I have finished setting up the detection phase but not the recognition phase because I don't require it now. I installed caffe-cpu on google colab but compiled the make -j8 using GPU. It is compiled successfully. make py is also compiled successfully. Now I am trying to run the demo_det.py. I have placed the models in the right place and are good to go. After running the
! python examples/text/demo_det.py
in colab deploy.prototxt prints successfully and starts creating the network. The cell signals the completion of the process but the code inside demo_det.py:
print("flag0")
net = caffe.Net(config['model_def'], # defines the structure of the model
config['model_weights'], # contains the trained weights
caffe.TEST) # use test mode (e.g., don't perform dropout)
print("flag1")
to my knowledge actually has not been fully executed. The cell does not signal any error.
I have finished setting up the detection phase but not the recognition phase because I don't require it now. I installed caffe-cpu on google colab but compiled the make -j8 using GPU. It is compiled successfully. make py is also compiled successfully. Now I am trying to run the demo_det.py. I have placed the models in the right place and are good to go. After running the
! python examples/text/demo_det.py
in colab deploy.prototxt prints successfully and starts creating the network. The cell signals the completion of the process but the code inside demo_det.py:
print("flag0") net = caffe.Net(config['model_def'], # defines the structure of the model config['model_weights'], # contains the trained weights caffe.TEST) # use test mode (e.g., don't perform dropout) print("flag1")
to my knowledge actually has not been fully executed. The cell does not signal any error.
Output: flag0 WARNING: Logging before InitGoogleLogging() is written to STDERR W0224 04:45:03.806703 21899 _caffe.cpp:123] DEPRECATION WARNING - deprecated use of Python interface W0224 04:45:03.806736 21899 _caffe.cpp:124] Use this instead (with the named "weights" parameter): W0224 04:45:03.806743 21899 _caffe.cpp:126] Net('./models/deploy.prototxt', 1, weights='./models/model_icdar15.caffemodel') I0224 04:45:03.813398 21899 upgrade_proto.cpp:67] Attempting to upgrade input file specified using deprecated input fields: ./models/deploy.prototxt I0224 04:45:03.813491 21899 upgrade_proto.cpp:70] Successfully upgraded file specified using deprecated input fields. W0224 04:45:03.813501 21899 upgrade_proto.cpp:72] Note that future Caffe releases will only support input layers and not input fields. I0224 04:45:03.813959 21899 net.cpp:58] Initializing net from parameters: name: "VGG_text_text_polygon_ic15_fix_order_384x384_deploy" state { phase: TEST level: 0 } layer { name: "input" type: "Input" top: "data" input_param { shape { dim: 1 dim: 3 dim: 768 dim: 768 } } } layer { name: "conv1_1" type: "Convolution" bottom: "data" top: "conv1_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 64 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu1_1" type: "ReLU" bottom: "conv1_1" top: "conv1_1" } layer { name: "conv1_2" type: "Convolution" bottom: "conv1_1" top: "conv1_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 64 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu1_2" type: "ReLU" bottom: "conv1_2" top: "conv1_2" } layer { name: "pool1" type: "Pooling" bottom: "conv1_2" top: "pool1" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv2_1" type: "Convolution" bottom: "pool1" top: "conv2_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu2_1" type: "ReLU" bottom: "conv2_1" top: "conv2_1" } layer { name: "conv2_2" type: "Convolution" bottom: "conv2_1" top: "conv2_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu2_2" type: "ReLU" bottom: "conv2_2" top: "conv2_2" } layer { name: "pool2" type: "Pooling" bottom: "conv2_2" top: "pool2" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv3_1" type: "Convolution" bottom: "pool2" top: "conv3_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu3_1" type: "ReLU" bottom: "conv3_1" top: "conv3_1" } layer { name: "conv3_2" type: "Convolution" bottom: "conv3_1" top: "conv3_2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu3_2" type: "ReLU" bottom: "conv3_2" top: "conv3_2" } layer { name: "conv3_3" type: "Convolution" bottom: "conv3_2" top: "conv3_3" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu3_3" type: "ReLU" bottom: "conv3_3" top: "conv3_3" } layer { name: "pool3" type: "Pooling" bottom: "conv3_3" top: "pool3" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv4_1" type: "Convolution" bottom: "pool3" top: "conv4_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu4_1" type: "ReLU" bottom: "conv4_1" top: "conv4_1" } layer { name: "conv4_2" type: "Convolution" bottom: "conv4_1" top: "conv4_2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu4_2" type: "ReLU" bottom: "conv4_2" top: "conv4_2" } layer { name: "conv4_3" type: "Convolution" bottom: "conv4_2" top: "conv4_3" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu4_3" type: "ReLU" bottom: "conv4_3" top: "conv4_3" } layer { name: "pool4" type: "Pooling" bottom: "conv4_3" top: "pool4" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv5_1" type: "Convolution" bottom: "pool4" top: "conv5_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } dilation: 1 } } layer { name: "relu5_1" type: "ReLU" bottom: "conv5_1" top: "conv5_1" } layer { name: "conv5_2" type: "Convolution" bottom: "conv5_1" top: "conv5_2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } dilation: 1 } } layer { name: "relu5_2" type: "ReLU" bottom: "conv5_2" top: "conv5_2" } layer { name: "conv5_3" type: "Convolution" bottom: "conv5_2" top: "conv5_3" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } dilation: 1 } } layer { name: "relu5_3" type: "ReLU" bottom: "conv5_3" top: "conv5_3" } layer { name: "pool5" type: "Pooling" bottom: "conv5_3" top: "pool5" pooling_param { pool: MAX kernel_size: 3 stride: 1 pad: 1 } } layer { name: "fc6" type: "Convolution" bottom: "pool5" top: "fc6" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 1024 pad: 6 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } dilation: 6 } } layer { name: "relu6" type: "ReLU" bottom: "fc6" top: "fc6" } layer { name: "fc7" type: "Convolution" bottom: "fc6" top: "fc7" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 1024 kernel_size: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu7" type: "ReLU" bottom: "fc7" top: "fc7" } layer { name: "conv6_1" type: "Convolution" bottom: "fc7" top: "conv6_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 0 kernel_size: 1 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv6_1_relu" type: "ReLU" bottom: "conv6_1" top: "conv6_1" } layer { name: "conv6_2" type: "Convolution" bottom: "conv6_1" top: "conv6_2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 stride: 2 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv6_2_relu" type: "ReLU" bottom: "conv6_2" top: "conv6_2" } layer { name: "conv7_1" type: "Convolution" bottom: "conv6_2" top: "conv7_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv7_1_relu" type: "ReLU" bottom: "conv7_1" top: "conv7_1" } layer { name: "conv7_2" type: "Convolution" bottom: "conv7_1" top: "conv7_2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 stride: 2 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv7_2_relu" type: "ReLU" bottom: "conv7_2" top: "conv7_2" } layer { name: "conv8_1" type: "Convolution" bottom: "conv7_2" top: "conv8_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv8_1_relu" type: "ReLU" bottom: "conv8_1" top: "conv8_1" } layer { name: "conv8_2" type: "Convolution" bottom: "conv8_1" top: "conv8_2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 0 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv8_2_relu" type: "ReLU" bottom: "conv8_2" top: "conv8_2" } layer { name: "conv9_1" type: "Convolution" bottom: "conv8_2" top: "conv9_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv9_1_relu" type: "ReLU" bottom: "conv9_1" top: "conv9_1" } layer { name: "conv9_2" type: "Convolution" bottom: "conv9_1" top: "conv9_2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 0 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv9_2_relu" type: "ReLU" bottom: "conv9_2" top: "conv9_2" } layer { name: "conv4_3_norm" type: "Normalize" bottom: "conv4_3" top: "conv4_3_norm" norm_param { across_spatial: false scale_filler { type: "constant" value: 20 } channel_shared: false } } layer { name: "conv4_3_norm_mbox_loc" type: "Convolution" bottom: "conv4_3_norm" top: "conv4_3_norm_mbox_loc" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 240 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad_h: 1 pad_w: 2 kernel_h: 3 kernel_w: 5 stride_h: 1 stride_w: 1 } } layer { name: "conv4_3_norm_mbox_loc_perm" type: "Permute" bottom: "conv4_3_norm_mbox_loc" top: "conv4_3_norm_mbox_loc_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "conv4_3_norm_mbox_loc_flat" type: "Flatten" bottom: "conv4_3_norm_mbox_loc_perm" top: "conv4_3_norm_mbox_loc_flat" flatten_param { axis: 1 } } layer { name: "conv4_3_norm_mbox_conf" type: "Convolution" bottom: "conv4_3_norm" top: "conv4_3_norm_mbox_conf" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 40 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad_h: 1 pad_w: 2 kernel_h: 3 kernel_w: 5 stride_h: 1 stride_w: 1 } } layer { name: "conv4_3_norm_mbox_conf_perm" type: "Permute" bottom: "conv4_3_norm_mbox_conf" top: "conv4_3_norm_mbox_conf_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "conv4_3_norm_mbox_conf_flat" type: "Flatten" bottom: "conv4_3_norm_mbox_conf_perm" top: "conv4_3_norm_mbox_conf_flat" flatten_param { axis: 1 } } layer { name: "conv4_3_norm_mbox_priorbox" type: "PriorBox" bottom: "conv4_3_norm" bottom: "data" top: "conv4_3_norm_mbox_priorbox" prior_box_param { min_size: 30 max_size: 60 aspect_ratio: 2 aspect_ratio: 3 aspect_ratio: 4 aspect_ratio: 5 flip: true clip: false variance: 0.1 variance: 0.1 variance: 0.2 variance: 0.2 step: 8 offset: 0.5 denser_prior_boxes: true } } layer { name: "fc7_mbox_loc" type: "Convolution" bottom: "fc7" top: "fc7_mbox_loc" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 240 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad_h: 1 pad_w: 2 kernel_h: 3 kernel_w: 5 stride_h: 1 stride_w: 1 } } layer { name: "fc7_mbox_loc_perm" type: "Permute" bottom: "fc7_mbox_loc" top: "fc7_mbox_loc_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "fc7_mbox_loc_flat" type: "Flatten" bottom: "fc7_mbox_loc_perm" top: "fc7_mbox_loc_flat" flatten_param { axis: 1 } } layer { name: "fc7_mbox_conf" type: "Convolution" bottom: "fc7" top: "fc7_mbox_conf" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 40 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad_h: 1 pad_w: 2 kernel_h: 3 kernel_w: 5 stride_h: 1 stride_w: 1 } } layer { name: "fc7_mbox_conf_perm" type: "Permute" bottom: "fc7_mbox_conf" top: "fc7_mbox_conf_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "fc7_mbox_conf_flat" type: "Flatten" bottom: "fc7_mbox_conf_perm" top: "fc7_mbox_conf_flat" flatten_param { axis: 1 } } layer { name: "fc7_mbox_priorbox" type: "PriorBox" bottom: "fc7" bottom: "data" top: "fc7_mbox_priorbox" prior_box_param { min_size: 30 max_size: 90 aspect_ratio: 2 aspect_ratio: 3 aspect_ratio: 4 aspect_ratio: 5 flip: true clip: false variance: 0.1 variance: 0.1 variance: 0.2 variance: 0.2 step: 16 offset: 0.5 denser_prior_boxes: true } } layer { name: "conv6_2_mbox_loc" type: "Convolution" bottom: "conv6_2" top: "conv6_2_mbox_loc" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 240 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad_h: 1 pad_w: 2 kernel_h: 3 kernel_w: 5 stride_h: 1 stride_w: 1 } } layer { name: "conv6_2_mbox_loc_perm" type: "Permute" bottom: "conv6_2_mbox_loc" top: "conv6_2_mbox_loc_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "conv6_2_mbox_loc_flat" type: "Flatten" bottom: "conv6_2_mbox_loc_perm" top: "conv6_2_mbox_loc_flat" flatten_param { axis: 1 } } layer { name: "conv6_2_mbox_conf" type: "Convolution" bottom: "conv6_2" top: "conv6_2_mbox_conf" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 40 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad_h: 1 pad_w: 2 kernel_h: 3 kernel_w: 5 stride_h: 1 stride_w: 1 } } layer { name: "conv6_2_mbox_conf_perm" type: "Permute" bottom: "conv6_2_mbox_conf" top: "conv6_2_mbox_conf_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "conv6_2_mbox_conf_flat" type: "Flatten" bottom: "conv6_2_mbox_conf_perm" top: "conv6_2_mbox_conf_flat" flatten_param { axis: 1 } } layer { name: "conv6_2_mbox_priorbox" type: "PriorBox" bottom: "conv6_2" bottom: "data" top: "conv6_2_mbox_priorbox" prior_box_param { min_size: 90 max_size: 150 aspect_ratio: 2 aspect_ratio: 3 aspect_ratio: 4 aspect_ratio: 5 flip: true clip: false variance: 0.1 variance: 0.1 variance: 0.2 variance: 0.2 step: 32 offset: 0.5 denser_prior_boxes: true } } layer { name: "conv7_2_mbox_loc" type: "Convolution" bottom: "conv7_2" top: "conv7_2_mbox_loc" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 240 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad_h: 1 pad_w: 2 kernel_h: 3 kernel_w: 5 stride_h: 1 stride_w: 1 } } layer { name: "conv7_2_mbox_loc_perm" type: "Permute" bottom: "conv7_2_mbox_loc" top: "conv7_2_mbox_loc_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "conv7_2_mbox_loc_flat" type: "Flatten" bottom: "conv7_2_mbox_loc_perm" top: "conv7_2_mbox_loc_flat" flatten_param { axis: 1 } } layer { name: "conv7_2_mbox_conf" type: "Convolution" bottom: "conv7_2" top: "conv7_2_mbox_conf" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 40 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad_h: 1 pad_w: 2 kernel_h: 3 kernel_w: 5 stride_h: 1 stride_w: 1 } } layer { name: "conv7_2_mbox_conf_perm" type: "Permute" bottom: "conv7_2_mbox_conf" top: "conv7_2_mbox_conf_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "conv7_2_mbox_conf_flat" type: "Flatten" bottom: "conv7_2_mbox_conf_perm" top: "conv7_2_mbox_conf_flat" flatten_param { axis: 1 } } layer { name: "conv7_2_mbox_priorbox" type: "PriorBox" bottom: "conv7_2" bottom: "data" top: "conv7_2_mbox_priorbox" prior_box_param { min_size: 150 max_size: 210 aspect_ratio: 2 aspect_ratio: 3 aspect_ratio: 4 aspect_ratio: 5 flip: true clip: false variance: 0.1 variance: 0.1 variance: 0.2 variance: 0.2 step: 64 offset: 0.5 denser_prior_boxes: true } } layer { name: "conv8_2_mbox_loc" type: "Convolution" bottom: "conv8_2" top: "conv8_2_mbox_loc" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 240 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad_h: 1 pad_w: 2 kernel_h: 3 kernel_w: 5 stride_h: 1 stride_w: 1 } } layer { name: "conv8_2_mbox_loc_perm" type: "Permute" bottom: "conv8_2_mbox_loc" top: "conv8_2_mbox_loc_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "conv8_2_mbox_loc_flat" type: "Flatten" bottom: "conv8_2_mbox_loc_perm" top: "conv8_2_mbox_loc_flat" flatten_param { axis: 1 } } layer { name: "conv8_2_mbox_conf" type: "Convolution" bottom: "conv8_2" top: "conv8_2_mbox_conf" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 40 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad_h: 1 pad_w: 2 kernel_h: 3 kernel_w: 5 stride_h: 1 stride_w: 1 } } layer { name: "conv8_2_mbox_conf_perm" type: "Permute" bottom: "conv8_2_mbox_conf" top: "conv8_2_mbox_conf_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "conv8_2_mbox_conf_flat" type: "Flatten" bottom: "conv8_2_mbox_conf_perm" top: "conv8_2_mbox_conf_flat" flatten_param { axis: 1 } } layer { name: "conv8_2_mbox_priorbox" type: "PriorBox" bottom: "conv8_2" bottom: "data" top: "conv8_2_mbox_priorbox" prior_box_param { min_size: 210 max_size: 270 aspect_ratio: 2 aspect_ratio: 3 aspect_ratio: 4 aspect_ratio: 5 flip: true clip: false variance: 0.1 variance: 0.1 variance: 0.2 variance: 0.2 step: 100 offset: 0.5 denser_prior_boxes: true } } layer { name: "conv9_2_mbox_loc" type: "Convolution" bottom: "conv9_2" top: "conv9_2_mbox_loc" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 240 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad_h: 1 pad_w: 2 kernel_h: 3 kernel_w: 5 stride_h: 1 stride_w: 1 } } layer { name: "conv9_2_mbox_loc_perm" type: "Permute" bottom: "conv9_2_mbox_loc" top: "conv9_2_mbox_loc_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "conv9_2_mbox_loc_flat" type: "Flatten" bottom: "conv9_2_mbox_loc_perm" top: "conv9_2_mbox_loc_flat" flatten_param { axis: 1 } } layer { name: "conv9_2_mbox_conf" type: "Convolution" bottom: "conv9_2" top: "conv9_2_mbox_conf" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 40 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } pad_h: 1 pad_w: 2 kernel_h: 3 kernel_w: 5 stride_h: 1 stride_w: 1 } } layer { name: "conv9_2_mbox_conf_perm" type: "Permute" bottom: "conv9_2_mbox_conf" top: "conv9_2_mbox_conf_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "conv9_2_mbox_conf_flat" type: "Flatten" bottom: "conv9_2_mbox_conf_perm" top: "conv9_2_mbox_conf_flat" flatten_param { axis: 1 } } layer { name: "conv9_2_mbox_priorbox" type: "PriorBox" bottom: "conv9_2" bottom: "data" top: "conv9_2_mbox_priorbox" prior_box_param { min_size: 270 max_size: 330 aspect_ratio: 2 aspect_ratio: 3 aspect_ratio: 4 aspect_ratio: 5 flip: true clip: false variance: 0.1 variance: 0.1 variance: 0.2 variance: 0.2 step: 300 offset: 0.5 denser_prior_boxes: true } } layer { name: "mbox_loc" type: "Concat" bottom: "conv4_3_norm_mbox_loc_flat" bottom: "fc7_mbox_loc_flat" bottom: "conv6_2_mbox_loc_flat" bottom: "conv7_2_mbox_loc_flat" bottom: "conv8_2_mbox_loc_flat" bottom: "conv9_2_mbox_loc_flat" top: "mbox_loc" concat_param { axis: 1 } } layer { name: "mbox_conf" type: "Concat" bottom: "conv4_3_norm_mbox_conf_flat" bottom: "fc7_mbox_conf_flat" bottom: "conv6_2_mbox_conf_flat" bottom: "conv7_2_mbox_conf_flat" bottom: "conv8_2_mbox_conf_flat" bottom: "conv9_2_mbox_conf_flat" top: "mbox_conf" concat_param { axis: 1 } } layer { name: "mbox_priorbox" type: "Concat" bottom: "conv4_3_norm_mbox_priorbox" bottom: "fc7_mbox_priorbox" bottom: "conv6_2_mbox_priorbox" bottom: "conv7_2_mbox_priorbox" bottom: "conv8_2_mbox_priorbox" bottom: "conv9_2_mbox_priorbox" top: "mbox_priorbox" concat_param { axis: 2 } } layer { name: "mbox_conf_reshape" type: "Reshape" bottom: "mbox_conf" top: "mbox_conf_reshape" reshape_param { shape { dim: 0 dim: -1 dim: 2 } } } layer { name: "mbox_conf_softmax" type: "Softmax" bottom: "mbox_conf_reshape" top: "mbox_conf_softmax" softmax_param { axis: 2 } } layer { name: "mbox_conf_flatten" type: "Flatten" bottom: "mbox_conf_softmax" top: "mbox_conf_flatten" flatten_param { axis: 1 } } layer { name: "detection_out" type: "DetectionOutput" bottom: "mbox_loc" bottom: "mbox_conf_flatten" bottom: "mbox_priorbox" top: "detection_out" include { phase: TEST } detection_output_param { num_classes: 2 share_location: true background_label_id: 0 nms_param { nms_threshold: 0.45 top_k: 400 } save_output_param { output_directory: "./data/text/results/text/text_polygon_ic15_fix_order_384x384/Main" output_name_prefix: "comp4_dettest" output_format: "VOC" label_map_file: "data/text/labelmap_voc.prototxt" num_test_image: 500 } code_type: CENTER_SIZE keep_top_k: 200 confidence_threshold: 0.01 use_polygon: true } } I0224 04:45:03.983318 21899 layer_factory.hpp:77] Creating layer input I0224 04:45:03.983523 21899 net.cpp:100] Creating Layer input I0224 04:45:03.983546 21899 net.cpp:408] input -> data I0224 04:45:03.983872 21899 net.cpp:150] Setting up input I0224 04:45:03.983886 21899 net.cpp:157] Top shape: 1 3 768 768 (1769472) I0224 04:45:03.983898 21899 net.cpp:165] Memory required for data: 7077888 I0224 04:45:03.983907 21899 layer_factory.hpp:77] Creating layer data_input_0_split I0224 04:45:03.984068 21899 net.cpp:100] Creating Layer data_input_0_split I0224 04:45:03.984081 21899 net.cpp:434] data_input_0_split <- data I0224 04:45:03.984091 21899 net.cpp:408] data_input_0_split -> data_input_0_split_0 I0224 04:45:03.984105 21899 net.cpp:408] data_input_0_split -> data_input_0_split_1 I0224 04:45:03.984117 21899 net.cpp:408] data_input_0_split -> data_input_0_split_2 I0224 04:45:03.984126 21899 net.cpp:408] data_input_0_split -> data_input_0_split_3 I0224 04:45:03.984136 21899 net.cpp:408] data_input_0_split -> data_input_0_split_4 I0224 04:45:03.984148 21899 net.cpp:408] data_input_0_split -> data_input_0_split_5 I0224 04:45:03.984156 21899 net.cpp:408] data_input_0_split -> data_input_0_split_6 I0224 04:45:03.984172 21899 net.cpp:150] Setting up data_input_0_split I0224 04:45:03.984180 21899 net.cpp:157] Top shape: 1 3 768 768 (1769472) I0224 04:45:03.984190 21899 net.cpp:157] Top shape: 1 3 768 768 (1769472) I0224 04:45:03.984198 21899 net.cpp:157] Top shape: 1 3 768 768 (1769472) I0224 04:45:03.984206 21899 net.cpp:157] Top shape: 1 3 768 768 (1769472) I0224 04:45:03.984215 21899 net.cpp:157] Top shape: 1 3 768 768 (1769472) I0224 04:45:03.984223 21899 net.cpp:157] Top shape: 1 3 768 768 (1769472) I0224 04:45:03.984231 21899 net.cpp:157] Top shape: 1 3 768 768 (1769472) I0224 04:45:03.984239 21899 net.cpp:165] Memory required for data: 56623104 I0224 04:45:03.984246 21899 layer_factory.hpp:77] Creating layer conv1_1 I0224 04:45:03.984426 21899 net.cpp:100] Creating Layer conv1_1 I0224 04:45:03.984436 21899 net.cpp:434] conv1_1 <- data_input_0_split_0 I0224 04:45:03.984447 21899 net.cpp:408] conv1_1 -> conv1_1
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I don't where is the issue. Please help!