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

Author: jaybdub

torch2trt/torch2trt.py

@@ -1,9 +1,10 @@
 import torch
 import tensorrt as trt
-from copy import copy
+import copy
 import numpy as np
 import io
 from collections import defaultdict
+import importlib
 
 from .calibration import (
     TensorBatchDataset,
@@ -297,30 +298,24 @@ def wrapper(*args, **kwargs):
 class ConversionHook(object):
     """Attaches TensorRT converter to PyTorch method call"""
 
-    def __init__(self, ctx, method, converter):
+    def __init__(self, ctx, key, converter):
         self.ctx = ctx
-        self.method_str = method
+        self.key = key
         self.converter = converter
 
     def _set_method(self, method):
-        exec("%s = method" % self.method_str)
+        module = self.converter['module']
+        exec('module.%s = method' % self.converter['qual_name'])
 
     def __enter__(self):
-        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
-                )
+        self._set_method(
+            attach_converter(
+                self.ctx, self.converter['method_impl'], self.converter, self.converter['method_str']
             )
+        )
 
     def __exit__(self, type, val, tb):
-        if self.method_impl:
-            self._set_method(self.method_impl)
+        self._set_method(self.converter['method_impl'])
 
 def default_input_names(num_inputs):
     return ["input_%d" % i for i in range(num_inputs)]
@@ -369,8 +364,8 @@ def __init__(self, network, converters=CONVERTERS):
         self.method_kwargs = None
         self.method_return = None
         self.hooks = [
-            ConversionHook(self, method, converter)
-            for method, converter in converters.items()
+            ConversionHook(self, key, converter)
+            for key, converter in converters.items()
         ]
 
     def __enter__(self):
@@ -569,11 +564,40 @@ 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):
-
+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 register_converter(converter):
-        CONVERTERS[method] = {"converter": converter, "is_real": is_real}
+        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
+        }
         return converter
 
     def pass_converter(converter):
@@ -584,4 +608,4 @@ def pass_converter(converter):
     else:
         return pass_converter
 
-    return register_converter
+    return register_converter