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MatCaffe3 : a powerful matlab interface for caffe
Added matcaffe3, a powerful matlab interface. To test it, run 'make mattest'
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,74 @@ | ||
| classdef test_net < matlab.unittest.TestCase | ||
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| properties | ||
| num_output | ||
| model_file | ||
| net | ||
| end | ||
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| methods (Static) | ||
| function model_file = simple_net_file(num_output) | ||
| model_file = tempname(); | ||
| fid = fopen(model_file, 'w'); | ||
| fprintf(fid, [ ... | ||
| 'name: "testnet" force_backward: true\n' ... | ||
| 'layer { type: "DummyData" name: "data" top: "data" top: "label"\n' ... | ||
| 'dummy_data_param { num: 5 channels: 2 height: 3 width: 4\n' ... | ||
| ' num: 5 channels: 1 height: 1 width: 1\n' ... | ||
| ' data_filler { type: "gaussian" std: 1 }\n' ... | ||
| ' data_filler { type: "constant" } } }\n' ... | ||
| 'layer { type: "Convolution" name: "conv" bottom: "data" top: "conv"\n' ... | ||
| ' convolution_param { num_output: 11 kernel_size: 2 pad: 3\n' ... | ||
| ' weight_filler { type: "gaussian" std: 1 }\n' ... | ||
| ' bias_filler { type: "constant" value: 2 } }\n' ... | ||
| ' param { decay_mult: 1 } param { decay_mult: 0 }\n' ... | ||
| ' }\n' ... | ||
| 'layer { type: "InnerProduct" name: "ip" bottom: "conv" top: "ip"\n' ... | ||
| ' inner_product_param { num_output: ' num2str(num_output) ... | ||
| ' weight_filler { type: "gaussian" std: 2.5 }\n' ... | ||
| ' bias_filler { type: "constant" value: -3 } } }\n' ... | ||
| 'layer { type: "SoftmaxWithLoss" name: "loss" bottom: "ip" bottom: "label"\n' ... | ||
| ' top: "loss" }' ]); | ||
| fclose(fid); | ||
| end | ||
| end | ||
| methods | ||
| function self = test_net() | ||
| self.num_output = 13; | ||
| self.model_file = caffe.test.test_net.simple_net_file(self.num_output); | ||
| self.net = caffe.Net(self.model_file, 'train'); | ||
| % also make sure get_solver runs | ||
| caffe.get_net(self.model_file, 'train'); | ||
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| % fill in valid labels | ||
| self.net.blobs('label').set_data(randi( ... | ||
| self.num_output - 1, self.net.blobs('label').shape)); | ||
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| delete(self.model_file); | ||
| end | ||
| end | ||
| methods (Test) | ||
| function test_forward_backward(self) | ||
| self.net.forward_prefilled(); | ||
| self.net.backward_prefilled(); | ||
| end | ||
| function test_inputs_outputs(self) | ||
| self.verifyEqual(self.net.inputs, cell(0, 1)) | ||
| self.verifyEqual(self.net.outputs, {'loss'}); | ||
| end | ||
| function test_save_and_read(self) | ||
| weights_file = tempname(); | ||
| self.net.save(weights_file); | ||
| model_file2 = caffe.test.test_net.simple_net_file(self.num_output); | ||
| net2 = caffe.Net(model_file2, weights_file, 'train'); | ||
| delete(model_file2); | ||
| delete(weights_file); | ||
| for l = 1:length(self.net.layer_vec) | ||
| for i = 1:length(self.net.layer_vec(l).params) | ||
| self.verifyEqual(self.net.layer_vec(l).params(i).get_data(), ... | ||
| net2.layer_vec(l).params(i).get_data()); | ||
| end | ||
| end | ||
| end | ||
| end | ||
| end |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,43 @@ | ||
| classdef test_solver < matlab.unittest.TestCase | ||
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| properties | ||
| num_output | ||
| solver | ||
| end | ||
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| methods | ||
| function self = test_solver() | ||
| self.num_output = 13; | ||
| model_file = caffe.test.test_net.simple_net_file(self.num_output); | ||
| solver_file = tempname(); | ||
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| fid = fopen(solver_file, 'w'); | ||
| fprintf(fid, [ ... | ||
| 'net: "' model_file '"\n' ... | ||
| 'test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9\n' ... | ||
| 'weight_decay: 0.0005 lr_policy: "inv" gamma: 0.0001 power: 0.75\n' ... | ||
| 'display: 100 max_iter: 100 snapshot_after_train: false\n' ]); | ||
| fclose(fid); | ||
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| self.solver = caffe.Solver(solver_file); | ||
| % also make sure get_solver runs | ||
| caffe.get_solver(solver_file); | ||
| caffe.set_mode_cpu(); | ||
| % fill in valid labels | ||
| self.solver.net.blobs('label').set_data(randi( ... | ||
| self.num_output - 1, self.solver.net.blobs('label').shape)); | ||
| self.solver.test_nets(1).blobs('label').set_data(randi( ... | ||
| self.num_output - 1, self.solver.test_nets(1).blobs('label').shape)); | ||
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| delete(solver_file); | ||
| delete(model_file); | ||
| end | ||
| end | ||
| methods (Test) | ||
| function test_solve(self) | ||
| self.verifyEqual(self.solver.iter(), 0) | ||
| self.solver.solve() | ||
| self.verifyEqual(self.solver.iter(), 100) | ||
| end | ||
| end | ||
| end |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,71 @@ | ||
| classdef Blob < handle | ||
| % Wrapper class of caffe::Blob in matlab | ||
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| properties (Access = private) | ||
| hBlob_self | ||
| end | ||
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| methods | ||
| function self = Blob(hBlob_blob) | ||
| CHECK(is_valid_handle(hBlob_blob), 'invalid input handle'); | ||
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| % setup self handle and attributes | ||
| self.hBlob_self = hBlob_blob; | ||
| end | ||
| function shape = shape(self) | ||
| shape = caffe_('blob_get_shape', self.hBlob_self); | ||
| end | ||
| function reshape(self, shape) | ||
| shape = self.check_and_preprocess_shape(shape); | ||
| caffe_('blob_reshape', self.hBlob_self, shape); | ||
| end | ||
| function data = get_data(self) | ||
| data = caffe_('blob_get_data', self.hBlob_self); | ||
| end | ||
| function set_data(self, data) | ||
| data = self.check_and_preprocess_data(data); | ||
| caffe_('blob_set_data', self.hBlob_self, data); | ||
| end | ||
| function diff = get_diff(self) | ||
| diff = caffe_('blob_get_diff', self.hBlob_self); | ||
| end | ||
| function set_diff(self, diff) | ||
| diff = self.check_and_preprocess_data(diff); | ||
| caffe_('blob_set_diff', self.hBlob_self, diff); | ||
| end | ||
| end | ||
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| methods (Access = private) | ||
| function shape = check_and_preprocess_shape(~, shape) | ||
| CHECK(isempty(shape) || isnumeric(shape) && isrow(shape), ... | ||
| 'shape must be a integer row vector'); | ||
| shape = double(shape); | ||
| end | ||
| function data = check_and_preprocess_data(self, data) | ||
| CHECK(isnumeric(data), 'data or diff must be numeric types'); | ||
| self.check_data_size_matches(data) | ||
| data = single(data); | ||
| end | ||
| function check_data_size_matches(self, data) | ||
| % check whether size of data matches shape of this blob | ||
| % note: matlab arrays always have at least 2 dimensions. To compare | ||
| % shape between size of data and shape of this blob, extend shape of | ||
| % this blob to have at least 2 dimensions | ||
| data_size = size(data); | ||
| self_shape_extended = self.shape; | ||
| if isempty(self_shape_extended) | ||
| % target blob is a scalar (0 dim) | ||
| self_shape_extended = [1, 1]; | ||
| elseif isscalar(self_shape_extended) | ||
| % target blob is a vector (1 dim) | ||
| self_shape_extended = [self_shape_extended, 1]; | ||
| end | ||
| is_matched = (length(self_shape_extended) == length(data_size)) ... | ||
| && all(self_shape_extended == data_size); | ||
| CHECK(is_matched, ... | ||
| sprintf('%s, data size: [ %s], blob shape: [ %s]', ... | ||
| 'data size does not match blob shape', ... | ||
| sprintf('%d ', data_size), sprintf('%d ', self_shape_extended))); | ||
| end | ||
| end | ||
| end |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,32 @@ | ||
| classdef Layer < handle | ||
| % Wrapper class of caffe::Layer in matlab | ||
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| properties (Access = private) | ||
| hLayer_self | ||
| attributes | ||
| % attributes fields: | ||
| % hBlob_blobs | ||
| end | ||
| properties (SetAccess = private) | ||
| params | ||
| end | ||
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| methods | ||
| function self = Layer(hLayer_layer) | ||
| CHECK(is_valid_handle(hLayer_layer), 'invalid input handle'); | ||
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| % setup self handle and attributes | ||
| self.hLayer_self = hLayer_layer; | ||
| self.attributes = caffe_('layer_get_attr', self.hLayer_self); | ||
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| % setup weights | ||
| self.params = caffe.Blob.empty(); | ||
| for n = 1:length(self.attributes.hBlob_blobs) | ||
| self.params(n) = caffe.Blob(self.attributes.hBlob_blobs(n)); | ||
| end | ||
| end | ||
| function layer_type = type(self) | ||
| layer_type = caffe_('layer_get_type', self.hLayer_self); | ||
| end | ||
| end | ||
| end |
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