leandron commented on a change in pull request #7366:
URL: https://github.com/apache/tvm/pull/7366#discussion_r567214252
##########
File path: python/tvm/driver/tvmc/common.py
##########
@@ -136,3 +138,46 @@ def tracker_host_port_from_cli(rpc_tracker_str):
logger.info("RPC tracker port: %s", rpc_port)
return rpc_hostname, rpc_port
+
+
+def parse_shape_string(inputs):
+ """Parse an input shape dictionary string to a usable dictionary.
+
+ Parameters
+ ----------
+ inputs: str
+ A string of the form "name:num1xnum2x...xnumN,name2:num1xnum2xnum3"
that indicates
+ the desired shape for specific model inputs.
+
+ Returns
+ -------
+ shape_dict: dict
+ A dictionary mapping input names to their shape for use in relay
frontend converters.
+ """
+ inputs = inputs.replace(" ", "")
+ # Check if the passed input is in the proper format.
+ valid_pattern =
re.compile("(\w+:(\d+(x|X))*(\d)+)(,(\w+:(\d+(x|X))*(\d)+))*")
+ result = re.fullmatch(valid_pattern, inputs)
+ if result is None:
+ raise argparse.ArgumentTypeError(
+ "--shapes argument must be of the form
'input_name:dim1xdim2x...xdimN,input_name2:dim1xdim2"
+ )
+ d = {}
+ # Break apart each specific input string
+ inputs = inputs.split(",")
+ for string in inputs:
+ # Split name from shape string.
+ string = string.split(":")
+ shapelist = []
+ # Separate each dimension in the shape.
+ string[1] = string[1].lower().split("x")
+ # Parse each dimension into an integer.
+ for x in string[1]:
+ x = int(x)
+ # Negative numbers are converted to dynamic axes.
+ if x < 0:
+ x = relay.Any()
+ shapelist.append(x)
+ # Assign dictionary key value pair.
+ d[string[0]] = shapelist
+ return d
Review comment:
I suggest renaming `d` to be `shape_dict`, according to the doctoring on
this function.
##########
File path: tests/python/driver/tvmc/test_common.py
##########
@@ -149,3 +149,27 @@ def
test_tracker_host_port_from_cli__only_hostname__default_port_is_9090():
assert expected_host == actual_host
assert expected_port == actual_port
+
+
+def test_shape_parser():
+ # Check that a valid input is parsed correctly
+ shape_string = "input:10x10x10"
+ shape_dict = tvmc.common.parse_shape_string(shape_string)
+ assert shape_dict == {"input": [10, 10, 10]}
+ # Check that multiple valid input shapes are parse correctly
+ shape_string = "input:10x10x10,input2:20x20x20x20"
+ shape_dict = tvmc.common.parse_shape_string(shape_string)
+ assert shape_dict == {"input": [10, 10, 10], "input2": [20, 20, 20, 20]}
+ # Check that alternate syntax parses correctly
+ shape_string = "input:10X10X10, input2:20X20X20X20"
+ shape_dict = tvmc.common.parse_shape_string(shape_string)
+ assert shape_dict == {"input": [10, 10, 10], "input2": [20, 20, 20, 20]}
+
+ # Check that invalid pattern raises expected error.
+ shape_string = "input:ax10"
+ with pytest.raises(argparse.ArgumentTypeError):
+ tvmc.common.parse_shape_string(shape_string)
+ # Check that input with invalid separators raises error.
+ shape_string = "input:5,10 input2:10,10"
+ with pytest.raises(argparse.ArgumentTypeError):
+ tvmc.common.parse_shape_string(shape_string)
Review comment:
I suggest splitting these test cases above in specific unit test - I see
5 self-contained unit tests here.
##########
File path: tests/python/driver/tvmc/test_compiler.py
##########
@@ -56,6 +53,15 @@ def test_compile_tflite_module(tflite_mobilenet_v1_1_quant):
assert type(dumps) is dict
+def test_compile_tflite_module(tflite_mobilenet_v1_1_quant):
+ # Check default compilation.
Review comment:
```suggestion
def test_compile_tflite_module(tflite_mobilenet_v1_1_quant):
# some CI environments wont offer flute, so skip in case it is not
present
pytest.importorskip("tflite")
```
Not all CI environments will offer `tflite`.
##########
File path: python/tvm/driver/tvmc/autotuner.py
##########
@@ -210,6 +210,13 @@ def add_tune_parser(subparsers):
# can be improved in future to add integration with a modelzoo
# or URL, for example.
parser.add_argument("FILE", help="path to the input model file")
+ parser.add_argument(
+ "--shapes",
Review comment:
Question: is the terminology `--shapes` specific enough for people to
always infer they are "input shapes"? I'm asking because in the PR we
previously had in discussion, we called it `--input-shapes`, and just want to
hear what others think.
cc @comaniac @ekalda
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