Class NetBuilder
Defined in File net_builder.h
Inheritance Relationships
Base Type
public cinn::frontend::BaseBuilder(Class BaseBuilder)
Class Documentation
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class
cinn::frontend::NetBuilder: public cinn::frontend::BaseBuilder Public Functions
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Variable
mul(const Variable &a, const Variable &b, int x_num_col_dims = 1, int y_num_col_dims = 1) Multiply two matrix.
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Variable
mulbias(const Variable &a, const Variable &b, const Variable &c, int x_num_col_dims = 1, int y_num_col_dims = 1) Multiply two matrix and add a bias.
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Variable
elementwise_add(const Variable &a, const Variable &b, int axis = -1) Add two tensors element-wise.
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const std::vector<Variable> &
elementwise_add_grad(const Variable &dout, const Variable &x, const Variable &y, int axis = -1) The gradient of elementwise_add.
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Variable
elementwise_mul(const Variable &a, const Variable &b, int axis = -1) Multiply two tensors element-wise.
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Variable
relu(const Variable &a) Apply Rectified Linear Unit on input Variable. Actually apply: outupt = max(input,0)
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Variable
relu_grad(const Variable &dout, const Variable &out) The gradient of Rectified Linear Unit. Actually apply: dx = dout * (out > 0)
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Variable
reverse(const Variable &x, const std::vector<int> &axis) This API reverses the Variable x along the given axis. Example 1: x = [[0, 1], [2, 3], [4, 5]], axis = [0] output = [[4, 5], [2, 3], [0, 1]] Example 2: x = [[0, 1], [2, 3], [4, 5]], axis = [0, 1] output = [[5, 4], [3, 2], [1, 0]]
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Variable
reduce_sum(const Variable &x, const std::vector<int> &dim, bool keep_dim = false) Compute the sum of Variable x along the given dim.
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Variable
conv2d(const Variable &a, const Variable &b, const std::vector<int> &strides = {1, 1}, const std::vector<int> &paddings = {0, 0}, const std::vector<int> &dilations = {1, 1}, int groups = 1, const std::string &data_format = "NCHW", const std::string &padding_algorithm = "EXPLICIT") The convolution2D layer calculates the output based on the input, filter and strides, paddings, dilations, groups parameters.
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Variable
depthwise_conv2d(const Variable &a, const Variable &b, const std::vector<int> &strides = {1, 1}, const std::vector<int> &paddings = {0, 0}, const std::vector<int> &dilations = {1, 1}, int groups = 1, const std::string &data_format = "NCHW", const std::string &padding_algorithm = "EXPLICIT")
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Variable
pool2d(const Variable &a, const std::string &pooling_type, const std::vector<int> &ksize, const std::vector<int> &strides = {1, 1}, const std::vector<int> &paddings = {0, 0}, bool ceil_mode = false, bool exclusive = true, bool global_pooling = false, const std::string &data_format = "NCHW", bool adaptive = false, const std::string &padding_algorithm = "EXPLICIT")
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std::vector<Variable>
batchnorm(const Variable &a, const Variable &scale, const Variable &bias, const Variable &mean, const Variable &variance, float epsilon = 1e-5f, float momentum = 0.9f, const std::string &data_layout = "NCHW", bool is_test = false) The batchnorm layer can be used as a normalizer function for convolution or fully_connected operations. is_test(true): batch norm infer (default), output={y} is_test(false): batch norm training, outputs={y, saved_mean, saved_variance, moving_mean, moving_variance}
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std::vector<Variable>
batch_norm_grad(const Variable &dy, const Variable &x, const Variable &scale, const Variable &save_mean, const Variable &save_variance, const float epsilon = 1e-5, const std::string &data_layout = "NCHW")
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Variable
scale(const Variable &a, float scale = 1.0f, float bias = 0.0f, bool bias_after_scale = true)
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Variable
slice(const Variable &a, const std::vector<int> &axes, const std::vector<int> &starts = {}, const std::vector<int> &ends = {}, const std::vector<int> &infer_flags = {}, const std::vector<int> &decrease_axis = {})
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Variable
dropout_infer(const Variable &a, float dropout_prob = 0.5f, const std::string &dropout_implementation = "downgrade_in_infer")
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std::vector<Variable>
conv2d_grad(const Variable &dy, const Variable &x, const Variable &w, const std::vector<int> &strides = {1, 1}, const std::vector<int> &paddings = {0, 0}, const std::vector<int> &dilations = {1, 1}, const int groups = 1, const std::string &data_format = "NCHW", const std::string &padding_algorithm = "EXPLICIT")
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Variable