This Pull resolves the issues with newer versions of Python and Pytorch

configs/rotated/e2e_ms_rcnn_R_101_FPN_1x.yaml

@@ -0,0 +1,43 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-101"
+  BACKBONE:
+    CONV_BODY: "R-101-FPN"
+  RESNETS:
+    BACKBONE_OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+  ROI_MASK_HEAD:
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  MASK_ON: True
+  MASKIOU_ON: True
+DATASETS:
+  TRAIN: ("smartflow_train", "smartflow_val")
+  TEST: ("smartflow_test",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  BASE_LR: 0.02
+  WEIGHT_DECAY: 0.0001
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
+OUTPUT_DIR: "/content/rotated_maskrcnn"

maskrcnn_benchmark/config/defaults.py

@@ -286,7 +286,7 @@
 _C.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION = 14
 _C.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO = 0
 _C.MODEL.ROI_BOX_HEAD.POOLER_SCALES = (1.0 / 16,)
-_C.MODEL.ROI_BOX_HEAD.NUM_CLASSES = 81
+_C.MODEL.ROI_BOX_HEAD.NUM_CLASSES = 200
 # Hidden layer dimension when using an MLP for the RoI box head
 _C.MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM = 1024
 # GN
@@ -326,7 +326,7 @@
 _C.MODEL.ROI_KEYPOINT_HEAD.MLP_HEAD_DIM = 1024
 _C.MODEL.ROI_KEYPOINT_HEAD.CONV_LAYERS = tuple(512 for _ in range(8))
 _C.MODEL.ROI_KEYPOINT_HEAD.RESOLUTION = 14
-_C.MODEL.ROI_KEYPOINT_HEAD.NUM_CLASSES = 17
+_C.MODEL.ROI_KEYPOINT_HEAD.NUM_CLASSES = 200
 _C.MODEL.ROI_KEYPOINT_HEAD.SHARE_BOX_FEATURE_EXTRACTOR = True
 
 
@@ -380,7 +380,7 @@
 _C.MODEL.RETINANET = CN()
 
 # This is the number of foreground classes and background.
-_C.MODEL.RETINANET.NUM_CLASSES = 81
+_C.MODEL.RETINANET.NUM_CLASSES = 200
 
 # Anchor aspect ratios to use
 _C.MODEL.RETINANET.ANCHOR_SIZES = (32, 64, 128, 256, 512)

maskrcnn_benchmark/config/paths_catalog.py

@@ -5,7 +5,7 @@
 
 
 class DatasetCatalog(object):
-    DATA_DIR = "datasets"
+    DATA_DIR = "/kaggle/working/training_dataset"
     DATASETS = {
         # CUSTOM STUFF START
         "cocopose_2014_debug": {
@@ -25,22 +25,27 @@ class DatasetCatalog(object):
             "ann_file": "/home/bot/Downloads/Rebin/labelled/coco_rebin.json"
         },
         # CUSTOM STUFF END
+
 
-        "coco_2017_train": {
-            "img_dir": "coco/train2017",
-            "ann_file": "coco/annotations/instances_train2017.json"
+        "smartflow_train": {
+            "img_dir": "train",
+            "ann_file": "train.json"
         },
-        "coco_2017_val": {
-            "img_dir": "coco/val2017",
-            "ann_file": "coco/annotations/instances_val2017.json"
+        "smartflow_val": {
+            "img_dir": "valid",
+            "ann_file": "valid.json"
+        },
+        "smartflow_test": {
+            "img_dir": "test",
+            "ann_file": "test.json"
         },
         "coco_2014_train": {
-            "img_dir": "coco/train2014",
-            "ann_file": "coco/annotations/instances_train2014.json"
+            "img_dir": "Images/train",
+            "ann_file": "Annotations/train.json"
         },
         "coco_2014_val": {
-            "img_dir": "coco/val2014",
-            "ann_file": "coco/annotations/instances_val2014.json"
+            "img_dir": "Images/test",
+            "ann_file": "Annotations/test.json"
         },
         "coco_2014_minival": {
             "img_dir": "coco/val2014",
@@ -150,6 +155,16 @@ def get(name):
                 factory="PascalVOCDataset",
                 args=args,
             )
+        elif "smartflow" in name:
+            data_dir = DatasetCatalog.DATA_DIR
+            attrs = DatasetCatalog.DATASETS[name]
+            args = dict(root=os.path.join(data_dir, attrs["img_dir"]),  # img_dir is the content of step a
+            ann_file=os.path.join(data_dir, attrs["ann_file"]),  # ann_file is the content of a step
+            )
+            return dict(
+                factory="COCODataset",  # MyDataset corresponds
+                args=args,
+            )
         raise RuntimeError("Dataset not available: {}".format(name))
 
 

maskrcnn_benchmark/csrc/cuda/deform_conv_cuda.cu

@@ -1,3 +1,8 @@
+#ifndef AT_CHECK
+#define AT_CHECK TORCH_CHECK 
+#endif
+
+
 // modify from
 // https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda.c
 

maskrcnn_benchmark/csrc/cuda/deform_pool_cuda.cu

@@ -1,3 +1,7 @@
+#ifndef AT_CHECK
+#define AT_CHECK TORCH_CHECK 
+#endif
+
 // modify from
 // https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/modulated_dcn_cuda.c
 

maskrcnn_benchmark/structures/segmentation_mask.py

@@ -398,7 +398,7 @@ def __getitem__(self, item):
         else:
             # advanced indexing on a single dimension
             selected_polygons = []
-            if isinstance(item, torch.Tensor) and item.dtype == torch.uint8:
+            if isinstance(item, torch.Tensor) and item.dtype == torch.bool:
                 item = item.nonzero()
                 item = item.squeeze(1) if item.numel() > 0 else item
                 item = item.tolist()
@@ -517,7 +517,7 @@ def convert_to_binarymask(self):
             )
         else:
             size = self.size
-            masks = torch.empty([0, size[1], size[0]], dtype=torch.uint8)
+            masks = torch.empty([0, size[1], size[0]], dtype=torch.bool)
 
         return BinaryMaskList(masks, size=self.size)
 
@@ -532,7 +532,7 @@ def __getitem__(self, item):
         else:
             # advanced indexing on a single dimension
             selected_polygons = []
-            if isinstance(item, torch.Tensor) and item.dtype == torch.uint8:
+            if isinstance(item, torch.Tensor) and item.dtype == torch.bool:
                 item = item.nonzero()
                 item = item.squeeze(1) if item.numel() > 0 else item
                 item = item.tolist()

maskrcnn_benchmark/utils/c2_model_loading.py

@@ -132,7 +132,7 @@ def _rename_weights_for_resnet(weights, stage_names):
 
 def _load_c2_pickled_weights(file_path):
     with open(file_path, "rb") as f:
-        if torch._six.PY3:
+        if torch._six.PY37:
             data = pickle.load(f, encoding="latin1")
         else:
             data = pickle.load(f)

maskrcnn_benchmark/utils/imports.py

@@ -1,7 +1,7 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
 import torch
 
-if torch._six.PY3:
+if torch._six.PY37:
     import importlib
     import importlib.util
     import sys