Refactor code hierarchy part 3: Unit test (#3037)

.gitignore

@@ -5,6 +5,10 @@
 /test/model_path/
 /test/temp.json
 /test/ut/sdk/*.pth
+/test/ut/tools/annotation/_generated/
+/ts/nni_manager/exp_profile.json
+/ts/nni_manager/metrics.json
+/ts/nni_manager/trial_jobs.json
 
 
 # Logs

docs/en_US/Compression/AutoPruningUsingTuners.md

@@ -7,13 +7,13 @@ It's convenient to implement auto model pruning with NNI compression and NNI tun
 You can easily compress a model with NNI compression. Take pruning for example, you can prune a pretrained model with LevelPruner like this
 
 ```python
-from nni.compression.torch import LevelPruner
+from nni.algorithms.compression.pytorch.pruning import LevelPruner
 config_list = [{ 'sparsity': 0.8, 'op_types': ['default'] }]
 pruner = LevelPruner(model, config_list)
 pruner.compress()
 ```
 
-The 'default' op_type stands for the module types defined in [default_layers.py](https://github.com/microsoft/nni/blob/v1.9/src/sdk/pynni/nni/compression/torch/default_layers.py) for pytorch.
+The 'default' op_type stands for the module types defined in [default_layers.py](https://github.com/microsoft/nni/blob/v1.9/src/sdk/pynni/nni/compression/pytorch/default_layers.py) for pytorch.
 
 Therefore ```{ 'sparsity': 0.8, 'op_types': ['default'] }```means that **all layers with specified op_types will be compressed with the same 0.8 sparsity**. When ```pruner.compress()``` called, the model is compressed with masks and after that you can normally fine tune this model and **pruned weights won't be updated** which have been masked.
 
@@ -71,7 +71,7 @@ Then we need to modify our codes for few lines
 
 ```python
 import nni
-from nni.compression.torch import *
+from nni.algorithms.compression.pytorch.pruning import *
 params = nni.get_parameters()
 conv0_sparsity = params['prune_method']['conv0_sparsity']
 conv1_sparsity = params['prune_method']['conv1_sparsity']

docs/en_US/Compression/CompressionReference.md

@@ -7,34 +7,34 @@
 ## Sensitivity Utilities
 
 ```eval_rst
-..  autoclass:: nni.compression.torch.utils.sensitivity_analysis.SensitivityAnalysis
+..  autoclass:: nni.compression.pytorch.utils.sensitivity_analysis.SensitivityAnalysis
     :members:
 
 ```
 
 ## Topology Utilities
 
 ```eval_rst
-..  autoclass:: nni.compression.torch.utils.shape_dependency.ChannelDependency
+..  autoclass:: nni.compression.pytorch.utils.shape_dependency.ChannelDependency
     :members:
 
-..  autoclass:: nni.compression.torch.utils.shape_dependency.GroupDependency
+..  autoclass:: nni.compression.pytorch.utils.shape_dependency.GroupDependency
     :members:
 
-..  autoclass:: nni.compression.torch.utils.mask_conflict.CatMaskPadding
+..  autoclass:: nni.compression.pytorch.utils.mask_conflict.CatMaskPadding
     :members:
 
-..  autoclass:: nni.compression.torch.utils.mask_conflict.GroupMaskConflict
+..  autoclass:: nni.compression.pytorch.utils.mask_conflict.GroupMaskConflict
     :members:
 
-..  autoclass:: nni.compression.torch.utils.mask_conflict.ChannelMaskConflict
+..  autoclass:: nni.compression.pytorch.utils.mask_conflict.ChannelMaskConflict
     :members:
 
 ```
 
 ## Model FLOPs/Parameters Counter
 
 ```eval_rst
-..  autofunction:: nni.compression.torch.utils.counter.count_flops_params
+..  autofunction:: nni.compression.pytorch.utils.counter.count_flops_params
 
-```
+```

docs/en_US/Compression/CompressionUtils.md

@@ -13,7 +13,7 @@ First, we provide a sensitivity analysis tool (**SensitivityAnalysis**) for user
 
 The following codes show the basic usage of the SensitivityAnalysis.
 ```python
-from nni.compression.torch.utils.sensitivity_analysis import SensitivityAnalysis
+from nni.compression.pytorch.utils.sensitivity_analysis import SensitivityAnalysis
 
 def val(model):
     model.eval()
@@ -88,7 +88,7 @@ If the layers have channel dependency are assigned with different sparsities (he
 
 #### Usage
 ```python
-from nni.compression.torch.utils.shape_dependency import ChannelDependency
+from nni.compression.pytorch.utils.shape_dependency import ChannelDependency
 data = torch.ones(1, 3, 224, 224).cuda()
 channel_depen = ChannelDependency(net, data)
 channel_depen.export('dependency.csv')
@@ -116,7 +116,7 @@ Set 12,layer4.1.conv1
 When the masks of different layers in a model have conflict (for example, assigning different sparsities for the layers that have channel dependency), we can fix the mask conflict by MaskConflict. Specifically, the MaskConflict loads the masks exported by the pruners(L1FilterPruner, etc), and check if there is mask conflict, if so, MaskConflict sets the conflicting masks to the same value.
 
 ```
-from nni.compression.torch.utils.mask_conflict import fix_mask_conflict
+from nni.compression.pytorch.utils.mask_conflict import fix_mask_conflict
 fixed_mask = fix_mask_conflict('./resnet18_mask', net, data)
 ```
 
@@ -125,10 +125,10 @@ We provide a model counter for calculating the model FLOPs and parameters. This
 
 ### Usage
 ```
-from nni.compression.torch.utils.counter import count_flops_params
+from nni.compression.pytorch.utils.counter import count_flops_params
 
 # Given input size (1, 1, 28, 28) 
 flops, params = count_flops_params(model, (1, 1, 28, 28))
 # Format output size to M (i.e., 10^6)
 print(f'FLOPs: {flops/1e6:.3f}M,  Params: {params/1e6:.3f}M)
-```
+```