From 4d92696f47c6bf84b660cadb1d8b715731a6cd97 Mon Sep 17 00:00:00 2001 From: Ashu Date: Sat, 13 Sep 2025 17:16:02 -0700 Subject: [PATCH] dinov3 example --- dinov3_example.py | 165 ++++++++++++++++++++++++++++++++++++++++++++++ pyproject.toml | 5 ++ uv.lock | 62 ++++++++--------- 3 files changed, 201 insertions(+), 31 deletions(-) create mode 100644 dinov3_example.py diff --git a/dinov3_example.py b/dinov3_example.py new file mode 100644 index 000000000..d4882fb12 --- /dev/null +++ b/dinov3_example.py @@ -0,0 +1,165 @@ +import torch +from transformers import AutoModel, AutoImageProcessor +from PIL import Image +import numpy as np + + +def dinov3_basic(): + print("=== DINOv3 Basic Example ===") + + # model loading + model_name = "facebook/dinov3-vits16-pretrain-lvd1689m" + print(f"Loading model: {model_name}") + + try: + model = AutoModel.from_pretrained(model_name) + processor = AutoImageProcessor.from_pretrained(model_name) + print(f"✓ Model loaded successfully") + print(f" Model type: {model.config.model_type}") + print(f" Hidden size: {model.config.hidden_size}") + print(f" Image size: {model.config.image_size}") + except Exception as e: + print(f"✗ Model loading failed: {e}") + return False + + print("Testing inference with dummy image...") + try: + dummy_image = Image.new('RGB', (224, 224), color=(128, 64, 192)) + + # Process the image + inputs = processor(images=dummy_image, return_tensors="pt") + print(f" Input tensor shape: {inputs.pixel_values.shape}") + + # Run inference + model.eval() + with torch.no_grad(): + outputs = model(**inputs) + + # Check outputs + print(f"✓ Inference successful") + if hasattr(outputs, 'pooler_output') and outputs.pooler_output is not None: + print(f" Pooler output shape: {outputs.pooler_output.shape}") + print(f" Pooler output (CLS token): {outputs.pooler_output.shape}") + if hasattr(outputs, 'last_hidden_state'): + print(f" Last hidden state shape: {outputs.last_hidden_state.shape}") + print(f" CLS token shape: {outputs.last_hidden_state[:, 0].shape}") + print(f" Patch tokens shape: {outputs.last_hidden_state[:, 1:].shape}") + + return True + + except Exception as e: + print(f"✗ Inference failed: {e}") + return False + + +def dinov3_variants(): + """different DINOv3 model variants availability.""" + print("=== Testing DINOv3 Model Variants ===") + + variants = [ + ("facebook/dinov3-vits16-pretrain-lvd1689m", "ViT-S/16", 384), + ("facebook/dinov3-vitb16-pretrain-lvd1689m", "ViT-B/16", 768), + ("facebook/dinov3-vitl16-pretrain-lvd1689m", "ViT-L/16", 1024), + ] + + available_variants = [] + for model_name, display_name, expected_dim in variants: + try: + print(f"Testing {display_name} ({model_name})...") + model = AutoModel.from_pretrained(model_name) + actual_dim = model.config.hidden_size + + print(f"✓ {display_name}") + print(f" Expected dim: {expected_dim}, Actual dim: {actual_dim}") + if actual_dim == expected_dim: + print(f" ✓ Dimension matches expected") + else: + print(f" ⚠ Dimension mismatch!") + + available_variants.append((model_name, display_name, actual_dim)) + + except Exception as e: + print(f"✗ {display_name} failed: {e}") + + return available_variants + + +def eva_compatibility(): + """compatibility with eva-probe patterns.""" + print("=== Testing eva-probe Compatibility ===") + + model_name = "facebook/dinov3-vits16-pretrain-lvd1689m" + + try: + model = AutoModel.from_pretrained(model_name) + processor = AutoImageProcessor.from_pretrained(model_name) + + # batch processing (similar to eva's batch sizes) + batch_size = 2 + dummy_images = [Image.new('RGB', (224, 224), color=(i*50, 100, 150)) for i in range(batch_size)] + + inputs = processor(images=dummy_images, return_tensors="pt") + print(f" Batch input shape: {inputs.pixel_values.shape}") + + model.eval() + with torch.no_grad(): + outputs = model(**inputs) + + # both CLS token (classification) and patch tokens (segmentation) modes + if hasattr(outputs, 'pooler_output') and outputs.pooler_output is not None: + cls_features = outputs.pooler_output + print(f"✓ CLS features (classification): {cls_features.shape}") + + if hasattr(outputs, 'last_hidden_state'): + # CLS token (first token) + cls_from_hidden = outputs.last_hidden_state[:, 0] + print(f"✓ CLS from hidden state: {cls_from_hidden.shape}") + + # Patch tokens (remaining tokens) + patch_tokens = outputs.last_hidden_state[:, 1:] + print(f"✓ Patch tokens (segmentation): {patch_tokens.shape}") + + # Calculate expected patch count (14x14 for 224x224 input with patch size 16) + expected_patches = (224 // 16) ** 2 # 196 patches + actual_patches = patch_tokens.shape[1] + print(f" Expected patches: {expected_patches}, Actual patches: {actual_patches}") + + return True + + except Exception as e: + print(f"✗ eva-probe compatibility failed: {e}") + return False + + +def main(): + """Run all DINOv3 validation tests.""" + print("DINOv3 Integration Validation for eva-probe") + print("=" * 50) + + # basic functionality + basic_success = dinov3_basic() + + # model variants + available_variants = dinov3_variants() + + # eva compatibility + eva_success = eva_compatibility() + + # Summary + print("" + "=" * 50) + print("VALIDATION SUMMARY") + print("=" * 50) + print(f"Basic functionality: {'✓ PASS' if basic_success else '✗ FAIL'}") + print(f"Available variants: {len(available_variants)}") + for model_name, display_name, dim in available_variants: + print(f" - {display_name}: {dim} dimensions") + print(f"eva-probe compatibility: {'✓ PASS' if eva_success else '✗ FAIL'}") + + if basic_success and eva_success and len(available_variants) > 0: + print("🎉 DINOv3 is ready for eva-probe integration!") + else: + print("⚠️ Some issues detected. Please review the output above.") + + +if __name__ == "__main__": + main() diff --git a/pyproject.toml b/pyproject.toml index a0a679094..7520d3835 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -47,6 +47,8 @@ dependencies = [ "rich>=13.7.1", "torchmetrics>=1.6.0", "nibabel>=3.2.2", + "huggingface-hub>=0.34.4", + "torchvision>=0.23.0", ] [project.urls] @@ -237,3 +239,6 @@ omit = [ [tool.coverage.comment] layout = "header, diff, flags, files, footer" + +[tool.uv.sources] +transformers = { git = "https://github.com/huggingface/transformers.git" } diff --git a/uv.lock b/uv.lock index d1f907c7f..05b0aafb8 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 2 +revision = 3 requires-python = ">=3.10" resolution-markers = [ "python_full_version >= '3.12' and sys_platform == 'darwin'", @@ -848,6 +848,7 @@ name = "kaiko-eva" version = "0.3.3" source = { editable = "." } dependencies = [ + { name = "huggingface-hub" }, { name = "jsonargparse", extra = ["omegaconf"] }, { name = "lightning" }, { name = "loguru" }, @@ -860,6 +861,7 @@ dependencies = [ { name = "toolz" }, { name = "torch" }, { name = "torchmetrics" }, + { name = "torchvision" }, { name = "transformers" }, ] @@ -911,6 +913,7 @@ requires-dist = [ { name = "gdown", marker = "extra == 'vision'", specifier = ">=5.2.0" }, { name = "h5py", marker = "extra == 'all'", specifier = ">=3.10.0" }, { name = "h5py", marker = "extra == 'vision'", specifier = ">=3.10.0" }, + { name = "huggingface-hub", specifier = ">=0.34.4" }, { name = "imagesize", marker = "extra == 'all'", specifier = ">=1.4.1" }, { name = "imagesize", marker = "extra == 'vision'", specifier = ">=1.4.1" }, { name = "jsonargparse", extras = ["omegaconf"], specifier = ">=4.30.0" }, @@ -941,9 +944,10 @@ requires-dist = [ { name = "toolz", specifier = ">=0.12.1" }, { name = "torch", specifier = ">=2.3.0" }, { name = "torchmetrics", specifier = ">=1.6.0" }, + { name = "torchvision", specifier = ">=0.23.0" }, { name = "torchvision", marker 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