Fix unit tests for Relay padding layer translation fixes

Xilinx · Apr 27, 2021 · bc290a5 · bc290a5
1 parent f69c345
commit bc290a5
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Showing 4 changed files with 42 additions and 43 deletions.
diff --git a/tests/unit/frontend/tvm/relay_tools/test_relay_l2_convolution.py b/tests/unit/frontend/tvm/relay_tools/test_relay_l2_convolution.py
@@ -380,15 +380,12 @@ def test_padding(self):
         xg = xf_relay.from_relay(mod, {})
         layers = xg.get_layers()
 
-        assert len(layers) == 3
+        assert len(layers) == 2
 
-        assert layers[0].type[0] == "Constant"
+        assert layers[0].type[0] == "Input"
         assert "relay_id" in layers[0].attrs
-
-        assert layers[1].type[0] == "Input"
-        assert "relay_id" in layers[1].attrs
 
-        X = layers[2]
+        X = layers[1]
         assert X.type[0] == "Pad"
         assert X.bottoms == ["var"]
         assert X.shapes == [-1, 2, 5, 5]

diff --git a/tests/unit/frontend/tvm/relay_tools/test_relay_l3_math_and_transform.py b/tests/unit/frontend/tvm/relay_tools/test_relay_l3_math_and_transform.py
@@ -175,6 +175,7 @@ def test_reshape(self):
         # 1
         c = relay.expr.const(np.ones((2, 3, 4), dtype=np.float32))
         net = relay.reshape(c, (4, 0, 2))
+        net = relay.Tuple([net])
         net = relay.Function([], net)
         mod = tvm.IRModule.from_expr(net)
         mod = relay.transform.InferType()(mod)
@@ -188,6 +189,7 @@ def test_reshape(self):
         # 2
         c = relay.expr.const(np.ones((2, 3, 4), dtype=np.float32))
         net = relay.reshape(c, (6, 1, -1))
+        net = relay.Tuple([net])
         net = relay.Function([], net)
         mod = tvm.IRModule.from_expr(net)
         mod = relay.transform.InferType()(mod)
@@ -201,6 +203,7 @@ def test_reshape(self):
         # 3
         c = relay.expr.const(np.ones((2, 3, 4), dtype=np.float32))
         net = relay.reshape(c, (-2,))
+        net = relay.Tuple([net])
         net = relay.Function([], net)
         mod = tvm.IRModule.from_expr(net)
         mod = relay.transform.InferType()(mod)
@@ -214,6 +217,7 @@ def test_reshape(self):
         # 4
         c = relay.expr.const(np.ones((2, 3, 4, 5), dtype=np.float32))
         net = relay.reshape(c, (-3, -3))
+        net = relay.Tuple([net])
         net = relay.Function([], net)
         mod = tvm.IRModule.from_expr(net)
         mod = relay.transform.InferType()(mod)
@@ -343,7 +347,7 @@ def test_transpose_constant(self):
         data = relay.var("data", relay.TensorType((1, 3, 2, 2), "float32"))
 
         net = relay.transpose(data, axes=(0, 2, 3, 1))
-
+        net = relay.Tuple([net])
         net = relay.Function(relay.analysis.free_vars(net), net)
 
         mod = tvm.IRModule.from_expr(net)

diff --git a/tests/unit/frontend/tvm/test_relay.py b/tests/unit/frontend/tvm/test_relay.py
@@ -75,18 +75,17 @@ def test_simple_network(self):
 
         layers = xgraph.get_layers()
 
-        assert layers[0].type[0] == "Constant"
-        assert layers[1].type[0] == "Input"
-        assert layers[2].type[0] == "Pad"
-        assert layers[3].type[0] == "Convolution"
-        assert layers[4].type[0] == "BatchNorm"
-        assert layers[5].type[0] == "ReLU"
-        assert layers[6].type[0] == "Mean"
-        assert layers[7].type[0] == "Reshape"
-        assert layers[8].type[0] == "Dense"
-        assert layers[9].type[0] == "BiasAdd"
-        assert layers[10].type[0] == "Softmax"
-        assert layers[11].type[0] == "Reshape"
+        assert layers[0].type[0] == "Input"
+        assert layers[1].type[0] == "Pad"
+        assert layers[2].type[0] == "Convolution"
+        assert layers[3].type[0] == "BatchNorm"
+        assert layers[4].type[0] == "ReLU"
+        assert layers[5].type[0] == "Mean"
+        assert layers[6].type[0] == "Reshape"
+        assert layers[7].type[0] == "Dense"
+        assert layers[8].type[0] == "BiasAdd"
+        assert layers[9].type[0] == "Softmax"
+        assert layers[10].type[0] == "Reshape"
 
     @unittest.skipIf(skip, "Could not import TVM and/or TVM frontend")
     def test_simple_network_cvx(self):
@@ -118,18 +117,18 @@ def test_simple_network_cvx(self):
         assert len(xgraph.get_input_names()) == 1
         layers = xgraph.get_layers()
 
-        assert layers[0].type[0] == "Constant"
-        assert layers[1].type[0] == "StrInput"
-        assert layers[1].shapes == [-1]
-        assert layers[2].type[0] == "Cvx"
-        assert layers[2].shapes == [-1, 3, 224, 224]
-        assert layers[3].type[0] == "Pad"
-        assert layers[4].type[0] == "Convolution"
-        assert layers[5].type[0] == "ReLU"
-
-        assert layers[1].tops == ["data_cvx"]
-        assert layers[2].bottoms == ["data"]
-        assert layers[2].tops[0][:7] == "nn.pad-"
+        # assert layers[0].type[0] == "Constant"
+        assert layers[0].type[0] == "StrInput"
+        assert layers[0].shapes == [-1]
+        assert layers[1].type[0] == "Cvx"
+        assert layers[1].shapes == [-1, 3, 224, 224]
+        assert layers[2].type[0] == "Pad"
+        assert layers[3].type[0] == "Convolution"
+        assert layers[4].type[0] == "ReLU"
+
+        assert layers[0].tops == ["data_cvx"]
+        assert layers[1].bottoms == ["data"]
+        assert layers[1].tops[0][:7] == "nn.pad-"
 
     @unittest.skipIf(skip, "Could not import TVM and/or TVM frontend")
     def test_conv2d_transpose(self):

diff --git a/tests/unit/frontend/tvm/test_relay_io.py b/tests/unit/frontend/tvm/test_relay_io.py
@@ -93,18 +93,17 @@ def test_simple_network(self):
 
         layers = xgraph.get_layers()
 
-        assert layers[0].type[0] == "Constant"
-        assert layers[1].type[0] == "Input"
-        assert layers[2].type[0] == "Pad"
-        assert layers[3].type[0] == "Convolution"
-        assert layers[4].type[0] == "BatchNorm"
-        assert layers[5].type[0] == "ReLU"
-        assert layers[6].type[0] == "Mean"
-        assert layers[7].type[0] == "Reshape"
-        assert layers[8].type[0] == "Dense"
-        assert layers[9].type[0] == "BiasAdd"
-        assert layers[10].type[0] == "Softmax"
-        assert layers[11].type[0] == "Reshape"
+        assert layers[0].type[0] == "Input"
+        assert layers[1].type[0] == "Pad"
+        assert layers[2].type[0] == "Convolution"
+        assert layers[3].type[0] == "BatchNorm"
+        assert layers[4].type[0] == "ReLU"
+        assert layers[5].type[0] == "Mean"
+        assert layers[6].type[0] == "Reshape"
+        assert layers[7].type[0] == "Dense"
+        assert layers[8].type[0] == "BiasAdd"
+        assert layers[9].type[0] == "Softmax"
+        assert layers[10].type[0] == "Reshape"
 
         os.remove(json_file)
         os.remove(params_file)