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]
[email protected]("ifm_shape", [(1, 299, 299, 3), (1, 55, 55, 3)])
[email protected]("kernel_shape", [(3, 2), (1, 3)])
[email protected]("padding", ["SAME", "VALID"])
[email protected]("strides, dilation", [((1, 1), (2, 1)), ((3, 2), (1,
1))])
[email protected]("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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