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hello, I encounter a problem when I try to use accuracy checker tools. the error said that my input dataset size(1,3,339,510) doesn't match my model's input size(1,1,339,510). But actually, my input image size is in 8 bits(only one channel) not 24bits(RGB channel). I don't know why this happened? and there is also one problem that even if I changed my lr_suffix and hr_suffix in the confiuration file(yml), the lr_suffix and hr_suffix in CMD_PROMPT is not changed
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Hello Liu Xue Chun.
We have successfully replicated the case and managed to obtain an accurate PSNR.
You are unable to obtain the correct value due to the .yml file created did not specify the Pre-processing and Post-processing configurations with some missing parameters in the Adapter section.
The attached file is the updated and validated version of your .yml file.
Regards,
Zulkifli
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I try to add the two lines in my configuration file, I don't know if it's correct to use "gray" flag. my input image is the Y channel which converted from RGB. now I successfully run the accuracy checker. but the PSNR I got is not correct. the PSNR should be around 28-30dB I suppose。
type: opencv_imread
reading_flag: gray
the PSNR got from the accuracy_checker.
the dataset I use only contain two y channel images
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Hello Liu Xue Chun,
Greetings to you.
Please share with us your model, command to use, and the necessary files (such as dataset, configuration files, etc) for us to reproduce the issue.
Regards,
Zulkifli
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hello, I will try to describe my necessary files later(some restrictions for uploading). and BTW when I tried to quantize my model I also meet this problem
my commad line is
pot -c SR-fsrcnn.json
my json configuration file is here
{
/* Model parameters */
"model": {
"model_name": "withoutd_s8_x4_fp32", // Model name
"model": "withoutd_s8_x4_fp32.xml", // Path to model (.xml format)
"weights": "withoutd_s8_x4_fp32.bin" // Path to weights (.bin format)
},
/* Parameters of the engine used for model inference */
"engine": {
"config": "AC-SR-FP32-fsrcnn.yml"
},
/* Optimization hyperparameters */
"compression": {
"target_device": "GPU", // Target device, the specificity of which will be taken
// into account during optimization
"algorithms": [
{
"name": "DefaultQuantization", // Optimization algorithm name
"params": {
"preset": "performance", // Preset [performance, mixed, accuracy] which control the quantization
// mode (symmetric, mixed (weights symmetric and activations asymmetric)
// and fully asymmetric respectively)
"stat_subset_size": 1 // Size of subset to calculate activations statistics that can be used
// for quantization parameters calculation
}
}
]
}
}
my yml configuration file is
models:
- name: SR-fsrcnn-x4-fp32
launchers:
- framework: dlsdk
model: withoutd_s8_x4_fp32.xml
weights: withoutd_s8_x4_fp32.bin
device: GPU
adapter:
type: super_resolution
reverse_channels: True
datasets:
- name: SR_fsrcnn_x4
reader:
type: opencv_imread
reading_flag: gray
data_source: dataset_x4
annotation_conversion:
converter: super_resolution
data_dir: dataset_x4
lr_suffix: lr_y_
#upsample_suffix: upsample_x4
hr_suffix: hr_y_
#two_streams: True
annotation: SR_fsrcnn_x4.pickle
metrics:
- type: psnr
scale_border: 0
presenter: print_vector
- type: ssim
presenter: print_vector
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Hello Liu Xue Chun.
Can you try to upload the model, dataset, and all the configuration files on google drive and send the link for us to download?
Regards,
Zulkifli
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hello, I have uploaded the two configuration files, the dataset(2 images) and the model, could you take a look at the problem in accuracy check and pot
my two commad line is
accuracy checker -c AC-SR-FP32-fsrcnn.yml
pot -c SR-fsrcnn.json
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Hello Liu Xue Chun.
Thank you for sharing the model and configuration files with us. It seems like the information you provided is not complete, the label and annotation files are missing. Please share the missing file with us.
Regards,
Zulkifli
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hello, I have no labels and annotation files because it's the super-resolution task. and when you mentioned annotation files, do you mean "SR_fsrcnn_x4.pickle". Actually it's generated from the command "accuracy checker -c AC-SR-FP32-fsrcnn.yml"
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The super-resolution task is to perform super-resolution on the image which is downsampled by four times on the original image, and then after the super-resolution, we calculate the PSNR between the super-resolution result and the original image to measure the performance of super-resolution. Therefore, the label of each downsampled image (lr_0801.png) is the original image (hr_0801.png).
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Hello Liu Xue Chun.
Thank you for your sharing all the information with us. We might need time to replicate and investigate this issue.
Regards,
Zulkifli
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Hello Liu Xue Chun.
We have successfully replicated the case and managed to obtain an accurate PSNR.
You are unable to obtain the correct value due to the .yml file created did not specify the Pre-processing and Post-processing configurations with some missing parameters in the Adapter section.
The attached file is the updated and validated version of your .yml file.
Regards,
Zulkifli
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hello,thank you very much!I know the reason. it's because our model's input layer is in range [0,1] but my input image is in range [0,255]
and besides do you have any ideas for this problem when I run "pot -c SR-fsrcnn.json"
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Hello Liu Xue Chun,
Since the initial issue has been resolved. Will it be okay if we close this case and focus the second question on this thread?
Regards,
Zulkifli
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Hello Liu Xue Chun.
This thread will no longer be monitored since this issue has been resolved. If you need any additional information from Intel, please submit a new question.
Regards,
Zulkifli
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hello,
the new problem is in this page Re:AttributeError: 'DatasetConversionInfo' object has no attribute 'identifier' - Intel Community
thanks~

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