Remove useless files in model compression examples

docs/en_US/CommunitySharings/ModelCompressionComparison.rst

@@ -50,24 +50,24 @@ The experiment result are shown in the following figures:
 CIFAR-10, VGG16:
 
 
-.. image:: ../../../examples/model_compress/comparison_of_pruners/img/performance_comparison_vgg16.png
-   :target: ../../../examples/model_compress/comparison_of_pruners/img/performance_comparison_vgg16.png
+.. image:: ../../../examples/model_compress/pruning/comparison_of_pruners/img/performance_comparison_vgg16.png
+   :target: ../../../examples/model_compress/pruning/comparison_of_pruners/img/performance_comparison_vgg16.png
    :alt: 
 
 
 CIFAR-10, ResNet18:
 
 
-.. image:: ../../../examples/model_compress/comparison_of_pruners/img/performance_comparison_resnet18.png
-   :target: ../../../examples/model_compress/comparison_of_pruners/img/performance_comparison_resnet18.png
+.. image:: ../../../examples/model_compress/pruning/comparison_of_pruners/img/performance_comparison_resnet18.png
+   :target: ../../../examples/model_compress/pruning/comparison_of_pruners/img/performance_comparison_resnet18.png
    :alt: 
 
 
 CIFAR-10, ResNet50:
 
 
-.. image:: ../../../examples/model_compress/comparison_of_pruners/img/performance_comparison_resnet50.png
-   :target: ../../../examples/model_compress/comparison_of_pruners/img/performance_comparison_resnet50.png
+.. image:: ../../../examples/model_compress/pruning/comparison_of_pruners/img/performance_comparison_resnet50.png
+   :target: ../../../examples/model_compress/pruning/comparison_of_pruners/img/performance_comparison_resnet50.png
    :alt: 
 
 

docs/en_US/Compression/ModelSpeedup.rst

@@ -37,7 +37,7 @@ Usage
    out = model(dummy_input)
    print('elapsed time: ', time.time() - start)
 
-For complete examples please refer to :githublink:`the code <examples/model_compress/model_speedup.py>`
+For complete examples please refer to :githublink:`the code <examples/model_compress/pruning/model_speedup.py>`
 
 NOTE: The current implementation supports PyTorch 1.3.1 or newer.
 
@@ -51,7 +51,7 @@ For PyTorch we can only replace modules, if functions in ``forward`` should be r
 Speedup Results of Examples
 ---------------------------
 
-The code of these experiments can be found :githublink:`here <examples/model_compress/model_speedup.py>`.
+The code of these experiments can be found :githublink:`here <examples/model_compress/pruning/model_speedup.py>`.
 
 slim pruner example
 ^^^^^^^^^^^^^^^^^^^

docs/en_US/Compression/Pruner.rst

@@ -133,7 +133,7 @@ We implemented one of the experiments in `Learning Efficient Convolutional Netwo
      - 88.5%
 
 
-The experiments code can be found at :githublink:`examples/model_compress <examples/model_compress/>`
+The experiments code can be found at :githublink:`examples/model_compress/pruning/reproduced/slim_torch_cifar10.py <examples/model_compress/pruning/reproduced/slim_torch_cifar10.py>`
 
 ----
 
@@ -252,7 +252,7 @@ We implemented one of the experiments in `PRUNING FILTERS FOR EFFICIENT CONVNETS
      - 64.0%
 
 
-The experiments code can be found at :githublink:`examples/model_compress <examples/model_compress/>`
+The experiments code can be found at :githublink:`examples/model_compress/pruning/reproduced/L1_torch_cifar10.py <examples/model_compress/pruning/reproduced/L1_torch_cifar10.py>`
 
 ----
 
@@ -316,7 +316,7 @@ PyTorch code
 
 Note: ActivationAPoZRankFilterPruner is used to prune convolutional layers within deep neural networks, therefore the ``op_types`` field supports only convolutional layers.
 
-You can view :githublink:`example <examples/model_compress/model_prune_torch.py>` for more information.
+You can view :githublink:`example <examples/model_compress/pruning/model_prune_torch.py>` for more information.
 
 User configuration for ActivationAPoZRankFilter Pruner
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -351,7 +351,7 @@ PyTorch code
 
 Note: ActivationMeanRankFilterPruner is used to prune convolutional layers within deep neural networks, therefore the ``op_types`` field supports only convolutional layers.
 
-You can view :githublink:`example <examples/model_compress/model_prune_torch.py>` for more information.
+You can view :githublink:`example <examples/model_compress/pruning/model_prune_torch.py>` for more information.
 
 User configuration for ActivationMeanRankFilterPruner
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -471,7 +471,7 @@ PyTorch code
 
    pruner.update_epoch(epoch)
 
-You can view :githublink:`example <examples/model_compress/model_prune_torch.py>` for more information.
+You can view :githublink:`example <examples/model_compress/pruning/model_prune_torch.py>` for more information.
 
 User configuration for AGP Pruner
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -511,7 +511,7 @@ PyTorch code
    pruner = NetAdaptPruner(model, config_list, short_term_fine_tuner=short_term_fine_tuner, evaluator=evaluator,base_algo='l1', experiment_data_dir='./')
    pruner.compress()
 
-You can view :githublink:`example <examples/model_compress/auto_pruners_torch.py>` for more information.
+You can view :githublink:`example <examples/model_compress/pruning/auto_pruners_torch.py>` for more information.
 
 User configuration for NetAdapt Pruner
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -552,7 +552,7 @@ PyTorch code
    pruner = SimulatedAnnealingPruner(model, config_list, evaluator=evaluator, base_algo='l1', cool_down_rate=0.9, experiment_data_dir='./')
    pruner.compress()
 
-You can view :githublink:`example <examples/model_compress/auto_pruners_torch.py>` for more information.
+You can view :githublink:`example <examples/model_compress/pruning/auto_pruners_torch.py>` for more information.
 
 User configuration for SimulatedAnnealing Pruner
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -593,7 +593,7 @@ PyTorch code
                cool_down_rate=0.9, admm_num_iterations=30, admm_training_epochs=5, experiment_data_dir='./')
    pruner.compress()
 
-You can view :githublink:`example <examples/model_compress/auto_pruners_torch.py>` for more information.
+You can view :githublink:`example <examples/model_compress/pruning/auto_pruners_torch.py>` for more information.
 
 User configuration for AutoCompress Pruner
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -631,7 +631,7 @@ PyTorch code
    pruner = AMCPruner(model, config_list, evaluator, val_loader, flops_ratio=0.5)
    pruner.compress()
 
-You can view :githublink:`example <examples/model_compress/amc/>` for more information.
+You can view :githublink:`example <examples/model_compress/pruning/amc/>` for more information.
 
 User configuration for AMC Pruner
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -659,7 +659,7 @@ We implemented one of the experiments in `AMC: AutoML for Model Compression and
      - 50%
 
 
-The experiments code can be found at :githublink:`examples/model_compress <examples/model_compress/amc/>`
+The experiments code can be found at :githublink:`examples/model_compress/pruning/ <examples/model_compress/pruning/amc/>`
 
 ADMM Pruner
 -----------
@@ -693,7 +693,7 @@ PyTorch code
    pruner = ADMMPruner(model, config_list, trainer=trainer, num_iterations=30, epochs=5)
    pruner.compress()
 
-You can view :githublink:`example <examples/model_compress/auto_pruners_torch.py>` for more information.
+You can view :githublink:`example <examples/model_compress/pruning/auto_pruners_torch.py>` for more information.
 
 User configuration for ADMM Pruner
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -754,7 +754,7 @@ User configuration for LotteryTicket Pruner
 Reproduced Experiment
 ^^^^^^^^^^^^^^^^^^^^^
 
-We try to reproduce the experiment result of the fully connected network on MNIST using the same configuration as in the paper. The code can be referred :githublink:`here <examples/model_compress/lottery_torch_mnist_fc.py>`. In this experiment, we prune 10 times, for each pruning we train the pruned model for 50 epochs.
+We try to reproduce the experiment result of the fully connected network on MNIST using the same configuration as in the paper. The code can be referred :githublink:`here <examples/model_compress/pruning/reproduced/lottery_torch_mnist_fc.py>`. In this experiment, we prune 10 times, for each pruning we train the pruned model for 50 epochs.
 
 
 .. image:: ../../img/lottery_ticket_mnist_fc.png

docs/en_US/Compression/Quantizer.rst

@@ -157,7 +157,7 @@ PyTorch code
    quantizer = BNNQuantizer(model, configure_list)
    model = quantizer.compress()
 
-You can view example :githublink:`examples/model_compress/BNN_quantizer_cifar10.py <examples/model_compress/BNN_quantizer_cifar10.py>` for more information.
+You can view example :githublink:`examples/model_compress/quantization/BNN_quantizer_cifar10.py <examples/model_compress/quantization/BNN_quantizer_cifar10.py>` for more information.
 
 User configuration for BNN Quantizer
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -181,4 +181,4 @@ We implemented one of the experiments in `Binarized Neural Networks: Training De
      - 86.93%
 
 
-The experiments code can be found at :githublink:`examples/model_compress/BNN_quantizer_cifar10.py <examples/model_compress/BNN_quantizer_cifar10.py>` 
+The experiments code can be found at :githublink:`examples/model_compress/quantization/BNN_quantizer_cifar10.py <examples/model_compress/quantization/BNN_quantizer_cifar10.py>` 

docs/en_US/Compression/QuickStart.rst

@@ -45,7 +45,7 @@ After training, you get accuracy of the pruned model. You can export model weigh
 
    pruner.export_model(model_path='pruned_vgg19_cifar10.pth', mask_path='mask_vgg19_cifar10.pth')
 
-The complete code of model compression examples can be found :githublink:`here <examples/model_compress/model_prune_torch.py>`.
+The complete code of model compression examples can be found :githublink:`here <examples/model_compress/pruning/model_prune_torch.py>`.
 
 Speed up the model
 ^^^^^^^^^^^^^^^^^^