arunm8489

def detection_preprocess(x,inp_dim,anchors,num_classes,CUDA=False):
        """
        This function will take input_dimension_of_image,anchors and number of classes as input 
        """
        # x --> 4D feature map
        batch_size = x.size(0)
        grid_size = x.size(2)
        stride =  inp_dim // x.size(2)   # factor by which current feature map reduced from input
        #grid_size = inp_dim // stride
    

arunm8489

elif module_type == 'yolo':
    anchors = self.module_list[i][0].anchors
                
    #Get the input dimensions
    inp_dim = int(self.net_info["height"])
    #Get the number of classes
    num_classes = int(module["classes"])
            
    #Transform 
    x = x.data   # get the data at that point

arunm8489

if module_type == "convolutional" or module_type == "upsample":
   x = self.module_list[i](x)
   outputs[i] = x

elif module_type == "route":
   layers = module["layers"]
   layers = [int(a) for a in layers]
   if len(layers) == 1:
      x = outputs[layers[0]]
   if len(layers) > 1:

arunm8489

def forward(self, x, CUDA=False):
    modules = self.blocks[1:]
    #We cache the outputs for the route layer
    outputs = {}   
    write = 0     
    for i, module in enumerate(modules):        
         module_type = (module["type"])

arunm8489

class Darknet(nn.Module):
    def __init__(self, cfgfile):
        super(Darknet, self).__init__()
        self.blocks = parse_cfg(cfgfile)
        self.net_info, self.module_list = model_initialization(self.blocks)

arunm8489

blocks =  parse_cfg(cfgfile)
details,modules = model_initialization(blocks)
modules

arunm8489

class DummyLayer(nn.Module):
    def __init__(self):
        super(DummyLayer, self).__init__()
        

        
class DetectionLayer(nn.Module):
    def __init__(self, anchors):
        super(DetectionLayer, self).__init__()
        self.anchors = anchors

arunm8489

modulelist.append(seq)
output_filters.append(filters)     
channels = filters

arunm8489

class DetectionLayer(nn.Module):
    def __init__(self, anchors):
        super(DetectionLayer, self).__init__()
        self.anchors = anchors

arunm8489

elif block["type"] == "yolo":
     mask = block["mask"].split(",")
     mask = [int(m) for m in mask]
     anchors = block["anchors"].split(",")
     anchors = [(int(anchors[i]), int(anchors[i + 1])) for i in range(0, len(anchors), 2)]
     anchors = [anchors[i] for i in mask]
     block["anchors"] = anchors
            
     detectorLayer = DetectionLayer(anchors)
     seq.add_module("Detection_{0}".format(i),detectorLayer)