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perf(loss): 批量计算
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‎py/lib/models/multi_part_loss.py

+130-72
Original file line numberDiff line numberDiff line change
@@ -37,6 +37,132 @@ def forward(self, preds, targets):
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:param targets: (N, S*S, (B*5+C))
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:return:
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"""
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# print('loss 1', self._process1(preds, targets))
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# print('loss 2', self._process2(preds, targets))
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return self._process3(preds, targets)
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def _process1(self, preds, targets):
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N = preds.shape[0]
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total_loss = 0.0
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for pred, target in zip(preds, targets):
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"""
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逐个图像计算
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pred: [S*S, (B*5+C)]
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target: [S*S, (B*5+C)]
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"""
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# 分类概率
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# [S*S, C]
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pred_probs = pred[:, :self.C]
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target_probs = target[:, :self.C]
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# 置信度
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# [S*S, B]
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pred_confidences = pred[:, self.C:(self.C + self.B)]
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target_confidences = target[:, self.C:(self.C + self.B)]
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# 边界框坐标
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pred_bboxs = pred[:, (self.C + self.B):]
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target_bboxs = target[:, (self.C + self.B):]
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for i in range(self.S * self.S):
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"""
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逐个网格计算
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"""
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pred_single_probs = pred_probs[i]
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target_single_probs = target_probs[i]
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pred_single_confidences = pred_confidences[i]
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target_single_confidences = target_confidences[i]
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pred_single_bboxs = pred_bboxs[i]
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target_single_bboxs = target_bboxs[i]
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# 是否存在置信度（如果存在，则target的置信度必然大于0）
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is_obj = target_single_confidences[0] > 0
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# 计算置信度损失 假定该网格不存在对象
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total_loss += self.noobj * self.sum_squared_error(pred_single_confidences, target_single_confidences)
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if is_obj:
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# 如果存在
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# 计算分类损失
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total_loss += self.sum_squared_error(pred_single_probs, target_single_probs)
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# 计算所有预测边界框和标注边界框的IoU
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pred_single_bboxs = pred_single_bboxs.reshape(-1, 4)
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target_single_bboxs = target_single_bboxs.reshape(-1, 4)
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scores = self.iou(pred_single_bboxs, target_single_bboxs)
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# 提取IoU最大的下标
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bbox_idx = torch.argmax(scores)
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# 计算置信度损失
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total_loss += (1 - self.noobj) * \
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self.sum_squared_error(pred_single_confidences[bbox_idx],
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target_single_confidences[bbox_idx])
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# 计算边界框损失
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total_loss += self.coord * self.bbox_loss(pred_single_bboxs[bbox_idx].reshape(-1, 4),
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target_single_bboxs[bbox_idx].reshape(-1, 4))
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return total_loss / N
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def _process2(self, preds, targets):
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N = preds.shape[0]
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total_loss = 0.0
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for pred, target in zip(preds, targets):
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"""
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逐个图像计算
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pred: [S*S, (B*5+C)]
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target: [S*S, (B*5+C)]
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"""
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# 分类概率
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# [S*S, C]
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pred_probs = pred[:, :self.C]
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target_probs = target[:, :self.C]
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# 置信度
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# [S*S, B]
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pred_confidences = pred[:, self.C:(self.C + self.B)]
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target_confidences = target[:, self.C:(self.C + self.B)]
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# 边界框坐标
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# [S*S, B*4] -> [S*S, B, 4]
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pred_bboxs = pred[:, (self.C + self.B):].reshape(self.S * self.S, self.B, 4)
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target_bboxs = target[:, (self.C + self.B):].reshape(self.S * self.S, self.B, 4)
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# 统一计算置信度损失
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total_loss += self.noobj * self.sum_squared_error(pred_confidences, target_confidences)
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# 计算每个网格预测边界框的IoU
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# Input: [S*S, B, 4] -> [S*S*B, 4]
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# Output: [S*S*B] -> [S*S, B]
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iou_scores = self.iou(pred_bboxs.reshape(-1, 4), target_bboxs.reshape(-1, 4)).reshape(self.S * self.S,
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self.B)
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# 计算其中最大IoU所属下标
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# [S*S]
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top_idxs = torch.argmax(iou_scores, dim=1)
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top_len = len(top_idxs)
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# 提取对应的边界框以及置信度
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# [S*S, 4]
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top_pred_bboxs = pred_bboxs[range(top_len), top_idxs]
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top_pred_confidences = pred_confidences[range(top_len), top_idxs]
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top_target_bboxs = target_bboxs[range(top_len), top_idxs]
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top_target_confidences = target_confidences[range(top_len), top_idxs]
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# 计算网格中是否存在目标
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# [S*S, C] -> [S*S]
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obj_idxs = torch.sum(target_probs, dim=1) > 0
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# 提取对应的目标分类概率、置信度以及边界框坐标
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# [S*S, C]
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obj_pred_probs = pred_probs[obj_idxs]
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obj_pred_confidences = top_pred_confidences[obj_idxs]
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obj_pred_bboxs = top_pred_bboxs[obj_idxs]
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obj_target_probs = target_probs[obj_idxs]
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obj_target_confidences = top_target_confidences[obj_idxs]
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obj_target_bboxs = top_target_bboxs[obj_idxs]
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# 计算置信度损失
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total_loss += (1 - self.noobj) * self.sum_squared_error(obj_pred_confidences, obj_target_confidences)
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# 分类概率损失
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total_loss += self.sum_squared_error(obj_pred_probs, obj_target_probs)
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# 坐标损失
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total_loss += self.coord * self.bbox_loss(obj_pred_bboxs, obj_target_bboxs)
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return total_loss / N
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def _process3(self, preds, targets):
40166
N = preds.shape[0]
41167
## 预测
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# 提取每个网格的分类概率
@@ -82,7 +208,7 @@ def forward(self, preds, targets):
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# print(top_pred_bboxs.shape)
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84210
# 选取存在目标的网格
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obj_idxs = torch.sum(target_probs, dim=1) == 1
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obj_idxs = torch.sum(target_probs, dim=1) > 0
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# print(obj_idxs)
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88214
obj_pred_confidences = top_pred_confidences[obj_idxs]
@@ -98,77 +224,12 @@ def forward(self, preds, targets):
98224
## 计算分类概率损失
99225
loss += self.sum_squared_error(obj_pred_probs, obj_target_probs)
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## 计算边界框坐标损失
101-
loss += self.sum_squared_error(obj_pred_bboxs[:, :2], obj_target_bboxs[:, :2])
102-
loss += self.sum_squared_error(torch.sqrt(obj_pred_bboxs[:, 2:]), torch.sqrt(obj_target_bboxs[:, 2:]))
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loss += self.coord * self.sum_squared_error(obj_pred_bboxs[:, :2], obj_target_bboxs[:, :2])
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loss += self.coord * self.sum_squared_error(torch.sqrt(obj_pred_bboxs[:, 2:]),
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torch.sqrt(obj_target_bboxs[:, 2:]))
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return loss / N
105232

106-
# N = preds.shape[0]
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# total_loss = 0.0
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# print(preds.shape)
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# print(targets.shape)
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# for pred, target in zip(preds, targets):
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# """
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# 逐个图像计算
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# pred: [S*S, (B*5+C)]
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# target: [S*S, (B*5+C)]
115-
# """
116-
# # 分类概率
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# pred_probs = pred[:, :self.C]
118-
# target_probs = target[:, :self.C]
119-
# # 置信度
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# pred_confidences = pred[:, self.C:(self.C + self.B)]
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# target_confidences = target[:, self.C:(self.C + self.B)]
122-
# # 边界框坐标
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# pred_bboxs = pred[:, (self.C + self.B):]
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# target_bboxs = target[:, (self.C + self.B):]
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#
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# for i in range(self.S * self.S):
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# """
128-
# 逐个网格计算
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# """
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# pred_single_probs = pred_probs[i]
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# target_single_probs = target_probs[i]
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#
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# pred_single_confidences = pred_confidences[i]
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# target_single_confidences = target_confidences[i]
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#
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# pred_single_bboxs = pred_bboxs[i]
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# target_single_bboxs = target_bboxs[i]
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#
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# # 是否存在置信度（如果存在，则target的置信度必然大于0）
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# is_obj = target_single_confidences[0] > 0
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# # 计算置信度损失 假定该网格不存在对象
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# total_loss += self.noobj * self.sum_squared_error(pred_single_confidences, target_single_confidences)
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# print(total_loss)
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# if is_obj:
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# print('i = %d' % (i))
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# # 如果存在
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# # 计算分类损失
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# total_loss += self.sum_squared_error(pred_single_probs, target_single_probs)
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# print(total_loss)
150-
#
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# # 计算所有预测边界框和标注边界框的IoU
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# pred_single_bboxs = pred_single_bboxs.reshape(-1, 4)
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# target_single_bboxs = target_single_bboxs.reshape(-1, 4)
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#
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# scores = self.iou(pred_single_bboxs, target_single_bboxs)
156-
# # 提取IoU最大的下标
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# bbox_idx = torch.argmax(scores)
158-
# # 计算置信度损失
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# total_loss += (1 - self.noobj) * \
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# self.sum_squared_error(pred_single_confidences[bbox_idx],
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# target_single_confidences[bbox_idx])
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# print(total_loss)
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# # 计算边界框损失
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# total_loss += self.coord * self.bbox_loss(pred_single_bboxs[bbox_idx].reshape(-1, 4),
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# target_single_bboxs[bbox_idx].reshape(-1, 4))
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# print(total_loss)
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#
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# print('done')
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#
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# return total_loss / N
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def sum_squared_error(self, preds, targets):
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return torch.sum((preds - targets) ** 2)
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@@ -241,9 +302,6 @@ def load_data(data_root_dir, cate_list, S=7, B=2, C=20):
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for inputs, labels in data_loader:
242303
inputs = inputs
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labels = labels
244-
print(inputs.shape)
245-
print(labels.shape)
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247305
with torch.set_grad_enabled(False):
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outputs = model(inputs)
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loss = criterion(outputs, labels)

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