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# 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 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()
# 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 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")
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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