| --- |
| library_name: transformers |
| tags: [] |
| --- |
| |
| # Model Card for Model ID |
|
|
| <!-- Provide a quick summary of what the model is/does. --> |
| ## Code to create model |
| ```python |
| import torch |
| from transformers import GroundingDinoConfig, GroundingDinoForObjectDetection, AutoProcessor |
| |
| model_id = 'IDEA-Research/grounding-dino-tiny' |
| config = GroundingDinoConfig.from_pretrained( |
| model_id, |
| decoder_layers=1, |
| decoder_attention_heads=2, |
| encoder_layers=1, |
| encoder_attention_heads=2, |
| text_config=dict( |
| num_attention_heads=2, |
| num_hidden_layers=1, |
| hidden_size=32, |
| ), |
| backbone_config=dict( |
| attention_probs_dropout_prob=0.0, |
| depths=[1, 1, 2, 1], |
| drop_path_rate=0.1, |
| embed_dim=12, |
| encoder_stride=32, |
| hidden_act="gelu", |
| hidden_dropout_prob=0.0, |
| hidden_size=48, |
| image_size=224, |
| initializer_range=0.02, |
| layer_norm_eps=1e-05, |
| mlp_ratio=4.0, |
| num_channels=3, |
| num_heads=[1, 2, 3, 4], |
| num_layers=4, |
| out_features=["stage2", "stage3", "stage4"], |
| out_indices=[2, 3, 4], |
| patch_size=4, |
| stage_names=["stem", "stage1", "stage2", "stage3", "stage4"], |
| window_size=7 |
| ) |
| ) |
| |
| # Create model and randomize all weights |
| model = GroundingDinoForObjectDetection(config) |
| |
| torch.manual_seed(0) # Set for reproducibility |
| for name, param in model.named_parameters(): |
| param.data = torch.randn_like(param) |
| |
| processor = AutoProcessor.from_pretrained(model_id) |
| |
| print(model.num_parameters()) # 7751525 |
| ``` |
|
|
| ## Code to export to ONNX |
|
|
| ```python |
| import requests |
| |
| import torch |
| from PIL import Image |
| from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection |
| from transformers.models.grounding_dino.modeling_grounding_dino import ( |
| GroundingDinoObjectDetectionOutput, |
| ) |
| |
| # torch.onnx.errors.UnsupportedOperatorError: Exporting the operator 'aten::__ior_' to ONNX opset version 16 is not supported. |
| # Please feel free to request support or submit a pull request on PyTorch GitHub: https://github.com/pytorch/pytorch/issues. |
| torch.Tensor.__ior__ = lambda self, other: self.__or__(other) |
| |
| # model_id = "IDEA-Research/grounding-dino-tiny" |
| model_id = "hf-internal-testing/tiny-random-GroundingDinoForObjectDetection" |
| processor = AutoProcessor.from_pretrained(model_id) |
| model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id) |
| |
| old_forward = model.forward |
| def new_forward(*args, **kwargs): |
| output = old_forward(*args, **kwargs, return_dict=True) |
| # Only return the logits and pred_boxes |
| return GroundingDinoObjectDetectionOutput( |
| logits=output.logits, pred_boxes=output.pred_boxes |
| ) |
| model.forward = new_forward |
| |
| image_url = "http://images.cocodataset.org/val2017/000000039769.jpg" |
| image = Image.open(requests.get(image_url, stream=True).raw).resize((800, 800)) |
| text = "a cat." # NB: text query need to be lowercased + end with a dot |
| |
| # Run python model |
| inputs = processor(images=image, text=text, return_tensors="pt") |
| with torch.no_grad(): |
| outputs = model(**inputs) |
| results = processor.post_process_grounded_object_detection( |
| outputs, |
| inputs.input_ids, |
| box_threshold=0.4, |
| text_threshold=0.3, |
| target_sizes=[image.size[::-1]], |
| ) |
| |
| text_axes = { |
| "input_ids": {1: "sequence_length"}, |
| "token_type_ids": {1: "sequence_length"}, |
| "attention_mask": {1: "sequence_length"}, |
| } |
| image_axes = {} |
| output_axes = { |
| "logits": {1: "num_queries"}, |
| "pred_boxes": {1: "num_queries"}, |
| } |
| input_names = [ |
| "pixel_values", |
| "input_ids", |
| "token_type_ids", |
| "attention_mask", |
| "pixel_mask", |
| ] |
| |
| # Input to the model |
| x = tuple(inputs[key] for key in input_names) |
| |
| # Export the model |
| torch.onnx.export( |
| model, # model being run |
| x, # model input (or a tuple for multiple inputs) |
| "model.onnx", # where to save the model (can be a file or file-like object) |
| export_params=True, # store the trained parameter weights inside the model file |
| opset_version=16, # the ONNX version to export the model to |
| do_constant_folding=True, # whether to execute constant folding for optimization |
| input_names=input_names, |
| output_names=list(output_axes.keys()), |
| dynamic_axes={ |
| **text_axes, |
| **image_axes, |
| **output_axes, |
| }, |
| ) |
| ``` |
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| <!-- Provide a longer summary of what this model is. --> |
|
|
| This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
|
|
| - **Developed by:** [More Information Needed] |
| - **Funded by [optional]:** [More Information Needed] |
| - **Shared by [optional]:** [More Information Needed] |
| - **Model type:** [More Information Needed] |
| - **Language(s) (NLP):** [More Information Needed] |
| - **License:** [More Information Needed] |
| - **Finetuned from model [optional]:** [More Information Needed] |
|
|
| ### Model Sources [optional] |
|
|
| <!-- Provide the basic links for the model. --> |
|
|
| - **Repository:** [More Information Needed] |
| - **Paper [optional]:** [More Information Needed] |
| - **Demo [optional]:** [More Information Needed] |
|
|
| ## Uses |
|
|
| <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
|
|
| ### Direct Use |
|
|
| <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
|
|
| [More Information Needed] |
|
|
| ### Downstream Use [optional] |
|
|
| <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
|
|
| [More Information Needed] |
|
|
| ### Out-of-Scope Use |
|
|
| <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
|
|
| [More Information Needed] |
|
|
| ## Bias, Risks, and Limitations |
|
|
| <!-- This section is meant to convey both technical and sociotechnical limitations. --> |
|
|
| [More Information Needed] |
|
|
| ### Recommendations |
|
|
| <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
|
|
| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
|
|
| ## How to Get Started with the Model |
|
|
| Use the code below to get started with the model. |
|
|
| [More Information Needed] |
|
|
| ## Training Details |
|
|
| ### Training Data |
|
|
| <!-- 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. --> |
|
|
| [More Information Needed] |
|
|
| ### Training Procedure |
|
|
| <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
|
|
| #### Preprocessing [optional] |
|
|
| [More Information Needed] |
|
|
|
|
| #### Training Hyperparameters |
|
|
| - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
|
|
| #### Speeds, Sizes, Times [optional] |
|
|
| <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
|
|
| [More Information Needed] |
|
|
| ## Evaluation |
|
|
| <!-- This section describes the evaluation protocols and provides the results. --> |
|
|
| ### Testing Data, Factors & Metrics |
|
|
| #### Testing Data |
|
|
| <!-- This should link to a Dataset Card if possible. --> |
|
|
| [More Information Needed] |
|
|
| #### Factors |
|
|
| <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
|
|
| [More Information Needed] |
|
|
| #### Metrics |
|
|
| <!-- These are the evaluation metrics being used, ideally with a description of why. --> |
|
|
| [More Information Needed] |
|
|
| ### Results |
|
|
| [More Information Needed] |
|
|
| #### Summary |
|
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|
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|
|
| ## Model Examination [optional] |
|
|
| <!-- Relevant interpretability work for the model goes here --> |
|
|
| [More Information Needed] |
|
|
| ## Environmental Impact |
|
|
| <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
|
|
| 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). |
|
|
| - **Hardware Type:** [More Information Needed] |
| - **Hours used:** [More Information Needed] |
| - **Cloud Provider:** [More Information Needed] |
| - **Compute Region:** [More Information Needed] |
| - **Carbon Emitted:** [More Information Needed] |
|
|
| ## Technical Specifications [optional] |
|
|
| ### 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. --> |
|
|
| **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] |
