Datasets:
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{
"cells": [
{
"cell_type": "markdown",
"id": "2051e891-2bcf-42a6-9a1e-f773baff8808",
"metadata": {},
"source": [
"<img src=\"./figs/IOAI-Logo.png\" alt=\"IOAI Logo\" width=\"200\" height=\"auto\">\n",
"\n",
"[IOAI 2025 (Beijing, China), Individual Contest](https://ioai-official.org/china-2025)\n",
"\n",
"[](https://colab.research.google.com/github/IOAI-official/IOAI-2025/blob/main/Individual-Contest/Pixel/Solution/Pixel_Solution.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "441c46a0-b20a-4dc1-9227-f659833a7d2f",
"metadata": {},
"source": [
"# Pixel Efficiency: Reference Solution"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2df5ae63",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import numpy as np\n",
"from PIL import Image\n",
"from tqdm import tqdm\n",
"import json\n",
"import torch\n",
"import torch.nn as nn\n",
"from datasets import load_from_disk\n",
"from transformers import CLIPProcessor, CLIPModel\n",
"from typing import Optional\n",
"from transformers.models.clip.modeling_clip import CLIPVisionTransformer, CLIPVisionConfig, BaseModelOutputWithPooling\n",
"\n",
"\n",
"# Dataset configuration\n",
"DATASET_PATH = os.environ.get(\"DATA_PATH\") + \"/test_dataset\"\n",
"SPLIT = \"test\"\n",
"\n",
"# Model Configuration\n",
"MODEL_PATH = \"./clip-vit-large-patch14\"\n",
"DEVICE = \"cuda\"\n",
"BACKGROUND_CLASS = \"other\"\n",
"\n",
"# Image and Masking Configuration\n",
"HEIGHT = 224\n",
"WIDTH = 224\n",
"RETAIN_RATIO = 0.0625\n",
"MEAN_COLOR = (0, 0, 0)\n",
"STRIDE = 2\n",
"TOP_K = 3\n",
"\n",
"# Load the dataset\n",
"print(\"Loading dataset...\")\n",
"dataset_whole = load_from_disk(DATASET_PATH)\n",
"dataset = dataset_whole[SPLIT]\n",
"\n",
"print(f\"Dataset loaded successfully! Total samples: {len(dataset)}\")\n",
"\n",
"print(f\"Loading CLIP model and processor: {MODEL_PATH}...\")\n",
"model = CLIPModel.from_pretrained(MODEL_PATH).to(DEVICE)\n",
"processor = CLIPProcessor.from_pretrained(MODEL_PATH)\n",
"print(\"Model and processor loaded successfully.\")\n",
"\n",
"\n",
"def generate_all_rectangular_regions(image_size=224, patch_size=14, retain_ratio=RETAIN_RATIO, max_aspect_ratio=1.2, stride=1):\n",
" \"\"\"Generate rectangular regions with optimizations for speed\"\"\"\n",
" max_pixels = int(retain_ratio * image_size * image_size)\n",
" patches_per_side = image_size // patch_size\n",
" patch_area = patch_size * patch_size\n",
" target_patches = max_pixels // patch_area\n",
" \n",
" min_patches = max(1, target_patches - 1)\n",
" max_patches = target_patches + 1\n",
" \n",
" regions = []\n",
" region_to_patches = []\n",
" \n",
" # Pre-compute valid rectangle dimensions\n",
" valid_dims = []\n",
" for width_patches in range(1, patches_per_side + 1):\n",
" for height_patches in range(1, patches_per_side + 1):\n",
" total_patches = width_patches * height_patches\n",
" if min_patches <= total_patches <= max_patches:\n",
" aspect_ratio = max(width_patches, height_patches) / min(width_patches, height_patches)\n",
" if aspect_ratio <= max_aspect_ratio:\n",
" valid_dims.append((width_patches, height_patches, total_patches))\n",
" \n",
" # Generate rectangles using stride for positions\n",
" for width_patches, height_patches, total_patches in valid_dims:\n",
" for top_patch in range(0, patches_per_side - height_patches + 1, stride):\n",
" for left_patch in range(0, patches_per_side - width_patches + 1, stride):\n",
" bottom_patch = top_patch + height_patches\n",
" right_patch = left_patch + width_patches\n",
" \n",
" pixel_coords = (\n",
" top_patch * patch_size,\n",
" left_patch * patch_size,\n",
" bottom_patch * patch_size,\n",
" right_patch * patch_size\n",
" )\n",
" regions.append(pixel_coords)\n",
" \n",
" covered_patches = []\n",
" for p_row in range(top_patch, bottom_patch):\n",
" for p_col in range(left_patch, right_patch):\n",
" patch_idx = p_row * patches_per_side + p_col\n",
" covered_patches.append(patch_idx)\n",
" region_to_patches.append(covered_patches)\n",
" \n",
" return regions, region_to_patches\n",
"\n",
"\n",
"class MaskCLIPVisionTransformer(CLIPVisionTransformer):\n",
" \"\"\"Modified CLIP Vision Transformer that supports mask tokens for all possible rectangular regions\"\"\"\n",
" \n",
" def __init__(self, config: CLIPVisionConfig, retain_ratio=RETAIN_RATIO):\n",
" super().__init__(config)\n",
" self.retain_ratio = retain_ratio\n",
" self.num_patches = (config.image_size // config.patch_size) ** 2\n",
" \n",
" self.regions, self.region_to_patches = generate_all_rectangular_regions(\n",
" image_size=config.image_size, \n",
" patch_size=config.patch_size, \n",
" retain_ratio=retain_ratio,\n",
" max_aspect_ratio=1.2,\n",
" stride=STRIDE\n",
" )\n",
" self.num_mask_tokens = len(self.regions)\n",
" \n",
" self.mask_tokens = nn.Parameter(torch.randn(1, self.num_mask_tokens, config.hidden_size))\n",
" \n",
" def create_mask_attention_matrix(self, batch_size):\n",
" \"\"\"Create attention mask matrix for all rectangular regions\"\"\"\n",
" N = self.num_patches\n",
" M = self.num_mask_tokens\n",
" total_tokens = N + 1 + M\n",
" \n",
" attention_mask = torch.zeros(total_tokens, total_tokens, dtype=torch.bool, device=self.mask_tokens.device)\n",
" \n",
" # Class token and image patches do NOT attend to mask tokens\n",
" attention_mask[:N+1, N+1:] = True\n",
" \n",
" # Each mask token attends to its specific image patches (not CLS)\n",
" attention_mask[N+1:, 1:N+1] = True\n",
" \n",
" # Then allow each mask token to attend to its assigned patches\n",
" for mask_idx in range(M):\n",
" covered_patches = self.region_to_patches[mask_idx]\n",
" for patch_idx in covered_patches:\n",
" token_pos = 1 + patch_idx\n",
" attention_mask[N + 1 + mask_idx, token_pos] = False\n",
" \n",
" # Mask tokens do NOT attend to each other\n",
" attention_mask[N+1:, N+1:] = True\n",
" # Allow self-attention for each mask token\n",
" for i in range(M):\n",
" attention_mask[N + 1 + i, N + 1 + i] = False\n",
" \n",
" return attention_mask\n",
" \n",
" def forward(\n",
" self,\n",
" pixel_values: Optional[torch.FloatTensor] = None,\n",
" output_attentions: Optional[bool] = None,\n",
" output_hidden_states: Optional[bool] = None,\n",
" interpolate_pos_encoding: Optional[bool] = False,\n",
" use_mask_tokens: bool = False,\n",
" ) -> BaseModelOutputWithPooling:\n",
" \"\"\"Forward pass with optional mask tokens\"\"\"\n",
" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n",
" output_hidden_states = (\n",
" output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n",
" )\n",
"\n",
" if pixel_values is None:\n",
" raise ValueError(\"You have to specify pixel_values\")\n",
"\n",
" # Get embeddings (patches + class token)\n",
" hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)\n",
" hidden_states = self.pre_layrnorm(hidden_states)\n",
" \n",
" if use_mask_tokens:\n",
" # Add mask tokens to the sequence\n",
" batch_size = hidden_states.shape[0]\n",
" \n",
" cls_token_embedding = hidden_states[:, 0:1, :]\n",
" mask_tokens_expanded = cls_token_embedding.expand(batch_size, self.num_mask_tokens, -1)\n",
" \n",
" if mask_tokens_expanded.device != hidden_states.device:\n",
" mask_tokens_expanded = mask_tokens_expanded.to(hidden_states.device)\n",
" \n",
" hidden_states = torch.cat([hidden_states, mask_tokens_expanded], dim=1)\n",
" \n",
" # Create custom attention mask\n",
" attention_mask = self.create_mask_attention_matrix(batch_size)\n",
" \n",
" seq_len = hidden_states.shape[1]\n",
" attention_mask_4d = attention_mask.unsqueeze(0).unsqueeze(0).expand(batch_size, 1, -1, -1)\n",
" attention_mask_4d = attention_mask_4d.float()\n",
" attention_mask_4d = attention_mask_4d.masked_fill(attention_mask_4d == 1, float('-inf'))\n",
" attention_mask_4d = attention_mask_4d.masked_fill(attention_mask_4d == 0, 0.0)\n",
" else:\n",
" attention_mask_4d = None\n",
"\n",
" # Process through encoder layers\n",
" encoder_outputs = self.encoder(\n",
" inputs_embeds=hidden_states,\n",
" attention_mask=attention_mask_4d,\n",
" causal_attention_mask=None,\n",
" output_attentions=output_attentions,\n",
" output_hidden_states=output_hidden_states,\n",
" )\n",
"\n",
" last_hidden_state = encoder_outputs.last_hidden_state\n",
" \n",
" if use_mask_tokens:\n",
" # Extract different token types\n",
" class_token_output = last_hidden_state[:, 0]\n",
" mask_tokens_output = last_hidden_state[:, self.num_patches + 1:]\n",
" \n",
" # Apply post layer norm\n",
" pooled_output = self.post_layernorm(class_token_output)\n",
" mask_tokens_output = self.post_layernorm(mask_tokens_output)\n",
" \n",
" return {\n",
" 'last_hidden_state': last_hidden_state,\n",
" 'pooler_output': pooled_output,\n",
" 'mask_tokens_output': mask_tokens_output,\n",
" 'hidden_states': encoder_outputs.hidden_states,\n",
" 'attentions': encoder_outputs.attentions,\n",
" }\n",
" else:\n",
" # Standard CLIP behavior\n",
" pooled_output = last_hidden_state[:, 0, :]\n",
" pooled_output = self.post_layernorm(pooled_output)\n",
"\n",
" return BaseModelOutputWithPooling(\n",
" last_hidden_state=last_hidden_state,\n",
" pooler_output=pooled_output,\n",
" hidden_states=encoder_outputs.hidden_states,\n",
" attentions=encoder_outputs.attentions,\n",
" )\n",
"\n",
"\n",
"def apply_mask_with_mean(image, mask, mean_rgb=MEAN_COLOR):\n",
" \"\"\"Apply arbitrary binary mask to image, replacing masked areas with mean values\"\"\"\n",
" img_array = np.array(image).copy()\n",
"\n",
" if isinstance(mask, Image.Image):\n",
" mask_array = np.array(mask.convert('L')) > 127\n",
" else:\n",
" mask_array = mask > 0\n",
"\n",
" mask_3d = np.stack([mask_array] * 3, axis=2)\n",
" mean_values = np.array([int(m * 255) for m in mean_rgb])\n",
" img_array = np.where(mask_3d, img_array, mean_values.reshape(1, 1, 3))\n",
"\n",
" return Image.fromarray(img_array.astype(np.uint8))\n",
"\n",
"\n",
"def compute_vision_features_once(model, image, mask_vision_model):\n",
" \"\"\"\n",
" Compute vision features once for efficient reuse across multiple mask selection functions.\n",
" This eliminates redundant forward passes.\n",
" \"\"\"\n",
" image_inputs = processor(images=image, return_tensors=\"pt\").to(DEVICE)\n",
" \n",
" with torch.no_grad():\n",
" vision_outputs = mask_vision_model(\n",
" pixel_values=image_inputs['pixel_values'],\n",
" use_mask_tokens=True\n",
" )\n",
" \n",
" full_image_features = vision_outputs['pooler_output']\n",
" if hasattr(model, 'visual_projection') and model.visual_projection is not None:\n",
" full_image_features = model.visual_projection(full_image_features)\n",
" \n",
" mask_tokens_features = vision_outputs['mask_tokens_output']\n",
" if hasattr(model, 'visual_projection') and model.visual_projection is not None:\n",
" batch_size, num_tokens, embed_dim = mask_tokens_features.shape\n",
" mask_tokens_features = mask_tokens_features.view(-1, embed_dim)\n",
" mask_tokens_features = model.visual_projection(mask_tokens_features)\n",
" mask_tokens_features = mask_tokens_features.view(batch_size, num_tokens, -1)\n",
" \n",
" full_image_features = full_image_features / full_image_features.norm(dim=-1, keepdim=True)\n",
" mask_tokens_features = mask_tokens_features / mask_tokens_features.norm(dim=-1, keepdim=True)\n",
" \n",
" return vision_outputs, full_image_features, mask_tokens_features\n",
"\n",
"\n",
"def find_best_mask_region_calibrated(model, image, class_names, mask_vision_model, text_features, \n",
" vision_outputs=None, full_image_features=None, mask_tokens_features=None, \n",
" return_detailed=False):\n",
" \"\"\"Find the best mask region using MaskCLIP approach with calibration for black-pixel masking\"\"\"\n",
" num_mask_tokens = mask_vision_model.num_mask_tokens\n",
" \n",
" # Use pre-computed features if provided, otherwise compute them\n",
" if vision_outputs is None or full_image_features is None or mask_tokens_features is None:\n",
" image_inputs = processor(images=image, return_tensors=\"pt\").to(DEVICE)\n",
" \n",
" with torch.no_grad():\n",
" vision_outputs = mask_vision_model(\n",
" pixel_values=image_inputs['pixel_values'],\n",
" use_mask_tokens=True\n",
" )\n",
" \n",
" full_image_features = vision_outputs['pooler_output']\n",
" if hasattr(model, 'visual_projection') and model.visual_projection is not None:\n",
" full_image_features = model.visual_projection(full_image_features)\n",
" \n",
" mask_tokens_features = vision_outputs['mask_tokens_output']\n",
" if hasattr(model, 'visual_projection') and model.visual_projection is not None:\n",
" batch_size, num_tokens, embed_dim = mask_tokens_features.shape\n",
" mask_tokens_features = mask_tokens_features.view(-1, embed_dim)\n",
" mask_tokens_features = model.visual_projection(mask_tokens_features)\n",
" mask_tokens_features = mask_tokens_features.view(batch_size, num_tokens, -1)\n",
" \n",
" full_image_features = full_image_features / full_image_features.norm(dim=-1, keepdim=True)\n",
" mask_tokens_features = mask_tokens_features / mask_tokens_features.norm(dim=-1, keepdim=True)\n",
" \n",
" # Compute similarity between full image and text\n",
" full_image_similarities = torch.matmul(full_image_features, text_features.T)\n",
" full_image_prediction = torch.argmax(full_image_similarities, dim=-1)\n",
" predicted_class_idx = full_image_prediction.item()\n",
" \n",
" # Compute similarities for each mask token\n",
" mask_similarities = torch.matmul(mask_tokens_features.squeeze(0), text_features.T)\n",
" mask_predictions = torch.argmax(mask_similarities, dim=-1)\n",
" \n",
" # Get candidates that predict the same class as full image, sorted by confidence\n",
" matching_masks = (mask_predictions == predicted_class_idx)\n",
" \n",
" if matching_masks.any():\n",
" candidate_indices = torch.where(matching_masks)[0]\n",
" candidate_confidences = mask_similarities[candidate_indices, predicted_class_idx]\n",
" sorted_indices = torch.argsort(candidate_confidences, descending=True)\n",
" sorted_candidates = candidate_indices[sorted_indices]\n",
" else:\n",
" # If no exact matches, use all candidates sorted by confidence for predicted class\n",
" candidate_confidences = mask_similarities[:, predicted_class_idx]\n",
" sorted_candidates = torch.topk(candidate_confidences, len(candidate_confidences)).indices\n",
" \n",
" # OPTIMIZATION: If TOP_K=1, skip calibration and return best candidate directly\n",
" if TOP_K == 1:\n",
" return sorted_candidates[0].item()\n",
" \n",
" # CALIBRATION STEP: Test top K candidates, return immediately when one is correct\n",
" calibration_results = []\n",
" candidates_to_test = sorted_candidates[:TOP_K]\n",
" \n",
" for i, candidate_idx in enumerate(candidates_to_test):\n",
" candidate_idx_item = candidate_idx.item()\n",
" \n",
" # Create masked image for this candidate\n",
" coordinates = mask_idx_to_coordinates(candidate_idx_item, mask_vision_model)\n",
" mask = generate_mask_from_coordinates(image, coordinates)\n",
" masked_image = apply_mask_with_mean(image, mask)\n",
" \n",
" # Test with actual forward pass\n",
" with torch.no_grad():\n",
" masked_image_inputs = processor(images=masked_image, return_tensors=\"pt\").to(DEVICE)\n",
" masked_image_features = model.get_image_features(**masked_image_inputs)\n",
" masked_image_features = masked_image_features / masked_image_features.norm(dim=-1, keepdim=True)\n",
" \n",
" masked_similarities = torch.matmul(masked_image_features, text_features.T)\n",
" masked_prediction = torch.argmax(masked_similarities, dim=-1).item()\n",
" masked_confidence = masked_similarities[0, predicted_class_idx].item()\n",
" \n",
" # If this candidate predicts correctly, return it immediately (early exit optimization)\n",
" if masked_prediction == predicted_class_idx:\n",
" return candidate_idx_item\n",
" \n",
" # Store failed calibration result\n",
" calibration_results.append((candidate_idx_item, masked_confidence))\n",
" \n",
" # If we reach here, all TOP_K candidates failed calibration\n",
" # Fall back to the next best candidate from sorted list WITHOUT additional calibration\n",
" if len(sorted_candidates) > TOP_K:\n",
" return sorted_candidates[TOP_K].item()\n",
" else:\n",
" # If no more candidates available, return the best failed calibration result\n",
" if calibration_results:\n",
" return max(calibration_results, key=lambda x: x[1])[0]\n",
" else:\n",
" # Ultimate fallback: return the best mask token prediction\n",
" return sorted_candidates[0].item()\n",
"\n",
"\n",
"def mask_idx_to_coordinates(mask_idx, mask_vision_model):\n",
" \"\"\"Convert mask token index to image coordinates using the pre-computed regions\"\"\"\n",
" if mask_idx >= len(mask_vision_model.regions):\n",
" raise ValueError(f\"mask_idx {mask_idx} is out of range. Only {len(mask_vision_model.regions)} regions available.\")\n",
" \n",
" top, left, bottom, right = mask_vision_model.regions[mask_idx]\n",
" return ((top, left), (bottom, right))\n",
"\n",
"\n",
"def generate_mask_from_coordinates(image, coordinates):\n",
" \"\"\"Generate a binary mask from crop coordinates\"\"\"\n",
" H, W = 224, 224\n",
" mask = np.zeros((H, W), dtype=np.int8)\n",
" \n",
" (top, left), (bottom, right) = coordinates\n",
" mask[top:bottom, left:right] = 1\n",
" \n",
" return mask\n",
"\n",
"\n",
"# Create the MaskCLIP model\n",
"print(\"Creating MaskCLIP model...\")\n",
"mask_vision_model = MaskCLIPVisionTransformer(model.vision_model.config, retain_ratio=RETAIN_RATIO)\n",
"mask_vision_model.load_state_dict(model.vision_model.state_dict(), strict=False)\n",
"mask_vision_model = mask_vision_model.to(DEVICE)\n",
"mask_vision_model.eval()\n",
"print(\"MaskCLIP model created successfully.\")\n",
"\n",
"dataset_eval = load_from_disk(DATASET_PATH)\n",
"dataset_eval = dataset_eval[SPLIT]\n",
"\n",
"# Get class names from training dataset for consistent evaluation \n",
"train_dataset = load_from_disk(\"/bohr/train-yzfn/v1/train_dataset\")[\"train\"] # TODO: This part needs changing!\n",
"class_names_eval = list(set([item['name'] for item in train_dataset])) + [BACKGROUND_CLASS]\n",
"\n",
"# Prepare text features once for efficiency\n",
"print(\"Preparing text features...\")\n",
"text_inputs_eval = processor(text=class_names_eval, return_tensors=\"pt\", padding=True).to(DEVICE)\n",
"with torch.no_grad():\n",
" text_features_eval = model.get_text_features(**text_inputs_eval)\n",
" text_features_eval = text_features_eval / text_features_eval.norm(dim=-1, keepdim=True)\n",
"print(\"Text features prepared.\")\n",
"\n",
"# Main evaluation loop\n",
"masks = {}\n",
"total_correct = 0\n",
"total_processed = 0\n",
"\n",
"for item in tqdm(dataset_eval):\n",
" image = item['image']\n",
" total_processed += 1\n",
"\n",
" try:\n",
" # Compute vision features once for efficiency (eliminates redundant forward passes)\n",
" vision_outputs, full_image_features, mask_tokens_features = compute_vision_features_once(\n",
" model, image, mask_vision_model\n",
" )\n",
" \n",
" # Get prediction from pre-computed features\n",
" full_image_similarities = torch.matmul(full_image_features, text_features_eval.T)\n",
" predicted_class_idx = torch.argmax(full_image_similarities, dim=-1).item()\n",
" \n",
" best_mask_idx = find_best_mask_region_calibrated(\n",
" model, image, class_names_eval, mask_vision_model, text_features_eval,\n",
" vision_outputs=vision_outputs, full_image_features=full_image_features, \n",
" mask_tokens_features=mask_tokens_features\n",
" )\n",
" \n",
" coordinates = mask_idx_to_coordinates(best_mask_idx, mask_vision_model)\n",
" \n",
" # Validate the mask\n",
" mask = generate_mask_from_coordinates(image, coordinates)\n",
" assert mask.shape == (224, 224), \"Mask should be 224x224\"\n",
" assert mask.sum() <= RETAIN_RATIO * 224 * 224, \"You should leave only 6.25% of pixels\"\n",
"\n",
" \n",
" # Save the coordinates\n",
" idx = item['idx']\n",
" masks[idx] = coordinates\n",
" \n",
" except Exception as e:\n",
" print(f\"Error processing image {item['idx']}: {e}\")\n",
" # Fallback to a small center region if there's an error\n",
" if len(mask_vision_model.regions) > 0:\n",
" region_sizes = [(r[2]-r[0])*(r[3]-r[1]) for r in mask_vision_model.regions]\n",
" min_region_idx = region_sizes.index(min(region_sizes))\n",
" fallback_coords = mask_idx_to_coordinates(min_region_idx, mask_vision_model)\n",
" else:\n",
" fallback_coords = ((84, 84), (140, 140))\n",
" masks[item['idx']] = fallback_coords\n",
"\n",
"# Save as JSONL (one JSON object per line) - much safer than pickle\n",
"with open('submission.jsonl', 'w') as f:\n",
" for idx, coordinates in masks.items():\n",
" json.dump({\"idx\": idx, \"coordinates\": coordinates}, f)\n",
" f.write('\\n')\n",
"\n",
"print(\"Masks saved to masks.jsonl\")\n"
]
}
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|