Apply Transformer to YOLO

test/test_models.py

@@ -2,6 +2,7 @@
 import torch
 
 from yolort.models.backbone_utils import darknet_pan_backbone
+from yolort.models.yolotr import darknet_pan_tr_backbone
 from yolort.models.anchor_utils import AnchorGenerator
 from yolort.models.box_head import YoloHead, PostProcess, SetCriterion
 
@@ -65,19 +66,61 @@ def _get_head_outputs(self, batch_size, h, w):
 
         return head_outputs
 
-    def _init_test_backbone_with_fpn(self):
+    def _init_test_backbone_with_pan_r3_1(self):
         backbone_name = 'darknet_s_r3_1'
         depth_multiple = 0.33
         width_multiple = 0.5
         backbone_with_fpn = darknet_pan_backbone(backbone_name, depth_multiple, width_multiple)
         return backbone_with_fpn
 
-    def test_backbone_with_fpn(self):
+    def test_backbone_with_pan_r3_1(self):
         N, H, W = 4, 416, 352
         out_shape = self._get_feature_shapes(H, W)
 
         x = torch.rand(N, 3, H, W)
-        model = self._init_test_backbone_with_fpn()
+        model = self._init_test_backbone_with_pan_r3_1()
+        out = model(x)
+
+        self.assertEqual(len(out), 3)
+        self.assertEqual(tuple(out[0].shape), (N, *out_shape[0]))
+        self.assertEqual(tuple(out[1].shape), (N, *out_shape[1]))
+        self.assertEqual(tuple(out[2].shape), (N, *out_shape[2]))
+        self.check_jit_scriptable(model, (x,))
+
+    def _init_test_backbone_with_pan_r4_0(self):
+        backbone_name = 'darknet_s_r4_0'
+        depth_multiple = 0.33
+        width_multiple = 0.5
+        backbone_with_fpn = darknet_pan_backbone(backbone_name, depth_multiple, width_multiple)
+        return backbone_with_fpn
+
+    def test_backbone_with_pan_r4_0(self):
+        N, H, W = 4, 416, 352
+        out_shape = self._get_feature_shapes(H, W)
+
+        x = torch.rand(N, 3, H, W)
+        model = self._init_test_backbone_with_pan_r4_0()
+        out = model(x)
+
+        self.assertEqual(len(out), 3)
+        self.assertEqual(tuple(out[0].shape), (N, *out_shape[0]))
+        self.assertEqual(tuple(out[1].shape), (N, *out_shape[1]))
+        self.assertEqual(tuple(out[2].shape), (N, *out_shape[2]))
+        self.check_jit_scriptable(model, (x,))
+
+    def _init_test_backbone_with_pan_tr(self):
+        backbone_name = 'darknet_s_r4_0'
+        depth_multiple = 0.33
+        width_multiple = 0.5
+        backbone_with_fpn_tr = darknet_pan_tr_backbone(backbone_name, depth_multiple, width_multiple)
+        return backbone_with_fpn_tr
+
+    def test_backbone_with_pan_tr(self):
+        N, H, W = 4, 416, 352
+        out_shape = self._get_feature_shapes(H, W)
+
+        x = torch.rand(N, 3, H, W)
+        model = self._init_test_backbone_with_pan_tr()
         out = model(x)
 
         self.assertEqual(len(out), 3)

test/test_onnx.py

@@ -10,7 +10,7 @@
 import unittest
 from torchvision.ops._register_onnx_ops import _onnx_opset_version
 
-from yolort.models import yolov5s, yolov5m
+from yolort.models import yolov5s, yolov5m, yolotr
 
 
 @unittest.skipIf(onnxruntime is None, 'ONNX Runtime unavailable')
@@ -135,6 +135,23 @@ def test_yolov5m_r40(self):
                        dynamic_axes={"images_tensors": [0, 1, 2], "outputs": [0, 1, 2]},
                        tolerate_small_mismatch=True)
 
+    def test_yolotr(self):
+        images_one, images_two = self.get_test_images()
+        images_dummy = [torch.ones(3, 100, 100) * 0.3]
+        model = yolotr(upstream_version='v4.0', export_friendly=True, pretrained=True)
+        model.eval()
+        model(images_one)
+        # Test exported model on images of different size, or dummy input
+        self.run_model(model, [(images_one,), (images_two,), (images_dummy,)], input_names=["images_tensors"],
+                       output_names=["outputs"],
+                       dynamic_axes={"images_tensors": [0, 1, 2], "outputs": [0, 1, 2]},
+                       tolerate_small_mismatch=True)
+        # Test exported model for an image with no detections on other images
+        self.run_model(model, [(images_dummy,), (images_one,)], input_names=["images_tensors"],
+                       output_names=["outputs"],
+                       dynamic_axes={"images_tensors": [0, 1, 2], "outputs": [0, 1, 2]},
+                       tolerate_small_mismatch=True)
+
 
 if __name__ == '__main__':
     unittest.main()

test/test_torchscript.py

@@ -2,7 +2,7 @@
 
 import torch
 
-from yolort.models import yolov5s, yolov5m, yolov5l
+from yolort.models import yolov5s, yolov5m, yolov5l, yolotr
 
 
 class TorchScriptTester(unittest.TestCase):
@@ -51,6 +51,20 @@ def test_yolov5l_script(self):
         self.assertTrue(out[0]["labels"].equal(out_script[0]["labels"]))
         self.assertTrue(out[0]["boxes"].equal(out_script[0]["boxes"]))
 
+    def test_yolotr_script(self):
+        model = yolotr(pretrained=True)
+        model.eval()
+
+        scripted_model = torch.jit.script(model)
+        scripted_model.eval()
+
+        x = [torch.rand(3, 416, 320), torch.rand(3, 480, 352)]
+
+        out = model(x)
+        out_script = scripted_model(x)
+        self.assertTrue(out[0]["scores"].equal(out_script[0]["scores"]))
+        self.assertTrue(out[0]["labels"].equal(out_script[0]["labels"]))
+        self.assertTrue(out[0]["boxes"].equal(out_script[0]["boxes"]))
 
 if __name__ == "__main__":
     unittest.main()

yolort/models/__init__.py

@@ -9,7 +9,7 @@
 from typing import Any
 
 
-def yolov5s(upstream_version: str ='v3.1', export_friendly: bool = False, **kwargs: Any):
+def yolov5s(upstream_version: str = 'v3.1', export_friendly: bool = False, **kwargs: Any):
     """
     Args:
         upstream_version (str): Determine the upstream YOLOv5 version.
@@ -28,7 +28,7 @@ def yolov5s(upstream_version: str ='v3.1', export_friendly: bool = False, **kwar
     return model
 
 
-def yolov5m(upstream_version: str ='v3.1', export_friendly: bool = False, **kwargs: Any):
+def yolov5m(upstream_version: str = 'v3.1', export_friendly: bool = False, **kwargs: Any):
     """
     Args:
         upstream_version (str): Determine the upstream YOLOv5 version.
@@ -47,7 +47,7 @@ def yolov5m(upstream_version: str ='v3.1', export_friendly: bool = False, **kwar
     return model
 
 
-def yolov5l(upstream_version: str ='v3.1', export_friendly: bool = False, **kwargs: Any):
+def yolov5l(upstream_version: str = 'v3.1', export_friendly: bool = False, **kwargs: Any):
     """
     Args:
         upstream_version (str): Determine the upstream YOLOv5 version.
@@ -66,6 +66,23 @@ def yolov5l(upstream_version: str ='v3.1', export_friendly: bool = False, **kwar
     return model
 
 
+def yolotr(upstream_version: str = 'v4.0', export_friendly: bool = False, **kwargs: Any):
+    """
+    Args:
+        upstream_version (str): Determine the upstream YOLOv5 version.
+        export_friendly (bool): Deciding whether to use (ONNX/TVM) export friendly mode.
+    """
+    if upstream_version == 'v4.0':
+        model = YOLOModule(arch="yolov5_darknet_pan_s_tr", **kwargs)
+    else:
+        raise NotImplementedError("Currently only supports v4.0 versions")
+
+    if export_friendly:
+        _export_module_friendly(model)
+
+    return model
+
+
 def _export_module_friendly(model):
     for m in model.modules():
         m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility

yolort/models/backbone_utils.py

@@ -4,7 +4,6 @@
 
 from . import darknet
 from .path_aggregation_network import PathAggregationNetwork
-from .common import BottleneckCSP, C3
 
 from typing import List, Optional
 
@@ -53,7 +52,7 @@ def darknet_pan_backbone(
     version: str = 'v4.0',
 ):
     """
-    Constructs a specified ResNet backbone with PAN on top. Freezes the specified number of
+    Constructs a specified DarkNet backbone with PAN on top. Freezes the specified number of
     layers in the backbone.
 
     Examples::
@@ -71,12 +70,12 @@ def darknet_pan_backbone(
         >>>    ('2', torch.Size([1, 512, 2, 2]))]
 
     Args:
-        backbone_name (string): resnet architecture. Possible values are 'DarkNet', 'darknet_s_r3_1',
+        backbone_name (string): darknet architecture. Possible values are 'DarkNet', 'darknet_s_r3_1',
            'darknet_m_r3_1', 'darknet_l_r3_1', 'darknet_s_r4_0', 'darknet_m_r4_0', 'darknet_l_r4_0'
         norm_layer (torchvision.ops): it is recommended to use the default value. For details visit:
             (https://github.com/facebookresearch/maskrcnn-benchmark/issues/267)
         pretrained (bool): If True, returns a model with backbone pre-trained on Imagenet
-        trainable_layers (int): number of trainable (not frozen) resnet layers starting from final block.
+        trainable_layers (int): number of trainable (not frozen) darknet layers starting from final block.
             Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable.
         version (str): ultralytics release version: v3.1 or v4.0
     """

yolort/models/experimental.py

@@ -5,7 +5,7 @@
 import torch
 import torch.nn as nn
 
-from ..models.common import Conv, DWConv
+from .common import Conv, DWConv
 
 
 class CrossConv(nn.Module):

yolort/models/yolo.py

@@ -1,5 +1,4 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-# Modified by Zhiqiang Wang (me@zhiqwang.com)
+# Copyright (c) 2020, Zhiqiang Wang. All Rights Reserved.
 import warnings
 
 import torch
@@ -8,13 +7,15 @@
 from torchvision.models.utils import load_state_dict_from_url
 
 from .backbone_utils import darknet_pan_backbone
+from .yolotr import darknet_pan_tr_backbone
 from .anchor_utils import AnchorGenerator
 from .box_head import YoloHead, SetCriterion, PostProcess
 
 from typing import Tuple, Any, List, Dict, Optional
 
 __all__ = ['YOLO', 'yolov5_darknet_pan_s_r31', 'yolov5_darknet_pan_m_r31', 'yolov5_darknet_pan_l_r31',
-           'yolov5_darknet_pan_s_r40', 'yolov5_darknet_pan_m_r40', 'yolov5_darknet_pan_l_r40']
+           'yolov5_darknet_pan_s_r40', 'yolov5_darknet_pan_m_r40', 'yolov5_darknet_pan_l_r40',
+           'yolov5_darknet_pan_s_tr']
 
 
 class YOLO(nn.Module):
@@ -133,6 +134,7 @@ def forward(
     'yolov5_darknet_pan_s_r40_coco': f'{model_urls_root}/yolov5_darknet_pan_s_r40_coco-e3fd213d.pt',
     'yolov5_darknet_pan_m_r40_coco': f'{model_urls_root}/yolov5_darknet_pan_m_r40_coco-d295cb02.pt',
     'yolov5_darknet_pan_l_r40_coco': f'{model_urls_root}/yolov5_darknet_pan_l_r40_coco-4416841f.pt',
+    'yolov5_darknet_pan_s_tr_coco': f'{model_urls_root}/yolov5_darknet_pan_s_tr_coco-f09f21f7.pt',
 }
 
 
@@ -299,3 +301,33 @@ def yolov5_darknet_pan_l_r40(pretrained: bool = False, progress: bool = True, nu
     version = 'v4.0'
     return _yolov5_darknet_pan(backbone_name, depth_multiple, width_multiple, version, weights_name,
                                pretrained=pretrained, progress=progress, num_classes=num_classes, **kwargs)
+
+
+def yolov5_darknet_pan_s_tr(pretrained: bool = False, progress: bool = True, num_classes: int = 80,
+                            **kwargs: Any) -> YOLO:
+    r"""yolov5 small with a transformer block model from
+    `"dingyiwei/yolov5" <https://github.com/ultralytics/yolov5/pull/2333>`_.
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    backbone_name = 'darknet_s_r4_0'
+    weights_name = 'yolov5_darknet_pan_s_tr_coco'
+    depth_multiple = 0.33
+    width_multiple = 0.5
+    version = 'v4.0'
+
+    backbone = darknet_pan_tr_backbone(backbone_name, depth_multiple, width_multiple, version=version)
+
+    anchor_grids = [[10, 13, 16, 30, 33, 23],
+                    [30, 61, 62, 45, 59, 119],
+                    [116, 90, 156, 198, 373, 326]]
+
+    model = YOLO(backbone, num_classes, anchor_grids, **kwargs)
+    if pretrained:
+        if model_urls.get(weights_name, None) is None:
+            raise ValueError(f"No checkpoint is available for model {weights_name}")
+        state_dict = load_state_dict_from_url(model_urls[weights_name], progress=progress)
+        model.load_state_dict(state_dict)
+
+    return model