def __init__(self):
self.inplanes = 64
super(DRNRes, self).__init__()
drn = drn_d_54(pretrained=True)
self.layer0 = drn.layer0
self.layer1 = drn.layer1
self.layer2 = drn.layer2
self.layer3 = drn.layer3
self.layer4 = drn.layer4
self.layer5 = drn.layer5
self.layer6 = drn.layer6
self.layer7 = drn.layer7
self.layer8 = drn.layer8
def __init__(self, options):
super(PlaneNet, self).__init__()
self.options = options
self.drn = drn_d_54(pretrained=True, out_map=32, num_classes=-1, out_middle=False)
self.pool = torch.nn.AvgPool2d((32 * options.height / options.width, 32))
self.plane_pred = nn.Linear(512, options.numOutputPlanes * 3)
self.pyramid = PyramidModule(options, 512, 128)
self.feature_conv = ConvBlock(1024, 512)
self.segmentation_pred = nn.Conv2d(512, options.numOutputPlanes + 1, kernel_size=1)
self.depth_pred = nn.Conv2d(512, 1, kernel_size=1)
self.upsample = torch.nn.Upsample(size=(options.outputHeight, options.outputWidth), mode='bilinear')
return
def __init__(self, options):
super(Model, self).__init__()
self.options = options
self.drn = drn_d_54(pretrained=True,
out_map=32,
num_classes=-1,
out_middle=False)
self.pyramid = PyramidModule(options, 512, 128)
self.feature_conv = ConvBlock(1024, 512)
self.segmentation_pred = nn.Conv2d(512,
NUM_CORNERS + NUM_ICONS + 2 +
NUM_ROOMS + 2,
kernel_size=1)
self.upsample = torch.nn.Upsample(size=(options.height, options.width),
mode='bilinear')
return
def __init__(self, type, num_classes):
super().__init__()
if type == "drn_d_38":
self.drn = drn_d_38(pretrained=True)
elif type == "drn_d_54":
self.drn = drn_d_54(pretrained=True)
elif type == "drn_d_105":
self.drn = drn_d_105(pretrained=True)
else:
raise Exception("Unsupported drn model type: '{}".format(type))
self.expand_channels = ExpandChannels2d(3)
self.bn = nn.BatchNorm2d(3)
self.avgpool = nn.AdaptiveAvgPool2d(output_size=1)
self.fc = nn.Conv2d(self.drn.out_dim,
num_classes,
kernel_size=1,
stride=1,
padding=0,
bias=True)
batch_size=test_batch_size,
sampler=vali_sampler)
test_dataset = image_list_folder.ImageListFolder(root='data/dev_data/',
transform=test_transform)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
num_workers=16,
batch_size=test_batch_size,
shuffle=True,
drop_last=False)
if model_name in pretrainedmodels.model_names:
backbone = pretrainedmodels.__dict__[model_name]()
backbone = nn.Sequential(*list(backbone.children())[:-2])
elif model_name == 'drn_d_54':
backbone = drn.drn_d_54(True, out_feat=True)
else:
raise Exception('\nModel {} not exist'.format(model_name))
# net = GAIN(backbone, num_classes, in_channels=in_channels)
solver = GAINSolver(backbone,
num_classes,
in_channels,
train_loader,
test_loader,
test_batch_size,
lr=lr,
loss_weights=loss_weights,
checkpoint_name=checkpoint_name,
devices=devices,
area_threshold=area_threshold,
optimizer=optimizer,