Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[KERAS] conv3d frontend operator support #5080

Merged
merged 2 commits into from
Mar 21, 2020
Merged

[KERAS] conv3d frontend operator support #5080

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
5b4d142
[KERAS]Conv3d support added
siju-samuel Mar 16, 2020
f7faac3
Keras conv3d testcase added
siju-samuel Mar 16, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
89 changes: 83 additions & 6 deletions python/tvm/relay/frontend/keras.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -319,6 +319,75 @@ def _convert_convolution(inexpr, keras_layer, etab):
out = _convert_activation(out, act_type, etab)
return out

def _convert_convolution3d(inexpr, keras_layer, etab):
_check_data_format(keras_layer)
weightList = keras_layer.get_weights()
weight = weightList[0]

if etab.data_layout == 'NDHWC':
kernel_layout = 'DHWIO'
else:
kernel_layout = 'OIDHW'
msg = 'Kernel layout with {} is not supported for operator Convolution3D ' \
'in frontend Keras.'
raise tvm.error.OpAttributeUnImplemented(msg.format(etab.data_layout))

dilation_rate = keras_layer.dilation_rate
if isinstance(dilation_rate, (list, tuple)):
dilation = [dilation_rate[0], dilation_rate[1], dilation_rate[2]]
else:
dilation = [dilation_rate, dilation_rate, dilation_rate]

kernel_d1 = weight.shape[0]
kernel_d2 = weight.shape[1]
kernel_d3 = weight.shape[2]
# in_channels = weight.shape[3]
FrozenGene marked this conversation as resolved.
Show resolved Hide resolved
n_filters = weight.shape[4]

dilated_kernel_d1 = (kernel_d1 - 1) * dilation[0] + 1
dilated_kernel_d2 = (kernel_d2 - 1) * dilation[1] + 1
dilated_kernel_d3 = (kernel_d3 - 1) * dilation[2] + 1
stride_d1, stride_d2, stride_d3 = keras_layer.strides
params = {'weight': etab.new_const(weight),
'kernel_size': [kernel_d1, kernel_d2, kernel_d3],
'strides': [stride_d1, stride_d2, stride_d3],
'dilation': dilation,
'padding': [0, 0, 0],
'data_layout': etab.data_layout,
'kernel_layout': kernel_layout}
params['channels'] = n_filters

if keras_layer.padding == 'valid':
pass
# calculate the padding values
elif keras_layer.padding == 'same':
in_d1 = keras_layer.input_shape[1]
in_d2 = keras_layer.input_shape[2]
in_d3 = keras_layer.input_shape[3]
pad_d1 = _get_pad_pair(in_d1, dilated_kernel_d1, stride_d1)
pad_d2 = _get_pad_pair(in_d2, dilated_kernel_d2, stride_d2)
pad_d3 = _get_pad_pair(in_d3, dilated_kernel_d3, stride_d3)
params['padding'] = [pad_d1[0], pad_d2[0], pad_d3[0], pad_d1[1], pad_d2[1], pad_d3[1]]
else:
msg = 'Padding with {} is not supported for operator Convolution ' \
'in frontend Keras.'
raise tvm.error.OpAttributeUnImplemented(msg.format(keras_layer.padding))
out = _op.nn.conv3d(data=inexpr, **params)

channel_axis = -1 if etab.data_layout == "NDHWC" else 1
if keras_layer.use_bias:
bias = etab.new_const(weightList[1])
out = _op.nn.bias_add(out, bias, channel_axis)

# defuse activation
if sys.version_info.major < 3:
act_type = keras_layer.activation.func_name
else:
act_type = keras_layer.activation.__name__
if act_type != 'linear':
out = _convert_activation(out, act_type, etab)

return out

def _convert_separable_convolution(inexpr, keras_layer, etab):
_check_data_format(keras_layer)
Expand Down Expand Up @@ -742,19 +811,27 @@ def _default_skip(inexpr, keras_layer, _): # pylint: disable=unused-argument
# 'GlobalMaxPooling1D' : _convert_pooling,
# 'Cropping1D' : _convert_cropping,
# 'UpSampling1D' : _convert_upsample,
# 'UpSampling3D' : _convert_upsample,
# 'Conv1D' : _convert_convolution1d,

'Conv3D' : _convert_convolution3d,
# 'Conv3DTranspose' : _convert_convolution3d,
# 'SeparableConv3D' : _convert_convolution3d,
# 'MaxPooling3D' : _convert_pooling3d,
# 'AveragePooling3D' : _convert_pooling3d,
# 'GlobalMaxPooling3D' : _convert_pooling3d,
# 'GlobalAveragePooling3D' : _convert_pooling3d,
# 'UpSampling3D' : _convert_upsample3d,

'SimpleRNN' : _convert_simple_rnn,
'LSTM' : _convert_lstm,
'GRU' : _convert_gru,
# 'Bidirectional' : _convert_bidirectional,
# 'TimeDistributed' : _default_skip,

'Average' : _convert_merge,
'Maximum' : _convert_merge,
'Dot' : _convert_merge,
'Permute' : _convert_permute,
'Average' : _convert_merge,
'Maximum' : _convert_merge,
'Dot' : _convert_merge,
'Permute' : _convert_permute,
# 'Embedding' : _convert_embedding,
# 'RepeatVector' : _convert_repeat_vector,

Expand Down Expand Up @@ -866,7 +943,7 @@ def _convert_input_layer(keras_layer):

etab = ExprTable()
# Set global data format.
assert layout in ['NCHW', 'NHWC'], "Layout must be one of 'NCHW' or NHWC"
assert layout in ['NCHW', 'NHWC', 'NDHWC'], "Layout must be one of 'NCHW', NHWC or NDHWC"
etab.data_layout = layout
for keras_layer in model.layers:
if isinstance(keras_layer, input_layer_class):
Expand Down
23 changes: 23 additions & 0 deletions tests/python/frontend/keras/test_forward.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -393,6 +393,28 @@ def test_forward_mobilenet(self, keras, layout='NCHW'):
input_shape=(224, 224, 3), classes=1000)
verify_keras_frontend(keras_model, layout=layout)

def test_forward_conv3d(self, keras):
data = keras.layers.Input(shape=(32, 32, 32, 3))
conv_funcs = [keras.layers.Conv3D(filters=10,
kernel_size=(3, 3, 3),
strides=(2, 2, 2),
padding='same'),
keras.layers.Conv3D(filters=10,
kernel_size=(3, 3, 3),
dilation_rate=(2, 2, 2),
padding='same'),
keras.layers.Conv3D(filters=1,
kernel_size=(3, 3, 3),
padding='valid',
use_bias=False),
keras.layers.Conv3D(filters=10,
kernel_size=(2, 2, 2),
padding='valid'),
]
for conv_func in conv_funcs:
x = conv_func(data)
keras_model = keras.models.Model(data, x)
verify_keras_frontend(keras_model, layout='NDHWC')

if __name__ == '__main__':
for k in [keras, tf_keras]:
Expand Down Expand Up @@ -421,3 +443,4 @@ def test_forward_mobilenet(self, keras, layout='NCHW'):
sut.test_forward_resnet50(keras=k, layout='NHWC')
sut.test_forward_mobilenet(keras=k)
sut.test_forward_mobilenet(keras=k, layout='NHWC')
sut.test_forward_conv3d(keras=k)