Fix integration tests

examples/model_compress/model_prune_torch.py

@@ -53,13 +53,13 @@
             'op_types': ['Conv2d']
         }]
     },
-    'l1': {
+    'l1filter': {
         'dataset_name': 'cifar10',
         'model_name': 'vgg16',
         'pruner_class': L1FilterPruner,
         'config_list': [{
             'sparsity': 0.5,
-            'op_types': ['default'],
+            'op_types': ['Conv2d'],
             'op_names': ['feature.0', 'feature.24', 'feature.27', 'feature.30', 'feature.34', 'feature.37']
         }]
     },
@@ -69,7 +69,7 @@
         'pruner_class': ActivationMeanRankFilterPruner,
         'config_list': [{
             'sparsity': 0.5,
-            'op_types': ['default'],
+            'op_types': ['Conv2d'],
             'op_names': ['feature.0', 'feature.24', 'feature.27', 'feature.30', 'feature.34', 'feature.37']
         }]
     },

examples/model_compress/model_speedup.py

@@ -25,7 +25,7 @@
         'model_name': 'vgg16',
         'device': 'cuda',
         'input_shape': [64, 3, 32, 32],
-        'masks_file': './checkpoints/mask_vgg16_cifar10_l1.pth'
+        'masks_file': './checkpoints/mask_vgg16_cifar10_l1filter.pth'
     },
     'fpgm': {
         'model_name': 'naive',

examples/trials/mnist-pytorch/mnist.py

@@ -15,14 +15,6 @@
 import torch.optim as optim
 from torchvision import datasets, transforms
 
-# Temporary patch this example until the MNIST dataset download issue get resolved
-# https://github.com/pytorch/vision/issues/1938
-import urllib
-
-opener = urllib.request.build_opener()
-opener.addheaders = [('User-agent', 'Mozilla/5.0')]
-urllib.request.install_opener(opener)
-
 logger = logging.getLogger('mnist_AutoML')
 
 
@@ -48,6 +40,8 @@ def forward(self, x):
 def train(args, model, device, train_loader, optimizer, epoch):
     model.train()
     for batch_idx, (data, target) in enumerate(train_loader):
+        if (args['batch_num'] is not None) and batch_idx >= args['batch_num']:
+            break
         data, target = data.to(device), target.to(device)
         optimizer.zero_grad()
         output = model(data)
@@ -119,16 +113,15 @@ def main(args):
         train(args, model, device, train_loader, optimizer, epoch)
         test_acc = test(args, model, device, test_loader)
 
-        if epoch < args['epochs']:
-            # report intermediate result
-            nni.report_intermediate_result(test_acc)
-            logger.debug('test accuracy %g', test_acc)
-            logger.debug('Pipe send intermediate result done.')
-        else:
-            # report final result
-            nni.report_final_result(test_acc)
-            logger.debug('Final result is %g', test_acc)
-            logger.debug('Send final result done.')
+        # report intermediate result
+        nni.report_intermediate_result(test_acc)
+        logger.debug('test accuracy %g', test_acc)
+        logger.debug('Pipe send intermediate result done.')
+
+    # report final result
+    nni.report_final_result(test_acc)
+    logger.debug('Final result is %g', test_acc)
+    logger.debug('Send final result done.')
 
 
 def get_params():
@@ -138,6 +131,7 @@ def get_params():
                         default='/tmp/pytorch/mnist/input_data', help="data directory")
     parser.add_argument('--batch_size', type=int, default=64, metavar='N',
                         help='input batch size for training (default: 64)')
+    parser.add_argument("--batch_num", type=int, default=None)
     parser.add_argument("--hidden_size", type=int, default=512, metavar='N',
                         help='hidden layer size (default: 512)')
     parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
@@ -165,6 +159,7 @@ def get_params():
         logger.debug(tuner_params)
         params = vars(get_params())
         params.update(tuner_params)
+        print(params)
         main(params)
     except Exception as exception:
         logger.exception(exception)

test/config/examples/mnist-pytorch.yml

@@ -1,7 +1,7 @@
 authorName: nni
 experimentName: default_test
 maxExecDuration: 15m
-maxTrialNum: 4
+maxTrialNum: 2
 trialConcurrency: 2
 searchSpacePath: ./mnist_pytorch_search_space.json
 
@@ -13,7 +13,7 @@ assessor:
     optimize_mode: maximize
 trial:
   codeDir: ../../../examples/trials/mnist-pytorch
-  command: python3 mnist.py  --epochs 1
+  command: python3 mnist.py  --epochs 1 --batch_num 10
   gpuNum: 0
 
 useAnnotation: false

test/config/integration_tests.yml

@@ -77,16 +77,15 @@ testCases:
     kwargs:
       expected_result_file: expected_metrics.json
 
-# to be enabled
-#- name: metrics-dict
-#  configFile: test/config/metrics_test/config_dict_metrics.yml
-#  config:
-#    maxTrialNum: 1
-#    trialConcurrency: 1
-#  validator:
-#    class: MetricsValidator
-#    kwargs:
-#      expected_result_file: expected_metrics_dict.json
+- name: metrics-dict
+  configFile: test/config/metrics_test/config_dict_metrics.yml
+  config:
+    maxTrialNum: 1
+    trialConcurrency: 1
+  validator:
+    class: MetricsValidator
+    kwargs:
+      expected_result_file: expected_metrics_dict.json
 
 - name: nnicli
   configFile: test/config/examples/sklearn-regression.yml

test/config/pr_tests.yml

@@ -31,16 +31,15 @@ testCases:
     kwargs:
       expected_result_file: expected_metrics.json
 
-# to be enabled
-#- name: metrics-dict
-#  configFile: test/config/metrics_test/config_dict_metrics.yml
-#  config:
-#    maxTrialNum: 1
-#    trialConcurrency: 1
-#  validator:
-#    class: MetricsValidator
-#    kwargs:
-#      expected_result_file: expected_metrics_dict.json
+- name: metrics-dict
+  configFile: test/config/metrics_test/config_dict_metrics.yml
+  config:
+    maxTrialNum: 1
+    trialConcurrency: 1
+  validator:
+    class: MetricsValidator
+    kwargs:
+      expected_result_file: expected_metrics_dict.json
 
 - name: nnicli
   configFile: test/config/examples/sklearn-regression.yml

test/nni_test/nnitest/validators.py

@@ -35,8 +35,8 @@ def check_metrics(self, nni_source_dir, **kwargs):
             assert len(trial_final_result) == 1, 'there should be 1 final result'
             assert trial_final_result[0] == expected_metrics['final_result']
             # encode dict/number into json string to compare them in set
-            assert set([json.dumps(x) for x in trial_intermediate_result]) \
-                == set([json.dumps(x) for x in expected_metrics['intermediate_result']])
+            assert set([json.dumps(x, sort_keys=True) for x in trial_intermediate_result]) \
+                == set([json.dumps(x, sort_keys=True) for x in expected_metrics['intermediate_result']])
 
     def get_metric_results(self, metrics):
         intermediate_result = {}

test/scripts/model_compression.sh

@@ -6,22 +6,14 @@ echo ""
 echo "===========================Testing: pruning and speedup==========================="
 cd ${CWD}/../examples/model_compress
 
-echo "testing slim pruning and speedup..."
-python3 model_prune_torch.py --pruner_name slim --pretrain_epochs 1 --prune_epochs 1
-python3 model_speedup.py --example_name slim --model_checkpoint ./checkpoints/pruned_vgg19_cifar10_slim.pth \
-    --masks_file ./checkpoints/mask_vgg19_cifar10_slim.pth
-
-echo "testing l1 pruning and speedup..."
-python3 model_prune_torch.py --pruner_name l1 --pretrain_epochs 1 --prune_epochs 1
-python3 model_speedup.py --example_name l1filter --model_checkpoint ./checkpoints/pruned_vgg16_cifar10_l1.pth \
-    --masks_file ./checkpoints/mask_vgg16_cifar10_l1.pth
-
-echo "testing apoz pruning and speedup..."
-python3 model_prune_torch.py --pruner_name apoz --pretrain_epochs 1 --prune_epochs 1
-python3 model_speedup.py --example_name apoz --model_checkpoint ./checkpoints/pruned_vgg16_cifar10_apoz.pth \
-    --masks_file ./checkpoints/mask_vgg16_cifar10_apoz.pth
-
-for name in level fpgm mean_activation
+for name in fpgm slim l1filter apoz
+do
+    echo "testing $name pruning and speedup..."
+    python3 model_prune_torch.py --pruner_name $name --pretrain_epochs 1 --prune_epochs 1
+    python3 model_speedup.py --example_name $name
+done
+
+for name in level mean_activation
 do
     echo "testing $name pruning..."
     python3 model_prune_torch.py --pruner_name $name --pretrain_epochs 1 --prune_epochs 1