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add loss_mean_squared_per_channel (#1863)
add loss_mean_squared_per_channel_and_pixel
This commit is contained in:
Adrià Arrufat
Davis E. King
parent
efd4e27488
commit
170877da88
144
dlib/dnn/loss.h
144
dlib/dnn/loss.h
@ -2891,7 +2891,149 @@ namespace dlib
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// ----------------------------------------------------------------------------------------
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class loss_dot_
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template<long _num_channels>
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class loss_mean_squared_per_channel_and_pixel_
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{
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public:
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typedef std::array<matrix<float>, _num_channels> training_label_type;
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typedef std::array<matrix<float>, _num_channels> output_label_type;
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template <
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typename SUB_TYPE,
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typename label_iterator
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>
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void to_label (
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const tensor& input_tensor,
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const SUB_TYPE& sub,
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label_iterator iter
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) const
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{
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DLIB_CASSERT(sub.sample_expansion_factor() == 1);
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const tensor& output_tensor = sub.get_output();
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DLIB_CASSERT(output_tensor.k() == _num_channels, "output k = " << output_tensor.k());
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DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples());
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const float* out_data = output_tensor.host();
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for (long i = 0; i < output_tensor.num_samples(); ++i, ++iter)
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{
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for (long k = 0; k < output_tensor.k(); ++k)
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{
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(*iter)[k].set_size(output_tensor.nr(), output_tensor.nc());
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for (long r = 0; r < output_tensor.nr(); ++r)
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{
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for (long c = 0; c < output_tensor.nc(); ++c)
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{
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(*iter)[k].operator()(r, c) = out_data[tensor_index(output_tensor, i, k, r, c)];
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}
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}
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}
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}
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}
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template <
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typename const_label_iterator,
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typename SUBNET
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>
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double compute_loss_value_and_gradient (
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const tensor& input_tensor,
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const_label_iterator truth,
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SUBNET& sub
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) const
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{
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const tensor& output_tensor = sub.get_output();
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tensor& grad = sub.get_gradient_input();
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DLIB_CASSERT(sub.sample_expansion_factor() == 1);
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DLIB_CASSERT(input_tensor.num_samples() != 0);
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DLIB_CASSERT(input_tensor.num_samples() % sub.sample_expansion_factor() == 0);
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DLIB_CASSERT(input_tensor.num_samples() == grad.num_samples());
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DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples());
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DLIB_CASSERT(output_tensor.k() == _num_channels);
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DLIB_CASSERT(output_tensor.nr() == grad.nr() &&
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output_tensor.nc() == grad.nc() &&
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output_tensor.k() == grad.k());
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for (long idx = 0; idx < output_tensor.num_samples(); ++idx)
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{
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const_label_iterator truth_matrix_ptr = (truth + idx);
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DLIB_CASSERT((*truth_matrix_ptr).size() == _num_channels);
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for (long k = 0; k < output_tensor.k(); ++k)
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{
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DLIB_CASSERT((*truth_matrix_ptr)[k].nr() == output_tensor.nr() &&
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(*truth_matrix_ptr)[k].nc() == output_tensor.nc(),
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"truth size = " << (*truth_matrix_ptr)[k].nr() << " x " << (*truth_matrix_ptr)[k].nc() << ", "
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"output size = " << output_tensor.nr() << " x " << output_tensor.nc());
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}
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}
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// The loss we output is the average loss over the mini-batch, and also over each element of the matrix output.
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const double scale = 1.0 / (output_tensor.num_samples() * output_tensor.k() * output_tensor.nr() * output_tensor.nc());
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double loss = 0;
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float* const g = grad.host();
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const float* out_data = output_tensor.host();
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for (long i = 0; i < output_tensor.num_samples(); ++i, ++truth)
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{
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for (long k = 0; k < output_tensor.k(); ++k)
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{
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for (long r = 0; r < output_tensor.nr(); ++r)
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{
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for (long c = 0; c < output_tensor.nc(); ++c)
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{
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const float y = (*truth)[k].operator()(r, c);
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const size_t idx = tensor_index(output_tensor, i, k, r, c);
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const float temp1 = y - out_data[idx];
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const float temp2 = scale*temp1;
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loss += temp2*temp1;
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g[idx] = -temp2;
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}
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}
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}
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}
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return loss;
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}
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friend void serialize(const loss_mean_squared_per_channel_and_pixel_& , std::ostream& out)
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{
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serialize("loss_mean_squared_per_channel_and_pixel_", out);
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}
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friend void deserialize(loss_mean_squared_per_channel_and_pixel_& , std::istream& in)
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{
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std::string version;
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deserialize(version, in);
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if (version != "loss_mean_squared_per_channel_and_pixel_")
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throw serialization_error("Unexpected version found while deserializing dlib::loss_mean_squared_per_channel_and_pixel_.");
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}
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friend std::ostream& operator<<(std::ostream& out, const loss_mean_squared_per_channel_and_pixel_& )
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{
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out << "loss_mean_squared_per_channel_and_pixel";
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return out;
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}
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friend void to_xml(const loss_mean_squared_per_channel_and_pixel_& /*item*/, std::ostream& out)
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{
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out << "<loss_mean_squared_per_channel_and_pixel/>";
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}
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private:
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static size_t tensor_index(const tensor& t, long sample, long k, long row, long column)
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{
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// See: https://github.com/davisking/dlib/blob/4dfeb7e186dd1bf6ac91273509f687293bd4230a/dlib/dnn/tensor_abstract.h#L38
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return ((sample * t.k() + k) * t.nr() + row) * t.nc() + column;
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}
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};
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template <long num_channels, typename SUBNET>
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using loss_mean_squared_per_channel_and_pixel = add_loss_layer<loss_mean_squared_per_channel_and_pixel_<num_channels>, SUBNET>;
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// ----------------------------------------------------------------------------------------
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class loss_dot_
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{
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public:
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@ -1495,7 +1495,68 @@ namespace dlib
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// ----------------------------------------------------------------------------------------
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class loss_dot_
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template<long _num_channels>
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class loss_mean_squared_per_channel_and_pixel_
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{
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/*!
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WHAT THIS OBJECT REPRESENTS
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This object implements the loss layer interface defined above by
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EXAMPLE_LOSS_LAYER_. In particular, it implements the mean squared loss,
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which is appropriate for regression problems. It is basically just like
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loss_mean_squared_per_pixel_ except that it computes the loss over all
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channels, not just the first one.
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!*/
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public:
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typedef std::array<matrix<float>, _num_channels> training_label_type;
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typedef std::array<matrix<float>, _num_channels> output_label_type;
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template <
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typename SUB_TYPE,
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typename label_iterator
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>
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void to_label (
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const tensor& input_tensor,
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const SUB_TYPE& sub,
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label_iterator iter
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) const;
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/*!
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This function has the same interface as EXAMPLE_LOSS_LAYER_::to_label() except
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it has the additional calling requirements that:
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- sub.get_output().num_samples() == input_tensor.num_samples()
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- sub.get_output().k() == _num_channels
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- sub.sample_expansion_factor() == 1
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and the output labels are the predicted continuous variables.
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!*/
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template <
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typename const_label_iterator,
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typename SUBNET
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>
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double compute_loss_value_and_gradient (
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const tensor& input_tensor,
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const_label_iterator truth,
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SUBNET& sub
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) const;
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/*!
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This function has the same interface as EXAMPLE_LOSS_LAYER_::compute_loss_value_and_gradient()
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except it has the additional calling requirements that:
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- sub.get_output().k() == _num_channels
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- sub.get_output().num_samples() == input_tensor.num_samples()
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- sub.sample_expansion_factor() == 1
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- for all idx such that 0 <= idx < sub.get_output().num_samples():
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- sub.get_output().nr() == (*(truth + idx)).nr()
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- sub.get_output().nc() == (*(truth + idx)).nc()
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!*/
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};
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template <long num_channels, typename SUBNET>
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using loss_mean_squared_per_channel_and_pixel = add_loss_layer<loss_mean_squared_per_channel_and_pixel_<num_channels>, SUBNET>;
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// ----------------------------------------------------------------------------------------
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class loss_dot_
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{
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/*!
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WHAT THIS OBJECT REPRESENTS
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@ -2495,6 +2495,66 @@ namespace
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DLIB_TEST_MSG(error_after < 1e-6, "Autoencoder error after training = " << error_after);
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}
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// ----------------------------------------------------------------------------------------
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void test_loss_mean_squared_per_channel_and_pixel()
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{
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print_spinner();
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const int num_samples = 1000;
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const long num_channels = 2;
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const long dimension = 3;
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::std::vector<matrix<float>> inputs;
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::std::vector<::std::array<matrix<float>, num_channels>> labels;
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for (int i = 0; i < num_samples; ++i)
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{
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matrix<float> x = matrix_cast<float>(randm(5, dimension));
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matrix<float> w = matrix_cast<float>(randm(num_channels, 5));
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matrix<float> y = w * x;
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DLIB_CASSERT(y.nr() == num_channels);
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::std::array<matrix<float>, num_channels> y_arr;
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// convert y to an array of matrices
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for (long c = 0; c < num_channels; ++c)
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{
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y_arr[c] = rowm(y, c);
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}
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inputs.push_back(::std::move(x));
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labels.push_back(::std::move(y_arr));
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}
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const long num_outputs = num_channels * dimension;
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using net_type = loss_mean_squared_per_channel_and_pixel<num_channels,
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extract<0, num_channels, 1, dimension,
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fc<num_outputs,
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relu<bn_fc<fc<500,
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input<matrix<float>>>>>>>>;
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net_type net;
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const auto compute_error = [&inputs, &labels, &net, num_channels]()
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{
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const auto out = net(inputs);
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double error = 0.0;
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for (size_t i = 0; i < out.size(); ++i)
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{
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for (size_t c = 0; c < num_channels; ++c)
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{
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error += mean(squared(out[i][c] - labels[i][c]));
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}
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}
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return error / out.size() / num_channels;
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};
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const auto error_before = compute_error();
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dnn_trainer<net_type> trainer(net);
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trainer.set_learning_rate(0.1);
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trainer.set_iterations_without_progress_threshold(500);
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trainer.set_min_learning_rate(1e-6);
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trainer.set_mini_batch_size(50);
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trainer.train(inputs, labels);
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const auto error_after = compute_error();
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DLIB_TEST_MSG(error_after < error_before, "multi channel error increased after training");
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}
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// ----------------------------------------------------------------------------------------
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void test_loss_multiclass_per_pixel_learned_params_on_trivial_single_pixel_task()
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@ -3252,6 +3312,7 @@ namespace
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test_simple_linear_regression_with_mult_prev();
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test_multioutput_linear_regression();
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test_simple_autoencoder();
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test_loss_mean_squared_per_channel_and_pixel();
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test_loss_multiclass_per_pixel_learned_params_on_trivial_single_pixel_task();
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test_loss_multiclass_per_pixel_activations_on_trivial_single_pixel_task();
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test_loss_multiclass_per_pixel_outputs_on_trivial_task();
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