Hello everyone I’m a newbie on PyTorch, previously I create small models not very complex, so now I’m trying to build and train bigger models, so I start with a pose estimation, this models use as in the literature indicate the first 10 layers of VGG19 I already figure it out how to do this by reading this forum but still I’m not sure if I’m doing this correctly or building the next layers on the right way:
Step1:
I took the vgg19 model and use the layers of interest
model_vgg19=models.vgg19(pretrained=True)
for param in model_vgg19.features.parameters(): param.requires_grad=False
my_vgga19=model_vgg19.features[:23]

Step2:
Here is where
I build the next part of the model:
class RMPPE(nn.Module):
    def __init___(self):
        super(RMPPE,self).__init__()
        self.ConvLayerStep2=nn.Sequential(
        nn.Conv2d(512,256,kernel_size=3,stride=1,padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(256,128,kernel_size=3,stride=1,padding=1),
        nn.ReLU(inplace=True))
        
        self.ConvLayerStep3a=nn.Sequential(
        nn.Conv2d(128,128,kernel_size=3,stride=1,padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(128,128,kernel_size=3,stride=1,padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(128,128,kernel_size=3,stride=1,padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(128,512,kernel_size=1,stride=1,padding=0),
        nn.ReLU(inplace=True),
        nn.Conv2d(512,38,kernel_size=1,stride=1,padding=0))
        
        self.ConvLayerStep3b=nn.Sequential(
        nn.Conv2d(128,128,kernel_size=3,stride=1,padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(128,128,kernel_size=3,stride=1,padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(128,128,kernel_size=3,stride=1,padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(128,512,kernel_size=1,stride=1,padding=0),
        nn.ReLU(inplace=True),
        nn.Conv2d(512,19,kernel_size=1,stride=1,padding=0))
   def forward(self,x):
        out_ConvLayerStep2=self.ConvLayerStep2(x)
        out_ConvLayerStep3a=self.ConvLayerStep3a(out_ConvLayerStep2)
        out_ConvLayerStep3b=self.ConvLayerStep3b(out_ConvLayerStep2)
        
        out=torch.cat((out_ConvLayerStep2,out_ConvLayerStep3a,out_ConvLayerStep3b),dims=1)
        return out


Step3:
Before to train or something like this I only want to visualize using print
model_pose = nn.Sequential(my_vgga19, RMPPE())
print(model_pose)

The Output is this
Sequential(
  (0): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU(inplace)
    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU(inplace)
    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (6): ReLU(inplace)
    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (8): ReLU(inplace)
    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU(inplace)
    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (13): ReLU(inplace)
    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): ReLU(inplace)
    (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (17): ReLU(inplace)
    (18): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (19): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (20): ReLU(inplace)
    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (22): ReLU(inplace)
  )
  (1): RMPPE()
)

So my current status is I have no clue what is happening, is something is missing or my approach isn’t the correct, thanks for your attention and time to read this big post and any guide or help would be awesome.