def __init__(self,
n_classes=7,
hidden_layers=256,
graph_hidden_layers=512):
super(LevelReasoning, self).__init__()
self.hidden_layers = hidden_layers
self.nclasses = n_classes
self.graph_transfer = nn.Conv1d(graph_hidden_layers,
graph_hidden_layers,
kernel_size=1,
stride=1)
self.graph_transfer_back = nn.Conv1d(graph_hidden_layers,
hidden_layers,
kernel_size=1,
stride=1)
self.attn1 = gcn.GraphContextReasoning(graph_hidden_layers, 'relu')
self.attn2 = gcn.GraphContextReasoning(graph_hidden_layers, 'relu')
self.attn3 = gcn.GraphContextReasoning(graph_hidden_layers, 'relu')
self.bn1 = SynchronizedBatchNorm1d(graph_hidden_layers)
self.bn2 = SynchronizedBatchNorm1d(graph_hidden_layers)
self.bn3 = SynchronizedBatchNorm1d(graph_hidden_layers)
def testSyncBatchNormSyncEval(self):
bn = nn.BatchNorm1d(10, eps=1e-5, affine=False)
sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
bn.cuda()
sync_bn.cuda()
self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), False, cuda=True)
def __init__(self, backbone, out_dim, pretrained_model=None):
super(enetv2, self).__init__()
self.enet = enet.EfficientNet.from_name(backbone)
if pretrained_model is not None:
self.enet.load_state_dict(torch.load(pretrained_model[backbone]))
self.myfc = nn.Linear(self.enet._fc.in_features, out_dim)
# self.batchnorm= nn.BatchNorm1d(self.enet._fc.in_features)
self.batchnorm = SynchronizedBatchNorm1d(self.enet._fc.in_features)
self.enet._fc = nn.Identity()
def __init__(self, backbone, out_dim):
super(enetv2, self).__init__()
self.enet = enet.EfficientNet.from_name(backbone)
self.batchnorm = SynchronizedBatchNorm1d(self.enet._fc.in_features)
self.myfc = nn.Linear(self.enet._fc.in_features, out_dim)
self.enet._fc = nn.Identity()
def testSyncBatchNormNormalEval(self):
bn = nn.BatchNorm1d(10)
sync_bn = SynchronizedBatchNorm1d(10)
self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), False)
