SebastianBoblest commented on code in PR #12028:
URL: https://github.com/apache/tvm/pull/12028#discussion_r915696791
##########
tests/python/frontend/tflite/test_forward.py:
##########
@@ -4317,10 +4326,10 @@ def convert_sub_dummy(self, op):
]
out = math_ops.subtract(in_data[0], in_data[1])
in_name = [x[1] for x in zip(in_data, ("in_0:0", "in_1:0"))]
- input_tensors = [x for x in in_data]
+ input_tensors = in_data
output_tensors = [out]
in_node = [0] * len(in_name)
- for i in range(len(in_name)):
+ for i, _ in enumerate(in_name):
in_node[i] = in_name[i].split(":")[0] if ":" in in_name[i] else
in_name[i]
Review Comment:
```suggestion
in_node[i] = in_name[i].split(":")[0]
```
If ":" is not found, split will return [in_node_[i]]
##########
tests/python/frontend/coreml/test_forward.py:
##########
@@ -566,6 +579,7 @@ def test_forward_unary():
@tvm.testing.uses_gpu
def test_forward_reduce():
+ """Reduce"""
from enum import Enum
Review Comment:
Couldn't this be imported at top-level?
##########
tests/python/frontend/tflite/test_forward.py:
##########
@@ -2194,14 +2209,14 @@ def __test_elemwise(in_data):
tf.quantization.fake_quant_with_min_max_args(
in_data[0], min=out_min, max=out_max, name="inq_0"
)
- if None != in_data[0]
+ if in_data[0] != None
Review Comment:
```suggestion
if in_data[0] is not None
```
##########
tests/python/frontend/tflite/test_forward.py:
##########
@@ -2194,14 +2209,14 @@ def __test_elemwise(in_data):
tf.quantization.fake_quant_with_min_max_args(
in_data[0], min=out_min, max=out_max, name="inq_0"
)
- if None != in_data[0]
+ if in_data[0] != None
else tf.quantization.fake_quant_with_min_max_args(
data[0], min=out_min, max=out_max, name="const_tensor0"
),
tf.quantization.fake_quant_with_min_max_args(
in_data[1], min=out_min, max=out_max, name="inq_1"
)
- if None != in_data[1]
+ if in_data[1] != None
Review Comment:
```suggestion
if in_data[1] is not None
```
##########
tests/python/frontend/coreml/test_forward.py:
##########
@@ -202,12 +205,12 @@ def verify_ConcatLayerParams(input1_dim, input2_dim):
@tvm.testing.uses_gpu
-def test_forward_ConcatLayerParams():
- verify_ConcatLayerParams((1, 1, 2, 2), (1, 2, 2, 2))
- verify_ConcatLayerParams((1, 2, 4, 4), (1, 3, 4, 4))
+def test_forward_concat_layer_params():
+ verify_concat_layer_params((1, 1, 2, 2), (1, 2, 2, 2))
+ verify_concat_layer_params((1, 2, 4, 4), (1, 3, 4, 4))
-def verify_UpsampleLayerParams(input_dim, scale, mode):
+def _verify_UpsampleLayerParams(input_dim, scale, mode):
Review Comment:
```suggestion
def _verify_upsample_layer_params(input_dim, scale, mode):
```
##########
tests/python/frontend/tflite/test_forward.py:
##########
@@ -2212,50 +2227,37 @@ def __test_elemwise(in_data):
for x in zip(
in_data, (("inq_0", (inq0_min, inq0_max)), ("inq_1",
(inq1_min, inq1_max)))
)
- if None != x[0]
+ if x[0] != None
Review Comment:
```suggestion
if x[0] is not None
```
##########
tests/python/frontend/tflite/test_forward.py:
##########
@@ -2212,50 +2227,37 @@ def __test_elemwise(in_data):
for x in zip(
in_data, (("inq_0", (inq0_min, inq0_max)), ("inq_1",
(inq1_min, inq1_max)))
)
- if None != x[0]
+ if x[0] != None
}
if math_op is math_ops.equal:
out = math_op(inq_data[0], inq_data[1])
out = with_fused_activation_function(out,
fused_activation_function)
- compare_tflite_with_tvm(
- [x[1] for x in zip(in_data, data) if None != x[0]],
- [x + ":0" for x in input_range.keys()],
- [x[1] for x in zip(in_data, inq_data) if None != x[0]],
- [out],
- )
- else:
- out = math_op(inq_data[0], inq_data[1])
- out = with_fused_activation_function(out,
fused_activation_function)
- out = tf.quantization.fake_quant_with_min_max_args(
- out, min=out_min, max=out_max, name="out"
- )
-
- # Note same_qnn_params uses experimental_new_converter as toco
failed
- compare_tflite_with_tvm(
- [x[1] for x in zip(in_data, data) if None != x[0]],
- [x + ":0" for x in input_range.keys()],
- [x[1] for x in zip(in_data, inq_data) if None != x[0]],
- [out],
- quantized=True,
- input_range=input_range,
- experimental_new_converter=same_qnn_params,
- )
+ # Note same_qnn_params uses experimental_new_converter as toco
failed
+ compare_tflite_with_tvm(
+ [x[1] for x in zip(in_data, data) if x[0] != None],
+ [x + ":0" for x in input_range.keys()],
+ [x[1] for x in zip(in_data, inq_data) if x[0] != None],
+ [out],
+ quantized=True,
+ input_range=input_range,
+ experimental_new_converter=same_qnn_params,
+ )
else:
out = math_op(
in_data[0]
- if None != in_data[0]
+ if in_data[0] != None
Review Comment:
```suggestion
if in_data[0] is not None
```
##########
tests/python/frontend/tflite/test_forward.py:
##########
@@ -2212,50 +2227,37 @@ def __test_elemwise(in_data):
for x in zip(
in_data, (("inq_0", (inq0_min, inq0_max)), ("inq_1",
(inq1_min, inq1_max)))
)
- if None != x[0]
+ if x[0] != None
}
if math_op is math_ops.equal:
out = math_op(inq_data[0], inq_data[1])
out = with_fused_activation_function(out,
fused_activation_function)
- compare_tflite_with_tvm(
- [x[1] for x in zip(in_data, data) if None != x[0]],
- [x + ":0" for x in input_range.keys()],
- [x[1] for x in zip(in_data, inq_data) if None != x[0]],
- [out],
- )
- else:
- out = math_op(inq_data[0], inq_data[1])
- out = with_fused_activation_function(out,
fused_activation_function)
- out = tf.quantization.fake_quant_with_min_max_args(
- out, min=out_min, max=out_max, name="out"
- )
-
- # Note same_qnn_params uses experimental_new_converter as toco
failed
- compare_tflite_with_tvm(
- [x[1] for x in zip(in_data, data) if None != x[0]],
- [x + ":0" for x in input_range.keys()],
- [x[1] for x in zip(in_data, inq_data) if None != x[0]],
- [out],
- quantized=True,
- input_range=input_range,
- experimental_new_converter=same_qnn_params,
- )
+ # Note same_qnn_params uses experimental_new_converter as toco
failed
+ compare_tflite_with_tvm(
+ [x[1] for x in zip(in_data, data) if x[0] != None],
+ [x + ":0" for x in input_range.keys()],
+ [x[1] for x in zip(in_data, inq_data) if x[0] != None],
+ [out],
+ quantized=True,
+ input_range=input_range,
+ experimental_new_converter=same_qnn_params,
+ )
else:
out = math_op(
in_data[0]
- if None != in_data[0]
+ if in_data[0] != None
else ops.convert_to_tensor(data[0], dtype=data[0].dtype),
in_data[1]
- if None != in_data[1]
+ if in_data[1] != None
else ops.convert_to_tensor(data[1], dtype=data[1].dtype),
)
out = with_fused_activation_function(out,
fused_activation_function)
compare_tflite_with_tvm(
- [x[1] for x in zip(in_data, data) if None != x[0]],
- [x[1] for x in zip(in_data, ("in_0:0", "in_1:0")) if None !=
x[0]],
- [x for x in in_data if None != x],
+ [x[1] for x in zip(in_data, data) if x[0] != None],
+ [x[1] for x in zip(in_data, ("in_0:0", "in_1:0")) if x[0] !=
None],
Review Comment:
```suggestion
[x[1] for x in zip(in_data, ("in_0:0", "in_1:0")) if x[0] is
not None],
```
##########
tests/python/frontend/tflite/test_forward.py:
##########
@@ -2212,50 +2227,37 @@ def __test_elemwise(in_data):
for x in zip(
in_data, (("inq_0", (inq0_min, inq0_max)), ("inq_1",
(inq1_min, inq1_max)))
)
- if None != x[0]
+ if x[0] != None
}
if math_op is math_ops.equal:
out = math_op(inq_data[0], inq_data[1])
out = with_fused_activation_function(out,
fused_activation_function)
- compare_tflite_with_tvm(
- [x[1] for x in zip(in_data, data) if None != x[0]],
- [x + ":0" for x in input_range.keys()],
- [x[1] for x in zip(in_data, inq_data) if None != x[0]],
- [out],
- )
- else:
- out = math_op(inq_data[0], inq_data[1])
- out = with_fused_activation_function(out,
fused_activation_function)
- out = tf.quantization.fake_quant_with_min_max_args(
- out, min=out_min, max=out_max, name="out"
- )
-
- # Note same_qnn_params uses experimental_new_converter as toco
failed
- compare_tflite_with_tvm(
- [x[1] for x in zip(in_data, data) if None != x[0]],
- [x + ":0" for x in input_range.keys()],
- [x[1] for x in zip(in_data, inq_data) if None != x[0]],
- [out],
- quantized=True,
- input_range=input_range,
- experimental_new_converter=same_qnn_params,
- )
+ # Note same_qnn_params uses experimental_new_converter as toco
failed
+ compare_tflite_with_tvm(
+ [x[1] for x in zip(in_data, data) if x[0] != None],
+ [x + ":0" for x in input_range.keys()],
+ [x[1] for x in zip(in_data, inq_data) if x[0] != None],
+ [out],
+ quantized=True,
+ input_range=input_range,
+ experimental_new_converter=same_qnn_params,
+ )
else:
out = math_op(
in_data[0]
- if None != in_data[0]
+ if in_data[0] != None
else ops.convert_to_tensor(data[0], dtype=data[0].dtype),
in_data[1]
- if None != in_data[1]
+ if in_data[1] != None
Review Comment:
```suggestion
if in_data[1] is not None
```
##########
tests/python/frontend/tflite/test_forward.py:
##########
@@ -2212,50 +2227,37 @@ def __test_elemwise(in_data):
for x in zip(
in_data, (("inq_0", (inq0_min, inq0_max)), ("inq_1",
(inq1_min, inq1_max)))
)
- if None != x[0]
+ if x[0] != None
}
if math_op is math_ops.equal:
out = math_op(inq_data[0], inq_data[1])
out = with_fused_activation_function(out,
fused_activation_function)
- compare_tflite_with_tvm(
- [x[1] for x in zip(in_data, data) if None != x[0]],
- [x + ":0" for x in input_range.keys()],
- [x[1] for x in zip(in_data, inq_data) if None != x[0]],
- [out],
- )
- else:
- out = math_op(inq_data[0], inq_data[1])
- out = with_fused_activation_function(out,
fused_activation_function)
- out = tf.quantization.fake_quant_with_min_max_args(
- out, min=out_min, max=out_max, name="out"
- )
-
- # Note same_qnn_params uses experimental_new_converter as toco
failed
- compare_tflite_with_tvm(
- [x[1] for x in zip(in_data, data) if None != x[0]],
- [x + ":0" for x in input_range.keys()],
- [x[1] for x in zip(in_data, inq_data) if None != x[0]],
- [out],
- quantized=True,
- input_range=input_range,
- experimental_new_converter=same_qnn_params,
- )
+ # Note same_qnn_params uses experimental_new_converter as toco
failed
+ compare_tflite_with_tvm(
+ [x[1] for x in zip(in_data, data) if x[0] != None],
+ [x + ":0" for x in input_range.keys()],
+ [x[1] for x in zip(in_data, inq_data) if x[0] != None],
+ [out],
+ quantized=True,
+ input_range=input_range,
+ experimental_new_converter=same_qnn_params,
+ )
else:
out = math_op(
in_data[0]
- if None != in_data[0]
+ if in_data[0] != None
else ops.convert_to_tensor(data[0], dtype=data[0].dtype),
in_data[1]
- if None != in_data[1]
+ if in_data[1] != None
else ops.convert_to_tensor(data[1], dtype=data[1].dtype),
)
out = with_fused_activation_function(out,
fused_activation_function)
compare_tflite_with_tvm(
- [x[1] for x in zip(in_data, data) if None != x[0]],
- [x[1] for x in zip(in_data, ("in_0:0", "in_1:0")) if None !=
x[0]],
- [x for x in in_data if None != x],
+ [x[1] for x in zip(in_data, data) if x[0] != None],
+ [x[1] for x in zip(in_data, ("in_0:0", "in_1:0")) if x[0] !=
None],
+ [x for x in in_data if x != None],
Review Comment:
```suggestion
[x for x in in_data if x is not None],
```
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