# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
-# pylint: disable=invalid-name, unused-argument
+# pylint: disable=invalid-name, unused-argument, too-many-lines
"""Tensorflow lite frontend."""
from __future__ import absolute_import as _abs
import math
'BATCH_TO_SPACE_ND': self.convert_batch_to_space_nd,
'SPACE_TO_BATCH_ND': self.convert_space_to_batch_nd,
'PRELU': self.convert_prelu,
+ 'TRANSPOSE_CONV': self.convert_transpose_conv,
}
def check_unsupported_ops(self):
return out
+ def convert_transpose_conv(self, op):
+ """Convert TFLite TRANSPOSE_CONV"""
+ try:
+ from tflite.BuiltinOptions import BuiltinOptions
+ from tflite.TensorType import TensorType
+ from tflite.Operator import Operator
+ from tflite.TransposeConvOptions import TransposeConvOptions
+ from tflite.Padding import Padding
+ except ImportError:
+ raise ImportError("The tflite package must be installed")
+
+ assert isinstance(op, Operator)
+ input_tensors = self.get_input_tensors(op)
+ assert len(input_tensors) == 3, "input tensors length should be 3"
+
+ # Input (data) Tensor. NHWC layout
+ input_tensor = input_tensors[2]
+ _, _, _, input_c = input_tensor.tensor.ShapeAsNumpy()
+ # Weights tensor. TFLite uses OHWI layout
+ weights_tensor = input_tensors[1]
+ out_channels, kernel_h, kernel_w, in_channels = weights_tensor.tensor.ShapeAsNumpy()
+ assert input_c == in_channels, \
+ "Input channel in the filter should match to channel in the input"
+ # output_shape Tensor. NHWC layout
+ output_shape_tensor = input_tensors[0]
+
+ output_tensors = self.get_output_tensors(op)
+ assert len(output_tensors) == 1, "output tensors length should be 1"
+ output_tensor = output_tensors[0]
+ output_tensor_type = output_tensor.tensor.Type()
+ output_tensor_type_str = self.get_tensor_type_str(output_tensor_type)
+
+ assert op.BuiltinOptionsType() == BuiltinOptions.TransposeConvOptions
+ op_options = op.BuiltinOptions()
+ deconv_options = TransposeConvOptions()
+ deconv_options.Init(op_options.Bytes, op_options.Pos)
+
+ padding = deconv_options.Padding()
+ stride_h = deconv_options.StrideH()
+ stride_w = deconv_options.StrideW()
+ assert padding in (Padding.VALID, Padding.SAME), \
+ 'Padding format {} is not supported for operator TRANSPOSE_CONV'.format(padding)
+
+ # Data
+ in_expr = self.get_expr(input_tensor.tensor_idx)
+
+ # Weights
+ weights_tensor_type = weights_tensor.tensor.Type()
+ # weights tensor type should be UINT8 (quantization) or FLOAT32
+ assert weights_tensor_type in (TensorType.UINT8, TensorType.FLOAT32)
+ weight_tensor_type_str = self.get_tensor_type_str(weights_tensor_type)
+ weight_value_ohwi = self.get_tensor_value(weights_tensor)
+ # Relay kernel_layout should be OIHW
+ # Relay weights layout should be different from kernel_layout - it should be IOHW
+ weight_value_iohw = np.transpose(weight_value_ohwi, (3, 0, 1, 2))
+ weight_expr_iohw = self.exp_tab.new_const(weight_value_iohw, dtype=weight_tensor_type_str)
+
+ # Output shape value
+ output_shape_value = self.get_tensor_value(output_shape_tensor)
+ # Relay expects filter output channel to match to output tensor channel.
+ assert out_channels == output_shape_value[3], \
+ "Output channel in the filter should match to channel in the output_shape"
+
+ # TF frontend supports 'SAME' padding for kernel 1x1 only. Lets do the same here
+ if padding == Padding.SAME:
+ assert (kernel_h, kernel_w) == (1, 1), \
+ "SAME padding is supported for kernel (1,1) only"
+
+ out = _op.nn.conv2d_transpose(in_expr, weight_expr_iohw,
+ strides=(stride_h, stride_w),
+ channels=int(out_channels),
+ kernel_size=(int(kernel_h), int(kernel_w)),
+ data_layout="NHWC",
+ kernel_layout="OIHW",
+ out_dtype=output_tensor_type_str)
+
+ return out
+
def get_expr(self, input_tensor_idx):
return self.exp_tab.get_expr(get_tensor_name(self.subgraph, input_tensor_idx))
#######################################################################
+# Transpose Convolution
+# ---------------------
+
+def _test_transpose_conv(tensor_in_sizes, filter_in_sizes, output_shape, strides, padding):
+ """ One iteration of transpose convolution with given shapes and attributes """
+
+ total_size_1 = 1
+ total_size_2 = 1
+ for s in tensor_in_sizes:
+ total_size_1 *= s
+ for s in filter_in_sizes:
+ total_size_2 *= s
+ # Initializes the input tensor with array containing incrementing
+ # numbers from 1.
+ data_array = [f * 1.0 for f in range(1, total_size_1 + 1)]
+ filter_array = [f * 1.0 for f in range(1, total_size_2 + 1)]
+
+ with tf.Graph().as_default():
+ in_data = array_ops.placeholder(shape=tensor_in_sizes, dtype='float32')
+ in_filter = constant_op.constant(filter_array, shape=filter_in_sizes, dtype='float32')
+ strides = [1] + strides + [1]
+ # in_filter layout is HWOI
+ out = nn_ops.conv2d_transpose(in_data,
+ in_filter,
+ output_shape=output_shape,
+ strides=strides,
+ padding=padding)
+ data_array = np.reshape(data_array, tensor_in_sizes).astype('float32')
+ compare_tflite_with_tvm(data_array, 'Placeholder:0', [in_data], [out])
+
+
+def test_forward_transpose_conv():
+ # kernel 3x3, padding VALID
+ _test_transpose_conv([4, 32, 32, 16], [3, 3, 5, 16], [4, 34, 34, 5], [1, 1], 'VALID')
+ _test_transpose_conv([1, 32, 32, 16], [3, 3, 5, 16], [1, 65, 65, 5], [2, 2], 'VALID')
+ _test_transpose_conv([1, 32, 32, 16], [3, 3, 5, 16], [1, 65, 34, 5], [2, 1], 'VALID')
+
+ # kernel 2x2, padding VALID
+ _test_transpose_conv([4, 32, 32, 16], [2, 2, 5, 16], [4, 33, 33, 5], [1, 1], 'VALID')
+ _test_transpose_conv([1, 32, 32, 16], [2, 2, 5, 16], [1, 64, 64, 5], [2, 2], 'VALID')
+ _test_transpose_conv([1, 32, 32, 16], [2, 2, 5, 16], [1, 64, 33, 5], [2, 1], 'VALID')
+
+ # kernel 1x1, padding VALID
+ _test_transpose_conv([4, 32, 32, 16], [1, 1, 5, 16], [4, 32, 32, 5], [1, 1], 'VALID')
+ _test_transpose_conv([1, 32, 32, 16], [1, 1, 5, 16], [1, 63, 63, 5], [2, 2], 'VALID')
+ _test_transpose_conv([1, 32, 32, 16], [1, 1, 5, 16], [1, 63, 32, 5], [2, 1], 'VALID')
+
+ # kernel 1x1, padding SAME
+ _test_transpose_conv([4, 32, 32, 16], [1, 1, 5, 16], [4, 32, 32, 5], [1, 1], 'SAME')
+ _test_transpose_conv([1, 32, 32, 16], [1, 1, 5, 16], [1, 63, 63, 5], [2, 2], 'SAME')
+ _test_transpose_conv([1, 32, 32, 16], [1, 1, 5, 16], [1, 63, 32, 5], [2, 1], 'SAME')
+
+
+#######################################################################
# Reshape
# -------
# NN
test_forward_convolution()
+ test_forward_transpose_conv()
test_forward_logistic()
test_forward_pooling()
test_forward_softmax()
projects / platform / upstream / tvm.git / commitdiff