Add Broadcast op (#365)
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@ -7,9 +7,11 @@
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- [ArgMin/ArgMax](#argminargmax)
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- [Batch2Space](#batch2space)
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- [BatchNorm](#batchnorm)
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- [Broadcast](#broadcast)
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- [Clip](#clip)
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- [Concat](#concat)
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- [Conv2d](#conv2d)
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- [Conv3d](#conv3d)
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- [DeConv2d](#deconv2d)
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- [DeConv1d](#deconv1d)
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- [DepthToSpace](#depthtospace)
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@ -22,9 +24,11 @@
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- [Minimum](#minimum)
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- [Maximum](#maximum)
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- [FloorDiv](#floordiv)
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- [Erf](#erf)
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- [FullyConnected](#fullyconnected)
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- [Gather](#gather)
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- [GatherNd](#gathernd)
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- [GroupedConv1d](#groupedconv1d)
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- [GroupedConv2d](#groupedconv2d)
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- [L2Normalization](#l2normalization)
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- [LocalResponseNormalization](#localresponsenormalization)
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@ -36,8 +40,12 @@
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- [MaxUnpool2d](#maxunpool2d)
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- [Moments](#moments)
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- [NBG](#nbg)
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- [OneHot](#onehot)
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- [Pad](#pad)
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- [Pool2d](#pool2d)
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- [Classic Pool2d](#classic-pool2d)
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- [Global Pool2d](#global-pool2d)
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- [Adaptive Pool2d](#adaptive-pool2d)
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- [ReduceMin](#reducemin)
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- [ReduceMax](#reducemax)
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- [ReduceAny](#reduceany)
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@ -78,6 +86,7 @@
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- [Squeeze](#squeeze)
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- [Stack](#stack)
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- [StridedSlice](#stridedslice)
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- [Svdf](#svdf)
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- [Tile](#tile)
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- [Transpose](#transpose)
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- [Unidirectional sequence lstm](#unidirectional-sequence-lstm)
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@ -108,12 +117,12 @@ Swish(x) : x * sigmoid(x)
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HardSwish(x) : 0 if x <= -3; x(x + 3)/6 if -3 < x < 3; x if x >= 3
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Mish(x) : x if x >= 0 else alpha * x
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HardSigmoid(x) : min(max(alpha*x + beta, 0), 1)
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SoftRelu(x) : log(1 + e^x). Also known as SoftPlus.
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Mish(x) : x * tanh(softrelu(x))
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LeakyRelu(x) : alpha * x if x <= 0; x if x > 0. alpha is a scalar.
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Prelu(x) : alpha * x if x <= 0; x if x > 0. alpha is a tensor.
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@ -153,7 +162,22 @@ rank as the input. This is the reverse transformation of Space2Batch.
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Carries out batch normalization as described in the paper
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https://arxiv.org/abs/1502.03167.
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Y = (X - Mean) / Sqrt( Var + Eps) * Gama + Beta
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$$\hat x_i\leftarrow \frac{x_i-\mu_\mathcal{B}}{\sqrt{\sigma_\mathcal{B}^2+\epsilon}}$$
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$$y_i=\gamma\hat x_i+\beta\equiv BN_{\gamma,\beta}(x_i)$$
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<a class="mk-toclify" id="broadcast"></a>
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## Broadcast
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Broadcast an array for a compatible shape. See also numpy.broadcast_to().
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Input:
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- input.
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Attribute:
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- shape: the shape which broadcast to.
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- dimensions(optional): Which dimension in the target shape each dimension
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of the operand shape corresponds to. For BroadcastInDim.
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<a class="mk-toclify" id="clip"></a>
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## Clip
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@ -189,6 +213,28 @@ Attribute:
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but the value is different. multiplier = weights / group.
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- layout : WHCN or CWHN.
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<a class="mk-toclify" id="conv3d"></a>
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## Conv3d
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Performs a 3-D convolution operation
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Input:
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- input [WHDCN].
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- kernel [ WHDIcOc ] (Ic: Input Channels. Oc: Output Channels).
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- bias [ O ]. Optional.
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Attribute:
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- weights : the output channel number for weight tensor.
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- ksize : the height and width for weight tensor.
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- padding : AUTO, VALID or SAME.
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- pad : pad value for each spatial axis. (left, right, top, bottom, front, rear).
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- stride : stride along each spatial axis.
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- dilation : dilation value along each spatial axis of the filter.
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- multiplier: function similar to group attribute on other framework,
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but the value is different. multiplier = weights / group.
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- input_layout : WHDCN or WHCDN.
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- kernel_layout : WHDIcOc
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<a class="mk-toclify" id="deconv2d"></a>
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## DeConv2d
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@ -292,6 +338,13 @@ Maximum(x, y) : max(x, y). This operation supports broadcasting.
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FloorDiv(x, y): floor( x / y ). This operation supports broadcasting.
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<a class="mk-toclify" id="erf"></a>
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## Erf
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Computes the Gauss error function of x element-wise.
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- no parameters
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<a class="mk-toclify" id="fullyconnected"></a>
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## FullyConnected
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@ -311,6 +364,26 @@ Gather slices from input, **axis** according to **indices**.
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An operation similar to Gather but gathers across multiple axis at once.
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<a class="mk-toclify" id="groupedconv1d"></a>
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## GroupedConv1d
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Performs a grouped 1-D convolution operation.
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Input:
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- input [WCN].
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- kernel [ WIcOc ] (Ic: Input Channels. Oc: Output Channels).Ic*group=C.
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- bias [ O ]. Optional.
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Attribute:
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- weights : the output channel number for weight tensor.
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- ksize : the height and width for weight tensor.
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- padding : AUTO, VALID or SAME.
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- pad : pad value for each spatial axis.
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- stride : stride along each spatial axis.
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- dilation : dilation value along each spatial axis of the filter.
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- group: Split conv to n group.
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- layout : WCN or CWN.
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<a class="mk-toclify" id="groupedconv2d"></a>
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## GroupedConv2d
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@ -415,24 +488,59 @@ If x is 1-D and axes = [0] this is just the mean and variance of a vector.
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Network Binary Graph is a precompile technology, which can compile a fuse graph into
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a bianry file.
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<a class="mk-toclify" id="onehot"></a>
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## OneHot
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Create a one-hot tensor.
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- depth : A scalar defining the depth of the one hot dimension.
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- on_value : A scalar defining the value to fill in output.
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- off_value : A scalar defining the value to fill in output.
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- axis : The axis to fill.
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<a class="mk-toclify" id="pad"></a>
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## Pad
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Pads a tensor.
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- const_val : the value to pad.
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- pad_mode : the mode of pad.
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- front_size : Add pad values to the left and top.
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- back_size : Add pad values to the right and bottom.
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<a class="mk-toclify" id="pool2d"></a>
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## Pool2d
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<a class="mk-toclify" id="classic-pool2d"></a>
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### Classic Pool2d
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Performs an 2-D pooling operation.
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- type : MAX, AVG, L2 or AVG_ANDROID.
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- padding : AUTO, VALID or SAME.
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- pad : Specify the number of pad values for left, right, top, and bottom.
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- ksize : filter size.
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- stride : stride along each spatial axis.
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- round_type : CEILING or FLOOR.
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<a class="mk-toclify" id="global-pool2d"></a>
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### Global Pool2d
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- type : MAX, AVG, L2 or AVG_ANDROID.
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- input_size : input size(only [W， H])
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- round_type : CEILING or FLOOR.
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<a class="mk-toclify" id="adaptive-pool2d"></a>
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### Adaptive Pool2d
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Same as torch.nn.AdaptiveXXXPool2d.
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- type : MAX, AVG, L2 or AVG_ANDROID.
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- input_size : input size(only [W， H])
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- output_size : output size(only [W， H])
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- round_type : CEILING or FLOOR.
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<a class="mk-toclify" id="reducemin"></a>
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## ReduceMin
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@ -714,12 +822,13 @@ Removes dimensions of size 1 from the shape of a tensor.
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## Stack
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Packs the list of tensors in inputs into a tensor with rank one higher than
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each tensor in values, by packing them along the **axis** dimension.
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each tensor in values, by packing them along the **axis** dimension.
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Dimensions below the dimension specified by axis will be packed together with other inputs.
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<a class="mk-toclify" id="stridedslice"></a>
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## StridedSlice
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Extracts a strided slice of a tensor.
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Extracts a strided slice of a tensor.Same as tensorflow.
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Roughly speaking, this op extracts a slice of size (end - begin) / stride from
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the given input tensor. Starting at the location specified by begin the slice
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@ -738,6 +847,15 @@ specification shrinks the dimensionality by 1, taking on the value at index begi
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In this case, the ith specification must define a slice of size 1,
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e.g. begin[i] = x, end[i] = x + 1.
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<a class="mk-toclify" id="svdf"></a>
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## Svdf
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Performs an 2-D pooling operation.
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- rank : The rank of the SVD approximation.
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- num_units : corresponds to the number of units.
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- spectrogram_length : corresponds to the fixed-size of the memory.
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<a class="mk-toclify" id="tile"></a>
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## Tile
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@ -765,4 +883,4 @@ how to bind input/output: take unidirectional_sequence_lstm_test.cc
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Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors.
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- axis : An int. The axis to unstack along. Defaults to the first dimension.
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Negative values wrap around, so the valid range is [-R, R).
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Negative values wrap around, so the valid range is [-R, R).
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@ -29,6 +29,7 @@
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#include "tim/vx/ops/arg.h"
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#include "tim/vx/ops/batch2space.h"
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#include "tim/vx/ops/batchnorm.h"
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#include "tim/vx/ops/broadcast.h"
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#include "tim/vx/ops/clip.h"
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#include "tim/vx/ops/concat.h"
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#include "tim/vx/ops/conv1d.h"
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@ -0,0 +1,61 @@
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/****************************************************************************
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*
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* Copyright (c) 2020 Vivante Corporation
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
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* to deal in the Software without restriction, including without limitation
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
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* and/or sell copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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*
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*****************************************************************************/
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#ifndef OVXLIBXX_OPERATIONS_BROADCAST_H_
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#define OVXLIBXX_OPERATIONS_BROADCAST_H_
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#include "tim/vx/direct_map_op.h"
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namespace tim {
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namespace vx {
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namespace ops {
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/**
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* ## Broadcast
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*
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* Broadcast an array for a compatible shape. See also numpy.broadcast_to().
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*
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* Input:
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* - input.
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*
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* Attribute:
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* - shape: the shape which broadcast to.
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* - dimensions(optional): Which dimension in the target shape each dimension
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* of the operand shape corresponds to. For BroadcastInDim.
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*/
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class Broadcast : public DirectMapOp {
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public:
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Broadcast(Graph* graph, const std::vector<int32_t>& shape, const std::vector<int32_t>& dimensions = {});
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std::shared_ptr<Operation> Clone(std::shared_ptr<Graph>& graph) const override;
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protected:
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const std::vector<int32_t> shape_;
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const std::vector<int32_t> dimensions_;
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};
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} // namespace ops
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} // namespace vx
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} // namespace tim
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#endif /* OVXLIBXX_OPERATIONS_BROADCAST_H_ */
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@ -104,6 +104,7 @@ Erf|ERF|Mapped|[tf.math.erf](https://tensorflow.google.cn/api_docs/python/tf/mat
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GroupedConv1d|GROUPED_CONV1D|Mapped|[tf.keras.layers.Conv1D](https://tensorflow.google.cn/api_docs/python/tf/keras/layers/Conv1D?hl=en)
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|SignalFrame|SIGNAL_FRAME|Mapped|[tf.signal.frame](https://tensorflow.google.cn/api_docs/python/tf/signal/frame)
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|RNNCell|RNNCELL_OVXLIB|Mapped|[ANEURALNETWORKS_RNN](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0acd2684ac9c73bb29767b534e78a332e8)
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|BroadCast|EXPAND_BROADCAST|Mapped|[numpy.broadcast_to](https://numpy.org/doc/stable/reference/generated/numpy.broadcast_to.html)
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||PROPOSAL| TBD |[Faster-RCNN Proposal Layer](https://github.com/intel/caffe/blob/master/examples/faster-rcnn/lib/rpn/proposal_layer.py)
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||ROI_POOL|Planned 22Q1 |[ANEURALNETWORKS_ROI_POOLING](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a6736198af337b2efbdb0b6b64dee7fe4)
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||ROI_ALIGN| TBD |[ANEURALNETWORKS_ROI_ALIGN](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a2848b39dd4bfba78f2438fda0d9397a4)
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@ -0,0 +1,57 @@
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/****************************************************************************
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*
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* Copyright (c) 2021 Vivante Corporation
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
|
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* to deal in the Software without restriction, including without limitation
|
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
|
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* and/or sell copies of the Software, and to permit persons to whom the
|
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
|
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
|
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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*
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*****************************************************************************/
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#include "tim/vx/ops/broadcast.h"
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#include <cassert>
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#include "direct_map_op_impl.h"
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#include "vsi_nn_pub.h"
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namespace tim {
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namespace vx {
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namespace ops {
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Broadcast::Broadcast(Graph* graph, const std::vector<int32_t>& shape,
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const std::vector<int32_t>& dimensions)
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: DirectMapOp(graph, VSI_NN_OP_EXPAND_BROADCAST),
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shape_(shape),
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dimensions_(dimensions) {
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this->impl()->node()->nn_param.expand_broadcast.dim_num = shape_.size();
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this->impl()->node()->nn_param.expand_broadcast.shape = (uint32_t*)shape_.data();
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this->impl()->node()->nn_param.expand_broadcast.dimensions_num = dimensions_.size();
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if (dimensions.size() > 0)
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{
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this->impl()->node()->nn_param.expand_broadcast.dimensions = (uint32_t*)dimensions_.data();
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} else {
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this->impl()->node()->nn_param.expand_broadcast.dimensions = nullptr;
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}
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}
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std::shared_ptr<Operation> Broadcast::Clone(
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std::shared_ptr<Graph>& graph) const {
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return graph->CreateOperation<Broadcast>(this->shape_, this->dimensions_);
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}
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} // namespace ops
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} // namespace vx
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} // namespace tim
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@ -0,0 +1,329 @@
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/****************************************************************************
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*
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* Copyright (c) 2021 Vivante Corporation
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
|
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* to deal in the Software without restriction, including without limitation
|
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
|
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* and/or sell copies of the Software, and to permit persons to whom the
|
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* Software is furnished to do so, subject to the following conditions:
|
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*
|
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* The above copyright notice and this permission notice shall be included in
|
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* all copies or substantial portions of the Software.
|
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*
|
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
|
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
|
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* DEALINGS IN THE SOFTWARE.
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*
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*****************************************************************************/
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#include "tim/vx/context.h"
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#include "tim/vx/graph.h"
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#include "tim/vx/ops/broadcast.h"
|
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#include "tim/transform/layout_inference.h"
|
||||
|
||||
#include "gtest/gtest.h"
|
||||
#include "test_utils.h"
|
||||
|
||||
static void CheckResult(std::shared_ptr<tim::vx::Graph>& graph,
|
||||
std::vector<float>& golden,
|
||||
std::shared_ptr<tim::vx::Tensor>& output_tensor) {
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
EXPECT_TRUE(graph->Run());
|
||||
|
||||
std::vector<float> output(golden.size() * sizeof(float));
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
|
||||
}
|
||||
|
||||
TEST(Broadcast, ScalarTo2D_2x3) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({1});
|
||||
tim::vx::ShapeType output_shape({3, 2});
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT);
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
std::vector<float> in_data = {
|
||||
2.25f,
|
||||
};
|
||||
std::vector<float> golden = {
|
||||
2.25f, 2.25f, 2.25f, 2.25f, 2.25f, 2.25f,
|
||||
};
|
||||
std::vector<int32_t> shape = {3, 2};
|
||||
|
||||
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
|
||||
in_data.size() * sizeof(float)));
|
||||
|
||||
auto op = graph->CreateOperation<tim::vx::ops::Broadcast>(shape);
|
||||
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
|
||||
|
||||
CheckResult(graph, golden, output_tensor);
|
||||
}
|
||||
|
||||
TEST(Broadcast, 1DTo2D) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({3});
|
||||
tim::vx::ShapeType output_shape({3, 2});
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT);
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
std::vector<float> in_data = {
|
||||
1.f, 2.f, 3.f,
|
||||
};
|
||||
std::vector<float> golden = {
|
||||
1.f, 2.f, 3.f, 1.f, 2.f, 3.f,
|
||||
};
|
||||
std::vector<int32_t> shape = {3, 2};
|
||||
|
||||
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
|
||||
in_data.size() * sizeof(float)));
|
||||
|
||||
auto op = graph->CreateOperation<tim::vx::ops::Broadcast>(shape);
|
||||
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
|
||||
|
||||
CheckResult(graph, golden, output_tensor);
|
||||
}
|
||||
|
||||
TEST(Broadcast, 1DTo2D_WithDims0) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({2});
|
||||
tim::vx::ShapeType output_shape({2, 2});
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT);
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
std::vector<float> in_data = {
|
||||
1.f, 2.f,
|
||||
};
|
||||
std::vector<float> golden = {
|
||||
1.f, 2.f,
|
||||
1.f, 2.f,
|
||||
};
|
||||
std::vector<int32_t> shape = {2, 2};
|
||||
std::vector<int32_t> dimensions = {0};
|
||||
|
||||
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
|
||||
in_data.size() * sizeof(float)));
|
||||
|
||||
auto op = graph->CreateOperation<tim::vx::ops::Broadcast>(shape, dimensions);
|
||||
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
|
||||
|
||||
CheckResult(graph, golden, output_tensor);
|
||||
}
|
||||
|
||||
TEST(Broadcast, 1DTo2D_WithDims1) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({2});
|
||||
tim::vx::ShapeType output_shape({2, 2});
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT);
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
std::vector<float> in_data = {
|
||||
1.f, 2.f,
|
||||
};
|
||||
std::vector<float> golden = {
|
||||
1.f, 1.f,
|
||||
2.f, 2.f,
|
||||
};
|
||||
std::vector<int32_t> shape = {2, 2};
|
||||
std::vector<int32_t> dimensions = {1};
|
||||
|
||||
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
|
||||
in_data.size() * sizeof(float)));
|
||||
|
||||
auto op = graph->CreateOperation<tim::vx::ops::Broadcast>(shape, dimensions);
|
||||
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
|
||||
|
||||
CheckResult(graph, golden, output_tensor);
|
||||
}
|
||||
|
||||
TEST(Broadcast, 1DTo3D_WithDims0) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({2});
|
||||
tim::vx::ShapeType output_shape({2, 2, 2});
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT);
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
std::vector<float> in_data = {
|
||||
1.f, 2.f,
|
||||
};
|
||||
std::vector<float> golden = {
|
||||
1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f,
|
||||
};
|
||||
std::vector<int32_t> shape = {2, 2, 2};
|
||||
std::vector<int32_t> dimensions = {0};
|
||||
|
||||
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
|
||||
in_data.size() * sizeof(float)));
|
||||
|
||||
auto op = graph->CreateOperation<tim::vx::ops::Broadcast>(shape, dimensions);
|
||||
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
|
||||
|
||||
CheckResult(graph, golden, output_tensor);
|
||||
}
|
||||
|
||||
TEST(Broadcast, 1DTo3D_WithDims1) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({2});
|
||||
tim::vx::ShapeType output_shape({2, 2, 2});
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT);
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
std::vector<float> in_data = {
|
||||
1.f, 2.f,
|
||||
};
|
||||
std::vector<float> golden = {
|
||||
1.f, 1.f, 2.f, 2.f, 1.f, 1.f, 2.f, 2.f,
|
||||
};
|
||||
std::vector<int32_t> shape = {2, 2, 2};
|
||||
std::vector<int32_t> dimensions = {1};
|
||||
|
||||
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
|
||||
in_data.size() * sizeof(float)));
|
||||
|
||||
auto op = graph->CreateOperation<tim::vx::ops::Broadcast>(shape, dimensions);
|
||||
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
|
||||
|
||||
CheckResult(graph, golden, output_tensor);
|
||||
}
|
||||
|
||||
TEST(Broadcast, 1DTo3D_WithDims2) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({2});
|
||||
tim::vx::ShapeType output_shape({2, 2, 2});
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT);
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
std::vector<float> in_data = {
|
||||
1.f, 2.f,
|
||||
};
|
||||
std::vector<float> golden = {
|
||||
1.f, 1.f, 1.f, 1.f, 2.f, 2.f, 2.f, 2.f,
|
||||
};
|
||||
std::vector<int32_t> shape = {2, 2, 2};
|
||||
std::vector<int32_t> dimensions = {2};
|
||||
|
||||
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
|
||||
in_data.size() * sizeof(float)));
|
||||
|
||||
auto op = graph->CreateOperation<tim::vx::ops::Broadcast>(shape, dimensions);
|
||||
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
|
||||
|
||||
CheckResult(graph, golden, output_tensor);
|
||||
}
|
||||
|
||||
TEST(Broadcast, 2DTo3D_WithDims02) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({2, 2});
|
||||
tim::vx::ShapeType output_shape({2, 2, 2});
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT);
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
std::vector<float> in_data = {
|
||||
1.f, 5.f, 2.f, 6.f
|
||||
};
|
||||
std::vector<float> golden = {
|
||||
1.f, 5.f, 1.f, 5.f, 2.f, 6.f, 2.f, 6.f,
|
||||
};
|
||||
std::vector<int32_t> shape = {2, 2, 2};
|
||||
std::vector<int32_t> dimensions = {0, 2};
|
||||
|
||||
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
|
||||
in_data.size() * sizeof(float)));
|
||||
|
||||
auto op = graph->CreateOperation<tim::vx::ops::Broadcast>(shape, dimensions);
|
||||
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
|
||||
|
||||
CheckResult(graph, golden, output_tensor);
|
||||
}
|
||||
|
||||
TEST(Broadcast, 2DTo3D_WithDims12) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({2, 2});
|
||||
tim::vx::ShapeType output_shape({2, 2, 2});
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT);
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
std::vector<float> in_data = {
|
||||
1.f, 5.f, 2.f, 6.f
|
||||
};
|
||||
std::vector<float> golden = {
|
||||
1.f, 1.f, 5.f, 5.f, 2.f, 2.f, 6.f, 6.f,
|
||||
};
|
||||
std::vector<int32_t> shape = {2, 2, 2};
|
||||
std::vector<int32_t> dimensions = {1, 2};
|
||||
|
||||
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
|
||||
in_data.size() * sizeof(float)));
|
||||
|
||||
auto op = graph->CreateOperation<tim::vx::ops::Broadcast>(shape, dimensions);
|
||||
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
|
||||
|
||||
CheckResult(graph, golden, output_tensor);
|
||||
}
|
||||
Loading…
Reference in New Issue