From: Siju Samuel Date: Thu, 18 Jun 2020 00:50:33 +0000 (+0530) Subject: [KERAS]RepeatVector, Conv3DTranspose op support added (#5833) X-Git-Tag: upstream/0.7.0~536 X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=f305b31d6343f207b913eb1aafc8d07782445e33;p=platform%2Fupstream%2Ftvm.git [KERAS]RepeatVector, Conv3DTranspose op support added (#5833) --- diff --git a/python/tvm/relay/frontend/keras.py b/python/tvm/relay/frontend/keras.py index ef76eb6..6972fb7 100644 --- a/python/tvm/relay/frontend/keras.py +++ b/python/tvm/relay/frontend/keras.py @@ -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, @@ -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: @@ -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 @@ -898,7 +914,7 @@ _convert_map = { # 'Conv1D' : _convert_convolution1d, 'Conv3D' : _convert_convolution3d, - # 'Conv3DTranspose' : _convert_convolution3d, + 'Conv3DTranspose' : _convert_convolution3d, # 'SeparableConv3D' : _convert_convolution3d, 'MaxPooling3D' : _convert_pooling3d, 'AveragePooling3D' : _convert_pooling3d, @@ -919,7 +935,7 @@ _convert_map = { 'Dot' : _convert_merge, 'Permute' : _convert_permute, 'Embedding' : _convert_embedding, - # 'RepeatVector' : _convert_repeat_vector, + 'RepeatVector' : _convert_repeat_vector, 'InputLayer' : _default_skip, 'Dropout' : _default_skip, diff --git a/tests/python/frontend/keras/test_forward.py b/tests/python/frontend/keras/test_forward.py index 9b963c3..8ddae96 100644 --- a/tests/python/frontend/keras/test_forward.py +++ b/tests/python/frontend/keras/test_forward.py @@ -422,6 +422,31 @@ class TestKeras: 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 @@ -483,6 +508,26 @@ class TestKeras: 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 @@ -523,8 +568,10 @@ if __name__ == '__main__': 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)