[GitHub] sxjscience commented on a change in pull request #10000: [MXNET-80] Fix average pooling kernel size assignment error
sxjscience commented on a change in pull request #1: [MXNET-80] Fix average
pooling kernel size assignment error
URL: https://github.com/apache/incubator-mxnet/pull/1#discussion_r176184133
##
File path: src/operator/pooling_v1-inl.h
##
@@ -215,18 +217,21 @@ class PoolingV1Prop : public OperatorProperty {
void Init(const std::vector >& kwargs)
override {
using namespace mshadow;
param_.Init(kwargs);
-if (param_.kernel.ndim() == 2) {
- if (param_.stride.ndim() == 0) param_.stride = Shape2(1, 1);
- if (param_.pad.ndim() == 0) param_.pad = Shape2(0, 0);
+if (param_.global_pool) {
Review comment:
We can use `if (! param_.global_pool) {...` instead.
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[GitHub] sxjscience commented on a change in pull request #10000: [MXNET-80] Fix average pooling kernel size assignment error
sxjscience commented on a change in pull request #1: [MXNET-80] Fix average
pooling kernel size assignment error
URL: https://github.com/apache/incubator-mxnet/pull/1#discussion_r176184133
##
File path: src/operator/pooling_v1-inl.h
##
@@ -215,18 +217,21 @@ class PoolingV1Prop : public OperatorProperty {
void Init(const std::vector >& kwargs)
override {
using namespace mshadow;
param_.Init(kwargs);
-if (param_.kernel.ndim() == 2) {
- if (param_.stride.ndim() == 0) param_.stride = Shape2(1, 1);
- if (param_.pad.ndim() == 0) param_.pad = Shape2(0, 0);
+if (param_.global_pool) {
Review comment:
if (! param_.global_pool) {...
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[GitHub] sxjscience commented on a change in pull request #10000: [MXNET-80] Fix average pooling kernel size assignment error
sxjscience commented on a change in pull request #1: [MXNET-80] Fix average
pooling kernel size assignment error
URL: https://github.com/apache/incubator-mxnet/pull/1#discussion_r176183569
##
File path: src/operator/nn/pooling.cc
##
@@ -98,28 +97,45 @@ static bool PoolingShape(const nnvm::NodeAttrs ,
<< "Pooling: Input data should be 3D in (batch, channel, x)"
<< " Or 4D in (batch, channel, y, x) "
<< " Or 5D in (batch, channel, d, y, x)";
+ CHECK_LE(dshape.ndim(), 5U)
+ << "Pooling: Input data should be 3D in (batch, channel, x)"
+ << " Or 4D in (batch, channel, y, x) "
+ << " Or 5D in (batch, channel, d, y, x)";
TShape oshape = dshape;
if (dshape.ndim() == 0) return false;
- if (param.kernel.ndim() == 1) {
+ if (param.global_pool) {
+ if (dshape.ndim() == 3) {
Review comment:
We can replace the if-clauses with a for-loop.
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[GitHub] sxjscience commented on a change in pull request #10000: [MXNET-80] Fix average pooling kernel size assignment error
sxjscience commented on a change in pull request #1: [MXNET-80] Fix average
pooling kernel size assignment error
URL: https://github.com/apache/incubator-mxnet/pull/1#discussion_r175856317
##
File path: src/operator/nn/pool.h
##
@@ -687,7 +687,7 @@ inline void pool(mshadow::Stream* s, const DType*
in_data, const TShape& is
LOG(FATAL) << "Unknown pooling type " << pool_type;
}
} else {
-LOG(FATAL) << "Unsupported " << kernel.ndim() << "-D pooling";
+LOG(FATAL) << "Unsupported " << kernel.ndim() << "-D non-avg pooling";
Review comment:
Why non-avg here?
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[GitHub] sxjscience commented on a change in pull request #10000: [MXNET-80] Fix average pooling kernel size assignment error
sxjscience commented on a change in pull request #1: [MXNET-80] Fix average
pooling kernel size assignment error
URL: https://github.com/apache/incubator-mxnet/pull/1#discussion_r174999488
##
File path: src/operator/nn/pooling-inl.h
##
@@ -132,19 +132,23 @@ class PoolingOp {
using namespace mshadow;
Stream *s = ctx.get_stream();
const TShape& ishape = in_data.shape_;
+TShape kernel = param_.kernel;
TShape padding = param_.pad;
+TShape stride = param_.stride;
if (param_.global_pool) {
- for (index_t i = 0; i < padding.ndim(); i++) {
+ kernel = TShape(ishape.data() + ishape.ndim()-param_.kernel.ndim(),
+ ishape.data() + ishape.ndim());
Review comment:
I think I’ve found the error. Here it should be ishape.data() +2,
ishape.data() + ishape.ndim())
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[GitHub] sxjscience commented on a change in pull request #10000: [MXNET-80] Fix average pooling kernel size assignment error
sxjscience commented on a change in pull request #1: [MXNET-80] Fix average
pooling kernel size assignment error
URL: https://github.com/apache/incubator-mxnet/pull/1#discussion_r174999516
##
File path: src/operator/nn/pooling-inl.h
##
@@ -154,20 +158,24 @@ class PoolingOp {
using namespace mshadow;
Stream *s = ctx.get_stream();
const TShape& ishape = in_data.shape_;
+TShape kernel = param_.kernel;
TShape padding = param_.pad;
+TShape stride = param_.stride;
if (param_.global_pool) {
- for (index_t i = 0; i < padding.ndim(); i++) {
+ kernel = TShape(ishape.data() + ishape.ndim() - param_.kernel.ndim(),
+ ishape.data() + ishape.ndim());
Review comment:
Same here
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[GitHub] sxjscience commented on a change in pull request #10000: [MXNET-80] Fix average pooling kernel size assignment error
sxjscience commented on a change in pull request #1: [MXNET-80] Fix average
pooling kernel size assignment error
URL: https://github.com/apache/incubator-mxnet/pull/1#discussion_r174552493
##
File path: src/operator/nn/pooling.cc
##
@@ -130,35 +153,30 @@ static bool PoolingShape(const nnvm::NodeAttrs ,
} else if (param.kernel.ndim() == 2) {
CHECK_EQ(dshape.ndim(), 4U)
<< "Pooling: Input data should be 4D in (batch, channel, y, x)";
-if (param.global_pool) {
- oshape[2] = 1;
- oshape[3] = 1;
+CHECK(param.kernel[0] <= dshape[2] + 2 * param.pad[0])
+<< "kernel size (" << param.kernel[0] << ") exceeds input ("
+<< dshape[2] << " padded to " << (dshape[2] + 2 * param.pad[0])
+<< ")";
+CHECK(param.kernel[1] <= dshape[3] + 2 * param.pad[1])
+<< "kernel size (" << param.kernel[1] << ") exceeds input ("
+<< dshape[3] << " padded to " << (dshape[3] + 2 * param.pad[1])
+<< ")";
+if (param.pooling_convention == pool_enum::kValid) {
+ oshape[2] = 1 +
+ (dshape[2] + 2 * param.pad[0] - param.kernel[0]) /
+ param.stride[0];
+ oshape[3] = 1 +
+ (dshape[3] + 2 * param.pad[1] - param.kernel[1]) /
+ param.stride[1];
} else {
Review comment:
Ignore this comment. I've misinterpreted the code.
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[GitHub] sxjscience commented on a change in pull request #10000: [MXNET-80] Fix average pooling kernel size assignment error
sxjscience commented on a change in pull request #1: [MXNET-80] Fix average
pooling kernel size assignment error
URL: https://github.com/apache/incubator-mxnet/pull/1#discussion_r174552138
##
File path: src/operator/nn/pooling.cc
##
@@ -130,35 +153,30 @@ static bool PoolingShape(const nnvm::NodeAttrs ,
} else if (param.kernel.ndim() == 2) {
CHECK_EQ(dshape.ndim(), 4U)
<< "Pooling: Input data should be 4D in (batch, channel, y, x)";
-if (param.global_pool) {
- oshape[2] = 1;
- oshape[3] = 1;
+CHECK(param.kernel[0] <= dshape[2] + 2 * param.pad[0])
+<< "kernel size (" << param.kernel[0] << ") exceeds input ("
+<< dshape[2] << " padded to " << (dshape[2] + 2 * param.pad[0])
+<< ")";
+CHECK(param.kernel[1] <= dshape[3] + 2 * param.pad[1])
+<< "kernel size (" << param.kernel[1] << ") exceeds input ("
+<< dshape[3] << " padded to " << (dshape[3] + 2 * param.pad[1])
+<< ")";
+if (param.pooling_convention == pool_enum::kValid) {
+ oshape[2] = 1 +
+ (dshape[2] + 2 * param.pad[0] - param.kernel[0]) /
+ param.stride[0];
+ oshape[3] = 1 +
+ (dshape[3] + 2 * param.pad[1] - param.kernel[1]) /
+ param.stride[1];
} else {
Review comment:
The `if (param.global_pool)` is removed and the `else` should also be
removed.
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[GitHub] sxjscience commented on a change in pull request #10000: [MXNET-80] Fix average pooling kernel size assignment error
sxjscience commented on a change in pull request #1: [MXNET-80] Fix average
pooling kernel size assignment error
URL: https://github.com/apache/incubator-mxnet/pull/1#discussion_r174551074
##
File path: src/operator/nn/pooling.cc
##
@@ -46,15 +46,20 @@ static void PoolingParamParser(nnvm::NodeAttrs *attrs) {
if (param.stride.ndim() == 0) param.stride = Shape2(1, 1);
if (param.pad.ndim() == 0) param.pad = Shape2(0, 0);
} else {
-CHECK_EQ(param.kernel.ndim(), 3U) << param.kernel.ndim()
- << "D pooling not supported";
+ // ignore kernel size only if global_pool not assigned false
+ if (param.global_pool == false) {
+CHECK_EQ(param.kernel.ndim(), 3U) << param.kernel.ndim()
+<< "D pooling not supported";
+ }
if (param.stride.ndim() == 0) param.stride = Shape3(1, 1, 1);
if (param.pad.ndim() == 0) param.pad = Shape3(0, 0, 0);
}
- CHECK_EQ(param.stride.ndim(), param.kernel.ndim())
- << "stride and kernel should have the same length";
- CHECK_EQ(param.pad.ndim(), param.kernel.ndim())
- << "pad and kernel should have the same length";
+ if (param.global_pool == false) {
+CHECK_EQ(param.stride.ndim(), param.kernel.ndim())
+<< "stride and kernel should have the same length";
+CHECK_EQ(param.pad.ndim(), param.kernel.ndim())
+<< "pad and kernel should have the same length";
+ }
Review comment:
Simplify the logic here. If you have used global_pool, there is no need to
check the stride, kernel or pad.
```c++
if(! param.global_pool) {
// CHECK kernel, pad, stride
}
```
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[GitHub] sxjscience commented on a change in pull request #10000: [MXNET-80] Fix average pooling kernel size assignment error
sxjscience commented on a change in pull request #1: [MXNET-80] Fix average
pooling kernel size assignment error
URL: https://github.com/apache/incubator-mxnet/pull/1#discussion_r174549092
##
File path: src/operator/nn/pooling.cc
##
@@ -98,9 +103,24 @@ static bool PoolingShape(const nnvm::NodeAttrs ,
<< "Pooling: Input data should be 3D in (batch, channel, x)"
<< " Or 4D in (batch, channel, y, x) "
<< " Or 5D in (batch, channel, d, y, x)";
+ CHECK_LE(dshape.ndim(), 5U)
+ << "Pooling: Input data should be 3D in (batch, channel, x)"
+ << " Or 4D in (batch, channel, y, x) "
+ << " Or 5D in (batch, channel, d, y, x)";
TShape oshape = dshape;
if (dshape.ndim() == 0) return false;
- if (param.kernel.ndim() == 1) {
+ if (param.global_pool) {
+ if (dshape.ndim() == 3) {
+ oshape[2] = 1;
+ } else if (dshape.ndim() == 4) {
+ oshape[2] = 1;
+ oshape[3] = 1;
+ } else if (dshape.ndim() == 5) {
+ oshape[2] = 1;
+ oshape[3] = 1;
+ oshape[4] = 1;
+ }
Review comment:
There is no need to revise this function. All you need to do is to prepare
the correct initial values. You can check the logic here
https://github.com/apache/incubator-mxnet/blob/master/src/operator/nn/pooling-inl.h#L135-L149
. If global_pool is used, kernel is set to be
`TShape(ishape.data()+ishape.ndim()-param_.kernel.ndim(),
ishape.data()+ishape.ndim())`, pad is set to be all 0 and stride is set to be
all 1. You can write a function to do this. For example:
```c++
TShape kernel = param_.kernel;
TShape padding = param_.pad;
TShape stride = param_.stride;
if(param_.global_pool) {
kernel = TShape(ishape.data()+ishape.ndim()-param_.kernel.ndim(),
ishape.data()+ishape.ndim());
padding = TShape(ishape.ndim() - 2);
for(int i = 0; i < ishape.ndim() - 2; i++) {
padding[i] = 0;
}
stride = TShape(ishape.ndim() - 2);
}
pool(s, in_data.dptr(), in_data.shape_, out_data.shape_,
kernel,
padding,
stride,
param_.pool_type, req, out_data.dptr());
```
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[GitHub] sxjscience commented on a change in pull request #10000: [MXNET-80] Fix average pooling kernel size assignment error
sxjscience commented on a change in pull request #1: [MXNET-80] Fix average
pooling kernel size assignment error
URL: https://github.com/apache/incubator-mxnet/pull/1#discussion_r174234577
##
File path: src/operator/nn/pooling.cc
##
@@ -98,9 +103,24 @@ static bool PoolingShape(const nnvm::NodeAttrs ,
<< "Pooling: Input data should be 3D in (batch, channel, x)"
<< " Or 4D in (batch, channel, y, x) "
<< " Or 5D in (batch, channel, d, y, x)";
+ CHECK_LE(dshape.ndim(), 5U)
+ << "Pooling: Input data should be 3D in (batch, channel, x)"
+ << " Or 4D in (batch, channel, y, x) "
+ << " Or 5D in (batch, channel, d, y, x)";
TShape oshape = dshape;
if (dshape.ndim() == 0) return false;
- if (param.kernel.ndim() == 1) {
+ if (param.global_pool) {
+ if (dshape.ndim() == 3) {
+ oshape[2] = 1;
+ } else if (dshape.ndim() == 4) {
+ oshape[2] = 1;
+ oshape[3] = 1;
+ } else if (dshape.ndim() == 5) {
+ oshape[2] = 1;
+ oshape[3] = 1;
+ oshape[4] = 1;
+ }
+ } else if (param.kernel.ndim() == 1) {
CHECK_EQ(dshape.ndim(), 3U)
<< "Pooling: Input data should be 3D in (batch, channel, x)";
if (param.global_pool) {
Review comment:
These `if` can be removed because the global_pool case is handled before.
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[GitHub] sxjscience commented on a change in pull request #10000: [MXNET-80] Fix average pooling kernel size assignment error
sxjscience commented on a change in pull request #1: [MXNET-80] Fix average
pooling kernel size assignment error
URL: https://github.com/apache/incubator-mxnet/pull/1#discussion_r174234503
##
File path: src/operator/nn/pooling.cc
##
@@ -98,9 +103,24 @@ static bool PoolingShape(const nnvm::NodeAttrs ,
<< "Pooling: Input data should be 3D in (batch, channel, x)"
<< " Or 4D in (batch, channel, y, x) "
<< " Or 5D in (batch, channel, d, y, x)";
+ CHECK_LE(dshape.ndim(), 5U)
+ << "Pooling: Input data should be 3D in (batch, channel, x)"
+ << " Or 4D in (batch, channel, y, x) "
+ << " Or 5D in (batch, channel, d, y, x)";
TShape oshape = dshape;
if (dshape.ndim() == 0) return false;
- if (param.kernel.ndim() == 1) {
+ if (param.global_pool) {
+ if (dshape.ndim() == 3) {
+ oshape[2] = 1;
+ } else if (dshape.ndim() == 4) {
+ oshape[2] = 1;
+ oshape[3] = 1;
+ } else if (dshape.ndim() == 5) {
+ oshape[2] = 1;
+ oshape[3] = 1;
+ oshape[4] = 1;
+ }
Review comment:
Also, you can use a for-loop instead of if.
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[GitHub] sxjscience commented on a change in pull request #10000: [MXNET-80] Fix average pooling kernel size assignment error
sxjscience commented on a change in pull request #1: [MXNET-80] Fix average
pooling kernel size assignment error
URL: https://github.com/apache/incubator-mxnet/pull/1#discussion_r174233854
##
File path: src/operator/nn/pooling.cc
##
@@ -98,9 +103,24 @@ static bool PoolingShape(const nnvm::NodeAttrs ,
<< "Pooling: Input data should be 3D in (batch, channel, x)"
<< " Or 4D in (batch, channel, y, x) "
<< " Or 5D in (batch, channel, d, y, x)";
+ CHECK_LE(dshape.ndim(), 5U)
+ << "Pooling: Input data should be 3D in (batch, channel, x)"
+ << " Or 4D in (batch, channel, y, x) "
+ << " Or 5D in (batch, channel, d, y, x)";
TShape oshape = dshape;
if (dshape.ndim() == 0) return false;
- if (param.kernel.ndim() == 1) {
+ if (param.global_pool) {
+ if (dshape.ndim() == 3) {
+ oshape[2] = 1;
+ } else if (dshape.ndim() == 4) {
+ oshape[2] = 1;
+ oshape[3] = 1;
+ } else if (dshape.ndim() == 5) {
+ oshape[2] = 1;
+ oshape[3] = 1;
+ oshape[4] = 1;
+ }
Review comment:
Need to push the oshape to `out_shape`. See
https://github.com/apache/incubator-mxnet/blob/master/src/operator/nn/pooling.cc#L163-L168.
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[GitHub] sxjscience commented on a change in pull request #10000: [MXNET-80] Fix average pooling kernel size assignment error
sxjscience commented on a change in pull request #1: [MXNET-80] Fix average
pooling kernel size assignment error
URL: https://github.com/apache/incubator-mxnet/pull/1#discussion_r174021284
##
File path: src/operator/nn/pooling.cc
##
@@ -54,11 +54,13 @@ static void PoolingParamParser(nnvm::NodeAttrs *attrs) {
if (param.stride.ndim() == 0) param.stride = Shape3(1, 1, 1);
if (param.pad.ndim() == 0) param.pad = Shape3(0, 0, 0);
}
- CHECK_EQ(param.stride.ndim(), param.kernel.ndim())
- << "stride and kernel should have the same length";
- CHECK_EQ(param.pad.ndim(), param.kernel.ndim())
- << "pad and kernel should have the same length";
- attrs->parsed = std::move(param);
+ if (param.global_pool == false) {
+CHECK_EQ(param.stride.ndim(), param.kernel.ndim())
+<< "stride and kernel should have the same length";
+CHECK_EQ(param.pad.ndim(), param.kernel.ndim())
+<< "pad and kernel should have the same length";
+attrs->parsed = std::move(param);
Review comment:
We still need to parse the param
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[GitHub] sxjscience commented on a change in pull request #10000: [MXNET-80] Fix average pooling kernel size assignment error
sxjscience commented on a change in pull request #1: [MXNET-80] Fix average
pooling kernel size assignment error
URL: https://github.com/apache/incubator-mxnet/pull/1#discussion_r174019453
##
File path: src/operator/nn/pooling-inl.h
##
@@ -56,11 +56,11 @@ struct PoolingParam : public dmlc::Parameter
{
DMLC_DECLARE_FIELD(cudnn_off).set_default(false)
.describe("Turn off cudnn pooling and use MXNet pooling operator. ");
-DMLC_DECLARE_FIELD(kernel)
+DMLC_DECLARE_FIELD(kernel).set_default(TShape()) // add default value here
.enforce_nonzero()
.describe("Pooling kernel size: (y, x) or (d, y, x)");
-DMLC_DECLARE_FIELD(pool_type)
+DMLC_DECLARE_FIELD(pool_type).set_default(pool_enum::kMaxPooling) // add
default pooling method
.add_enum("max", pool_enum::kMaxPooling)
.add_enum("avg", pool_enum::kAvgPooling)
.add_enum("sum", pool_enum::kSumPooling)
Review comment:
There's no need to check this if global_pool is turned on.
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[GitHub] sxjscience commented on a change in pull request #10000: [MXNET-80] Fix average pooling kernel size assignment error
sxjscience commented on a change in pull request #1: [MXNET-80] Fix average
pooling kernel size assignment error
URL: https://github.com/apache/incubator-mxnet/pull/1#discussion_r173895781
##
File path: tests/python/gpu/test_operator_gpu.py
##
@@ -904,86 +904,87 @@ def test_1d_pooling(pool_type):
kernel = (4,)
pad = (2,)
stride = (2,)
-
+
ctx_list = []
sym_list = []
-
+
pooling_convention = 'valid'
-
+
ctx_list.append({'ctx': mx.cpu(0), 'pool_data': data, 'type_dict':
{'pool_data': np.float32}})
-sym_list.append(mx.sym.Pooling(kernel=kernel, pad=pad, stride=stride,
pool_type=pool_type,
+sym_list.append(mx.sym.Pooling(pad=pad, stride=stride,
pool_type=pool_type,
pooling_convention=pooling_convention,
global_pool=True, name='pool'))
-
+
ctx_list.append({'ctx': mx.cpu(0), 'pool_data': data, 'type_dict':
{'pool_data': np.float32}})
-sym_list.append(mx.sym.Pooling(kernel=kernel, pool_type=pool_type,
+sym_list.append(mx.sym.Pooling(pool_type=pool_type,
pooling_convention=pooling_convention,
global_pool=True, name='pool'))
-
+
ctx_list.append({'ctx': mx.gpu(0), 'pool_data': data, 'type_dict':
{'pool_data': np.float32}})
-sym_list.append(mx.sym.Pooling(kernel=kernel, pad=pad, stride=stride,
pool_type=pool_type,
+sym_list.append(mx.sym.Pooling(pad=pad, stride=stride,
pool_type=pool_type,
pooling_convention=pooling_convention,
global_pool=True, cudnn_off=False, name='pool'))
-
+
ctx_list.append({'ctx': mx.gpu(0), 'pool_data': data, 'type_dict':
{'pool_data': np.float32}})
-sym_list.append(mx.sym.Pooling(kernel=kernel, pool_type=pool_type,
+sym_list.append(mx.sym.Pooling(pool_type=pool_type,
pooling_convention=pooling_convention,
global_pool=True, cudnn_off=False, name='pool'))
-
+
ctx_list.append({'ctx': mx.gpu(0), 'pool_data': data, 'type_dict':
{'pool_data': np.float32}})
-sym_list.append(mx.sym.Pooling(kernel=kernel, pad=pad, stride=stride,
pool_type=pool_type,
+sym_list.append(mx.sym.Pooling(pad=pad, stride=stride,
pool_type=pool_type,
pooling_convention=pooling_convention,
global_pool=True, cudnn_off=True, name='pool'))
-
+
ctx_list.append({'ctx': mx.gpu(0), 'pool_data': data, 'type_dict':
{'pool_data': np.float32}})
-sym_list.append(mx.sym.Pooling(kernel=kernel, pool_type=pool_type,
+sym_list.append(mx.sym.Pooling(pool_type=pool_type,
pooling_convention=pooling_convention,
global_pool=True, cudnn_off=True, name='pool'))
-
+
check_consistency(sym_list, ctx_list)
-
+
def test_2d_pooling(pool_type):
data = (2, 3, 20, 20)
kernel = (4, 4)
pad = (2, 2)
stride = (2, 2)
-
+
ctx_list = []
sym_list = []
-
+
pooling_convention = 'valid'
-
+
ctx_list.append({'ctx': mx.cpu(0), 'pool_data': data, 'type_dict':
{'pool_data': np.float32}})
sym_list.append(mx.sym.Pooling_v1(kernel=kernel, pad=pad,
stride=stride, pool_type=pool_type,
Review comment:
@CoinCheung, we can then change to append new tests in the list instead of
replacing the current tests. We could change the code following the comment by
@TaoLv
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