[Retiarii] refactor based on the new launch approach (#3185)

test/convert_test/base_mnasnet.py → test/retiarii_test/mnasnet/base_mnasnet.py

@@ -9,6 +9,7 @@ import sys
 from pathlib import Path
 sys.path.append(str(Path(__file__).resolve().parents[2]))
 import nni.retiarii.nn.pytorch as nn
+from nni.retiarii import register_module
 
 # Paper suggests 0.9997 momentum, for TensorFlow. Equivalent PyTorch momentum is
 # 1.0 - tensorflow.
@@ -109,7 +110,7 @@ def _get_depths(depths, alpha):
     rather than down. """
     return [_round_to_multiple_of(depth * alpha, 8) for depth in depths]
 
-
+@register_module()
 class MNASNet(nn.Module):
     """ MNASNet, as described in https://arxiv.org/pdf/1807.11626.pdf. This
     implements the B1 variant of the model.
@@ -126,8 +127,7 @@ class MNASNet(nn.Module):
 
     def __init__(self, alpha, depths, convops, kernel_sizes, num_layers,
                  skips, num_classes=1000, dropout=0.2):
-        super(MNASNet, self).__init__(alpha, depths, convops, kernel_sizes, num_layers,
-                 skips, num_classes, dropout)
+        super(MNASNet, self).__init__()
         assert alpha > 0.0
         assert len(depths) == len(convops) == len(kernel_sizes) == len(num_layers) == len(skips) == 7
         self.alpha = alpha

test/convert_test/test.py → test/retiarii_test/mnasnet/test.py

@@ -3,20 +3,11 @@ import sys
 import torch
 from pathlib import Path
 
-sys.path.append(str(Path(__file__).resolve().parents[2]))
-from nni.retiarii.converter.graph_gen import convert_to_graph
-from nni.retiarii.converter.visualize import visualize_model
-from nni.retiarii.codegen.pytorch import model_to_pytorch_script
-
-from nni.retiarii import nn
 from nni.retiarii.trainer import PyTorchImageClassificationTrainer
-from nni.retiarii.utils import TraceClassArguments
 
 from base_mnasnet import MNASNet
-from nni.experiment import RetiariiExperiment, RetiariiExpConfig
+from nni.retiarii.experiment import RetiariiExperiment, RetiariiExeConfig
 
-#from simple_strategy import SimpleStrategy
-#from tpe_strategy import TPEStrategy
 from nni.retiarii.strategies import TPEStrategy
 from mutator import BlockMutator
 
@@ -27,7 +18,6 @@ if __name__ == '__main__':
     _DEFAULT_KERNEL_SIZES = [3, 3, 5, 5, 3, 5, 3]
     _DEFAULT_NUM_LAYERS = [1, 3, 3, 3, 2, 4, 1]
 
-    with TraceClassArguments() as tca:
     base_model = MNASNet(0.5, _DEFAULT_DEPTHS, _DEFAULT_CONVOPS, _DEFAULT_KERNEL_SIZES,
                     _DEFAULT_NUM_LAYERS, _DEFAULT_SKIPS)
     trainer = PyTorchImageClassificationTrainer(base_model, dataset_cls="CIFAR10",
@@ -36,15 +26,6 @@ if __name__ == '__main__':
             optimizer_kwargs={"lr": 1e-3},
             trainer_kwargs={"max_epochs": 1})
 
-    '''script_module = torch.jit.script(base_model)
-    model = convert_to_graph(script_module, base_model, tca.recorded_arguments)
-    code_script = model_to_pytorch_script(model)
-    print(code_script)
-    print("Model: ", model)
-    graph_ir = model._dump()
-    print(graph_ir)
-    visualize_model(graph_ir)'''
-
     # new interface
     applied_mutators = []
     applied_mutators.append(BlockMutator('mutable_0'))
@@ -52,11 +33,12 @@ if __name__ == '__main__':
 
     simple_startegy = TPEStrategy()
 
-    exp = RetiariiExperiment(base_model, trainer, applied_mutators, simple_startegy, tca)
+    exp = RetiariiExperiment(base_model, trainer, applied_mutators, simple_startegy)
 
-    exp_config = RetiariiExpConfig.create_template('local')
+    exp_config = RetiariiExeConfig('local')
     exp_config.experiment_name = 'mnasnet_search'
     exp_config.trial_concurrency = 2
     exp_config.max_trial_number = 10
+    exp_config.training_service.use_active_gpu = False
 
     exp.run(exp_config, 8081, debug=True)