Inference example for image_classification and unit_test for "inference" #8020
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@sidgoyal78 will you update this PR? Since you said #7995 can resolve the problem of PR and #7995 is merged. Also, there are conflicts with the develop branch. |
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Yes, I have updated the PR. Now, this works. |
Xreki
reviewed
Feb 6, 2018
| fetch_targets] = fluid.io.load_inference_model(save_dirname, exe) | ||
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| # The input's dimension of conv should be 4-D or 5-D. | ||
| tensor_img = numpy.random.rand(1, 3, 32, 32).astype("float32") |
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What is the range of input data?
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Range is from 0 to 1 (https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/dataset/cifar.py#L52)
| if acc_value > 0.01: # Low threshold for speeding up | ||
| fluid.io.save_inference_model(save_dirname, ["pixel"], | ||
| [predict], exe) | ||
| early_terminate = True |
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Maybe we can change the origin main to train, and define another main function to run train and infer, so that we can use return here and do not need the boolean early_terminate.
| fetch_list=[avg_cost, acc]) | ||
| acc_list.append(float(acc_t)) | ||
| avg_loss_list.append(float(loss_t)) | ||
| break # just use 1 segment for now |
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Change the comment to : use 1 segment for speeding up CI
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This addresses one of the 2 tasks in #7999. With the new changes from #7995, it is now running successfully.
It checks for both vgg and resnet versions.
EDIT: removed previously pasted log