Update py/README.md

py/README.md

@@ -7,26 +7,24 @@ Torch-TensorRT is a compiler for PyTorch/TorchScript, targeting NVIDIA GPUs via
 ## Example Usage
 
 ``` python
-import torch
-import torchvision
 import torch_tensorrt
 
-# Get a model
-model = torchvision.models.alexnet(pretrained=True).eval().cuda()
+...
 
-# Create some example data
-data = torch.randn((1, 3, 224, 224)).to("cuda")
+trt_ts_module = torch_tensorrt.compile(torch_script_module,
+    inputs = [example_tensor, # Provide example tensor for input shape or...
+        torch_tensorrt.Input( # Specify input object with shape and dtype
+            min_shape=[1, 3, 224, 224],
+            opt_shape=[1, 3, 512, 512],
+            max_shape=[1, 3, 1024, 1024],
+            # For static size shape=[1, 3, 224, 224]
+            dtype=torch.half) # Datatype of input tensor. Allowed options torch.(float|half|int8|int32|bool)
+    ],
+    enabled_precisions = {torch.half}, # Run with FP16)
 
-# Trace the module with example data
-traced_model = torch.jit.trace(model, [data])
+result = trt_ts_module(input_data) # run inference
+torch.jit.save(trt_ts_module, "trt_torchscript_module.ts") # save the TRT embedded Torchscript
 
-# Compile module
-compiled_trt_model = torch_tensorrt.compile(traced_model, {
-    "inputs": [torch_tensorrt.Input(data.shape)],
-    "enabled_precisions": {torch.float, torch.half}, # Run with FP16
-})
-
-results = compiled_trt_model(data.half())
 ```
 
 ## Installation