Merge branch 'master' into patch-1

Author: gadcam

CMakeLists.txt

@@ -38,7 +38,7 @@ target_link_libraries(caffe2_detectron_custom_ops caffe2_library)
 install(TARGETS caffe2_detectron_custom_ops DESTINATION lib)
 
 # Install custom GPU ops lib, if gpu is present.
-if (${CAFFE2_FOUND_CUDA})
+if (CAFFE2_USE_CUDA OR CAFFE2_FOUND_CUDA)
   # Additional -I prefix is required for CMake versions before commit (< 3.7):
   # https://github.com/Kitware/CMake/commit/7ded655f7ba82ea72a82d0555449f2df5ef38594
   list(APPEND CUDA_INCLUDE_DIRS -I${CAFFE2_INCLUDE_DIRS})

INSTALL.md

@@ -69,7 +69,7 @@ Set up Python modules:
 cd $DETECTRON && make
 ```
 
-Check that Detectron tests pass (e.g. for [`SpatialNarrowAsOp test`](tests/test_spatial_narrow_as_op.py)):
+Check that Detectron tests pass (e.g. for [`SpatialNarrowAsOp test`](detectron/tests/test_spatial_narrow_as_op.py)):
 
 ```
 python2 $DETECTRON/detectron/tests/test_spatial_narrow_as_op.py
@@ -115,7 +115,7 @@ cd $DETECTRON/docker
 docker build -t detectron:c2-cuda9-cudnn7 .
 ```
 
-Run the image (e.g. for [`BatchPermutationOp test`](tests/test_batch_permutation_op.py)):
+Run the image (e.g. for [`BatchPermutationOp test`](detectron/tests/test_batch_permutation_op.py)):
 
 ```
 nvidia-docker run --rm -it detectron:c2-cuda9-cudnn7 python2 detectron/tests/test_batch_permutation_op.py
@@ -129,7 +129,7 @@ In case of Caffe2 installation problems, please read the troubleshooting section
 
 Caffe2 comes with performance [`profiling`](https://github.com/caffe2/caffe2/tree/master/caffe2/contrib/prof)
 support which you may find useful for benchmarking or debugging your operators
-(see [`BatchPermutationOp test`](tests/test_batch_permutation_op.py) for example usage).
+(see [`BatchPermutationOp test`](detectron/tests/test_batch_permutation_op.py) for example usage).
 Profiling support is not built by default and you can enable it by setting
 the `-DUSE_PROF=ON` flag when running Caffe2 CMake.
 

cmake/Summary.cmake

@@ -11,9 +11,11 @@ function (detectron_print_config_summary)
   message(STATUS "  CXX flags            : ${CMAKE_CXX_FLAGS}")
   message(STATUS "  Caffe2 version       : ${CAFFE2_VERSION}")
   message(STATUS "  Caffe2 include path  : ${CAFFE2_INCLUDE_DIRS}")
-  message(STATUS "  Caffe2 found CUDA    : ${CAFFE2_FOUND_CUDA}")
-  if (${CAFFE2_FOUND_CUDA})
+  if (CAFFE2_USE_CUDA OR CAFFE2_FOUND_CUDA)
+    message(STATUS "  Caffe2 found CUDA    : True")
     message(STATUS "    CUDA version       : ${CUDA_VERSION}")
     message(STATUS "    CuDNN version      : ${CUDNN_VERSION}")
+  else()
+    message(STATUS "  Caffe2 found CUDA    : False")
   endif()
 endfunction()

configs/04_2018_gn_baselines/scratch_e2e_mask_rcnn_R-101-FPN_3x_gn.yaml

@@ -37,6 +37,7 @@ MRCNN:
   CONV_INIT: MSRAFill  # default GaussianFill
 TRAIN:
   # WEIGHTS: N/A
+  FREEZE_AT: 0
   DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
   SCALES: (800,)
   MAX_SIZE: 1333

configs/04_2018_gn_baselines/scratch_e2e_mask_rcnn_R-50-FPN_3x_gn.yaml

@@ -37,6 +37,7 @@ MRCNN:
   CONV_INIT: MSRAFill  # default GaussianFill
 TRAIN:
   # WEIGHTS: N/A
+  FREEZE_AT: 0
   DATASETS: ('coco_2014_train', 'coco_2014_valminusminival')
   SCALES: (800,)
   MAX_SIZE: 1333

detectron/core/config.py

@@ -189,6 +189,12 @@
 # output directory
 __C.TRAIN.AUTO_RESUME = True
 
+# Training will copy TRAIN.WEIGHTS and treat it as a candidate checkpoint
+__C.TRAIN.COPY_WEIGHTS = False
+
+# Add StopGrad at a specified stage so the bottom layers are frozen
+__C.TRAIN.FREEZE_AT = 2
+
 
 # ---------------------------------------------------------------------------- #
 # Data loader options (see detectron/roi_data/loader.py for more info)
@@ -776,7 +782,7 @@
 
 
 # ---------------------------------------------------------------------------- #
-# Keyoint Mask R-CNN options ("KRCNN" = Mask R-CNN with Keypoint support)
+# Keypoint Mask R-CNN options ("KRCNN" = Mask R-CNN with Keypoint support)
 # ---------------------------------------------------------------------------- #
 __C.KRCNN = AttrDict()
 
@@ -966,6 +972,9 @@
 # Absolute and relative tolerance to use when comparing to EXPECTED_RESULTS
 __C.EXPECTED_RESULTS_RTOL = 0.1
 __C.EXPECTED_RESULTS_ATOL = 0.005
+# When the expected value specifies a mean and standard deviation, we check
+# that the actual value is within mean +/- SIGMA_TOL * std
+__C.EXPECTED_RESULTS_SIGMA_TOL = 4
 # Set to send email in case of an EXPECTED_RESULTS failure
 __C.EXPECTED_RESULTS_EMAIL = b''
 
@@ -988,7 +997,7 @@
 # If an option is removed from the code and you don't want to break existing
 # yaml configs, you can add the full config key as a string to the set below.
 # ---------------------------------------------------------------------------- #
-_DEPCRECATED_KEYS = set(
+_DEPRECATED_KEYS = set(
     {
         'FINAL_MSG',
         'MODEL.DILATION',
@@ -1189,7 +1198,7 @@ def _merge_a_into_b(a, b, stack=None):
 
 
 def _key_is_deprecated(full_key):
-    if full_key in _DEPCRECATED_KEYS:
+    if full_key in _DEPRECATED_KEYS:
         logger.warn(
             'Deprecated config key (ignoring): {}'.format(full_key)
         )

detectron/datasets/data/README.md

@@ -51,7 +51,8 @@ VOC<year>
 |  |_ ...
 |  |_ <im-N-name>.jpg
 |_ annotations
-|  |_ voc_<year>_trainval.json
+|  |_ voc_<year>_train.json
+|  |_ voc_<year>_val.json
 |  |_ ...
 |_ VOCdevkit<year>
 ```

detectron/datasets/dataset_catalog.py

@@ -162,11 +162,19 @@
         _ANN_FN:
             _DATA_DIR + '/coco/annotations/image_info_test-dev2015.json'
     },
-    'voc_2007_trainval': {
+    'voc_2007_train': {
         _IM_DIR:
             _DATA_DIR + '/VOC2007/JPEGImages',
         _ANN_FN:
-            _DATA_DIR + '/VOC2007/annotations/voc_2007_trainval.json',
+            _DATA_DIR + '/VOC2007/annotations/voc_2007_train.json',
+        _DEVKIT_DIR:
+            _DATA_DIR + '/VOC2007/VOCdevkit2007'
+    },
+    'voc_2007_val': {
+        _IM_DIR:
+            _DATA_DIR + '/VOC2007/JPEGImages',
+        _ANN_FN:
+            _DATA_DIR + '/VOC2007/annotations/voc_2007_val.json',
         _DEVKIT_DIR:
             _DATA_DIR + '/VOC2007/VOCdevkit2007'
     },
@@ -178,11 +186,19 @@
         _DEVKIT_DIR:
             _DATA_DIR + '/VOC2007/VOCdevkit2007'
     },
-    'voc_2012_trainval': {
+    'voc_2012_train': {
+        _IM_DIR:
+            _DATA_DIR + '/VOC2012/JPEGImages',
+        _ANN_FN:
+            _DATA_DIR + '/VOC2012/annotations/voc_2012_train.json',
+        _DEVKIT_DIR:
+            _DATA_DIR + '/VOC2012/VOCdevkit2012'
+    },
+    'voc_2012_val': {
         _IM_DIR:
             _DATA_DIR + '/VOC2012/JPEGImages',
         _ANN_FN:
-            _DATA_DIR + '/VOC2012/annotations/voc_2012_trainval.json',
+            _DATA_DIR + '/VOC2012/annotations/voc_2012_val.json',
         _DEVKIT_DIR:
             _DATA_DIR + '/VOC2012/VOCdevkit2012'
     }

detectron/datasets/json_dataset.py

@@ -43,6 +43,7 @@
 from detectron.utils.timer import Timer
 import detectron.datasets.dataset_catalog as dataset_catalog
 import detectron.utils.boxes as box_utils
+import detectron.utils.segms as segm_utils
 
 logger = logging.getLogger(__name__)
 
@@ -167,8 +168,8 @@ def _add_gt_annotations(self, entry):
         width = entry['width']
         height = entry['height']
         for obj in objs:
-            # crowd regions are RLE encoded and stored as dicts
-            if isinstance(obj['segmentation'], list):
+            # crowd regions are RLE encoded
+            if segm_utils.is_poly(obj['segmentation']):
                 # Valid polygons have >= 3 points, so require >= 6 coordinates
                 obj['segmentation'] = [
                     p for p in obj['segmentation'] if len(p) >= 6

detectron/datasets/task_evaluation.py

@@ -195,6 +195,11 @@ def check_expected_results(results, atol=0.005, rtol=0.1):
     Expected results should take the form of a list of expectations, each
     specified by four elements: [dataset, task, metric, expected value]. For
     example: [['coco_2014_minival', 'box_proposal', 'AR@1000', 0.387], ...].
+
+    The expected value may also be formatted as a list [mean, std] providing
+    an empirical mean and standard deviation from which a valid range is computed
+    using cfg.EXPECTED_RESULTS_SIGMA_TOL. For example:
+    [['coco_2014_minival', 'box_proposal', 'AR@1000', [0.387, 0.001]], ...]
     """
     # cfg contains a reference set of results that we want to check against
     if len(cfg.EXPECTED_RESULTS) == 0:
@@ -206,13 +211,28 @@ def check_expected_results(results, atol=0.005, rtol=0.1):
         assert metric in results[dataset][task], \
             'Metric {} not in results'.format(metric)
         actual_val = results[dataset][task][metric]
-        err = abs(actual_val - expected_val)
-        tol = atol + rtol * abs(expected_val)
-        msg = (
-            '{} > {} > {} sanity check (actual vs. expected): '
-            '{:.3f} vs. {:.3f}, err={:.3f}, tol={:.3f}'
-        ).format(dataset, task, metric, actual_val, expected_val, err, tol)
-        if err > tol:
+        ok = False
+        if isinstance(expected_val, list):
+            assert len(expected_val) == 2, (
+                'Expected result must be in (mean, std) format'
+            )
+            mean, std = expected_val
+            lo = mean - cfg.EXPECTED_RESULTS_SIGMA_TOL * std
+            hi = mean + cfg.EXPECTED_RESULTS_SIGMA_TOL * std
+            ok = (lo < actual_val) and (actual_val < hi)
+            msg = (
+                '{} > {} > {} sanity check (actual vs. expected): '
+                '{:.3f} vs. mean={:.4f}, std={:.4}, range=({:.4f}, {:.4f})'
+            ).format(dataset, task, metric, actual_val, mean, std, lo, hi)
+        else:
+            err = abs(actual_val - expected_val)
+            tol = atol + rtol * abs(expected_val)
+            ok = (err > tol)
+            msg = (
+                '{} > {} > {} sanity check (actual vs. expected): '
+                '{:.3f} vs. {:.3f}, err={:.3f}, tol={:.3f}'
+            ).format(dataset, task, metric, actual_val, expected_val, err, tol)
+        if not ok:
             msg = 'FAIL: ' + msg
             logger.error(msg)
             if cfg.EXPECTED_RESULTS_EMAIL != '':