rm useless file (#5042)

test/ut/sdk/models/pytorch_models/__init__.py

 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
-from .layer_choice_only import LayerChoiceOnlySearchSpace
 from .mutable_scope import SpaceWithMutableScope
 from .naive import NaiveSearchSpace
 from .nested import NestedSpace

test/ut/sdk/models/pytorch_models/layer_choice_only.py

-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from nni.nas.pytorch.mutables import LayerChoice
-
-
-class LayerChoiceOnlySearchSpace(nn.Module):
-    def __init__(self, test_case):
-        super().__init__()
-        self.test_case = test_case
-        self.conv1 = LayerChoice([nn.Conv2d(3, 6, 3, padding=1), nn.Conv2d(3, 6, 5, padding=2)])
-        self.pool = nn.MaxPool2d(2, 2)
-        self.conv2 = LayerChoice([nn.Conv2d(6, 16, 3, padding=1), nn.Conv2d(6, 16, 5, padding=2)],
-                                 return_mask=True)
-        self.conv3 = nn.Conv2d(16, 16, 1)
-        self.bn = nn.BatchNorm2d(16)
-
-        self.gap = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Linear(16, 10)
-
-    def forward(self, x):
-        bs = x.size(0)
-
-        x = self.pool(F.relu(self.conv1(x)))
-        x0, mask = self.conv2(x)
-        self.test_case.assertEqual(mask.size(), torch.Size([2]))
-        x1 = F.relu(self.conv3(x0))
-
-        x = self.pool(self.bn(x1))
-        self.test_case.assertEqual(mask.size(), torch.Size([2]))
-
-        x = self.gap(x).view(bs, -1)
-        x = self.fc(x)
-        return x