# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # 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 ConvNext + UperNet checkpoints from mmsegmentation.""" import argparse import json import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation def get_upernet_config(model_name): auxiliary_in_channels = 384 if "tiny" in model_name: depths = [3, 3, 9, 3] hidden_sizes = [96, 192, 384, 768] if "small" in model_name: depths = [3, 3, 27, 3] hidden_sizes = [96, 192, 384, 768] if "base" in model_name: depths = [3, 3, 27, 3] hidden_sizes = [128, 256, 512, 1024] auxiliary_in_channels = 512 if "large" in model_name: depths = [3, 3, 27, 3] hidden_sizes = [192, 384, 768, 1536] auxiliary_in_channels = 768 if "xlarge" in model_name: depths = [3, 3, 27, 3] hidden_sizes = [256, 512, 1024, 2048] auxiliary_in_channels = 1024 # set label information num_labels = 150 repo_id = "huggingface/label-files" filename = "ade20k-id2label.json" id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) id2label = {int(k): v for k, v in id2label.items()} label2id = {v: k for k, v in id2label.items()} backbone_config = ConvNextConfig( depths=depths, hidden_sizes=hidden_sizes, out_features=["stage1", "stage2", "stage3", "stage4"] ) config = UperNetConfig( backbone_config=backbone_config, auxiliary_in_channels=auxiliary_in_channels, num_labels=num_labels, id2label=id2label, label2id=label2id, ) return config # here we list all keys to be renamed (original name on the left, our name on the right) def create_rename_keys(config): rename_keys = [] # fmt: off # stem rename_keys.append(("backbone.downsample_layers.0.0.weight", "backbone.embeddings.patch_embeddings.weight")) rename_keys.append(("backbone.downsample_layers.0.0.bias", "backbone.embeddings.patch_embeddings.bias")) rename_keys.append(("backbone.downsample_layers.0.1.weight", "backbone.embeddings.layernorm.weight")) rename_keys.append(("backbone.downsample_layers.0.1.bias", "backbone.embeddings.layernorm.bias")) # stages for i in range(len(config.backbone_config.depths)): for j in range(config.backbone_config.depths[i]): rename_keys.append((f"backbone.stages.{i}.{j}.gamma", f"backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter")) rename_keys.append((f"backbone.stages.{i}.{j}.depthwise_conv.weight", f"backbone.encoder.stages.{i}.layers.{j}.dwconv.weight")) rename_keys.append((f"backbone.stages.{i}.{j}.depthwise_conv.bias", f"backbone.encoder.stages.{i}.layers.{j}.dwconv.bias")) rename_keys.append((f"backbone.stages.{i}.{j}.norm.weight", f"backbone.encoder.stages.{i}.layers.{j}.layernorm.weight")) rename_keys.append((f"backbone.stages.{i}.{j}.norm.bias", f"backbone.encoder.stages.{i}.layers.{j}.layernorm.bias")) rename_keys.append((f"backbone.stages.{i}.{j}.pointwise_conv1.weight", f"backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight")) rename_keys.append((f"backbone.stages.{i}.{j}.pointwise_conv1.bias", f"backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias")) rename_keys.append((f"backbone.stages.{i}.{j}.pointwise_conv2.weight", f"backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight")) rename_keys.append((f"backbone.stages.{i}.{j}.pointwise_conv2.bias", f"backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias")) if i > 0: rename_keys.append((f"backbone.downsample_layers.{i}.0.weight", f"backbone.encoder.stages.{i}.downsampling_layer.0.weight")) rename_keys.append((f"backbone.downsample_layers.{i}.0.bias", f"backbone.encoder.stages.{i}.downsampling_layer.0.bias")) rename_keys.append((f"backbone.downsample_layers.{i}.1.weight", f"backbone.encoder.stages.{i}.downsampling_layer.1.weight")) rename_keys.append((f"backbone.downsample_layers.{i}.1.bias", f"backbone.encoder.stages.{i}.downsampling_layer.1.bias")) rename_keys.append((f"backbone.norm{i}.weight", f"backbone.hidden_states_norms.stage{i+1}.weight")) rename_keys.append((f"backbone.norm{i}.bias", f"backbone.hidden_states_norms.stage{i+1}.bias")) # decode head rename_keys.extend( [ ("decode_head.conv_seg.weight", "decode_head.classifier.weight"), ("decode_head.conv_seg.bias", "decode_head.classifier.bias"), ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"), ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"), ] ) # fmt: on return rename_keys def rename_key(dct, old, new): val = dct.pop(old) dct[new] = val def convert_upernet_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub): model_name_to_url = { "upernet-convnext-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth", "upernet-convnext-small": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth", "upernet-convnext-base": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth", "upernet-convnext-large": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth", "upernet-convnext-xlarge": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth", } checkpoint_url = model_name_to_url[model_name] state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")["state_dict"] config = get_upernet_config(model_name) model = UperNetForSemanticSegmentation(config) model.eval() # replace "bn" => "batch_norm" for key in state_dict.copy().keys(): val = state_dict.pop(key) if "bn" in key: key = key.replace("bn", "batch_norm") state_dict[key] = val # rename keys rename_keys = create_rename_keys(config) for src, dest in rename_keys: rename_key(state_dict, src, dest) model.load_state_dict(state_dict) # verify on image url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg" image = Image.open(requests.get(url, stream=True).raw).convert("RGB") processor = SegformerImageProcessor() pixel_values = processor(image, return_tensors="pt").pixel_values with torch.no_grad(): outputs = model(pixel_values) if model_name == "upernet-convnext-tiny": expected_slice = torch.tensor( [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]] ) elif model_name == "upernet-convnext-small": expected_slice = torch.tensor( [[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]] ) elif model_name == "upernet-convnext-base": expected_slice = torch.tensor( [[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]] ) elif model_name == "upernet-convnext-large": expected_slice = torch.tensor( [[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]] ) elif model_name == "upernet-convnext-xlarge": expected_slice = torch.tensor( [[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]] ) print("Logits:", outputs.logits[0, 0, :3, :3]) assert torch.allclose(outputs.logits[0, 0, :3, :3], expected_slice, atol=1e-4) print("Looks ok!") if pytorch_dump_folder_path is not None: print(f"Saving model {model_name} to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) print(f"Saving processor to {pytorch_dump_folder_path}") processor.save_pretrained(pytorch_dump_folder_path) if push_to_hub: print(f"Pushing model and processor for {model_name} to hub") model.push_to_hub(f"openmmlab/{model_name}") processor.push_to_hub(f"openmmlab/{model_name}") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default="upernet-convnext-tiny", type=str, choices=[f"upernet-convnext-{size}" for size in ["tiny", "small", "base", "large", "xlarge"]], help="Name of the ConvNext UperNet model you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." ) args = parser.parse_args() convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)