Rename x-axis label of Intermediate result "Trial No.x"

examples/model_compress/model_prune_torch.py

@@ -142,10 +142,10 @@ def create_model(model_name='naive'):
     else:
         return VGG(19)
 
-def create_pruner(model, pruner_name, optimizer=None):
+def create_pruner(model, pruner_name):
     pruner_class = prune_config[pruner_name]['pruner_class']
     config_list = prune_config[pruner_name]['config_list']
-    return pruner_class(model, config_list, optimizer)
+    return pruner_class(model, config_list)
 
 def train(model, device, train_loader, optimizer):
     model.train()
@@ -179,7 +179,6 @@ def test(model, device, test_loader):
 
 def main(args):
     device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
-    os.makedirs(args.checkpoints_dir, exist_ok=True)
 
     model_name = prune_config[args.pruner_name]['model_name']
     dataset_name = prune_config[args.pruner_name]['dataset_name']
@@ -204,6 +203,8 @@ def main(args):
 
     print('start model pruning...')
 
+    if not os.path.exists(args.checkpoints_dir):
+        os.makedirs(args.checkpoints_dir)
     model_path = os.path.join(args.checkpoints_dir, 'pruned_{}_{}_{}.pth'.format(model_name, dataset_name, args.pruner_name))
     mask_path = os.path.join(args.checkpoints_dir, 'mask_{}_{}_{}.pth'.format(model_name, dataset_name, args.pruner_name))
 

examples/trials/mnist-pytorch/mnist.py

@@ -15,13 +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')
 

src/sdk/pynni/nni/compression/torch/activation_rank_filter_pruners.py

@@ -16,7 +16,7 @@ class ActivationRankFilterPruner(Pruner):
     to achieve a preset level of network sparsity.
     """
 
-    def __init__(self, model, config_list, optimizer=None, activation='relu', statistics_batch_num=1):
+    def __init__(self, model, config_list, optimizer, activation='relu', statistics_batch_num=1):
         """
         Parameters
         ----------
@@ -25,8 +25,6 @@ def __init__(self, model, config_list, optimizer=None, activation='relu', statis
         config_list : list
             support key for each list item:
                 - sparsity: percentage of convolutional filters to be pruned.
-        optimizer: torch.optim.Optimizer
-            Optimizer used to train model
         activation : str
             Activation function
         statistics_batch_num : int
@@ -107,7 +105,7 @@ class ActivationAPoZRankFilterPruner(ActivationRankFilterPruner):
     https://arxiv.org/abs/1607.03250
     """
 
-    def __init__(self, model, config_list, optimizer=None, activation='relu', statistics_batch_num=1):
+    def __init__(self, model, config_list, optimizer, activation='relu', statistics_batch_num=1):
         """
         Parameters
         ----------
@@ -116,8 +114,6 @@ def __init__(self, model, config_list, optimizer=None, activation='relu', statis
         config_list : list
             support key for each list item:
                 - sparsity: percentage of convolutional filters to be pruned.
-        optimizer: torch.optim.Optimizer
-            Optimizer used to train model
         activation : str
             Activation function
         statistics_batch_num : int
@@ -181,7 +177,7 @@ class ActivationMeanRankFilterPruner(ActivationRankFilterPruner):
     https://arxiv.org/abs/1611.06440
     """
 
-    def __init__(self, model, config_list, optimizer=None, activation='relu', statistics_batch_num=1):
+    def __init__(self, model, config_list, optimizer, activation='relu', statistics_batch_num=1):
         """
         Parameters
         ----------
@@ -190,8 +186,6 @@ def __init__(self, model, config_list, optimizer=None, activation='relu', statis
         config_list : list
             support key for each list item:
                 - sparsity: percentage of convolutional filters to be pruned.
-        optimizer: torch.optim.Optimizer
-            Optimizer used to train model
         activation : str
             Activation function
         statistics_batch_num : int

src/sdk/pynni/nni/compression/torch/compressor.py

@@ -27,7 +27,7 @@ class Compressor:
     Abstract base PyTorch compressor
     """
 
-    def __init__(self, model, config_list, optimizer=None):
+    def __init__(self, model, config_list, optimizer):
         """
         Record necessary info in class members
 
@@ -235,8 +235,7 @@ def new_step(_, *args, **kwargs):
                     task()
                 return output
             return new_step
-        if self.optimizer is not None:
-            self.optimizer.step = types.MethodType(patch_step(self.optimizer.step), self.optimizer)
+        self.optimizer.step = types.MethodType(patch_step(self.optimizer.step), self.optimizer)
 
 class PrunerModuleWrapper(torch.nn.Module):
     def __init__(self, module, module_name, module_type, config, pruner):
@@ -291,10 +290,9 @@ class Pruner(Compressor):
 
     """
 
-    def __init__(self, model, config_list, optimizer=None):
+    def __init__(self, model, config_list, optimizer):
         super().__init__(model, config_list, optimizer)
-        if optimizer is not None:
-            self.patch_optimizer(self.update_mask)
+        self.patch_optimizer(self.update_mask)
 
     def compress(self):
         self.update_mask()

src/sdk/pynni/nni/compression/torch/pruners.py

@@ -16,16 +16,14 @@ class LevelPruner(Pruner):
     Prune to an exact pruning level specification
     """
 
-    def __init__(self, model, config_list, optimizer=None):
+    def __init__(self, model, config_list, optimizer):
         """
         Parameters
         ----------
         model : torch.nn.module
             Model to be pruned
         config_list : list
             List on pruning configs
-        optimizer: torch.optim.Optimizer
-            Optimizer used to train model
         """
 
         super().__init__(model, config_list, optimizer)
@@ -80,13 +78,9 @@ def __init__(self, model, config_list, optimizer):
             Model to be pruned
         config_list : list
             List on pruning configs
-        optimizer: torch.optim.Optimizer
-            Optimizer used to train model
         """
 
         super().__init__(model, config_list, optimizer)
-        assert isinstance(optimizer, torch.optim.Optimizer), "AGP pruner is an iterative pruner, please pass optimizer of the model to it"
-
         self.now_epoch = 0
         self.set_wrappers_attribute("if_calculated", False)
 
@@ -182,17 +176,13 @@ class SlimPruner(Pruner):
     https://arxiv.org/pdf/1708.06519.pdf
     """
 
-    def __init__(self, model, config_list, optimizer=None):
+    def __init__(self, model, config_list, optimizer):
         """
         Parameters
         ----------
-        model : torch.nn.module
-            Model to be pruned
         config_list : list
             support key for each list item:
                 - sparsity: percentage of convolutional filters to be pruned.
-        optimizer: torch.optim.Optimizer
-            Optimizer used to train model
         """
 
         super().__init__(model, config_list, optimizer)
@@ -254,7 +244,7 @@ class LotteryTicketPruner(Pruner):
     5. Repeat step 2, 3, and 4.
     """
 
-    def __init__(self, model, config_list, optimizer=None, lr_scheduler=None, reset_weights=True):
+    def __init__(self, model, config_list, optimizer, lr_scheduler=None, reset_weights=True):
         """
         Parameters
         ----------

src/sdk/pynni/nni/compression/torch/weight_rank_filter_pruners.py

@@ -15,7 +15,7 @@ class WeightRankFilterPruner(Pruner):
     importance criterion in convolution layers to achieve a preset level of network sparsity.
     """
 
-    def __init__(self, model, config_list, optimizer=None):
+    def __init__(self, model, config_list, optimizer):
         """
         Parameters
         ----------
@@ -24,8 +24,6 @@ def __init__(self, model, config_list, optimizer=None):
         config_list : list
             support key for each list item:
                 - sparsity: percentage of convolutional filters to be pruned.
-        optimizer: torch.optim.Optimizer
-            Optimizer used to train model
         """
 
         super().__init__(model, config_list, optimizer)
@@ -85,7 +83,7 @@ class L1FilterPruner(WeightRankFilterPruner):
     https://arxiv.org/abs/1608.08710
     """
 
-    def __init__(self, model, config_list, optimizer=None):
+    def __init__(self, model, config_list, optimizer):
         """
         Parameters
         ----------
@@ -94,8 +92,6 @@ def __init__(self, model, config_list, optimizer=None):
         config_list : list
             support key for each list item:
                 - sparsity: percentage of convolutional filters to be pruned.
-        optimizer: torch.optim.Optimizer
-            Optimizer used to train model
         """
 
         super().__init__(model, config_list, optimizer)
@@ -135,7 +131,7 @@ class L2FilterPruner(WeightRankFilterPruner):
     smallest L2 norm of the weights.
     """
 
-    def __init__(self, model, config_list, optimizer=None):
+    def __init__(self, model, config_list, optimizer):
         """
         Parameters
         ----------
@@ -144,8 +140,6 @@ def __init__(self, model, config_list, optimizer=None):
         config_list : list
             support key for each list item:
                 - sparsity: percentage of convolutional filters to be pruned.
-        optimizer: torch.optim.Optimizer
-            Optimizer used to train model
         """
 
         super().__init__(model, config_list, optimizer)
@@ -193,11 +187,8 @@ def __init__(self, model, config_list, optimizer):
         config_list: list
             support key for each list item:
                 - sparsity: percentage of convolutional filters to be pruned.
-        optimizer: torch.optim.Optimizer
-            Optimizer used to train model
         """
         super().__init__(model, config_list, optimizer)
-        assert isinstance(optimizer, torch.optim.Optimizer), "FPGM pruner is an iterative pruner, please pass optimizer of the model to it"
 
     def get_mask(self, base_mask, weight, num_prune):
         """

src/sdk/pynni/nni/curvefitting_assessor/curvefitting_assessor.py

@@ -4,7 +4,6 @@
 import logging
 import datetime
 from nni.assessor import Assessor, AssessResult
-from nni.utils import extract_scalar_history
 from .model_factory import CurveModel
 
 logger = logging.getLogger('curvefitting_Assessor')
@@ -92,11 +91,10 @@ def assess_trial(self, trial_job_id, trial_history):
         Exception
             unrecognize exception in curvefitting_assessor
         """
-        scalar_trial_history = extract_scalar_history(trial_history)
-        self.trial_history = scalar_trial_history
+        self.trial_history = trial_history
         if not self.set_best_performance:
             return AssessResult.Good
-        curr_step = len(scalar_trial_history)
+        curr_step = len(trial_history)
         if curr_step < self.start_step:
             return AssessResult.Good
 
@@ -108,7 +106,7 @@ def assess_trial(self, trial_job_id, trial_history):
             start_time = datetime.datetime.now()
             # Predict the final result
             curvemodel = CurveModel(self.target_pos)
-            predict_y = curvemodel.predict(scalar_trial_history)
+            predict_y = curvemodel.predict(trial_history)
             logger.info('Prediction done. Trial job id = %s. Predict value = %s', trial_job_id, predict_y)
             if predict_y is None:
                 logger.info('wait for more information to predict precisely')

src/sdk/pynni/nni/medianstop_assessor/medianstop_assessor.py

@@ -3,7 +3,6 @@
 
 import logging
 from nni.assessor import Assessor, AssessResult
-from nni.utils import extract_scalar_history
 
 logger = logging.getLogger('medianstop_Assessor')
 
@@ -92,12 +91,20 @@ def assess_trial(self, trial_job_id, trial_history):
         if curr_step < self._start_step:
             return AssessResult.Good
 
-        scalar_trial_history = extract_scalar_history(trial_history)
-        self._update_data(trial_job_id, scalar_trial_history)
+        try:
+            num_trial_history = [float(ele) for ele in trial_history]
+        except (TypeError, ValueError) as error:
+            logger.warning('incorrect data type or value:')
+            logger.exception(error)
+        except Exception as error:
+            logger.warning('unrecognized exception in medianstop_assessor:')
+            logger.exception(error)
+
+        self._update_data(trial_job_id, num_trial_history)
         if self._high_better:
-            best_history = max(scalar_trial_history)
+            best_history = max(trial_history)
         else:
-            best_history = min(scalar_trial_history)
+            best_history = min(trial_history)
 
         avg_array = []
         for id_ in self._completed_avg_history:

src/sdk/pynni/nni/msg_dispatcher.py

@@ -234,5 +234,4 @@ def _earlystop_notify_tuner(self, data):
         if multi_thread_enabled():
             self._handle_final_metric_data(data)
         else:
-            data['value'] = to_json(data['value'])
             self.enqueue_command(CommandType.ReportMetricData, data)

src/sdk/pynni/nni/utils.py

@@ -62,13 +62,6 @@ def extract_scalar_reward(value, scalar_key='default'):
     """
     Extract scalar reward from trial result.
 
-    Parameters
-    ----------
-    value : int, float, dict
-        the reported final metric data
-    scalar_key : str
-        the key name that indicates the numeric number
-
     Raises
     ------
     RuntimeError
@@ -85,26 +78,6 @@ def extract_scalar_reward(value, scalar_key='default'):
     return reward
 
 
-def extract_scalar_history(trial_history, scalar_key='default'):
-    """
-    Extract scalar value from a list of intermediate results.
-
-    Parameters
-    ----------
-    trial_history : list
-        accumulated intermediate results of a trial
-    scalar_key : str
-        the key name that indicates the numeric number
-
-    Raises
-    ------
-    RuntimeError
-        Incorrect final result: the final result should be float/int,
-        or a dict which has a key named "default" whose value is float/int.
-    """
-    return [extract_scalar_reward(ele, scalar_key) for ele in trial_history]
-
-
 def convert_dict2tuple(value):
     """
     convert dict type to tuple to solve unhashable problem.
@@ -117,9 +90,7 @@ def convert_dict2tuple(value):
 
 
 def init_dispatcher_logger():
-    """
-    Initialize dispatcher logging configuration
-    """
+    """ Initialize dispatcher logging configuration"""
     logger_file_path = 'dispatcher.log'
     if dispatcher_env_vars.NNI_LOG_DIRECTORY is not None:
         logger_file_path = os.path.join(dispatcher_env_vars.NNI_LOG_DIRECTORY, logger_file_path)