Merge pull request #318 from lucienwang1009/dropout

set shape and dtype for RandomNormal and Dropout ops

Author: pengwa

tests/test_backend.py

@@ -376,6 +376,21 @@ def test_depthwiseconv_1(self):
         # rtol is a bit high, 2 values have a bit high error. Maybe use different input data.
         self._run_test_case([_OUTPUT], {_INPUT: x_val}, rtol=0.08)
 
+    def test_dropout(self):
+        is_training = tf.placeholder_with_default(False, (), "is_training")
+        x_val = np.ones([1, 24, 24, 3], dtype=np.float32)
+        # Define a scope for reusing the variables
+        x = tf.placeholder(tf.float32, shape=x_val.shape, name="input_1")
+        x_ = tf.identity(x)
+
+        fc1 = tf.layers.dropout(x_, rate=.1, training=is_training)
+
+        _ = tf.identity(fc1, name="output")
+        feed_dict = {"input_1:0": x_val}
+        input_names_with_port = ["input_1:0"]
+        output_names_with_port = ["output:0"]
+        self.run_test_case(feed_dict, input_names_with_port, output_names_with_port)
+
     def test_conv2d_with_input_transpose(self):
         x_shape = [2, 32, 32, 3]
         kernel_shape = [3, 3, 3, 3]

tf2onnx/rewriter/random_uniform.py

@@ -69,9 +69,11 @@ def create_onnx_random_uniform_op(g, tmax, tmin, ru_op, output):
     if ru_op.inputs[0].type == "Shape":
         shape_node = ru_op.inputs[0]
         new_node = g.make_node("RandomUniformLike", inputs=[shape_node.input[0]], name=op_name,
-                               attr={"low": tmin, "high": tmax, "dtype": dtype})
+                               attr={"low": tmin, "high": tmax, "dtype": dtype},
+                               shapes=shape_node.output_shapes, dtypes=[dtype])
     else:
         shape = g.get_shape(output.output[0])
         new_node = g.make_node("RandomUniform", [], name=op_name,
-                               attr={"low": tmin, "high": tmax, "dtype": dtype, "shape": shape})
+                               attr={"low": tmin, "high": tmax, "dtype": dtype, "shape": shape},
+                               shapes=[shape], dtypes=[dtype])
     return new_node

tf2onnx/tfonnx.py

@@ -1962,7 +1962,15 @@ def rewrite_dropout(g, ops):
         outputs = match.get_op('outputs')
         op_name = utils.make_name("Dropout")
         out_name = port_name(op_name)
-        new_node = g.make_node("Dropout", [inputs2.input[0]], outputs=[out_name], name=op_name, attr={"ratio": 1.0})
+        new_node = g.make_node(
+            "Dropout",
+            [inputs2.input[0]],
+            outputs=[out_name],
+            name=op_name,
+            attr={"ratio": 1.0},
+            shapes=[g.get_shape(inputs2.input[0])],
+            dtypes=[g.get_dtype(inputs2.input[0])]
+        )
         ops = g.replace_subgraph(ops, match, [inputs2], [outputs], [new_node], [new_node])
 
     return ops