Closed SherlockHolmes221 closed 4 years ago
Sorry to bother you,I have some doubts about the codes in TIN_HICO.py
Question1: I see the code: pool5_O = self.crop_pool_layer(head, self.O_boxes[:self.H_num, :], 'Crop_O') You only use the positive labels, and I want to ask the shape of your O branch(cls_prob_O) in the below code: self.predictions["cls_prob_HO"] = cls_prob_sp * (cls_prob_O + cls_prob_H) Cause I found the the shape of cls_prob_sp and cls_prob_H is (pos+neg, 600), while the shape of cls_prob_O is (pos, 600) when rewrite it in pytorch, they can't add
Question2: tf.reshape(cls_prob_binary, [1, self.num_binary]) And: tf.reshape(cls_prob_H, [1, self.num_classes]) tf.reshape(cls_prob_O, [1, self.num_classes]) tf.reshape(cls_prob_sp, [1, self.num_classes]) Are they useless cause I found they are not given to a variable?
I found the reason just now and will close it! Sorry to bother you again!
Sorry to bother you,I have some doubts about the codes in TIN_HICO.py
Question1: I see the code: pool5_O = self.crop_pool_layer(head, self.O_boxes[:self.H_num, :], 'Crop_O') You only use the positive labels, and I want to ask the shape of your O branch(cls_prob_O) in the below code: self.predictions["cls_prob_HO"] = cls_prob_sp * (cls_prob_O + cls_prob_H) Cause I found the the shape of cls_prob_sp and cls_prob_H is (pos+neg, 600), while the shape of cls_prob_O is (pos, 600) when rewrite it in pytorch, they can't add
Question2: tf.reshape(cls_prob_binary, [1, self.num_binary]) And: tf.reshape(cls_prob_H, [1, self.num_classes]) tf.reshape(cls_prob_O, [1, self.num_classes]) tf.reshape(cls_prob_sp, [1, self.num_classes]) Are they useless cause I found they are not given to a variable?