Upload model
Browse files- README.md +199 -0
- config.json +54 -0
- model.safetensors +3 -0
- modelling_uniformer.py +412 -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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"_name_or_path": "/datasets/work/hb-mlaifsp-mm/work/checkpoints/uniformer_base_tl_384",
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"architectures": [
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"UniFormerModel"
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],
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"attention_probs_dropout_prob": 0.0,
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"attn_drop_rate": 0.0,
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"auto_map": {
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"AutoModel": "modelling_uniformer.UniFormerModel"
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},
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"conv_stem": false,
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"depth": [
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5,
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8,
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20,
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7
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],
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"drop_path_rate": 0.3,
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"drop_rate": 0.0,
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"embed_dim": [
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64,
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128,
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320,
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512
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],
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"encoder_stride": 16,
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"head_dim": 64,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.0,
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"hidden_size": 768,
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"image_size": 384,
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"in_chans": 3,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-06,
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"mlp_ratio": 4,
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"model_type": "vit",
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"num_attention_heads": 12,
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"num_channels": 3,
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"num_classes": 1000,
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"num_hidden_layers": 12,
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"patch_size": [
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4,
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2,
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2,
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2
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],
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"projection_size": null,
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"qk_scale": null,
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"qkv_bias": true,
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"representation_size": null,
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"torch_dtype": "float32",
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"transformers_version": "4.40.2"
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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 sha256:0571c333da6cbecab2e8f03af5ca2b87608c1a5e269fbb5b070e175f02e6fd34
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size 197150032
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modelling_uniformer.py
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|
1 |
+
from collections import OrderedDict
|
2 |
+
from functools import partial
|
3 |
+
from typing import Optional, Tuple, Union
|
4 |
+
from math import isqrt
|
5 |
+
|
6 |
+
import torch
|
7 |
+
import torch.nn as nn
|
8 |
+
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
|
9 |
+
from transformers import ViTConfig
|
10 |
+
from transformers.modeling_outputs import ModelOutput
|
11 |
+
from transformers.modeling_utils import PreTrainedModel
|
12 |
+
from transformers.utils import logging
|
13 |
+
|
14 |
+
logger = logging.get_logger(__name__)
|
15 |
+
|
16 |
+
|
17 |
+
layer_scale = False
|
18 |
+
init_value = 1e-6
|
19 |
+
|
20 |
+
|
21 |
+
class Mlp(nn.Module):
|
22 |
+
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
|
23 |
+
super().__init__()
|
24 |
+
out_features = out_features or in_features
|
25 |
+
hidden_features = hidden_features or in_features
|
26 |
+
self.fc1 = nn.Linear(in_features, hidden_features)
|
27 |
+
self.act = act_layer()
|
28 |
+
self.fc2 = nn.Linear(hidden_features, out_features)
|
29 |
+
self.drop = nn.Dropout(drop)
|
30 |
+
|
31 |
+
def forward(self, x):
|
32 |
+
x = self.fc1(x)
|
33 |
+
x = self.act(x)
|
34 |
+
x = self.drop(x)
|
35 |
+
x = self.fc2(x)
|
36 |
+
x = self.drop(x)
|
37 |
+
return x
|
38 |
+
|
39 |
+
|
40 |
+
class CMlp(nn.Module):
|
41 |
+
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
|
42 |
+
super().__init__()
|
43 |
+
out_features = out_features or in_features
|
44 |
+
hidden_features = hidden_features or in_features
|
45 |
+
self.fc1 = nn.Conv2d(in_features, hidden_features, 1)
|
46 |
+
self.act = act_layer()
|
47 |
+
self.fc2 = nn.Conv2d(hidden_features, out_features, 1)
|
48 |
+
self.drop = nn.Dropout(drop)
|
49 |
+
|
50 |
+
def forward(self, x):
|
51 |
+
x = self.fc1(x)
|
52 |
+
x = self.act(x)
|
53 |
+
x = self.drop(x)
|
54 |
+
x = self.fc2(x)
|
55 |
+
x = self.drop(x)
|
56 |
+
return x
|
57 |
+
|
58 |
+
|
59 |
+
class Attention(nn.Module):
|
60 |
+
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
|
61 |
+
super().__init__()
|
62 |
+
self.num_heads = num_heads
|
63 |
+
head_dim = dim // num_heads
|
64 |
+
self.scale = qk_scale or head_dim ** -0.5
|
65 |
+
|
66 |
+
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
67 |
+
self.attn_drop = nn.Dropout(attn_drop)
|
68 |
+
self.proj = nn.Linear(dim, dim)
|
69 |
+
self.proj_drop = nn.Dropout(proj_drop)
|
70 |
+
|
71 |
+
def forward(self, x):
|
72 |
+
B, N, C = x.shape
|
73 |
+
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
74 |
+
q, k, v = qkv[0], qkv[1], qkv[2]
|
75 |
+
|
76 |
+
attn = (q @ k.transpose(-2, -1)) * self.scale
|
77 |
+
attn = attn.softmax(dim=-1)
|
78 |
+
attn = self.attn_drop(attn)
|
79 |
+
|
80 |
+
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
|
81 |
+
x = self.proj(x)
|
82 |
+
x = self.proj_drop(x)
|
83 |
+
return x
|
84 |
+
|
85 |
+
|
86 |
+
class CBlock(nn.Module):
|
87 |
+
def __init__(self, dim, mlp_ratio=4., drop=0., drop_path=0., act_layer=nn.GELU):
|
88 |
+
super().__init__()
|
89 |
+
self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim)
|
90 |
+
self.norm1 = nn.BatchNorm2d(dim)
|
91 |
+
self.conv1 = nn.Conv2d(dim, dim, 1)
|
92 |
+
self.conv2 = nn.Conv2d(dim, dim, 1)
|
93 |
+
self.attn = nn.Conv2d(dim, dim, 5, padding=2, groups=dim)
|
94 |
+
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
95 |
+
self.norm2 = nn.BatchNorm2d(dim)
|
96 |
+
mlp_hidden_dim = int(dim * mlp_ratio)
|
97 |
+
self.mlp = CMlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
|
98 |
+
|
99 |
+
def forward(self, x):
|
100 |
+
x = x + self.pos_embed(x)
|
101 |
+
x = x + self.module_1(x)
|
102 |
+
x = x + self.module_2(x)
|
103 |
+
return x
|
104 |
+
|
105 |
+
def module_1(self, x):
|
106 |
+
x = self.norm1(x.to(dtype=self.norm1.weight.dtype)) # Won't autocast to the dtype of the parameters of nn.BatchNorm2d.
|
107 |
+
x = self.conv1(x)
|
108 |
+
x = self.attn(x)
|
109 |
+
x = self.conv2(x)
|
110 |
+
x = self.drop_path(x)
|
111 |
+
return x
|
112 |
+
|
113 |
+
def module_2(self, x):
|
114 |
+
x = self.norm2(x.to(dtype=self.norm2.weight.dtype)) # Won't autocast to the dtype of the parameters of nn.BatchNorm2d.
|
115 |
+
x = self.mlp(x)
|
116 |
+
x = self.drop_path(x)
|
117 |
+
return x
|
118 |
+
|
119 |
+
class SABlock(nn.Module):
|
120 |
+
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
|
121 |
+
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
|
122 |
+
super().__init__()
|
123 |
+
self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim)
|
124 |
+
self.norm1 = norm_layer(dim)
|
125 |
+
self.attn = Attention(
|
126 |
+
dim,
|
127 |
+
num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
|
128 |
+
attn_drop=attn_drop, proj_drop=drop)
|
129 |
+
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
130 |
+
self.norm2 = norm_layer(dim)
|
131 |
+
mlp_hidden_dim = int(dim * mlp_ratio)
|
132 |
+
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
|
133 |
+
global layer_scale
|
134 |
+
self.ls = layer_scale
|
135 |
+
if self.ls:
|
136 |
+
global init_value
|
137 |
+
print(f"Use layer_scale: {layer_scale}, init_values: {init_value}")
|
138 |
+
self.gamma_1 = nn.Parameter(init_value * torch.ones((dim)),requires_grad=True)
|
139 |
+
self.gamma_2 = nn.Parameter(init_value * torch.ones((dim)),requires_grad=True)
|
140 |
+
|
141 |
+
def forward(self, x):
|
142 |
+
x = x + self.pos_embed(x)
|
143 |
+
B, N, H, W = x.shape
|
144 |
+
x = x.flatten(2).transpose(1, 2)
|
145 |
+
if self.ls:
|
146 |
+
x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x)))
|
147 |
+
x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
|
148 |
+
else:
|
149 |
+
x = x + self.drop_path(self.attn(self.norm1(x)))
|
150 |
+
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
151 |
+
x = x.transpose(1, 2).reshape(B, N, H, W)
|
152 |
+
return x
|
153 |
+
|
154 |
+
|
155 |
+
class HeadEmbedding(nn.Module):
|
156 |
+
def __init__(self, in_channels, out_channels):
|
157 |
+
super(HeadEmbedding, self).__init__()
|
158 |
+
|
159 |
+
self.proj = nn.Sequential(
|
160 |
+
nn.Conv2d(in_channels, out_channels // 2, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
|
161 |
+
nn.BatchNorm2d(out_channels // 2),
|
162 |
+
nn.GELU(),
|
163 |
+
nn.Conv2d(out_channels // 2, out_channels, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
|
164 |
+
nn.BatchNorm2d(out_channels),
|
165 |
+
)
|
166 |
+
|
167 |
+
def forward(self, x):
|
168 |
+
x = self.proj(x)
|
169 |
+
return x
|
170 |
+
|
171 |
+
|
172 |
+
class MiddleEmbedding(nn.Module):
|
173 |
+
def __init__(self, in_channels, out_channels):
|
174 |
+
super(MiddleEmbedding, self).__init__()
|
175 |
+
|
176 |
+
self.proj = nn.Sequential(
|
177 |
+
nn.Conv2d(in_channels, out_channels, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
|
178 |
+
nn.BatchNorm2d(out_channels),
|
179 |
+
)
|
180 |
+
|
181 |
+
def forward(self, x):
|
182 |
+
x = self.proj(x)
|
183 |
+
return x
|
184 |
+
|
185 |
+
|
186 |
+
class PatchEmbed(nn.Module):
|
187 |
+
def __init__(self, image_size=224, patch_size=16, in_chans=3, embed_dim=768):
|
188 |
+
super().__init__()
|
189 |
+
image_size = to_2tuple(image_size)
|
190 |
+
patch_size = to_2tuple(patch_size)
|
191 |
+
num_patches_height = image_size[0] // patch_size[0]
|
192 |
+
num_patches_width = image_size[1] // patch_size[1]
|
193 |
+
num_patches = num_patches_height * num_patches_width
|
194 |
+
self.image_size = image_size
|
195 |
+
self.patch_size = patch_size
|
196 |
+
self.num_patches = num_patches
|
197 |
+
|
198 |
+
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
|
199 |
+
self.norm = nn.LayerNorm(embed_dim)
|
200 |
+
|
201 |
+
def forward(self, x):
|
202 |
+
_, _, H, W = x.shape
|
203 |
+
assert H == self.image_size[0] and W == self.image_size[1], \
|
204 |
+
f"Input image size ({H}*{W}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})."
|
205 |
+
x = self.proj(x)
|
206 |
+
B, _, H, W = x.shape
|
207 |
+
x = x.flatten(2).transpose(1, 2)
|
208 |
+
x = self.norm(x)
|
209 |
+
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
|
210 |
+
return x
|
211 |
+
|
212 |
+
|
213 |
+
class UniFormer(nn.Module):
|
214 |
+
def __init__(self, depth=[3, 4, 8, 3], image_size=224, in_chans=3, num_classes=1000, embed_dim=[64, 128, 320, 512],
|
215 |
+
head_dim=64, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None, patch_size=[4, 2, 2, 2],
|
216 |
+
drop_rate=0., attn_drop_rate=0., drop_path_rate=0., conv_stem=False, layer_norm_eps=1e-6, **kwargs):
|
217 |
+
super().__init__()
|
218 |
+
self.num_classes = num_classes
|
219 |
+
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
|
220 |
+
norm_layer = partial(nn.LayerNorm, eps=layer_norm_eps)
|
221 |
+
if conv_stem:
|
222 |
+
self.patch_embed1 = HeadEmbedding(in_channels=in_chans, out_channels=embed_dim[0])
|
223 |
+
self.patch_embed2 = MiddleEmbedding(in_channels=embed_dim[0], out_channels=embed_dim[1])
|
224 |
+
self.patch_embed3 = MiddleEmbedding(in_channels=embed_dim[1], out_channels=embed_dim[2])
|
225 |
+
self.patch_embed4 = MiddleEmbedding(in_channels=embed_dim[2], out_channels=embed_dim[3])
|
226 |
+
else:
|
227 |
+
self.patch_embed1 = PatchEmbed(
|
228 |
+
image_size=image_size, patch_size=patch_size[0], in_chans=in_chans, embed_dim=embed_dim[0])
|
229 |
+
self.patch_embed2 = PatchEmbed(
|
230 |
+
image_size=image_size // patch_size[0], patch_size=patch_size[1], in_chans=embed_dim[0], embed_dim=embed_dim[1])
|
231 |
+
self.patch_embed3 = PatchEmbed(
|
232 |
+
image_size=image_size // (patch_size[0]*patch_size[1]), patch_size=patch_size[2], in_chans=embed_dim[1], embed_dim=embed_dim[2])
|
233 |
+
self.patch_embed4 = PatchEmbed(
|
234 |
+
image_size=image_size // (patch_size[0]*patch_size[1]*patch_size[2]), patch_size=patch_size[3], in_chans=embed_dim[2], embed_dim=embed_dim[3])
|
235 |
+
|
236 |
+
self.pos_drop = nn.Dropout(p=drop_rate)
|
237 |
+
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depth))] # stochastic depth decay rule
|
238 |
+
num_heads = [dim // head_dim for dim in embed_dim]
|
239 |
+
self.blocks1 = nn.ModuleList([
|
240 |
+
CBlock(dim=embed_dim[0], mlp_ratio=mlp_ratio, drop=drop_rate, drop_path=dpr[i])
|
241 |
+
for i in range(depth[0])])
|
242 |
+
self.blocks2 = nn.ModuleList([
|
243 |
+
CBlock(dim=embed_dim[1], mlp_ratio=mlp_ratio, drop=drop_rate, drop_path=dpr[i+depth[0]])
|
244 |
+
for i in range(depth[1])])
|
245 |
+
self.blocks3 = nn.ModuleList([
|
246 |
+
SABlock(
|
247 |
+
dim=embed_dim[2], num_heads=num_heads[2], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
|
248 |
+
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]+depth[1]], norm_layer=norm_layer)
|
249 |
+
for i in range(depth[2])])
|
250 |
+
self.blocks4 = nn.ModuleList([
|
251 |
+
SABlock(
|
252 |
+
dim=embed_dim[3], num_heads=num_heads[3], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
|
253 |
+
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+depth[0]+depth[1]+depth[2]], norm_layer=norm_layer)
|
254 |
+
for i in range(depth[3])])
|
255 |
+
self.norm = nn.BatchNorm2d(embed_dim[-1])
|
256 |
+
|
257 |
+
# Representation layer
|
258 |
+
if representation_size:
|
259 |
+
self.num_features = representation_size
|
260 |
+
self.pre_logits = nn.Sequential(OrderedDict([
|
261 |
+
('fc', nn.Linear(embed_dim, representation_size)),
|
262 |
+
('act', nn.Tanh())
|
263 |
+
]))
|
264 |
+
else:
|
265 |
+
self.pre_logits = nn.Identity()
|
266 |
+
|
267 |
+
def forward_features(self, x):
|
268 |
+
x = self.patch_embed1(x)
|
269 |
+
x = self.pos_drop(x)
|
270 |
+
for blk in self.blocks1:
|
271 |
+
x = blk(x)
|
272 |
+
x = self.patch_embed2(x)
|
273 |
+
for blk in self.blocks2:
|
274 |
+
x = blk(x)
|
275 |
+
x = self.patch_embed3(x)
|
276 |
+
for blk in self.blocks3:
|
277 |
+
x = blk(x)
|
278 |
+
x = self.patch_embed4(x)
|
279 |
+
for blk in self.blocks4:
|
280 |
+
x = blk(x)
|
281 |
+
x = self.norm(x.to(dtype=self.norm.weight.dtype)) # Won't autocast to the dtype of the parameters of nn.BatchNorm2d.
|
282 |
+
x = self.pre_logits(x)
|
283 |
+
return x
|
284 |
+
|
285 |
+
def forward(self, x):
|
286 |
+
x = self.forward_features(x)
|
287 |
+
return x
|
288 |
+
|
289 |
+
|
290 |
+
class UniFormerPreTrainedModel(PreTrainedModel):
|
291 |
+
"""
|
292 |
+
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
293 |
+
models.
|
294 |
+
"""
|
295 |
+
|
296 |
+
config_class = ViTConfig
|
297 |
+
base_model_prefix = "vit"
|
298 |
+
main_input_name = "pixel_values"
|
299 |
+
|
300 |
+
def _init_weights(self, m):
|
301 |
+
if isinstance(m, nn.Linear):
|
302 |
+
trunc_normal_(m.weight, std=.02)
|
303 |
+
if isinstance(m, nn.Linear) and m.bias is not None:
|
304 |
+
nn.init.constant_(m.bias, 0)
|
305 |
+
elif isinstance(m, nn.LayerNorm):
|
306 |
+
nn.init.constant_(m.bias, 0)
|
307 |
+
nn.init.constant_(m.weight, 1.0)
|
308 |
+
|
309 |
+
|
310 |
+
class UniFormerProjectionHead(torch.nn.Module):
|
311 |
+
|
312 |
+
def __init__(self, config) -> None:
|
313 |
+
super().__init__()
|
314 |
+
|
315 |
+
# Layer normalisation before projection:
|
316 |
+
self.layer_norm = torch.nn.LayerNorm(config.embed_dim[-1], eps=config.layer_norm_eps)
|
317 |
+
|
318 |
+
# No bias as following layer normalisation with bias:
|
319 |
+
self.projection = torch.nn.Linear(config.embed_dim[-1], config.projection_size, bias=False)
|
320 |
+
|
321 |
+
|
322 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
323 |
+
x = self.layer_norm(x)
|
324 |
+
x = self.projection(x)
|
325 |
+
return x
|
326 |
+
|
327 |
+
|
328 |
+
class UniFormerModel(UniFormerPreTrainedModel):
|
329 |
+
def __init__(self, config):
|
330 |
+
super().__init__(config)
|
331 |
+
|
332 |
+
self.uniformer = UniFormer(**vars(config))
|
333 |
+
|
334 |
+
# Initialize weights and apply final processing:
|
335 |
+
self.post_init()
|
336 |
+
|
337 |
+
def forward(
|
338 |
+
self,
|
339 |
+
pixel_values: Optional[torch.Tensor] = None,
|
340 |
+
output_hidden_states: Optional[bool] = None,
|
341 |
+
return_dict: Optional[bool] = None,
|
342 |
+
) -> Union[Tuple, ModelOutput]:
|
343 |
+
|
344 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
345 |
+
|
346 |
+
last_hidden_state = self.uniformer(pixel_values)
|
347 |
+
|
348 |
+
# Flatten h x w:
|
349 |
+
last_hidden_state = torch.flatten(last_hidden_state, 2)
|
350 |
+
|
351 |
+
# Permute last hidden state:
|
352 |
+
last_hidden_state = torch.permute(last_hidden_state, [0, 2, 1])
|
353 |
+
|
354 |
+
# return last_hidden_state
|
355 |
+
if not return_dict:
|
356 |
+
return last_hidden_state
|
357 |
+
|
358 |
+
return ModelOutput(last_hidden_state=last_hidden_state)
|
359 |
+
|
360 |
+
|
361 |
+
class MultiUniFormerWithProjectionHead(UniFormerPreTrainedModel):
|
362 |
+
def __init__(self, config):
|
363 |
+
super().__init__(config)
|
364 |
+
|
365 |
+
self.uniformer = UniFormer(**vars(config))
|
366 |
+
self.projection_head = UniFormerProjectionHead(config)
|
367 |
+
|
368 |
+
# Initialize weights and apply final processing:
|
369 |
+
self.post_init()
|
370 |
+
|
371 |
+
def forward(
|
372 |
+
self,
|
373 |
+
pixel_values: Optional[torch.Tensor] = None,
|
374 |
+
output_hidden_states: Optional[bool] = None,
|
375 |
+
return_dict: Optional[bool] = None,
|
376 |
+
) -> Union[Tuple, ModelOutput]:
|
377 |
+
|
378 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
379 |
+
|
380 |
+
# Flatten the batch and study_id dimensions:
|
381 |
+
assert len(pixel_values.shape) == 5, 'pixel_values must be B, S, C, H, W, where S is the max number of images for a study in the batch.'
|
382 |
+
last_hidden_state = self.uniformer(pixel_values.view(-1, *pixel_values.shape[2:]))
|
383 |
+
# last_hidden_state = self.uniformer(pixel_values.flatten(start_dim=0, end_dim=1))
|
384 |
+
|
385 |
+
# Flatten h x w:
|
386 |
+
last_hidden_state = torch.flatten(last_hidden_state, 2)
|
387 |
+
|
388 |
+
# Project the features for each spatial position to the decoder's hidden size:
|
389 |
+
projection = self.projection_head(torch.permute(last_hidden_state, [0, 2, 1]))
|
390 |
+
|
391 |
+
# Concatenate the features for each chest X-ray:
|
392 |
+
projection = projection.view(pixel_values.shape[0], -1, projection.shape[-1])
|
393 |
+
|
394 |
+
# Derive the attention mask from the pixel values:
|
395 |
+
mask = (pixel_values[:, :, 0, 0, 0] != 0.0)[:, :, None]
|
396 |
+
attention_mask = torch.ones(
|
397 |
+
[projection.shape[0], pixel_values.shape[1], projection.shape[1] // pixel_values.shape[1]],
|
398 |
+
dtype=torch.long,
|
399 |
+
device=mask.device,
|
400 |
+
)
|
401 |
+
attention_mask = attention_mask * mask
|
402 |
+
attention_mask = attention_mask.view(attention_mask.shape[0], -1)
|
403 |
+
|
404 |
+
if not return_dict:
|
405 |
+
return projection
|
406 |
+
|
407 |
+
return ModelOutput(last_hidden_state=projection, attention_mask=attention_mask)
|
408 |
+
|
409 |
+
|
410 |
+
if __name__ == '__main__':
|
411 |
+
y = PatchEmbed()
|
412 |
+
y(torch.randn(2, 3, 224, 224))
|