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--- |
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library_name: transformers |
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pipeline_tag: mask-generation |
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inference: true |
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widget: |
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- text: Hello! |
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example_title: Hello world |
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group: Python |
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base_model: |
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- facebook/sam3 |
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--- |
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This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [facebook/sam3](https://huggingface.co/facebook/sam3). |
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### Example usage: |
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```python |
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import requests |
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import torch |
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from PIL import Image |
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from transformers import Sam3Model, Sam3Processor |
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from transformers.models.sam3.modeling_sam3 import Sam3Config |
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model_id = "tiny-random/sam3" |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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model = Sam3Model.from_pretrained(model_id).to(device) |
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processor = Sam3Processor.from_pretrained(model_id) |
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kitchen_url = "http://images.cocodataset.org/val2017/000000136466.jpg" |
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kitchen_image = Image.open(requests.get( |
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kitchen_url, stream=True).raw).convert("RGB") |
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# Segment "handle" but exclude the oven handle using a negative box |
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text = "handle" |
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# Negative box covering oven handle area (xyxy): [40, 183, 318, 204] |
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oven_handle_box = [40, 183, 318, 204] |
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input_boxes = [[oven_handle_box]] |
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inputs = processor( |
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images=kitchen_image, |
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text=text, |
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input_boxes=input_boxes, |
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input_boxes_labels=[[0]], # 0 = negative (exclude this region) |
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return_tensors="pt" |
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).to(device) |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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# Post-process results |
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results = processor.post_process_instance_segmentation( |
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outputs, |
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threshold=0.5, |
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mask_threshold=0.5, |
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target_sizes=inputs.get("original_sizes").tolist() |
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)[0] |
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print(results) |
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# This will segment pot handles but exclude the oven handle |
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``` |
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### Codes to create this repo: |
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```python |
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import json |
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from pathlib import Path |
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import accelerate |
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import torch |
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from huggingface_hub import file_exists, hf_hub_download |
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from transformers import ( |
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AutoConfig, |
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AutoModelForCausalLM, |
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AutoProcessor, |
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GenerationConfig, |
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Sam3Processor, |
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set_seed, |
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) |
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from transformers.models.sam3.modeling_sam3 import Sam3Config, Sam3Model |
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source_model_id = "facebook/sam3" |
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save_folder = "/tmp/tiny-random/sam3" |
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processor = Sam3Processor.from_pretrained( |
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source_model_id, trust_remote_code=True) |
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processor.save_pretrained(save_folder) |
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with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model'), 'r', encoding='utf-8') as f: |
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config_json = json.load(f) |
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HIDDEN_SIZE = 16 |
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INTERMEDIATE_SIZE = 32 |
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NUM_ATTENTION_HEADS = 2 |
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config_json['detector_config']['detr_decoder_config'].update({ |
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'hidden_size': HIDDEN_SIZE, |
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'intermediate_size': INTERMEDIATE_SIZE, |
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'num_attention_heads': NUM_ATTENTION_HEADS, |
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}) |
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config_json['detector_config']['detr_encoder_config'].update({ |
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'hidden_size': HIDDEN_SIZE, |
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'intermediate_size': INTERMEDIATE_SIZE, |
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'num_attention_heads': NUM_ATTENTION_HEADS, |
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}) |
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config_json['detector_config']['geometry_encoder_config'].update({ |
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'hidden_size': HIDDEN_SIZE, |
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'intermediate_size': INTERMEDIATE_SIZE, |
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'num_attention_heads': NUM_ATTENTION_HEADS, |
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}) |
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config_json['detector_config']['mask_decoder_config'].update({ |
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'hidden_size': HIDDEN_SIZE, |
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'intermediate_size': INTERMEDIATE_SIZE, |
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'num_attention_heads': NUM_ATTENTION_HEADS, |
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}) |
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config_json['detector_config']['text_config'].update({ |
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'hidden_size': HIDDEN_SIZE, |
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'intermediate_size': INTERMEDIATE_SIZE, |
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'num_attention_heads': NUM_ATTENTION_HEADS, |
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'projection_dim': HIDDEN_SIZE, |
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'num_hidden_layers': 2, |
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}) |
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config_json['detector_config']['vision_config']['backbone_config'].update({ |
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'hidden_size': HIDDEN_SIZE, |
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'intermediate_size': INTERMEDIATE_SIZE, |
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'num_attention_heads': NUM_ATTENTION_HEADS, |
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'fpn_hidden_size': HIDDEN_SIZE, |
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'global_attn_indexes': [1, 3, 5, 7], |
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'num_hidden_layers': 8, |
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}) |
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config_json['detector_config']['vision_config'].update({ |
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'fpn_hidden_size': HIDDEN_SIZE, |
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}) |
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config_json['tracker_config']['mask_decoder_config'].update({ |
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'hidden_size': HIDDEN_SIZE, |
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'iou_head_hidden_dim': HIDDEN_SIZE, |
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'num_attention_heads': NUM_ATTENTION_HEADS, |
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}) |
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config_json['tracker_config'].update({ |
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'mask_downsampler_embed_dim': HIDDEN_SIZE, |
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'memory_attention_feed_forward_hidden_size': HIDDEN_SIZE, |
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'memory_attention_hidden_size': HIDDEN_SIZE, |
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'memory_encoder_hidden_size': HIDDEN_SIZE, |
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'memory_fuser_embed_dim': HIDDEN_SIZE, |
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'memory_fuser_intermediate_dim': INTERMEDIATE_SIZE, |
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}) |
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config_json['tracker_config']['prompt_encoder_config'].update({ |
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'hidden_size': HIDDEN_SIZE, |
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'intermediate_size': INTERMEDIATE_SIZE, |
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'num_attention_heads': NUM_ATTENTION_HEADS, |
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}) |
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config_json['tracker_config']['vision_config']['backbone_config'].update({ |
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'hidden_size': HIDDEN_SIZE, |
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'intermediate_size': INTERMEDIATE_SIZE, |
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'num_attention_heads': NUM_ATTENTION_HEADS, |
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'global_attn_indexes': [1, 3, 5, 7], |
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'num_hidden_layers': 8, |
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}) |
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config_json['tracker_config']['vision_config'].update({ |
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'fpn_hidden_size': HIDDEN_SIZE, |
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}) |
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with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f: |
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json.dump(config_json, f, indent=2) |
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config = Sam3Config.from_pretrained( |
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save_folder, |
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trust_remote_code=True, |
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) |
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print(config) |
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torch.set_default_dtype(torch.float32) |
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model = Sam3Model(config) |
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set_seed(42) |
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model = model.cpu() |
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with torch.no_grad(): |
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for name, p in sorted(model.named_parameters()): |
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torch.nn.init.normal_(p, 0, 0.1) |
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print(name, p.shape) |
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model.save_pretrained(save_folder) |
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# print(list(model.state_dict().keys())) |
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# there is some bug in model.save_pretrained... Re-save the model weights here. |
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import safetensors.torch |
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safetensors.torch.save_file( |
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tensors=model.state_dict(), |
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filename=f"{save_folder}/model.safetensors" |
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) |
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``` |
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### Printing the model: |
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```text |
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Sam3Model( |
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(vision_encoder): Sam3VisionModel( |
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(backbone): Sam3ViTModel( |
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(embeddings): Sam3ViTEmbeddings( |
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(patch_embeddings): Sam3ViTPatchEmbeddings( |
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(projection): Conv2d(3, 16, kernel_size=(14, 14), stride=(14, 14), bias=False) |
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) |
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(dropout): Dropout(p=0.0, inplace=False) |
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) |
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(layer_norm): LayerNorm((16,), eps=1e-06, elementwise_affine=True) |
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(layers): ModuleList( |
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(0-7): 8 x Sam3ViTLayer( |
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(layer_norm1): LayerNorm((16,), eps=1e-06, elementwise_affine=True) |
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(rotary_emb): Sam3ViTRotaryEmbedding() |
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(attention): Sam3ViTRoPEAttention( |
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(q_proj): Linear(in_features=16, out_features=16, bias=True) |
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(k_proj): Linear(in_features=16, out_features=16, bias=True) |
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(v_proj): Linear(in_features=16, out_features=16, bias=True) |
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(o_proj): Linear(in_features=16, out_features=16, bias=True) |
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) |
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(layer_norm2): LayerNorm((16,), eps=1e-06, elementwise_affine=True) |
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(mlp): Sam3MLP( |
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(activation_fn): GELUActivation() |
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(fc1): Linear(in_features=16, out_features=32, bias=True) |
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(fc2): Linear(in_features=32, out_features=16, bias=True) |
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(dropout): Dropout(p=0.0, inplace=False) |
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) |
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(dropout): Dropout(p=0.0, inplace=False) |
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) |
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) |
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) |
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(neck): Sam3VisionNeck( |
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(position_encoding): Sam3SinePositionEmbedding() |
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(fpn_layers): ModuleList( |
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(0): Sam3FPNLayer( |
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(scale_layers): ModuleList( |
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(0): ConvTranspose2d(16, 8, kernel_size=(2, 2), stride=(2, 2)) |
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(1): GELU(approximate='none') |
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(2): ConvTranspose2d(8, 4, kernel_size=(2, 2), stride=(2, 2)) |
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) |
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(proj1): Conv2d(4, 16, kernel_size=(1, 1), stride=(1, 1)) |
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(proj2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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) |
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(1): Sam3FPNLayer( |
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(scale_layers): ModuleList( |
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(0): ConvTranspose2d(16, 8, kernel_size=(2, 2), stride=(2, 2)) |
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) |
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(proj1): Conv2d(8, 16, kernel_size=(1, 1), stride=(1, 1)) |
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(proj2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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) |
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(2): Sam3FPNLayer( |
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(scale_layers): ModuleList() |
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(proj1): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1)) |
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(proj2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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) |
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(3): Sam3FPNLayer( |
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(scale_layers): ModuleList( |
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(0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) |
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) |
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(proj1): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1)) |
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(proj2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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) |
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) |
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) |
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) |
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(text_encoder): CLIPTextModelWithProjection( |
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(text_model): CLIPTextTransformer( |
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(embeddings): CLIPTextEmbeddings( |
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(token_embedding): Embedding(49408, 16) |
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(position_embedding): Embedding(32, 16) |
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) |
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(encoder): CLIPEncoder( |
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(layers): ModuleList( |
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(0-1): 2 x CLIPEncoderLayer( |
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(self_attn): CLIPAttention( |
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(k_proj): Linear(in_features=16, out_features=16, bias=True) |
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(v_proj): Linear(in_features=16, out_features=16, bias=True) |
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(q_proj): Linear(in_features=16, out_features=16, bias=True) |
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(out_proj): Linear(in_features=16, out_features=16, bias=True) |
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) |
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(layer_norm1): LayerNorm((16,), eps=1e-05, elementwise_affine=True) |
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(mlp): CLIPMLP( |
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(activation_fn): GELUActivation() |
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(fc1): Linear(in_features=16, out_features=32, bias=True) |
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(fc2): Linear(in_features=32, out_features=16, bias=True) |
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) |
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(layer_norm2): LayerNorm((16,), eps=1e-05, elementwise_affine=True) |
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) |
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) |
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) |
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(final_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) |
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) |
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(text_projection): Linear(in_features=16, out_features=16, bias=False) |
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) |
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(text_projection): Linear(in_features=16, out_features=16, bias=True) |
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(geometry_encoder): Sam3GeometryEncoder( |
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(position_encoding): Sam3SinePositionEmbedding() |
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(label_embed): Embedding(2, 16) |
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(cls_embed): Embedding(1, 16) |
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(boxes_direct_project): Linear(in_features=4, out_features=16, bias=True) |
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(boxes_pool_project): Conv2d(16, 16, kernel_size=(7, 7), stride=(1, 1)) |
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(boxes_pos_enc_project): Linear(in_features=18, out_features=16, bias=True) |
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(vision_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) |
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(final_proj): Linear(in_features=16, out_features=16, bias=True) |
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(prompt_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) |
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(layers): ModuleList( |
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(0-2): 3 x Sam3GeometryEncoderLayer( |
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(layer_norm1): LayerNorm((16,), eps=1e-05, elementwise_affine=True) |
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(self_attn): Sam3Attention( |
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(q_proj): Linear(in_features=16, out_features=16, bias=True) |
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(k_proj): Linear(in_features=16, out_features=16, bias=True) |
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(v_proj): Linear(in_features=16, out_features=16, bias=True) |
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(o_proj): Linear(in_features=16, out_features=16, bias=True) |
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) |
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(dropout): Dropout(p=0.1, inplace=False) |
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(cross_attn): Sam3Attention( |
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(q_proj): Linear(in_features=16, out_features=16, bias=True) |
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(k_proj): Linear(in_features=16, out_features=16, bias=True) |
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(v_proj): Linear(in_features=16, out_features=16, bias=True) |
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(o_proj): Linear(in_features=16, out_features=16, bias=True) |
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) |
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(layer_norm2): LayerNorm((16,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sam3MLP( |
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(activation_fn): ReLU() |
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(fc1): Linear(in_features=16, out_features=32, bias=True) |
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(fc2): Linear(in_features=32, out_features=16, bias=True) |
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(dropout): Dropout(p=0.0, inplace=False) |
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) |
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(layer_norm3): LayerNorm((16,), eps=1e-05, elementwise_affine=True) |
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) |
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) |
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(output_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) |
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) |
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(detr_encoder): Sam3DetrEncoder( |
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(layers): ModuleList( |
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(0-5): 6 x Sam3DetrEncoderLayer( |
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(layer_norm1): LayerNorm((16,), eps=1e-05, elementwise_affine=True) |
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(self_attn): Sam3Attention( |
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(q_proj): Linear(in_features=16, out_features=16, bias=True) |
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(k_proj): Linear(in_features=16, out_features=16, bias=True) |
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(v_proj): Linear(in_features=16, out_features=16, bias=True) |
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(o_proj): Linear(in_features=16, out_features=16, bias=True) |
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) |
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(dropout): Dropout(p=0.1, inplace=False) |
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(cross_attn): Sam3Attention( |
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(q_proj): Linear(in_features=16, out_features=16, bias=True) |
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(k_proj): Linear(in_features=16, out_features=16, bias=True) |
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(v_proj): Linear(in_features=16, out_features=16, bias=True) |
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(o_proj): Linear(in_features=16, out_features=16, bias=True) |
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) |
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(layer_norm2): LayerNorm((16,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sam3MLP( |
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(activation_fn): ReLU() |
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(fc1): Linear(in_features=16, out_features=32, bias=True) |
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(fc2): Linear(in_features=32, out_features=16, bias=True) |
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(dropout): Dropout(p=0.0, inplace=False) |
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) |
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(layer_norm3): LayerNorm((16,), eps=1e-05, elementwise_affine=True) |
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) |
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) |
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) |
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(detr_decoder): Sam3DetrDecoder( |
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(layers): ModuleList( |
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(0-5): 6 x Sam3DetrDecoderLayer( |
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(self_attn): Sam3Attention( |
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(q_proj): Linear(in_features=16, out_features=16, bias=True) |
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(k_proj): Linear(in_features=16, out_features=16, bias=True) |
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(v_proj): Linear(in_features=16, out_features=16, bias=True) |
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(o_proj): Linear(in_features=16, out_features=16, bias=True) |
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) |
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(self_attn_dropout): Dropout(p=0.1, inplace=False) |
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(self_attn_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) |
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(text_cross_attn): Sam3Attention( |
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(q_proj): Linear(in_features=16, out_features=16, bias=True) |
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(k_proj): Linear(in_features=16, out_features=16, bias=True) |
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(v_proj): Linear(in_features=16, out_features=16, bias=True) |
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(o_proj): Linear(in_features=16, out_features=16, bias=True) |
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) |
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(text_cross_attn_dropout): Dropout(p=0.1, inplace=False) |
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(text_cross_attn_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) |
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(vision_cross_attn): Sam3Attention( |
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(q_proj): Linear(in_features=16, out_features=16, bias=True) |
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(k_proj): Linear(in_features=16, out_features=16, bias=True) |
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(v_proj): Linear(in_features=16, out_features=16, bias=True) |
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(o_proj): Linear(in_features=16, out_features=16, bias=True) |
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) |
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(vision_cross_attn_dropout): Dropout(p=0.1, inplace=False) |
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(vision_cross_attn_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) |
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(mlp): Sam3MLP( |
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(activation_fn): ReLU() |
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(fc1): Linear(in_features=16, out_features=32, bias=True) |
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(fc2): Linear(in_features=32, out_features=16, bias=True) |
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(dropout): Dropout(p=0.0, inplace=False) |
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) |
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(mlp_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) |
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(mlp_dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(output_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) |
|
|
(box_head): Sam3DecoderMLP( |
|
|
(layer1): Linear(in_features=16, out_features=16, bias=True) |
|
|
(layer2): Linear(in_features=16, out_features=16, bias=True) |
|
|
(layer3): Linear(in_features=16, out_features=4, bias=True) |
|
|
) |
|
|
(query_embed): Embedding(200, 16) |
|
|
(reference_points): Embedding(200, 4) |
|
|
(presence_token): Embedding(1, 16) |
|
|
(presence_head): Sam3DecoderMLP( |
|
|
(layer1): Linear(in_features=16, out_features=16, bias=True) |
|
|
(layer2): Linear(in_features=16, out_features=16, bias=True) |
|
|
(layer3): Linear(in_features=16, out_features=1, bias=True) |
|
|
) |
|
|
(presence_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) |
|
|
(ref_point_head): Sam3DecoderMLP( |
|
|
(layer1): Linear(in_features=32, out_features=16, bias=True) |
|
|
(layer2): Linear(in_features=16, out_features=16, bias=True) |
|
|
) |
|
|
(box_rpb_embed_x): Sam3DecoderMLP( |
|
|
(layer1): Linear(in_features=2, out_features=16, bias=True) |
|
|
(layer2): Linear(in_features=16, out_features=2, bias=True) |
|
|
) |
|
|
(box_rpb_embed_y): Sam3DecoderMLP( |
|
|
(layer1): Linear(in_features=2, out_features=16, bias=True) |
|
|
(layer2): Linear(in_features=16, out_features=2, bias=True) |
|
|
) |
|
|
(position_encoding): Sam3SinePositionEmbedding() |
|
|
) |
|
|
(mask_decoder): Sam3MaskDecoder( |
|
|
(pixel_decoder): Sam3PixelDecoder( |
|
|
(conv_layers): ModuleList( |
|
|
(0-2): 3 x Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
|
) |
|
|
(norms): ModuleList( |
|
|
(0-2): 3 x GroupNorm(8, 16, eps=1e-05, affine=True) |
|
|
) |
|
|
) |
|
|
(mask_embedder): Sam3MaskEmbedder( |
|
|
(layers): ModuleList( |
|
|
(0-2): 3 x Linear(in_features=16, out_features=16, bias=True) |
|
|
) |
|
|
(activation): ReLU() |
|
|
) |
|
|
(instance_projection): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1)) |
|
|
(semantic_projection): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1)) |
|
|
(prompt_cross_attn): Sam3Attention( |
|
|
(q_proj): Linear(in_features=16, out_features=16, bias=True) |
|
|
(k_proj): Linear(in_features=16, out_features=16, bias=True) |
|
|
(v_proj): Linear(in_features=16, out_features=16, bias=True) |
|
|
(o_proj): Linear(in_features=16, out_features=16, bias=True) |
|
|
) |
|
|
(prompt_cross_attn_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) |
|
|
(prompt_cross_attn_dropout): Dropout(p=0.0, inplace=False) |
|
|
) |
|
|
(dot_product_scoring): Sam3DotProductScoring( |
|
|
(text_mlp): Sam3DecoderMLP( |
|
|
(layer1): Linear(in_features=16, out_features=32, bias=True) |
|
|
(layer2): Linear(in_features=32, out_features=16, bias=True) |
|
|
) |
|
|
(text_mlp_dropout): Dropout(p=0.1, inplace=False) |
|
|
(text_mlp_out_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True) |
|
|
(text_proj): Linear(in_features=16, out_features=16, bias=True) |
|
|
(query_proj): Linear(in_features=16, out_features=16, bias=True) |
|
|
) |
|
|
) |
|
|
``` |
