jshang-bdai
commited on
Commit
•
a6f613c
1
Parent(s):
48f2884
Upload model
Browse files- README.md +199 -0
- config.json +50 -0
- model.safetensors +3 -0
- theia_model.py +1495 -0
README.md
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---
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library_name: transformers
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tags: []
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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config.json
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{
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"architectures": [
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"TheiaModel"
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],
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"auto_map": {
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"AutoConfig": "theia_model.TheiaConfig",
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"AutoModel": "theia_model.TheiaModel"
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},
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"backbone": "facebook/deit-small-patch16-224",
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"feature_neck": false,
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"feature_neck_hidden_dim": 256,
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"feature_neck_nonlinearity": "relu",
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"feature_reduce_method": null,
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"forward_neck": false,
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"image_size": 224,
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"num_reg_tokens": 0,
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"pretrained": false,
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"target_feature_sizes": {
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"LiheYoung/depth-anything-large-hf": [
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32,
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64,
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64
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],
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"facebook/dinov2-large": [
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1024,
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16,
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16
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],
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"facebook/sam-vit-huge": [
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256,
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64,
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64
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],
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"google/vit-huge-patch14-224-in21k": [
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1280,
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16,
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16
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],
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"openai/clip-vit-large-patch14": [
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1024,
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16,
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16
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]
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},
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"target_loss_weights": null,
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"torch_dtype": "float32",
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"transformers_version": "4.45.1",
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"translator_hidden_size_factor": 1.0,
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"translator_type": "lconv"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:480e75f2d90e92b141ca485618ade82e4712b28302b4b40da21fa9d14c66c626
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size 211636624
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theia_model.py
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|
1 |
+
# Copyright (c) 2024 Boston Dynamics AI Institute LLC. All rights reserved.
|
2 |
+
|
3 |
+
import math
|
4 |
+
from itertools import chain
|
5 |
+
from typing import Any, Optional
|
6 |
+
from omegaconf import OmegaConf
|
7 |
+
|
8 |
+
import torch
|
9 |
+
import torch.nn as nn
|
10 |
+
import torch.nn.functional as F
|
11 |
+
from torch.nn.functional import interpolate
|
12 |
+
from einops.layers.torch import Rearrange
|
13 |
+
|
14 |
+
from transformers import PretrainedConfig, PreTrainedModel
|
15 |
+
from transformers import AutoConfig, AutoModel, AutoProcessor, AutoImageProcessor
|
16 |
+
from transformers.models.vit.modeling_vit import ViTEmbeddings, ViTModel
|
17 |
+
|
18 |
+
def handle_feature_output(
|
19 |
+
x: torch.Tensor, feature_reduce_method: Optional[str] = None, num_discard_tokens: int = 0
|
20 |
+
) -> torch.Tensor:
|
21 |
+
"""Handle feature output from transformer.
|
22 |
+
|
23 |
+
Args:
|
24 |
+
x (torch.Tensor): input feature to be handled. shape is
|
25 |
+
[B, 1+H*W+N, C] if including both CLS and register tokens.
|
26 |
+
[B, 1+H*W, C] for standard model (N=0).
|
27 |
+
[B, H*W, C] for model without CLS.
|
28 |
+
feature_reduce_method (Optional[str]): method to select token. Options:
|
29 |
+
- `mean_pooling`: average over spatial tokens (non CLS tokens), output shape = [B, C].
|
30 |
+
- `max_pooling`: max over spatial tokens, output shape = [B, C].
|
31 |
+
- `cls`: return CLS token only, output shape = [B, C].
|
32 |
+
- `identity`: return the feature without touching it, output shape = input shape.
|
33 |
+
- `None`: return spatial tokens, output shape = [B, H*W, C] (assuming input is [B, 1+H*W, C]).
|
34 |
+
suppose raw feature is in shape [B, 1+H*W, C], `1` corresponds to CLS token.
|
35 |
+
num_discard_tokens (int):
|
36 |
+
number of tokens to be discarded. Assuming they are at the end of the sequence.
|
37 |
+
Returns:
|
38 |
+
torch.Tensor: selected feature tokens.
|
39 |
+
"""
|
40 |
+
|
41 |
+
match feature_reduce_method:
|
42 |
+
case "mean_pooling":
|
43 |
+
return torch.mean(x[:, 1 : x.size(1) - num_discard_tokens], dim=1) # [B, C]
|
44 |
+
case "max_pooling":
|
45 |
+
return torch.amax(x[:, 1 : x.size(1) - num_discard_tokens], dim=1) # [B, C]
|
46 |
+
case "cls":
|
47 |
+
return x[:, 0] # [B, C]
|
48 |
+
case "identity":
|
49 |
+
return x
|
50 |
+
case None:
|
51 |
+
return x[:, 1 : x.size(1) - num_discard_tokens]
|
52 |
+
case _:
|
53 |
+
raise NotImplementedError(f"feature_reduce_method {feature_reduce_method} it not implemented.")
|
54 |
+
|
55 |
+
|
56 |
+
# Modified from huggingface transformers ViTEmbeddings
|
57 |
+
# Original Copyright 2021 The HuggingFace Inc. team. All rights reserved.
|
58 |
+
#
|
59 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
60 |
+
# you may not use this file except in compliance with the License.
|
61 |
+
# You may obtain a copy of the License at
|
62 |
+
#
|
63 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
64 |
+
#
|
65 |
+
# Unless required by applicable law or agreed to in writing, software
|
66 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
67 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
68 |
+
# See the License for the specific language governing permissions and
|
69 |
+
# limitations under the License.
|
70 |
+
class ViTEmbeddingsNoCLS(ViTEmbeddings):
|
71 |
+
"""ViT Embedding Module without CLS token."""
|
72 |
+
|
73 |
+
def __init__(self, config: AutoConfig, use_mask_token: bool = False):
|
74 |
+
"""Initialization.
|
75 |
+
|
76 |
+
Args:
|
77 |
+
config (AutoConfig): config for ViT.
|
78 |
+
use_mask_token (bool, optional): whether to use mask token. Defaults to False.
|
79 |
+
"""
|
80 |
+
super(ViTEmbeddingsNoCLS, self).__init__(config, use_mask_token=use_mask_token)
|
81 |
+
self.cls_token = None
|
82 |
+
|
83 |
+
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
|
84 |
+
"""
|
85 |
+
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
|
86 |
+
resolution images.
|
87 |
+
|
88 |
+
Source:
|
89 |
+
https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
|
90 |
+
"""
|
91 |
+
|
92 |
+
num_patches = embeddings.shape[1]
|
93 |
+
num_positions = self.position_embeddings.shape[1] - 1
|
94 |
+
if num_patches == num_positions and height == width:
|
95 |
+
return self.position_embeddings
|
96 |
+
patch_pos_embed = self.position_embeddings[:, 1:]
|
97 |
+
dim = embeddings.shape[-1]
|
98 |
+
h0 = height // self.config.patch_size
|
99 |
+
w0 = width // self.config.patch_size
|
100 |
+
# we add a small number to avoid floating point error in the interpolation
|
101 |
+
# see discussion at https://github.com/facebookresearch/dino/issues/8
|
102 |
+
h0, w0 = h0 + 0.1, w0 + 0.1
|
103 |
+
patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim)
|
104 |
+
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
|
105 |
+
patch_pos_embed = nn.functional.interpolate(
|
106 |
+
patch_pos_embed,
|
107 |
+
scale_factor=(h0 / math.sqrt(num_positions), w0 / math.sqrt(num_positions)),
|
108 |
+
mode="bicubic",
|
109 |
+
align_corners=False,
|
110 |
+
)
|
111 |
+
assert int(h0) == patch_pos_embed.shape[-2] and int(w0) == patch_pos_embed.shape[-1]
|
112 |
+
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
|
113 |
+
return patch_pos_embed
|
114 |
+
|
115 |
+
def forward(
|
116 |
+
self,
|
117 |
+
pixel_values: torch.Tensor,
|
118 |
+
bool_masked_pos: Optional[torch.BoolTensor] = None,
|
119 |
+
interpolate_pos_encoding: bool = False,
|
120 |
+
) -> torch.Tensor:
|
121 |
+
batch_size, num_channels, height, width = pixel_values.shape
|
122 |
+
embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
|
123 |
+
|
124 |
+
if bool_masked_pos is not None:
|
125 |
+
seq_length = embeddings.shape[1]
|
126 |
+
mask_tokens = self.mask_token.expand(batch_size, seq_length, -1)
|
127 |
+
# replace the masked visual tokens by mask_tokens
|
128 |
+
mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
|
129 |
+
embeddings = embeddings * (1.0 - mask) + mask_tokens * mask
|
130 |
+
|
131 |
+
# add positional encoding to each token
|
132 |
+
if interpolate_pos_encoding:
|
133 |
+
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
|
134 |
+
else:
|
135 |
+
embeddings = embeddings + self.position_embeddings[:, 1:]
|
136 |
+
|
137 |
+
embeddings = self.dropout(embeddings)
|
138 |
+
|
139 |
+
return embeddings
|
140 |
+
|
141 |
+
|
142 |
+
# modified from huggingface transformers ViTModel
|
143 |
+
class ViTModelNoCLS(ViTModel):
|
144 |
+
"""ViT Model without CLS token."""
|
145 |
+
|
146 |
+
def __init__(self, config: AutoConfig, add_pooling_layer: bool = True, use_mask_token: bool = False) -> None:
|
147 |
+
super(ViTModelNoCLS, self).__init__(config, add_pooling_layer, use_mask_token)
|
148 |
+
self.embeddings = ViTEmbeddingsNoCLS(config, use_mask_token=use_mask_token)
|
149 |
+
self.no_cls = True
|
150 |
+
|
151 |
+
def _init_weights(self, module: nn.Linear | nn.Conv2d | nn.LayerNorm) -> None:
|
152 |
+
"""Initialize the weights"""
|
153 |
+
if isinstance(module, (nn.Linear, nn.Conv2d)):
|
154 |
+
# Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
|
155 |
+
# `trunc_normal_cpu` not implemented in `half` issues
|
156 |
+
module.weight.data = nn.init.trunc_normal_(
|
157 |
+
module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range
|
158 |
+
).to(module.weight.dtype)
|
159 |
+
if module.bias is not None:
|
160 |
+
module.bias.data.zero_()
|
161 |
+
elif isinstance(module, nn.LayerNorm):
|
162 |
+
module.bias.data.zero_()
|
163 |
+
module.weight.data.fill_(1.0)
|
164 |
+
elif isinstance(module, ViTEmbeddings):
|
165 |
+
module.position_embeddings.data = nn.init.trunc_normal_(
|
166 |
+
module.position_embeddings.data.to(torch.float32),
|
167 |
+
mean=0.0,
|
168 |
+
std=self.config.initializer_range,
|
169 |
+
).to(module.position_embeddings.dtype)
|
170 |
+
|
171 |
+
|
172 |
+
# modified from huggingface transformers ViTEmbeddings
|
173 |
+
class ViTEmbeddingsReg(ViTEmbeddings):
|
174 |
+
"""
|
175 |
+
ViT Embedding Module with register tokens. https://openreview.net/forum?id=2dnO3LLiJ1
|
176 |
+
"""
|
177 |
+
|
178 |
+
def __init__(self, config: AutoConfig, use_mask_token: bool = False, num_reg_tokens: int = 7):
|
179 |
+
super(ViTEmbeddingsReg, self).__init__(config, use_mask_token=use_mask_token)
|
180 |
+
self.reg_token = nn.Parameter(torch.randn(1, num_reg_tokens, config.hidden_size))
|
181 |
+
self.num_reg_tokens = num_reg_tokens
|
182 |
+
self.reg_pos_embed = nn.Parameter(torch.randn(1, num_reg_tokens, config.hidden_size))
|
183 |
+
|
184 |
+
self.reg_pos_embed.data = nn.init.trunc_normal_(
|
185 |
+
self.reg_pos_embed.data.to(torch.float32),
|
186 |
+
mean=0.0,
|
187 |
+
std=self.config.initializer_range,
|
188 |
+
).to(self.reg_pos_embed.dtype)
|
189 |
+
|
190 |
+
self.reg_token.data = nn.init.trunc_normal_(
|
191 |
+
self.reg_token.data.to(torch.float32),
|
192 |
+
mean=0.0,
|
193 |
+
std=self.config.initializer_range,
|
194 |
+
).to(self.reg_token.dtype)
|
195 |
+
|
196 |
+
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
|
197 |
+
"""
|
198 |
+
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
|
199 |
+
resolution images.
|
200 |
+
|
201 |
+
Source:
|
202 |
+
https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
|
203 |
+
"""
|
204 |
+
|
205 |
+
num_patches = embeddings.shape[1] - 1 - self.num_reg_tokens
|
206 |
+
num_positions = self.position_embeddings.shape[1] - 1
|
207 |
+
if num_patches == num_positions and height == width:
|
208 |
+
return self.position_embeddings
|
209 |
+
class_pos_embed = self.position_embeddings[:, 0]
|
210 |
+
patch_pos_embed = self.position_embeddings[:, 1:]
|
211 |
+
reg_pos_embed = self.reg_pos_embed
|
212 |
+
dim = embeddings.shape[-1]
|
213 |
+
h0 = height // self.config.patch_size
|
214 |
+
w0 = width // self.config.patch_size
|
215 |
+
# we add a small number to avoid floating point error in the interpolation
|
216 |
+
# see discussion at https://github.com/facebookresearch/dino/issues/8
|
217 |
+
h0, w0 = h0 + 0.1, w0 + 0.1
|
218 |
+
patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim)
|
219 |
+
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
|
220 |
+
patch_pos_embed = nn.functional.interpolate(
|
221 |
+
patch_pos_embed,
|
222 |
+
scale_factor=(h0 / math.sqrt(num_positions), w0 / math.sqrt(num_positions)),
|
223 |
+
mode="bicubic",
|
224 |
+
align_corners=False,
|
225 |
+
)
|
226 |
+
assert int(h0) == patch_pos_embed.shape[-2] and int(w0) == patch_pos_embed.shape[-1]
|
227 |
+
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
|
228 |
+
return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed, reg_pos_embed), dim=1)
|
229 |
+
|
230 |
+
def forward(
|
231 |
+
self,
|
232 |
+
pixel_values: torch.Tensor,
|
233 |
+
bool_masked_pos: Optional[torch.BoolTensor] = None,
|
234 |
+
interpolate_pos_encoding: bool = False,
|
235 |
+
) -> torch.Tensor:
|
236 |
+
batch_size, num_channels, height, width = pixel_values.shape
|
237 |
+
embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
|
238 |
+
|
239 |
+
if bool_masked_pos is not None:
|
240 |
+
seq_length = embeddings.shape[1]
|
241 |
+
mask_tokens = self.mask_token.expand(batch_size, seq_length, -1)
|
242 |
+
# replace the masked visual tokens by mask_tokens
|
243 |
+
mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
|
244 |
+
embeddings = embeddings * (1.0 - mask) + mask_tokens * mask
|
245 |
+
|
246 |
+
# add the [CLS] token to the embedded patch tokens
|
247 |
+
cls_tokens = self.cls_token.expand(batch_size, -1, -1)
|
248 |
+
reg_tokens = self.reg_token.expand(batch_size, -1, -1)
|
249 |
+
embeddings = torch.cat((cls_tokens, embeddings, reg_tokens), dim=1)
|
250 |
+
|
251 |
+
# add positional encoding to each token
|
252 |
+
if interpolate_pos_encoding:
|
253 |
+
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
|
254 |
+
else:
|
255 |
+
embeddings = embeddings + torch.cat([self.position_embeddings, self.reg_pos_embed], dim=1)
|
256 |
+
|
257 |
+
embeddings = self.dropout(embeddings)
|
258 |
+
|
259 |
+
return embeddings
|
260 |
+
|
261 |
+
|
262 |
+
# modified from huggingface transformers ViTModel
|
263 |
+
class ViTModelReg(ViTModel):
|
264 |
+
"""ViT Model with register tokens. https://openreview.net/forum?id=2dnO3LLiJ1"""
|
265 |
+
|
266 |
+
def __init__(
|
267 |
+
self, config: AutoConfig, add_pooling_layer: bool = True, use_mask_token: bool = False, num_reg_tokens: int = 7
|
268 |
+
):
|
269 |
+
super(ViTModelReg, self).__init__(config, add_pooling_layer, use_mask_token)
|
270 |
+
self.embeddings = ViTEmbeddingsReg(config, use_mask_token=use_mask_token, num_reg_tokens=num_reg_tokens)
|
271 |
+
self.num_reg_tokens = num_reg_tokens
|
272 |
+
|
273 |
+
def _init_weights(self, module: nn.Linear | nn.Conv2d | nn.LayerNorm) -> None:
|
274 |
+
"""Initialize the weights"""
|
275 |
+
if isinstance(module, (nn.Linear, nn.Conv2d)):
|
276 |
+
# Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
|
277 |
+
# `trunc_normal_cpu` not implemented in `half` issues
|
278 |
+
module.weight.data = nn.init.trunc_normal_(
|
279 |
+
module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range
|
280 |
+
).to(module.weight.dtype)
|
281 |
+
if module.bias is not None:
|
282 |
+
module.bias.data.zero_()
|
283 |
+
elif isinstance(module, nn.LayerNorm):
|
284 |
+
module.bias.data.zero_()
|
285 |
+
module.weight.data.fill_(1.0)
|
286 |
+
elif isinstance(module, ViTEmbeddings):
|
287 |
+
module.position_embeddings.data = nn.init.trunc_normal_(
|
288 |
+
module.position_embeddings.data.to(torch.float32),
|
289 |
+
mean=0.0,
|
290 |
+
std=self.config.initializer_range,
|
291 |
+
).to(module.position_embeddings.dtype)
|
292 |
+
module.cls_token.data = nn.init.trunc_normal_(
|
293 |
+
module.cls_token.data.to(torch.float32),
|
294 |
+
mean=0.0,
|
295 |
+
std=self.config.initializer_range,
|
296 |
+
).to(module.cls_token.dtype)
|
297 |
+
|
298 |
+
|
299 |
+
class DeiT(nn.Module):
|
300 |
+
"""DeiT model.
|
301 |
+
|
302 |
+
Paper: Training data-efficient image transformers & distillation through attention
|
303 |
+
https://arxiv.org/abs/2012.12877
|
304 |
+
Huggingface Reference: https://huggingface.co/docs/transformers/en/model_doc/deit
|
305 |
+
|
306 |
+
Attributes:
|
307 |
+
model_name (str): name of the model.
|
308 |
+
pretrained (bool): whether to use pretrained weights.
|
309 |
+
"""
|
310 |
+
|
311 |
+
def __init__(
|
312 |
+
self,
|
313 |
+
model_name: str = "facebook/deit-small-patch16-224",
|
314 |
+
pretrained: bool = False,
|
315 |
+
image_size: int = 224,
|
316 |
+
):
|
317 |
+
super().__init__()
|
318 |
+
self.image_size = image_size
|
319 |
+
model = AutoModel.from_pretrained(model_name)
|
320 |
+
if pretrained:
|
321 |
+
self.model = model
|
322 |
+
else:
|
323 |
+
deit_config = model.config
|
324 |
+
self.model = AutoModel.from_config(deit_config)
|
325 |
+
del model
|
326 |
+
|
327 |
+
self.model.pooler = nn.Identity()
|
328 |
+
|
329 |
+
self.processor = AutoProcessor.from_pretrained(model_name)
|
330 |
+
|
331 |
+
def get_feature_size(
|
332 |
+
self,
|
333 |
+
keep_spatial: bool = False,
|
334 |
+
return_torch_size: bool = False,
|
335 |
+
) -> torch.Size | tuple[int, ...]:
|
336 |
+
"""Get the size of the feature.
|
337 |
+
|
338 |
+
Args:
|
339 |
+
keep_spatial (bool): keep spatial dim of the feature shape. Defaults to False.
|
340 |
+
return_torch_size (bool): if true, return torch.Size type. Defaults to False.
|
341 |
+
|
342 |
+
Returns:
|
343 |
+
torch.Size | tuple[int, ...]: returned feature shape.
|
344 |
+
"""
|
345 |
+
with torch.inference_mode():
|
346 |
+
image_size = (224, 224)
|
347 |
+
x = torch.zeros((1, *image_size, 3), dtype=torch.uint8)
|
348 |
+
y = self.forward(x)[:, 1:] # for getting feature size, discard cls token
|
349 |
+
size = y.size()[1:][::-1]
|
350 |
+
if keep_spatial:
|
351 |
+
assert math.isqrt(size[-1])
|
352 |
+
h = w = int(math.sqrt(size[-1]))
|
353 |
+
size = (size[0], h, w)
|
354 |
+
if return_torch_size:
|
355 |
+
size = torch.Size(size)
|
356 |
+
return size
|
357 |
+
|
358 |
+
def forward(
|
359 |
+
self,
|
360 |
+
x: torch.Tensor,
|
361 |
+
do_resize: bool = True,
|
362 |
+
interpolate_pos_encoding: Optional[bool] = None,
|
363 |
+
do_rescale: bool = True,
|
364 |
+
do_normalize: bool = True,
|
365 |
+
) -> torch.Tensor:
|
366 |
+
"""Forward pass of the model
|
367 |
+
|
368 |
+
Args:
|
369 |
+
x (torch.Tensor): model input.
|
370 |
+
|
371 |
+
- arguments for self.processor. Details can be find at
|
372 |
+
https://huggingface.co/docs/transformers/v4.41.3/en/model_doc/deit#transformers.DeiTImageProcessor
|
373 |
+
do_resize (bool): if do resizing in processor. Defaults to True.
|
374 |
+
interpolate_pos_encoding (bool): if interpolate the positional embedding. Defaults to None.
|
375 |
+
do_rescale (bool): if do rescaling (0-255 -> 0-1) in processor. Defaults to True.
|
376 |
+
do_normalize (bool): if do normalize in processor. Defaults to True.
|
377 |
+
|
378 |
+
Returns:
|
379 |
+
torch.Tensor: model output.
|
380 |
+
"""
|
381 |
+
input = self.processor(
|
382 |
+
x, return_tensors="pt", do_resize=do_resize, do_rescale=do_rescale, do_normalize=do_normalize
|
383 |
+
).to(self.model.device)
|
384 |
+
y = self.model(**input, interpolate_pos_encoding=interpolate_pos_encoding)
|
385 |
+
return y.last_hidden_state
|
386 |
+
|
387 |
+
|
388 |
+
class DeiTNoCLS(nn.Module):
|
389 |
+
"""Modified DeiT model without CLS token."""
|
390 |
+
|
391 |
+
def __init__(
|
392 |
+
self, model_name: str = "nocls-facebook/deit-small-patch16-224", pretrained: bool = False, image_size: int = 224
|
393 |
+
):
|
394 |
+
super().__init__()
|
395 |
+
self.image_size = image_size
|
396 |
+
pretrained_model_name = model_name.replace("nocls-", "")
|
397 |
+
deit_config = AutoConfig.from_pretrained(pretrained_model_name)
|
398 |
+
self.model = ViTModelNoCLS(deit_config)
|
399 |
+
if pretrained:
|
400 |
+
pretrained_model = AutoModel.from_pretrained(pretrained_model_name)
|
401 |
+
pretrained_dict = {k: v for k, v in pretrained_model.state_dict().items() if k in self.model.state_dict()}
|
402 |
+
self.load_state_dict(pretrained_dict, strict=False)
|
403 |
+
del pretrained_model, pretrained_dict
|
404 |
+
|
405 |
+
self.model.pooler = nn.Identity()
|
406 |
+
self.processor = AutoProcessor.from_pretrained(pretrained_model_name)
|
407 |
+
self.no_cls = True
|
408 |
+
|
409 |
+
def get_feature_size(
|
410 |
+
self,
|
411 |
+
keep_spatial: bool = False,
|
412 |
+
return_torch_size: bool = False,
|
413 |
+
) -> torch.Size | tuple[int, ...]:
|
414 |
+
"""Get the size of the feature.
|
415 |
+
|
416 |
+
Args:
|
417 |
+
keep_spatial (bool): keep spatial dim of the feature shape. Defaults to False.
|
418 |
+
return_torch_size (bool): if true, return torch.Size type. Defaults to False.
|
419 |
+
|
420 |
+
Returns:
|
421 |
+
torch.Size | tuple[int, ...]: returned feature shape.
|
422 |
+
"""
|
423 |
+
with torch.inference_mode():
|
424 |
+
image_size = (self.image_size, self.image_size)
|
425 |
+
x = torch.zeros((1, *image_size, 3), dtype=torch.uint8)
|
426 |
+
y = self.forward(x)
|
427 |
+
size = y.size()[1:][::-1]
|
428 |
+
if keep_spatial:
|
429 |
+
assert math.isqrt(size[-1])
|
430 |
+
h = w = int(math.sqrt(size[-1]))
|
431 |
+
size = (size[0], h, w)
|
432 |
+
if return_torch_size:
|
433 |
+
size = torch.Size(size)
|
434 |
+
return size
|
435 |
+
|
436 |
+
def forward(
|
437 |
+
self,
|
438 |
+
x: torch.Tensor,
|
439 |
+
do_resize: bool = True,
|
440 |
+
interpolate_pos_encoding: Optional[bool] = None,
|
441 |
+
do_rescale: bool = True,
|
442 |
+
do_normalize: bool = True,
|
443 |
+
) -> torch.Tensor:
|
444 |
+
"""Forward pass of the model
|
445 |
+
|
446 |
+
Args:
|
447 |
+
x (torch.Tensor): model input.
|
448 |
+
|
449 |
+
- arguments for self.processor. Details can be find at
|
450 |
+
https://huggingface.co/docs/transformers/v4.41.3/en/model_doc/deit#transformers.DeiTImageProcessor
|
451 |
+
do_resize (bool): if do resizing in processor. Defaults to True.
|
452 |
+
do_rescale (bool): if do rescaling (0-255 -> 0-1) in processor. Defaults to True.
|
453 |
+
do_normalize (bool): if do normalize in processor. Defaults to True.
|
454 |
+
|
455 |
+
- argument for forward
|
456 |
+
interpolate_pos_encoding (bool): if interpolate the positional embedding. Defaults to None.
|
457 |
+
|
458 |
+
Returns:
|
459 |
+
torch.Tensor: model output.
|
460 |
+
"""
|
461 |
+
input = self.processor(
|
462 |
+
x, return_tensors="pt", do_resize=do_resize, do_rescale=do_rescale, do_normalize=do_normalize
|
463 |
+
).to(self.model.device)
|
464 |
+
y = self.model(**input, interpolate_pos_encoding=interpolate_pos_encoding)
|
465 |
+
return y.last_hidden_state
|
466 |
+
|
467 |
+
|
468 |
+
class DeiTReg(nn.Module):
|
469 |
+
"""Modified DeiT model with register tokens."""
|
470 |
+
|
471 |
+
def __init__(
|
472 |
+
self,
|
473 |
+
model_name: str = "reg-facebook/deit-small-patch16-224",
|
474 |
+
pretrained: bool = False,
|
475 |
+
image_size: int = 224,
|
476 |
+
num_reg_tokens: int = 7,
|
477 |
+
):
|
478 |
+
super().__init__()
|
479 |
+
self.image_size = image_size
|
480 |
+
pretrained_model_name = model_name.replace("reg-", "")
|
481 |
+
deit_config = AutoConfig.from_pretrained(pretrained_model_name)
|
482 |
+
self.model = ViTModelReg(deit_config, num_reg_tokens=num_reg_tokens)
|
483 |
+
if pretrained:
|
484 |
+
pretrained_model = AutoModel.from_pretrained(pretrained_model_name)
|
485 |
+
pretrained_dict = {k: v for k, v in pretrained_model.state_dict().items() if k in self.model.state_dict()}
|
486 |
+
self.load_state_dict(pretrained_dict, strict=False)
|
487 |
+
del pretrained_model, pretrained_dict
|
488 |
+
|
489 |
+
self.model.pooler = nn.Identity()
|
490 |
+
self.processor = AutoProcessor.from_pretrained(pretrained_model_name)
|
491 |
+
self.num_reg_tokens = num_reg_tokens
|
492 |
+
|
493 |
+
def get_feature_size(
|
494 |
+
self,
|
495 |
+
keep_spatial: bool = False,
|
496 |
+
return_torch_size: bool = False,
|
497 |
+
) -> torch.Size | tuple[int, ...]:
|
498 |
+
"""Get the size of the feature.
|
499 |
+
|
500 |
+
Args:
|
501 |
+
keep_spatial (bool): keep spatial dim of the feature shape. Defaults to False.
|
502 |
+
return_torch_size (bool): if true, return torch.Size type. Defaults to False.
|
503 |
+
|
504 |
+
Returns:
|
505 |
+
torch.Size | tuple[int, ...]: returned feature shape.
|
506 |
+
"""
|
507 |
+
with torch.inference_mode():
|
508 |
+
image_size = (self.image_size, self.image_size)
|
509 |
+
x = torch.zeros((1, *image_size, 3), dtype=torch.uint8)
|
510 |
+
y = self.forward(x)[:, 1 : -self.num_reg_tokens]
|
511 |
+
size = y.size()[1:][::-1]
|
512 |
+
if keep_spatial:
|
513 |
+
assert math.isqrt(size[-1])
|
514 |
+
h = w = int(math.sqrt(size[-1]))
|
515 |
+
size = (size[0], h, w)
|
516 |
+
if return_torch_size:
|
517 |
+
size = torch.Size(size)
|
518 |
+
return size
|
519 |
+
|
520 |
+
def forward(
|
521 |
+
self,
|
522 |
+
x: torch.Tensor,
|
523 |
+
do_resize: bool = True,
|
524 |
+
interpolate_pos_encoding: Optional[bool] = None,
|
525 |
+
do_rescale: bool = True,
|
526 |
+
do_normalize: bool = True,
|
527 |
+
) -> torch.Tensor:
|
528 |
+
"""Forward pass of the model
|
529 |
+
|
530 |
+
Args:
|
531 |
+
x (torch.Tensor): model input.
|
532 |
+
|
533 |
+
- arguments for self.processor. Details can be find at
|
534 |
+
https://huggingface.co/docs/transformers/v4.41.3/en/model_doc/deit#transformers.DeiTImageProcessor
|
535 |
+
do_resize (bool): if do resizing in processor. Defaults to True.
|
536 |
+
interpolate_pos_encoding (bool): if interpolate the positional embedding. Defaults to None.
|
537 |
+
do_rescale (bool): if do rescaling (0-255 -> 0-1) in processor. Defaults to True.
|
538 |
+
do_normalize (bool): if do normalize in processor. Defaults to True.
|
539 |
+
|
540 |
+
Returns:
|
541 |
+
torch.Tensor: model output.
|
542 |
+
"""
|
543 |
+
input = self.processor(
|
544 |
+
x, return_tensors="pt", do_resize=do_resize, do_rescale=do_rescale, do_normalize=do_normalize
|
545 |
+
).to(self.model.device)
|
546 |
+
y = self.model(**input, interpolate_pos_encoding=interpolate_pos_encoding)
|
547 |
+
return y.last_hidden_state
|
548 |
+
|
549 |
+
|
550 |
+
def build_backbone(model_name: str, pretrained: bool = False, image_size: int = 224, **kwargs: Any) -> nn.Module:
|
551 |
+
"""Build the backbone visual encoder of robot vision foundation model.
|
552 |
+
|
553 |
+
Args:
|
554 |
+
model_name (str): name of the model.
|
555 |
+
pretrained (bool): whether to use pretrained weights. Defaults to False.
|
556 |
+
image_size (int): size of the image. Assume a square image. Defaults to 224
|
557 |
+
kwargs (Any): any kwargs specific to some models. For example,
|
558 |
+
`num_reg_tokens` for `DeiTReg` when `"reg"` in `model_name`
|
559 |
+
|
560 |
+
Returns:
|
561 |
+
nn.Module: backbone network.
|
562 |
+
"""
|
563 |
+
if "reg" in model_name:
|
564 |
+
return DeiTReg(model_name=model_name, pretrained=pretrained, image_size=image_size, **kwargs)
|
565 |
+
elif "nocls" in model_name:
|
566 |
+
return DeiTNoCLS(model_name=model_name, pretrained=pretrained, image_size=image_size, **kwargs)
|
567 |
+
elif "deit" in model_name:
|
568 |
+
return DeiT(model_name=model_name, pretrained=pretrained, image_size=image_size)
|
569 |
+
else:
|
570 |
+
raise NotImplementedError(f"Requested {model_name} is not implemented.")
|
571 |
+
|
572 |
+
class Interpolation(nn.Module):
|
573 |
+
"""Interpolation nn.Module wrap for nn.functional.interpolate.
|
574 |
+
|
575 |
+
Attributes:
|
576 |
+
target_size (tuple[int, int] | torch.Size): target spatial size of this interpolation.
|
577 |
+
"""
|
578 |
+
|
579 |
+
def __init__(self, target_size: tuple[int, int] | torch.Size) -> None:
|
580 |
+
super().__init__()
|
581 |
+
self.target_size = target_size
|
582 |
+
|
583 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
584 |
+
"""Very simple forward pass to call interpolate()."""
|
585 |
+
return interpolate(x, self.target_size)
|
586 |
+
|
587 |
+
|
588 |
+
class LinearAdapterHead(nn.Module):
|
589 |
+
"""Adapter head contains a single linear layer."""
|
590 |
+
def __init__(
|
591 |
+
self, source_size: tuple[int, ...] | torch.Size, target_size: tuple[int, ...] | torch.Size
|
592 |
+
):
|
593 |
+
"""Initialization function for LinearAdapterHead.
|
594 |
+
Args:
|
595 |
+
source_size (tuple[int, ...] | torch.Size): the size of the source feature.
|
596 |
+
target_size (tuple[int, ...] | torch.Size): the size of the target feature.
|
597 |
+
num_layer (int): number of MLP layers (One linear layer if num_layer = 1).
|
598 |
+
"""
|
599 |
+
super().__init__()
|
600 |
+
|
601 |
+
self.source_size = source_size
|
602 |
+
self.target_size = target_size
|
603 |
+
|
604 |
+
source_channel_size = self.source_size[0]
|
605 |
+
target_channel_size = self.target_size[0]
|
606 |
+
|
607 |
+
self.adapter = nn.Sequential(
|
608 |
+
nn.Linear(source_channel_size, target_channel_size),
|
609 |
+
)
|
610 |
+
|
611 |
+
def forward(self, x: torch.Tensor, backbone_no_cls: bool = False) -> torch.Tensor:
|
612 |
+
"""Forward pass for the adapter. """
|
613 |
+
assert backbone_no_cls == False
|
614 |
+
# x: [B, (1+H*W), C]
|
615 |
+
# LinearAdapterHead is used only when there is cls token in the backbone.
|
616 |
+
x = x[:, 0]
|
617 |
+
x = self.adapter(x)
|
618 |
+
return x # [B, (H*W), C]
|
619 |
+
|
620 |
+
|
621 |
+
class MLPAdapterHead(nn.Module):
|
622 |
+
"""MLP Adapter module.
|
623 |
+
|
624 |
+
Transforms features in shape source size [B, (H_s*W_s), C_s] to target size [B, (H_t*W_t), C_t].
|
625 |
+
Will first do interpolation to match the spatial size [H_t, W_t],
|
626 |
+
followed by MLP to project to the target channel dimension [C_t].
|
627 |
+
|
628 |
+
Attributes:
|
629 |
+
source_size (tuple[int, ...] | torch.Size): the size of the source feature. [C, H, W]
|
630 |
+
target_size (tuple[int, ...] | torch.Size): the size of the target feature. [C, H, W]
|
631 |
+
adapter (nn.Module): the adapter module.
|
632 |
+
interpolation (nn.Module): interpolation to adjust sizes before MLP.
|
633 |
+
"""
|
634 |
+
|
635 |
+
def __init__(
|
636 |
+
self, source_size: tuple[int, ...] | torch.Size, target_size: tuple[int, ...] | torch.Size, num_layer: int
|
637 |
+
):
|
638 |
+
"""Initialization function for MLPAdapter.
|
639 |
+
|
640 |
+
Args:
|
641 |
+
source_size (tuple[int, ...] | torch.Size): the size of the source feature.
|
642 |
+
target_size (tuple[int, ...] | torch.Size): the size of the target feature.
|
643 |
+
num_layer (int): number of MLP layers (One linear layer if num_layer = 1).
|
644 |
+
"""
|
645 |
+
super().__init__()
|
646 |
+
assert num_layer >= 1, f"`num_layer` in {self._get_name()} should >= 1. Got {num_layer}"
|
647 |
+
|
648 |
+
self.source_size = source_size
|
649 |
+
self.target_size = target_size
|
650 |
+
|
651 |
+
source_channel_size = self.source_size[0]
|
652 |
+
target_channel_size = self.target_size[0]
|
653 |
+
|
654 |
+
self.interpolation = nn.Sequential(
|
655 |
+
nn.Identity(),
|
656 |
+
)
|
657 |
+
if self.source_size[1] != self.target_size[1]:
|
658 |
+
self.interpolation = nn.Sequential(
|
659 |
+
Rearrange("b (h w) c-> b c h w", h=self.source_size[1], w=self.source_size[2]),
|
660 |
+
Interpolation(self.target_size[1:]),
|
661 |
+
Rearrange("b c h w-> b (h w) c"),
|
662 |
+
)
|
663 |
+
|
664 |
+
if num_layer == 1:
|
665 |
+
self.adapter = nn.Sequential(
|
666 |
+
nn.Linear(source_channel_size, target_channel_size),
|
667 |
+
)
|
668 |
+
elif num_layer >= 2:
|
669 |
+
hidden_dim = source_channel_size * 2
|
670 |
+
self.adapter = nn.Sequential(
|
671 |
+
nn.Linear(source_channel_size, hidden_dim),
|
672 |
+
*list(
|
673 |
+
chain.from_iterable([[nn.ReLU(), nn.Linear(hidden_dim, hidden_dim)] for _ in range(num_layer - 2)])
|
674 |
+
),
|
675 |
+
nn.ReLU(),
|
676 |
+
nn.Linear(hidden_dim, target_channel_size),
|
677 |
+
)
|
678 |
+
|
679 |
+
def forward(self, x: torch.Tensor, backbone_no_cls: bool = False) -> torch.Tensor:
|
680 |
+
"""Forward pass for the adapter. First interpolation then MLP."""
|
681 |
+
# x: [B, (1)+H*W, C]
|
682 |
+
if not backbone_no_cls:
|
683 |
+
x = x[:, 1:]
|
684 |
+
# x: [B, (H*W), C]
|
685 |
+
x = self.interpolation(x)
|
686 |
+
x = self.adapter(x)
|
687 |
+
return x # [B, (H*W), C]
|
688 |
+
|
689 |
+
|
690 |
+
class ConvAdapterHead(nn.Module):
|
691 |
+
"""Convolutional Adapter module.
|
692 |
+
|
693 |
+
Transforms features in shape source size [B, (H_s*W_s), C_s] to target size [B, (H_t*W_t), C_t].
|
694 |
+
Uses CNN to map channel and spatial sizes jointly.
|
695 |
+
Note: only work for (16, 16), (any, any), any <= 14, and (64, 64) spatial sizes for now.
|
696 |
+
|
697 |
+
Attributes:
|
698 |
+
source_size (tuple[int, ...] | torch.Size): the size of the source feature.
|
699 |
+
target_size (tuple[int, ...] | torch.Size): the size of the target feature.
|
700 |
+
adapter (nn.Module): the adapter module.
|
701 |
+
interpolation (nn.Module): interpolation to adjust sizes before MLP.
|
702 |
+
"""
|
703 |
+
|
704 |
+
def __init__(
|
705 |
+
self,
|
706 |
+
source_size: tuple[int, ...] | torch.Size,
|
707 |
+
target_size: tuple[int, ...] | torch.Size,
|
708 |
+
):
|
709 |
+
"""Initialization function for ConvAdapter.
|
710 |
+
|
711 |
+
Args:
|
712 |
+
source_size (tuple[int, ...] | torch.Size): the size of the source feature.
|
713 |
+
target_size (tuple[int, ...] | torch.Size): the size of the target feature.
|
714 |
+
"""
|
715 |
+
super().__init__()
|
716 |
+
self.source_size = source_size
|
717 |
+
self.target_size = target_size
|
718 |
+
|
719 |
+
hidden_dim = self.source_size[0] * 2
|
720 |
+
source_channel_size = self.source_size[0]
|
721 |
+
target_channel_size = self.target_size[0]
|
722 |
+
|
723 |
+
if self.source_size[1] < 12:
|
724 |
+
raise NotImplementedError("feature spatial size smaller than 12x12 is not supported.")
|
725 |
+
elif self.source_size[1] < 16: # pad (any, any), any <= 14 to (16, 16)
|
726 |
+
self.pad = nn.Sequential(
|
727 |
+
Rearrange("b (h w) c-> b c h w", h=self.source_size[1], w=self.source_size[2]),
|
728 |
+
nn.ConvTranspose2d(
|
729 |
+
source_channel_size,
|
730 |
+
source_channel_size,
|
731 |
+
kernel_size=3,
|
732 |
+
stride=1,
|
733 |
+
output_padding=14 - self.source_size[1],
|
734 |
+
),
|
735 |
+
)
|
736 |
+
self.source_size = (self.source_size[0], 16, 16)
|
737 |
+
elif self.source_size[1] == 16 or self.source_size[1] == 64: # do nothing for (16, 16) and (64, 64)
|
738 |
+
self.pad = nn.Sequential(
|
739 |
+
Rearrange("b (h w) c-> b c h w", h=self.source_size[1], w=self.source_size[2]),
|
740 |
+
)
|
741 |
+
else:
|
742 |
+
raise NotImplementedError("feature spatial size (>=16x16) other than 16x16 and 64x64 is not supported.")
|
743 |
+
|
744 |
+
if self.source_size[1] < self.target_size[1]: # (16, 16) / (14, 14) to (64, 64)
|
745 |
+
self.adapter = nn.Sequential(
|
746 |
+
nn.LayerNorm(self.source_size),
|
747 |
+
nn.ConvTranspose2d(source_channel_size, hidden_dim, kernel_size=3, stride=2, padding=1), # 31
|
748 |
+
nn.ReLU(),
|
749 |
+
nn.LayerNorm([hidden_dim, 31, 31]),
|
750 |
+
nn.ConvTranspose2d(hidden_dim, hidden_dim, kernel_size=3, stride=2, output_padding=1), # 64
|
751 |
+
nn.ReLU(),
|
752 |
+
nn.LayerNorm([hidden_dim, 64, 64]),
|
753 |
+
nn.ConvTranspose2d(hidden_dim, target_channel_size, kernel_size=3, stride=1, padding=1), # 64
|
754 |
+
Rearrange("b c h w-> b (h w) c"),
|
755 |
+
)
|
756 |
+
elif self.source_size[1] == self.target_size[1]: # (16, 16) to (16, 16)
|
757 |
+
self.adapter = nn.Sequential(
|
758 |
+
nn.LayerNorm(self.source_size),
|
759 |
+
nn.Conv2d(source_channel_size, hidden_dim, kernel_size=3, padding=1), # 16
|
760 |
+
nn.ReLU(),
|
761 |
+
nn.LayerNorm([hidden_dim, *self.source_size[1:]]),
|
762 |
+
nn.Conv2d(hidden_dim, hidden_dim, kernel_size=3, padding=1), # 16
|
763 |
+
nn.ReLU(),
|
764 |
+
nn.LayerNorm([hidden_dim, *self.source_size[1:]]),
|
765 |
+
nn.Conv2d(hidden_dim, target_channel_size, kernel_size=3, padding=1), # 16
|
766 |
+
Rearrange("b c h w-> b (h w) c"),
|
767 |
+
)
|
768 |
+
else: # (64, 64) to (16, 16)
|
769 |
+
self.adapter = nn.Sequential(
|
770 |
+
nn.LayerNorm(self.source_size),
|
771 |
+
nn.Conv2d(source_channel_size, hidden_dim, kernel_size=3, stride=2, padding=1), # 32
|
772 |
+
nn.ReLU(),
|
773 |
+
nn.LayerNorm([hidden_dim, 32, 32]),
|
774 |
+
nn.Conv2d(hidden_dim, hidden_dim, kernel_size=3, stride=2, padding=1), # 16
|
775 |
+
nn.ReLU(),
|
776 |
+
nn.LayerNorm([hidden_dim, 16, 16]),
|
777 |
+
nn.Conv2d(hidden_dim, target_channel_size, kernel_size=3, padding=1), # 16
|
778 |
+
Rearrange("b c h w-> b (h w) c"),
|
779 |
+
)
|
780 |
+
|
781 |
+
def forward(self, x: torch.Tensor, backbone_no_cls: bool = False) -> torch.Tensor:
|
782 |
+
"""Forward pass for ConvAdapter"""
|
783 |
+
# x: [B, (1)+H*W, C]
|
784 |
+
if not backbone_no_cls:
|
785 |
+
x = x[:, 1:]
|
786 |
+
# x: [B, H*W, C]
|
787 |
+
x = self.pad(x)
|
788 |
+
x = self.adapter(x)
|
789 |
+
return x # B, (H*W), C
|
790 |
+
|
791 |
+
|
792 |
+
class LightConvAdapterHead(nn.Module):
|
793 |
+
"""Light Convolutional Adapter module.
|
794 |
+
|
795 |
+
Transforms features from source size in [B, (H_s*W_s), C_s] to target size [B, (H_t*W_t), C_t].
|
796 |
+
Uses CNN to map channel and spatial sizes jointly.
|
797 |
+
Note: only work for source sizes (H_s, W_s): (16, 16), (any, any), 12 <= any <= 14,
|
798 |
+
and target sizes (H_t, W_t): (16, 16) and (64, 64) for now.
|
799 |
+
|
800 |
+
Attributes:
|
801 |
+
source_size (tuple[int, ...] | torch.Size): the size of the source feature,
|
802 |
+
channel first (C, H, W).
|
803 |
+
target_size (tuple[int, ...] | torch.Size): the size of the target feature,
|
804 |
+
channel first (C, H, W).
|
805 |
+
adapter (nn.Module): the adapter module.
|
806 |
+
interpolation (nn.Module): interpolation to adjust sizes before MLP.
|
807 |
+
"""
|
808 |
+
|
809 |
+
def __init__(
|
810 |
+
self,
|
811 |
+
source_size: tuple[int, ...] | torch.Size,
|
812 |
+
target_size: tuple[int, ...] | torch.Size,
|
813 |
+
hidden_size_factor: int | float = 1.0,
|
814 |
+
):
|
815 |
+
"""Initialization function for ConvAdapter.
|
816 |
+
|
817 |
+
Args:
|
818 |
+
source_size (tuple[int, ...] | torch.Size): the size of the source feature.
|
819 |
+
target_size (tuple[int, ...] | torch.Size): the size of the target feature.
|
820 |
+
hidden_size_factor (int | float): the size of hidden dim of feature translator
|
821 |
+
as a factor of input feature hidden dim.
|
822 |
+
"""
|
823 |
+
super().__init__()
|
824 |
+
if source_size[1] != source_size[2] or target_size[1] != target_size[2]:
|
825 |
+
raise NotImplementedError(
|
826 |
+
"Currently does not support non-square feature maps like source size"
|
827 |
+
"{source_size} and target size {target_size}."
|
828 |
+
)
|
829 |
+
self.source_size = source_size
|
830 |
+
self.target_size = target_size
|
831 |
+
self.hidden_size_factor = hidden_size_factor
|
832 |
+
|
833 |
+
hidden_dim = int(self.source_size[0] * hidden_size_factor)
|
834 |
+
source_channel_size = self.source_size[0]
|
835 |
+
target_channel_size = self.target_size[0]
|
836 |
+
|
837 |
+
if self.source_size[1] < 12:
|
838 |
+
raise NotImplementedError("feature spatial size smaller than 12x12 is not supported.")
|
839 |
+
elif self.source_size[1] < 16 and self.target_size[1] >= 16: # pad (any, any), any <= 14 to (16, 16)
|
840 |
+
self.pad = nn.Sequential(
|
841 |
+
Rearrange("b (h w) c-> b c h w", h=self.source_size[1], w=self.source_size[2]),
|
842 |
+
nn.ConvTranspose2d(
|
843 |
+
source_channel_size,
|
844 |
+
source_channel_size,
|
845 |
+
kernel_size=3,
|
846 |
+
stride=1,
|
847 |
+
output_padding=14 - self.source_size[1],
|
848 |
+
),
|
849 |
+
)
|
850 |
+
self.source_size = (self.source_size[0], 16, 16)
|
851 |
+
elif (self.source_size[1] == 16 or self.source_size[1] == 64) or \
|
852 |
+
(self.source_size[1] == 14 and self.target_size[1] == 14):
|
853 |
+
# no padding for (16, 16), (64, 64) and (14, 14) <-> (14, 14)
|
854 |
+
self.pad = nn.Sequential(
|
855 |
+
Rearrange("b (h w) c-> b c h w", h=self.source_size[1], w=self.source_size[2]),
|
856 |
+
)
|
857 |
+
elif self.target_size[1] < 14:
|
858 |
+
self.pad = nn.Sequential(
|
859 |
+
Rearrange("b (h w) c-> b c h w", h=self.source_size[1], w=self.source_size[2]),
|
860 |
+
)
|
861 |
+
else:
|
862 |
+
raise NotImplementedError("feature spatial size larger than 16x16 (other than 64x64) is not supported.")
|
863 |
+
|
864 |
+
if self.source_size[1] == 16 and self.target_size[1] == 64: # (16, 16) to (64, 64)
|
865 |
+
self.adapter = nn.Sequential(
|
866 |
+
nn.LayerNorm(self.source_size),
|
867 |
+
nn.ConvTranspose2d(source_channel_size, hidden_dim, kernel_size=3, stride=2, padding=1), # 31
|
868 |
+
nn.ReLU(),
|
869 |
+
nn.LayerNorm([hidden_dim, 31, 31]),
|
870 |
+
nn.ConvTranspose2d(hidden_dim, hidden_dim, kernel_size=3, stride=2, output_padding=1), # 64
|
871 |
+
nn.ReLU(),
|
872 |
+
nn.LayerNorm([hidden_dim, 64, 64]),
|
873 |
+
Rearrange("b c h w-> b (h w) c"),
|
874 |
+
nn.Linear(hidden_dim, target_channel_size),
|
875 |
+
)
|
876 |
+
elif self.source_size[1] == self.target_size[1]: # (16, 16) to (16, 16)
|
877 |
+
self.adapter = nn.Sequential(
|
878 |
+
nn.LayerNorm(self.source_size),
|
879 |
+
nn.Conv2d(source_channel_size, hidden_dim, kernel_size=3, padding=1), # 16
|
880 |
+
nn.ReLU(),
|
881 |
+
nn.LayerNorm([hidden_dim, *self.source_size[1:]]),
|
882 |
+
nn.Conv2d(hidden_dim, hidden_dim, kernel_size=3, padding=1), # 16
|
883 |
+
nn.ReLU(),
|
884 |
+
nn.LayerNorm([hidden_dim, *self.source_size[1:]]),
|
885 |
+
Rearrange("b c h w-> b (h w) c"),
|
886 |
+
nn.Linear(hidden_dim, target_channel_size),
|
887 |
+
)
|
888 |
+
elif self.source_size[1] == 64 and self.target_size[1] == 16: # (64, 64) to (16, 16)
|
889 |
+
self.adapter = nn.Sequential(
|
890 |
+
nn.LayerNorm(self.source_size),
|
891 |
+
nn.Conv2d(source_channel_size, hidden_dim, kernel_size=3, stride=2, padding=1), # 32
|
892 |
+
nn.ReLU(),
|
893 |
+
nn.LayerNorm([hidden_dim, 32, 32]),
|
894 |
+
nn.Conv2d(hidden_dim, hidden_dim, kernel_size=3, stride=2, padding=1), # 16
|
895 |
+
nn.ReLU(),
|
896 |
+
nn.LayerNorm([hidden_dim, 16, 16]),
|
897 |
+
Rearrange("b c h w-> b (h w) c"),
|
898 |
+
nn.Linear(hidden_dim, target_channel_size),
|
899 |
+
)
|
900 |
+
elif self.target_size[1] == 7:
|
901 |
+
self.adapter = nn.Sequential(
|
902 |
+
nn.LayerNorm(self.source_size),
|
903 |
+
nn.Conv2d(source_channel_size, hidden_dim, kernel_size=4, stride=2, padding=1), #14x14 -> 7x7
|
904 |
+
nn.ReLU(),
|
905 |
+
nn.LayerNorm([hidden_dim, 7, 7]),
|
906 |
+
Rearrange("b c h w-> b (h w) c"),
|
907 |
+
nn.Linear(hidden_dim, target_channel_size)
|
908 |
+
)
|
909 |
+
else:
|
910 |
+
NotImplementedError(f"{self.source_size} to {self.target_size} is not supported.")
|
911 |
+
|
912 |
+
def forward(self, x: torch.Tensor, backbone_no_cls: bool = False) -> torch.Tensor:
|
913 |
+
"""Forward pass for ConvAdapter"""
|
914 |
+
# x: [B, (1)+H*W, C]
|
915 |
+
if not backbone_no_cls:
|
916 |
+
x = x[:, 1:]
|
917 |
+
x = self.pad(x)
|
918 |
+
x = self.adapter(x)
|
919 |
+
return x # [B, H*W, C]
|
920 |
+
|
921 |
+
|
922 |
+
class FeatureTranslator(nn.Module):
|
923 |
+
"""Base class for the feature translator.
|
924 |
+
|
925 |
+
The flow is backbone_adapter -> translator_stem -> translator_heads.
|
926 |
+
|
927 |
+
Attributes:
|
928 |
+
backbone_feature_size (torch.Size): the size of features of the backbone.
|
929 |
+
target_feature_sizes (dict[str, torch.Size | tuple[int, ...]]): the sizes of features of target models.
|
930 |
+
translator_hidden_size (int): the hidden dim of the translator. Defaults to 2048.
|
931 |
+
target_model_names (list[str]): convenient attribute to hold all the names of the target models.
|
932 |
+
|
933 |
+
backbone_adapter (nn.Module): the adapter to map channel dim of backbone to the translator hidden dim.
|
934 |
+
translator_stem (nn.Module): the shared stem for all target models.
|
935 |
+
translator_heads (nn.ModuleDict): specific heads for different target models.
|
936 |
+
"""
|
937 |
+
|
938 |
+
def __init__(
|
939 |
+
self,
|
940 |
+
backbone_feature_size: torch.Size,
|
941 |
+
target_feature_sizes: dict[str, torch.Size | tuple[int, ...]],
|
942 |
+
translator_hidden_size: int = 1024,
|
943 |
+
) -> None:
|
944 |
+
"""Initalization function for FeatureTranslator.
|
945 |
+
|
946 |
+
Args:
|
947 |
+
backbone_feature_size (torch.Size): the size of features of the backbone.
|
948 |
+
target_feature_sizes (dict[str, torch.Size | tuple[int, ...]]): the sizes of features of target models.
|
949 |
+
translator_hidden_size (int): the hidden dim of the translator. Defaults to 2048.
|
950 |
+
"""
|
951 |
+
super().__init__()
|
952 |
+
self.backbone_feature_size = backbone_feature_size # (C, H, W)
|
953 |
+
self.target_feature_sizes = target_feature_sizes # [(C, H, W)]
|
954 |
+
self.translator_hidden_size = translator_hidden_size # C
|
955 |
+
self.target_model_names = list(target_feature_sizes.keys())
|
956 |
+
self.legit_target_model_name_map: dict[str, str] = {t: t.replace(".", "_") for t in self.target_model_names}
|
957 |
+
self.translator_heads: nn.ModuleDict = None
|
958 |
+
|
959 |
+
self.backbone_adapter = nn.Sequential(
|
960 |
+
nn.LayerNorm(self.backbone_feature_size[0]), # do a pre-norm
|
961 |
+
nn.Linear(
|
962 |
+
self.backbone_feature_size[0], # C in [C,H,W]
|
963 |
+
self.translator_hidden_size,
|
964 |
+
),
|
965 |
+
)
|
966 |
+
self.translator_stem: nn.Module = nn.Identity()
|
967 |
+
self.build_translator_heads()
|
968 |
+
|
969 |
+
def build_translator_heads(self) -> None:
|
970 |
+
"""Build translator heads to match the dimension of each target feature set.
|
971 |
+
|
972 |
+
Example:
|
973 |
+
translator_heads: dict[str, nn.Module] = ...
|
974 |
+
self.translator_heads = nn.ModuleDict(translator_heads)
|
975 |
+
"""
|
976 |
+
raise NotImplementedError("build_translator_heads() should be overridden")
|
977 |
+
|
978 |
+
def forward(
|
979 |
+
self, x: torch.Tensor, target_model_names: Optional[list[str]] = None, backbone_no_cls: bool = False
|
980 |
+
) -> torch.Tensor:
|
981 |
+
"""Forward pass for a base feature translator.
|
982 |
+
|
983 |
+
Args:
|
984 |
+
x (torch.Tensor): input features from the backbone. [B, (1)+H*W, C].
|
985 |
+
(1) means optional CLS token. If `backbone_no_cls==True`, then [B, H*W, C].
|
986 |
+
target_model_names (Optional[list[str]]): names of the target models.
|
987 |
+
backbone_no_cls (bool): indicate backbone has cls token or not.
|
988 |
+
Can use it to customize whether to drop cls.
|
989 |
+
|
990 |
+
Returns:
|
991 |
+
dict[str, torch.Tensor]: predicted features for target models.
|
992 |
+
"""
|
993 |
+
# x: [B, (1)+H*W, C]
|
994 |
+
x = self.backbone_adapter(x)
|
995 |
+
x = self.translator_stem(x)
|
996 |
+
target_model_names = target_model_names if target_model_names is not None else self.target_model_names
|
997 |
+
features = {t: self.translator_heads[self.legit_target_model_name_map[t]](x, backbone_no_cls=backbone_no_cls) for t in target_model_names}
|
998 |
+
return features
|
999 |
+
|
1000 |
+
|
1001 |
+
class MLPFeatureTranslator(FeatureTranslator):
|
1002 |
+
def __init__(
|
1003 |
+
self,
|
1004 |
+
backbone_feature_size: torch.Size,
|
1005 |
+
target_feature_sizes: dict[str, torch.Size | tuple[int, ...]],
|
1006 |
+
translator_hidden_size: int = 1024,
|
1007 |
+
translator_n_layer: int = 3,
|
1008 |
+
) -> None:
|
1009 |
+
"""Initalization function for MLPFeatureTranslator.
|
1010 |
+
|
1011 |
+
Args:
|
1012 |
+
backbone_feature_size (torch.Size): the size of features of the backbone.
|
1013 |
+
target_feature_sizes (dict[str, torch.Size | tuple[int, ...]]): the sizes of features of target models.
|
1014 |
+
translator_hidden_size (Optional[int]): the hidden dim of the translator. Defaults to 2048.
|
1015 |
+
translator_n_layer (int): number of MLP layers. Defaults to 3.
|
1016 |
+
"""
|
1017 |
+
self.translator_n_layer = translator_n_layer
|
1018 |
+
|
1019 |
+
super().__init__(
|
1020 |
+
backbone_feature_size=backbone_feature_size,
|
1021 |
+
target_feature_sizes=target_feature_sizes,
|
1022 |
+
translator_hidden_size=translator_hidden_size,
|
1023 |
+
)
|
1024 |
+
|
1025 |
+
def build_translator_heads(self) -> nn.ModuleDict:
|
1026 |
+
"""Build MLP translator heads to match the dimension of each target feature set."""
|
1027 |
+
translator_heads = {}
|
1028 |
+
source_size = (self.translator_hidden_size, *self.backbone_feature_size[1:])
|
1029 |
+
for target_model, target_size in self.target_feature_sizes.items():
|
1030 |
+
head = MLPAdapterHead(source_size=source_size, target_size=target_size, num_layer=self.translator_n_layer)
|
1031 |
+
translator_heads[self.legit_target_model_name_map[target_model]] = head
|
1032 |
+
self.translator_heads = nn.ModuleDict(translator_heads)
|
1033 |
+
|
1034 |
+
|
1035 |
+
class ConvFeatureTranslator(FeatureTranslator):
|
1036 |
+
def __init__(
|
1037 |
+
self,
|
1038 |
+
backbone_feature_size: torch.Size,
|
1039 |
+
target_feature_sizes: dict[str, torch.Size | tuple[int, ...]],
|
1040 |
+
translator_hidden_size: int = 1024,
|
1041 |
+
) -> None:
|
1042 |
+
"""Initalization function for ConvFeatureTranslator.
|
1043 |
+
|
1044 |
+
Args:
|
1045 |
+
backbone_feature_size (torch.Size): the size of features of the backbone.
|
1046 |
+
target_feature_sizes (dict[str, torch.Size | tuple[int, ...]]): the sizes of features of target models.
|
1047 |
+
translator_hidden_size (Optional[int]): the hidden dim of the translator. Defaults to 2048.
|
1048 |
+
"""
|
1049 |
+
super().__init__(
|
1050 |
+
backbone_feature_size=backbone_feature_size,
|
1051 |
+
target_feature_sizes=target_feature_sizes,
|
1052 |
+
translator_hidden_size=translator_hidden_size,
|
1053 |
+
)
|
1054 |
+
|
1055 |
+
def build_translator_heads(self) -> nn.ModuleDict:
|
1056 |
+
"""Build translator heads to match the dimension of each target feature set.
|
1057 |
+
|
1058 |
+
Returns:
|
1059 |
+
nn.ModuleDict: the translator heads.
|
1060 |
+
"""
|
1061 |
+
translator_heads = {}
|
1062 |
+
source_size = (self.translator_hidden_size, *self.backbone_feature_size[1:])
|
1063 |
+
for target_model, target_size in self.target_feature_sizes.items():
|
1064 |
+
head = ConvAdapterHead(source_size=source_size, target_size=target_size)
|
1065 |
+
translator_heads[self.legit_target_model_name_map[target_model]] = head
|
1066 |
+
self.translator_heads = nn.ModuleDict(translator_heads)
|
1067 |
+
|
1068 |
+
|
1069 |
+
class LightConvFeatureTranslator(FeatureTranslator):
|
1070 |
+
def __init__(
|
1071 |
+
self,
|
1072 |
+
backbone_feature_size: torch.Size,
|
1073 |
+
target_feature_sizes: dict[str, torch.Size | tuple[int, ...]],
|
1074 |
+
translator_hidden_size: int = 1024,
|
1075 |
+
hidden_size_factor: int | float = 1.0,
|
1076 |
+
) -> None:
|
1077 |
+
"""Initalization function for LightConvFeatureTranslator.
|
1078 |
+
It's for a smaller translator compared to ConvFeatureTranslator.
|
1079 |
+
|
1080 |
+
Args:
|
1081 |
+
backbone_feature_size (torch.Size): the size of features of the backbone.
|
1082 |
+
target_feature_sizes (dict[str, torch.Size | tuple[int, ...]]): the sizes of features of target models.
|
1083 |
+
translator_hidden_size (Optional[int]): the hidden dim of the translator. Defaults to 1024.
|
1084 |
+
hidden_size_factor: the size of hidden dim of feature translator
|
1085 |
+
as a factor of input feature hidden dim. Defaults to 1.0
|
1086 |
+
"""
|
1087 |
+
self.hidden_size_factor = hidden_size_factor
|
1088 |
+
super().__init__(
|
1089 |
+
backbone_feature_size=backbone_feature_size,
|
1090 |
+
target_feature_sizes=target_feature_sizes,
|
1091 |
+
translator_hidden_size=translator_hidden_size,
|
1092 |
+
)
|
1093 |
+
self.backbone_adapter = nn.Identity()
|
1094 |
+
|
1095 |
+
def build_translator_heads(self) -> nn.ModuleDict:
|
1096 |
+
"""Build translator heads to match the dimension of each target feature set.
|
1097 |
+
|
1098 |
+
Returns:
|
1099 |
+
nn.ModuleDict: the translator heads.
|
1100 |
+
"""
|
1101 |
+
translator_heads = {}
|
1102 |
+
for target_model, target_size in self.target_feature_sizes.items():
|
1103 |
+
if "_cls" in target_model:
|
1104 |
+
head = LinearAdapterHead(
|
1105 |
+
source_size=self.backbone_feature_size,
|
1106 |
+
target_size=target_size
|
1107 |
+
)
|
1108 |
+
else:
|
1109 |
+
head = LightConvAdapterHead(
|
1110 |
+
source_size=self.backbone_feature_size,
|
1111 |
+
target_size=target_size,
|
1112 |
+
hidden_size_factor=self.hidden_size_factor
|
1113 |
+
)
|
1114 |
+
translator_heads[self.legit_target_model_name_map[target_model]] = head
|
1115 |
+
self.translator_heads = nn.ModuleDict(translator_heads)
|
1116 |
+
|
1117 |
+
|
1118 |
+
class TransformerFreatureTranslator(FeatureTranslator):
|
1119 |
+
def __init__(
|
1120 |
+
self,
|
1121 |
+
backbone_feature_size: torch.Size,
|
1122 |
+
target_feature_sizes: dict[str, torch.Size | tuple[int, int]],
|
1123 |
+
translator_hidden_size: int = 1024,
|
1124 |
+
translator_n_layers: int = 2,
|
1125 |
+
translator_n_heads: int = 8,
|
1126 |
+
translator_activation: str = "gelu",
|
1127 |
+
) -> None:
|
1128 |
+
super().__init__(
|
1129 |
+
backbone_feature_size=backbone_feature_size,
|
1130 |
+
target_feature_sizes=target_feature_sizes,
|
1131 |
+
translator_hidden_size=translator_hidden_size,
|
1132 |
+
)
|
1133 |
+
|
1134 |
+
self.translator_stem = nn.TransformerDecoder(
|
1135 |
+
nn.TransformerDecoderLayer(
|
1136 |
+
d_model=translator_hidden_size,
|
1137 |
+
nhead=translator_n_heads,
|
1138 |
+
dim_feedforward=translator_hidden_size * 2,
|
1139 |
+
activation=translator_activation,
|
1140 |
+
batch_first=True,
|
1141 |
+
norm_first=True,
|
1142 |
+
),
|
1143 |
+
num_layers=translator_n_layers,
|
1144 |
+
)
|
1145 |
+
|
1146 |
+
self.decode_tokens = nn.Parameter(
|
1147 |
+
torch.randn((1, math.prod(self.backbone_feature_size[1:]), translator_hidden_size))
|
1148 |
+
)
|
1149 |
+
|
1150 |
+
self.target_model_emb = nn.ParameterDict(
|
1151 |
+
{
|
1152 |
+
self.legit_target_model_name_map[t]: torch.randn(1, 1, translator_hidden_size)
|
1153 |
+
for t in self.target_model_names
|
1154 |
+
}
|
1155 |
+
)
|
1156 |
+
|
1157 |
+
def build_translator_heads(self) -> None:
|
1158 |
+
"""Build Transformer translator heads to match the dimension of each target feature set."""
|
1159 |
+
translator_heads = {}
|
1160 |
+
for target_model, target_size in self.target_feature_sizes.items():
|
1161 |
+
head = MLPAdapterHead(
|
1162 |
+
source_size=(self.translator_hidden_size, *self.backbone_feature_size[1:]),
|
1163 |
+
target_size=target_size,
|
1164 |
+
num_layer=2,
|
1165 |
+
)
|
1166 |
+
translator_heads[self.legit_target_model_name_map[target_model]] = head
|
1167 |
+
self.translator_heads = nn.ModuleDict(translator_heads)
|
1168 |
+
|
1169 |
+
def forward(
|
1170 |
+
self, x: torch.Tensor, target_model_names: Optional[list[str]] = None, backbone_no_cls: bool = False
|
1171 |
+
) -> torch.Tensor:
|
1172 |
+
"""Forward pass for a simple linear translator.
|
1173 |
+
|
1174 |
+
Args:
|
1175 |
+
x (torch.Tensor): input features from the backbone.
|
1176 |
+
target_model_names (Optional[str]): names of the target models.
|
1177 |
+
backbone_no_cls (bool): indicate backbone has cls token or not.
|
1178 |
+
Can use it to customize whether to drop cls.
|
1179 |
+
|
1180 |
+
Returns:
|
1181 |
+
dict[str, torch.Tensor]: predicted features for target models.
|
1182 |
+
"""
|
1183 |
+
if not backbone_no_cls:
|
1184 |
+
x = x[:, 1:]
|
1185 |
+
x = self.backbone_adapter(x)
|
1186 |
+
features = {}
|
1187 |
+
target_model_names = target_model_names if target_model_names is not None else self.target_model_names
|
1188 |
+
for t in target_model_names:
|
1189 |
+
feature = self.translator_stem(
|
1190 |
+
torch.cat(
|
1191 |
+
[
|
1192 |
+
self.decode_tokens.repeat(x.size(0), 1, 1),
|
1193 |
+
self.target_model_emb[self.legit_target_model_name_map[t]].repeat(x.size(0), 1, 1),
|
1194 |
+
],
|
1195 |
+
dim=1,
|
1196 |
+
),
|
1197 |
+
memory=x,
|
1198 |
+
)[:, 1:, ...]
|
1199 |
+
features[t] = self.translator_heads[self.legit_target_model_name_map[t]](feature)
|
1200 |
+
return features
|
1201 |
+
|
1202 |
+
|
1203 |
+
def build_feature_translator(translator_type: str, **kwargs: Any) -> FeatureTranslator:
|
1204 |
+
"""Handy function to build feature translators given the type
|
1205 |
+
|
1206 |
+
Args:
|
1207 |
+
translator_type (str): the type of the translator,
|
1208 |
+
one in `"mlp"`, `"conv"`, `"lconv"`, `"transformer"` (or `"trans"`).
|
1209 |
+
At the moment we are actively using `"lconv"`.
|
1210 |
+
|
1211 |
+
Returns:
|
1212 |
+
FeatureTranslator: the corresponding FeatureTranslator
|
1213 |
+
"""
|
1214 |
+
if translator_type == "mlp":
|
1215 |
+
return MLPFeatureTranslator(**kwargs)
|
1216 |
+
elif translator_type == "conv":
|
1217 |
+
return ConvFeatureTranslator(**kwargs)
|
1218 |
+
elif translator_type == "lconv":
|
1219 |
+
return LightConvFeatureTranslator(**kwargs)
|
1220 |
+
elif translator_type == "transformer" or translator_type == "trans":
|
1221 |
+
return TransformerFreatureTranslator(**kwargs)
|
1222 |
+
else:
|
1223 |
+
raise NotImplementedError(f"Requested {translator_type} is not implemented yet.")
|
1224 |
+
|
1225 |
+
|
1226 |
+
class TheiaConfig(PretrainedConfig):
|
1227 |
+
def __init__(
|
1228 |
+
self,
|
1229 |
+
backbone: str | nn.Module = "facebook/deit-tiny-patch16-224",
|
1230 |
+
pretrained: bool = False,
|
1231 |
+
target_feature_sizes: Optional[dict[str, torch.Size | tuple[int, ...]]] = None,
|
1232 |
+
translator_type: str = "lconv",
|
1233 |
+
translator_hidden_size_factor: float | int = 1.0,
|
1234 |
+
target_loss_weights: Optional[dict[str, float]] = None,
|
1235 |
+
feature_reduce_method: Optional[str] = None,
|
1236 |
+
feature_neck: bool = False,
|
1237 |
+
feature_neck_hidden_dim: int = 256,
|
1238 |
+
forward_neck: bool = False,
|
1239 |
+
feature_neck_nonlinearity: str = "relu",
|
1240 |
+
iamge_size: int = 224,
|
1241 |
+
num_reg_tokens: int = 0,
|
1242 |
+
**kwargs: Any
|
1243 |
+
):
|
1244 |
+
self.backbone = backbone
|
1245 |
+
self.pretrained = pretrained
|
1246 |
+
self.target_feature_sizes = target_feature_sizes
|
1247 |
+
self.translator_type = translator_type
|
1248 |
+
self.translator_hidden_size_factor = translator_hidden_size_factor
|
1249 |
+
self.target_loss_weights = target_loss_weights
|
1250 |
+
self.feature_reduce_method = feature_reduce_method
|
1251 |
+
self.feature_neck = feature_neck
|
1252 |
+
self.feature_neck_hidden_dim = feature_neck_hidden_dim
|
1253 |
+
self.forward_neck = forward_neck
|
1254 |
+
self.feature_neck_nonlinearity = feature_neck_nonlinearity
|
1255 |
+
self.image_size = 224
|
1256 |
+
self.num_reg_tokens = num_reg_tokens
|
1257 |
+
super().__init__(**kwargs)
|
1258 |
+
|
1259 |
+
class TheiaModel(PreTrainedModel):
|
1260 |
+
config_class = TheiaConfig
|
1261 |
+
|
1262 |
+
def __init__(self, config: TheiaConfig):
|
1263 |
+
super().__init__(config)
|
1264 |
+
|
1265 |
+
self.target_feature_sizes = config.target_feature_sizes
|
1266 |
+
self.preprocessor = None
|
1267 |
+
self.pretrained = config.pretrained
|
1268 |
+
|
1269 |
+
# backbone
|
1270 |
+
self.image_size = config.image_size
|
1271 |
+
if "reg" in config.backbone:
|
1272 |
+
self.backbone: nn.Module = build_backbone(config.backbone, config.pretrained, image_size=config.image_size, num_reg_tokens = config.num_reg_tokens)
|
1273 |
+
else:
|
1274 |
+
self.backbone: nn.Module = build_backbone(config.backbone, config.pretrained, image_size=config.image_size)
|
1275 |
+
|
1276 |
+
# handle output feature (feature reduce)
|
1277 |
+
self.feature_reduce_method = config.feature_reduce_method
|
1278 |
+
self.no_cls = hasattr(self.backbone, "no_cls")
|
1279 |
+
self.num_reg_tokens = self.backbone.num_reg_tokens if hasattr(self.backbone, "num_reg_tokens") else 0
|
1280 |
+
|
1281 |
+
# translator
|
1282 |
+
backbone_feature_size = self.backbone.get_feature_size(keep_spatial=True)
|
1283 |
+
if self.target_feature_sizes:
|
1284 |
+
translator_kwargs = {
|
1285 |
+
"hidden_size_factor": config.translator_hidden_size_factor
|
1286 |
+
}
|
1287 |
+
translator_kwargs["backbone_feature_size"] = backbone_feature_size
|
1288 |
+
translator_kwargs["target_feature_sizes"] = config.target_feature_sizes
|
1289 |
+
self.translator = build_feature_translator(
|
1290 |
+
config.translator_type, **translator_kwargs
|
1291 |
+
)
|
1292 |
+
else:
|
1293 |
+
self.translator = None
|
1294 |
+
|
1295 |
+
self.feature_neck = config.feature_neck
|
1296 |
+
self.feature_neck_hidden_dim = config.feature_neck_hidden_dim
|
1297 |
+
self.forward_neck = config.forward_neck
|
1298 |
+
if self.feature_neck:
|
1299 |
+
num_tokens_edge = self.backbone.model.config.image_size // self.backbone.model.config.patch_size
|
1300 |
+
self.neck = nn.Sequential(
|
1301 |
+
Rearrange("b (h w) c -> b c h w", h=num_tokens_edge, w=num_tokens_edge),
|
1302 |
+
nn.Conv2d(self.backbone.model.config.hidden_size, self.feature_neck_hidden_dim, kernel_size=4, stride=2, padding=1), #14x14 -> 7x7
|
1303 |
+
nn.ReLU() if config.feature_neck_nonlinearity == 'relu' else nn.Tanh(), # just to keep the same as super class
|
1304 |
+
nn.Conv2d(self.feature_neck_hidden_dim, self.feature_neck_hidden_dim, kernel_size=3, stride=2), #7x7 -> 3x3
|
1305 |
+
nn.ReLU() if config.feature_neck_nonlinearity == 'relu' else nn.Tanh(),
|
1306 |
+
nn.Conv2d(self.feature_neck_hidden_dim, self.feature_neck_hidden_dim, kernel_size=3, stride=1), #3x3 -> 1x1
|
1307 |
+
nn.ReLU() if config.feature_neck_nonlinearity == 'relu' else nn.Tanh(),
|
1308 |
+
nn.Flatten()
|
1309 |
+
)
|
1310 |
+
else:
|
1311 |
+
self.neck = None
|
1312 |
+
|
1313 |
+
# loss
|
1314 |
+
self.mse_loss = nn.MSELoss()
|
1315 |
+
self.l1_loss = nn.SmoothL1Loss()
|
1316 |
+
self.cos_loss = nn.CosineEmbeddingLoss()
|
1317 |
+
self.cos_target = torch.ones((1), dtype=torch.int, requires_grad=False)
|
1318 |
+
self.target_loss_weights = config.target_loss_weights
|
1319 |
+
|
1320 |
+
def load_pretrained_weights(self, checkpoint_path: str) -> None:
|
1321 |
+
"""
|
1322 |
+
Load weights from `checkpoint_path` manually.
|
1323 |
+
|
1324 |
+
Args:
|
1325 |
+
checkpoint_path (str): path to the weights.
|
1326 |
+
"""
|
1327 |
+
# load theia weights
|
1328 |
+
if checkpoint_path:
|
1329 |
+
weights_dict = torch.load(checkpoint_path, map_location="cpu")
|
1330 |
+
# Filter out unnecessary keys
|
1331 |
+
pretrained_dict = {k: v for k, v in weights_dict.items() if k in self.state_dict()}
|
1332 |
+
self.load_state_dict(pretrained_dict, strict=False)
|
1333 |
+
|
1334 |
+
def freeze_translator(self) -> None:
|
1335 |
+
"""Freeze feature translators `self.translator`."""
|
1336 |
+
if self.translator is not None:
|
1337 |
+
for param in self.translator.parameters():
|
1338 |
+
param.requires_grad = False
|
1339 |
+
|
1340 |
+
def freeze_backbone(self) -> None:
|
1341 |
+
"""Freeze backbone (encoder) `self.backbone`. """
|
1342 |
+
self.freeze_encoder()
|
1343 |
+
|
1344 |
+
def freeze_encoder(self) -> None:
|
1345 |
+
"""Freeze backbone (encoder) `self.backbone`. """
|
1346 |
+
for param in self.backbone.parameters():
|
1347 |
+
param.requires_grad = False
|
1348 |
+
|
1349 |
+
def freeze_neck(self) -> None:
|
1350 |
+
"""Freeze feature neck `self.neck`."""
|
1351 |
+
if self.neck is not None:
|
1352 |
+
for param in self.neck.parameters():
|
1353 |
+
param.requires_grad = False
|
1354 |
+
|
1355 |
+
def freeze_everything(self) -> None:
|
1356 |
+
"""Freeze all parameters in the model."""
|
1357 |
+
self.freeze_translator()
|
1358 |
+
self.freeze_neck()
|
1359 |
+
self.freeze_encoder()
|
1360 |
+
|
1361 |
+
def unfreeze_translator(self) -> None:
|
1362 |
+
if self.translator is not None:
|
1363 |
+
for param in self.translator.parameters():
|
1364 |
+
param.requires_grad = True
|
1365 |
+
|
1366 |
+
def unfreeze_backbone(self) -> None:
|
1367 |
+
"Set parameters in backbone (encoder) `self.backbone` trainable."
|
1368 |
+
self.unfreeze_encoder()
|
1369 |
+
|
1370 |
+
def unfreeze_encoder(self) -> None:
|
1371 |
+
"Set parameters in backbone (encoder) `self.backbone` trainable."
|
1372 |
+
for param in self.backbone.parameters():
|
1373 |
+
param.requires_grad = True
|
1374 |
+
|
1375 |
+
def unfreeze_neck(self) -> None:
|
1376 |
+
"Set parameters in feature neck `self.neck` trainable."
|
1377 |
+
if self.neck is not None:
|
1378 |
+
for param in self.neck.parameters():
|
1379 |
+
param.requires_grad = True
|
1380 |
+
|
1381 |
+
def unfreeze_everything(self) -> None:
|
1382 |
+
"""Set all parameters trainable."""
|
1383 |
+
self.unfreeze_translator()
|
1384 |
+
self.unfreeze_neck()
|
1385 |
+
self.unfreeze_encoder()
|
1386 |
+
|
1387 |
+
def set_forward_neck(self, forward_neck: bool = True) -> None:
|
1388 |
+
"""
|
1389 |
+
Set `self.forward_neck` to `forward_neck` value.
|
1390 |
+
|
1391 |
+
Args:
|
1392 |
+
forward_neck (bool): whether forward the feature through the random initialized neck.
|
1393 |
+
If set to True, the output from `self.forward()` will be in shape [batch_size, self.config.feature_neck_hidden_dim]
|
1394 |
+
"""
|
1395 |
+
self.forward_neck = forward_neck
|
1396 |
+
|
1397 |
+
def forward_feature(self, x: torch.Tensor, **kwargs: Any) -> torch.Tensor:
|
1398 |
+
"""Forward RVFM feature only (before translators).
|
1399 |
+
|
1400 |
+
Args:
|
1401 |
+
x (torch.Tensor): input image. By default it accepts images
|
1402 |
+
in shape [B, H, W, C] or [B, C, H, W], pixel range [0,255], torch.uint8.
|
1403 |
+
kwargs (Any): kwargs including mainly those for huggingface preprocessor:
|
1404 |
+
`do_resize` (bool) defaults to True.
|
1405 |
+
`interpolate_pos_encoding` (Optional[bool]) defaults to None.
|
1406 |
+
`do_rescale` (bool) defaults to True.
|
1407 |
+
`do_normalize` (bool) defaults to True.
|
1408 |
+
|
1409 |
+
Returns:
|
1410 |
+
torch.Tensor: RVFM feature.
|
1411 |
+
"""
|
1412 |
+
feature = self.backbone(x, **kwargs)
|
1413 |
+
# [B, 1+H*W+N, C] if including both CLS and register tokens.
|
1414 |
+
# [B, 1+H*W, C] for standard model (N=0).
|
1415 |
+
# [B, H*W, C] for model without CLS.
|
1416 |
+
return handle_feature_output(feature, num_discard_tokens=self.num_reg_tokens)
|
1417 |
+
|
1418 |
+
def forward(self, x: torch.Tensor, target_model_names: Optional[list[str]] = None, **kwargs: Any) -> dict[str, torch.Tensor] | torch.Tensor:
|
1419 |
+
"""Forward pass of Robot Vision Foundation Model.
|
1420 |
+
|
1421 |
+
Args:
|
1422 |
+
x (torch.Tensor): input image. By default it accepts images
|
1423 |
+
in shape [B, H, W, C] or [B, C, H, W], pixel range [0,255], torch.uint8.
|
1424 |
+
target_model_names (Optional[list[str]]): names of the target foundation models.
|
1425 |
+
kwargs (Any): kwargs including mainly those for huggingface preprocessor:
|
1426 |
+
`do_resize` (bool) defaults to True.
|
1427 |
+
`interpolate_pos_encoding` (Optional[bool]) defaults to None.
|
1428 |
+
`do_rescale` (bool) defaults to True.
|
1429 |
+
`do_normalize` (bool) defaults to True.
|
1430 |
+
|
1431 |
+
Returns:
|
1432 |
+
if `self.forward_neck`:
|
1433 |
+
torch.Tensor: compact vector feature passed through the neck. [B, C_neck]
|
1434 |
+
else:
|
1435 |
+
dict[str, torch.Tensor]: features that match to each foundation model.
|
1436 |
+
Each feature is in [B, (H*W), C] or [B, C].
|
1437 |
+
"""
|
1438 |
+
if self.forward_neck:
|
1439 |
+
x = self.forward_feature(x)
|
1440 |
+
return self.neck(x)
|
1441 |
+
else:
|
1442 |
+
x = self.backbone(x, **kwargs)
|
1443 |
+
if self.num_reg_tokens > 0:
|
1444 |
+
x = x[:, :-self.num_reg_tokens] # [B, (1)+H*W, C]
|
1445 |
+
features = self.translator(x, target_model_names, backbone_no_cls=self.no_cls) # each is [B, H*W, C] or [B, C]
|
1446 |
+
return features
|
1447 |
+
|
1448 |
+
def get_loss(self, pred_features: dict[str, torch.Tensor], y: dict[str, torch.Tensor]) -> dict[str, Any]:
|
1449 |
+
"""Get loss terms given predictions and targets.
|
1450 |
+
|
1451 |
+
Args:
|
1452 |
+
pred_features (dict[str, torch.Tensor]): predictions.
|
1453 |
+
y (dict[str, torch.Tensor]): targets.
|
1454 |
+
|
1455 |
+
Returns:
|
1456 |
+
tuple[Any, ...]: loss terms
|
1457 |
+
"""
|
1458 |
+
mse_loss_avg, cos_loss_avg, l1_loss_avg = 0, 0, 0
|
1459 |
+
mse_losses_per_model = {}
|
1460 |
+
cos_losses_per_model = {}
|
1461 |
+
l1_losses_per_model = {}
|
1462 |
+
|
1463 |
+
for t in pred_features:
|
1464 |
+
pred = pred_features[t]
|
1465 |
+
target = y[t]
|
1466 |
+
|
1467 |
+
# mse loss
|
1468 |
+
mse_loss = self.mse_loss(pred, target)
|
1469 |
+
weight = self.target_loss_weights if self.target_loss_weights else 1.0 / len(pred_features)
|
1470 |
+
|
1471 |
+
# l1 loss
|
1472 |
+
l1_loss = self.l1_loss(pred, target)
|
1473 |
+
|
1474 |
+
# cos loss
|
1475 |
+
pred_norm = F.normalize(pred.flatten(start_dim=1), dim=1, p=2)
|
1476 |
+
target_norm = F.normalize(target.flatten(start_dim=1), dim=1, p=2)
|
1477 |
+
target = self.cos_target.repeat(pred.size(0)).to(pred.device)
|
1478 |
+
cos_loss = self.cos_loss(pred_norm, target_norm, target)
|
1479 |
+
|
1480 |
+
mse_loss_avg += mse_loss * weight
|
1481 |
+
cos_loss_avg += cos_loss / len(pred_features) # balance cos by default for meaningful eval
|
1482 |
+
l1_loss_avg += l1_loss * weight
|
1483 |
+
|
1484 |
+
mse_losses_per_model[t] = mse_loss.item()
|
1485 |
+
cos_losses_per_model[t] = cos_loss.item()
|
1486 |
+
l1_losses_per_model[t] = l1_loss.item()
|
1487 |
+
|
1488 |
+
return {
|
1489 |
+
"mse_loss": mse_loss_avg,
|
1490 |
+
"cos_loss": cos_loss_avg,
|
1491 |
+
"l1_loss": l1_loss_avg,
|
1492 |
+
"mse_losses_per_model": mse_losses_per_model,
|
1493 |
+
"cos_losses_per_model": cos_losses_per_model,
|
1494 |
+
"l1_losses_per_model": l1_losses_per_model,
|
1495 |
+
}
|