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[Frontend][Relay][Keras] fix a wrong assertion about the kernal_layout of DepthwiseConv2D #15124

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[Frontend][Relay][Keras] fix a wrong assertion about the kernal_layout of DepthwiseConv2D #15124

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24fd39f
fix a wrong assertion about kernel_layout in conv2d
jikechao Jun 20, 2023
34fc738
add new test cases to capture the bug in depthwiseconv2d conversion
jikechao Jun 20, 2023
6905323
Update test_forward.py
jikechao Jun 20, 2023
9331f5f
Update keras.py
jikechao Jun 21, 2023
0f6b7b1
Update x86.py
jikechao Jun 21, 2023
5da0485
Update test_forward.py
jikechao Jun 22, 2023
2b30f9a
Merge branch 'apache:main' into fix_DepthwiseConv2D
jikechao Jun 22, 2023
ba90f28
Merge branch 'apache:main' into fix_DepthwiseConv2D
jikechao Jun 23, 2023
699072c
Update keras.py
jikechao Jul 7, 2023
bed51de
Merge branch 'apache:main' into fix_DepthwiseConv2D
jikechao Jul 7, 2023
da75c28
Merge branch 'apache:main' into fix_DepthwiseConv2D
jikechao Oct 8, 2023
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2 changes: 1 addition & 1 deletion python/tvm/relay/op/strategy/x86.py
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Original file line number Diff line number Diff line change
Expand Up @@ -139,7 +139,7 @@ def conv2d_strategy_cpu(attrs, inputs, out_type, target):
assert _OIHWio_matcher.match(kernel_layout) # check if kernel is OIHWio
return conv2d_NCHWc_strategy_cpu(attrs, inputs, out_type, target)
elif layout == "NHWC":
assert kernel_layout == "HWIO"
assert kernel_layout == "HWOI"
if (not need_auto_scheduler_layout) and (not need_meta_schedule_layout):
logger.warning("conv2d NHWC layout is not optimized for x86 with autotvm.")
if "dnnl" in target.libs:
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8 changes: 8 additions & 0 deletions tests/python/frontend/keras/test_forward.py
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Expand Up @@ -315,6 +315,14 @@ def test_forward_conv(self, keras_mod):
x = conv_func(data)
keras_model = keras_mod.models.Model(data, x)
verify_keras_frontend(keras_model)
# check the DepthwiseConv2D with single-channel
data = keras_mod.layers.Input(shape=(32, 32, 1))
conv_func = keras_mod.layers.DepthwiseConv2D(
kernel_size=(2, 2), strides= (2, 2), data_format="channels_last"
)
x = conv_func(data)
keras_model = keras_mod.models.Model(data, x)
verify_keras_frontend(keras_model)

def test_forward_conv_transpose(self, keras_mod):
"""test_forward_conv_transpose"""
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