[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #15905: [Numpy] Basic indexing in symbolic interface
haojin2 commented on a change in pull request #15905: [Numpy] Basic indexing in
symbolic interface
URL: https://github.com/apache/incubator-mxnet/pull/15905#discussion_r314582942
##
File path: src/operator/numpy/np_matrix_op.cc
##
@@ -135,17 +136,165 @@ bool NumpyReshapeInferShape(const mxnet::TShape& src,
mxnet::TShape* dst) {
}
}
+bool NumpyXReshapeInferShape(const mxnet::TShape& src,
+ const mxnet::Tuple& target,
+ mxnet::TShape* output) {
+ bool target_shape_is_known = true;
+ dim_t target_size = 1;
+ for (int i = 0; i < target.ndim(); ++i) {
+if (target[i] < 0) {
+ target_shape_is_known = false;
+ target_size = -1;
+ break;
+} else {
+ target_size *= target[i];
+}
+ }
+ if (shape_is_known(src) && target_shape_is_known) {
+CHECK_EQ(src.Size(), target_size) << "Cannot reshape array of size "
+ << src.Size() << " into shape " <<
target;
+*output = TShape(target.begin(), target.end());
+return true;
+ } else if (!shape_is_known(src) || target.ndim() == -1) {
+return false;
+ } else {
+int unknown_axis = -1;
+dim_t known_dim_size_prod = 1;
+std::vector output_shape_vector;
+int src_inx = 0;
+for (int i = 0; i < target.ndim(); ++i) {
+ dim_t proposed_dim = target[i];
+ CHECK(proposed_dim >= -6)
+<< "Dimension size must be greater than -6, received " << proposed_dim;
+ if (proposed_dim == -1) {
+// infer the known dimension
+CHECK_LT(unknown_axis, 0)
+ << "One and only one dim can be inferred";
+unknown_axis = output_shape_vector.size();
+output_shape_vector.push_back(1);
+src_inx++;
+ } else if (proposed_dim == -2) {
+// copy the dimension from src to output
+CHECK_LT(src_inx, src.ndim())
+ << "Unmatching dimension of proposed new shape";
+known_dim_size_prod *= src[src_inx];
+output_shape_vector.push_back(src[src_inx++]);
+ } else if (proposed_dim == -3) {
+// skip the source dimension if and only if it is one
+CHECK_EQ(src[src_inx], 1)
+ <<"-3 index should only be used to skip dimision size 1";
+src_inx++;
+ } else if (proposed_dim == -4) {
+// copy all remaining dims from source
+while (src_inx < src.ndim()) {
+ known_dim_size_prod *= src[src_inx];
+ const int dn = src[src_inx++];
+ output_shape_vector.push_back(dn);
+}
+ } else if (proposed_dim == -5) {
+// merge two dims from source
+CHECK_LT(src_inx, src.ndim()-1)
+ <<"Not enough dimensions left for the product";
+const int d1 = src[src_inx++];
+const int d2 = src[src_inx++];
+if (!mxnet::dim_size_is_known(d1) || !mxnet::dim_size_is_known(d2)) {
+ CHECK_LT(unknown_axis, 0)
+ << "One and only one dim can be inferred";
+ unknown_axis = output_shape_vector.size();
+ output_shape_vector.push_back(-1);
+} else {
+ known_dim_size_prod *= d1*d2;
+ output_shape_vector.push_back(d1 * d2);
+}
+ } else if (proposed_dim == -6) {
+// split the source dim s into two dims
+// read the left dim and then the right dim (either can be -1)
+CHECK_LT(i + 2, target.ndim());
+CHECK_LT(src_inx, src.ndim());
+const int d0 = src[src_inx++];
+dim_t d1 = target[++i];
+dim_t d2 = target[++i];
+CHECK(d1 != -1 || d2 != -1) << "Split dims cannot both be -1.";
+if (d1 == -1 && d0 >= 0) d1 = d0 / d2; // d0 must be known to do this
+if (d2 == -1 && d0 >= 0) d2 = d0 / d1; // d0 must be known to do this
+CHECK(d1 * d2 == static_cast(d0) || static_cast(d0) ==
dim_t(-1))
+ <<"Split dims " << d1 << ", " << d2 << " do not divide original dim
" << d0;
+if (d1 == -1) {
+ CHECK_LT(unknown_axis, 0)
+ << "One and only one dim can be inferred";
+ unknown_axis = output_shape_vector.size();
+} else if (d2 == -1) {
+ CHECK_LT(unknown_axis, 0)
+ << "One and only one dim can be inferred";
+ unknown_axis = output_shape_vector.size() + 1;
+}
+known_dim_size_prod *= d0 == -1 ? 1 : d0;
+output_shape_vector.push_back(d1);
+output_shape_vector.push_back(d2);
+ } else {
+// greater than 0, new shape
+known_dim_size_prod *= proposed_dim;
+output_shape_vector.push_back(proposed_dim);
+src_inx++;
+ }
+}
+
+if (unknown_axis > -1) {
+ // if the input in zero size tensor, the output must be of known shape
of zero size
+ CHECK_NE(known_dim_size_prod, 0) << "Cannot reshape array of size "
+ << src.Size() << " into shape " <<
target;
+ CHECK(src.Size() % known_dim_size_prod == 0)
+
[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #15905: [Numpy] Basic indexing in symbolic interface
haojin2 commented on a change in pull request #15905: [Numpy] Basic indexing in
symbolic interface
URL: https://github.com/apache/incubator-mxnet/pull/15905#discussion_r314582869
##
File path: src/operator/numpy/np_matrix_op.cc
##
@@ -135,17 +136,165 @@ bool NumpyReshapeInferShape(const mxnet::TShape& src,
mxnet::TShape* dst) {
}
}
+bool NumpyXReshapeInferShape(const mxnet::TShape& src,
+ const mxnet::Tuple& target,
+ mxnet::TShape* output) {
+ bool target_shape_is_known = true;
+ dim_t target_size = 1;
+ for (int i = 0; i < target.ndim(); ++i) {
+if (target[i] < 0) {
+ target_shape_is_known = false;
+ target_size = -1;
+ break;
+} else {
+ target_size *= target[i];
+}
+ }
+ if (shape_is_known(src) && target_shape_is_known) {
+CHECK_EQ(src.Size(), target_size) << "Cannot reshape array of size "
+ << src.Size() << " into shape " <<
target;
+*output = TShape(target.begin(), target.end());
+return true;
+ } else if (!shape_is_known(src) || target.ndim() == -1) {
+return false;
+ } else {
+int unknown_axis = -1;
+dim_t known_dim_size_prod = 1;
+std::vector output_shape_vector;
+int src_inx = 0;
+for (int i = 0; i < target.ndim(); ++i) {
+ dim_t proposed_dim = target[i];
+ CHECK(proposed_dim >= -6)
+<< "Dimension size must be greater than -6, received " << proposed_dim;
+ if (proposed_dim == -1) {
+// infer the known dimension
+CHECK_LT(unknown_axis, 0)
+ << "One and only one dim can be inferred";
+unknown_axis = output_shape_vector.size();
+output_shape_vector.push_back(1);
+src_inx++;
+ } else if (proposed_dim == -2) {
+// copy the dimension from src to output
+CHECK_LT(src_inx, src.ndim())
+ << "Unmatching dimension of proposed new shape";
+known_dim_size_prod *= src[src_inx];
+output_shape_vector.push_back(src[src_inx++]);
+ } else if (proposed_dim == -3) {
+// skip the source dimension if and only if it is one
+CHECK_EQ(src[src_inx], 1)
+ <<"-3 index should only be used to skip dimision size 1";
+src_inx++;
+ } else if (proposed_dim == -4) {
+// copy all remaining dims from source
+while (src_inx < src.ndim()) {
+ known_dim_size_prod *= src[src_inx];
+ const int dn = src[src_inx++];
+ output_shape_vector.push_back(dn);
+}
+ } else if (proposed_dim == -5) {
+// merge two dims from source
+CHECK_LT(src_inx, src.ndim()-1)
+ <<"Not enough dimensions left for the product";
+const int d1 = src[src_inx++];
+const int d2 = src[src_inx++];
+if (!mxnet::dim_size_is_known(d1) || !mxnet::dim_size_is_known(d2)) {
+ CHECK_LT(unknown_axis, 0)
+ << "One and only one dim can be inferred";
+ unknown_axis = output_shape_vector.size();
+ output_shape_vector.push_back(-1);
+} else {
+ known_dim_size_prod *= d1*d2;
+ output_shape_vector.push_back(d1 * d2);
+}
+ } else if (proposed_dim == -6) {
+// split the source dim s into two dims
+// read the left dim and then the right dim (either can be -1)
+CHECK_LT(i + 2, target.ndim());
+CHECK_LT(src_inx, src.ndim());
+const int d0 = src[src_inx++];
+dim_t d1 = target[++i];
+dim_t d2 = target[++i];
+CHECK(d1 != -1 || d2 != -1) << "Split dims cannot both be -1.";
+if (d1 == -1 && d0 >= 0) d1 = d0 / d2; // d0 must be known to do this
+if (d2 == -1 && d0 >= 0) d2 = d0 / d1; // d0 must be known to do this
+CHECK(d1 * d2 == static_cast(d0) || static_cast(d0) ==
dim_t(-1))
+ <<"Split dims " << d1 << ", " << d2 << " do not divide original dim
" << d0;
+if (d1 == -1) {
+ CHECK_LT(unknown_axis, 0)
+ << "One and only one dim can be inferred";
+ unknown_axis = output_shape_vector.size();
+} else if (d2 == -1) {
+ CHECK_LT(unknown_axis, 0)
+ << "One and only one dim can be inferred";
+ unknown_axis = output_shape_vector.size() + 1;
+}
+known_dim_size_prod *= d0 == -1 ? 1 : d0;
+output_shape_vector.push_back(d1);
+output_shape_vector.push_back(d2);
+ } else {
+// greater than 0, new shape
+known_dim_size_prod *= proposed_dim;
+output_shape_vector.push_back(proposed_dim);
+src_inx++;
+ }
+}
+
+if (unknown_axis > -1) {
+ // if the input in zero size tensor, the output must be of known shape
of zero size
+ CHECK_NE(known_dim_size_prod, 0) << "Cannot reshape array of size "
+ << src.Size() << " into shape " <<
target;
Review comment:
Alignment:
```c++
[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #15905: [Numpy] Basic indexing in symbolic interface
haojin2 commented on a change in pull request #15905: [Numpy] Basic indexing in
symbolic interface
URL: https://github.com/apache/incubator-mxnet/pull/15905#discussion_r314582829
##
File path: src/operator/numpy/np_matrix_op.cc
##
@@ -135,17 +136,165 @@ bool NumpyReshapeInferShape(const mxnet::TShape& src,
mxnet::TShape* dst) {
}
}
+bool NumpyXReshapeInferShape(const mxnet::TShape& src,
+ const mxnet::Tuple& target,
+ mxnet::TShape* output) {
+ bool target_shape_is_known = true;
+ dim_t target_size = 1;
+ for (int i = 0; i < target.ndim(); ++i) {
+if (target[i] < 0) {
+ target_shape_is_known = false;
+ target_size = -1;
+ break;
+} else {
+ target_size *= target[i];
+}
+ }
+ if (shape_is_known(src) && target_shape_is_known) {
+CHECK_EQ(src.Size(), target_size) << "Cannot reshape array of size "
+ << src.Size() << " into shape " <<
target;
+*output = TShape(target.begin(), target.end());
+return true;
+ } else if (!shape_is_known(src) || target.ndim() == -1) {
+return false;
+ } else {
+int unknown_axis = -1;
+dim_t known_dim_size_prod = 1;
+std::vector output_shape_vector;
+int src_inx = 0;
+for (int i = 0; i < target.ndim(); ++i) {
+ dim_t proposed_dim = target[i];
+ CHECK(proposed_dim >= -6)
+<< "Dimension size must be greater than -6, received " << proposed_dim;
+ if (proposed_dim == -1) {
+// infer the known dimension
+CHECK_LT(unknown_axis, 0)
+ << "One and only one dim can be inferred";
+unknown_axis = output_shape_vector.size();
+output_shape_vector.push_back(1);
+src_inx++;
+ } else if (proposed_dim == -2) {
+// copy the dimension from src to output
+CHECK_LT(src_inx, src.ndim())
+ << "Unmatching dimension of proposed new shape";
+known_dim_size_prod *= src[src_inx];
+output_shape_vector.push_back(src[src_inx++]);
+ } else if (proposed_dim == -3) {
+// skip the source dimension if and only if it is one
+CHECK_EQ(src[src_inx], 1)
+ <<"-3 index should only be used to skip dimision size 1";
+src_inx++;
+ } else if (proposed_dim == -4) {
+// copy all remaining dims from source
+while (src_inx < src.ndim()) {
+ known_dim_size_prod *= src[src_inx];
+ const int dn = src[src_inx++];
+ output_shape_vector.push_back(dn);
+}
+ } else if (proposed_dim == -5) {
+// merge two dims from source
+CHECK_LT(src_inx, src.ndim()-1)
+ <<"Not enough dimensions left for the product";
+const int d1 = src[src_inx++];
+const int d2 = src[src_inx++];
+if (!mxnet::dim_size_is_known(d1) || !mxnet::dim_size_is_known(d2)) {
+ CHECK_LT(unknown_axis, 0)
+ << "One and only one dim can be inferred";
+ unknown_axis = output_shape_vector.size();
+ output_shape_vector.push_back(-1);
+} else {
+ known_dim_size_prod *= d1*d2;
+ output_shape_vector.push_back(d1 * d2);
+}
+ } else if (proposed_dim == -6) {
+// split the source dim s into two dims
+// read the left dim and then the right dim (either can be -1)
+CHECK_LT(i + 2, target.ndim());
+CHECK_LT(src_inx, src.ndim());
+const int d0 = src[src_inx++];
+dim_t d1 = target[++i];
+dim_t d2 = target[++i];
+CHECK(d1 != -1 || d2 != -1) << "Split dims cannot both be -1.";
+if (d1 == -1 && d0 >= 0) d1 = d0 / d2; // d0 must be known to do this
+if (d2 == -1 && d0 >= 0) d2 = d0 / d1; // d0 must be known to do this
+CHECK(d1 * d2 == static_cast(d0) || static_cast(d0) ==
dim_t(-1))
+ <<"Split dims " << d1 << ", " << d2 << " do not divide original dim
" << d0;
+if (d1 == -1) {
+ CHECK_LT(unknown_axis, 0)
+ << "One and only one dim can be inferred";
Review comment:
Same for all applicable places.
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[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #15905: [Numpy] Basic indexing in symbolic interface
haojin2 commented on a change in pull request #15905: [Numpy] Basic indexing in
symbolic interface
URL: https://github.com/apache/incubator-mxnet/pull/15905#discussion_r314582898
##
File path: src/operator/numpy/np_matrix_op.cc
##
@@ -135,17 +136,165 @@ bool NumpyReshapeInferShape(const mxnet::TShape& src,
mxnet::TShape* dst) {
}
}
+bool NumpyXReshapeInferShape(const mxnet::TShape& src,
+ const mxnet::Tuple& target,
+ mxnet::TShape* output) {
+ bool target_shape_is_known = true;
+ dim_t target_size = 1;
+ for (int i = 0; i < target.ndim(); ++i) {
+if (target[i] < 0) {
+ target_shape_is_known = false;
+ target_size = -1;
+ break;
+} else {
+ target_size *= target[i];
+}
+ }
+ if (shape_is_known(src) && target_shape_is_known) {
+CHECK_EQ(src.Size(), target_size) << "Cannot reshape array of size "
+ << src.Size() << " into shape " <<
target;
+*output = TShape(target.begin(), target.end());
+return true;
+ } else if (!shape_is_known(src) || target.ndim() == -1) {
+return false;
+ } else {
+int unknown_axis = -1;
+dim_t known_dim_size_prod = 1;
+std::vector output_shape_vector;
+int src_inx = 0;
+for (int i = 0; i < target.ndim(); ++i) {
+ dim_t proposed_dim = target[i];
+ CHECK(proposed_dim >= -6)
+<< "Dimension size must be greater than -6, received " << proposed_dim;
+ if (proposed_dim == -1) {
+// infer the known dimension
+CHECK_LT(unknown_axis, 0)
+ << "One and only one dim can be inferred";
+unknown_axis = output_shape_vector.size();
+output_shape_vector.push_back(1);
+src_inx++;
+ } else if (proposed_dim == -2) {
+// copy the dimension from src to output
+CHECK_LT(src_inx, src.ndim())
+ << "Unmatching dimension of proposed new shape";
+known_dim_size_prod *= src[src_inx];
+output_shape_vector.push_back(src[src_inx++]);
+ } else if (proposed_dim == -3) {
+// skip the source dimension if and only if it is one
+CHECK_EQ(src[src_inx], 1)
+ <<"-3 index should only be used to skip dimision size 1";
+src_inx++;
+ } else if (proposed_dim == -4) {
+// copy all remaining dims from source
+while (src_inx < src.ndim()) {
+ known_dim_size_prod *= src[src_inx];
+ const int dn = src[src_inx++];
+ output_shape_vector.push_back(dn);
+}
+ } else if (proposed_dim == -5) {
+// merge two dims from source
+CHECK_LT(src_inx, src.ndim()-1)
+ <<"Not enough dimensions left for the product";
+const int d1 = src[src_inx++];
+const int d2 = src[src_inx++];
+if (!mxnet::dim_size_is_known(d1) || !mxnet::dim_size_is_known(d2)) {
+ CHECK_LT(unknown_axis, 0)
+ << "One and only one dim can be inferred";
+ unknown_axis = output_shape_vector.size();
+ output_shape_vector.push_back(-1);
+} else {
+ known_dim_size_prod *= d1*d2;
+ output_shape_vector.push_back(d1 * d2);
+}
+ } else if (proposed_dim == -6) {
+// split the source dim s into two dims
+// read the left dim and then the right dim (either can be -1)
+CHECK_LT(i + 2, target.ndim());
+CHECK_LT(src_inx, src.ndim());
+const int d0 = src[src_inx++];
+dim_t d1 = target[++i];
+dim_t d2 = target[++i];
+CHECK(d1 != -1 || d2 != -1) << "Split dims cannot both be -1.";
+if (d1 == -1 && d0 >= 0) d1 = d0 / d2; // d0 must be known to do this
+if (d2 == -1 && d0 >= 0) d2 = d0 / d1; // d0 must be known to do this
+CHECK(d1 * d2 == static_cast(d0) || static_cast(d0) ==
dim_t(-1))
+ <<"Split dims " << d1 << ", " << d2 << " do not divide original dim
" << d0;
+if (d1 == -1) {
+ CHECK_LT(unknown_axis, 0)
+ << "One and only one dim can be inferred";
+ unknown_axis = output_shape_vector.size();
+} else if (d2 == -1) {
+ CHECK_LT(unknown_axis, 0)
+ << "One and only one dim can be inferred";
+ unknown_axis = output_shape_vector.size() + 1;
+}
+known_dim_size_prod *= d0 == -1 ? 1 : d0;
+output_shape_vector.push_back(d1);
+output_shape_vector.push_back(d2);
+ } else {
+// greater than 0, new shape
+known_dim_size_prod *= proposed_dim;
+output_shape_vector.push_back(proposed_dim);
+src_inx++;
+ }
+}
+
+if (unknown_axis > -1) {
+ // if the input in zero size tensor, the output must be of known shape
of zero size
+ CHECK_NE(known_dim_size_prod, 0) << "Cannot reshape array of size "
+ << src.Size() << " into shape " <<
target;
+ CHECK(src.Size() % known_dim_size_prod == 0)
+
[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #15905: [Numpy] Basic indexing in symbolic interface
haojin2 commented on a change in pull request #15905: [Numpy] Basic indexing in
symbolic interface
URL: https://github.com/apache/incubator-mxnet/pull/15905#discussion_r314582779
##
File path: src/operator/numpy/np_matrix_op.cc
##
@@ -135,17 +136,165 @@ bool NumpyReshapeInferShape(const mxnet::TShape& src,
mxnet::TShape* dst) {
}
}
+bool NumpyXReshapeInferShape(const mxnet::TShape& src,
+ const mxnet::Tuple& target,
+ mxnet::TShape* output) {
+ bool target_shape_is_known = true;
+ dim_t target_size = 1;
+ for (int i = 0; i < target.ndim(); ++i) {
+if (target[i] < 0) {
+ target_shape_is_known = false;
+ target_size = -1;
+ break;
+} else {
+ target_size *= target[i];
+}
+ }
+ if (shape_is_known(src) && target_shape_is_known) {
+CHECK_EQ(src.Size(), target_size) << "Cannot reshape array of size "
+ << src.Size() << " into shape " <<
target;
+*output = TShape(target.begin(), target.end());
+return true;
+ } else if (!shape_is_known(src) || target.ndim() == -1) {
+return false;
+ } else {
+int unknown_axis = -1;
+dim_t known_dim_size_prod = 1;
+std::vector output_shape_vector;
+int src_inx = 0;
+for (int i = 0; i < target.ndim(); ++i) {
+ dim_t proposed_dim = target[i];
+ CHECK(proposed_dim >= -6)
+<< "Dimension size must be greater than -6, received " << proposed_dim;
+ if (proposed_dim == -1) {
+// infer the known dimension
+CHECK_LT(unknown_axis, 0)
+ << "One and only one dim can be inferred";
+unknown_axis = output_shape_vector.size();
+output_shape_vector.push_back(1);
+src_inx++;
+ } else if (proposed_dim == -2) {
+// copy the dimension from src to output
+CHECK_LT(src_inx, src.ndim())
+ << "Unmatching dimension of proposed new shape";
+known_dim_size_prod *= src[src_inx];
+output_shape_vector.push_back(src[src_inx++]);
+ } else if (proposed_dim == -3) {
+// skip the source dimension if and only if it is one
+CHECK_EQ(src[src_inx], 1)
+ <<"-3 index should only be used to skip dimision size 1";
+src_inx++;
+ } else if (proposed_dim == -4) {
+// copy all remaining dims from source
+while (src_inx < src.ndim()) {
+ known_dim_size_prod *= src[src_inx];
+ const int dn = src[src_inx++];
+ output_shape_vector.push_back(dn);
+}
+ } else if (proposed_dim == -5) {
+// merge two dims from source
+CHECK_LT(src_inx, src.ndim()-1)
+ <<"Not enough dimensions left for the product";
+const int d1 = src[src_inx++];
+const int d2 = src[src_inx++];
+if (!mxnet::dim_size_is_known(d1) || !mxnet::dim_size_is_known(d2)) {
+ CHECK_LT(unknown_axis, 0)
+ << "One and only one dim can be inferred";
+ unknown_axis = output_shape_vector.size();
+ output_shape_vector.push_back(-1);
+} else {
+ known_dim_size_prod *= d1*d2;
+ output_shape_vector.push_back(d1 * d2);
+}
+ } else if (proposed_dim == -6) {
+// split the source dim s into two dims
+// read the left dim and then the right dim (either can be -1)
+CHECK_LT(i + 2, target.ndim());
+CHECK_LT(src_inx, src.ndim());
+const int d0 = src[src_inx++];
+dim_t d1 = target[++i];
+dim_t d2 = target[++i];
+CHECK(d1 != -1 || d2 != -1) << "Split dims cannot both be -1.";
+if (d1 == -1 && d0 >= 0) d1 = d0 / d2; // d0 must be known to do this
+if (d2 == -1 && d0 >= 0) d2 = d0 / d1; // d0 must be known to do this
+CHECK(d1 * d2 == static_cast(d0) || static_cast(d0) ==
dim_t(-1))
+ <<"Split dims " << d1 << ", " << d2 << " do not divide original dim
" << d0;
+if (d1 == -1) {
+ CHECK_LT(unknown_axis, 0)
+ << "One and only one dim can be inferred";
Review comment:
indent here:
```c++
CHECK_LT(unknown_axis, 0)
<< "One and only one dim can be inferred";
```
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[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #15905: [Numpy] Basic indexing in symbolic interface
haojin2 commented on a change in pull request #15905: [Numpy] Basic indexing in
symbolic interface
URL: https://github.com/apache/incubator-mxnet/pull/15905#discussion_r314582546
##
File path: python/mxnet/symbol/numpy/_symbol.py
##
@@ -39,25 +46,90 @@ def _num_outputs(sym):
@set_module('mxnet.symbol.numpy')
class _Symbol(Symbol):
-def __getitem__(self, key):
-num_outputs = _num_outputs(self)
-if num_outputs == 1:
-raise NotImplementedError
-if not isinstance(key, int):
+def __init__(self, handle):
+super(_Symbol, self).__init__(handle)
+self._output_is_list = False
+
+def __getitem__(self, key): # pylint: disable =
too-many-return-statements, inconsistent-return-statements
+num_outputs = len(self)
+# print("Num of outputs is ", num_outputs)
+if num_outputs == 1: # pylint: disable = too-many-nested-blocks
+# If number of output is one and is not a list, perform ndarray
basic slicing
+if not self._output_is_list:
+if isinstance(key, integer_types):
+sliced = _npi.slice(self, key, key+1)
+return _npi.reshape(sliced, (-3, -4))
+elif isinstance(key, py_slice):
+if key.step is None or key.step != 0:
+start = [None] if key.start is None else key.start
+stop = [None] if key.stop is None else key.stop
+return _npi.slice(self, start, stop, key.step)
+else:
+raise ValueError("slice step cannot be zero")
+elif isinstance(key, list):
+raise NotImplementedError
+elif isinstance(key, tuple):
+begin = []
+end = []
+step = []
+new_shape = ()
+for index in key:
+if isinstance(index, py_slice):
+if index.step is not None and index.step == 0:
+raise ValueError("slice step cannot be zero")
+begin.append(index.start)
+end.append(index.stop)
+step.append(index.step)
+new_shape += (-2,)
+elif isinstance(index, integer_types):
+begin.append(index)
+end.append(index+1)
+step.append(1)
+new_shape += (-3,)
+new_shape += (-4,)
+sliced = _npi.slice(self, begin, end, step)
+return _npi.reshape(sliced, new_shape)
+# perform trivial list slicing on length one list represented by
flag
+else:
+if isinstance(key, integer_types):
+if key in [-1, 0]:
+self._output_is_list = False
+return self
+else:
+raise IndexError
+elif isinstance(key, py_slice):
+if (key.start is None or key.start <= 0) and (key.stop is
None or key.stop > 0):
+return self
+else:
+raise ValueError
+else:
+raise IndexError
+# list slicing on several nodes of outputs
+elif num_outputs > 1:
+if isinstance(key, py_slice):
+start = 0 if key.start is None else key.start
+stop = num_outputs if key.stop is None else key.stop
+step = 1 if key.step is None else key.step
+return Group([self[i] for i in range(start, stop, step)],
_Symbol)
+elif isinstance(key, integer_types):
+if key >= num_outputs:
+# Important, python determines the end by this exception
+raise IndexError
+handle = SymbolHandle()
+check_call(_LIB.MXSymbolGetOutput(
+self.handle, mx_uint(key), ctypes.byref(handle)))
+return _Symbol(handle=handle)
+else:
+raise NotImplementedError
+else:
raise NotImplementedError
-if key >= num_outputs:
-# Important, python determines the end by this exception
-raise IndexError
-handle = SymbolHandle()
-check_call(_LIB.MXSymbolGetOutput(
-self.handle, mx_uint(key), ctypes.byref(handle)))
-return _Symbol(handle=handle)
+
Review comment:
get rid of the extra blank line.
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[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #15905: [Numpy] Basic indexing in symbolic interface
haojin2 commented on a change in pull request #15905: [Numpy] Basic indexing in
symbolic interface
URL: https://github.com/apache/incubator-mxnet/pull/15905#discussion_r314582455
##
File path: python/mxnet/symbol/numpy/_symbol.py
##
@@ -39,25 +46,90 @@ def _num_outputs(sym):
@set_module('mxnet.symbol.numpy')
class _Symbol(Symbol):
-def __getitem__(self, key):
-num_outputs = _num_outputs(self)
-if num_outputs == 1:
-raise NotImplementedError
-if not isinstance(key, int):
+def __init__(self, handle):
+super(_Symbol, self).__init__(handle)
+self._output_is_list = False
+
+def __getitem__(self, key): # pylint: disable =
too-many-return-statements, inconsistent-return-statements
+num_outputs = len(self)
+# print("Num of outputs is ", num_outputs)
Review comment:
Get rid of dead code.
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