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[KERAS]RepeatVector, Conv3DTranspose op support added (apache#5833)
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@siju-samuel
siju-samuel authored and Trevor Morris committed Jun 30, 2020
1 parent 3862ad1 commit 96d4bd3
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Showing 2 changed files with 85 additions and 22 deletions.
60 changes: 38 additions & 22 deletions python/tvm/relay/frontend/keras.py
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Original file line number Diff line number Diff line change
Expand Up @@ -336,25 +336,28 @@ def _convert_convolution3d(inexpr, keras_layer, etab):
'in frontend Keras.'
raise tvm.error.OpAttributeUnImplemented(msg.format(etab.data_layout))

is_deconv = type(keras_layer).__name__ == 'Conv3DTranspose'

if is_deconv:
kernel_d, kernel_h, kernel_w, n_filters, _ = weight.shape
if kernel_layout == 'OIDHW':
weight = weight.transpose([4, 3, 2, 0, 1])
else:
kernel_d, kernel_h, kernel_w, _, n_filters = weight.shape

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]
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
dilated_kernel_d = (kernel_d - 1) * dilation[0] + 1
dilated_kernel_h = (kernel_h - 1) * dilation[1] + 1
dilated_kernel_w = (kernel_w - 1) * dilation[2] + 1
stride_d, stride_h, stride_w = keras_layer.strides
params = {'weight': etab.new_const(weight),
'kernel_size': [kernel_d1, kernel_d2, kernel_d3],
'strides': [stride_d1, stride_d2, stride_d3],
'kernel_size': [kernel_d, kernel_h, kernel_w],
'strides': [stride_d, stride_h, stride_w],
'dilation': dilation,
'padding': [0, 0, 0],
'data_layout': etab.data_layout,
Expand All @@ -365,18 +368,21 @@ def _convert_convolution3d(inexpr, keras_layer, etab):
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]]
in_d = keras_layer.input_shape[1]
in_h = keras_layer.input_shape[2]
in_w = keras_layer.input_shape[3]
pad_d = _get_pad_pair(in_d, dilated_kernel_d, stride_d)
pad_h = _get_pad_pair(in_h, dilated_kernel_h, stride_h)
pad_w = _get_pad_pair(in_w, dilated_kernel_w, stride_w)
params['padding'] = [pad_d[0], pad_h[0], pad_w[0], pad_d[1], pad_h[1], pad_w[1]]
else:
msg = 'Padding with {} is not supported for operator Convolution3D ' \
'in frontend Keras.'
raise tvm.error.OpAttributeUnImplemented(msg.format(keras_layer.padding))
out = _op.nn.conv3d(data=inexpr, **params)
if is_deconv:
out = _op.nn.conv3d_transpose(data=inexpr, **params)
else:
out = _op.nn.conv3d(data=inexpr, **params)

channel_axis = -1 if etab.data_layout == "NDHWC" else 1
if keras_layer.use_bias:
Expand Down Expand Up @@ -849,6 +855,16 @@ def _convert_gru(inexpr, keras_layer, etab):
return [output, output]


def _convert_repeat_vector(inexpr, keras_layer, _):
input_shape = list(keras_layer.input_shape)
repeats = keras_layer.n
out_shape = [-1, repeats] + input_shape[1:]
out = _op.repeat(inexpr, repeats=repeats, axis=0)
out = _op.reshape(out, out_shape)

return out


def _default_skip(inexpr, keras_layer, _): # pylint: disable=unused-argument
"""Layers that can be skipped because they are train time only."""
return inexpr
Expand Down Expand Up @@ -898,7 +914,7 @@ def _default_skip(inexpr, keras_layer, _): # pylint: disable=unused-argument
# 'Conv1D' : _convert_convolution1d,

'Conv3D' : _convert_convolution3d,
# 'Conv3DTranspose' : _convert_convolution3d,
'Conv3DTranspose' : _convert_convolution3d,
# 'SeparableConv3D' : _convert_convolution3d,
'MaxPooling3D' : _convert_pooling3d,
'AveragePooling3D' : _convert_pooling3d,
Expand All @@ -919,7 +935,7 @@ def _default_skip(inexpr, keras_layer, _): # pylint: disable=unused-argument
'Dot' : _convert_merge,
'Permute' : _convert_permute,
'Embedding' : _convert_embedding,
# 'RepeatVector' : _convert_repeat_vector,
'RepeatVector' : _convert_repeat_vector,

'InputLayer' : _default_skip,
'Dropout' : _default_skip,
Expand Down
47 changes: 47 additions & 0 deletions tests/python/frontend/keras/test_forward.py
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Original file line number Diff line number Diff line change
Expand Up @@ -422,6 +422,31 @@ def test_forward_conv3d(self, keras):
keras_model = keras.models.Model(data, x)
verify_keras_frontend(keras_model, layout='NDHWC')


def test_forward_conv3d_transpose(self, keras):
data = keras.layers.Input(shape=(32, 32, 32, 3))
conv_funcs = [keras.layers.Conv3DTranspose(filters=10,
kernel_size=(3, 3, 3),
strides=(2, 2, 2),
padding='same'),
keras.layers.Conv3DTranspose(filters=10,
kernel_size=(1, 1, 1),
dilation_rate=(1, 1, 1),
padding='same'),
keras.layers.Conv3DTranspose(filters=1,
kernel_size=(3, 3, 3),
padding='valid',
use_bias=False),
keras.layers.Conv3DTranspose(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')


def test_forward_pool3d(self, keras):
data = keras.layers.Input(shape=(32, 32, 32, 1))
pool_funcs = [# maxpool
Expand Down Expand Up @@ -483,6 +508,26 @@ def test_forward_embedding(self, keras):
keras_model = keras.models.Model(data, x)
verify_keras_frontend(keras_model, need_transpose=False)


def test_forward_repeat_vector(self, keras):
data = keras.layers.Input(shape=(5,), dtype="float32")
x = keras.layers.Dense(6)(data)
x = keras.layers.RepeatVector(2)(x)

keras_model = keras.models.Model(data, x)
verify_keras_frontend(keras_model, need_transpose=False)

data = keras.layers.Input(shape=(10,), dtype="float32")
x = keras.layers.RepeatVector(3)(data)
keras_model = keras.models.Model(data, x)
verify_keras_frontend(keras_model, need_transpose=False)

data = keras.layers.Input(shape=(4,), dtype="float32")
x = keras.layers.RepeatVector(1)(data)
keras_model = keras.models.Model(data, x)
verify_keras_frontend(keras_model, need_transpose=False)


def test_forward_global_pool3d(self, keras):
data = keras.layers.Input(shape=(32, 32, 32, 1))
pool_funcs = [# global maxpool
Expand Down Expand Up @@ -523,8 +568,10 @@ def test_forward_global_pool3d(self, keras):
sut.test_forward_mobilenet(keras=k)
sut.test_forward_mobilenet(keras=k, layout='NHWC')
sut.test_forward_conv3d(keras=k)
sut.test_forward_conv3d_transpose(keras=k)
sut.test_forward_pool3d(keras=k)
sut.test_forward_global_pool3d(keras=k)
sut.test_forward_upsample3d(keras=k)
sut.test_forward_zero_padding3d(keras=k)
sut.test_forward_embedding(keras=k)
sut.test_forward_repeat_vector(keras=k)

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