Updated README.md

Signed-off-by: Bo Wang <[email protected]>

Author: bowang007

examples/README.md

@@ -106,22 +106,31 @@ converter and use it in our application:
 import torch
 import trtorch
 
+# After "python3 setup install", you should find this .so file under generated "build" directory
 torch.ops.load_library('./build/lib.linux-x86_64-3.6/elu_converter.cpython-36m-x86_64-linux-gnu.so')
 
+
 class Elu(torch.nn.Module):
+
     def __init__(self):
         super(Elu, self).__init__()
         self.elu = torch.nn.ELU()
 
     def forward(self, x):
         return self.elu(x)
 
+
+def MaxDiff(pytorch_out, trtorch_out):
+    diff = torch.sub(pytorch_out, trtorch_out)
+    abs_diff = torch.abs(diff)
+    max_diff = torch.max(abs_diff)
+    print("Maximum differnce between TRTorch and PyTorch: \n", max_diff)
+
+
 def main():
-    data = torch.randn((1, 1, 2, 2)).to("cuda")
     model = Elu().eval()  #.cuda()
 
     scripted_model = torch.jit.script(model)
-    print(scripted_model.graph)
     compile_settings = {
         "input_shapes": [{
             "min": [1024, 1, 32, 32],
@@ -133,10 +142,14 @@ def main():
     }
     trt_ts_module = trtorch.compile(scripted_model, compile_settings)
     input_data = torch.randn((1024, 1, 32, 32))
-    print(input_data[0, :, :, 0])
     input_data = input_data.half().to("cuda")
-    result = trt_ts_module(input_data)
-    print(result[0, :, :, 0])
+    pytorch_out = model.forward(input_data)
+
+    trtorch_out = trt_ts_module(input_data)
+    print('PyTorch output: \n', pytorch_out[0, :, :, 0])
+    print('TRTorch output: \n', trtorch_out[0, :, :, 0])
+    MaxDiff(pytorch_out, trtorch_out)
+
 
 if __name__ == "__main__":
     main()