pdarts update (#1753)

docs/en_US/AdvancedFeature/MultiPhase.md

@@ -79,7 +79,7 @@ With this information, the tuner could know which trial is requesting a configur
 
 ### Tuners support multi-phase experiments:
 
-[TPE](../Tuner/HyperoptTuner.md), [Random](../Tuner/HyperoptTuner.md), [Anneal](../Tuner/HyperoptTuner.md), [Evolution](../Tuner/EvolutionTuner.md), [SMAC](../Tuner/SmacTuner.md), [NetworkMorphism](../Tuner/NetworkmorphismTuner.md), [MetisTuner](../Tuner/MetisTuner.md), [BOHB](../Tuner/BohbAdvisor.md), [Hyperband](../Tuner/HyperbandAdvisor.md), [ENAS tuner](https://github.com/countif/enas_nni/blob/master/nni/examples/tuners/enas/nni_controller_ptb.py).
+[TPE](../Tuner/HyperoptTuner.md), [Random](../Tuner/HyperoptTuner.md), [Anneal](../Tuner/HyperoptTuner.md), [Evolution](../Tuner/EvolutionTuner.md), [SMAC](../Tuner/SmacTuner.md), [NetworkMorphism](../Tuner/NetworkmorphismTuner.md), [MetisTuner](../Tuner/MetisTuner.md), [BOHB](../Tuner/BohbAdvisor.md), [Hyperband](../Tuner/HyperbandAdvisor.md).
 
 ### Training services support multi-phase experiment:
 [Local Machine](../TrainingService/LocalMode.md), [Remote Servers](../TrainingService/RemoteMachineMode.md), [OpenPAI](../TrainingService/PaiMode.md)

docs/en_US/NAS/Overview.md

-# Neural Architecture Search (NAS) on NNI
-
-Automatic neural architecture search is taking an increasingly important role on finding better models. Recent research works have proved the feasibility of automatic NAS, and also found some models that could beat manually designed and tuned models. Some of representative works are [NASNet][2], [ENAS][1], [DARTS][3], [Network Morphism][4], and [Evolution][5]. There are new innovations keeping emerging.
-
-However, it takes great efforts to implement NAS algorithms, and it is hard to reuse code base of existing algorithms in new one. To facilitate NAS innovations (e.g., design and implement new NAS models, compare different NAS models side-by-side), an easy-to-use and flexible programming interface is crucial.
-
-With this motivation, our ambition is to provide a unified architecture in NNI, to accelerate innovations on NAS, and apply state-of-art algorithms on real world problems faster.
-
-## Supported algorithms
-
-NNI supports below NAS algorithms now, and being adding more. User can reproduce an algorithm, or use it on owned dataset. we also encourage user to implement other algorithms with [NNI API](#use-nni-api), to benefit more people.
-
-Note, these algorithms run standalone without nnictl, and supports PyTorch only.
-
-### DARTS
-
-The main contribution of [DARTS: Differentiable Architecture Search][3] on algorithm is to introduce a novel algorithm for differentiable network architecture search on bilevel optimization.
-
-#### Usage
-
-```bash
-### In case NNI code is not cloned.
-git clone https://github.com/Microsoft/nni.git
-
-cd examples/nas/darts
-python search.py
-```
-
-### P-DARTS
-
-[Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation](https://arxiv.org/abs/1904.12760) bases on DARTS(#DARTS). It main contribution on algorithm is to introduce an efficient algorithm which allows the depth of searched architectures to grow gradually during the training procedure.
-
-#### Usage
-
-```bash
-### In case NNI code is not cloned.
-git clone https://github.com/Microsoft/nni.git
-
-cd examples/nas/pdarts
-python main.py
-```
-
-## Use NNI API
-
-NOTE, we are trying to support various NAS algorithms with unified programming interface, and it's in very experimental stage. It means the current programing interface may be updated significantly.
-
-*previous [NAS annotation](../AdvancedFeature/GeneralNasInterfaces.md) interface will be deprecated soon.*
-
-### Programming interface
-
-The programming interface of designing and searching a model is often demanded in two scenarios.
-
-1. When designing a neural network, there may be multiple operation choices on a layer, sub-model, or connection, and it's undetermined which one or combination performs  best. So it needs an easy way to express the candidate layers or sub-models.
-2. When applying NAS on a neural network, it needs an unified way to express the search space of architectures, so that it doesn't need to update trial code for different searching algorithms.
-
-NNI proposed API is [here](https://github.com/microsoft/nni/tree/dev-nas-refactor/src/sdk/pynni/nni/nas/pytorch). And [here](https://github.com/microsoft/nni/tree/dev-nas-refactor/examples/nas/darts) is an example of NAS implementation, which bases on NNI proposed interface.
-
-[1]: https://arxiv.org/abs/1802.03268
-[2]: https://arxiv.org/abs/1707.07012
-[3]: https://arxiv.org/abs/1806.09055
-[4]: https://arxiv.org/abs/1806.10282
-[5]: https://arxiv.org/abs/1703.01041
+# Neural Architecture Search (NAS) on NNI
+
+Automatic neural architecture search is taking an increasingly important role on finding better models. Recent research works have proved the feasibility of automatic NAS, and also found some models that could beat manually designed and tuned models. Some of representative works are [NASNet][2], [ENAS][1], [DARTS][3], [Network Morphism][4], and [Evolution][5]. There are new innovations keeping emerging.
+
+However, it takes great efforts to implement NAS algorithms, and it is hard to reuse code base of existing algorithms in new one. To facilitate NAS innovations (e.g., design and implement new NAS models, compare different NAS models side-by-side), an easy-to-use and flexible programming interface is crucial.
+
+With this motivation, our ambition is to provide a unified architecture in NNI, to accelerate innovations on NAS, and apply state-of-art algorithms on real world problems faster.
+
+## Supported algorithms
+
+NNI supports below NAS algorithms now and being adding more. User can reproduce an algorithm or use it on owned dataset. we also encourage user to implement other algorithms with [NNI API](#use-nni-api), to benefit more people.
+
+Note, these algorithms run standalone without nnictl, and supports PyTorch only.
+
+### Dependencies
+
+* Install latest NNI
+* PyTorch 1.2+
+* git
+
+### DARTS
+
+The main contribution of [DARTS: Differentiable Architecture Search][3] on algorithm is to introduce a novel algorithm for differentiable network architecture search on bilevel optimization.
+
+#### Usage
+
+```bash
+# In case NNI code is not cloned. If the code is cloned already, ignore this line and enter code folder.
+git clone https://github.com/Microsoft/nni.git
+
+# search the best architecture
+cd examples/nas/darts
+python3 search.py
+
+# train the best architecture
+python3 retrain.py --arc-checkpoint ./checkpoints/epoch_49.json
+```
+
+### P-DARTS
+
+[Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation](https://arxiv.org/abs/1904.12760) bases on [DARTS](#DARTS). It's contribution on algorithm is to introduce an efficient algorithm which allows the depth of searched architectures to grow gradually during the training procedure.
+
+#### Usage
+
+```bash
+# In case NNI code is not cloned. If the code is cloned already, ignore this line and enter code folder.
+git clone https://github.com/Microsoft/nni.git
+
+# search the best architecture
+cd examples/nas/pdarts
+python3 search.py
+
+# train the best architecture, it's the same progress as darts.
+cd examples/nas/darts
+python3 retrain.py --arc-checkpoint ./checkpoints/epoch_2.json
+```
+
+## Use NNI API
+
+NOTE, we are trying to support various NAS algorithms with unified programming interface, and it's in very experimental stage. It means the current programing interface may be updated significantly.
+
+*previous [NAS annotation](../AdvancedFeature/GeneralNasInterfaces.md) interface will be deprecated soon.*
+
+### Programming interface
+
+The programming interface of designing and searching a model is often demanded in two scenarios.
+
+1. When designing a neural network, there may be multiple operation choices on a layer, sub-model, or connection, and it's undetermined which one or combination performs  best. So, it needs an easy way to express the candidate layers or sub-models.
+2. When applying NAS on a neural network, it needs an unified way to express the search space of architectures, so that it doesn't need to update trial code for different searching algorithms.
+
+NNI proposed API is [here](https://github.com/microsoft/nni/tree/master/src/sdk/pynni/nni/nas/pytorch). And [here](https://github.com/microsoft/nni/tree/master/examples/nas/darts) is an example of NAS implementation, which bases on NNI proposed interface.
+
+[1]: https://arxiv.org/abs/1802.03268
+[2]: https://arxiv.org/abs/1707.07012
+[3]: https://arxiv.org/abs/1806.09055
+[4]: https://arxiv.org/abs/1806.10282
+[5]: https://arxiv.org/abs/1703.01041

docs/en_US/Tutorial/SearchSpaceSpec.md

@@ -73,12 +73,6 @@ All types of sampling strategies and their parameter are listed here:
   * Which means the variable value is a value like `round(exp(normal(mu, sigma)) / q) * q`
   * Suitable for a discrete variable with respect to which the objective is smooth and gets smoother with the size of the variable, which is bounded from one side.
 
-* `{"_type": "mutable_layer", "_value": {mutable_layer_infomation}}`
-  * Type for [Neural Architecture Search Space][1]. Value is also a dictionary, which contains key-value pairs representing respectively name and search space of each mutable_layer.
-  * For now, users can only use this type of search space with annotation, which means that there is no need to define a json file for search space since it will be automatically generated according to the annotation in trial code.
-  * The following HPO tuners can be adapted to tune this search space: TPE, Random, Anneal, Evolution, Grid Search,
-  Hyperband and BOHB.
-  * For detailed usage, please refer to [General NAS Interfaces][1].
 
 ## Search Space Types Supported by Each Tuner
 
@@ -105,5 +99,3 @@ Known Limitations:
     * Only Random Search/TPE/Anneal/Evolution tuner supports nested search space
 
     * We do not support nested search space "Hyper Parameter" in visualization now, the enhancement is being considered in [#1110](https://github.com/microsoft/nni/issues/1110), any suggestions or discussions or contributions are warmly welcomed
-
-[1]: ../AdvancedFeature/GeneralNasInterfaces.md

docs/en_US/advanced.rst

@@ -3,5 +3,3 @@ Advanced Features
 
 ..  toctree::
     MultiPhase<./AdvancedFeature/MultiPhase>
-    AdvancedNas<./AdvancedFeature/AdvancedNas>
-    NAS Programming Interface<./AdvancedFeature/GeneralNasInterfaces>
\ No newline at end of file

examples/nas/darts/retrain.py

 import logging
+import time
 from argparse import ArgumentParser
 
 import torch
@@ -10,8 +11,17 @@ from model import CNN
 from nni.nas.pytorch.fixed import apply_fixed_architecture
 from nni.nas.pytorch.utils import AverageMeter
 
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
+logger = logging.getLogger()
+
+fmt = '[%(asctime)s] %(levelname)s (%(name)s/%(threadName)s) %(message)s'
+logging.Formatter.converter = time.localtime
+formatter = logging.Formatter(fmt, '%m/%d/%Y, %I:%M:%S %p')
+
+std_out_info = logging.StreamHandler()
+std_out_info.setFormatter(formatter)
+logger.setLevel(logging.INFO)
+logger.addHandler(std_out_info)
+
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 

examples/nas/darts/search.py

+import logging
+import time
 from argparse import ArgumentParser
 
-import datasets
 import torch
 import torch.nn as nn
 
+import datasets
 from model import CNN
-from nni.nas.pytorch.callbacks import LearningRateScheduler, ArchitectureCheckpoint
+from nni.nas.pytorch.callbacks import (ArchitectureCheckpoint,
+                                       LearningRateScheduler)
 from nni.nas.pytorch.darts import DartsTrainer
 from utils import accuracy
 
+logger = logging.getLogger()
+
+fmt = '[%(asctime)s] %(levelname)s (%(name)s/%(threadName)s) %(message)s'
+logging.Formatter.converter = time.localtime
+formatter = logging.Formatter(fmt, '%m/%d/%Y, %I:%M:%S %p')
+
+std_out_info = logging.StreamHandler()
+std_out_info.setFormatter(formatter)
+logger.setLevel(logging.INFO)
+logger.addHandler(std_out_info)
 
 if __name__ == "__main__":
     parser = ArgumentParser("darts")

examples/nas/enas/search.py

+import logging
+import time
 from argparse import ArgumentParser
 
 import torch
@@ -10,6 +12,17 @@ from nni.nas.pytorch import enas
 from nni.nas.pytorch.callbacks import LearningRateScheduler, ArchitectureCheckpoint
 from utils import accuracy, reward_accuracy
 
+logger = logging.getLogger()
+
+fmt = '[%(asctime)s] %(levelname)s (%(name)s/%(threadName)s) %(message)s'
+logging.Formatter.converter = time.localtime
+formatter = logging.Formatter(fmt, '%m/%d/%Y, %I:%M:%S %p')
+
+std_out_info = logging.StreamHandler()
+std_out_info.setFormatter(formatter)
+logger.setLevel(logging.INFO)
+logger.addHandler(std_out_info)
+
 if __name__ == "__main__":
     parser = ArgumentParser("enas")
     parser.add_argument("--batch-size", default=128, type=int)

examples/nas/pdarts/datasets.py

-from torchvision import transforms
-from torchvision.datasets import CIFAR10
-
-
-def get_dataset(cls):
-    MEAN = [0.49139968, 0.48215827, 0.44653124]
-    STD = [0.24703233, 0.24348505, 0.26158768]
-    transf = [
-        transforms.RandomCrop(32, padding=4),
-        transforms.RandomHorizontalFlip()
-    ]
-    normalize = [
-        transforms.ToTensor(),
-        transforms.Normalize(MEAN, STD)
-    ]
-
-    train_transform = transforms.Compose(transf + normalize)
-    valid_transform = transforms.Compose(normalize)
-
-    if cls == "cifar10":
-        dataset_train = CIFAR10(root="./data", train=True, download=True, transform=train_transform)
-        dataset_valid = CIFAR10(root="./data", train=False, download=True, transform=valid_transform)
-    else:
-        raise NotImplementedError
-    return dataset_train, dataset_valid

examples/nas/pdarts/main.py → examples/nas/pdarts/search.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import logging
+import sys
+import time
 from argparse import ArgumentParser
 
-import datasets
 import torch
 import torch.nn as nn
-import nni.nas.pytorch as nas
-from nni.nas.pytorch.pdarts import PdartsTrainer
-from nni.nas.pytorch.darts import CnnNetwork, CnnCell
-
 
-def accuracy(output, target, topk=(1,)):
-    """ Computes the precision@k for the specified values of k """
-    maxk = max(topk)
-    batch_size = target.size(0)
+from nni.nas.pytorch.callbacks import ArchitectureCheckpoint
+from nni.nas.pytorch.pdarts import PdartsTrainer
 
-    _, pred = output.topk(maxk, 1, True, True)
-    pred = pred.t()
-    # one-hot case
-    if target.ndimension() > 1:
-        target = target.max(1)[1]
+# prevent it to be reordered.
+if True:
+    sys.path.append('../darts')
+    from utils import accuracy
+    from model import CNN
+    import datasets
 
-    correct = pred.eq(target.view(1, -1).expand_as(pred))
+logger = logging.getLogger()
 
-    res = dict()
-    for k in topk:
-        correct_k = correct[:k].view(-1).float().sum(0)
-        res["acc{}".format(k)] = correct_k.mul_(1.0 / batch_size).item()
-    return res
+fmt = '[%(asctime)s] %(levelname)s (%(name)s/%(threadName)s) %(message)s'
+logging.Formatter.converter = time.localtime
+formatter = logging.Formatter(fmt, '%m/%d/%Y, %I:%M:%S %p')
 
+std_out_info = logging.StreamHandler()
+std_out_info.setFormatter(formatter)
+logger.setLevel(logging.INFO)
+logger.addHandler(std_out_info)
 
 if __name__ == "__main__":
-    parser = ArgumentParser("darts")
-    parser.add_argument("--layers", default=5, type=int)
+    parser = ArgumentParser("pdarts")
     parser.add_argument('--add_layers', action='append',
                         default=[0, 6, 12], help='add layers')
     parser.add_argument("--nodes", default=4, type=int)
-    parser.add_argument("--batch-size", default=128, type=int)
+    parser.add_argument("--layers", default=5, type=int)
+    parser.add_argument("--batch-size", default=64, type=int)
     parser.add_argument("--log-frequency", default=1, type=int)
+    parser.add_argument("--epochs", default=50, type=int)
     args = parser.parse_args()
 
+    logger.info("loading data")
     dataset_train, dataset_valid = datasets.get_dataset("cifar10")
 
-    def model_creator(layers, n_nodes):
-        model = CnnNetwork(3, 16, 10, layers, n_nodes=n_nodes, cell_type=CnnCell)
-        loss = nn.CrossEntropyLoss()
+    def model_creator(layers):
+        model = CNN(32, 3, 16, 10, layers, n_nodes=args.nodes)
+        criterion = nn.CrossEntropyLoss()
+
+        optim = torch.optim.SGD(model.parameters(), 0.025, momentum=0.9, weight_decay=3.0E-4)
+        lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optim, args.epochs, eta_min=0.001)
 
-        model_optim = torch.optim.SGD(model.parameters(), 0.025,
-                                      momentum=0.9, weight_decay=3.0E-4)
-        n_epochs = 50
-        lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(model_optim, n_epochs, eta_min=0.001)
-        return model, loss, model_optim, lr_scheduler
+        return model, criterion, optim, lr_scheduler
 
+    logger.info("initializing trainer")
     trainer = PdartsTrainer(model_creator,
+                            layers=args.layers,
                             metrics=lambda output, target: accuracy(output, target, topk=(1,)),
-                            num_epochs=50,
                             pdarts_num_layers=[0, 6, 12],
                             pdarts_num_to_drop=[3, 2, 2],
+                            num_epochs=args.epochs,
                             dataset_train=dataset_train,
                             dataset_valid=dataset_valid,
-                            layers=args.layers,
-                            n_nodes=args.nodes,
                             batch_size=args.batch_size,
-                            log_frequency=args.log_frequency)
+                            log_frequency=args.log_frequency,
+                            callbacks=[ArchitectureCheckpoint("./checkpoints")])
+    logger.info("training")
     trainer.train()
-    trainer.export()

src/sdk/pynni/nni/nas/pytorch/darts/cnn_cell.py

-
-import torch
-import torch.nn as nn
-
-import nni.nas.pytorch as nas
-from nni.nas.pytorch.modules import RankedModule
-
-from .cnn_ops import OPS, PRIMITIVES, FactorizedReduce, StdConv
-
-
-class CnnCell(RankedModule):
-    """
-    Cell for search.
-    """
-
-    def __init__(self, n_nodes, channels_pp, channels_p, channels, reduction_p, reduction):
-        """
-        Initialization a search cell.
-
-        Parameters
-        ----------
-        n_nodes: int
-            Number of nodes in current DAG.
-        channels_pp: int
-            Number of output channels from previous previous cell.
-        channels_p: int
-            Number of output channels from previous cell.
-        channels: int
-            Number of channels that will be used in the current DAG.
-        reduction_p: bool
-            Flag for whether the previous cell is reduction cell or not.
-        reduction: bool
-            Flag for whether the current cell is reduction cell or not.
-        """
-        super(CnnCell, self).__init__(rank=1, reduction=reduction)
-        self.n_nodes = n_nodes
-
-        # If previous cell is reduction cell, current input size does not match with
-        # output size of cell[k-2]. So the output[k-2] should be reduced by preprocessing.
-        if reduction_p:
-            self.preproc0 = FactorizedReduce(channels_pp, channels, affine=False)
-        else:
-            self.preproc0 = StdConv(channels_pp, channels, 1, 1, 0, affine=False)
-        self.preproc1 = StdConv(channels_p, channels, 1, 1, 0, affine=False)
-
-        # generate dag
-        self.mutable_ops = nn.ModuleList()
-        for depth in range(self.n_nodes):
-            self.mutable_ops.append(nn.ModuleList())
-            for i in range(2 + depth):  # include 2 input nodes
-                # reduction should be used only for input node
-                stride = 2 if reduction and i < 2 else 1
-                m_ops = []
-                for primitive in PRIMITIVES:
-                    op = OPS[primitive](channels, stride, False)
-                    m_ops.append(op)
-                op = nas.mutables.LayerChoice(m_ops, key="r{}_d{}_i{}".format(reduction, depth, i))
-                self.mutable_ops[depth].append(op)
-
-    def forward(self, s0, s1):
-        # s0, s1 are the outputs of previous previous cell and previous cell, respectively.
-        tensors = [self.preproc0(s0), self.preproc1(s1)]
-        for ops in self.mutable_ops:
-            assert len(ops) == len(tensors)
-            cur_tensor = sum(op(tensor) for op, tensor in zip(ops, tensors))
-            tensors.append(cur_tensor)
-
-        output = torch.cat(tensors[2:], dim=1)
-        return output

src/sdk/pynni/nni/nas/pytorch/darts/cnn_network.py

-
-import torch.nn as nn
-
-from .cnn_cell import CnnCell
-
-
-class CnnNetwork(nn.Module):
-    """
-    Search CNN model
-    """
-
-    def __init__(self, in_channels, channels, n_classes, n_layers, n_nodes=4, stem_multiplier=3, cell_type=CnnCell):
-        """
-        Initializing a search channelsNN.
-
-        Parameters
-        ----------
-        in_channels: int
-            Number of channels in images.
-        channels: int
-            Number of channels used in the network.
-        n_classes: int
-            Number of classes.
-        n_layers: int
-            Number of cells in the whole network.
-        n_nodes: int
-            Number of nodes in a cell.
-        stem_multiplier: int
-            Multiplier of channels in STEM.
-        """
-        super().__init__()
-        self.in_channels = in_channels
-        self.channels = channels
-        self.n_classes = n_classes
-        self.n_layers = n_layers
-
-        c_cur = stem_multiplier * self.channels
-        self.stem = nn.Sequential(
-            nn.Conv2d(in_channels, c_cur, 3, 1, 1, bias=False),
-            nn.BatchNorm2d(c_cur)
-        )
-
-        # for the first cell, stem is used for both s0 and s1
-        # [!] channels_pp and channels_p is output channel size, but c_cur is input channel size.
-        channels_pp, channels_p, c_cur = c_cur, c_cur, channels
-
-        self.cells = nn.ModuleList()
-        reduction_p, reduction = False, False
-        for i in range(n_layers):
-            reduction_p, reduction = reduction, False
-            # Reduce featuremap size and double channels in 1/3 and 2/3 layer.
-            if i in [n_layers // 3, 2 * n_layers // 3]:
-                c_cur *= 2
-                reduction = True
-
-            cell = cell_type(n_nodes, channels_pp, channels_p, c_cur, reduction_p, reduction)
-            self.cells.append(cell)
-            c_cur_out = c_cur * n_nodes
-            channels_pp, channels_p = channels_p, c_cur_out
-
-        self.gap = nn.AdaptiveAvgPool2d(1)
-        self.linear = nn.Linear(channels_p, n_classes)
-
-    def forward(self, x):
-        s0 = s1 = self.stem(x)
-
-        for cell in self.cells:
-            s0, s1 = s1, cell(s0, s1)
-
-        out = self.gap(s1)
-        out = out.view(out.size(0), -1)  # flatten
-        logits = self.linear(out)
-        return logits

src/sdk/pynni/nni/nas/pytorch/darts/cnn_ops.py

-import torch
-import torch.nn as nn
-
-PRIMITIVES = [
-    'none',
-    'max_pool_3x3',
-    'avg_pool_3x3',
-    'skip_connect',  # identity
-    'sep_conv_3x3',
-    'sep_conv_5x5',
-    'dil_conv_3x3',
-    'dil_conv_5x5',
-]
-
-OPS = {
-    'none': lambda C, stride, affine: Zero(stride),
-    'avg_pool_3x3': lambda C, stride, affine: PoolBN('avg', C, 3, stride, 1, affine=affine),
-    'max_pool_3x3': lambda C, stride, affine: PoolBN('max', C, 3, stride, 1, affine=affine),
-    'skip_connect': lambda C, stride, affine: Identity() if stride == 1 else FactorizedReduce(C, C, affine=affine),
-    'sep_conv_3x3': lambda C, stride, affine: SepConv(C, C, 3, stride, 1, affine=affine),
-    'sep_conv_5x5': lambda C, stride, affine: SepConv(C, C, 5, stride, 2, affine=affine),
-    'sep_conv_7x7': lambda C, stride, affine: SepConv(C, C, 7, stride, 3, affine=affine),
-    'dil_conv_3x3': lambda C, stride, affine: DilConv(C, C, 3, stride, 2, 2, affine=affine),  # 5x5
-    'dil_conv_5x5': lambda C, stride, affine: DilConv(C, C, 5, stride, 4, 2, affine=affine),  # 9x9
-    'conv_7x1_1x7': lambda C, stride, affine: FacConv(C, C, 7, stride, 3, affine=affine)
-}
-
-
-def drop_path_(x, drop_prob, training):
-    if training and drop_prob > 0.:
-        keep_prob = 1. - drop_prob
-        # per data point mask; assuming x in cuda.
-        mask = torch.cuda.FloatTensor(x.size(0), 1, 1, 1).bernoulli_(keep_prob)
-        x.div_(keep_prob).mul_(mask)
-
-    return x
-
-
-class DropPath_(nn.Module):
-    def __init__(self, p=0.):
-        """ [!] DropPath is inplace module
-        Args:
-            p: probability of an path to be zeroed.
-        """
-        super().__init__()
-        self.p = p
-
-    def extra_repr(self):
-        return 'p={}, inplace'.format(self.p)
-
-    def forward(self, x):
-        drop_path_(x, self.p, self.training)
-
-        return x
-
-
-class PoolBN(nn.Module):
-    """
-    AvgPool or MaxPool - BN
-    """
-
-    def __init__(self, pool_type, C, kernel_size, stride, padding, affine=True):
-        """
-        Args:
-            pool_type: 'max' or 'avg'
-        """
-        super().__init__()
-        if pool_type.lower() == 'max':
-            self.pool = nn.MaxPool2d(kernel_size, stride, padding)
-        elif pool_type.lower() == 'avg':
-            self.pool = nn.AvgPool2d(kernel_size, stride, padding, count_include_pad=False)
-        else:
-            raise ValueError()
-
-        self.bn = nn.BatchNorm2d(C, affine=affine)
-
-    def forward(self, x):
-        out = self.pool(x)
-        out = self.bn(out)
-        return out
-
-
-class StdConv(nn.Module):
-    """ Standard conv
-    ReLU - Conv - BN
-    """
-
-    def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
-        super().__init__()
-        self.net = nn.Sequential(
-            nn.ReLU(),
-            nn.Conv2d(C_in, C_out, kernel_size, stride, padding, bias=False),
-            nn.BatchNorm2d(C_out, affine=affine)
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-class FacConv(nn.Module):
-    """ Factorized conv
-    ReLU - Conv(Kx1) - Conv(1xK) - BN
-    """
-
-    def __init__(self, C_in, C_out, kernel_length, stride, padding, affine=True):
-        super().__init__()
-        self.net = nn.Sequential(
-            nn.ReLU(),
-            nn.Conv2d(C_in, C_in, (kernel_length, 1), stride, padding, bias=False),
-            nn.Conv2d(C_in, C_out, (1, kernel_length), stride, padding, bias=False),
-            nn.BatchNorm2d(C_out, affine=affine)
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-class DilConv(nn.Module):
-    """ (Dilated) depthwise separable conv
-    ReLU - (Dilated) depthwise separable - Pointwise - BN
-    If dilation == 2, 3x3 conv => 5x5 receptive field
-                    5x5 conv => 9x9 receptive field
-    """
-
-    def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine=True):
-        super().__init__()
-        self.net = nn.Sequential(
-            nn.ReLU(),
-            nn.Conv2d(C_in, C_in, kernel_size, stride, padding, dilation=dilation, groups=C_in, bias=False),
-            nn.Conv2d(C_in, C_out, 1, stride=1, padding=0, bias=False),
-            nn.BatchNorm2d(C_out, affine=affine)
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-class SepConv(nn.Module):
-    """ Depthwise separable conv
-    DilConv(dilation=1) * 2
-    """
-
-    def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
-        super().__init__()
-        self.net = nn.Sequential(
-            DilConv(C_in, C_in, kernel_size, stride, padding, dilation=1, affine=affine),
-            DilConv(C_in, C_out, kernel_size, 1, padding, dilation=1, affine=affine)
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-class Identity(nn.Module):
-
-    def forward(self, x):
-        return x
-
-
-class Zero(nn.Module):
-    def __init__(self, stride):
-        super().__init__()
-        self.stride = stride
-
-    def forward(self, x):
-        if self.stride == 1:
-            return x * 0.
-
-        # re-sizing by stride
-        return x[:, :, ::self.stride, ::self.stride] * 0.
-
-
-class FactorizedReduce(nn.Module):
-    """
-    Reduce feature map size by factorized pointwise(stride=2).
-    """
-
-    def __init__(self, C_in, C_out, affine=True):
-        super().__init__()
-        self.relu = nn.ReLU()
-        self.conv1 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
-        self.conv2 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
-        self.bn = nn.BatchNorm2d(C_out, affine=affine)
-
-    def forward(self, x):
-        x = self.relu(x)
-        out = torch.cat([self.conv1(x), self.conv2(x[:, :, 1:, 1:])], dim=1)
-        out = self.bn(out)
-        return out

src/sdk/pynni/nni/nas/pytorch/darts/trainer.py

 import copy
+import logging
 
 import torch
 from torch import nn as nn
 
 from nni.nas.pytorch.trainer import Trainer
 from nni.nas.pytorch.utils import AverageMeterGroup
+
 from .mutator import DartsMutator
 
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+
 
 class DartsTrainer(Trainer):
     def __init__(self, model, loss, metrics,
@@ -72,7 +77,8 @@ class DartsTrainer(Trainer):
             metrics["loss"] = loss.item()
             meters.update(metrics)
             if self.log_frequency is not None and step % self.log_frequency == 0:
-                print("Epoch [{}/{}] Step [{}/{}]  {}".format(epoch, self.num_epochs, step, len(self.train_loader), meters))
+                logger.info("Epoch [%s/%s] Step [%s/%s]  %s", epoch+1,
+                            self.num_epochs, step+1, len(self.train_loader), meters)
 
     def validate_one_epoch(self, epoch):
         self.model.eval()
@@ -86,7 +92,8 @@ class DartsTrainer(Trainer):
                 metrics = self.metrics(logits, y)
                 meters.update(metrics)
                 if self.log_frequency is not None and step % self.log_frequency == 0:
-                    print("Epoch [{}/{}] Step [{}/{}]  {}".format(epoch, self.num_epochs, step, len(self.valid_loader), meters))
+                    logger.info("Epoch [%s/%s] Step [%s/%s]  %s", epoch+1,
+                                self.num_epochs, step+1, len(self.test_loader), meters)
 
     def _unrolled_backward(self, trn_X, trn_y, val_X, val_y, backup_model, lr):
         """

src/sdk/pynni/nni/nas/pytorch/enas/trainer.py

+import logging
 import torch
 import torch.optim as optim
 
@@ -6,6 +7,10 @@ from nni.nas.pytorch.utils import AverageMeterGroup
 from .mutator import EnasMutator
 
 
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+
+
 class EnasTrainer(Trainer):
     def __init__(self, model, loss, metrics, reward_function,
                  optimizer, num_epochs, dataset_train, dataset_valid,
@@ -70,8 +75,8 @@ class EnasTrainer(Trainer):
             meters.update(metrics)
 
             if self.log_frequency is not None and step % self.log_frequency == 0:
-                print("Model Epoch [{}/{}] Step [{}/{}]  {}".format(epoch, self.num_epochs,
-                                                                    step, len(self.train_loader), meters))
+                logger.info("Model Epoch [%s/%s] Step [%s/%s]  %s", epoch,
+                            self.num_epochs, step, len(self.train_loader), meters)
 
         # Train sampler (mutator)
         self.model.eval()
@@ -109,9 +114,8 @@ class EnasTrainer(Trainer):
                     self.mutator_optim.zero_grad()
 
                 if self.log_frequency is not None and step % self.log_frequency == 0:
-                    print("RL Epoch [{}/{}] Step [{}/{}]  {}".format(epoch, self.num_epochs,
-                                                                     mutator_step // self.mutator_steps_aggregate,
-                                                                     self.mutator_steps, meters))
+                    logger.info("RL Epoch [%s/%s] Step [%s/%s]  %s", epoch, self.num_epochs,
+                                mutator_step // self.mutator_steps_aggregate, self.mutator_steps, meters)
                 mutator_step += 1
                 if mutator_step >= total_mutator_steps:
                     break

src/sdk/pynni/nni/nas/pytorch/modules.py

-
-from torch import nn as nn
-
-
-class RankedModule(nn.Module):
-    def __init__(self, rank=None, reduction=False):
-        super(RankedModule, self).__init__()
-        self.rank = rank
-        self.reduction = reduction

src/sdk/pynni/nni/nas/pytorch/mutables.py

+import logging
+
 import torch.nn as nn
 
 from nni.nas.pytorch.utils import global_mutable_counting
 
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+
 
 class Mutable(nn.Module):
     """
@@ -20,7 +25,7 @@ class Mutable(nn.Module):
         if key is not None:
             if not isinstance(key, str):
                 key = str(key)
-                print("Warning: key \"{}\" is not string, converted to string.".format(key))
+                logger.warning("Warning: key \"%s\" is not string, converted to string.", key)
             self._key = key
         else:
             self._key = self.__class__.__name__ + str(global_mutable_counting())

src/sdk/pynni/nni/nas/pytorch/pdarts/__init__.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from .trainer import PdartsTrainer

src/sdk/pynni/nni/nas/pytorch/pdarts/mutator.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import copy
 
 import numpy as np
-import torch
-from torch import nn as nn
 from torch.nn import functional as F
 
 from nni.nas.pytorch.darts import DartsMutator
@@ -11,24 +12,27 @@ from nni.nas.pytorch.mutables import LayerChoice
 
 class PdartsMutator(DartsMutator):
 
-    def __init__(self, pdarts_epoch_index, pdarts_num_to_drop, switches=None):
+    def __init__(self, model, pdarts_epoch_index, pdarts_num_to_drop, switches={}):
         self.pdarts_epoch_index = pdarts_epoch_index
         self.pdarts_num_to_drop = pdarts_num_to_drop
-        self.switches = switches
+        if switches is None:
+            self.switches = {}
+        else:
+            self.switches = switches
 
-        super(PdartsMutator, self).__init__()
+        super(PdartsMutator, self).__init__(model)
 
-    def before_build(self):
-        self.choices = nn.ParameterDict()
-        if self.switches is None:
-            self.switches = {}
+        for mutable in self.mutables:
+            if isinstance(mutable, LayerChoice):
+
+                switches = self.switches.get(mutable.key, [True for j in range(mutable.length)])
+
+                for index in range(len(switches)-1, -1, -1):
+                    if switches[index] == False:
+                        del(mutable.choices[index])
+                        mutable.length -= 1
 
-    def named_mutables(self, model):
-        key2module = dict()
-        for name, module in model.named_modules():
-            if isinstance(module, LayerChoice):
-                key2module[module.key] = module
-                yield name, module, True
+                self.switches[mutable.key] = switches
 
     def drop_paths(self):
         for key in self.switches:
@@ -49,22 +53,6 @@ class PdartsMutator(DartsMutator):
                 switches[idxs[idx]] = False
         return self.switches
 
-    def on_init_layer_choice(self, mutable: LayerChoice):
-        switches = self.switches.get(
-            mutable.key, [True for j in range(mutable.length)])
-
-        for index in range(len(switches)-1, -1, -1):
-            if switches[index] == False:
-                del(mutable.choices[index])
-                mutable.length -= 1
-
-        self.switches[mutable.key] = switches
-
-        self.choices[mutable.key] = nn.Parameter(1.0E-3 * torch.randn(mutable.length))
-
-    def on_calc_layer_choice_mask(self, mutable: LayerChoice):
-        return F.softmax(self.choices[mutable.key], dim=-1)
-
     def get_min_k(self, input_in, k):
         index = []
         for _ in range(k):

src/sdk/pynni/nni/nas/pytorch/pdarts/trainer.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+import logging
+from nni.nas.pytorch.callbacks import LearningRateScheduler
 from nni.nas.pytorch.darts import DartsTrainer
-from nni.nas.pytorch.trainer import Trainer
+from nni.nas.pytorch.trainer import BaseTrainer
 
 from .mutator import PdartsMutator
 
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
 
-class PdartsTrainer(Trainer):
 
-    def __init__(self, model_creator, metrics, num_epochs, dataset_train, dataset_valid,
-                 layers=5, n_nodes=4, pdarts_num_layers=[0, 6, 12], pdarts_num_to_drop=[3, 2, 2],
-                 mutator=None, batch_size=64, workers=4, device=None, log_frequency=None):
+class PdartsTrainer(BaseTrainer):
+
+    def __init__(self, model_creator, layers, metrics,
+                 num_epochs, dataset_train, dataset_valid,
+                 pdarts_num_layers=[0, 6, 12], pdarts_num_to_drop=[3, 2, 2],
+                 mutator=None, batch_size=64, workers=4, device=None, log_frequency=None, callbacks=None):
+        super(PdartsTrainer, self).__init__()
         self.model_creator = model_creator
         self.layers = layers
-        self.n_nodes = n_nodes
         self.pdarts_num_layers = pdarts_num_layers
         self.pdarts_num_to_drop = pdarts_num_to_drop
         self.pdarts_epoch = len(pdarts_num_to_drop)
@@ -25,29 +33,41 @@ class PdartsTrainer(Trainer):
             "device": device,
             "log_frequency": log_frequency
         }
+        self.callbacks = callbacks if callbacks is not None else []
 
     def train(self):
         layers = self.layers
-        n_nodes = self.n_nodes
         switches = None
         for epoch in range(self.pdarts_epoch):
 
             layers = self.layers+self.pdarts_num_layers[epoch]
-            model, loss, model_optim, _ = self.model_creator(
-                layers, n_nodes)
-            mutator = PdartsMutator(model, epoch, self.pdarts_num_to_drop, switches)  # pylint: disable=too-many-function-args
+            model, criterion, optim, lr_scheduler = self.model_creator(layers)
+            self.mutator = PdartsMutator(model, epoch, self.pdarts_num_to_drop, switches)
+
+            for callback in self.callbacks:
+                callback.build(model, self.mutator, self)
+                callback.on_epoch_begin(epoch)
+
+            darts_callbacks = []
+            if lr_scheduler is not None:
+                darts_callbacks.append(LearningRateScheduler(lr_scheduler))
 
-            self.trainer = DartsTrainer(model, loss=loss, optimizer=model_optim,
-                                        mutator=mutator, **self.darts_parameters)
-            print("start pdrats training %s..." % epoch)
+            self.trainer = DartsTrainer(model, mutator=self.mutator, loss=criterion, optimizer=optim,
+                                        callbacks=darts_callbacks, **self.darts_parameters)
+            logger.info("start pdarts training %s...", epoch)
 
             self.trainer.train()
 
-            # with open('log/parameters_%d.txt' % epoch, "w") as f:
-            #     f.write(str(model.parameters))
+            switches = self.mutator.drop_paths()
 
-            switches = mutator.drop_paths()
+            for callback in self.callbacks:
+                callback.on_epoch_end(epoch)
+
+    def validate(self):
+        self.model.validate()
 
     def export(self):
-        if (self.trainer is not None) and hasattr(self.trainer, "export"):
-            self.trainer.export()
+        self.mutator.export()
+
+    def checkpoint(self):
+        raise NotImplementedError("Not implemented yet")

src/sdk/pynni/nni/nas/pytorch/trainer.py

@@ -7,6 +7,7 @@ import torch
 from .base_trainer import BaseTrainer
 
 _logger = logging.getLogger(__name__)
+_logger.setLevel(logging.INFO)
 
 
 class TorchTensorEncoder(json.JSONEncoder):
@@ -59,12 +60,12 @@ class Trainer(BaseTrainer):
                 callback.on_epoch_begin(epoch)
 
             # training
-            print("Epoch {} Training".format(epoch))
+            _logger.info("Epoch %d Training", epoch)
             self.train_one_epoch(epoch)
 
             if validate:
                 # validation
-                print("Epoch {} Validating".format(epoch))
+                _logger.info("Epoch %d Validating", epoch)
                 self.validate_one_epoch(epoch)
 
             for callback in self.callbacks: