Merge branch 'release/v01.03.01' v01.03.01
authorYuan Zhao <yuanzhao@ti.com>
Fri, 6 Sep 2019 21:44:40 +0000 (16:44 -0500)
committerYuan Zhao <yuanzhao@ti.com>
Fri, 6 Sep 2019 21:44:40 +0000 (16:44 -0500)
TIDL-API 1.3.1 for Processor SDK 6.1

51 files changed:
docs/source/changelog.rst
docs/source/example.rst
docs/source/faq/out_of_memory.rst
docs/source/using_api.rst
examples/Makefile
examples/classification/main.cpp
examples/imagenet/main.cpp
examples/one_eo_per_frame/main.cpp
examples/pybind/imagenet.py [new file with mode: 0755]
examples/pybind/mnist.py
examples/pybind/one_eo_per_frame.py
examples/pybind/two_eo_per_frame.py
examples/pybind/two_eo_per_frame_opt.py
examples/ssd_multibox/main.cpp
examples/test/main.cpp
examples/test/testvecs/config/tidl_models/jsegnet21/tidl_net_jsegnet21v2.bin
examples/test/testvecs/config/tidl_models/tidl_inception_v1_net.bin
examples/test/testvecs/config/tidl_models/tidl_inception_v1_param.bin
examples/test/testvecs/config/tidl_models/tidl_net_cifar_jacintonet11v2.bin
examples/test/testvecs/config/tidl_models/tidl_net_dense_1x1.bin
examples/test/testvecs/config/tidl_models/tidl_net_dense_varibale_block_size.bin
examples/test/testvecs/config/tidl_models/tidl_net_imagenet_jacintonet11v2.bin
examples/test/testvecs/config/tidl_models/tidl_net_imagenet_jacintonet11v2_bn.bin
examples/test/testvecs/config/tidl_models/tidl_net_imagenet_jacintonet11v2_dense.bin
examples/test/testvecs/config/tidl_models/tidl_net_imagenet_jacintonet11v2_prelu.bin
examples/test/testvecs/config/tidl_models/tidl_net_jacintonet_cntrllayers.bin
examples/test/testvecs/config/tidl_models/tidl_net_jdetNet_ssd.bin
examples/test/testvecs/config/tidl_models/tidl_net_jdetnet_voc_dense.bin
examples/test/testvecs/config/tidl_models/tidl_net_jseg21_cityscapes_dense.bin
examples/test/testvecs/config/tidl_models/tidl_net_jsegnet21v2.bin
examples/test/testvecs/config/tidl_models/tidl_net_mnist_lenet.bin
examples/test/testvecs/config/tidl_models/tidl_net_mobilenet_1_224.bin
examples/test/testvecs/config/tidl_models/tidl_net_squeezeNet1.1.bin
examples/test/testvecs/config/tidl_models/tidl_net_toydogs_jacintonet11v2.bin
examples/test/testvecs/config/tidl_models/tidl_param_mobilenet_1_224.bin
examples/test/testvecs/config/tidl_models/tidl_param_squeezeNet1.1.bin
examples/test/testvecs/reference/squeeze1_1_ref.bin
examples/two_eo_per_frame/main.cpp
examples/two_eo_per_frame_opt/main.cpp
tidl_api/Makefile
tidl_api/dsp/Makefile [deleted file]
tidl_api/dsp/ocl_wrapper.cl [deleted file]
tidl_api/make.buildid
tidl_api/src/configuration.cpp
tidl_api/src/execution_object.cpp
tidl_api/src/executor.cpp
tidl_api/src/executor_impl.h
tidl_api/src/ocl_device.cpp
tidl_api/src/ocl_device.h
tidl_api/src/parameters.h
tidl_api_manifest.html

index e69b6e44dbf1508cd5d1f76a9bc73280f16c5b4e..e170f5e7e09406ea9cf9bbe7a3cd081619bdd5ce 100644 (file)
@@ -2,6 +2,20 @@
 Changelog
 #########
 
+1.3.1 [Processor Linux SDK 6.1]
+===============================
+**Added**
+
+#. Added imagenet python example.
+
+**Changed**
+
+#. Trace data now also include dataQ/minValue/maxValue info.
+
+#. ssd_multibox example now can also run the whole netework on a single core.
+
+#. Shipped network binaries are in new file format, of size 484384 bytes.
+
 1.3.0 [Processor Linux SDK 5.3]
 ===============================
 **Added**
@@ -30,7 +44,7 @@ Changelog
     Enable a two phase approach to generating execution graphs. Use the
     following API function to enable timestamp generation:
 
-    .. code::
+    .. code:: cpp
 
         bool EnableTimeStamps(const std::string& file = "timestamp.log", size_t num_frames=32);
 
@@ -38,7 +52,7 @@ Changelog
 
 #. Added Python 3 bindings for TIDL API. See the ``examples/pybind`` directory for examples of using the Python bindings. Set PYTHONPATH to the location of ``tidl.so``.
 
-   .. code::
+   .. code:: bash
 
         root@am57xx-evm:~# export PYTHONPATH=/home/root/tidl-api/tidl_api
         root@am57xx-evm:~# python3
index ff70804267e5179aab6316088b66020e00a8a4a5..669020fe8345a5a00e8553344617257f5050fb9c 100644 (file)
@@ -35,13 +35,13 @@ Examples
      - OpenCV used to read input image from file or capture from camera.
    * - ssd_multibox
      - Object detection
-     - EVE and C66x (network is split across both EVE and C66x)
+     - EVE and C66x (network is split across both EVE and C66x), EVE or C66x (full network on each core)
      - OpenCV used to read input image from file or capture from camera.
    * - mnist
      - handwritten digits recognition (MNIST).  This example illustrates
        low TIDL API overhead (~1.8%) for small networks with low compute
        requirements (<5ms).
-     - EVE
+     - EVE or C66x
      - Pre-processed white-on-black images read from file, with or without
        MNIST database file headers.
    * - classification
@@ -273,6 +273,14 @@ versus ExecutionObject level.
      - 1630 ms
      - 1408 ms
 
+When there is a requirement to run the SSD networks non-partitioned,
+for example, the SoC only has C66x cores but not EVE cores,
+use ``-e 0`` to run the full network only on C66x cores, without partitioning.
+When running full network with C66x DSP cores, jdetnet works fine with default
+heap sizes.  jdetnet_voc requires slightly larger heap size and can be run
+with environment variable, e.g.
+``TIDL_NETWORK_HEAP_SIZE_DSP=75000000 ./ssd_multibox -d 1 -e 0``.
+
 .. _mnist-example:
 
 MNIST
index ee97e58eb00d5b66cc7c3afda189aafee94934a6..d7c8e1f89c2a00da0f7fdee2cbf5bf833e9582e3 100644 (file)
@@ -4,7 +4,7 @@ Why do I get an assertion failure from malloc_ddr?
 
 Application execution fails with the following error message:
 
-.. code:: shell
+.. code:: console
 
    tidl: device_alloc.h:31: T* tidl::malloc_ddr(size_t) [with T = char; size_t = unsigned int]: Assertion `val != nullptr' failed
 
@@ -28,7 +28,7 @@ Insufficient OpenCL global memory
 +++++++++++++++++++++++++++++++++
 Another possible reason is that total memory requirement specified in the ``Configuration`` using NETWORK_HEAP_SIZE and PARAM_HEAP_SIZE exceeds default memory available for OpenCL.  Follow the instructions below to increase the amount of CMEM (contiguous memory available for OpenCL) from 192MB (0xc000000) to 384MB (0x18000000):
 
-.. code:: bash
+.. code:: diff
 
    $ sudo apt-get install device-tree-compiler # In case dtc is not already installed
    $ scp root@am57:/boot/am57xx-evm-reva3.dtb .
index 47c0d9c6db38636089083b1d80d5d7c2e3f721f0..80d8a7490ba99f7be077b89b60b81d3fe377c098 100644 (file)
@@ -280,7 +280,20 @@ and the ``configuration.showHeapStats = true`` line can be removed.
 
     The memory for parameter and network heaps is itself allocated from OpenCL global memory (CMEM). Refer :ref:`opencl-global-memory` for details.
 
+In addition, the following environment variables are provided to overwrite
+the heap sizes and heap allocation optimization level (1 or 2) that are
+specified by default or by application.
 
+.. code-block:: bash
+
+    TIDL_PARAM_HEAP_SIZE_EVE
+    TIDL_PARAM_HEAP_SIZE_DSP
+    TIDL_NETWORK_HEAP_SIZE_EVE
+    TIDL_NETWORK_HEAP_SIZE_DSP
+    TIDL_EXTMEM_ALLOC_OPT_EVE
+    TIDL_EXTMEM_ALLOC_OPT_DSP
+    # # for example,
+    # TIDL_PARAM_HEAP_SIZE_EVE=3000000 TIDL_NETWORK_HEAP_SIZE_EVE=21000000 TIDL_PARAM_HEAP_SIZE_DSP=3000000 TIDL_NETWORK_HEAP_SIZE_DSP=2000000 ./tidl_classification -g 2 -d 1 -e 4 -l ./imagenet.txt -s ./classlist.txt -i ./clips/test10.mp4 -c ./stream_config_j11_v2.txt
 
 .. _network_layer_output:
 
index 52b3ff6508b233093e8ff3b83a20f9b473351146..9217380d00d1c235dcc66307193f77c7b87bf8b6 100644 (file)
@@ -34,7 +34,7 @@ RUN_DIRS := $(filter-out classification, $(DIRS))
 define make_in_dirs
        @for dir in $(1); do \
          echo "=============== " $$dir " =================" ; \
-         $(MAKE) --no-print-directory $(MAKEFILE) -C $$dir $(2); \
+         $(MAKE) --no-print-directory $(MAKEFILE) -C $$dir $(2) || exit 1; \
        done
 endef
 
index 020004bb2263322e3660f9ebf05274ec66402e03..25361f8a210a1de37c37a3030d6d3bfc55adf988 100644 (file)
@@ -699,7 +699,7 @@ int tf_postprocess(uchar *in, int size, int roi_idx, int frame_idx, int f_id)
   int rpt_id = -1;
 
   typedef std::pair<uchar, int> val_index;
-  auto constexpr cmp = [](val_index &left, val_index &right) { return left.first > right.first; };
+  auto cmp = [](val_index &left, val_index &right) { return left.first > right.first; };
   std::priority_queue<val_index, std::vector<val_index>, decltype(cmp)> queue(cmp);
   // initialize priority queue with smallest value on top
   for (int i = 0; i < k; i++) {
index 937f4671fdb2bda3099d4c3e6874bf8853d4296c..6aa7155a03c1e44c32c31032ea9a52b07463e64f 100644 (file)
@@ -306,10 +306,14 @@ bool WriteFrameOutput(const ExecutionObjectPipeline &eop,
     const int k = 5;
     unsigned char *out = (unsigned char *) eop.GetOutputBufferPtr();
     int out_size = eop.GetOutputBufferSizeInBytes();
+    // Tensorflow trained network outputs 1001 probabilities,
+    // with 0-index being background, thus we need to subtract 1 when
+    // reporting classified object from 1000 categories
+    int background_offset = out_size == 1001 ? 1 : 0;
 
     // sort and get k largest values and corresponding indices
     typedef pair<unsigned char, int> val_index;
-    auto constexpr cmp = [](val_index &left, val_index &right)
+    auto cmp = [](val_index &left, val_index &right)
                          { return left.first > right.first; };
     priority_queue<val_index, vector<val_index>, decltype(cmp)> queue(cmp);
     // initialize priority queue with smallest value on top
@@ -338,8 +342,9 @@ bool WriteFrameOutput(const ExecutionObjectPipeline &eop,
     for (int i = k - 1; i >= 0; i--)
     {
         if (sorted[i].first * 100 < min_prob_255)  break;
-        cout << k-i << ": "
-             << object_classes->At(sorted[i].second).label
+        int imagenet_index = sorted[i].second - background_offset;
+        cout << k-i << ": [" << imagenet_index << "] "
+             << object_classes->At(imagenet_index).label
              << ",   prob = " << setprecision(4)
              << ((sorted[i].first * 100) / 255.0f) << "%" << endl;
     }
index 8872c06438d3aed18c54d8ebbcbd0bcf6fa320cd..2665bb6f5718184106efdc67cf1b7bb1bd50f5b3 100644 (file)
@@ -100,7 +100,9 @@ bool Run(const string& config_file, int num_eve, int num_dsp,
 
     // Heap sizes for this network determined using Configuration::showHeapStats
     c.PARAM_HEAP_SIZE   = (3 << 20); // 3MB
-    c.NETWORK_HEAP_SIZE = (20 << 20); // 20MB
+    c.NETWORK_HEAP_SIZE = (34 << 20); // 34MB
+    // AM572x default CMEM size is 160MB, 4 EVEs + 2DSPs won't fit
+    if (num_eve > 2)  num_eve = 2;
 
     c.numFrames = 16;
 
diff --git a/examples/pybind/imagenet.py b/examples/pybind/imagenet.py
new file mode 100755 (executable)
index 0000000..4d83bbe
--- /dev/null
@@ -0,0 +1,217 @@
+#!/usr/bin/python3
+
+# Copyright (c) 2019 Texas Instruments Incorporated - http://www.ti.com/
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+# * Redistributions of source code must retain the above copyright
+# notice, this list of conditions and the following disclaimer.
+# * Redistributions in binary form must reproduce the above copyright
+# notice, this list of conditions and the following disclaimer in the
+# documentation and/or other materials provided with the distribution.
+# * Neither the name of Texas Instruments Incorporated nor the
+# names of its contributors may be used to endorse or promote products
+# derived from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+# THE POSSIBILITY OF SUCH DAMAGE.
+
+""" Process each frame using a single ExecutionObject.
+    Increase throughput by using multiple ExecutionObjects.
+"""
+
+import os
+import argparse
+import json
+import heapq
+import logging
+import numpy as np
+import cv2
+
+from tidl import DeviceId, DeviceType, Configuration, TidlError
+from tidl import Executor, ExecutionObjectPipeline
+from tidl import allocate_memory, free_memory
+
+
+def main():
+    """Read the configuration and run the network"""
+    #logging.basicConfig(level=logging.INFO)
+
+    args = parse_args()
+
+    config_file = '../test/testvecs/config/infer/tidl_config_j11_v2.txt'
+    labels_file = '../imagenet/imagenet_objects.json'
+
+    configuration = Configuration()
+    configuration.read_from_file(config_file)
+
+    if os.path.isfile(args.input_file):
+        configuration.in_data = args.input_file
+    else:
+        print('Input image {} does not exist'.format(args.input_file))
+        return
+    print('Input: {}'.format(args.input_file))
+
+    num_eve = Executor.get_num_devices(DeviceType.EVE)
+    num_dsp = Executor.get_num_devices(DeviceType.DSP)
+
+    if num_eve == 0 and num_dsp == 0:
+        print('No TIDL API capable devices available')
+        return
+
+    # use 1 EVE or DSP since input is a single image
+    # If input is a stream of images, feel free to use all EVEs and/or DSPs
+    if num_eve > 0:
+        num_eve = 1
+        num_dsp = 0
+    else:
+        num_dsp = 1
+
+    run(num_eve, num_dsp, configuration, labels_file)
+
+    return
+
+
+DESCRIPTION = 'Run the imagenet network on input image.'
+DEFAULT_INFILE = '../test/testvecs/input/objects/cat-pet-animal-domestic-104827.jpeg'
+
+def parse_args():
+    """Parse input arguments"""
+
+    parser = argparse.ArgumentParser(description=DESCRIPTION)
+    parser.add_argument('-i', '--input_file',
+                        default=DEFAULT_INFILE,
+                        help='input image file (that OpenCV can read)')
+    args = parser.parse_args()
+
+    return args
+
+PIPELINE_DEPTH = 2
+
+def run(num_eve, num_dsp, configuration, labels_file):
+    """ Run the network on the specified device type and number of devices"""
+
+    logging.info('Running network across {} EVEs, {} DSPs'.format(num_eve,
+                                                                  num_dsp))
+
+    dsp_device_ids = set([DeviceId.ID0, DeviceId.ID1,
+                          DeviceId.ID2, DeviceId.ID3][0:num_dsp])
+    eve_device_ids = set([DeviceId.ID0, DeviceId.ID1,
+                          DeviceId.ID2, DeviceId.ID3][0:num_eve])
+
+    # Heap sizes for this network determined using Configuration.showHeapStats
+    configuration.param_heap_size = (3 << 20)
+    configuration.network_heap_size = (34 << 20)
+
+
+    try:
+        logging.info('TIDL API: performing one time initialization ...')
+
+        # Collect all EOs from EVE and DSP executors
+        eos = []
+
+        if eve_device_ids:
+            eve = Executor(DeviceType.EVE, eve_device_ids, configuration, 1)
+            for i in range(eve.get_num_execution_objects()):
+                eos.append(eve.at(i))
+
+        if dsp_device_ids:
+            dsp = Executor(DeviceType.DSP, dsp_device_ids, configuration, 1)
+            for i in range(dsp.get_num_execution_objects()):
+                eos.append(dsp.at(i))
+
+        eops = []
+        num_eos = len(eos)
+        for j in range(PIPELINE_DEPTH):
+            for i in range(num_eos):
+                eops.append(ExecutionObjectPipeline([eos[i]]))
+
+        allocate_memory(eops)
+
+        # open labels file
+        with open(labels_file) as json_file:
+            labels_data = json.load(json_file)
+
+        configuration.num_frames = 1
+        logging.info('TIDL API: processing {} input frames ...'.format(
+                                                     configuration.num_frames))
+
+        num_eops = len(eops)
+        for frame_index in range(configuration.num_frames+num_eops):
+            eop = eops[frame_index % num_eops]
+
+            if eop.process_frame_wait():
+                process_output(eop, labels_data)
+
+            if read_frame(eop, frame_index, configuration):
+                eop.process_frame_start_async()
+
+        free_memory(eops)
+
+    except TidlError as err:
+        print(err)
+
+def read_frame(eo, frame_index, configuration):
+    """Read a frame into the ExecutionObject input buffer"""
+
+    if frame_index >= configuration.num_frames:
+        return False
+
+    # Read into the EO's input buffer
+    arg_info = eo.get_input_buffer()
+    np_arg = np.asarray(arg_info)
+
+    img = cv2.imread(configuration.in_data)
+    resized = cv2.resize(img, (224, 224), interpolation=cv2.INTER_AREA)
+    b_frame, g_frame, r_frame = cv2.split(resized)
+    np_arg[0*224*224:1*224*224] = np.reshape(b_frame, 224*224)
+    np_arg[1*224*224:2*224*224] = np.reshape(g_frame, 224*224)
+    np_arg[2*224*224:3*224*224] = np.reshape(r_frame, 224*224)
+
+    eo.set_frame_index(frame_index)
+
+    return True
+
+def process_output(eo, labels_data):
+    """Display the inference result using labels."""
+
+    # keep top k predictions in heap
+    k = 5
+    # output predictions with probability of 10/255 or higher
+    threshold = 10
+
+    out_buffer = eo.get_output_buffer()
+    output_array = np.asarray(out_buffer)
+
+    k_heap = []
+    for i in range(k):
+        heapq.heappush(k_heap, (output_array[i], i))
+
+    for i in range(k, out_buffer.size()):
+        if output_array[i] > k_heap[0][0]:
+            heapq.heappushpop(k_heap, (output_array[i], i))
+
+    k_sorted = []
+    for i in range(k):
+        k_sorted.insert(0, heapq.heappop(k_heap))
+
+    for i in range(k):
+        if k_sorted[i][0] > threshold:
+            print('{}: {},   prob = {:5.2f}%'.format(i+1, \
+                             labels_data['objects'][k_sorted[i][1]]['label'], \
+                             k_sorted[i][0]/255.0*100))
+
+    return 0
+
+if __name__ == '__main__':
+    main()
index 4029982dcefb3ded32a67349c00a8e5153c8362f..f3368f4b680f08888e151924afc8e4ab7ff5a708 100755 (executable)
@@ -108,8 +108,8 @@ def run(num_eve, num_dsp, configuration, labels_file):
 
         eops = []
         num_eos = len(eos)
-        for j in range(num_eos):
-            for i in range(PIPELINE_DEPTH):
+        for j in range(PIPELINE_DEPTH):
+            for i in range(num_eos):
                 eops.append(ExecutionObjectPipeline([eos[i]]))
 
         allocate_memory(eops)
index 81c9e220f1d71f41c0f1236b75dcc901269a6de5..dda97127efad53180013e7b3660b38bd09751a29 100755 (executable)
@@ -54,6 +54,9 @@ def main():
 
     num_dsp = Executor.get_num_devices(DeviceType.DSP)
     num_eve = Executor.get_num_devices(DeviceType.EVE)
+    # AM572x default CMEM size is 160MB, 4 EVEs + 2DSPs won't fit
+    if num_eve > 2:
+        num_eve = 2
 
     if num_dsp == 0 and num_eve == 0:
         print('No TIDL API capable devices available')
@@ -93,7 +96,7 @@ def run(num_eve, num_dsp, configuration):
 
     # Heap sizes for this network determined using Configuration.showHeapStats
     configuration.param_heap_size = (3 << 20)
-    configuration.network_heap_size = (20 << 20)
+    configuration.network_heap_size = (34 << 20)
 
     try:
         print('TIDL API: performing one time initialization ...')
index 953c691efb62921905f744be3e6d51091d49d199..6090d7bff7e70ffa05f5af4c518aae52ca5a0fe8 100755 (executable)
@@ -55,10 +55,13 @@ def main():
 
     # Heap sizes for this network determined using Configuration.showHeapStats
     configuration.param_heap_size = (3 << 20)
-    configuration.network_heap_size = (20 << 20)
+    configuration.network_heap_size = (34 << 20)
 
     num_dsp = Executor.get_num_devices(DeviceType.DSP)
     num_eve = Executor.get_num_devices(DeviceType.EVE)
+    # AM572x default CMEM size is 160MB, 4 EVEs + 2DSPs won't fit
+    if num_eve > 2:
+        num_eve = 2
 
     if num_dsp == 0 or num_eve == 0:
         print('This example requires EVEs and DSPs.')
index f46ca08e8cddfe123f7161fc4f79f3eae80cd710..120251150999a56da65326dd44e833f4476f1eeb 100755 (executable)
@@ -51,10 +51,13 @@ def main():
 
     # Heap sizes for this network determined using Configuration.showHeapStats
     configuration.param_heap_size = (3 << 20)
-    configuration.network_heap_size = (20 << 20)
+    configuration.network_heap_size = (34 << 20)
 
     num_dsp = Executor.get_num_devices(DeviceType.DSP)
     num_eve = Executor.get_num_devices(DeviceType.EVE)
+    # AM572x default CMEM size is 160MB, 4 EVEs + 2DSPs won't fit
+    if num_eve > 2:
+        num_eve = 2
 
     if num_dsp == 0 or num_eve == 0:
         print('This example requires EVEs and DSPs.')
@@ -100,8 +103,8 @@ def run(num_eve, num_dsp, c):
 
         eops = []
         num_pipe = max(num_eve_eos, num_dsp_eos)
-        for i in range(num_pipe):
-            for i in range(PIPELINE_DEPTH):
+        for j in range(PIPELINE_DEPTH):
+            for i in range(num_pipe):
                 eops.append(ExecutionObjectPipeline([eve.at(i % num_eve_eos),
                                                      dsp.at(i % num_dsp_eos)]))
 
index 315834501227ccf166ce54cb29b96136d98fa6ea..eaa61bd2d40708f7352a49b7c00eae62a7f4bbc9 100644 (file)
@@ -102,9 +102,9 @@ int main(int argc, char *argv[])
     // If there are no devices capable of offloading TIDL on the SoC, exit
     uint32_t num_eves = Executor::GetNumDevices(DeviceType::EVE);
     uint32_t num_dsps = Executor::GetNumDevices(DeviceType::DSP);
-    if (num_eves == 0 || num_dsps == 0)
+    if (num_eves == 0 && num_dsps == 0)
     {
-        cout << "ssd_multibox requires both EVE and DSP for execution." << endl;
+        cout << "ssd_multibox requires EVE or DSP for execution." << endl;
         return EXIT_SUCCESS;
     }
 
@@ -112,8 +112,8 @@ int main(int argc, char *argv[])
     cmdline_opts_t opts;
     opts.config = DEFAULT_CONFIG;
     opts.object_classes_list_file = DEFAULT_OBJECT_CLASSES_LIST_FILE;
-    opts.num_eves = 1;
-    opts.num_dsps = 1;
+    opts.num_eves = num_eves > 0 ? 1 : 0;
+    opts.num_dsps = num_dsps > 0 ? 1 : 0;
     opts.input_file = DEFAULT_INPUT;
     opts.output_prob_threshold = DEFAULT_OUTPUT_PROB_THRESHOLD;
     if (! ProcessArgs(argc, argv, opts))
@@ -121,7 +121,7 @@ int main(int argc, char *argv[])
         DisplayHelp();
         exit(EXIT_SUCCESS);
     }
-    assert(opts.num_dsps != 0 && opts.num_eves != 0);
+    assert(opts.num_dsps != 0 || opts.num_eves != 0);
     if (opts.num_frames == 0)
         opts.num_frames = (opts.is_camera_input || opts.is_video_input) ?
                           NUM_VIDEO_FRAMES :
@@ -164,6 +164,12 @@ bool RunConfiguration(const cmdline_opts_t& opts)
         return false;
     }
     c.enableApiTrace = opts.verbose;
+    if (opts.num_eves == 0 || opts.num_dsps == 0)
+        c.runFullNet = true;
+    // DSP only execution requires larger NETWORK HEAP size
+    if (opts.num_eves == 0)
+        c.NETWORK_HEAP_SIZE = 75000000;
+
     // setup camera/video input
     VideoCapture cap;
     if (! SetVideoInputOutput(cap, opts, "SSD_Multibox"))  return false;
@@ -198,43 +204,81 @@ bool RunConfiguration(const cmdline_opts_t& opts)
         // DSP will run layersGroupId 2 in the network
         Executor* e_eve = CreateExecutor(DeviceType::EVE, opts.num_eves, c, 1);
         Executor* e_dsp = CreateExecutor(DeviceType::DSP, opts.num_dsps, c, 2);
-
-        // Construct ExecutionObjectPipeline that utilizes multiple
-        // ExecutionObjects to process a single frame, each ExecutionObject
-        // processes one layerGroup of the network
-        //
-        // Pipeline depth can enable more optimized pipeline execution:
-        // Given one EVE and one DSP as an example, with different
-        //     pipeline_depth, we have different execution behavior:
-        // If pipeline_depth is set to 1,
-        //    we create one EOP: eop0 (eve0, dsp0)
-        //    pipeline execution of multiple frames over time is as follows:
-        //    --------------------- time ------------------->
-        //    eop0: [eve0...][dsp0]
-        //    eop0:                [eve0...][dsp0]
-        //    eop0:                               [eve0...][dsp0]
-        //    eop0:                                              [eve0...][dsp0]
-        // If pipeline_depth is set to 2,
-        //    we create two EOPs: eop0 (eve0, dsp0), eop1(eve0, dsp0)
-        //    pipeline execution of multiple frames over time is as follows:
-        //    --------------------- time ------------------->
-        //    eop0: [eve0...][dsp0]
-        //    eop1:          [eve0...][dsp0]
-        //    eop0:                   [eve0...][dsp0]
-        //    eop1:                            [eve0...][dsp0]
-        // Additional benefit of setting pipeline_depth to 2 is that
-        //    it can also overlap host ReadFrame() with device processing:
-        //    --------------------- time ------------------->
-        //    eop0: [RF][eve0...][dsp0]
-        //    eop1:     [RF]     [eve0...][dsp0]
-        //    eop0:                    [RF][eve0...][dsp0]
-        //    eop1:                             [RF][eve0...][dsp0]
         vector<ExecutionObjectPipeline *> eops;
-        uint32_t pipeline_depth = 2;  // 2 EOs in EOP -> depth 2
-        for (uint32_t j = 0; j < pipeline_depth; j++)
-            for (uint32_t i = 0; i < max(opts.num_eves, opts.num_dsps); i++)
-                eops.push_back(new ExecutionObjectPipeline(
+
+        if (e_eve != nullptr && e_dsp != nullptr)
+        {
+            // Construct ExecutionObjectPipeline that utilizes multiple
+            // ExecutionObjects to process a single frame, each ExecutionObject
+            // processes one layerGroup of the network
+            //
+            // Pipeline depth can enable more optimized pipeline execution:
+            // Given one EVE and one DSP as an example, with different
+            //     pipeline_depth, we have different execution behavior:
+            // If pipeline_depth is set to 1,
+            //    we create one EOP: eop0 (eve0, dsp0)
+            //    pipeline execution of multiple frames over time is as follows:
+            //    --------------------- time ------------------->
+            //    eop0: [eve0...][dsp0]
+            //    eop0:                [eve0...][dsp0]
+            //    eop0:                               [eve0...][dsp0]
+            //    eop0:                                              [eve0...][dsp0]
+            // If pipeline_depth is set to 2,
+            //    we create two EOPs: eop0 (eve0, dsp0), eop1(eve0, dsp0)
+            //    pipeline execution of multiple frames over time is as follows:
+            //    --------------------- time ------------------->
+            //    eop0: [eve0...][dsp0]
+            //    eop1:          [eve0...][dsp0]
+            //    eop0:                   [eve0...][dsp0]
+            //    eop1:                            [eve0...][dsp0]
+            // Additional benefit of setting pipeline_depth to 2 is that
+            //    it can also overlap host ReadFrame() with device processing:
+            //    --------------------- time ------------------->
+            //    eop0: [RF][eve0...][dsp0]
+            //    eop1:     [RF]     [eve0...][dsp0]
+            //    eop0:                    [RF][eve0...][dsp0]
+            //    eop1:                             [RF][eve0...][dsp0]
+            uint32_t pipeline_depth = 2;  // 2 EOs in EOP -> depth 2
+            for (uint32_t j = 0; j < pipeline_depth; j++)
+                for (uint32_t i = 0; i < max(opts.num_eves, opts.num_dsps); i++)
+                    eops.push_back(new ExecutionObjectPipeline(
                       {(*e_eve)[i%opts.num_eves], (*e_dsp)[i%opts.num_dsps]}));
+        }
+        else
+        {
+            // Construct ExecutionObjectPipeline that utilizes a
+            // ExecutionObject to process a single frame, each ExecutionObject
+            // processes the full network
+            //
+            // Use duplicate EOPs to do double buffering on frame input/output
+            //    because each EOP has its own set of input/output buffers,
+            //    so that host ReadFrame() can overlap device processing
+            // Use one EO as an example, with different buffer_factor,
+            //    we have different execution behavior:
+            // If buffer_factor is set to 1 -> single buffering
+            //    we create one EOP: eop0 (eo0)
+            //    pipeline execution of multiple frames over time is as follows:
+            //    --------------------- time ------------------->
+            //    eop0: [RF][eo0.....][WF]
+            //    eop0:                   [RF][eo0.....][WF]
+            //    eop0:                                     [RF][eo0.....][WF]
+            // If buffer_factor is set to 2 -> double buffering
+            //    we create two EOPs: eop0 (eo0), eop1(eo0)
+            //    pipeline execution of multiple frames over time is as follows:
+            //    --------------------- time ------------------->
+            //    eop0: [RF][eo0.....][WF]
+            //    eop1:     [RF]      [eo0.....][WF]
+            //    eop0:                   [RF]  [eo0.....][WF]
+            //    eop1:                             [RF]  [eo0.....][WF]
+            uint32_t buffer_factor = 2;  // set to 1 for single buffering
+            for (uint32_t j = 0; j < buffer_factor; j++)
+            {
+                for (uint32_t i = 0; i < opts.num_eves; i++)
+                    eops.push_back(new ExecutionObjectPipeline({(*e_eve)[i]}));
+                for (uint32_t i = 0; i < opts.num_dsps; i++)
+                    eops.push_back(new ExecutionObjectPipeline({(*e_dsp)[i]}));
+            }
+        }
         uint32_t num_eops = eops.size();
 
         // Allocate input/output memory for each EOP
@@ -252,9 +296,7 @@ bool RunConfiguration(const cmdline_opts_t& opts)
 
             // Wait for previous frame on the same eop to finish processing
             if (eop->ProcessFrameWait())
-            {
                 WriteFrameOutput(*eop, c, opts, (float)prob_slider);
-            }
 
             // Read a frame and start processing it with current eo
             if (ReadFrame(*eop, frame_idx, c, opts, cap, ifs))
@@ -291,7 +333,9 @@ Executor* CreateExecutor(DeviceType dt, uint32_t num, const Configuration& c,
     for (uint32_t i = 0; i < num; i++)
         ids.insert(static_cast<DeviceId>(i));
 
-    return new Executor(dt, ids, c, layers_group_id);
+    Executor* e = new Executor(dt, ids, c, layers_group_id);
+    assert(e != nullptr);
+    return e;
 }
 
 bool ReadFrame(ExecutionObjectPipeline& eop, uint32_t frame_idx,
index f6dfb64ae9a751329ccd97a7821df7d9b6af58fc..df9fa88d4f8022aadb764cdf0d99fcff0bcd1e11 100644 (file)
@@ -244,6 +244,9 @@ bool RunAllConfigurations(int32_t num_devices, DeviceType device_type)
     int errors = 0;
     for (auto config : configurations)
     {
+        // Skip smallRoi, not a real network anyway
+        if (config.compare("smallRoi") == 0)  continue;
+
         std::string config_file = "testvecs/config/infer/tidl_config_"
                                   + config + ".txt";
         std::cout << "Running " << config << " on " << num_devices
@@ -259,9 +262,6 @@ bool RunAllConfigurations(int32_t num_devices, DeviceType device_type)
 
         if (!status) { errors++; continue; }
 
-        // Skip smallRoi reference checking, will investigate
-        if (config.compare("smallRoi") == 0)  continue;
-
         // Check output against reference output
         std::string reference_output = "testvecs/reference/"
                                        + config + "_ref.bin";
index 35948a2f856834a14cdc363fe1d762fa9c1075b4..017a14c898d30fdc79c5f114a796e0ed5c9379c5 100755 (executable)
Binary files a/examples/test/testvecs/config/tidl_models/jsegnet21/tidl_net_jsegnet21v2.bin and b/examples/test/testvecs/config/tidl_models/jsegnet21/tidl_net_jsegnet21v2.bin differ
index 4325274962a3ea17174928f1b680fbb40bae8a0f..38600047dccb32ebd38abfaab4d475e3e6eaa56b 100644 (file)
Binary files a/examples/test/testvecs/config/tidl_models/tidl_inception_v1_net.bin and b/examples/test/testvecs/config/tidl_models/tidl_inception_v1_net.bin differ
index 7e3cb57302cae334adb549afcb7a331c484c5452..5f395de590c351b369b0603df23eca0c3e4e3d28 100644 (file)
@@ -12,21 +12,21 @@ jfefVY]fblldhwsghbj_mhgc
 ý\b\82þe÷Q\1f÷\b\15\ 5´\a­\ 4"\aÕ\a²õ³\ 4\9a\ 4ÏóÌ\v»\ 2ª\ 6ºò2\ 4\ f\ 2#\b\eýèûS\ 4ÿ\7f³æ©ü\1dÿd\11h\a)
 x\f»î¯ÿ®\ 6ñ\14\92øB\fÉ\0ð\ 1\8d\1a0\0\81ùU\a0\a\8d\b[\0M
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