model_converted[k] = model_to_convert[old_k].detach() model_to_save = {"model": model_converted} torch.save(model_to_save, args.output_model) if __name__ == "__main__": main() ```
rentainhe commented 1 year ago

This issue has been removed to this discussion: https://github.com/IDEA-Research/detrex/discussions/223