SSD-Tensorflow 512x512 訓練配置

搞了幾天終於把這個給搞得差不多了，遇到的錯誤這里也記錄一下：

一、配置【配置什么的300和512其實差不多，這里只舉一個例子來分析一下】

　之前的文件修改什么的和300x300的一樣：https://www.cnblogs.com/GrPhoenix/p/10018072.html

從自己訓練的ssd_300_vgg模型開始訓練ssd_512_vgg的模型

      因ssd_300_vgg中沒有block12,又因為block7,block8,block9,block10,block11,中的參數張量兩個網絡模型中不匹配，因此ssd_512_vgg中這幾個模塊的參數不從ssd_300_vgg模型中繼承，因此使用checkpoint_exclude_scopes命令指出。

         因為所有的參數均需要訓練，因此不使用命令--trainable_scopes

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1  #/bin/bash   2  DATASET_DIR = / home / data / xxx / imagedata / xing_tf / train_tf /   3  TRAIN_DIR = / home / data / xxx / model / xing300512_model /   4  CHECKPOINT_PATH = / home / data / xxx / model / xing300_model / model.ckpt - 60000    #加載的ssd_300_vgg模型   5  python3 . / train_ssd_network.py \   6         - - train_dir = ${TRAIN_DIR} \   7         - - dataset_dir = ${DATASET_DIR} \   8         - - dataset_name = pascalvoc_2007 \   9         - - dataset_split_name = train \ 10         - - model_name = ssd_512_vgg \ 11         - - checkpoint_path = ${CHECKPOINT_PATH} \ 12         - - checkpoint_model_scope = ssd_300_vgg \ 13         - - checkpoint_exclude_scopes = ssd_512_vgg / block7,ssd_512_vgg / block7_box,ssd_512_vgg / block8,ssd_512_vgg / block8_box,ssd_512_vgg / block9,ssd_512_vgg / block9_box,ssd_512_vgg / block10,ssd_512_vgg / block10_box,ssd_512_vgg / block11,ssd_512_vgg / b    lock11_box,ssd_512_vgg / block12,ssd_512_vgg / block12_box \ 14         #--trainable_scopes=ssd_300_vgg/conv6,ssd_300_vgg/conv7,ssd_300_vgg/block8,ssd_300_vgg/block9,ssd_300_vgg/block10,ssd_300_vgg/block11,ssd_300_vgg/block4_box,ssd_300_vgg/block7_box,ssd_300_vgg/block8_box,ssd_300_vgg/block9_box,ssd_3    00_vgg/block10_box,ssd_300_vgg/block11_box \ 15         - - save_summaries_secs = 28800  \ 16         - - save_interval_secs = 28800  \ 17         - - weight_decay = 0.0005  \ 18         - - optimizer = adam \ 19         - - learning_rate_decay_factor = 0.94  \ 20         - - batch_size = 16  \ 21         - - num_classes = 4  \ 22         - gpu_memory_fraction = 0.8  \

另外由300轉512后還需修改：

1. 首先修改ssd_vgg_512.py的訓練類別

2.修改train_ssd_network.py的model_name

   修改為ssd_512_vgg

3. 修改nets/np_methods.py

    修改：將300改為512， 將類別改為自己數據的類別（+背景）

4. 修改preprocessing/ssd_vgg_preprocessing.py

    修改：將300改為512

5. 修改ssd_notbook.ipynb

   a  將文件中數字“300”改為“512”

其他修改可以參考：http://blog.csdn.net/liuyan20062010/article/details/78905517

二、我遇到的錯誤：

InvalidArgumentError (see above for traceback): Restoring from checkpoint failed. This is most likely due to a mismatch between the current graph and the graph from the checkpoint. Please ensure that you have not altered the graph expected based on the checkpoint. Original error:

Assign requires shapes of both tensors to match. lhs shape= [84] rhs shape= [8]
	 [[{{node save/Assign_20}} = Assign[T=DT_FLOAT, _class=["loc:@ssd_512_vgg/block12_box/conv_cls/biases"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](ssd_512_vgg/block12_box/conv_cls/biases, save/RestoreV2/_41)]]
	 [[{{node save/RestoreV2/_104}} = _Send[T=DT_FLOAT, client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device_incarnation=1, tensor_name="edge_110_save/RestoreV2", _device="/job:localhost/replica:0/task:0/device:CPU:0"](save/RestoreV2:52)]]

　　這類的問題本質上來說還是自己的配置不對，這個問題我查了很久，最后發現實在是太simpleT-T。

　　我的問題的話：在從300轉到512的時候忘記改ssd_vgg_512.py的類別導致test的時候文件配置和訓練的tensor  shape不匹配TT...