Merge pull request #218 from microsoft/master

merge master

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.nn.functional as F
+
+from nni.nas.pytorch.darts import DartsMutator
+from nni.nas.pytorch.mutables import LayerChoice
+
+
+class PdartsMutator(DartsMutator):
+
+    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
+        if switches is None:
+            self.switches = {}
+        else:
+            self.switches = switches
+
+        super(PdartsMutator, self).__init__(model)
+
+        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
+
+                self.switches[mutable.key] = switches
+
+    def drop_paths(self):
+        for key in self.switches:
+            prob = F.softmax(self.choices[key], dim=-1).data.cpu().numpy()
+
+            switches = self.switches[key]
+            idxs = []
+            for j in range(len(switches)):
+                if switches[j]:
+                    idxs.append(j)
+            if self.pdarts_epoch_index == len(self.pdarts_num_to_drop) - 1:
+                # for the last stage, drop all Zero operations
+                drop = self.get_min_k_no_zero(prob, idxs, self.pdarts_num_to_drop[self.pdarts_epoch_index])
+            else:
+                drop = self.get_min_k(prob, self.pdarts_num_to_drop[self.pdarts_epoch_index])
+
+            for idx in drop:
+                switches[idxs[idx]] = False
+        return self.switches
+
+    def get_min_k(self, input_in, k):
+        index = []
+        for _ in range(k):
+            idx = np.argmin(input)
+            index.append(idx)
+
+        return index
+
+    def get_min_k_no_zero(self, w_in, idxs, k):
+        w = copy.deepcopy(w_in)
+        index = []
+        if 0 in idxs:
+            zf = True
+        else:
+            zf = False
+        if zf:
+            w = w[1:]
+            index.append(0)
+            k = k - 1
+        for _ in range(k):
+            idx = np.argmin(w)
+            w[idx] = 1
+            if zf:
+                idx = idx + 1
+            index.append(idx)
+        return index

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

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import json
+import logging
+
+from nni.nas.pytorch.callbacks import LRSchedulerCallback
+from nni.nas.pytorch.darts import DartsTrainer
+from nni.nas.pytorch.trainer import BaseTrainer, TorchTensorEncoder
+
+from .mutator import PdartsMutator
+
+logger = logging.getLogger(__name__)
+
+
+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.pdarts_num_layers = pdarts_num_layers
+        self.pdarts_num_to_drop = pdarts_num_to_drop
+        self.pdarts_epoch = len(pdarts_num_to_drop)
+        self.darts_parameters = {
+            "metrics": metrics,
+            "num_epochs": num_epochs,
+            "dataset_train": dataset_train,
+            "dataset_valid": dataset_valid,
+            "batch_size": batch_size,
+            "workers": workers,
+            "device": device,
+            "log_frequency": log_frequency
+        }
+        self.callbacks = callbacks if callbacks is not None else []
+
+    def train(self):
+        layers = self.layers
+        switches = None
+        for epoch in range(self.pdarts_epoch):
+
+            layers = self.layers+self.pdarts_num_layers[epoch]
+            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(LRSchedulerCallback(lr_scheduler))
+
+            self.trainer = DartsTrainer(model, mutator=self.mutator, loss=criterion, optimizer=optim,
+                                        callbacks=darts_callbacks, **self.darts_parameters)
+            logger.info("start pdarts training epoch %s...", epoch)
+
+            self.trainer.train()
+
+            switches = self.mutator.drop_paths()
+
+            for callback in self.callbacks:
+                callback.on_epoch_end(epoch)
+
+    def validate(self):
+        self.model.validate()
+
+    def export(self, file):
+        mutator_export = self.mutator.export()
+        with open(file, "w") as f:
+            json.dump(mutator_export, f, indent=2, sort_keys=True, cls=TorchTensorEncoder)
+
+    def checkpoint(self):
+        raise NotImplementedError("Not implemented yet")

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

+from .mutator import RandomMutator
\ No newline at end of file

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

+import torch
+import torch.nn.functional as F
+
+from nni.nas.pytorch.mutator import Mutator
+from nni.nas.pytorch.mutables import LayerChoice, InputChoice
+
+
+class RandomMutator(Mutator):
+    def sample_search(self):
+        result = dict()
+        for mutable in self.mutables:
+            if isinstance(mutable, LayerChoice):
+                gen_index = torch.randint(high=mutable.length, size=(1, ))
+                result[mutable.key] = F.one_hot(gen_index, num_classes=mutable.length).view(-1).bool()
+            elif isinstance(mutable, InputChoice):
+                if mutable.n_chosen is None:
+                    result[mutable.key] = torch.randint(high=2, size=(mutable.n_candidates,)).view(-1).bool()
+                else:
+                    perm = torch.randperm(mutable.n_candidates)
+                    mask = [i in perm[:mutable.n_chosen] for i in range(mutable.n_candidates)]
+                    result[mutable.key] = torch.tensor(mask, dtype=torch.bool)  # pylint: disable=not-callable
+        return result
+
+    def sample_final(self):
+        return self.sample_search()

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

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import json
+import logging
+from abc import abstractmethod
+
+import torch
+
+from .base_trainer import BaseTrainer
+
+_logger = logging.getLogger(__name__)
+
+
+class TorchTensorEncoder(json.JSONEncoder):
+    def default(self, o):  # pylint: disable=method-hidden
+        if isinstance(o, torch.Tensor):
+            olist = o.tolist()
+            if "bool" not in o.type().lower() and all(map(lambda d: d == 0 or d == 1, olist)):
+                _logger.warning("Every element in %s is either 0 or 1. "
+                                "You might consider convert it into bool.", olist)
+            return olist
+        return super().default(o)
+
+
+class Trainer(BaseTrainer):
+    def __init__(self, model, mutator, loss, metrics, optimizer, num_epochs,
+                 dataset_train, dataset_valid, batch_size, workers, device, log_frequency, callbacks):
+        """
+        Trainer initialization.
+
+        Parameters
+        ----------
+        model : nn.Module
+            Model with mutables.
+        mutator : BaseMutator
+            A mutator object that has been initialized with the model.
+        loss : callable
+            Called with logits and targets. Returns a loss tensor.
+        metrics : callable
+            Returns a dict that maps metrics keys to metrics data.
+        optimizer : Optimizer
+            Optimizer that optimizes the model.
+        num_epochs : int
+            Number of epochs of training.
+        dataset_train : torch.utils.data.Dataset
+            Dataset of training.
+        dataset_valid : torch.utils.data.Dataset
+            Dataset of validation/testing.
+        batch_size : int
+            Batch size.
+        workers : int
+            Number of workers used in data preprocessing.
+        device : torch.device
+            Device object. Either `torch.device("cuda")` or torch.device("cpu")`. When `None`, trainer will
+            automatic detects GPU and selects GPU first.
+        log_frequency : int
+            Number of mini-batches to log metrics.
+        callbacks : list of Callback
+            Callbacks to plug into the trainer. See Callbacks.
+        """
+
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if device is None else device
+        self.model = model
+        self.mutator = mutator
+        self.loss = loss
+
+        self.metrics = metrics
+        self.optimizer = optimizer
+
+        self.model.to(self.device)
+        self.mutator.to(self.device)
+        self.loss.to(self.device)
+
+        self.num_epochs = num_epochs
+        self.dataset_train = dataset_train
+        self.dataset_valid = dataset_valid
+        self.batch_size = batch_size
+        self.workers = workers
+        self.log_frequency = log_frequency
+        self.callbacks = callbacks if callbacks is not None else []
+        for callback in self.callbacks:
+            callback.build(self.model, self.mutator, self)
+
+    @abstractmethod
+    def train_one_epoch(self, epoch):
+        pass
+
+    @abstractmethod
+    def validate_one_epoch(self, epoch):
+        pass
+
+    def train(self, validate=True):
+        for epoch in range(self.num_epochs):
+            for callback in self.callbacks:
+                callback.on_epoch_begin(epoch)
+
+            # training
+            _logger.info("Epoch %d Training", epoch)
+            self.train_one_epoch(epoch)
+
+            if validate:
+                # validation
+                _logger.info("Epoch %d Validating", epoch)
+                self.validate_one_epoch(epoch)
+
+            for callback in self.callbacks:
+                callback.on_epoch_end(epoch)
+
+    def validate(self):
+        self.validate_one_epoch(-1)
+
+    def export(self, file):
+        mutator_export = self.mutator.export()
+        with open(file, "w") as f:
+            json.dump(mutator_export, f, indent=2, sort_keys=True, cls=TorchTensorEncoder)
+
+    def checkpoint(self):
+        raise NotImplementedError("Not implemented yet")

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

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+from collections import OrderedDict
+
+_counter = 0
+
+
+def global_mutable_counting():
+    global _counter
+    _counter += 1
+    return _counter
+
+
+class AverageMeterGroup:
+
+    def __init__(self):
+        self.meters = OrderedDict()
+
+    def update(self, data):
+        for k, v in data.items():
+            if k not in self.meters:
+                self.meters[k] = AverageMeter(k, ":4f")
+            self.meters[k].update(v)
+
+    def __str__(self):
+        return "  ".join(str(v) for _, v in self.meters.items())
+
+
+class AverageMeter:
+    """Computes and stores the average and current value"""
+
+    def __init__(self, name, fmt=':f'):
+        """
+        Initialization of AverageMeter
+
+        Parameters
+        ----------
+        name : str
+            Name to display.
+        fmt : str
+            Format string to print the values.
+        """
+        self.name = name
+        self.fmt = fmt
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+    def __str__(self):
+        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
+        return fmtstr.format(**self.__dict__)
+
+
+class StructuredMutableTreeNode:
+    """
+    A structured representation of a search space.
+    A search space comes with a root (with `None` stored in its `mutable`), and a bunch of children in its `children`.
+    This tree can be seen as a "flattened" version of the module tree. Since nested mutable entity is not supported yet,
+    the following must be true: each subtree corresponds to a ``MutableScope`` and each leaf corresponds to a
+    ``Mutable`` (other than ``MutableScope``).
+    """
+
+    def __init__(self, mutable):
+        self.mutable = mutable
+        self.children = []
+
+    def add_child(self, mutable):
+        self.children.append(StructuredMutableTreeNode(mutable))
+        return self.children[-1]
+
+    def type(self):
+        return type(self.mutable)
+
+    def __iter__(self):
+        return self.traverse()
+
+    def traverse(self, order="pre", deduplicate=True, memo=None):
+        """
+        Return a generator that generates a list of mutables in this tree.
+
+        Parameters
+        ----------
+        order : str
+            pre or post. If pre, current mutable is yield before children. Otherwise after.
+        deduplicate : bool
+            If true, mutables with the same key will not appear after the first appearance.
+        memo : dict
+            An auxiliary dict that memorize keys seen before, so that deduplication is possible.
+
+        Returns
+        -------
+        generator of Mutable
+        """
+        if memo is None:
+            memo = set()
+        assert order in ["pre", "post"]
+        if order == "pre":
+            if self.mutable is not None:
+                if not deduplicate or self.mutable.key not in memo:
+                    memo.add(self.mutable.key)
+                    yield self.mutable
+        for child in self.children:
+            for m in child.traverse(order=order, deduplicate=deduplicate, memo=memo):
+                yield m
+        if order == "post":
+            if self.mutable is not None:
+                if not deduplicate or self.mutable.key not in memo:
+                    memo.add(self.mutable.key)
+                    yield self.mutable

src/sdk/pynni/nni/nas_utils.py

-# Copyright (c) Microsoft Corporation. All rights reserved.
-#
-# MIT License
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
-# associated documentation files (the "Software"), to deal in the Software without restriction,
-# including without limitation the rights to use, copy, modify, merge, publish, distribute,
-# sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all copies or
-# substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
-# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
-# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT
-# OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-# ==================================================================================================
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import functools
 import logging
 

src/sdk/pynni/nni/networkmorphism_tuner/bayesian.py

-# Copyright (c) Microsoft Corporation. All rights reserved.
-#
-# MIT License
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
-# associated documentation files (the "Software"), to deal in the Software without restriction,
-# including without limitation the rights to use, copy, modify, merge, publish, distribute,
-# sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all copies or
-# substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
-# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
-# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT
-# OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-# ==================================================================================================
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
 
 import math
 import random

src/sdk/pynni/nni/networkmorphism_tuner/graph.py

-# Copyright (c) Microsoft Corporation. All rights reserved.
-#
-# MIT License
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
-# associated documentation files (the "Software"), to deal in the Software without restriction,
-# including without limitation the rights to use, copy, modify, merge, publish, distribute,
-# sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all copies or
-# substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
-# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
-# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT
-# OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-# ==================================================================================================
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
 
 import json
 from collections.abc import Iterable

src/sdk/pynni/nni/networkmorphism_tuner/graph_transformer.py

-# Copyright (c) Microsoft Corporation. All rights reserved.
-#
-# MIT License
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
-# associated documentation files (the "Software"), to deal in the Software without restriction,
-# including without limitation the rights to use, copy, modify, merge, publish, distribute,
-# sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all copies or
-# substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
-# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
-# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT
-# OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-# ==================================================================================================
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
 
 from copy import deepcopy
 

src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py

-# Copyright (c) Microsoft Corporation. All rights reserved.
-#
-# MIT License
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
-# associated documentation files (the "Software"), to deal in the Software without restriction,
-# including without limitation the rights to use, copy, modify, merge, publish, distribute,
-# sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all copies or
-# substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
-# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
-# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT
-# OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-# ==================================================================================================
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
 
 import numpy as np
 

src/sdk/pynni/nni/networkmorphism_tuner/layers.py

-# Copyright (c) Microsoft Corporation. All rights reserved.
-#
-# MIT License
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
-# associated documentation files (the "Software"), to deal in the Software without restriction,
-# including without limitation the rights to use, copy, modify, merge, publish, distribute,
-# sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all copies or
-# substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
-# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
-# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT
-# OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-# ==================================================================================================
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
 
 from abc import abstractmethod
 from collections.abc import Iterable

src/sdk/pynni/nni/networkmorphism_tuner/networkmorphism_tuner.py

-# Copyright (c) Microsoft Corporation. All rights reserved.
-#
-# MIT License
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
-# associated documentation files (the "Software"), to deal in the Software without restriction,
-# including without limitation the rights to use, copy, modify, merge, publish, distribute,
-# sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all copies or
-# substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
-# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
-# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT
-# OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-# ==================================================================================================
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 """
 networkmorphsim_tuner.py
 """

src/sdk/pynni/nni/networkmorphism_tuner/nn.py

-# Copyright (c) Microsoft Corporation. All rights reserved.
-#
-# MIT License
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
-# associated documentation files (the "Software"), to deal in the Software without restriction,
-# including without limitation the rights to use, copy, modify, merge, publish, distribute,
-# sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all copies or
-# substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
-# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
-# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT
-# OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-# ==================================================================================================
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
 
 from abc import abstractmethod
 

src/sdk/pynni/nni/networkmorphism_tuner/test_networkmorphism_tuner.py

-# Copyright (c) Microsoft Corporation. All rights reserved.
-#
-# MIT License
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
-# associated documentation files (the "Software"), to deal in the Software without restriction,
-# including without limitation the rights to use, copy, modify, merge, publish, distribute,
-# sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all copies or
-# substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
-# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
-# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT
-# OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-# ==================================================================================================
-
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
 
 import json
 from unittest import TestCase, main

src/sdk/pynni/nni/networkmorphism_tuner/utils.py

-# Copyright (c) Microsoft Corporation. All rights reserved.
-#
-# MIT License
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
-# associated documentation files (the "Software"), to deal in the Software without restriction,
-# including without limitation the rights to use, copy, modify, merge, publish, distribute,
-# sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all copies or
-# substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
-# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
-# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT
-# OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-# ==================================================================================================
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
 
 
 class Constant:

src/sdk/pynni/nni/parameter_expressions.py

-# Copyright (c) Microsoft Corporation
-# All rights reserved.
-#
-# MIT License
-#
-# Permission is hereby granted, free of charge,
-# to any person obtaining a copy of this software and associated
-# documentation files (the "Software"), to deal in the Software without restriction,
-# including without limitation the rights to use, copy, modify, merge, publish,
-# distribute, sublicense, and/or sell copies of the Software, and
-# to permit persons to whom the Software is furnished to do so, subject to the following conditions:
-# The above copyright notice and this permission notice shall be included
-# in all copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING
-# BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
-# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 '''
 parameter_expression.py
 '''

src/sdk/pynni/nni/platform/__init__.py

-# Copyright (c) Microsoft Corporation. All rights reserved.
-#
-# MIT License
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
-# associated documentation files (the "Software"), to deal in the Software without restriction,
-# including without limitation the rights to use, copy, modify, merge, publish, distribute,
-# sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all copies or
-# substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
-# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
-# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT
-# OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-# ==================================================================================================
-
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
 
 from ..env_vars import trial_env_vars
 

src/sdk/pynni/nni/platform/local.py

-# Copyright (c) Microsoft Corporation. All rights reserved.
-#
-# MIT License
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
-# associated documentation files (the "Software"), to deal in the Software without restriction,
-# including without limitation the rights to use, copy, modify, merge, publish, distribute,
-# sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all copies or
-# substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
-# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
-# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT
-# OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-# ==================================================================================================
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
 
 import os
 import sys
@@ -101,4 +84,4 @@ def get_trial_id():
     return trial_env_vars.NNI_TRIAL_JOB_ID
 
 def get_sequence_id():
-    return trial_env_vars.NNI_TRIAL_SEQ_ID
+    return int(trial_env_vars.NNI_TRIAL_SEQ_ID)