def __init__(self, num_class):
self.inplanes = 128
super(SpatialOCRNetasDec, self).__init__()
self.num_classes=num_class
in_channels = [1024, 2048]
self.conv_3x3 = nn.Sequential(
nn.Conv2d(in_channels[1], 512, kernel_size=3, stride=1, padding=1),
BatchNorm2d(512),
nn.ReLU(inplace=True)
)
from models.ocr_modules.spatial_ocr_block import SpatialGather_Module, SpatialOCR_Module
self.spatial_context_head = SpatialGather_Module(self.num_classes)
self.spatial_ocr_head = SpatialOCR_Module(in_channels=512,
key_channels=256,
out_channels=512,
scale=1,
dropout=0.05
)
# self.head = nn.Conv2d(512, self.num_classes, kernel_size=1, stride=1, padding=0, bias=True)
self.dsn_head = nn.Sequential(
nn.Conv2d(in_channels[0], 512, kernel_size=3, stride=1, padding=1),
BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Dropout2d(0.05),
nn.Conv2d(512, self.num_classes, kernel_size=1, stride=1, padding=0, bias=True)
)
def __init__(self, net_enc, crit, args, deep_sup_scale=None):
super(ClipOCRNet, self).__init__()
self.args = args
if self.args.use_memory:
self.memory = []
self.crit = crit
self.deep_sup_scale = deep_sup_scale
self.encoder = net_enc
self.inplanes = 128
self.num_classes = args.num_class
in_channels = [1024, 2048]
self.conv_3x3 = nn.Sequential(
nn.Conv2d(in_channels[1], 512, kernel_size=3, stride=1, padding=1),
BatchNorm2d(512), nn.ReLU(inplace=True))
self.spatial_context_head = SpatialTemporalGather_Module(
self.num_classes)
self.spatial_ocr_head = SpatialOCR_Module(in_channels=512,
key_channels=256,
out_channels=512,
scale=1,
dropout=0.05)
self.head = nn.Conv2d(512,
self.num_classes,
kernel_size=1,
stride=1,
padding=0,
bias=True)
self.dsn_head = nn.Sequential(
nn.Conv2d(in_channels[0], 512, kernel_size=3, stride=1, padding=1),
BatchNorm2d(512), nn.ReLU(inplace=True), nn.Dropout2d(0.05),
nn.Conv2d(512,
self.num_classes,
kernel_size=1,
stride=1,
padding=0,
bias=True))