Revert "allow direct method setting to support custom layers (#460)"

This reverts commit 81024cc.

torch2trt/torch2trt.py

@@ -1,10 +1,9 @@
 import torch
 import tensorrt as trt
-import copy
+from copy import copy
 import numpy as np
 import io
 from collections import defaultdict
-import importlib
 
 from .calibration import (
     TensorBatchDataset,
@@ -298,24 +297,30 @@ def wrapper(*args, **kwargs):
 class ConversionHook(object):
     """Attaches TensorRT converter to PyTorch method call"""
 
-    def __init__(self, ctx, key, converter):
+    def __init__(self, ctx, method, converter):
         self.ctx = ctx
-        self.key = key
+        self.method_str = method
         self.converter = converter
 
     def _set_method(self, method):
-        module = self.converter['module']
-        exec('module.%s = method' % self.converter['qual_name'])
+        exec("%s = method" % self.method_str)
 
     def __enter__(self):
-        self._set_method(
-            attach_converter(
-                self.ctx, self.converter['method_impl'], self.converter, self.converter['method_str']
+        try:
+            self.method_impl = eval(self.method_str)
+        except AttributeError:
+            self.method_impl = None
+
+        if self.method_impl:
+            self._set_method(
+                attach_converter(
+                    self.ctx, self.method_impl, self.converter, self.method_str
+                )
             )
-        )
 
     def __exit__(self, type, val, tb):
-        self._set_method(self.converter['method_impl'])
+        if self.method_impl:
+            self._set_method(self.method_impl)
 
 def default_input_names(num_inputs):
     return ["input_%d" % i for i in range(num_inputs)]
@@ -364,8 +369,8 @@ def __init__(self, network, converters=CONVERTERS):
         self.method_kwargs = None
         self.method_return = None
         self.hooks = [
-            ConversionHook(self, key, converter)
-            for key, converter in converters.items()
+            ConversionHook(self, method, converter)
+            for method, converter in converters.items()
         ]
 
     def __enter__(self):
@@ -564,40 +569,11 @@ def torch2trt(module,
 
 # DEFINE ALL CONVERSION FUNCTIONS
 
-def get_module_qualname(name):
-    s = name.split('.')
-
-    for i in range(len(s)):
-        idx = len(s) - i - 1
-        modulename, qualname = ".".join(s[:idx]), ".".join(s[idx:])
-        try:
-            module = importlib.import_module(modulename)
-            return module, modulename, qualname
-        except:
-            pass
-
-    raise RuntimeError("Could not import module")
-
 
-def tensorrt_converter(method, is_real=True, enabled=True, imports=[]):
-
-    if isinstance(method, str):
-        module, module_name, qual_name = get_module_qualname(method)
-    else:
-        module, module_name, qual_name = importlib.import_module(method.__module__), method.__module__, method.__qualname__
-
-    method_impl = eval('copy.deepcopy(module.%s)' % qual_name)
-
+def tensorrt_converter(method, is_real=True, enabled=True):
+
     def register_converter(converter):
-        CONVERTERS[method] = {
-            "converter": converter, 
-            "is_real": is_real, 
-            "module": module,
-            "module_name": module_name,
-            "qual_name": qual_name,
-            "method_str": module_name + '.' + qual_name,
-            "method_impl": method_impl
-        }
+        CONVERTERS[method] = {"converter": converter, "is_real": is_real}
         return converter
 
     def pass_converter(converter):
@@ -608,4 +584,4 @@ def pass_converter(converter):
     else:
         return pass_converter
 
-    return register_converter
+    return register_converter