yuyadanyadan commented 8 years ago

Hi, I build a project with your code in ubuntu14.04 with Nsight Eclipse. Error: my caffe::Caffe don't have SetGetcuDNNAlgorithmFunc and SetSetcuDNNAlgorithmFunc. Are they your own functions? Hoping for your reply! Thank you !

lltcggie commented 8 years ago

こちらのCaffeを使っています。ただし、Ubuntuではビルドしたことがないのでエラーが出る可能性があります。 https://github.com/lltcggie/caffe

nagadomi commented 8 years ago

Yes, waifu2x-caffe is required lltcggie's modified version of caffe. I've provided lltcggie's caffe and waifu2x-caffe for ubuntu. Maybe it will helpful for you https://github.com/nagadomi/waifu2x-caffe/blob/ubuntu/INSTALL-linux.md

yuyadanyadan commented 8 years ago

I'm grateful! Thank you so much !

在 2016-08-16 17:41:59，"nagadomi" notifications@github.com 写道：

Yes, waifu2x-caffe is required lltcggie's customized caffe. I provide lltcggie's caffe and waifu2x-caffe for ubuntu. Maybe it will helpful for you https://github.com/nagadomi/waifu2x-caffe/blob/ubuntu/INSTALL-linux.md

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yuyadanyadan commented 7 years ago

Hi, I use your caffe to run the waifu2x net . And I get: Check failed: status == CUDNN_STATUS_SUCCESS (3 vs. 0) CUDNN_STATUS_BAD_PARAM (#241)

在 2016-08-16 16:44:58，"lltcggie" notifications@github.com 写道：

こちらのCaffeを使っています。ただし、Ubuntuではビルドしたことがないのでエラーが出る可能性があります。 https://github.com/lltcggie/caffe

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yuyadanyadan commented 7 years ago

This is my net! Could you help me? Thank you so much

name: "upconv_7" layer { name: "data" type: "Data" top: "input_data" include { phase: TRAIN } transform_param { scale: 0.00392156 mirror: true mean_file: "/home/yuyadan/src/sourceCode/lltcggie-caffe/data/waifu2x_trainning_cpp/waifu2x_mean.binaryproto" } data_param { source: "/home/yuyadan/src/sourceCode/lltcggie-caffe/examples/waifu2x_trainning_cpp/waifu2x_train0_lmdb" batch_size: 10 backend: LMDB } } layer { name: "data" type: "Data" top: "input_data" include { phase: TEST } transform_param { scale: 0.00392156 mirror: true mean_file: "/home/yuyadan/src/sourceCode/lltcggie-caffe/data/waifu2x_trainning_cpp/waifu2x_mean.binaryproto" } data_param { source: "/home/yuyadan/src/sourceCode/lltcggie-caffe/examples/waifu2x_trainning_cpp/waifu2x_val0_lmdb" batch_size: 10 backend: LMDB } } layer { name: "conv1_layer" type: "Convolution" bottom: "input_data" top: "conv1" convolution_param { num_output: 16 kernel_size: 3 stride: 1 bias_filler{ type: "constant" value: 0 } weight_filler { type: "gaussian" std: 0.01 } } } layer { name: "conv1_relu_layer" type: "ReLU" bottom: "conv1" top: "conv1" relu_param { negative_slope: 0.1 } } layer { name: "conv2_layer" type: "Convolution" bottom: "conv1" top: "conv2" convolution_param { num_output: 32 kernel_size: 3 stride: 1 bias_term: false weight_filler { type: "gaussian" std: 0.01 } } } layer { name: "conv2_relu_layer" type: "ReLU" bottom: "conv2" top: "conv2" relu_param { negative_slope: 0.1 } } layer { name: "conv3_layer" type: "Convolution" bottom: "conv2" top: "conv3" convolution_param { num_output: 64 kernel_size: 3 stride: 1 bias_term: false weight_filler { type: "gaussian" std: 0.01 } } } layer { name: "conv3_relu_layer" type: "ReLU" bottom: "conv3" top: "conv3" relu_param { negative_slope: 0.1 } } layer { name: "conv4_layer" type: "Convolution" bottom: "conv3" top: "conv4" convolution_param { num_output: 128 kernel_size: 3 stride: 1 bias_term: false weight_filler { type: "gaussian" std: 0.01 } } } layer { name: "conv4_relu_layer" type: "ReLU" bottom: "conv4" top: "conv4" relu_param { negative_slope: 0.1 } } layer { name: "conv5_layer" type: "Convolution" bottom: "conv4" top: "conv5" convolution_param { num_output: 128 kernel_size: 3 stride: 1 bias_term: false weight_filler { type: "gaussian" std: 0.01 } } } layer { name: "conv5_relu_layer" type: "ReLU" bottom: "conv5" top: "conv5" relu_param { negative_slope: 0.1 } } layer { name: "conv6_layer" type: "Convolution" bottom: "conv5" top: "conv6" convolution_param { num_output: 256 kernel_size: 3 stride: 1 bias_term: false weight_filler { type: "gaussian" std: 0.01 } } } layer { name: "conv6_relu_layer" type: "ReLU" bottom: "conv6" top: "conv6" relu_param { negative_slope: 0.1 } } layer { name: "conv7_layer" type: "Deconvolution" bottom: "conv6" top: "conv7" convolution_param { num_output: 3 kernel_size: 4 stride: 2 pad: 3 bias_term: false weight_filler { type: "gaussian" std: 0.01 } } param { lr_mult: 0 decay_mult: 0 } } layer { name: "target" type: "Data" top: "target" transform_param{ scale: 0.00392156 mirror: true mean_file: "/home/yuyadan/src/sourceCode/lltcggie-caffe/data/waifu2x_trainning_cpp/waifu2x_mean_out.binaryproto" } data_param { source: "/home/yuyadan/src/sourceCode/lltcggie-caffe/examples/waifu2x_trainning_cpp/waifu2x_train1_lmdb" batch_size: 10 backend: LMDB } include: { phase: TRAIN } } layer { name: "target" type: "Data" top: "target" transform_param{ scale: 0.00392156 mirror: true mean_file: "/home/yuyadan/src/sourceCode/lltcggie-caffe/data/waifu2x_trainning_cpp/waifu2x_mean_out.binaryproto" } data_param { source: "/home/yuyadan/src/sourceCode/lltcggie-caffe/examples/waifu2x_trainning_cpp/waifu2x_val1_lmdb" batch_size: 10 backend: LMDB } include: { phase: TEST } } layer { name: "loss" type: "EuclideanLoss" bottom: "conv7" bottom: "target" top: "loss"

include: { phase: TRAIN }

}

在 2016-08-16 16:44:58，"lltcggie" notifications@github.com 写道：

こちらのCaffeを使っています。ただし、Ubuntuではビルドしたことがないのでエラーが出る可能性があります。 https://github.com/lltcggie/caffe

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lltcggie / waifu2x-caffe

my caffe have no “SetGetcuDNNAlgorithmFunc ” #46

include: { phase: TRAIN }