fix(//py): Fix some api import issues

Signed-off-by: Naren Dasan <[email protected]>
Signed-off-by: Naren Dasan <[email protected]>

Author: narendasan

py/torch_tensorrt/_Input.py

@@ -125,7 +125,7 @@ def __str__(self) -> str:
             return "Input(shape={}, dtype={}, format={})".format(self.shape, str(self.dtype), str(self.format))
         elif self.shape_mode == Input._ShapeMode.DYNAMIC:
             return "Input(min_shape={}, opt_shape={}, max_shape={}, dtype={}, format={})".format(
-                self.shape["min_shape"], self.shape["min_shape"], self.shape["min_shape"], str(self.dtype),
+                self.shape["min_shape"], self.shape["opt_shape"], self.shape["max_shape"], str(self.dtype),
                 str(self.format))
         else:
             raise RuntimeError("Unknown input shape mode")
@@ -145,14 +145,14 @@ def _to_internal(self) -> _C.Input:
                     "Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: "
                     + str(type(self.shape["opt_shape"])) + " for opt_shape")
             else:
-                internal_in.min = self.shape["op_shape"]
+                internal_in.opt = self.shape["opt_shape"]
 
             if not Input._supported_input_size_type(self.shape["max_shape"]):
                 raise TypeError(
                     "Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: "
                     + str(type(self.shape["max_shape"])) + " for max_shape")
             else:
-                internal_in.min = self.shape["opt_shape"]
+                internal_in.max = self.shape["max_shape"]
             internal_in.input_is_dynamic = True
         else:
             if not Input._supported_input_size_type(self.shape):

py/torch_tensorrt/_compile.py

@@ -3,8 +3,10 @@
 import torch_tensorrt.ts
 from torch_tensorrt import logging
 import torch
+from torch import fx
 from enum import Enum
 
+print(torch.fx.GraphModule)
 
 class _IRType(Enum):
     """Enum to set the minimum required logging level to print a message to stdout
@@ -89,6 +91,7 @@ def compile(module: Any, ir="default", inputs=[], enabled_precisions=set([_enums
         ts_mod = module
         if isinstance(module, torch.nn.Module):
             logging.log(
+                logging.Level.Info,
                 "Module was provided as a torch.nn.Module, trying to script the module with torch.jit.script. In the event of a failure please preconvert your module to TorchScript"
             )
             ts_mod = torch.jit.script(module)