[WIP] Parameter replacement. Div,Gemm,Mul,Reshape,Resize,Sub,Transpose

README.md

@@ -41,7 +41,7 @@ Self-Created Tools to convert ONNX files (NCHW) to TensorFlow format (NHWC). The
 $ docker run --rm -it \
 -v `pwd`:/workdir \
 -w /workdir \
-ghcr.io/pinto0309/onnx2tf:0.0.25
+ghcr.io/pinto0309/onnx2tf:0.0.26
 
 or
 
@@ -72,6 +72,7 @@ usage: onnx2tf
 [-rlr]
 [-rpw]
 [-me]
+[-prf PARAM_REPLACEMENT_FILE]
 [-n]
 
 optional arguments:
@@ -130,6 +131,9 @@ optional arguments:
     (input_tensor - mean) / tf.sqrt(variance + mvn_epsilon)
     Default: 0.0000000001
 
+  -prf PARAM_REPLACEMENT_FILE, --param_replacement_file PARAM_REPLACEMENT_FILE
+    Parameter replacement file path. (.json)
+
   -n, --non_verbose
     Do not show all information logs. Only error logs are displayed.
 ```
@@ -155,6 +159,7 @@ convert(
   replace_leakyrelu_to_pseudo_leakyrelu: Union[bool, NoneType] = False,
   replace_power_to_pseudo_power: Optional[bool] = False,
   mvn_epsilon: Union[float, NoneType] = 0.0000000001,
+  param_replacement_file: Optional[str] = '',
   non_verbose: Union[bool, NoneType] = False
 ) -> keras.engine.training.Model
 
@@ -222,6 +227,9 @@ convert(
         (input_tensor - mean) / tf.sqrt(variance + mvn_epsilon)
         Default: 0.0000000001
 
+    param_replacement_file: Optional[str]
+        Parameter replacement file path. (.json)
+
     non_verbose: Optional[bool]
         Do not show all information logs. Only error logs are displayed.
         Only one of replace_argmax_to_reducemax_and_indicies_is_int64 and
@@ -235,7 +243,7 @@ convert(
 ```
 
 ## [WIP] Parameter replacement
-This tool is used to convert `NCW` to `NWC`, `NCHW` to `NHWC`, `NCDHW` to `NDHWC`, `NCDDHW` to `NDDHWC`, `NCDDDDDDHW` to `NDDDDDDHWC`. Therefore, as stated in the Key Concepts, the conversion will inevitably break down at some point in the model. You need to look at the entire conversion log to see which OP transpositions are failing and correct them yourself. I dare to explain very little because I know that no matter how much detail I put in the README, you guys will not read it at all.
+This tool is used to convert `NCW` to `NWC`, `NCHW` to `NHWC`, `NCDHW` to `NDHWC`, `NCDDHW` to `NDDHWC`, `NCDDDDDDHW` to `NDDDDDDHWC`. Therefore, as stated in the Key Concepts, the conversion will inevitably break down at some point in the model. You need to look at the entire conversion log to see which OP transpositions are failing and correct them yourself. I dare to explain very little because I know that no matter how much detail I put in the README, you guys will not read it at all. `attribute` or `INPUT constant` or `INPUT Initializer` can be replaced with the specified value.
 
 "A conversion error occurs." Please don't post such low level questions as issues.
 
@@ -255,6 +263,12 @@ This tool is used to convert `NCW` to `NWC`, `NCHW` to `NHWC`, `NCDHW` to `NDHWC
       "param_target": "attributes", # attributes or inputs
       "param_name": "axes",
       "values": [2] # Disable parameter transposition or overwrite parameters
+    },
+    {
+      "op_name": "Resize__697",
+      "param_target": "inputs",
+      "param_name": "Concat__696:0",
+      "values": [26,26] # Replacement of unk__x (Resize OP, sizes height/width parameter)
     }
   ]
 }

onnx2tf/__init__.py

@@ -1,3 +1,3 @@
 from onnx2tf.onnx2tf import convert, main
 
-__version__ = '0.0.25'
+__version__ = '0.0.26'

onnx2tf/onnx2tf.py

@@ -114,7 +114,7 @@ def convert(
         Default: 0.0000000001
 
     param_replacement_file: Optional[str]
-        Parameter replacement path. (.json)
+        Parameter replacement file path. (.json)
 
     non_verbose: Optional[bool]
         Do not show all information logs. Only error logs are displayed.\n
@@ -453,7 +453,7 @@ def main():
         '--param_replacement_file',
         type=str,
         default='',
-        help='Parameter replacement path. (.json)'
+        help='Parameter replacement file path. (.json)'
     )
     parser.add_argument(
         '-n',

onnx2tf/ops/Div.py

@@ -5,6 +5,8 @@
 import tensorflow as tf
 import onnx_graphsurgeon as gs
 from onnx2tf.utils.common_functions import (
+    get_replacement_parameter,
+    replace_parameter,
     get_constant_or_variable,
     print_node_info,
     inverted_operation_enable_disable,
@@ -14,6 +16,7 @@
 
 @print_node_info
 @inverted_operation_enable_disable
+@get_replacement_parameter
 def make_node(
     *,
     graph_node: gs.Node,
@@ -57,6 +60,25 @@ def make_node(
         'dtype': dtype,
     }
 
+    input_tensor_1 = tf_layers_dict[graph_node_input_1.name]['tf_node'] \
+        if isinstance(graph_node_input_1, gs.Variable) else graph_node_input_1
+    input_tensor_2 = tf_layers_dict[graph_node_input_2.name]['tf_node'] \
+        if isinstance(graph_node_input_2, gs.Variable) else graph_node_input_2
+
+    # Param replacement
+    input_tensor_1 = replace_parameter(
+        value_before_replacement=input_tensor_1,
+        param_target='inputs',
+        param_name=graph_node.inputs[0].name,
+        **kwargs,
+    )
+    input_tensor_2 = replace_parameter(
+        value_before_replacement=input_tensor_2,
+        param_target='inputs',
+        param_name=graph_node.inputs[1].name,
+        **kwargs,
+    )
+
     # Generation of TF OP
     target_cast_dtype = [
         np.int8,
@@ -65,16 +87,9 @@ def make_node(
         np.int64,
     ]
 
-    input_tensor_1 = tf_layers_dict[graph_node_input_1.name]['tf_node'] \
-        if isinstance(graph_node_input_1, gs.Variable) else graph_node_input_1
-    input_tensor_2 = tf_layers_dict[graph_node_input_2.name]['tf_node'] \
-        if isinstance(graph_node_input_2, gs.Variable) else graph_node_input_2
-
     divided_tensor = tf.math.divide(
-        x=tf_layers_dict[graph_node_input_1.name]['tf_node'] \
-            if isinstance(graph_node_input_1, gs.Variable) else graph_node_input_1,
-        y=tf_layers_dict[graph_node_input_2.name]['tf_node'] \
-            if isinstance(graph_node_input_2, gs.Variable) else graph_node_input_2,
+        x=input_tensor_1,
+        y=input_tensor_2,
         name=graph_node.name,
     )
 

onnx2tf/ops/Gemm.py

@@ -5,6 +5,8 @@
 import tensorflow as tf
 import onnx_graphsurgeon as gs
 from onnx2tf.utils.common_functions import (
+    get_replacement_parameter,
+    replace_parameter,
     get_constant_or_variable,
     print_node_info,
     inverted_operation_enable_disable,
@@ -14,6 +16,7 @@
 
 @print_node_info
 @inverted_operation_enable_disable
+@get_replacement_parameter
 def make_node(
     *,
     graph_node: gs.Node,
@@ -71,10 +74,8 @@ def make_node(
     shape = graph_node_output.shape
     dtype = graph_node_output.dtype
 
-    if bool(graph_node.attrs.get('transA', 0)):
-        x = tf.transpose(x)
-    if bool(graph_node.attrs.get('transB', 0)):
-        y = tf.transpose(y)
+    transA = bool(graph_node.attrs.get('transA', 0))
+    transB = bool(graph_node.attrs.get('transB', 0))
     alpha = graph_node.attrs.get('alpha', 1.0)
     beta = graph_node.attrs.get('beta', 1.0)
 
@@ -85,7 +86,55 @@ def make_node(
         'dtype': dtype,
     }
 
+    # Param replacement
+    x = replace_parameter(
+        value_before_replacement=x,
+        param_target='inputs',
+        param_name=graph_node.inputs[0].name,
+        **kwargs,
+    )
+    y = replace_parameter(
+        value_before_replacement=y,
+        param_target='inputs',
+        param_name=graph_node.inputs[1].name,
+        **kwargs,
+    )
+    z = replace_parameter(
+        value_before_replacement=z,
+        param_target='inputs',
+        param_name=graph_node.inputs[2].name,
+        **kwargs,
+    )
+    alpha = replace_parameter(
+        value_before_replacement=alpha,
+        param_target='attributes',
+        param_name='alpha',
+        **kwargs,
+    )
+    beta = replace_parameter(
+        value_before_replacement=beta,
+        param_target='attributes',
+        param_name='beta',
+        **kwargs,
+    )
+    transA = replace_parameter(
+        value_before_replacement=transA,
+        param_target='attributes',
+        param_name='transA',
+        **kwargs,
+    )
+    transB = replace_parameter(
+        value_before_replacement=transB,
+        param_target='attributes',
+        param_name='transB',
+        **kwargs,
+    )
+
     # Generation of TF OP
+    if transA == True:
+        x = tf.transpose(x)
+    if transB == True:
+        y = tf.transpose(y)
     # We cast to either input or attribute data type to preserve precision
     if input_tensor_x_dtype in [tf.float64]:
         # cast to input data type

onnx2tf/ops/Mul.py

@@ -5,6 +5,8 @@
 import tensorflow as tf
 import onnx_graphsurgeon as gs
 from onnx2tf.utils.common_functions import (
+    get_replacement_parameter,
+    replace_parameter,
     get_constant_or_variable,
     print_node_info,
     inverted_operation_enable_disable,
@@ -14,6 +16,7 @@
 
 @print_node_info
 @inverted_operation_enable_disable
+@get_replacement_parameter
 def make_node(
     *,
     graph_node: gs.Node,
@@ -57,12 +60,26 @@ def make_node(
         'dtype': dtype,
     }
 
-    # Generation of TF OP
     input_tensor_1 = tf_layers_dict[graph_node_input_1.name]['tf_node'] \
         if isinstance(graph_node_input_1, gs.Variable) else graph_node_input_1
     input_tensor_2 = tf_layers_dict[graph_node_input_2.name]['tf_node'] \
         if isinstance(graph_node_input_2, gs.Variable) else graph_node_input_2
 
+    # Param replacement
+    input_tensor_1 = replace_parameter(
+        value_before_replacement=input_tensor_1,
+        param_target='inputs',
+        param_name=graph_node.inputs[0].name,
+        **kwargs,
+    )
+    input_tensor_2 = replace_parameter(
+        value_before_replacement=input_tensor_2,
+        param_target='inputs',
+        param_name=graph_node.inputs[1].name,
+        **kwargs,
+    )
+
+    # Generation of TF OP
     tf_layers_dict[graph_node_output.name]['tf_node'] = \
         tf.math.multiply(
             x=input_tensor_1,

onnx2tf/ops/Reshape.py

@@ -5,6 +5,8 @@
 import tensorflow as tf
 import onnx_graphsurgeon as gs
 from onnx2tf.utils.common_functions import (
+    get_replacement_parameter,
+    replace_parameter,
     get_constant_or_variable,
     convert_axis,
     print_node_info,
@@ -16,6 +18,7 @@
 
 @print_node_info
 @inverted_operation_enable_disable
+@get_replacement_parameter
 def make_node(
     *,
     graph_node: gs.Node,
@@ -106,6 +109,20 @@ def make_node(
     else:
         transposed_reshape_shape = reshape_shape
 
+    # Param replacement
+    transposed_tensor = replace_parameter(
+        value_before_replacement=transposed_tensor,
+        param_target='inputs',
+        param_name=graph_node.inputs[0].name,
+        **kwargs,
+    )
+    transposed_reshape_shape = replace_parameter(
+        value_before_replacement=transposed_reshape_shape,
+        param_target='inputs',
+        param_name=graph_node.inputs[1].name,
+        **kwargs,
+    )
+
     # Reshape
     tf_layers_dict[graph_node_output.name]['tf_node'] = \
         tf.reshape(

onnx2tf/ops/Resize.py

@@ -6,6 +6,8 @@
 from tensorflow.keras.layers import Lambda # type: ignore
 import onnx_graphsurgeon as gs
 from onnx2tf.utils.common_functions import (
+    get_replacement_parameter,
+    replace_parameter,
     get_constant_or_variable,
     print_node_info,
     inverted_operation_enable_disable,
@@ -15,6 +17,7 @@
 
 @print_node_info
 @inverted_operation_enable_disable
+@get_replacement_parameter
 def make_node(
     *,
     graph_node: gs.Node,
@@ -84,7 +87,6 @@ def make_node(
     }
 
 
-
     def upsampling2d_bilinear(input_tensor, new_size, align_corners, half_pixel_centers, name):
         return tf.compat.v1.image.resize_bilinear(
             images=input_tensor,
@@ -151,6 +153,51 @@ def upsampling2d_nearest(input_tensor, new_size, align_corners, half_pixel_cente
                 new_values[new_idx] = graph_node_output.shape[idx]
             new_size = new_values[-3:-1]
 
+    # Param replacement
+    input_tensor = replace_parameter(
+        value_before_replacement=input_tensor,
+        param_target='inputs',
+        param_name=graph_node.inputs[0].name,
+        **kwargs,
+    )
+    roi = replace_parameter(
+        value_before_replacement=roi,
+        param_target='inputs',
+        param_name=graph_node.inputs[1].name,
+        **kwargs,
+    )
+    scales = replace_parameter(
+        value_before_replacement=scales,
+        param_target='inputs',
+        param_name=graph_node.inputs[2].name,
+        **kwargs,
+    )
+    new_size = replace_parameter(
+        value_before_replacement=new_size,
+        param_target='inputs',
+        param_name=graph_node.inputs[3].name,
+        **kwargs,
+    )
+
+    coordinate_transformation_mode = replace_parameter(
+        value_before_replacement=coordinate_transformation_mode,
+        param_target='attributes',
+        param_name='coordinate_transformation_mode',
+        **kwargs,
+    )
+    extrapolation_value = replace_parameter(
+        value_before_replacement=extrapolation_value,
+        param_target='attributes',
+        param_name='extrapolation_value',
+        **kwargs,
+    )
+    mode = replace_parameter(
+        value_before_replacement=mode,
+        param_target='attributes',
+        param_name='mode',
+        **kwargs,
+    )
+
     # TODO: upsampling2d_bilinear_5d
     # TODO: upsampling2d_nearest_5d
     resized_tensor = None