Open youthM opened 5 years ago
When I run the code from GitHub,which is “Face_Alignment_Two_Stage_Re-initialization”,I got the following error: I0911 08:08:29.921020 12228 layer_factory.hpp:88] Creating layer input I0911 08:08:29.922020 12228 net.cpp:100] Creating Layer input I0911 08:08:29.922020 12228 net.cpp:418] input -> data I0911 08:08:29.923020 12228 net.cpp:150] Setting up input I0911 08:08:29.924021 12228 net.cpp:157] Top shape: 1 3 448 448 (602112) I0911 08:08:29.927022 12228 net.cpp:165] Memory required for data: 2408448 I0911 08:08:29.927022 12228 layer_factory.hpp:88] Creating layer data_input_0_split I0911 08:08:29.928025 12228 net.cpp:100] Creating Layer data_input_0_split I0911 08:08:29.928025 12228 net.cpp:444] data_input_0_split <- data I0911 08:08:29.929024 12228 net.cpp:418] data_input_0_split -> data_input_0_split_0 I0911 08:08:29.930024 12228 net.cpp:418] data_input_0_split -> data_input_0_split_1 I0911 08:08:29.930024 12228 net.cpp:150] Setting up data_input_0_split I0911 08:08:29.930024 12228 net.cpp:157] Top shape: 1 3 448 448 (602112) I0911 08:08:29.932025 12228 net.cpp:157] Top shape: 1 3 448 448 (602112) I0911 08:08:29.932025 12228 net.cpp:165] Memory required for data: 7225344 I0911 08:08:29.932025 12228 layer_factory.hpp:88] Creating layer downsample_data I0911 08:08:29.932025 12228 net.cpp:100] Creating Layer downsample_data I0911 08:08:29.932025 12228 net.cpp:444] downsample_data <- data_input_0_split_0 I0911 08:08:29.932025 12228 net.cpp:418] downsample_data -> downsample_data I0911 08:08:29.932025 12228 net.cpp:150] Setting up downsample_data I0911 08:08:29.932025 12228 net.cpp:157] Top shape: (0) I0911 08:08:29.932025 12228 net.cpp:165] Memory required for data: 7225344 I0911 08:08:29.933027 12228 layer_factory.hpp:88] Creating layer net1_conv1 I0911 08:08:29.933027 12228 net.cpp:100] Creating Layer net1_conv1 I0911 08:08:29.933027 12228 net.cpp:444] net1_conv1 <- downsample_data I0911 08:08:29.933027 12228 net.cpp:418] net1_conv1 -> net1_conv1 F0911 08:08:29.933027 12228 blob.hpp:122] Check failed: axis_index < num_axes() (1 vs. 0) axis 1 out of range for 0-D Blob with shape (0) Check failure stack trace:
The part of the deploy is as follows.
name: "facial_point_net" input: "data" input_dim: 1 input_dim: 3 input_dim: 448 input_dim: 448
layer { name: "downsample_data" type: "SubsamplePooling" bottom: "data" top: "downsample_data" subsample_pooling_param { output_H: 60 output_W: 60 } } ########################################## layer { name: "net1_conv1" type: "Convolution" bottom: "downsample_data" top: "net1_conv1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 20 pad: 0 kernel_size: 5 stride: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "net1_PReLU1" type: "PReLU" bottom: "net1_conv1" top: "net1_conv1" }
layer { name: "net1_pool1" type: "Pooling" bottom: "net1_conv1" top: "net1_pool1" pooling_param { pool: MAX kernel_size: 2 stride: 2 } }
Does anyone know what the problem is ?
How did you go about this issue? I'm facing similar problem can you assist.
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When I run the code from GitHub,which is “Face_Alignment_Two_Stage_Re-initialization”,I got the following error: I0911 08:08:29.921020 12228 layer_factory.hpp:88] Creating layer input I0911 08:08:29.922020 12228 net.cpp:100] Creating Layer input I0911 08:08:29.922020 12228 net.cpp:418] input -> data I0911 08:08:29.923020 12228 net.cpp:150] Setting up input I0911 08:08:29.924021 12228 net.cpp:157] Top shape: 1 3 448 448 (602112) I0911 08:08:29.927022 12228 net.cpp:165] Memory required for data: 2408448 I0911 08:08:29.927022 12228 layer_factory.hpp:88] Creating layer data_input_0_split I0911 08:08:29.928025 12228 net.cpp:100] Creating Layer data_input_0_split I0911 08:08:29.928025 12228 net.cpp:444] data_input_0_split <- data I0911 08:08:29.929024 12228 net.cpp:418] data_input_0_split -> data_input_0_split_0 I0911 08:08:29.930024 12228 net.cpp:418] data_input_0_split -> data_input_0_split_1 I0911 08:08:29.930024 12228 net.cpp:150] Setting up data_input_0_split I0911 08:08:29.930024 12228 net.cpp:157] Top shape: 1 3 448 448 (602112) I0911 08:08:29.932025 12228 net.cpp:157] Top shape: 1 3 448 448 (602112) I0911 08:08:29.932025 12228 net.cpp:165] Memory required for data: 7225344 I0911 08:08:29.932025 12228 layer_factory.hpp:88] Creating layer downsample_data I0911 08:08:29.932025 12228 net.cpp:100] Creating Layer downsample_data I0911 08:08:29.932025 12228 net.cpp:444] downsample_data <- data_input_0_split_0 I0911 08:08:29.932025 12228 net.cpp:418] downsample_data -> downsample_data I0911 08:08:29.932025 12228 net.cpp:150] Setting up downsample_data I0911 08:08:29.932025 12228 net.cpp:157] Top shape: (0) I0911 08:08:29.932025 12228 net.cpp:165] Memory required for data: 7225344 I0911 08:08:29.933027 12228 layer_factory.hpp:88] Creating layer net1_conv1 I0911 08:08:29.933027 12228 net.cpp:100] Creating Layer net1_conv1 I0911 08:08:29.933027 12228 net.cpp:444] net1_conv1 <- downsample_data I0911 08:08:29.933027 12228 net.cpp:418] net1_conv1 -> net1_conv1 F0911 08:08:29.933027 12228 blob.hpp:122] Check failed: axis_index < num_axes() (1 vs. 0) axis 1 out of range for 0-D Blob with shape (0) Check failure stack trace:
The part of the deploy is as follows.
name: "facial_point_net" input: "data" input_dim: 1 input_dim: 3 input_dim: 448 input_dim: 448
layer { name: "downsample_data" type: "SubsamplePooling" bottom: "data" top: "downsample_data" subsample_pooling_param { output_H: 60 output_W: 60 } } ########################################## layer { name: "net1_conv1" type: "Convolution" bottom: "downsample_data" top: "net1_conv1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 20 pad: 0 kernel_size: 5 stride: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "net1_PReLU1" type: "PReLU" bottom: "net1_conv1" top: "net1_conv1" }
layer { name: "net1_pool1" type: "Pooling" bottom: "net1_conv1" top: "net1_pool1" pooling_param { pool: MAX kernel_size: 2 stride: 2 } }
Does anyone know what the problem is ?