Hi, all,
I’m using the computational graph output from relay, which is like the following.
%0 = nn.pad(%input_1, pad_width=[[0, 0], [0, 0], [3, 3], [3, 3]])
%1 = nn.conv2d(%0, %v_param_1, strides=[2, 2], channels=64, kernel_size=[7, 7])
%2 = nn.bias_add(%1, %v_param_2)
%3 = nn.batch_norm(%2, %v_param_3, %v_param_4, %v_param_5, %v_param_6, epsilon=0.001)
%4 = %3.0
%5 = nn.relu(%4)
%6 = nn.pad(%5, pad_width=[[0, 0], [0, 0], [1, 1], [1, 1]])
%7 = nn.max_pool2d(%6, pool_size=[3, 3], strides=[2, 2])
%8 = nn.conv2d(%7, %v_param_7, channels=64, kernel_size=[1, 1])
%9 = nn.bias_add(%8, %v_param_8)
%10 = nn.batch_norm(%9, %v_param_9, %v_param_10, %v_param_11, %v_param_12, epsilon=0.001)
%11 = %10.0
%12 = nn.relu(%11)
Most of these expressions are lucid, except ’ %4 = %3.0’ and ’ %11 = %10.0’. Could anyone tell me what they mean? Quantification? And it would be better if you tell me where I can read a relevant introduction.
Thanks in advance.