<li><spanclass="math">\(Out\)</span>: Output value, the shape of <spanclass="math">\(Out\)</span> and <spanclass="math">\(X\)</span> may be different.</li>
<p>The convolution2D transpose layer calculates the output based on the input,
filter, and dilations, strides, paddings. Input(Input) and output(Output)
are in NCHW format. Where N is batch size, C is the number of channels,
H is the height of the feature, and W is the width of the feature.
Parameters(dilations, strides, paddings) are two elements. These two elements
represent height and width, respectively. The details of convolution transpose
layer, please refer to the following explanation and references <aclass="reference external"href="http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf">therein</a>.</p>
<p>For each input <spanclass="math">\(X\)</span>, the equation is:</p>
<divclass="math">
\[Out = W \ast X\]</div>
<p>In the above equation:</p>
<ulclass="simple">
<li><spanclass="math">\(X\)</span>: Input value, a tensor with NCHW format.</li>
<li><spanclass="math">\(W\)</span>: Filter value, a tensor with MCHW format.</li>
<li><spanclass="math">\(Out\)</span>: Output value, the shape of <spanclass="math">\(Out\)</span> and <spanclass="math">\(X\)</span> may be different.</li>
<trclass="field-even field"><thclass="field-name">Raises:</th><tdclass="field-body"><pclass="first last"><codeclass="xref py py-exc docutils literal"><spanclass="pre">ValueError</span></code>– If the shapes of input, filter_size, stride, padding and groups mismatch.</p>
"comment":"\nConvolution3D Transpose Operator.\n\nThe convolution transpose operation calculates the output based on the input, filter\nand dilations, strides, paddings, groups parameters. The size of each dimension of the\nparameters is checked in the infer-shape.\nInput(Input) and output(Output) are in NCDHW format. Where N is batch size, C is the\nnumber of channels, D is the depth of the feature, H is the height of the feature,\nand W is the width of the feature.\nFilter(Input) is in MCDHW format. Where M is the number of input feature channels,\nC is the number of output feature channels, D is the depth of the filter,H is the\nheight of the filter, and W is the width of the filter.\nParameters(strides, paddings) are three elements. These three elements represent\ndepth, height and width, respectively.\nThe input(X) size and output(Out) size may be different.\n\nExample: \n Input:\n Input shape: $(N, C_{in}, D_{in}, H_{in}, W_{in})$\n Filter shape: $(C_{in}, C_{out}, D_f, H_f, W_f)$\n Output:\n Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$\n Where\n $$\n D_{out} = (D_{in} - 1) * strides[0] - 2 * paddings[0] + D_f \\\\\n H_{out} = (H_{in} - 1) * strides[1] - 2 * paddings[1] + H_f \\\\\n W_{out} = (W_{in} - 1) * strides[2] - 2 * paddings[2] + W_f\n $$\n",
"comment":"\nConvolution3D Transpose Operator.\n\nThe convolution transpose operation calculates the output based on the input, filter\nand dilations, strides, paddings, groups parameters. The size of each dimension of the\nparameters is checked in the infer-shape.\nInput(Input) and output(Output) are in NCDHW format. Where N is batch size, C is the\nnumber of channels, D is the depth of the feature, H is the height of the feature,\nand W is the width of the feature.\nFilter(Input) is in MCDHW format. Where M is the number of input feature channels,\nC is the number of output feature channels, D is the depth of the filter,H is the\nheight of the filter, and W is the width of the filter.\nParameters(strides, paddings) are three elements. These three elements represent\ndepth, height and width, respectively.\nThe input(X) size and output(Out) size may be different.\n\nExample: \n Input:\n Input shape: $(N, C_{in}, D_{in}, H_{in}, W_{in})$\n Filter shape: $(C_{in}, C_{out}, D_f, H_f, W_f)$\n Output:\n Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$\n Where\n $$\n D_{out} = (D_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (D_f - 1) + 1 \\\\\n H_{out} = (H_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (H_f - 1) + 1 \\\\\n W_{out} = (W_{in} - 1) * strides[2] - 2 * paddings[2] + dilations[2] * (W_f - 1) + 1\n $$\n",
"inputs":[
{
"name":"Input",
...
...
@@ -1805,7 +1805,7 @@
"attrs":[]
},{
"type":"conv2d_transpose",
"comment":"\nConvolution2D Transpose Operator.\n\nThe convolution transpose operation calculates the output based on the input, filter\nand dilations, strides, paddings, groups parameters. The size of each dimension of the\nparameters is checked in the infer-shape.\nInput(Input) and output(Output) are in NCHW format. Where N is batchsize, C is the\nnumber of channels, H is the height of the feature, and W is the width of the feature.\nFilter(Input) is in MCHW format. Where M is the number of input feature channels,\nC is the number of output feature channels, H is the height of the filter,\nand W is the width of the filter.\nParameters(strides, paddings) are two elements. These two elements represent height\nand width, respectively.\nThe input(X) size and output(Out) size may be different.\n\nExample:\n Input:\n Input shape: $(N, C_{in}, H_{in}, W_{in})$\n Filter shape: $(C_{in}, C_{out}, H_f, W_f)$\n Output:\n Output shape: $(N, C_{out}, H_{out}, W_{out})$\n Where\n $$\n H_{out} = (H_{in} - 1) * strides[0] - 2 * paddings[0] + H_f \\\\\n W_{out} = (W_{in} - 1) * strides[1] - 2 * paddings[1] + W_f\n $$\n",
"comment":"\nConvolution2D Transpose Operator.\n\nThe convolution transpose operation calculates the output based on the input, filter\nand dilations, strides, paddings, groups parameters. The size of each dimension of the\nparameters is checked in the infer-shape.\nInput(Input) and output(Output) are in NCHW format. Where N is batchsize, C is the\nnumber of channels, H is the height of the feature, and W is the width of the feature.\nFilter(Input) is in MCHW format. Where M is the number of input feature channels,\nC is the number of output feature channels, H is the height of the filter,\nand W is the width of the filter.\nParameters(strides, paddings) are two elements. These two elements represent height\nand width, respectively.\nThe input(X) size and output(Out) size may be different.\n\nExample:\n Input:\n Input shape: $(N, C_{in}, H_{in}, W_{in})$\n Filter shape: $(C_{in}, C_{out}, H_f, W_f)$\n Output:\n Output shape: $(N, C_{out}, H_{out}, W_{out})$\n Where\n $$\n H_{out} = (H_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (H_f - 1) + 1 \\\\\n W_{out} = (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1\n $$\n",
<li><spanclass="math">\(Out\)</span>: Output value, the shape of <spanclass="math">\(Out\)</span> and <spanclass="math">\(X\)</span> may be different.</li>
<p>The convolution2D transpose layer calculates the output based on the input,
filter, and dilations, strides, paddings. Input(Input) and output(Output)
are in NCHW format. Where N is batch size, C is the number of channels,
H is the height of the feature, and W is the width of the feature.
Parameters(dilations, strides, paddings) are two elements. These two elements
represent height and width, respectively. The details of convolution transpose
layer, please refer to the following explanation and references <aclass="reference external"href="http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf">therein</a>.</p>
<p>For each input <spanclass="math">\(X\)</span>, the equation is:</p>
<divclass="math">
\[Out = W \ast X\]</div>
<p>In the above equation:</p>
<ulclass="simple">
<li><spanclass="math">\(X\)</span>: Input value, a tensor with NCHW format.</li>
<li><spanclass="math">\(W\)</span>: Filter value, a tensor with MCHW format.</li>
<li><spanclass="math">\(Out\)</span>: Output value, the shape of <spanclass="math">\(Out\)</span> and <spanclass="math">\(X\)</span> may be different.</li>
<trclass="field-even field"><thclass="field-name">Raises:</th><tdclass="field-body"><pclass="first last"><codeclass="xref py py-exc docutils literal"><spanclass="pre">ValueError</span></code>– If the shapes of input, filter_size, stride, padding and groups mismatch.</p>
