Revision models.detection.yolo

pl_bolts/models/detection/yolo/yolo_config.py

@@ -6,10 +6,8 @@
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
 from pl_bolts.models.detection.yolo import yolo_layers
-from pl_bolts.utils.stability import under_review
 
 
-@under_review()
 class YOLOConfiguration:
     """This class can be used to parse the configuration files of the Darknet YOLOv4 implementation.
 
@@ -149,7 +147,6 @@ def convert(key, value):
         return sections
 
 
-@under_review()
 def _create_layer(config: dict, num_inputs: List[int]) -> Tuple[nn.Module, int]:
     """Calls one of the ``_create_<layertype>(config, num_inputs)`` functions to create a PyTorch module from the
     layer config.
@@ -173,8 +170,7 @@ def _create_layer(config: dict, num_inputs: List[int]) -> Tuple[nn.Module, int]:
     return create_func[config["type"]](config, num_inputs)
 
 
-@under_review()
-def _create_convolutional(config, num_inputs):
+def _create_convolutional(config: dict, num_inputs: int) -> Tuple[nn.Module, int]:
     module = nn.Sequential()
 
     batch_normalize = config.get("batch_normalize", False)
@@ -210,15 +206,13 @@ def _create_convolutional(config, num_inputs):
     return module, config["filters"]
 
 
-@under_review()
-def _create_maxpool(config, num_inputs):
+def _create_maxpool(config: dict, num_inputs: int) -> Tuple[nn.Module, int]:
     padding = (config["size"] - 1) // 2
     module = nn.MaxPool2d(config["size"], config["stride"], padding)
     return module, num_inputs[-1]
 
 
-@under_review()
-def _create_route(config, num_inputs):
+def _create_route(config: dict, num_inputs: int) -> Tuple[nn.Module, int]:
     num_chunks = config.get("groups", 1)
     chunk_idx = config.get("group_id", 0)
 
@@ -234,20 +228,17 @@ def _create_route(config, num_inputs):
     return module, num_outputs
 
 
-@under_review()
-def _create_shortcut(config, num_inputs):
+def _create_shortcut(config: dict, num_inputs: int) -> Tuple[nn.Module, int]:
     module = yolo_layers.ShortcutLayer(config["from"])
     return module, num_inputs[-1]
 
 
-@under_review()
-def _create_upsample(config, num_inputs):
+def _create_upsample(config: dict, num_inputs: int) -> Tuple[nn.Module, int]:
     module = nn.Upsample(scale_factor=config["stride"], mode="nearest")
     return module, num_inputs[-1]
 
 
-@under_review()
-def _create_yolo(config, num_inputs):
+def _create_yolo(config: dict, num_inputs: int) -> Tuple[nn.Module, int]:
     # The "anchors" list alternates width and height.
     anchor_dims = config["anchors"]
     anchor_dims = [(anchor_dims[i], anchor_dims[i + 1]) for i in range(0, len(anchor_dims), 2)]
@@ -264,8 +255,10 @@ def _create_yolo(config, num_inputs):
         overlap_loss_func = yolo_layers.SELoss()
     elif overlap_loss_name == "giou":
         overlap_loss_func = yolo_layers.GIoULoss()
-    else:
+    elif overlap_loss_name == "iou":
         overlap_loss_func = yolo_layers.IoULoss()
+    else:
+        assert False, "Unknown overlap loss: " + overlap_loss_name
 
     module = yolo_layers.DetectionLayer(
         num_classes=config["classes"],

pl_bolts/models/detection/yolo/yolo_layers.py

@@ -1,11 +1,10 @@
-from typing import Callable, Dict, List, Optional, Tuple
+from typing import Callable, Dict, List, Optional, Tuple, Union
 
 import torch
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from torch import Tensor, nn
 
-from pl_bolts.utils import _TORCHVISION_AVAILABLE
-from pl_bolts.utils.stability import under_review
+from pl_bolts.utils import _TORCH_MESHGRID_REQUIRES_INDEXING, _TORCHVISION_AVAILABLE
 from pl_bolts.utils.warnings import warn_missing_pkg
 
 if _TORCHVISION_AVAILABLE:
@@ -21,7 +20,6 @@
     warn_missing_pkg("torchvision")
 
 
-@under_review()
 def _corner_coordinates(xy: Tensor, wh: Tensor) -> Tensor:
     """Converts box center points and sizes to corner coordinates.
 
@@ -38,7 +36,6 @@ def _corner_coordinates(xy: Tensor, wh: Tensor) -> Tensor:
     return torch.cat((top_left, bottom_right), -1)
 
 
-@under_review()
 def _aligned_iou(dims1: Tensor, dims2: Tensor) -> Tensor:
     """Calculates a matrix of intersections over union from box dimensions, assuming that the boxes are located at
     the same coordinates.
@@ -61,7 +58,6 @@ def _aligned_iou(dims1: Tensor, dims2: Tensor) -> Tensor:
     return inter / union
 
 
-@under_review()
 class SELoss(nn.MSELoss):
     def __init__(self):
         super().__init__(reduction="none")
@@ -70,13 +66,11 @@ def forward(self, inputs: Tensor, target: Tensor) -> Tensor:
         return super().forward(inputs, target).sum(1)
 
 
-@under_review()
 class IoULoss(nn.Module):
     def forward(self, inputs: Tensor, target: Tensor) -> Tensor:
         return 1.0 - box_iou(inputs, target).diagonal()
 
 
-@under_review()
 class GIoULoss(nn.Module):
     def __init__(self) -> None:
         super().__init__()
@@ -89,7 +83,6 @@ def forward(self, inputs: Tensor, target: Tensor) -> Tensor:
         return 1.0 - generalized_box_iou(inputs, target).diagonal()
 
 
-@under_review()
 class DetectionLayer(nn.Module):
     """A YOLO detection layer.
 
@@ -263,7 +256,10 @@ def _global_xy(self, xy: Tensor, image_size: Tensor) -> Tensor:
 
         x_range = torch.arange(width, device=xy.device)
         y_range = torch.arange(height, device=xy.device)
-        grid_y, grid_x = torch.meshgrid(y_range, x_range)
+        if _TORCH_MESHGRID_REQUIRES_INDEXING:
+            grid_y, grid_x = torch.meshgrid(y_range, x_range, indexing="ij")
+        else:
+            grid_y, grid_x = torch.meshgrid(y_range, x_range)
         offset = torch.stack((grid_x, grid_y), -1)  # [height, width, 2]
         offset = offset.unsqueeze(2)  # [height, width, 1, 2]
 
@@ -468,15 +464,13 @@ def _calculate_losses(
         return losses, hits
 
 
-@under_review()
 class Mish(nn.Module):
     """Mish activation."""
 
-    def forward(self, x):
+    def forward(self, x: Tensor) -> Tensor:
         return x * torch.tanh(nn.functional.softplus(x))
 
 
-@under_review()
 class RouteLayer(nn.Module):
     """Route layer concatenates the output (or part of it) from given layers."""
 
@@ -492,12 +486,11 @@ def __init__(self, source_layers: List[int], num_chunks: int, chunk_idx: int) ->
         self.num_chunks = num_chunks
         self.chunk_idx = chunk_idx
 
-    def forward(self, x, outputs):
+    def forward(self, x, outputs: List[Union[Tensor, None]]) -> Tensor:
         chunks = [torch.chunk(outputs[layer], self.num_chunks, dim=1)[self.chunk_idx] for layer in self.source_layers]
         return torch.cat(chunks, dim=1)
 
 
-@under_review()
 class ShortcutLayer(nn.Module):
     """Shortcut layer adds a residual connection from the source layer."""
 
@@ -510,5 +503,5 @@ def __init__(self, source_layer: int) -> None:
         super().__init__()
         self.source_layer = source_layer
 
-    def forward(self, x, outputs):
+    def forward(self, x, outputs: List[Union[Tensor, None]]) -> Tensor:
         return outputs[-1] + outputs[self.source_layer]

pl_bolts/models/detection/yolo/yolo_module.py

@@ -11,7 +11,6 @@
 from pl_bolts.models.detection.yolo.yolo_layers import DetectionLayer, RouteLayer, ShortcutLayer
 from pl_bolts.optimizers.lr_scheduler import LinearWarmupCosineAnnealingLR
 from pl_bolts.utils import _TORCHVISION_AVAILABLE
-from pl_bolts.utils.stability import under_review
 from pl_bolts.utils.warnings import warn_missing_pkg
 
 if _TORCHVISION_AVAILABLE:
@@ -23,7 +22,6 @@
 log = logging.getLogger(__name__)
 
 
-@under_review()
 class YOLO(LightningModule):
     """PyTorch Lightning implementation of YOLOv3 and YOLOv4.
 
@@ -179,7 +177,7 @@ def forward(
             )
         for layer_idx, layer_hits in enumerate(hits):
             hit_rate = torch.true_divide(layer_hits, total_hits) if total_hits > 0 else 1.0
-            self.log(f"layer_{layer_idx}_hit_rate", hit_rate, sync_dist=False)
+            self.log(f"layer_{layer_idx}_hit_rate", hit_rate, sync_dist=False, batch_size=images.size(0))
 
         def total_loss(loss_name):
             """Returns the sum of the loss over detection layers."""
@@ -233,8 +231,8 @@ def validation_step(self, batch: Tuple[List[Tensor], List[Dict[str, Tensor]]], b
         total_loss = torch.stack(tuple(losses.values())).sum()
 
         for name, value in losses.items():
-            self.log(f"val/{name}_loss", value, sync_dist=True)
-        self.log("val/total_loss", total_loss, sync_dist=True)
+            self.log(f"val/{name}_loss", value, sync_dist=True, batch_size=images.size(0))
+        self.log("val/total_loss", total_loss, sync_dist=True, batch_size=images.size(0))
 
     def test_step(self, batch: Tuple[List[Tensor], List[Dict[str, Tensor]]], batch_idx: int):
         """Evaluates a batch of data from the test set.
@@ -455,7 +453,6 @@ def _filter_detections(self, detections: Dict[str, Tensor]) -> Dict[str, List[Te
         return {"boxes": out_boxes, "scores": out_scores, "classprobs": out_classprobs, "labels": out_labels}
 
 
-@under_review()
 class Resize:
     """Rescales the image and target to given dimensions.
 
@@ -486,7 +483,6 @@ def __call__(self, image: Tensor, target: Dict[str, Any]):
         return image, target
 
 
-@under_review()
 def run_cli():
     from argparse import ArgumentParser
 

pl_bolts/utils/__init__.py

@@ -6,7 +6,6 @@
 from pl_bolts.callbacks.verification.batch_gradient import BatchGradientVerification  # type: ignore
 
 _NATIVE_AMP_AVAILABLE: bool = module_available("torch.cuda.amp") and hasattr(torch.cuda.amp, "autocast")
-
 _TORCHVISION_AVAILABLE: bool = module_available("torchvision")
 _GYM_AVAILABLE: bool = module_available("gym")
 _SKLEARN_AVAILABLE: bool = module_available("sklearn")
@@ -20,6 +19,7 @@
 _PL_GREATER_EQUAL_1_4_5 = compare_version("pytorch_lightning", operator.ge, "1.4.5")
 _TORCH_ORT_AVAILABLE = module_available("torch_ort")
 _TORCH_MAX_VERSION_SPARSEML = compare_version("torch", operator.lt, "1.11.0")
+_TORCH_MESHGRID_REQUIRES_INDEXING = compare_version("torch", operator.ge, "1.10.0")
 _SPARSEML_AVAILABLE = module_available("sparseml") and _PL_GREATER_EQUAL_1_4_5 and _TORCH_MAX_VERSION_SPARSEML
 
 __all__ = ["BatchGradientVerification"]

tests/data/yolo_giou.cfg

@@ -0,0 +1,81 @@
+[net]
+width=256
+height=256
+channels=3
+
+[convolutional]
+batch_normalize=1
+filters=8
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers=-1
+groups=2
+group_id=1
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=2
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=4
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=14
+activation=linear
+
+[yolo]
+mask=2,3
+anchors=1,2, 3,4, 5,6, 9,10
+classes=2
+iou_loss=giou
+scale_x_y=1.05
+cls_normalizer=1.0
+iou_normalizer=0.07
+ignore_thresh=0.7
+
+[route]
+layers = -4
+
+[upsample]
+stride=2
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=14
+activation=linear
+
+[yolo]
+mask=0,1
+anchors=1,2, 3,4, 5,6, 9,10
+classes=2
+iou_loss=giou
+scale_x_y=1.05
+cls_normalizer=1.0
+iou_normalizer=0.07
+ignore_thresh=0.7