Merge pull request apache#31 from heliqi/paddle_frontend

paddle frontend

python/tvm/relay/frontend/paddlepaddle.py

@@ -80,6 +80,26 @@ def _infer_value(x, params):
             return x
 
 
+def _convert_dtype_value(val):
+    """converts a Paddle type id to a string."""
+
+    convert_dtype_map = {
+        21: "int8",
+        20: "uint8",
+        6: "float64",
+        5: "float32",
+        4: "float16",
+        3: "int64",
+        2: "int32",
+        1: "int16",
+        0: "bool",
+    }
+    if val not in convert_dtype_map:
+        msg = "Paddle data type value %d is not handled yet." % (val)
+        raise NotImplementedError(msg)
+    return convert_dtype_map[val]
+
+
 def convert_unary_op(g, op, block):
     """Operator converter for all the activation."""
 
@@ -162,14 +182,12 @@ def convert_assign(g, op, block):
 def convert_assign_value(g, op, block):
     """Operator converter for assign_value."""
 
-    keys = ["fp32_values", "int32_values", "int64_values"]
+    keys = ["bool_values", "fp32_values", "int32_values", "int64_values"]
     for key in keys:
         value = np.array(op.attr(key))
         if value is not None and value.size >= 1:
             break
     shape = op.attr("shape")
-    dtype = block.var(op.output("Out")[0]).dtype
-    dtype = str(dtype).strip().split(".")[1]
     value = value.reshape(shape)
     out = _op.const(value)
     g.add_node(op.output("Out")[0], out)
@@ -306,8 +324,8 @@ def convert_interpolate(g, op, block):
 def convert_cast(g, op, block):
     """Operator converter for cast."""
 
-    dtype = block.var(op.output("Out")[0]).dtype
-    dtype = str(dtype).strip().split(".")[1]
+    dtype = op.attr("out_dtype")
+    dtype = _convert_dtype_value(dtype)
     x = g.get_node(op.input("X")[0])
     out = _op.cast(x, dtype=dtype)
     g.add_node(op.output("Out")[0], out)
@@ -576,8 +594,8 @@ def convert_fill_constant(g, op, block):
 
     value = op.attr("value")
     shape = block.var(op.output("Out")[0]).shape
-    dtype = block.var(op.output("Out")[0]).dtype
-    dtype = str(dtype).strip().split(".")[1]
+    dtype = op.attr("dtype")
+    dtype = _convert_dtype_value(dtype)
     value = _expr.const(value).astype(dtype)
     if op.input("ValueTensor"):
         shape = g.get_node(op.input("ValueTensor")[0])
@@ -598,9 +616,9 @@ def convert_fill_constant_batch_size_like(g, op, block):
     shape = op.attr("shape")
     input_dim_idx = op.attr("input_dim_idx")
     output_dim_idx = op.attr("output_dim_idx")
+    dtype = op.attr("dtype")
 
-    dtype = block.var(op.output("Out")[0]).dtype
-    dtype = str(dtype).strip().split(".")[1]
+    dtype = _convert_dtype_value(dtype)
     input_shape = shape_of(x)
     batch = _op.strided_slice(input_shape, begin=[input_dim_idx], end=[input_dim_idx + 1]).astype(
         "int32"