[GitHub] [tvm] ekalda commented on a change in pull request #9508: [microNPU] Update Conv2D Tests to Use TF API to Gen Test Cases

[GitBox <gi...@apache.org>]

ekalda commented on a change in pull request #9508:
URL: https://github.com/apache/tvm/pull/9508#discussion_r761038853



##########
File path: tests/python/contrib/test_ethosu/test_legalize.py
##########
@@ -221,135 +221,135 @@ def get_shape_expr(in_expr, out_expr):
     return shape
 
 
+def compute_ofm_shape(ifm_shape, padding, kernel_shape, strides, dilation):
+    if padding.lower() == "valid":
+        h = math.ceil((ifm_shape[1] - (kernel_shape[0] - 1) * dilation[0]) / strides[0])
+        w = math.ceil((ifm_shape[2] - (kernel_shape[1] - 1) * dilation[1]) / strides[1])
+    if padding.lower() == "same":
+        h = math.ceil(ifm_shape[1] / strides[0])
+        w = math.ceil(ifm_shape[2] / strides[1])
+    ofm_shape = [ifm_shape[0], h, w, kernel_shape[3]]
+    return ofm_shape

Review comment:
       Friendly ping on @lhutton1's question there :) 

##########
File path: tests/python/contrib/test_ethosu/test_legalize.py
##########
@@ -221,135 +222,137 @@ def get_shape_expr(in_expr, out_expr):
     return shape
 
 
+def compute_ofm_shape(ifm_shape, padding, kernel_shape, strides, dilation):
+    if padding.lower() == "valid":
+        h = math.ceil((ifm_shape[1] - (kernel_shape[0] - 1) * dilation[0]) / strides[0])
+        w = math.ceil((ifm_shape[2] - (kernel_shape[1] - 1) * dilation[1]) / strides[1])
+    if padding.lower() == "same":
+        h = math.ceil(ifm_shape[1] / strides[0])
+        w = math.ceil(ifm_shape[2] / strides[1])
+    ofm_shape = [ifm_shape[0], h, w, kernel_shape[3]]
+    return ofm_shape
+
+
 INVERSE_LAYOUT_TRANSFORM_OHWI_MAP = {
     "HWIO": [1, 2, 3, 0],
     "HWOI": [1, 2, 0, 3],
     "OWHI": [0, 1, 2, 3],
 }
 
 
-def test_ethosu_conv2d_legalize():
-    def create_graph_single(input_tensor_name, input_tensor_shape, input_tensor_dtype):
-        c1_params = relay_ir_builder.QnnConv2DParams(input_tensor_dtype)
-        c1_params.ifm.shape = input_tensor_shape
-        c1_params.kernel.shape = (3, 3, c1_params.ifm.shape[3], 32)
-        c1_params.strides = (1, 1)
-        c1_params.pad = "VALID"
-        c1_params.activation = "CLIP"
-        c1_params.clip_min = 23
-        c1_params.clip_max = 180
-        input0 = relay.var(input_tensor_name, shape=c1_params.ifm.shape, dtype=c1_params.ifm.dtype)
-        c1, new_params = relay_ir_builder.create_qnn_conv2d(c1_params, input0)
-        c1_params.ofm.shape = get_shape_expr(input0, c1)
-
-        f = relay.Function([input0], c1)
-        mod = tvm.IRModule()
-        mod["main"] = f
-        return mod, [c1_params]
-
-    def create_graph_double(input_tensor_name, input_tensor_shape, input_tensor_dtype):
-        c1_params = relay_ir_builder.QnnConv2DParams(input_tensor_dtype)
-        c1_params.ifm.shape = input_tensor_shape
-        c1_params.kernel.shape = (7, 7, c1_params.ifm.shape[3], 8)
-        c1_params.strides = (2, 2)
-        c1_params.pad = "VALID"
-        c1_params.activation = "CLIP"
-        c1_params.clip_min = 10
-        c1_params.clip_max = 240
-        input0 = relay.var(input_tensor_name, shape=c1_params.ifm.shape, dtype=c1_params.ifm.dtype)
-        c1, new_params = relay_ir_builder.create_qnn_conv2d(c1_params, input0)
-        c1_params.ofm.shape = get_shape_expr(input0, c1)
-
-        c2_params = relay_ir_builder.QnnConv2DParams(input_tensor_dtype)
-        c2_params.ifm.shape = c1_params.ofm.shape
-        c2_params.kernel.shape = (5, 5, c2_params.ifm.shape[3], 16)
-        c2_params.strides = (1, 1)
-        c2_params.pad = "SAME"
-        c2, new_params = relay_ir_builder.create_qnn_conv2d(c2_params, c1)
-        c2_params.ofm.shape = get_shape_expr(input0, c2)
-
-        f = relay.Function([input0], c2)
-        mod = tvm.IRModule()
-        mod["main"] = f
-        return mod, [c2_params, c1_params]
+@pytest.mark.parametrize("ifm_shape", [(1, 299, 299, 3), (1, 55, 55, 3)])
+@pytest.mark.parametrize("kernel_shape", [(3, 2), (1, 3)])
+@pytest.mark.parametrize("padding", ["SAME", "VALID"])
+@pytest.mark.parametrize("strides, dilation", [((1, 1), (2, 1)), ((3, 2), (1, 1))])
+@pytest.mark.parametrize("activation", [None, "RELU"])
+def test_tflite_conv_2d_legalize(ifm_shape, kernel_shape, padding, strides, dilation, activation):

Review comment:
       ```suggestion
   def test_tflite_conv2d_legalize(ifm_shape, kernel_shape, padding, strides, dilation, activation):
   ```
   to confirm with the convention used in the rest of the code




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