Plurk paste

將目標檢測模型的頭部改為 Transformer-style 架構是個很好的想法，這可以讓模型獲得更強的上下文建模能力和對象關係理解。以下是如何實現這一改進的研究和代碼框架：

### Transformer-Style 檢測頭的優勢

1. **全局上下文**：Transformer 可以通過自注意力機制捕獲圖像的全局上下文關係
2. **位置靈敏度**：使用位置編碼可以讓模型更好地理解對象的空間位置
3. **跨尺度特徵融合**：可以更有效地整合不同尺度的特徵
4. **對小物體敏感**：對於小目標的檢測往往有更好的性能

### DETR (DEtection TRansformer) 風格的檢測頭

以下是一個受 DETR 啟發的 Transformer-style 檢測頭的實現方案：

```python
import torch
import torch.nn as nn
import torch.nn.functional as F

class TransformerDetectionHead(nn.Module):
def __init__(self, in_channels, num_classes, num_queries=100, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6):
super().__init__()
self.num_queries = num_queries
self.num_classes = num_classes

# 將特徵圖轉換為序列
self.conv = nn.Conv2d(in_channels, hidden_dim, kernel_size=1)

# 位置編碼
self.pos_encoder = PositionEmbedding2D(hidden_dim)

# 查詢嵌入 (object queries)
self.query_embed = nn.Embedding(num_queries, hidden_dim)

# Transformer 編碼器-解碼器
encoder_layer = nn.TransformerEncoderLayer(d_model=hidden_dim, nhead=nheads)
self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_encoder_layers)

decoder_layer = nn.TransformerDecoderLayer(d_model=hidden_dim, nhead=nheads)
self.transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=num_decoder_layers)

# 預測頭: 類別和邊界框
self.class_embed = nn.Linear(hidden_dim, num_classes + 1) # +1 for background
self.bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3) # (cx, cy, w, h)

# 輸出激活
self.activation = nn.Sigmoid()

def forward(self, x):
# 特徵圖轉換
h = self.conv(x)

# 保存原始尺寸
bs, c, h, w = h.shape

# 添加位置編碼
pos = self.pos_encoder(h)

# 展平特徵圖為序列
h_flatten = h.flatten(2).permute(2, 0, 1) # (hw, bs, c)
pos_flatten = pos.flatten(2).permute(2, 0, 1) # (hw, bs, c)

# Transformer 編碼
memory = self.transformer_encoder(h_flatten + pos_flatten)

# 準備解碼器的查詢嵌入
query_embed = self.query_embed.weight.unsqueeze(1).repeat(1, bs, 1) # (num_queries, bs, hidden_dim)

# Transformer 解碼
tgt = torch.zeros_like(query_embed)
hs = self.transformer_decoder(tgt, memory) # (num_queries, bs, hidden_dim)

# 預測類別和邊界框
outputs_class = self.class_embed(hs) # (num_queries, bs, num_classes+1)
outputs_coord = self.activation(self.bbox_embed(hs)) # (num_queries, bs, 4), 值範圍 [0, 1]

# 重新排列輸出
outputs_class = outputs_class.permute(1, 0, 2) # (bs, num_queries, num_classes+1)
outputs_coord = outputs_coord.permute(1, 0, 2) # (bs, num_queries, 4)

return outputs_class, outputs_coord

# 輔助子模塊
class PositionEmbedding2D(nn.Module):
def __init__(self, hidden_dim=256):
super().__init__()
self.row_embed = nn.Embedding(50, hidden_dim // 2)
self.col_embed = nn.Embedding(50, hidden_dim // 2)

def forward(self, x):
h, w = x.shape[-2:]

# 限制最大尺寸
h = min(h, 50)
w = min(w, 50)

# 生成網格坐標
i = torch.arange(w, device=x.device)
j = torch.arange(h, device=x.device)

# 獲取位置嵌入
x_emb = self.col_embed(i)
y_emb = self.row_embed(j)

# 生成位置編碼網格
pos = torch.cat([
x_emb.unsqueeze(0).repeat(h, 1, 1),
y_emb.unsqueeze(1).repeat(1, w, 1),
], dim=-1).permute(2, 0, 1).unsqueeze(0).repeat(x.shape[0], 1, 1, 1)

return pos

class MLP(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
super().__init__()
self.num_layers = num_layers
h = [hidden_dim] * (num_layers - 1)
self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))

def forward(self, x):
for i, layer in enumerate(self.layers):
x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
return x
```

### 整合到現有模型中

這種 Transformer-style 檢測頭可以替換原來基於卷積的檢測頭，完整的模型架構會是：

```python
class TransformerObjectDetectionModel(nn.Module):
def __init__(self, unet, num_classes):
super().__init__()
self.encoder = UNetEncoder(unet)
self.detection_head = TransformerDetectionHead(
in_channels=1280, # UNet 特徵維度
num_classes=num_classes,
num_queries=100, # 每幅圖像查詢的目標數量
hidden_dim=256,
nheads=8
)

def forward(self, x, t=0):
# 通過encoder獲取特徵
feat = self.encoder(x, t)
# 通過Transformer檢測頭
return self.detection_head(feat)
```

### 改進損失函數

對於 Transformer-style 檢測頭，通常使用二分匹配損失（Hungarian Loss）：

```python
class HungarianLoss(nn.Module):
def __init__(self, num_classes, weight_class=1, weight_bbox=5, weight_giou=2):
super().__init__()
self.num_classes = num_classes
self.weight_class = weight_class
self.weight_bbox = weight_bbox
self.weight_giou = weight_giou

# 類別損失
self.class_loss = nn.CrossEntropyLoss(reduction='none')

def forward(self, outputs, targets, device):
# 解包輸出
pred_logits, pred_boxes = outputs # (bs, num_queries, num_classes+1), (bs, num_queries, 4)

# 為每個批次個別處理
batch_size = pred_logits.shape[0]
total_loss = 0

for b in range(batch_size):
batch_targets = targets[b] # (n_targets, 6) - [class_idx, cx, cy, w, h, conf]

if batch_targets.shape[0] == 0:
# 如果沒有目標，只計算類別損失（所有都應該是背景）
background_pred = pred_logits[b, :, 0]
background_target = torch.ones_like(background_pred)
total_loss += self.class_loss(pred_logits[b], torch.zeros(pred_logits.shape[1], dtype=torch.long, device=device)).sum()
continue

# 目標類別
tgt_classes = batch_targets[:, 0].long()

# 目標邊界框 (cx, cy, w, h)
tgt_boxes = batch_targets[:, 1:5]

# 匈牙利匹配算法
indices = self.hungarian_matcher(pred_logits[b], pred_boxes[b], tgt_classes, tgt_boxes, device)
idx_pred, idx_tgt = indices

# 類別損失
class_loss = self.class_loss(pred_logits[b, idx_pred], tgt_classes[idx_tgt])

# 邊界框損失
# L1 損失
l1_loss = F.l1_loss(pred_boxes[b, idx_pred], tgt_boxes[idx_tgt], reduction='none').sum(-1)

# IoU/GIoU 損失
iou_loss = 1 - self.box_iou(
self.box_cxcywh_to_xyxy(pred_boxes[b, idx_pred]),
self.box_cxcywh_to_xyxy(tgt_boxes[idx_tgt])
)[0].diag()

# 總損失
loss = (
self.weight_class * class_loss +
self.weight_bbox * l1_loss +
self.weight_giou * iou_loss
).sum()

total_loss += loss

return total_loss / batch_size, {
'class_loss': self.weight_class * class_loss.mean().item() if 'class_loss' in locals() else 0.0,
'l1_loss': self.weight_bbox * l1_loss.mean().item() if 'l1_loss' in locals() else 0.0,
'iou_loss': self.weight_giou * iou_loss.mean().item() if 'iou_loss' in locals() else 0.0,
'total_loss': total_loss.item() / batch_size
}

# 匈牙利匹配器（略）
def hungarian_matcher(self, pred_logits, pred_boxes, tgt_classes, tgt_boxes, device):
# 簡易版本 - 實際應該使用Hungarian算法
n_preds = pred_logits.shape[0]
n_targets = tgt_classes.shape[0]

if n_targets == 0:
return (torch.arange(0, n_preds, device=device),
torch.arange(0, n_preds, device=device))

# 計算成本矩陣
cost_class = -pred_logits[:, tgt_classes]

# L1 距離
cost_bbox = torch.cdist(pred_boxes, tgt_boxes, p=1)

# IoU 距離
cost_giou = -self.generalized_box_iou(
self.box_cxcywh_to_xyxy(pred_boxes),
self.box_cxcywh_to_xyxy(tgt_boxes)
)

# 最終成本
cost = (
self.weight_class * cost_class +
self.weight_bbox * cost_bbox +
self.weight_giou * cost_giou
)

# 簡單貪婪匹配 (實際應使用匈牙利算法)
indices = []
for i in range(min(n_targets, n_preds)):
# 找到成本最小的匹配
if cost.numel() == 0:
break
i, j = torch.unravel_index(torch.argmin(cost), cost.shape)
indices.append((i.item(), j.item()))

# 移除已匹配的預測和目標
cost[i, :] = float('inf')
cost[:, j] = float('inf')

if not indices:
return torch.tensor([], device=device, dtype=torch.int64), torch.tensor([], device=device, dtype=torch.int64)

indices = list(zip(*indices))
return torch.tensor(indices[0], device=device, dtype=torch.int64), torch.tensor(indices[1], device=device, dtype=torch.int64)

# 邊界框工具函數
def box_cxcywh_to_xyxy(self, x):
x_c, y_c, w, h = x.unbind(-1)
b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
(x_c + 0.5 * w), (y_c + 0.5 * h)]
return torch.stack(b, dim=-1)

def box_iou(self, boxes1, boxes2):
area1 = self.box_area(boxes1)
area2 = self.box_area(boxes2)

lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]

wh = (rb - lt).clamp(min=0) # [N,M,2]
inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]

union = area1[:, None] + area2 - inter

iou = inter / union
return iou, inter

def box_area(self, boxes):
return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])

def generalized_box_iou(self, boxes1, boxes2):
# 計算IoU
iou, inter = self.box_iou(boxes1, boxes2)

# 計算包含兩個框的最小外接矩形的面積
lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])
rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])

wh = (rb - lt).clamp(min=0) # [N,M,2]
area = wh[:, :, 0] * wh[:, :, 1]

return iou - (area - union) / area
```

### 訓練調整

使用Transformer架構的模型通常需要更多的訓練時間和更好的初始化：

1. **更長的訓練時間**：Transformer模型通常需要更多的訓練輪次
2. **更平緩的學習率**：建議使用較小的初始學習率，例如 1e-5
3. **權重初始化**：特別關注Transformer權重的初始化
4. **批次大小**：如果可能，使用較大的批次大小
5. **數據增強**：更多的數據增強有助於預防過擬合

### 結論與建議

轉換到Transformer-style的檢測頭是一個很好的想法，特別是如果您需要處理複雜場景和多物體檢測。然而，這也帶來了以下挑戰：

1. **計算成本**：Transformer模型的訓練和推理成本更高
2. **數據需求**：通常需要更多的訓練數據
3. **超參數敏感**：對學習率、批次大小等超參數更敏感

對於您的腰果檢測任務，如果數據集規模不大，建議先嘗試一個簡化版的Transformer架構，或者考慮使用預訓練的DETR或Mask R-CNN模型進行微調。

希望這些見解和代碼幫助能夠改進您的目標檢測性能！