add pt2tf tool

pt2tf/onnx-tensorflow/onnx_tf/handlers/backend/conv_integer.py

@@ -0,0 +1,63 @@
+import tensorflow as tf
+
+from onnx_tf.handlers.backend_handler import BackendHandler
+from onnx_tf.handlers.handler import onnx_op
+from .conv_mixin import ConvMixin
+
+
+@onnx_op("ConvInteger")
+class ConvInteger(ConvMixin, BackendHandler):
+
+  @classmethod
+  def _apply_zero_point(cls, base, zero_point):
+    base = tf.cast(base, tf.float32)
+    zero_point = tf.cast(zero_point, tf.float32)
+    return base - zero_point
+
+  @classmethod
+  def version_10(cls, node, **kwargs):
+    tensor_dict = kwargs["tensor_dict"]
+    x = tensor_dict[node.inputs[0]]
+    w = tensor_dict[node.inputs[1]]
+
+    def process_conv(new_x, new_w):
+      # Remove zero-points from inputs
+      if len(node.inputs) == 4:
+        node.inputs.remove(node.inputs[3])
+      if len(node.inputs) == 3:
+        node.inputs.remove(node.inputs[2])
+
+      new_dict = { node.inputs[0]:new_x, node.inputs[1]:new_w } 
+
+      # Use common conv handling
+      conv_node = cls.conv(node, new_dict)
+
+      return conv_node
+
+    # Apply x_zero_point first
+    x = cls._apply_zero_point(
+        x, tensor_dict[node.inputs[2]]) if len(node.inputs) > 2 else tf.cast(
+            x, tf.float32)
+
+    # Apply w_zero_point next
+    if len(node.inputs) == 4:
+      w_zero_point = tensor_dict[node.inputs[3]]
+      if w_zero_point.shape.rank == 0:
+        # Simply apply w_zero_point for scalar
+        w = cls._apply_zero_point(w, w_zero_point)
+      elif w_zero_point.shape.rank == 1:
+        # Need additional processing for 1d w_zero_point
+        tensor_list = []
+        process_shape = [1] + [w.shape[i] for i in range(1, len(w.shape))]
+        for i in range(w.shape.as_list()[0]):
+          # Apply w_zero_point for each element in 1d tensor
+          out_tensor = cls._apply_zero_point(w[i], w_zero_point[i])
+          tensor_list.append(tf.reshape(out_tensor, process_shape))
+        w = tf.concat(tensor_list, 0)
+      else:
+        raise ValueError("Unsupported w zero point: {}".format(w_zero_point))
+    else:
+      # Just cast without processing w
+      w = tf.cast(w, tf.float32)
+
+    return [tf.cast(process_conv(x, w)[0], tf.int32)]