Merge branch 'dev-nas-refactor' of github.com:Microsoft/nni into dev-nas-refactor

.gitignore

@@ -80,6 +80,7 @@ venv.bak/
 
 # VSCode
 .vscode
+.vs
 
 # In case you place source code in ~/nni/
 /experiments

examples/nas/.gitignore

-data
+data

examples/nas/darts/search.py

 from argparse import ArgumentParser
 
-import datasets
 import torch
 import torch.nn as nn
 
-from model import SearchCNN
-from nni.nas.pytorch.darts import DartsTrainer
+import datasets
+from nni.nas.pytorch.darts import CnnNetwork, DartsTrainer
 from utils import accuracy
 
-
 if __name__ == "__main__":
     parser = ArgumentParser("darts")
-    parser.add_argument("--layers", default=4, type=int)
-    parser.add_argument("--nodes", default=2, type=int)
+    parser.add_argument("--layers", default=5, type=int)
+    parser.add_argument("--nodes", default=4, type=int)
     parser.add_argument("--batch-size", default=128, type=int)
     parser.add_argument("--log-frequency", default=1, type=int)
     args = parser.parse_args()
 
     dataset_train, dataset_valid = datasets.get_dataset("cifar10")
 
-    model = SearchCNN(3, 16, 10, args.layers, n_nodes=args.nodes)
+    model = CnnNetwork(3, 16, 10, args.layers, n_nodes=args.nodes)
     criterion = nn.CrossEntropyLoss()
 
     optim = torch.optim.SGD(model.parameters(), 0.025, momentum=0.9, weight_decay=3.0E-4)

examples/nas/pdarts/.gitignore

+data/*
+log

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

+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)
+
+    _, pred = output.topk(maxk, 1, True, True)
+    pred = pred.t()
+    # one-hot case
+    if target.ndimension() > 1:
+        target = target.max(1)[1]
+
+    correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+    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
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser("darts")
+    parser.add_argument("--layers", default=5, type=int)
+    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("--log-frequency", default=1, type=int)
+    args = parser.parse_args()
+
+    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()
+
+        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
+
+    trainer = PdartsTrainer(model_creator,
+                            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],
+                            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)
+    trainer.train()
+    trainer.export()

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

 from .mutator import DartsMutator
 from .trainer import DartsTrainer
+from .cnn_cell import CnnCell
+from .cnn_network import CnnNetwork

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

examples/nas/darts/model.py → src/sdk/pynni/nni/nas/pytorch/darts/cnn_network.py

-import torch
-import torch.nn as nn
-
-import ops
-from nni.nas import pytorch as nas
-
-
-class SearchCell(nn.Module):
-    """
-    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().__init__()
-        self.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 = ops.FactorizedReduce(channels_pp, channels, affine=False)
-        else:
-            self.preproc0 = ops.StdConv(channels_pp, channels, 1, 1, 0, affine=False)
-        self.preproc1 = ops.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
-                op = nas.mutables.LayerChoice([ops.PoolBN('max', channels, 3, stride, 1, affine=False),
-                                               ops.PoolBN('avg', channels, 3, stride, 1, affine=False),
-                                               ops.Identity() if stride == 1 else
-                                               ops.FactorizedReduce(channels, channels, affine=False),
-                                               ops.SepConv(channels, channels, 3, stride, 1, affine=False),
-                                               ops.SepConv(channels, channels, 5, stride, 2, affine=False),
-                                               ops.DilConv(channels, channels, 3, stride, 2, 2, affine=False),
-                                               ops.DilConv(channels, channels, 5, stride, 4, 2, affine=False),
-                                               ops.Zero(stride)],
-                                              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)
+import torch.nn as nn
 
-        output = torch.cat(tensors[2:], dim=1)
-        return output
+from .cnn_cell import CnnCell
 
 
-class SearchCNN(nn.Module):
+class CnnNetwork(nn.Module):
     """
     Search CNN model
     """
 
-    def __init__(self, in_channels, channels, n_classes, n_layers, n_nodes=4, stem_multiplier=3):
+    def __init__(self, in_channels, channels, n_classes, n_layers, n_nodes=4, stem_multiplier=3, cell_type=CnnCell):
         """
         Initializing a search channelsNN.
 
@@ -121,7 +53,7 @@ class SearchCNN(nn.Module):
                 c_cur *= 2
                 reduction = True
 
-            cell = SearchCell(n_nodes, channels_pp, channels_p, c_cur, reduction_p, reduction)
+            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

examples/nas/darts/ops.py → 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
+    'skip_connect',  # identity
     'sep_conv_3x3',
     'sep_conv_5x5',
     'dil_conv_3x3',
     'dil_conv_5x5',
-    'none'
 ]
 
 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),
+    '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
+    '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)
 }
 
@@ -60,6 +58,7 @@ class PoolBN(nn.Module):
     """
     AvgPool or MaxPool - BN
     """
+
     def __init__(self, pool_type, C, kernel_size, stride, padding, affine=True):
         """
         Args:
@@ -85,6 +84,7 @@ 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(
@@ -101,6 +101,7 @@ 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(
@@ -118,14 +119,14 @@ 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
+                    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_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)
         )
@@ -138,6 +139,7 @@ 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(
@@ -172,6 +174,7 @@ 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()

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

@@ -94,7 +94,8 @@ class DartsTrainer(Trainer):
         with torch.no_grad():
             for step, (X, y) in enumerate(self.valid_loader):
                 X, y = X.to(self.device), y.to(self.device)
-                logits = self.model(X)
+                with self.mutator.forward_pass():
+                    logits = self.model(X)
                 metrics = self.metrics(logits, y)
                 meters.update(metrics)
                 if self.log_frequency is not None and step % self.log_frequency == 0:

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

@@ -40,7 +40,7 @@ class EnasMutator(PyTorchMutator):
         self.attn_query = nn.Linear(self.lstm_size, self.lstm_size, bias=False)
         self.v_attn = nn.Linear(self.lstm_size, 1, bias=False)
         self.g_emb = nn.Parameter(torch.randn(1, self.lstm_size) * 0.1)
-        self.skip_targets = nn.Parameter(torch.tensor([1.0 - self.skip_target, self.skip_target]), requires_grad=False)
+        self.skip_targets = nn.Parameter(torch.Tensor([1.0 - self.skip_target, self.skip_target]), requires_grad=False)
         self.cross_entropy_loss = nn.CrossEntropyLoss()
 
     def after_build(self, model):
@@ -79,7 +79,7 @@ class EnasMutator(PyTorchMutator):
         self._lstm_next_step()
         logit = self.soft(self._h[-1])
         if self.tanh_constant is not None:
-           logit = self.tanh_constant * torch.tanh(logit)
+            logit = self.tanh_constant * torch.tanh(logit)
         branch_id = torch.multinomial(F.softmax(logit, dim=-1), 1).view(-1)
         log_prob = self.cross_entropy_loss(logit, branch_id)
         self.sample_log_prob += log_prob

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

@@ -56,9 +56,6 @@ class PyTorchMutable(nn.Module):
                 "Mutator not set for {}. Did you initialize a mutable on the fly in forward pass? Move to __init__"
                 "so that trainer can locate all your mutables. See NNI docs for more details.".format(self))
 
-    def __repr__(self):
-        return "{} ({})".format(self.name, self.key)
-
 
 class MutableScope(PyTorchMutable):
     """
@@ -85,6 +82,9 @@ class LayerChoice(PyTorchMutable):
         self.reduction = reduction
         self.return_mask = return_mask
 
+    def __len__(self):
+        return self.length
+
     def forward(self, *inputs):
         out, mask = self.mutator.on_forward(self, *inputs)
         if self.return_mask:
@@ -116,4 +116,4 @@ class InputChoice(PyTorchMutable):
 
     def similar(self, other):
         return type(self) == type(other) and \
-               self.n_candidates == other.n_candidates and self.n_selected and other.n_selected
+            self.n_candidates == other.n_candidates and self.n_selected and other.n_selected

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

+from .trainer import PdartsTrainer

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

+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
+from nni.nas.pytorch.mutables import LayerChoice
+
+
+class PdartsMutator(DartsMutator):
+
+    def __init__(self, model, pdarts_epoch_index, pdarts_num_to_drop, switches=None):
+        self.pdarts_epoch_index = pdarts_epoch_index
+        self.pdarts_num_to_drop = pdarts_num_to_drop
+        self.switches = switches
+
+        super(PdartsMutator, self).__init__(model)
+
+    def before_build(self, model):
+        self.choices = nn.ParameterDict()
+        if self.switches is None:
+            self.switches = {}
+
+    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
+
+    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 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):
+            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

+from nni.nas.pytorch.darts import DartsTrainer
+from nni.nas.pytorch.trainer import Trainer
+
+from .mutator import PdartsMutator
+
+
+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):
+        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)
+        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
+        }
+
+    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, lr_scheduler = self.model_creator(
+                layers, n_nodes)
+            mutator = PdartsMutator(
+                model, epoch, self.pdarts_num_to_drop, switches)
+
+            self.trainer = DartsTrainer(model, loss=loss, model_optim=model_optim,
+                                        lr_scheduler=lr_scheduler, mutator=mutator, **self.darts_parameters)
+            print("start pdrats training %s..." % epoch)
+
+            self.trainer.train()
+
+            # with open('log/parameters_%d.txt' % epoch, "w") as f:
+            #     f.write(str(model.parameters))
+
+            switches = mutator.drop_paths()
+
+    def export(self):
+        if (self.trainer is not None) and hasattr(self.trainer, "export"):
+            self.trainer.export()