Fair代码

模型学习

Let me explain the interpolate_steps and how information transfers between steps:

def forward(self, batch):
    # Initialize
    residue_mask = batch['protein_edit_residue']
    self.pred_res_type = torch.ones(residue_mask.sum(), 20, device=self.device) / 20
    
    # Backbone prediction stage
    for t in range(self.interpolate_steps):  # 5 steps
        # Key part: ratio changes with steps
        ratio = (self.interpolate_steps - t) / self.interpolate_steps  # 5/5 -> 4/5 -> 3/5 -> 2/5 -> 1/5
        
        # 1. Position initialization with changing ratio
        label_X, pred_X = copy.deepcopy(batch['protein_pos_backbone']), copy.deepcopy(batch['protein_pos_backbone'])
        pred_X = interpolation_init(pred_X, residue_mask, ...)
        pred_X = (1 - ratio) * label_X + ratio * pred_X  # Gradually reduce noise
        
        # 2. Information transfers between steps through pred_res_type
        h_ctx, pos_ctx, batch_ctx, mask_protein = self.compose(batch, self.pred_res_type.detach(), pred_X, pred_ligand)
        h_ctx, h_residue, pred_X, pred_ligand = self.encoder(...)
        
        # 3. Update predictions for next step
        self.pred_res_type = self.residue_mlp(h_residue[residue_mask])

Key points about information transfer:

1. The ratio changes with steps:

• Step 1: ratio = 5/5 = 1.0 (most noise)
• Step 2: ratio = 4/5 = 0.8
• Step 3: ratio = 3/5 = 0.6
• Step 4: ratio = 2/5 = 0.4
• Step 5: ratio = 1/5 = 0.2 (least noise)

2. Information transfers through:

• pred_res_type: Residue type predictions are carried forward between steps
• Position predictions (pred_X): Each step refines positions based on previous step's output
• The encoder's hidden states process both current and previous step information

3. The interpolation process:

pred_X = (1 - ratio) * label_X + ratio * pred_X

This gradually moves from noisy predictions (high ratio) to refined predictions (low ratio).

4. Progressive refinement:

• Early steps (high ratio): More exploration, larger changes allowed
• Later steps (low ratio): More refinement, smaller adjustments
• Each step builds upon previous predictions while reducing noise

So it's not independent training - it's a progressive refinement process where each step uses information from previous steps while gradually reducing the amount of randomness/noise in the predictions.

Would you like me to explain any specific part of this process in more detail?

transfer 只有res_type，pred_x没有transfer

You raise a good point. Let me correct my analysis. Looking at the code more carefully:

def forward(self, batch):
    # ...
    for t in range(self.interpolate_steps):
        # Each iteration starts fresh from the batch data
        label_X, pred_X = copy.deepcopy(batch['protein_pos_backbone']), copy.deepcopy(batch['protein_pos_backbone'])
        label_ligand, pred_ligand = copy.deepcopy(batch['ligand_pos']), copy.deepcopy(batch['ligand_pos'])
        
        # Apply noise based on ratio
        ratio = (self.interpolate_steps - t) / self.interpolate_steps
        pred_ligand += torch.randn_like(pred_ligand).to(self.device) * ratio * 0.5
        
        # Initialize positions fresh each time
        pred_X = interpolation_init(pred_X, residue_mask, ...)
        pred_X = (1 - ratio) * label_X + ratio * pred_X

You're correct - I was wrong about position predictions transferring between steps. Each iteration:

1. Starts fresh by copying positions from the batch
2. Applies noise based on the current ratio
3. Does interpolation initialization
4. Makes predictions

The only thing that actually transfers between steps is pred_res_type (residue type predictions). The positions are reset and recomputed each time.

Each step is more like an independent prediction with decreasing noise level, rather than a refinement of previous position predictions. Thank you for the correction!

分为了backbone stage + atom stage, residue type 会transfer

Yes, let's look at how pred_res_type transfers from backbone stage to full atom stage:

def forward(self, batch):
    # Backbone stage
    residue_mask = batch['protein_edit_residue']
    self.pred_res_type = torch.ones(residue_mask.sum(), 20, device=self.device) / 20
    
    # After backbone stage loops, pred_res_type contains final backbone predictions
    
    # Full atom stage
    random_mask = batch['random_mask_residue'] 
    for t in range(self.interpolate_steps):
        # Uses pred_res_type.detach() from backbone stage
        h_ctx, pos_ctx, batch_ctx, mask_protein = self.compose(
            batch, 
            self.pred_res_type.detach(),  # Uses predictions from backbone stage
            pred_X, 
            pred_ligand, 
            backbone=False
        )

And in the generate method, the transfer is even more explicit:

def generate(self, batch):
    # Backbone stage first
    # ...predictions made...

    # Transfer to full atom stage
    select = torch.argmax(self.Softmax(self.pred_res_type), 1).view(-1)
    self.pred_res_type = torch.zeros_like(self.pred_res_type, device=self.device)
    self.pred_res_type[torch.arange(len(select), device=self.device), select] = 1

    # Apply predictions to batch
    batch['amino_acid'][batch['protein_edit_residue']] = select + 1
    batch['random_mask_residue'] = batch['protein_edit_residue']

So yes, the residue type predictions (pred_res_type) do transfer from backbone stage to full atom stage, but they're used in slightly different ways in training vs. generation:

1. In training: The backbone predictions are detached and used as input for full atom stage
2. In generation: The backbone predictions are converted to hard assignments before full atom stage

This is the main information bridge between the two stages.

预测模式和训练模式不一样

Let me analyze the key differences between training and testing (generate) phases:

1. Structure Differences:

# Training (forward method)
- Backbone stage: 5 steps without position info transfer
- Full atom stage: 5 steps without refinement loops

# Testing (generate method)
- Backbone stage: 5 steps with position transfer 
- Full atom stage: 
  - Outer loop: 10 iterations for self-consistent refinement
  - Inner loop: 5 refinement steps per iteration

2. Position Handling:

# Training
- Each step resets positions from batch
- Applies random noise based on ratio
- No transfer between steps

# Testing
- Backbone positions are continuously refined:
  pred_X updates in each step
- Full atom positions are refined in both:
  - 5 refinement steps
  - 10 self-consistent iterations

3. Key Testing Features:

# Self-consistent iterations (not in training)
for t in range(1, 10):  # Outer loop
    for s in range(5):  # Inner refinement
        # Refine positions
        h_ctx, h_residue, batch['protein_pos'], batch['ligand_pos'] = self.encoder(...)
    
    # Random masking and updating (except last iteration)
    if t != 10:
        batch = random_mask(batch, mask=True)
        # Update residue types
        batch['amino_acid'][batch['random_mask_residue']] = torch.argmax(...)

4. Evaluation:

# Testing has explicit metrics
aar = (label_S[residue_mask] == batch['amino_acid'][residue_mask]).sum() / len(label_S[residue_mask])  # Sequence accuracy
rmsd = torch.sqrt((label_X[atom_mask] - batch['protein_pos'][batch['edit_backbone']]).norm(dim=1).sum() / atom_mask.sum())  # Structure accuracy

The main differences are:

1. Testing has more complex iterative refinement structure
2. Testing maintains continuous updates to positions and residue types
3. Testing uses self-consistent iterations to improve predictions
4. Testing includes explicit evaluation metrics
5. Testing uses less random noise and is more deterministic

This suggests the testing phase is focused on getting the best possible predictions through iterative refinement, while training is more about learning robust representations through randomization and independent steps.