[Docs] Converters for converting official weights into detrex format #223
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Converters for DETRconvert DETR-R50 & DETR-R101# 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# 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() |
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Converters for DAB-DETRconvert DAB-DETR-R50-DC5 & DAB-DETR-R101-DC5# 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# 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() |
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Converters for DN-DETRconvert DN-DETR-R50# 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]
label_enc_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, 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.0.backbone." + k
k = "backbone." + k
# k = "detr." + k
# add new convert content
if "label_enc" in k:
k = k.replace("label_enc", "label_encoder")
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
if "label_enc" in old_k:
v = model_to_convert[old_k].detach()
print("Label enc conversion:", v.shape[0])
if v.shape[0] == 92:
shape_old = v.shape
model_converted[k] = v[label_enc_coco_idx]
print(
"Label enc 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 DN-DETR-R50-DC5# 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]
label_enc_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, 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
# k = "detr." + k
# add new convert content
if "label_enc" in k:
k = k.replace("label_enc", "label_encoder")
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
if "label_enc" in old_k:
v = model_to_convert[old_k].detach()
print("Label enc conversion:", v.shape[0])
if v.shape[0] == 92:
shape_old = v.shape
model_converted[k] = v[label_enc_coco_idx]
print(
"Label enc 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() |
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Converters for Anchor-DETRconvert Anchor-DETR-R50 (DC5) & Anchor-DETR-R101 (DC5)# 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
if "input_proj.0.0" in k:
k = k.replace("input_proj.0.0", "input_proj.0")
if "input_proj.0.1" in k:
k = k.replace("input_proj.0.1", "input_proj.1")
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() |
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Converters for DETAconvert DETA# 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
# ------------------------------------------------------------------------------------------------
import argparse
import numpy as np
import torch
def parse_args():
parser = argparse.ArgumentParser("detrex deformable-detr 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
# add input_proj converter
if "input_proj" in k:
if "input_proj.0.0" in k:
k = k.replace("input_proj.0.0", "neck.convs.0.conv")
if "input_proj.0.1" in k:
k = k.replace("input_proj.0.1", "neck.convs.0.norm")
if "input_proj.1.0" in k:
k = k.replace("input_proj.1.0", "neck.convs.1.conv")
if "input_proj.1.1" in k:
k = k.replace("input_proj.1.1", "neck.convs.1.norm")
if "input_proj.2.0" in k:
k = k.replace("input_proj.2.0", "neck.convs.2.conv")
if "input_proj.2.1" in k:
k = k.replace("input_proj.2.1", "neck.convs.2.norm")
if "input_proj.3.0" in k:
k = k.replace("input_proj.3.0", "neck.extra_convs.0.conv")
if "input_proj.3.1" in k:
k = k.replace("input_proj.3.1", "neck.extra_convs.0.norm")
if "input_proj.4.0" in k:
k = k.replace("input_proj.4.0", "neck.extra_convs.1.conv")
if "input_proj.4.1" in k:
k = k.replace("input_proj.4.1", "neck.extra_convs.1.norm")
# add new convert content
if "encoder.layers" in k:
if "self_attn" in k:
k = k.replace("self_attn", "attentions.0")
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 "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.1")
elif "norm2" in k:
k = k.replace("norm2", "norms.0")
elif "norm3" in k:
k = k.replace("norm3", "norms.2")
elif "self_attn" in k:
k = k.replace("self_attn", "attentions.0.attn")
elif "cross_attn" in k:
k = k.replace("cross_attn", "attentions.1")
if "level_embed" in k:
k = k.replace("level_embed", "level_embeds")
if "query_embed" in k:
k = k.replace("query_embed", "query_embedding")
# k = "detr." + k
print(old_k, "->", k)
if "class_embed.6" in old_k:
v = model_to_convert[old_k].detach()
shape_old = v.shape
v = v[:80]
model_converted[k] = v
print(
"Head conversion: changing shape from {} to {}".format(
shape_old, model_converted[k].shape
)
)
continue
elif "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() |
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We've provide some converters for users to convert their official training weights into detrex format, which means users can use some practical tools in detrex to analysis your model:
All of the converters can be run and evaluation as:
python tools/train_net.py --config-file /path/to/config.py --eval-only train.init_checkpoint="path/to/converted_weights.pth"We will also add this tutorials in our documentation.
Quick Link
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