Add AdvProp-AA weights ported from TF impl

Author: rwightman

README.md

@@ -16,6 +16,9 @@ Aside from the default model configs, there is a lot of flexibility to facilitat
 
 ## Updates
 
+### 2021-04-30
+* Add EfficientDet AdvProp-AA weights for D0-D5 from https://github.com/google/automl/blob/master/efficientdet/Det-AdvProp.md
+
 ### 2021-02-18
 * Add some new model weights with bilinear interpolation for upsample and downsample in FPN.
   * 40.9 mAP - `efficientdet_q1`  (replace prev model at 40.6)
@@ -114,25 +117,31 @@ The table below contains models with pretrained weights. There are quite a numbe
 
 | Variant | mAP (val2017) | mAP (test-dev2017) | mAP (TF official val2017) | mAP (TF official test-dev2017) | Params (M) |
 | --- | :---: | :---: | :---: | :---: | :---: |
-| tf_efficientdet_lite0.pth | 32.0 | TBD | N/A | N/A | 3.24 |
-| efficientdet_d0.pth | 33.6 | TBD | 33.5 | 33.8 | 3.88 |
-| tf_efficientdet_d0.pth | 34.2 | TBD | 34.3 | 34.6 | 3.88 |
-| efficientdet_q0.pth | 35.7 | TBD | N/A | N/A | 4.13 |
-| efficientdet_d1.pth | 39.4 | 39.5 | 39.1 | 39.6 | 6.62 |
-| tf_efficientdet_d1.pth | 40.1 | TBD | 40.2 | 40.5 | 6.63 |
-| efficientdet_q1.pth | 40.9 | TBD | N/A | N/A | 6.98 |
+| tf_efficientdet_lite0 | 32.0 | TBD | N/A | N/A | 3.24 |
+| efficientdet_d0 | 33.6 | TBD | 33.5 | 33.8 | 3.88 |
+| tf_efficientdet_d0 | 34.2 | TBD | 34.3 | 34.6 | 3.88 |
+| tf_efficientdet_d0_ap | 34.8 | TBD | 35.2 | 35.3 | 3.88 |
+| efficientdet_q0 | 35.7 | TBD | N/A | N/A | 4.13 |
+| efficientdet_d1 | 39.4 | 39.5 | 39.1 | 39.6 | 6.62 |
+| tf_efficientdet_d1 | 40.1 | TBD | 40.2 | 40.5 | 6.63 |
+| tf_efficientdet_d1_ap | 40.8 | TBD | 40.9 | 40.8 | 6.63 |
+| efficientdet_q1 | 40.9 | TBD | N/A | N/A | 6.98 |
 | cspresdext50pan | 41.2 | TBD | N/A | N/A | 22.2 |
 | resdet50 | 41.6 | TBD | N/A | N/A | 27.6 |
-| efficientdet_q2.pth | 43.1 | TBD | N/A | N/A | 8.81 |
+| efficientdet_q2 | 43.1 | TBD | N/A | N/A | 8.81 |
 | cspresdet50 | 43.2 | TBD | N/A | N/A | 24.3 |
-| tf_efficientdet_d2.pth | 43.4 | TBD | 42.5 | 43 | 8.10 |
+| tf_efficientdet_d2 | 43.4 | TBD | 42.5 | 43 | 8.10 |
+| tf_efficientdet_d2_ap | 44.2 | TBD | 44.3 | 44.3 | 8.10 |
 | cspdarkdet53m | 45.2 | TBD | N/A | N/A | 35.6 |
-| tf_efficientdet_d3.pth | 47.1 | TBD | 47.2 | 47.5 | 12.0 |
-| tf_efficientdet_d4.pth | 49.2 | TBD | 49.3 | 49.7 | 20.7 |
-| tf_efficientdet_d5.pth | 51.2 | TBD | 51.2 | 51.5 | 33.7 |
-| tf_efficientdet_d6.pth | 52.0 | TBD | 52.1 | 52.6 | 51.9 |
-| tf_efficientdet_d7.pth | 53.1 | 53.4 | 53.4 | 53.7 | 51.9 |
-| tf_efficientdet_d7x.pth | 54.3 | TBD | 54.4 | 55.1 | 77.1 |
+| tf_efficientdet_d3 | 47.1 | TBD | 47.2 | 47.5 | 12.0 |
+| tf_efficientdet_d3_ap | 47.7 | TBD | 48.0 | 47.7 | 12.0 |
+| tf_efficientdet_d4 | 49.2 | TBD | 49.3 | 49.7 | 20.7 |
+| tf_efficientdet_d4_ap | 50.2 | TBD | 50.4 | 50.4 | 20.7 |
+| tf_efficientdet_d5 | 51.2 | TBD | 51.2 | 51.5 | 33.7 |
+| tf_efficientdet_d6 | 52.0 | TBD | 52.1 | 52.6 | 51.9 |
+| tf_efficientdet_d5_ap | 52.1 | TBD | 52.2 | 52.5 | 33.7 |
+| tf_efficientdet_d7 | 53.1 | 53.4 | 53.4 | 53.7 | 51.9 |
+| tf_efficientdet_d7x | 54.3 | TBD | 54.4 | 55.1 | 77.1 |
 
 
 See [model configurations](effdet/config/model_config.py) for model checkpoint urls and differences.

effdet/config/model_config.py

@@ -532,6 +532,87 @@ def default_detection_model_configs():
         url='https://github.com/rwightman/efficientdet-pytorch/releases/download/v0.1/tf_efficientdet_d7x-f390b87c.pth'
     ),
 
+    #  Models ported from Tensorflow AdvProp+AA weights
+    #  https://github.com/google/automl/blob/master/efficientdet/Det-AdvProp.md
+    tf_efficientdet_d0_ap=dict(
+        name='tf_efficientdet_d0_ap',
+        backbone_name='tf_efficientnet_b0',
+        image_size=(512, 512),
+        fpn_channels=64,
+        fpn_cell_repeats=3,
+        box_class_repeats=3,
+        mean=(0.5, 0.5, 0.5),
+        std=(0.5, 0.5, 0.5),
+        fill_color=0,
+        backbone_args=dict(drop_path_rate=0.2),
+        url='https://github.com/rwightman/efficientdet-pytorch/releases/download/v0.1/tf_efficientdet_d0_ap-d0cdbd0a.pth',
+    ),
+    tf_efficientdet_d1_ap=dict(
+        name='tf_efficientdet_d1_ap',
+        backbone_name='tf_efficientnet_b1',
+        image_size=(640, 640),
+        fpn_channels=88,
+        fpn_cell_repeats=4,
+        box_class_repeats=3,
+        mean=(0.5, 0.5, 0.5),
+        std=(0.5, 0.5, 0.5),
+        fill_color=0,
+        backbone_args=dict(drop_path_rate=0.2),
+        url='https://github.com/rwightman/efficientdet-pytorch/releases/download/v0.1/tf_efficientdet_d1_ap-7721d075.pth'
+    ),
+    tf_efficientdet_d2_ap=dict(
+        name='tf_efficientdet_d2_ap',
+        backbone_name='tf_efficientnet_b2',
+        image_size=(768, 768),
+        fpn_channels=112,
+        fpn_cell_repeats=5,
+        box_class_repeats=3,
+        mean=(0.5, 0.5, 0.5),
+        std=(0.5, 0.5, 0.5),
+        fill_color=0,
+        backbone_args=dict(drop_path_rate=0.2),
+        url='https://github.com/rwightman/efficientdet-pytorch/releases/download/v0.1/tf_efficientdet_d2_ap-a2995c19.pth',
+    ),
+    tf_efficientdet_d3_ap=dict(
+        name='tf_efficientdet_d3_ap',
+        backbone_name='tf_efficientnet_b3',
+        image_size=(896, 896),
+        fpn_channels=160,
+        fpn_cell_repeats=6,
+        box_class_repeats=4,
+        mean=(0.5, 0.5, 0.5),
+        std=(0.5, 0.5, 0.5),
+        fill_color=0,
+        backbone_args=dict(drop_path_rate=0.2),
+        url='https://github.com/rwightman/efficientdet-pytorch/releases/download/v0.1/tf_efficientdet_d3_ap-e4a2feab.pth',
+    ),
+    tf_efficientdet_d4_ap=dict(
+        name='tf_efficientdet_d4_ap',
+        backbone_name='tf_efficientnet_b4',
+        image_size=(1024, 1024),
+        fpn_channels=224,
+        fpn_cell_repeats=7,
+        box_class_repeats=4,
+        mean=(0.5, 0.5, 0.5),
+        std=(0.5, 0.5, 0.5),
+        fill_color=0,
+        backbone_args=dict(drop_path_rate=0.2),
+        url='https://github.com/rwightman/efficientdet-pytorch/releases/download/v0.1/tf_efficientdet_d4_ap-f601a5fc.pth',
+    ),
+    tf_efficientdet_d5_ap=dict(
+        name='tf_efficientdet_d5_ap',
+        backbone_name='tf_efficientnet_b5',
+        image_size=(1280, 1280),
+        fpn_channels=288,
+        fpn_cell_repeats=7,
+        box_class_repeats=4,
+        mean=(0.5, 0.5, 0.5),
+        std=(0.5, 0.5, 0.5),
+        fill_color=0,
+        backbone_args=dict(drop_path_rate=0.2),
+        url='https://github.com/rwightman/efficientdet-pytorch/releases/download/v0.1/tf_efficientdet_d5_ap-3673ae5d.pth',
+    ),
+
     # The lite configs are in TF automl repository but no weights yet and listed as 'not final'
     tf_efficientdet_lite0=dict(
         name='tf_efficientdet_lite0',

effdet/factory.py

@@ -23,7 +23,8 @@ def create_model_from_config(
         pretrained_backbone = False  # no point in loading backbone weights
 
     # Config overrides, override some config values via kwargs.
-    overrides = ('redundant_bias', 'label_smoothing', 'legacy_focal', 'jit_loss', 'soft_nms', 'max_det_per_image')
+    overrides = (
+        'redundant_bias', 'label_smoothing', 'legacy_focal', 'jit_loss', 'soft_nms', 'max_det_per_image', 'image_size')
     for ov in overrides:
         value = kwargs.pop(ov, None)
         if value is not None:

effdet/version.py

@@ -1 +1 @@
-__version__ = '0.2.3'
+__version__ = '0.2.4'

validate.py

@@ -107,6 +107,9 @@ def validate(args):
 
     # create model
     with set_layer_config(scriptable=args.torchscript):
+        extra_args = {}
+        if args.img_size is not None:
+            extra_args = dict(image_size=(args.img_size, args.img_size))
         bench = create_model(
             args.model,
             bench_task='predict',
@@ -116,6 +119,7 @@ def validate(args):
             soft_nms=args.soft_nms,
             checkpoint_path=args.checkpoint,
             checkpoint_ema=args.use_ema,
+            **extra_args,
         )
     model_config = bench.config