Delete modeling_sybil.py

modeling_sybil.py

@@ -1,385 +0,0 @@
-"""PyTorch Sybil model for lung cancer risk prediction"""
-
-import torch
-import torch.nn as nn
-import torchvision
-from transformers import PreTrainedModel
-from transformers.modeling_outputs import BaseModelOutput
-from typing import Optional, Dict, List, Tuple
-import numpy as np
-from dataclasses import dataclass
-
-try:
-    from .configuration_sybil import SybilConfig
-except ImportError:
-    from configuration_sybil import SybilConfig
-
-
-@dataclass
-class SybilOutput(BaseModelOutput):
-    """
-    Base class for Sybil model outputs.
-
-    Args:
-        risk_scores: (`torch.FloatTensor` of shape `(batch_size, max_followup)`):
-            Predicted risk scores for each year up to max_followup.
-        image_attention: (`torch.FloatTensor` of shape `(batch_size, num_slices, height, width)`, *optional*):
-            Attention weights over image pixels.
-        volume_attention: (`torch.FloatTensor` of shape `(batch_size, num_slices)`, *optional*):
-            Attention weights over CT scan slices.
-        hidden_states: (`torch.FloatTensor` of shape `(batch_size, hidden_dim)`, *optional*):
-            Hidden states from the pooling layer.
-    """
-    risk_scores: torch.FloatTensor = None
-    image_attention: Optional[torch.FloatTensor] = None
-    volume_attention: Optional[torch.FloatTensor] = None
-    hidden_states: Optional[torch.FloatTensor] = None
-
-
-class CumulativeProbabilityLayer(nn.Module):
-    """Cumulative probability layer for survival prediction"""
-
-    def __init__(self, hidden_dim: int, max_followup: int = 6):
-        super().__init__()
-        self.max_followup = max_followup
-        self.fc = nn.Linear(hidden_dim, max_followup)
-
-    def forward(self, x):
-        logits = self.fc(x)
-        # Apply cumulative sum for monotonic risk scores
-        cumsum = torch.cumsum(torch.sigmoid(logits), dim=-1)
-        # Normalize to [0, 1] range
-        return cumsum / self.max_followup
-
-
-class MultiAttentionPool(nn.Module):
-    """Multi-attention pooling layer for CT scan aggregation"""
-
-    def __init__(self, channels: int = 512):
-        super().__init__()
-        self.channels = channels
-
-        # Volume-level attention (across slices)
-        self.volume_attention = nn.Sequential(
-            nn.Conv3d(channels, 128, kernel_size=1),
-            nn.ReLU(),
-            nn.Conv3d(128, 1, kernel_size=1)
-        )
-
-        # Image-level attention (within slices)
-        self.image_attention = nn.Sequential(
-            nn.Conv3d(channels, 128, kernel_size=1),
-            nn.ReLU(),
-            nn.Conv3d(128, 1, kernel_size=1)
-        )
-
-    def forward(self, x):
-        batch_size = x.shape[0]
-
-        # Compute attention weights
-        volume_att = self.volume_attention(x)  # [B, 1, D, H, W]
-        image_att = self.image_attention(x)    # [B, 1, D, H, W]
-
-        # Apply softmax for normalization
-        volume_att_flat = volume_att.view(batch_size, -1)
-        volume_att_weights = torch.softmax(volume_att_flat, dim=-1)
-        volume_att_weights = volume_att_weights.view_as(volume_att)
-
-        image_att_2d = image_att.squeeze(1)  # [B, D, H, W]
-        for i in range(image_att_2d.shape[1]):  # For each slice
-            slice_att = image_att_2d[:, i, :, :].contiguous()
-            slice_att_flat = slice_att.view(batch_size, -1)
-            slice_att_weights = torch.softmax(slice_att_flat, dim=-1)
-            image_att_2d[:, i, :, :] = slice_att_weights.view_as(slice_att)
-        image_att = image_att_2d.unsqueeze(1)
-
-        # Apply attention and pool
-        attended = x * volume_att_weights * image_att
-        hidden = attended.mean(dim=[2, 3, 4])  # Global average pooling
-
-        return {
-            'hidden': hidden,
-            'volume_attention_1': volume_att_weights.squeeze(1),
-            'image_attention_1': image_att.squeeze(1)
-        }
-
-
-class SybilPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface
-    for downloading and loading pretrained models.
-    """
-    config_class = SybilConfig
-    base_model_prefix = "sybil"
-    supports_gradient_checkpointing = False
-
-    def _init_weights(self, module):
-        """Initialize the weights"""
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Conv3d):
-            nn.init.kaiming_normal_(module.weight, mode='fan_out', nonlinearity='relu')
-            if module.bias is not None:
-                module.bias.data.zero_()
-
-
-class SybilForRiskPrediction(SybilPreTrainedModel):
-    """
-    Sybil model for lung cancer risk prediction from CT scans.
-
-    This model takes 3D CT scan volumes as input and predicts cancer risk scores
-    for multiple future time points (typically 1-6 years).
-    """
-
-    def __init__(self, config: SybilConfig):
-        super().__init__(config)
-        self.config = config
-
-        # Use pretrained R3D-18 as backbone
-        encoder = torchvision.models.video.r3d_18(pretrained=True)
-        self.image_encoder = nn.Sequential(*list(encoder.children())[:-2])
-
-        # Multi-attention pooling
-        self.pool = MultiAttentionPool(channels=512)
-
-        # Classification layers
-        self.relu = nn.ReLU(inplace=False)
-        self.dropout = nn.Dropout(p=config.dropout)
-
-        # Risk prediction layer
-        self.prob_of_failure_layer = CumulativeProbabilityLayer(
-            config.hidden_dim,
-            max_followup=config.max_followup
-        )
-
-        # Calibrator for ensemble predictions
-        self.calibrator = None
-        if config.calibrator_data:
-            self.set_calibrator(config.calibrator_data)
-
-        # Initialize weights
-        self.post_init()
-
-    def set_calibrator(self, calibrator_data: Dict):
-        """Set calibration data for risk score adjustment"""
-        self.calibrator = calibrator_data
-
-    def _calibrate_scores(self, scores: torch.Tensor) -> torch.Tensor:
-        """Apply calibration to raw risk scores"""
-        if self.calibrator is None:
-            return scores
-
-        # Convert to numpy for calibration
-        scores_np = scores.detach().cpu().numpy()
-        calibrated = np.zeros_like(scores_np)
-
-        # Apply calibration for each year
-        for year in range(scores_np.shape[1]):
-            year_key = f"Year{year + 1}"
-            if year_key in self.calibrator:
-                # Apply calibration transformation
-                calibrated[:, year] = self._apply_calibration(
-                    scores_np[:, year],
-                    self.calibrator[year_key]
-                )
-            else:
-                calibrated[:, year] = scores_np[:, year]
-
-        return torch.from_numpy(calibrated).to(scores.device)
-
-    def _apply_calibration(self, scores: np.ndarray, calibrator_params: Dict) -> np.ndarray:
-        """Apply specific calibration transformation"""
-        # Simplified calibration - in practice, this would use the full calibration model
-        # from the original Sybil implementation
-        return scores  # Placeholder for now
-
-    def forward(
-        self,
-        pixel_values: torch.FloatTensor,
-        return_attentions: bool = False,
-        return_dict: bool = True,
-    ) -> SybilOutput:
-        """
-        Forward pass of the Sybil model.
-
-        Args:
-            pixel_values: (`torch.FloatTensor` of shape `(batch_size, channels, depth, height, width)`):
-                Pixel values of CT scan volumes.
-            return_attentions: (`bool`, *optional*, defaults to `False`):
-                Whether to return attention weights.
-            return_dict: (`bool`, *optional*, defaults to `True`):
-                Whether to return a `SybilOutput` instead of a plain tuple.
-
-        Returns:
-            `SybilOutput` or tuple
-        """
-        # Extract features using 3D CNN backbone
-        features = self.image_encoder(pixel_values)
-
-        # Apply multi-attention pooling
-        pool_output = self.pool(features)
-
-        # Apply ReLU and dropout
-        hidden = self.relu(pool_output['hidden'])
-        hidden = self.dropout(hidden)
-
-        # Predict risk scores
-        risk_logits = self.prob_of_failure_layer(hidden)
-        risk_scores = torch.sigmoid(risk_logits)
-
-        # Apply calibration if available
-        risk_scores = self._calibrate_scores(risk_scores)
-
-        if not return_dict:
-            outputs = (risk_scores,)
-            if return_attentions:
-                outputs = outputs + (pool_output.get('image_attention_1'),
-                                   pool_output.get('volume_attention_1'))
-            return outputs
-
-        return SybilOutput(
-            risk_scores=risk_scores,
-            image_attention=pool_output.get('image_attention_1') if return_attentions else None,
-            volume_attention=pool_output.get('volume_attention_1') if return_attentions else None,
-            hidden_states=hidden if return_attentions else None
-        )
-
-    @classmethod
-    def from_pretrained_ensemble(
-        cls,
-        pretrained_model_name_or_path,
-        checkpoint_paths: List[str],
-        calibrator_path: Optional[str] = None,
-        **kwargs
-    ):
-        """
-        Load an ensemble of Sybil models from checkpoints.
-
-        Args:
-            pretrained_model_name_or_path: Path to the pretrained model or model identifier.
-            checkpoint_paths: List of paths to individual model checkpoints.
-            calibrator_path: Path to calibration data.
-            **kwargs: Additional keyword arguments for model initialization.
-
-        Returns:
-            SybilEnsemble: An ensemble of Sybil models.
-        """
-        config = kwargs.pop("config", None)
-        if config is None:
-            config = SybilConfig.from_pretrained(pretrained_model_name_or_path)
-
-        # Load calibrator if provided
-        calibrator_data = None
-        if calibrator_path:
-            import json
-            with open(calibrator_path, 'r') as f:
-                calibrator_data = json.load(f)
-            config.calibrator_data = calibrator_data
-
-        # Create ensemble
-        models = []
-        for checkpoint_path in checkpoint_paths:
-            model = cls(config)
-            # Load checkpoint weights
-            checkpoint = torch.load(checkpoint_path, map_location='cpu')
-            # Remove 'model.' prefix from state dict keys if present
-            state_dict = {}
-            for k, v in checkpoint['state_dict'].items():
-                if k.startswith('model.'):
-                    state_dict[k[6:]] = v
-                else:
-                    state_dict[k] = v
-
-            # Map to new model structure
-            mapped_state_dict = model._map_checkpoint_weights(state_dict)
-            model.load_state_dict(mapped_state_dict, strict=False)
-            models.append(model)
-
-        return SybilEnsemble(models, config)
-
-    def _map_checkpoint_weights(self, state_dict: Dict) -> Dict:
-        """Map original Sybil checkpoint weights to new structure"""
-        mapped = {}
-
-        # Map encoder weights
-        for k, v in state_dict.items():
-            if k.startswith('image_encoder'):
-                mapped[k] = v
-            elif k.startswith('pool'):
-                # Map pooling layer weights
-                mapped[k] = v
-            elif k.startswith('prob_of_failure_layer'):
-                # Map final prediction layer
-                mapped[k] = v
-
-        return mapped
-
-
-class SybilEnsemble:
-    """Ensemble of Sybil models for improved predictions"""
-
-    def __init__(self, models: List[SybilForRiskPrediction], config: SybilConfig):
-        self.models = models
-        self.config = config
-        self.device = None
-
-    def to(self, device):
-        """Move all models to device"""
-        self.device = device
-        for model in self.models:
-            model.to(device)
-        return self
-
-    def eval(self):
-        """Set all models to evaluation mode"""
-        for model in self.models:
-            model.eval()
-
-    def __call__(
-        self,
-        pixel_values: torch.FloatTensor,
-        return_attentions: bool = False,
-    ) -> SybilOutput:
-        """
-        Run inference with ensemble voting.
-
-        Args:
-            pixel_values: Input CT scan volumes.
-            return_attentions: Whether to return attention maps.
-
-        Returns:
-            SybilOutput with averaged predictions from all models.
-        """
-        all_risk_scores = []
-        all_image_attentions = []
-        all_volume_attentions = []
-
-        with torch.no_grad():
-            for model in self.models:
-                output = model(
-                    pixel_values=pixel_values,
-                    return_attentions=return_attentions
-                )
-                all_risk_scores.append(output.risk_scores)
-
-                if return_attentions:
-                    all_image_attentions.append(output.image_attention)
-                    all_volume_attentions.append(output.volume_attention)
-
-        # Average predictions
-        risk_scores = torch.stack(all_risk_scores).mean(dim=0)
-
-        # Average attentions if requested
-        image_attention = None
-        volume_attention = None
-        if return_attentions:
-            image_attention = torch.stack(all_image_attentions).mean(dim=0)
-            volume_attention = torch.stack(all_volume_attentions).mean(dim=0)
-
-        return SybilOutput(
-            risk_scores=risk_scores,
-            image_attention=image_attention,
-            volume_attention=volume_attention
-        )