mirror of
https://github.com/huggingface/lerobot.git
synced 2026-05-16 17:20:05 +00:00
Jade Choghari
55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
from transformers import AutoModel, AutoProcessor
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import json_numpy
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import numpy as np
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import torch
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from PIL import Image
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model = AutoModel.from_pretrained(
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"2toINF/X-VLA-WidowX",
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trust_remote_code=True
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)
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processor = AutoProcessor.from_pretrained("2toINF/X-VLA-WidowX", trust_remote_code=True)
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# append 3 random image to a list
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def make_random_pil_images(num_images=3, H=480, W=640):
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images = []
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for _ in range(num_images):
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# Random RGB image
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arr = np.random.randint(0, 256, (H, W, 3), dtype=np.uint8)
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img = Image.fromarray(arr)
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images.append(img)
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return images
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# Example:
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images = make_random_pil_images()
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language_instruction = "This is a random image"
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# Multimodal preprocessing by processor
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inputs = processor(images, language_instruction)
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if not {"input_ids", "image_input", "image_mask"}.issubset(inputs):
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raise ValueError("Processor did not return the expected keys.")
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proprio = torch.randn(1, 20)
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domain_id = torch.tensor([int(0)], dtype=torch.long)
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# Align to model's device/dtype
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device = model.device
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dtype = next(model.parameters()).dtype
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def to_model(t: torch.Tensor) -> torch.Tensor:
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if not isinstance(t, torch.Tensor):
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t = torch.as_tensor(t)
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# cast floats to model dtype, keep integral/bool as-is
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return t.to(device=device, dtype=dtype) if t.is_floating_point() else t.to(device=device)
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inputs = {k: to_model(v) for k, v in inputs.items()}
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inputs.update({
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"proprio": to_model(proprio),
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"domain_id": domain_id.to(device),
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})
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# Inference
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action = model.generate_actions(**inputs, steps=10).squeeze(0).float().cpu().numpy()
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breakpoint()
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