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Posted to commits@systemml.apache.org by du...@apache.org on 2017/06/28 19:25:20 UTC
[1/2] systemml git commit: [SYSTEMML-1678] Add a new 1D top_k utility
function
Repository: systemml
Updated Branches:
refs/heads/master 9f808c43e -> 5e7e57774
[SYSTEMML-1678] Add a new 1D top_k utility function
This function computes the top k values (i.e. probabilities) and
associated indices (i.e. classes) from the input matrix X. A typical
use case is that in which X is the output of a softmax layer, and
values and indices will contain rows with the top k probabilities and
class indices.
Closes #551.
Project: http://git-wip-us.apache.org/repos/asf/systemml/repo
Commit: http://git-wip-us.apache.org/repos/asf/systemml/commit/2e78eb9a
Tree: http://git-wip-us.apache.org/repos/asf/systemml/tree/2e78eb9a
Diff: http://git-wip-us.apache.org/repos/asf/systemml/diff/2e78eb9a
Branch: refs/heads/master
Commit: 2e78eb9a56148f9e27ea1cb646e2d6dd528d251e
Parents: 9f808c4
Author: Fei Hu <hu...@gmail.com>
Authored: Wed Jun 28 12:23:08 2017 -0700
Committer: Mike Dusenberry <mw...@us.ibm.com>
Committed: Wed Jun 28 12:23:08 2017 -0700
----------------------------------------------------------------------
scripts/nn/test/run_tests.dml | 2 ++
scripts/nn/test/test.dml | 66 +++++++++++++++++++++++++++++++++++++-
scripts/nn/util.dml | 59 ++++++++++++++++++++++++++++++++++
3 files changed, 126 insertions(+), 1 deletion(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/systemml/blob/2e78eb9a/scripts/nn/test/run_tests.dml
----------------------------------------------------------------------
diff --git a/scripts/nn/test/run_tests.dml b/scripts/nn/test/run_tests.dml
index cca0d0d..4cc2875 100644
--- a/scripts/nn/test/run_tests.dml
+++ b/scripts/nn/test/run_tests.dml
@@ -98,6 +98,8 @@ test::padding()
test::tanh()
test::threshold()
test::transpose_NCHW_to_CNHW()
+test::top_k_row()
+test::top_k()
print("---")
print("Other tests complete -- look for any ERRORs or WARNINGs.")
http://git-wip-us.apache.org/repos/asf/systemml/blob/2e78eb9a/scripts/nn/test/test.dml
----------------------------------------------------------------------
diff --git a/scripts/nn/test/test.dml b/scripts/nn/test/test.dml
index b0899e3..b1190fc 100644
--- a/scripts/nn/test/test.dml
+++ b/scripts/nn/test/test.dml
@@ -768,7 +768,7 @@ tanh = function() {
threshold = function() {
/*
- * Test for threshold function.
+ * Test for the threshold function.
*/
print("Testing the threshold function.")
@@ -788,3 +788,67 @@ threshold = function() {
out = test_util::check_all_equal(indicator_matrix, target_matrix)
}
+top_k_row = function() {
+ /*
+ * Test for the top_k function.
+ */
+ print("Testing the top_k_row function.")
+
+ #Generate data
+ X = matrix("2 3 2 1 19 20 31 12 3 4 5 60 9 2
+ 3 6 15 18 6 12 1 17 3 0 4 19 1 6", rows=2, cols=14)
+ r = 2
+ k = 3
+ expected_values = matrix("19
+ 18
+ 17", rows=1, cols=3)
+ expected_indices = matrix("12
+ 4
+ 8", rows=1, cols=3)
+
+ # Test the top 3 for the second row
+ [values, indices] = util::top_k_row(X, 2, 3)
+ check_values = test_util::check_all_equal(values, expected_values)
+ check_indices = test_util::check_all_equal(indices, expected_indices)
+}
+
+top_k = function() {
+ /*
+ * Test for the top_k function.
+ */
+ print("Testing the top_k function.")
+
+ # Generate data
+ X = matrix("0.1 0.3 0.2 0.4
+ 0.1 0.3 0.3 0.2", rows=2, cols=4)
+ expected_values_top1 = matrix("0.4
+ 0.3", rows=2, cols=1)
+ expected_indices_top1 = matrix("4
+ 2", rows=2, cols=1)
+ expected_values_top2 = matrix("0.4 0.3
+ 0.3 0.3", rows=2, cols=2)
+ expected_indices_top2 = matrix("4 2
+ 2 3", rows=2, cols=2)
+ expected_values_topAll = matrix("0.4 0.3 0.2 0.1
+ 0.3 0.3 0.2 0.1", rows=2, cols=4)
+ expected_indices_topAll = matrix("4 2 3 1
+ 2 3 4 1", rows=2, cols=4)
+
+ # test top_1
+ print("Case 1: test top_1")
+ [values_top1, indices_top1] = util::top_k(X, 1)
+ check_values_top1 = test_util::check_all_equal(values_top1, expected_values_top1)
+ check_indices_top1 = test_util::check_all_equal(indices_top1, expected_indices_top1)
+
+ # test top_2
+ print("Case 2: test top_2")
+ [values_top2, indices_top2] = util::top_k(X, 2)
+ check_values_top2 = test_util::check_all_equal(values_top2, expected_values_top2)
+ check_indices_top2 = test_util::check_all_equal(indices_top2, expected_indices_top2)
+
+ # test top_All
+ print("Case 3: test top_All")
+ [values_topAll, indices_topAll] = util::top_k(X, 4)
+ check_values_topAll = test_util::check_all_equal(values_topAll, expected_values_topAll)
+ check_indices_topAll = test_util::check_all_equal(indices_topAll, expected_indices_topAll)
+}
http://git-wip-us.apache.org/repos/asf/systemml/blob/2e78eb9a/scripts/nn/util.dml
----------------------------------------------------------------------
diff --git a/scripts/nn/util.dml b/scripts/nn/util.dml
index 329f22f..fb54c43 100644
--- a/scripts/nn/util.dml
+++ b/scripts/nn/util.dml
@@ -230,6 +230,8 @@ transpose_NCHW_to_CNHW = function(matrix[double] X, int C) return (matrix[double
* Outputs:
* - out: Transposed output with C rows.
*/
+
+ if(1==1){}
N = nrow(X)
D = ncol(X) / C
@@ -268,3 +270,60 @@ transpose_NCHW_to_CNHW = function(matrix[double] X, int C) return (matrix[double
*/
out = matrix(permut %*% matrix(X, rows=N*C, cols=D), rows=C, cols=N*D)
}
+
+top_k_row = function(matrix[double] X, integer r, integer k)
+ return (matrix[double] values, matrix[double] indices) {
+ /*
+ * Computes the top k values (i.e. probabilities) and associated
+ * indices (i.e. classes) in the rth row of the input matrix X
+ *
+ * Inputs:
+ * - X: Inputs, of shape (N D).
+ * - r: Input row number of X to look for
+ * - k: Input number of top elements to look for
+ *
+ * Outputs:
+ * - values: The top k values at the rth row, of shape
+ * (1, k)
+ * - indices: The class indices, of shape (1, k)
+ */
+
+ #TODO: do r & k need to be checked in the valid range
+ row = X[r, ]
+ row_t = t(row)
+ indices = order(target=row_t, by=1, decreasing=TRUE, index.return=TRUE)
+ indices = t(indices)
+ indices = indices[1, 1:k]
+
+ values = matrix(0, rows=1, cols=k)
+ for (i in 1:k) {
+ values[1, i] = row[1, as.scalar(indices[1, i])]
+ }
+}
+
+top_k = function(matrix[double] X, integer k)
+ return (matrix[double] values, matrix[double] indices) {
+ /*
+ * Computes the top k values (i.e. probabilities) and associated
+ * indices (i.e. classes) for the input matrix X.
+ *
+ * Inputs:
+ * - X: Inputs, of shape (N D).
+ * - k: Input number of top elements to look for
+ *
+ * Outputs:
+ * - values: The top k values along a certain dimension, of shape
+ * (N, k)
+ * - indices: The indices of classes, of shape (N, K)
+ */
+ N = nrow(X)
+ D = ncol(X)
+ values = matrix(0, rows=N, cols=k)
+ indices = matrix(0, rows=N, cols=k)
+
+ parfor (r in 1:N) {
+ [value, index] = top_k_row(X, r, k)
+ values[r, ] = value
+ indices[r, ] = index
+ }
+}
[2/2] systemml git commit: [SYSTEMML-1736] Add a new 2D top_k utility
function
Posted by du...@apache.org.
[SYSTEMML-1736] Add a new 2D top_k utility function
This function computes the top k values (i.e. probabilities) and
associated indices (i.e. classes) from the input matrix X. A typical use
case here is that in which X is the output of a 2D softmax layer, so
each channel contains a set of normalized class probalilities, and
values and indices will contain the top k probabilities and indices
along the channel axis. The scenario will be common in an image
segmentation problem.
Closes #551.
Project: http://git-wip-us.apache.org/repos/asf/systemml/repo
Commit: http://git-wip-us.apache.org/repos/asf/systemml/commit/5e7e5777
Tree: http://git-wip-us.apache.org/repos/asf/systemml/tree/5e7e5777
Diff: http://git-wip-us.apache.org/repos/asf/systemml/diff/5e7e5777
Branch: refs/heads/master
Commit: 5e7e57774b1936736d8551bacf3dffd60bc45071
Parents: 2e78eb9
Author: Fei Hu <hu...@gmail.com>
Authored: Wed Jun 28 12:23:13 2017 -0700
Committer: Mike Dusenberry <mw...@us.ibm.com>
Committed: Wed Jun 28 12:23:13 2017 -0700
----------------------------------------------------------------------
scripts/nn/test/run_tests.dml | 1 +
scripts/nn/test/test.dml | 85 ++++++++++++++++++++++++++++++++++----
scripts/nn/util.dml | 62 ++++++++++++++++++++-------
3 files changed, 127 insertions(+), 21 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/systemml/blob/5e7e5777/scripts/nn/test/run_tests.dml
----------------------------------------------------------------------
diff --git a/scripts/nn/test/run_tests.dml b/scripts/nn/test/run_tests.dml
index 4cc2875..f4c33d8 100644
--- a/scripts/nn/test/run_tests.dml
+++ b/scripts/nn/test/run_tests.dml
@@ -100,6 +100,7 @@ test::threshold()
test::transpose_NCHW_to_CNHW()
test::top_k_row()
test::top_k()
+test::top_k2d()
print("---")
print("Other tests complete -- look for any ERRORs or WARNINGs.")
http://git-wip-us.apache.org/repos/asf/systemml/blob/5e7e5777/scripts/nn/test/test.dml
----------------------------------------------------------------------
diff --git a/scripts/nn/test/test.dml b/scripts/nn/test/test.dml
index b1190fc..a1bb6cd 100644
--- a/scripts/nn/test/test.dml
+++ b/scripts/nn/test/test.dml
@@ -493,17 +493,17 @@ transpose_NCHW_to_CNHW = function() {
* Test for `transpose_NCHW_to_CNHW` function.
*/
print("Testing transpose_NCHW_to_CNHW function.")
-
+
# Generate data
N = 2
C = 3
H = 4
W = 5
X = matrix(seq(1, N*C*H*W), rows=N, cols=C*H*W)
-
+
out = util::transpose_NCHW_to_CNHW(X, C)
-
- target =
+
+ target =
matrix("1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
@@ -511,7 +511,7 @@ transpose_NCHW_to_CNHW = function() {
41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120",
rows=C, cols=N*H*W)
-
+
# Equivalency check
for (i in 1:nrow(out)) {
for(j in 1:ncol(out)) {
@@ -790,7 +790,7 @@ threshold = function() {
top_k_row = function() {
/*
- * Test for the top_k function.
+ * Test for the top_k_row function.
*/
print("Testing the top_k_row function.")
@@ -806,7 +806,7 @@ top_k_row = function() {
4
8", rows=1, cols=3)
- # Test the top 3 for the second row
+ # Test the top 3 for the second row.
[values, indices] = util::top_k_row(X, 2, 3)
check_values = test_util::check_all_equal(values, expected_values)
check_indices = test_util::check_all_equal(indices, expected_indices)
@@ -852,3 +852,74 @@ top_k = function() {
check_values_topAll = test_util::check_all_equal(values_topAll, expected_values_topAll)
check_indices_topAll = test_util::check_all_equal(indices_topAll, expected_indices_topAll)
}
+
+top_k2d = function() {
+ /*
+ * Test for the top_k2d function.
+ */
+ print("Testing the top_k2d function.")
+ # Generate data, of shape (2, 3, 3, 4)
+ k = 2
+ X = matrix("0.1 0.4 0.4 0.5
+ 0.4 0.1 0.6 0.1
+ 0.7 0.7 0.3 0.2
+
+ 0.2 0.5 0.4 0.5
+ 0.4 0.1 0.6 0.1
+ 0.7 0.8 0.3 0.2
+
+ 0.3 0.4 0.4 0.5
+ 0.4 0.1 0.6 0.1
+ 0.7 0.2 0.3 0.2
+
+ 0.1 0.4 0.4 0.5
+ 0.4 0.1 0.6 0.1
+ 0.7 0.7 0.3 0.2
+
+ 0.2 0.5 0.4 0.5
+ 0.4 0.1 0.6 0.1
+ 0.7 0.8 0.3 0.2
+
+ 0.3 0.4 0.4 0.5
+ 0.4 0.1 0.6 0.1
+ 0.7 0.2 0.3 0.2", rows=2, cols=3*3*4)
+
+ expected_values = matrix("0.3 0.5 0.4 0.5
+ 0.4 0.1 0.6 0.1
+ 0.7 0.8 0.3 0.2
+
+ 0.2 0.4 0.4 0.5
+ 0.4 0.1 0.6 0.1
+ 0.7 0.7 0.3 0.2
+
+ 0.3 0.5 0.4 0.5
+ 0.4 0.1 0.6 0.1
+ 0.7 0.8 0.3 0.2
+
+ 0.2 0.4 0.4 0.5
+ 0.4 0.1 0.6 0.1
+ 0.7 0.7 0.3 0.2", rows=2, cols=2*3*4)
+
+ expected_indices = matrix("3 2 1 1
+ 1 1 1 1
+ 1 2 1 1
+
+ 2 1 2 2
+ 2 2 2 2
+ 2 1 2 2
+
+ 3 2 1 1
+ 1 1 1 1
+ 1 2 1 1
+
+ 2 1 2 2
+ 2 2 2 2
+ 2 1 2 2", rows=2, cols=24)
+
+ [values, indices] = util::top_k2d(X, k, 3, 3, 4)
+
+ # Equivalency check
+ check_values = test_util::check_all_equal(values, expected_values)
+ check_indices = test_util::check_all_equal(indices, expected_indices)
+}
+
http://git-wip-us.apache.org/repos/asf/systemml/blob/5e7e5777/scripts/nn/util.dml
----------------------------------------------------------------------
diff --git a/scripts/nn/util.dml b/scripts/nn/util.dml
index fb54c43..102a507 100644
--- a/scripts/nn/util.dml
+++ b/scripts/nn/util.dml
@@ -204,7 +204,7 @@ threshold = function(matrix[double] X, double thresh)
return (matrix[double] out) {
/*
* Computes an indicator matrix with values in {0, 1} depending on
- * whether or not the values in X are above the input threshold
+ * whether or not the values in X are above the input threshold.
*
* Inputs:
* - X: Inputs, of shape (any, any).
@@ -231,7 +231,6 @@ transpose_NCHW_to_CNHW = function(matrix[double] X, int C) return (matrix[double
* - out: Transposed output with C rows.
*/
- if(1==1){}
N = nrow(X)
D = ncol(X) / C
@@ -275,17 +274,17 @@ top_k_row = function(matrix[double] X, integer r, integer k)
return (matrix[double] values, matrix[double] indices) {
/*
* Computes the top k values (i.e. probabilities) and associated
- * indices (i.e. classes) in the rth row of the input matrix X
+ * indices (i.e. classes) in the rth row of the input matrix X.
*
* Inputs:
- * - X: Inputs, of shape (N D).
- * - r: Input row number of X to look for
- * - k: Input number of top elements to look for
+ * - X: Inputs, of shape (N, D).
+ * - r: Input row number of X to look for.
+ * - k: Input number of top elements to look for.
*
* Outputs:
- * - values: The top k values at the rth row, of shape
- * (1, k)
- * - indices: The class indices, of shape (1, k)
+ * - values: The top k values at the rth row, of shape
+ * (1, k).
+ * - indices: The class indices, of shape (1, k).
*/
#TODO: do r & k need to be checked in the valid range
@@ -308,13 +307,13 @@ top_k = function(matrix[double] X, integer k)
* indices (i.e. classes) for the input matrix X.
*
* Inputs:
- * - X: Inputs, of shape (N D).
- * - k: Input number of top elements to look for
+ * - X: Inputs, of shape (N, D).
+ * - k: Input number of top elements to look for.
*
* Outputs:
- * - values: The top k values along a certain dimension, of shape
- * (N, k)
- * - indices: The indices of classes, of shape (N, K)
+ * - values: The top k values along a certain dimension, of shape
+ * (N, k).
+ * - indices: The indices of classes, of shape (N, K).
*/
N = nrow(X)
D = ncol(X)
@@ -327,3 +326,38 @@ top_k = function(matrix[double] X, integer k)
indices[r, ] = index
}
}
+
+top_k2d = function(matrix[double] X, int k, int C, int Hin, int Win)
+ return (matrix[double] values, matrix[double] indices) {
+ /*
+ * Computes the top k values (i.e. probabilities) and associated
+ * indices (i.e. classes) for the input matrix X.
+ *
+ * Inputs:
+ * - X: Inputs, of shape (N, C*Hin*Win).
+ * - k: Input number of top elements to look for.
+ * - C: Number of input channels (dimensionality of input depth).
+ * - Hin: Input height.
+ * - Win: Input width.
+ *
+ * Outputs:
+ * - values: The top k values along a certain dimension, of shape
+ * (N, k*Hin*Win).
+ * - indices: The indices of classes, of shape (N, k*Hin*Win).
+ */
+ N = nrow(X)
+
+ # Reshape the input matrix (N, C*Hin*Win) to (N*Hin*Win, C)
+ X_C_NHW = transpose_NCHW_to_CNHW(X, C)
+ X_NHW_C = t(X_C_NHW)
+
+ # Compute the top k for the reshape matrix.
+ [values_NHW_K, indices_NHW_K] = top_k(X_NHW_C, k) # shape: (N*Hin*Win, k)
+
+ values_K_NHW = t(values_NHW_K)
+ indices_K_NHW = t(indices_NHW_K)
+
+ values = transpose_NCHW_to_CNHW(values_K_NHW, N)
+ indices = transpose_NCHW_to_CNHW(indices_K_NHW, N)
+}
+