MultiPaste operator #2583

TheTimemaster · 2021-01-03T12:28:19Z

Why we need this PR?

It adds new features, notably MultiPaste operator, needed in the implementation of Mosaic augmentation in the YOLOv4 pipeline.

What happened in this PR?

What solution was applied:
Currently, MultiPaste simply runs (new) paste kernel K * batch_size times and is for now only implemented for CPU. To support multiple pastes, tensor describing regions and indexes have one additional dimension accounting for K iterations.
Affected modules and functionalities:
Nothing was changed nor removed. New operator - Multipaste was added performing K * batch_size pastes of regions of images.
Key points relevant for the review:
There were a few issues I am aware of, described in TODO comments, now mostly fixed.
Validation and testing:
Manually tested in Mosaic implementation ([NEW VERSION] https://pastebin.com/DPuBFEUu, not sure where to put it), automatic python tests added to test/python directory, unsure if they should be registered elsewhere
Documentation (including examples):
Examples were not updated. Documentation for the operator has been created in-code.

JIRA TASK: NA

Signed-off-by: Piotr Kowalewski <piotr.kowalewski.main@gmail.com>

awolant · 2021-01-05T13:02:01Z

It would be useful to split this into two PRs - one per added operator.

dali/kernels/imgproc/paste/CMakeLists.txt

…d few TODO items Signed-off-by: Piotr Kowalewski <piotr.kowalewski.main@gmail.com>

mzient · 2021-01-12T13:30:46Z

dali/kernels/imgproc/paste/paste.h

+#define X_AXIS 0
+#define Y_AXIS 1
+#define C_AXIS 2


This is wrong (reversed). Also, #define? Really?

Changed X and Y order and changed #define to const int

mzient · 2021-01-12T13:32:43Z

dali/kernels/imgproc/paste/paste.h

+  using Image = InTensorCPU<InputType, 3>;
+  using OutImage = OutTensorCPU<OutputType>;


Why limit ourselves to 2D?
Why does the input have fixed dimensionality, but not the output?

Limited to 2D for simplicity's sake. Missing dimensionality in Out was a typo, fixed now.

mzient · 2021-01-12T13:35:31Z

dali/kernels/imgproc/paste/paste.h

+  void copyRoi(const OutTensorCPU<OutputType> &out, const Image &in, int inXAnchor, int inYAnchor,
+               int inXShape, int inYShape, int outXAnchor, int outYAnchor) {


Suggested change

void copyRoi(const OutTensorCPU<OutputType> &out, const Image &in, int inXAnchor, int inYAnchor,

int inXShape, int inYShape, int outXAnchor, int outYAnchor) {

void CopyROI(const OutImage &out. int outXAnchor, int outYAnchor,

const Image &in, int inXAnchor, int inYAnchor, int inXShape, int inYShape) {

I think that output anchors should go next to output and input anchors/shapes next to input.

mzient · 2021-01-12T13:38:37Z

dali/operators/image/paste/multipaste.cc

+.InputDox(1, "in_ids", "1D TesorList of shape [K] and type int",
+R"code(Indexes from what inputs to paste data in each iteration.)code")
+.InputDox(2, "in_anchors", "2D TesorList of shape [K, 2] and type int",
+R"code(Absolute values of LU corner of the selection for each iteration.)code")
+.InputDox(3, "in_shapes", "2D TesorList of shape [K, 2] and type int",
+R"code(Absolute values of size of the selection for each iteration.)code")
+.InputDox(4, "out_anchors", "2D TesorList of shape [K, 2] and type int",
+R"code(Absolute values of LU corner of the paste for each iteration.)code")
+.InputDox(5, "out_ids", "1D TesorList of shape [K] and type int",
+R"code(Indexes to what outputs to paste data in each iteration.
+If ommitted, i-th tensor pastes to i-th output. )code")


All of these should be tensor arguments, not regular inputs.

Changed in the new commit, tests were modified accordingly and pass.

mzient · 2021-01-12T13:38:57Z

dali/operators/image/paste/multipaste.cc

+.AddArg("output_width",
+R"code(Output width.)code", DALI_INT32, true)
+
+.AddArg("output_height",
+R"code(Output height.)code", DALI_INT32, true)


Use one "size" argument instead.

Changed in the new commit

mzient · 2021-01-12T13:40:01Z

dali/operators/image/paste/multipaste.cc

+.AddOptionalArg("input_out_ids",
+R"code(If true, the operator takes the last, out_ids input.)code", false)


Unnecessary when using named arguments?

Removed as suggested

dali/operators/image/paste/multipaste.cc

Signed-off-by: Piotr Kowalewski <piotr.kowalewski.main@gmail.com>

mzient · 2021-01-12T15:33:33Z

dali/operators/image/paste/multipaste.cc

+.InputDox(4, "out_anchors", "2D TesorList of shape [K, 2] and type int",
+R"code(Absolute values of LU corner of the paste for each iteration.)code")
+.InputDox(5, "out_ids", "1D TesorList of shape [K] and type int",
+R"code(Indexes to what outputs to paste data in each iteration.


This kind of mapping is both redundant and dangerous.
Redundancy stems from the fact that you can just permute the input to get the desired result.
Danger comes in two flavors:

race condition: two samples write to the same index

missing sample: some output is never specified

It also complicates batch size reduction - and when we finally allow the batch size to change across operators in the pipeline, just specifying as many input parameter sets as there are desired output samples is the way to go.

It is very much possible to have a specific use case, where one would like to perform no paste to one of the outputs - it should be left blank in such case, as no pastes target it. As for race condition, there are 3 worker strategies:

if no_intersections is assumed, a separate work item is launched for each paste

if no_intersections is not assumed, but out_ids is not given, one work item runs all the pastes for a given output

if no_intersections is not assumed and out_ids is given, there is no easy way to parallelize, so one worker runs all the pastes

[EDIT After reading last comments] Ok now I understand. So the shapes of the tensors in TensorList need not be uniform, so I can put any number (for example 0) pastes for each index separately, and that's how many pastes will run to the corresponding output.

Got rid of this argument and fixed tests accordingly

mzient · 2021-01-12T15:34:12Z

dali/operators/image/paste/multipaste.cc

+          {
+            using Kernel = TheKernel<OutputType, InputType>;
+            if (no_intersections_) {
+              for (int i = 0; i < batch_size; i++) {


This should iterate over output batch, not input - see my comments about out_ids

mzient

use named argument inputs instead of inputs for indices and parameters
remove out_ids unless absolutely necessary
iterate over output batch size

minor stuff in the comments

mzient · 2021-01-12T15:58:42Z

I think that current assumption of having fixed K inputs is a strong one. In gerenal, each output image can be composed of an arbitrary number of source images.
The whole operator should be driven by output, not input.
You have N outputs to fill.
Each output is described by:
output size
list of source indices
list of source ROIs
list of placement positions (where to paste given input in the output)
When you look at it like this, there's nothing that needs to change when the input image batch is of different length than output batch (and parameter batches).

Invarians:

number of samples in the output must match the number of samples in lists of indices, ROIs and placement positions
number of source indices, ROIs and placement positions must be the same for each sample (but in general does not need to be the same for different output samples).

TheTimemaster · 2021-01-12T21:55:00Z

How can I fix DCO for a commit that was made by auto-applying suggested changes (I did not know we should not do it)?

JanuszL · 2021-01-12T22:00:17Z

How can I fix DCO for a commit that was made by auto-applying suggested changes (I did not know we should not do it)?

Try interactive rebase, edit commit message and force push.

TheTimemaster · 2021-01-12T22:01:45Z

I have noticed one issue while testing. Outputs are not zero-ed when declared. Do I have to run another kernel to zero out the output before running pastes (current paste only writes where the paste should be), or is there a way to signify (in SetupImpl in OutputDesc maybe?) that allocated memory should be zeroed before we get it?

For now python tests just skip checking pixels where no paste should output.

JanuszL · 2021-01-12T22:11:17Z

Do I have to run another kernel to zero out the output before running pastes (current paste only writes where the paste should be), or is there a way to signify (in SetupImpl in OutputDesc maybe?) that allocated memory should be zeroed before we get it?

Zeroing memory is just running a code that sets 0 there (cudaMemset). As GPU is about the perf it is up to the user to initialize it if needed. In this case I would test what is faster - zero the whole memory ahead, or paste and then zero the remaining memory (I guess the second one).

… output_size. Signed-off-by: Piotr Kowalewski <piotr.kowalewski.main@gmail.com>

Signed-off-by: Piotr Kowalewski <piotr.kowalewski.main@gmail.com>

mzient · 2021-02-08T10:59:28Z

dali/test/python/test_operator_multipaste.py

+        out_size=None,
+        even_paste_count=False,
+        k=4,
+        dtype=types.DALIDataType.UINT8,


Suggested change

dtype=types.DALIDataType.UINT8,

dtype=types.UINT8,

jantonguirao · 2021-02-10T08:13:11Z

dali/kernels/imgproc/paste/paste.h

+  using Coords = InTensorCPU<const int, 1>;
+
+  /**
+   * Pastes regions of inputs onto the output.


Suggested change

* Pastes regions of inputs onto the output.

* @brief Pastes regions of inputs onto the output.

Nitpick (not 100% sure is needed)

jantonguirao · 2021-02-10T08:20:41Z

dali/operators/image/paste/multipaste.cc

+DALI_REGISTER_OPERATOR(MultiPaste, MultiPasteCpu, CPU)
+
+bool MultiPasteCpu::SetupImpl(std::vector<OutputDesc> &output_desc,
+                                      const workspace_t<CPUBackend> &ws) {


nitpick: Fix indentation here

jantonguirao · 2021-02-10T08:23:14Z

dali/operators/image/paste/multipaste.cc

+          {
+            using Kernel = kernels::PasteCPU<OutputType, InputType>;
+            auto in_view = view<const InputType, 3>(images);
+            auto out_view = view<OutputType, 3>(output);
+            for (int i = 0; i < batch_size; i++) {
+              auto paste_count = in_idx_[i].shape[0];
+              memset(out_view[i].data, 0,
+                     out_view[i].shape[0] * out_view[i].shape[1] * out_view[i].shape[2]);
+
+              if (no_intersections_[i]) {
+                for (int iter = 0; iter < paste_count; iter++) {
+                  int from_sample = in_idx_[i].data[iter];
+                  int to_sample = i;
+
+                  tp.AddWork(
+                      [&, i, iter, from_sample, to_sample, in_view, out_view](int thread_id) {
+                        kernels::KernelContext ctx;
+                        auto tvin = in_view[from_sample];
+                        auto tvout = out_view[to_sample];
+
+                        auto in_anchor_view = GetInAnchors(i, iter);
+                        auto in_shape_view = GetShape(i, iter, Coords(
+                            raw_input_size_mem_.data() + 2 * from_sample, dali::TensorShape<>(2)));
+                        auto out_anchor_view = GetOutAnchors(i, iter);
+                        kernel_manager_.Run<Kernel>(thread_id, to_sample, ctx, tvout, tvin,
+                                        in_anchor_view, in_shape_view, out_anchor_view);
+                      },
+                      out_shape.tensor_size(to_sample));
+                }
+              } else {
+                tp.AddWork(
+                    [&, i, paste_count, in_view, out_view](int thread_id) {
+                      for (int iter = 0; iter < paste_count; iter++) {
+                        int from_sample = in_idx_[i].data[iter];
+                        int to_sample = i;
+
+                        kernels::KernelContext ctx;
+                        auto tvin = in_view[from_sample];
+                        auto tvout = out_view[to_sample];
+
+                        auto in_anchor_view = GetInAnchors(i, iter);
+                        auto in_shape_view = GetShape(i, iter, Coords(
+                            raw_input_size_mem_.data() + 2 * from_sample, dali::TensorShape<>(2)));
+                        auto out_anchor_view = GetOutAnchors(i, iter);
+                        kernel_manager_.Run<Kernel>(thread_id, to_sample, ctx, tvout, tvin,
+                                        in_anchor_view, in_shape_view, out_anchor_view);
+                      }
+                    },
+                    paste_count * out_shape.tensor_size(0));
+              }
+            }
+          }


I was about to suggest the same :)

jantonguirao · 2021-02-10T08:27:55Z

dali/operators/image/paste/multipaste.h

+        DALI_ENFORCE(in_anchors_[i].shape[0] == n_paste,
+                     "in_anchors must be same length as in_idx");
+        DALI_ENFORCE(in_anchors_[i].shape[1] == spatial_ndim,
+         "in_anchors must have number of coordinates equal to that of input images - 1 (channel)");


Just a suggestion, I think it'd be better for the user to have something like:

Suggested change

"in_anchors must have number of coordinates equal to that of input images - 1 (channel)");

make_string("Unexpected number of dimensions for ``in_anchors``. Expected ", spatial_dim, ", got ", in_anchors_[i].shape[1]));

jantonguirao · 2021-02-10T08:33:46Z

dali/operators/image/paste/multipaste.h

+
+    output_size_.Acquire(spec, ws, curr_batch_size, true);
+    in_idx_.Acquire(spec, ws, curr_batch_size, false);
+    if (out_anchors_.IsDefined()) {


This is a suggestion so that you don't need to do manual validation below:

TensorListShape<2> expected_sh(curr_batch_size); for (int i = 0; I < curr_batch_size; i++) { expected_sh.set_tensor_shape(i, TensorShape<2>{in_idx_[i].shape[0], static_ndim}); } if (out_anchors_.IsDefined()) { out_anchors_.Acquire(spec, ws, curr_batch_size, expected_sh); } if (in_anchors_.IsDefined()) { in_anchors_.Acquire(spec, ws, curr_batch_size, expected_sh); } if (in_shapes_.IsDefined()) { in_shapes_.Acquire(spec, ws, curr_batch_size, expected_sh); }

If you do this, the shape is already enforced to be expected_sh so you can remove the validation done below (lines 120-136).

It seems this only works when enforcing uniform shapes, the last argument of this variant of Acquire is a const TensorShape, not a TensorListShape

You are right, my mistake

dali/operators/image/paste/multipaste.h

jantonguirao · 2021-02-10T08:50:03Z

dali/test/python/test_operator_multipaste.py

+        in_idx_l, in_anchors_l, shapes_l, out_anchors_l = prepare_cuts(
+            k, batch_size, in_size, out_size, even_paste_count,
+            no_intersections, full_input, in_anchor_top_left, out_anchor_top_left)
+        in_idx = fn.external_source(numpyize(in_idx_l))


I don't get why the function is called numpyize. The input is already a numpy array. In my opinion lambda: in_idx_l would read better.

It was left over from the time when it would do much more logic and the input was not in numpy array. Applying this

jantonguirao · 2021-02-10T08:51:09Z

dali/test/python/test_operator_multipaste.py

+import math
+import os
+import cv2
+#from test_utils import get_dali_extra_path


I think before we merge we can revert to use the one from test_utils.

jantonguirao · 2021-02-10T08:51:24Z

dali/test/python/test_operator_multipaste.py

+
+
+def test_operator_multipaste():
+    tests = [


can you at least add a comment here mentioning the order of those tests arguments. Right now I have to scroll up to check what those numbers and bools mean.

…emoved intersection checking bug and memory alloc bug. Signed-off-by: Piotr Kowalewski <piotr.kowalewski.main@gmail.com>

mzient · 2021-02-12T10:41:15Z

dali/operators/image/paste/multipaste.cc

+template<typename InputType, typename OutputType>
+void MultiPasteCPU::RunImplExplicitlyTyped(workspace_t<CPUBackend> &ws) {
+    const auto &images = ws.template InputRef<CPUBackend>(0);
+    auto &output = ws.template OutputRef<CPUBackend>(0);
+
+    output.SetLayout(images.GetLayout());
+    auto out_shape = output.shape();
+
+    auto& tp = ws.GetThreadPool();
+
+    auto batch_size = output.shape().num_samples();
+
+    using Kernel = kernels::PasteCPU<OutputType, InputType>;
+    auto in_view = view<const InputType, 3>(images);
+    auto out_view = view<OutputType, 3>(output);
+    for (int i = 0; i < batch_size; i++) {
+        auto paste_count = in_idx_[i].shape[0];
+        memset(out_view[i].data, 0, out_view[i].num_elements() * sizeof(OutputType));
+
+        if (no_intersections_[i]) {
+            for (int iter = 0; iter < paste_count; iter++) {
+                int from_sample = in_idx_[i].data[iter];
+                int to_sample = i;
+
+                tp.AddWork(
+                        [&, i, iter, from_sample, to_sample, in_view, out_view](int thread_id) {
+                            kernels::KernelContext ctx;
+                            auto tvin = in_view[from_sample];
+                            auto tvout = out_view[to_sample];
+
+                            auto in_anchor_view = GetInAnchors(i, iter);
+                            auto in_shape_view = GetShape(i, iter, Coords(
+                                    raw_input_size_mem_.data() + 2 * from_sample,
+                                    dali::TensorShape<>(2)));
+                            auto out_anchor_view = GetOutAnchors(i, iter);
+                            kernel_manager_.Run<Kernel>(
+                                    thread_id, to_sample, ctx, tvout, tvin,
+                                    in_anchor_view, in_shape_view, out_anchor_view);
+                        },
+                        out_shape.tensor_size(to_sample));
+            }
+        } else {
+            tp.AddWork(
+                    [&, i, paste_count, in_view, out_view](int thread_id) {
+                        for (int iter = 0; iter < paste_count; iter++) {
+                            int from_sample = in_idx_[i].data[iter];
+                            int to_sample = i;
+
+                            kernels::KernelContext ctx;
+                            auto tvin = in_view[from_sample];
+                            auto tvout = out_view[to_sample];
+
+                            auto in_anchor_view = GetInAnchors(i, iter);
+                            auto in_shape_view = GetShape(i, iter, Coords(
+                                    raw_input_size_mem_.data() + 2 * from_sample,
+                                    dali::TensorShape<>(2)));
+                            auto out_anchor_view = GetOutAnchors(i, iter);
+                            kernel_manager_.Run<Kernel>(
+                                    thread_id, to_sample, ctx, tvout, tvin,
+                                    in_anchor_view, in_shape_view, out_anchor_view);
+                        }
+                    },
+                    paste_count * out_shape.tensor_size(0));
+        }
+    }
+    tp.RunAll();
+}


Tab size is 4. Should be 2.

Signed-off-by: Piotr Kowalewski <piotr.kowalewski.main@gmail.com>

awolant · 2021-02-15T11:36:43Z

!build

dali-automaton · 2021-02-15T11:40:58Z

CI MESSAGE: [2076628]: BUILD STARTED

jantonguirao · 2021-02-15T11:59:46Z

dali/operators/image/paste/multipaste.cc

+                    in_anchor_view, in_shape_view, out_anchor_view);
+          }
+        },
+        paste_count * out_shape.tensor_size(0));


Suggested change

paste_count * out_shape.tensor_size(0));

paste_count * out_shape.tensor_size(i));

You should use the size of the current tensor, I think

jantonguirao · 2021-02-15T12:02:12Z

dali/operators/image/paste/multipaste.cc

+
+
+template<typename InputType, typename OutputType>
+void MultiPasteCPU::RunImplExplicitlyTyped(workspace_t<CPUBackend> &ws) {


nitpick: The name is a bit too long. Perhaps RunImplExplicitlyTyped?
(You can disregard this suggestion)

jantonguirao · 2021-02-15T12:04:27Z

dali/operators/image/paste/multipaste.h

+
+    output_size_.Acquire(spec, ws, curr_batch_size, true);
+    in_idx_.Acquire(spec, ws, curr_batch_size, false);
+    if (out_anchors_.IsDefined()) {


You are right, my mistake

dali-automaton · 2021-02-15T12:54:20Z

CI MESSAGE: [2076628]: BUILD FAILED

awolant · 2021-02-15T13:06:03Z

Failed test in CI:

ERROR: Failure: SyntaxError (invalid syntax (test_operator_multipaste.py, line 238))
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/nose/failure.py", line 39, in runTest
    raise self.exc_val.with_traceback(self.tb)
  File "/usr/local/lib/python3.7/dist-packages/nose/loader.py", line 418, in loadTestsFromName
    addr.filename, addr.module)
  File "/usr/local/lib/python3.7/dist-packages/nose/importer.py", line 47, in importFromPath
    return self.importFromDir(dir_path, fqname)
  File "/usr/local/lib/python3.7/dist-packages/nose/importer.py", line 94, in importFromDir
    mod = load_module(part_fqname, fh, filename, desc)
  File "/usr/lib/python3.7/imp.py", line 234, in load_module
    return load_source(name, filename, file)
  File "/usr/lib/python3.7/imp.py", line 171, in load_source
    module = _load(spec)
  File "<frozen importlib._bootstrap>", line 696, in _load
  File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 724, in exec_module
  File "<frozen importlib._bootstrap_external>", line 860, in get_code
  File "<frozen importlib._bootstrap_external>", line 791, in source_to_code
  File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
  File "/opt/dali/dali/test/python/test_operator_multipaste.py", line 238
    yield check_operator_multipaste, *t
                                     ^
SyntaxError: invalid syntax
----------------------------------------------------------------------
Ran 1 test in 0.001s
FAILED (errors=1)

Signed-off-by: Piotr Kowalewski <piotr.kowalewski.main@gmail.com>

awolant · 2021-02-16T18:16:41Z

!build

dali-automaton · 2021-02-16T18:26:36Z

CI MESSAGE: [2079881]: BUILD STARTED

dali-automaton · 2021-02-16T20:49:46Z

CI MESSAGE: [2079881]: BUILD FAILED

JanuszL · 2021-02-16T22:50:28Z

Still:

Failure: SyntaxError (invalid syntax (test_operator_multipaste.py, line 238)) ... ERROR
======================================================================
ERROR: Failure: SyntaxError (invalid syntax (test_operator_multipaste.py, line 238))
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/nose/failure.py", line 39, in runTest
    raise self.exc_val.with_traceback(self.tb)
  File "/usr/local/lib/python3.6/dist-packages/nose/loader.py", line 418, in loadTestsFromName
    addr.filename, addr.module)
  File "/usr/local/lib/python3.6/dist-packages/nose/importer.py", line 47, in importFromPath
    return self.importFromDir(dir_path, fqname)
  File "/usr/local/lib/python3.6/dist-packages/nose/importer.py", line 94, in importFromDir
    mod = load_module(part_fqname, fh, filename, desc)
  File "/usr/lib/python3.6/imp.py", line 235, in load_module
    return load_source(name, filename, file)
  File "/usr/lib/python3.6/imp.py", line 172, in load_source
    module = _load(spec)
  File "<frozen importlib._bootstrap>", line 684, in _load
  File "<frozen importlib._bootstrap>", line 665, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 674, in exec_module
  File "<frozen importlib._bootstrap_external>", line 781, in get_code
  File "<frozen importlib._bootstrap_external>", line 741, in source_to_code
  File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
  File "/opt/dali/dali/test/python/test_operator_multipaste.py", line 238
    yield check_operator_multipaste, *t
                                     ^
SyntaxError: invalid syntax

TheTimemaster · 2021-02-17T12:41:04Z

Can I get exact commands used to run those tests? I used nosetests as suggested and it does not reproduce

JanuszL · 2021-02-17T12:46:51Z

Can I get exact commands used to run those tests? I used nosetests as suggested and it does not reproduce

pip install nose numpy>=1.17 opencv-python pillow librosa scipy nvidia-ml-py==11.450.51
export DALI_EXTRA_PATH=YOUR_PATH_TO_DALI_EXTRA
nosetests --verbose --attr '!slow' test_operator_multipaste.py

Please download DALI_extra (it requires git-lfs).
You can read more about nosetests here.

Signed-off-by: Piotr Kowalewski <piotr.kowalewski.main@gmail.com>

TheTimemaster · 2021-02-17T14:21:53Z

It seems that "Python 3.6 does not support unpacking without parenthesis" and I was running it with 3.8. Tried with 3.6, reproduced the error and fixed by adding parenthesis.

JanuszL · 2021-02-17T17:33:55Z

!build

dali-automaton · 2021-02-17T17:41:52Z

CI MESSAGE: [2083312]: BUILD STARTED

dali-automaton · 2021-02-17T19:07:05Z

CI MESSAGE: [2083312]: BUILD PASSED

TheTimemaster added 3 commits December 21, 2020 13:26

First version for multipaste

67772b3

Signed-off-by: Piotr Kowalewski <piotr.kowalewski.main@gmail.com>

Actually run the copy

7c12a8e

Signed-off-by: Piotr Kowalewski <piotr.kowalewski.main@gmail.com>

Implementation is now working. Fixed documentation comments

6e2843f

Signed-off-by: Piotr Kowalewski <piotr.kowalewski.main@gmail.com>

awolant reviewed Jan 5, 2021

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dali/kernels/imgproc/paste/CMakeLists.txt Outdated Show resolved Hide resolved

Merge branch 'master' of github.com:NVIDIA/DALI

111993c

jantonguirao self-assigned this Jan 7, 2021

Removed BatchIndex operator, Multipaste out_idx is now optional. Fixe…

cd6d288

…d few TODO items Signed-off-by: Piotr Kowalewski <piotr.kowalewski.main@gmail.com>

TheTimemaster force-pushed the master branch from 5a1e9b6 to cd6d288 Compare January 7, 2021 13:56

TheTimemaster changed the title ~~[WIP] Creation of MultiPaste operator and BatchIndex operator~~ [WIP] Creation of MultiPaste operator Jan 7, 2021

jantonguirao assigned mzient Jan 11, 2021

mzient reviewed Jan 12, 2021

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dali/operators/image/paste/multipaste.cc Outdated Show resolved Hide resolved

Added python tests

c2e67d1

Signed-off-by: Piotr Kowalewski <piotr.kowalewski.main@gmail.com>

TheTimemaster changed the title ~~[WIP] Creation of MultiPaste operator~~ Creation of MultiPaste operator Jan 12, 2021

mzient reviewed Jan 12, 2021

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mzient previously requested changes Jan 12, 2021

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TheTimemaster added 2 commits January 12, 2021 23:13

Most inputs are now named arguments. Output_width+height changed into…

9a3b3f4

… output_size. Signed-off-by: Piotr Kowalewski <piotr.kowalewski.main@gmail.com>

Removed redundant 'input_out_ids' argument

3ba0a5f

Signed-off-by: Piotr Kowalewski <piotr.kowalewski.main@gmail.com>

mzient reviewed Feb 8, 2021

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jantonguirao reviewed Feb 10, 2021

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Extracted large code blocks from TYPE_SWITCH to separate functions. R…

ce70740

…emoved intersection checking bug and memory alloc bug. Signed-off-by: Piotr Kowalewski <piotr.kowalewski.main@gmail.com>

TheTimemaster requested review from mzient and jantonguirao February 11, 2021 21:24

mzient reviewed Feb 12, 2021

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Changed tab size in RunImplExplicitlyTyped

8fea944

Signed-off-by: Piotr Kowalewski <piotr.kowalewski.main@gmail.com>

TheTimemaster requested a review from mzient February 13, 2021 14:32

TheTimemaster added 2 commits February 13, 2021 16:42

Tab fix

02f8ebf

Signed-off-by: Piotr Kowalewski <piotr.kowalewski.main@gmail.com>

Tab fix

c1b3bce

Signed-off-by: Piotr Kowalewski <piotr.kowalewski.main@gmail.com>

mzient approved these changes Feb 15, 2021

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jantonguirao approved these changes Feb 15, 2021

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Python test fix

28a90a1

Signed-off-by: Piotr Kowalewski <piotr.kowalewski.main@gmail.com>

Test parenthesis fix

1640dfc

Signed-off-by: Piotr Kowalewski <piotr.kowalewski.main@gmail.com>

awolant merged commit 8547df3 into NVIDIA:master Feb 18, 2021

JanuszL mentioned this pull request May 19, 2021

DALI 2021 roadmap #2978

Closed

		using Image = InTensorCPU<InputType, 3>;
		using OutImage = OutTensorCPU<OutputType>;

		void copyRoi(const OutTensorCPU<OutputType> &out, const Image &in, int inXAnchor, int inYAnchor,
		int inXShape, int inYShape, int outXAnchor, int outYAnchor) {

		.AddOptionalArg("input_out_ids",
		R"code(If true, the operator takes the last, out_ids input.)code", false)

	* Pastes regions of inputs onto the output.
	* @brief Pastes regions of inputs onto the output.

	"in_anchors must have number of coordinates equal to that of input images - 1 (channel)");
	make_string("Unexpected number of dimensions for ``in_anchors``. Expected ", spatial_dim, ", got ", in_anchors_[i].shape[1]));

	paste_count * out_shape.tensor_size(0));
	paste_count * out_shape.tensor_size(i));



		template<typename InputType, typename OutputType>
		void MultiPasteCPU::RunImplExplicitlyTyped(workspace_t<CPUBackend> &ws) {

MultiPaste operator #2583

MultiPaste operator #2583

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TheTimemaster commented Jan 3, 2021 • edited Loading

Why we need this PR?

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