add weight conversion script for other model versions

convert_sam_hq_to_hf.py

@@ -0,0 +1,172 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. 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.
+"""
+Convert SAM checkpoints from the original repository.
+
+URL: https://github.com/facebookresearch/segment-anything.
+
+Also supports converting the SlimSAM checkpoints from https://github.com/czg1225/SlimSAM/tree/master.
+"""
+import sys
+sys.path.append("../")
+
+import argparse
+import re
+import torch
+from safetensors.torch import save_model
+from huggingface_hub import hf_hub_download
+from transformers import (
+ SamImageProcessor,
+ SamProcessor,
+ SamVisionConfig,
+)
+from sam_hq_vit_huge.modeling_sam_hq import SamHQModel
+from sam_hq_vit_huge.configuration_sam_hq import SamHQConfig
+
+
+def get_config(model_name):
+ if "sam_hq_vit_b" in model_name:
+ vision_config = SamVisionConfig()
+ elif "sam_hq_vit_l" in model_name:
+ vision_config = SamVisionConfig(
+ hidden_size=1024,
+ num_hidden_layers=24,
+ num_attention_heads=16,
+ global_attn_indexes=[5, 11, 17, 23],
+ )
+ elif "sam_hq_vit_h" in model_name:
+ vision_config = SamVisionConfig(
+ hidden_size=1280,
+ num_hidden_layers=32,
+ num_attention_heads=16,
+ global_attn_indexes=[7, 15, 23, 31],
+ )
+
+ config = SamHQConfig(
+ vision_config=vision_config,
+ )
+
+ return config
+
+
+KEYS_TO_MODIFY_MAPPING = {
+ # Vision Encoder
+ "image_encoder": "vision_encoder",
+ "patch_embed.proj": "patch_embed.projection",
+ "blocks.": "layers.",
+ "neck.0": "neck.conv1",
+ "neck.1": "neck.layer_norm1",
+ "neck.2": "neck.conv2",
+ "neck.3": "neck.layer_norm2",
+
+ # Prompt Encoder
+ "mask_downscaling.0": "mask_embed.conv1",
+ "mask_downscaling.1": "mask_embed.layer_norm1",
+ "mask_downscaling.3": "mask_embed.conv2",
+ "mask_downscaling.4": "mask_embed.layer_norm2",
+ "mask_downscaling.6": "mask_embed.conv3",
+ "point_embeddings": "point_embed",
+ "pe_layer.positional_encoding_gaussian_matrix": "shared_embedding.positional_embedding",
+
+ # Mask Decoder
+ "iou_prediction_head.layers.0": "iou_prediction_head.proj_in",
+ "iou_prediction_head.layers.1": "iou_prediction_head.layers.0",
+ "iou_prediction_head.layers.2": "iou_prediction_head.proj_out",
+ "mask_decoder.output_upscaling.0": "mask_decoder.upscale_conv1",
+ "mask_decoder.output_upscaling.1": "mask_decoder.upscale_layer_norm",
+ "mask_decoder.output_upscaling.3": "mask_decoder.upscale_conv2",
+ ".norm": ".layer_norm",
+
+ # SAM HQ Extra (in Mask Decoder)
+ "hf_mlp.layers.0": "hf_mlp.proj_in",
+ "hf_mlp.layers.1": "hf_mlp.layers.0",
+ "hf_mlp.layers.2": "hf_mlp.proj_out", 
+}
+
+
+def replace_keys(state_dict):
+ model_state_dict = {}
+ state_dict.pop("pixel_mean", None)
+ state_dict.pop("pixel_std", None)
+
+ output_hypernetworks_mlps_pattern = r".*.output_hypernetworks_mlps.(\d+).layers.(\d+).*"
+
+ for key, value in state_dict.items():
+ for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
+ if key_to_modify in key:
+ key = key.replace(key_to_modify, new_key)
+
+ if re.match(output_hypernetworks_mlps_pattern, key):
+ layer_nb = int(re.match(output_hypernetworks_mlps_pattern, key).group(2))
+ if layer_nb == 0:
+ key = key.replace("layers.0", "proj_in")
+ elif layer_nb == 1:
+ key = key.replace("layers.1", "layers.0")
+ elif layer_nb == 2:
+ key = key.replace("layers.2", "proj_out")
+ break
+
+ model_state_dict[key] = value.cpu()
+
+ model_state_dict["shared_image_embedding.positional_embedding"] = model_state_dict[
+ "prompt_encoder.shared_embedding.positional_embedding"
+ ].cpu().clone()
+
+ return model_state_dict
+
+
+def convert_sam_checkpoint(model_name, checkpoint_path, output_dir):
+ config = get_config(model_name)
+
+ state_dict = torch.load(checkpoint_path, map_location="cpu")
+ state_dict = replace_keys(state_dict)
+ # print(state_dict.keys())
+
+ hf_model = SamHQModel(config)
+ hf_model.eval()
+
+ hf_model.load_state_dict(state_dict)
+
+ if output_dir is not None:
+ save_model(hf_model, f"{output_dir}/{model_name}.safetensors", metadata={"format": "pt"})
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ choices = ["sam_hq_vit_b", "sam_hq_vit_l", "sam_hq_vit_h"]
+ parser.add_argument(
+ "--model_name",
+ default="sam_hq_vit_h",
+ choices=choices,
+ type=str,
+ help="Name of the original model to convert",
+ )
+ parser.add_argument(
+ "--checkpoint_path",
+ type=str,
+ required=False,
+ help="Path to the original checkpoint",
+ )
+ parser.add_argument("--output_dir", default=".", type=str, help="Path to the output PyTorch model.")
+
+ args = parser.parse_args()
+
+ if args.checkpoint_path is not None:
+ checkpoint_path = args.checkpoint_path
+ else:
+ checkpoint_path = hf_hub_download("lkeab/hq-sam", f"{args.model_name}.pth")
+ print(checkpoint_path)
+
+ convert_sam_checkpoint(args.model_name, checkpoint_path, args.output_dir)