# coding=utf-8 # Copyright 2023 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 DPT 3.1 checkpoints from the MiDaS repository. URL: https://github.com/isl-org/MiDaS""" import argparse from pathlib import Path import requests import torch from PIL import Image from transformers import BeitConfig, DPTConfig, DPTForDepthEstimation, DPTImageProcessor from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) def get_dpt_config(model_name): hidden_size = 768 num_hidden_layers = 12 num_attention_heads = 12 intermediate_size = 3072 out_features = ["stage3", "stage6", "stage9", "stage12"] # beit-base-384 uses [2, 5, 8, 11] if "large" in model_name: hidden_size = 1024 num_hidden_layers = 24 num_attention_heads = 16 intermediate_size = 4096 out_features = ["stage6", "stage12", "stage18", "stage24"] # beit-large-512 uses [5, 11, 17, 23] if "512" in model_name: image_size = 512 elif "384" in model_name: image_size = 384 else: raise ValueError("Model not supported") backbone_config = BeitConfig( image_size=image_size, num_hidden_layers=num_hidden_layers, hidden_size=hidden_size, intermediate_size=intermediate_size, num_attention_heads=num_attention_heads, use_relative_position_bias=True, reshape_hidden_states=False, out_features=out_features, ) neck_hidden_sizes = [256, 512, 1024, 1024] if "large" in model_name else [96, 192, 384, 768] config = DPTConfig(backbone_config=backbone_config, neck_hidden_sizes=neck_hidden_sizes) return config, image_size # 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(("pretrained.model.cls_token", "backbone.embeddings.cls_token")) rename_keys.append(("pretrained.model.patch_embed.proj.weight", "backbone.embeddings.patch_embeddings.projection.weight")) rename_keys.append(("pretrained.model.patch_embed.proj.bias", "backbone.embeddings.patch_embeddings.projection.bias")) # Transfomer encoder for i in range(config.backbone_config.num_hidden_layers): rename_keys.append((f"pretrained.model.blocks.{i}.gamma_1", f"backbone.encoder.layer.{i}.lambda_1")) rename_keys.append((f"pretrained.model.blocks.{i}.gamma_2", f"backbone.encoder.layer.{i}.lambda_2")) rename_keys.append((f"pretrained.model.blocks.{i}.norm1.weight", f"backbone.encoder.layer.{i}.layernorm_before.weight")) rename_keys.append((f"pretrained.model.blocks.{i}.norm1.bias", f"backbone.encoder.layer.{i}.layernorm_before.bias")) rename_keys.append((f"pretrained.model.blocks.{i}.norm2.weight", f"backbone.encoder.layer.{i}.layernorm_after.weight")) rename_keys.append((f"pretrained.model.blocks.{i}.norm2.bias", f"backbone.encoder.layer.{i}.layernorm_after.bias")) rename_keys.append((f"pretrained.model.blocks.{i}.mlp.fc1.weight", f"backbone.encoder.layer.{i}.intermediate.dense.weight")) rename_keys.append((f"pretrained.model.blocks.{i}.mlp.fc1.bias", f"backbone.encoder.layer.{i}.intermediate.dense.bias")) rename_keys.append((f"pretrained.model.blocks.{i}.mlp.fc2.weight", f"backbone.encoder.layer.{i}.output.dense.weight")) rename_keys.append((f"pretrained.model.blocks.{i}.mlp.fc2.bias", f"backbone.encoder.layer.{i}.output.dense.bias")) rename_keys.append((f"pretrained.model.blocks.{i}.attn.proj.weight", f"backbone.encoder.layer.{i}.attention.output.dense.weight")) rename_keys.append((f"pretrained.model.blocks.{i}.attn.proj.bias", f"backbone.encoder.layer.{i}.attention.output.dense.bias")) rename_keys.append((f"pretrained.model.blocks.{i}.attn.relative_position_bias_table", f"backbone.encoder.layer.{i}.attention.attention.relative_position_bias.relative_position_bias_table")) rename_keys.append((f"pretrained.model.blocks.{i}.attn.relative_position_index", f"backbone.encoder.layer.{i}.attention.attention.relative_position_bias.relative_position_index")) # activation postprocessing (readout projections + resize blocks) for i in range(4): rename_keys.append((f"pretrained.act_postprocess{i+1}.0.project.0.weight", f"neck.reassemble_stage.readout_projects.{i}.0.weight")) rename_keys.append((f"pretrained.act_postprocess{i+1}.0.project.0.bias", f"neck.reassemble_stage.readout_projects.{i}.0.bias")) rename_keys.append((f"pretrained.act_postprocess{i+1}.3.weight", f"neck.reassemble_stage.layers.{i}.projection.weight")) rename_keys.append((f"pretrained.act_postprocess{i+1}.3.bias", f"neck.reassemble_stage.layers.{i}.projection.bias")) if i != 2: rename_keys.append((f"pretrained.act_postprocess{i+1}.4.weight", f"neck.reassemble_stage.layers.{i}.resize.weight")) rename_keys.append((f"pretrained.act_postprocess{i+1}.4.bias", f"neck.reassemble_stage.layers.{i}.resize.bias")) # refinenet (tricky here) mapping = {1:3, 2:2, 3:1, 4:0} for i in range(1, 5): j = mapping[i] rename_keys.append((f"scratch.refinenet{i}.out_conv.weight", f"neck.fusion_stage.layers.{j}.projection.weight")) rename_keys.append((f"scratch.refinenet{i}.out_conv.bias", f"neck.fusion_stage.layers.{j}.projection.bias")) rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv1.weight", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution1.weight")) rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv1.bias", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution1.bias")) rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv2.weight", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution2.weight")) rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv2.bias", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution2.bias")) rename_keys.append((f"scratch.refinenet{i}.resConfUnit2.conv1.weight", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution1.weight")) rename_keys.append((f"scratch.refinenet{i}.resConfUnit2.conv1.bias", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution1.bias")) rename_keys.append((f"scratch.refinenet{i}.resConfUnit2.conv2.weight", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution2.weight")) rename_keys.append((f"scratch.refinenet{i}.resConfUnit2.conv2.bias", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution2.bias")) # scratch convolutions for i in range(4): rename_keys.append((f"scratch.layer{i+1}_rn.weight", f"neck.convs.{i}.weight")) # head for i in range(0, 5, 2): rename_keys.append((f"scratch.output_conv.{i}.weight", f"head.head.{i}.weight")) rename_keys.append((f"scratch.output_conv.{i}.bias", f"head.head.{i}.bias")) return rename_keys def remove_ignore_keys_(state_dict): ignore_keys = ["pretrained.model.head.weight", "pretrained.model.head.bias"] for k in ignore_keys: state_dict.pop(k, None) # we split up the matrix of each encoder layer into queries, keys and values def read_in_q_k_v(state_dict, config): hidden_size = config.backbone_config.hidden_size for i in range(config.backbone_config.num_hidden_layers): # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias) in_proj_weight = state_dict.pop(f"pretrained.model.blocks.{i}.attn.qkv.weight") q_bias = state_dict.pop(f"pretrained.model.blocks.{i}.attn.q_bias") v_bias = state_dict.pop(f"pretrained.model.blocks.{i}.attn.v_bias") # next, add query, keys and values (in that order) to the state dict state_dict[f"backbone.encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[:hidden_size, :] state_dict[f"backbone.encoder.layer.{i}.attention.attention.query.bias"] = q_bias state_dict[f"backbone.encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[ hidden_size : hidden_size * 2, : ] state_dict[f"backbone.encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[-hidden_size:, :] state_dict[f"backbone.encoder.layer.{i}.attention.attention.value.bias"] = v_bias def rename_key(dct, old, new): val = dct.pop(old) dct[new] = val # We will verify our results on an image of cute cats def prepare_img(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" im = Image.open(requests.get(url, stream=True).raw) return im @torch.no_grad() def convert_dpt_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub): """ Copy/paste/tweak model's weights to our DPT structure. """ name_to_url = { "dpt-beit-large-512": "https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_large_512.pt", "dpt-beit-large-384": "https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_large_384.pt", "dpt-beit-base-384": "https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_base_384.pt", } # define DPT configuration based on URL checkpoint_url = name_to_url[model_name] config, image_size = get_dpt_config(model_name) # load original state_dict from URL state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu") # remove certain keys remove_ignore_keys_(state_dict) # rename keys rename_keys = create_rename_keys(config) for src, dest in rename_keys: rename_key(state_dict, src, dest) # read in qkv matrices read_in_q_k_v(state_dict, config) # load HuggingFace model model = DPTForDepthEstimation(config) missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False) print("Missing keys:", missing_keys) print("Unexpected keys:", unexpected_keys) assert missing_keys == [] # assert unexpected_keys == ["pretrained.model.fc_norm.weight", "pretrained.model.fc_norm.bias"] model.eval() # Check outputs on an image # We set `keep_aspect_ratio=False` as our current BEiT does not support arbitrary window sizes processor = DPTImageProcessor( size={"height": image_size, "width": image_size}, keep_aspect_ratio=False, ensure_multiple_of=32 ) image = prepare_img() pixel_values = processor(image, return_tensors="pt").pixel_values print("First values of pixel values:", pixel_values[0, 0, :3, :3]) print("Mean of pixel values:", pixel_values.mean().item()) print("Shape of pixel values:", pixel_values.shape) import requests from PIL import Image from torchvision import transforms url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) transforms = transforms.Compose( [ transforms.Resize((image_size, image_size)), transforms.ToTensor(), ] ) pixel_values = transforms(image).unsqueeze(0) # forward pass with torch.no_grad(): outputs = model(pixel_values) predicted_depth = outputs.predicted_depth print("Shape of predicted depth:", predicted_depth.shape) print("First values of predicted depth:", predicted_depth[0, :3, :3]) # assert logits # TODO there's still a small difference with the original logits if model_name == "dpt-beit-large-512": # OK, checked expected_shape = torch.Size([1, 512, 512]) expected_slice = torch.tensor( [[2804.6260, 2792.5708, 2812.9263], [2772.0288, 2780.1118, 2796.2529], [2748.1094, 2766.6558, 2766.9834]] ) elif model_name == "dpt-beit-large-384": # OK, checked expected_shape = torch.Size([1, 384, 384]) expected_slice = torch.tensor( [[1783.2273, 1780.5729, 1792.6453], [1759.9817, 1765.5359, 1778.5002], [1739.1633, 1754.7903, 1757.1990]], ) elif model_name == "dpt-beit-base-384": # OK, checked expected_shape = torch.Size([1, 384, 384]) expected_slice = torch.tensor( [[2898.4482, 2891.3750, 2904.8079], [2858.6685, 2877.2615, 2894.4507], [2842.1235, 2854.1023, 2861.6328]], ) assert predicted_depth.shape == torch.Size(expected_shape) assert torch.allclose(predicted_depth[0, :3, :3], expected_slice) print("Looks ok!") if pytorch_dump_folder_path is not None: Path(pytorch_dump_folder_path).mkdir(exist_ok=True) print(f"Saving model and processor to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) processor.save_pretrained(pytorch_dump_folder_path) if push_to_hub: print("Pushing model and processor to hub...") model.push_to_hub(repo_id=f"nielsr/{model_name}") processor.push_to_hub(repo_id=f"nielsr/{model_name}") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default="dpt-beit-large-512", type=str, choices=["dpt-beit-large-512", "dpt-beit-large-384", "dpt-beit-base-384"], help="Name of the 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 to push the model to the hub after conversion.", ) args = parser.parse_args() convert_dpt_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)