[fbsync] Adding FLOPs and size to model metadata (#6936)

Summary:
* Adding FLOPs and size to model metadata

* Adding weight size to quantization models

* Small refactor of rich metadata

* Removing unused code

* Fixing wrong entries

* Adding .DS_Store to gitignore

* Renaming _flops to _ops

* Adding number of operations to quantization models

* Reflecting _flops change to _ops

* Renamed ops and weight size in individual model doc pages

* Linter fixes

* Rounding ops to first decimal

* Rounding num ops and sizes to 3 decimals

* Change naming of columns.

* Update tables

Reviewed By: NicolasHug

Differential Revision: D41265180

fbshipit-source-id: e6f8629ba3f2177411716113430b87c1710982c0

Co-authored-by: Toni Blaslov <tblaslov@fb.com>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

docs/source/conf.py

@@ -362,6 +362,14 @@ def inject_weight_metadata(app, what, name, obj, options, lines):
                     max_visible = 3
                     v_sample = ", ".join(v[:max_visible])
                     v = f"{v_sample}, ... ({len(v)-max_visible} omitted)" if len(v) > max_visible else v_sample
+                elif k == "_ops":
+                    if obj.__name__.endswith("_QuantizedWeights"):
+                        v = f"{v} giga instructions per sec"
+                    else:
+                        v = f"{v} giga floating-point operations per sec"
+                elif k == "_weight_size":
+                    v = f"{v} MB (file size)"
+
                 table.append((str(k), str(v)))
             table = tabulate(table, tablefmt="rst")
             lines += [".. rst-class:: table-weights"]  # Custom CSS class, see custom_torchvision.css
@@ -385,27 +393,38 @@ def generate_weights_table(module, table_name, metrics, dataset, include_pattern
     if exclude_patterns is not None:
         weights = [w for w in weights if all(p not in str(w) for p in exclude_patterns)]
 
+    ops_name = "GIPS" if "QuantizedWeights" in weights_endswith else "GFLOPS"
+
     metrics_keys, metrics_names = zip(*metrics)
-    column_names = ["Weight"] + list(metrics_names) + ["Params", "Recipe"]
+    column_names = (
+        ["Weight"] + list(metrics_names) + ["Params"] + [ops_name, "Size (MB)", "Recipe"]
+    )  # Final column order
     column_names = [f"**{name}**" for name in column_names]  # Add bold
 
-    content = [
-        (
+    content = []
+    for w in weights:
+        row = [
             f":class:`{w} <{type(w).__name__}>`",
             *(w.meta["_metrics"][dataset][metric] for metric in metrics_keys),
             f"{w.meta['num_params']/1e6:.1f}M",
+            f"{w.meta['_ops']:.3f}",
+            f"{round(w.meta['_weight_size'], 1):.1f}",
             f"`link <{w.meta['recipe']}>`__",
-        )
-        for w in weights
-    ]
+        ]
+
+        content.append(row)
+
+    column_widths = ["110"] + ["18"] * len(metrics_names) + ["18"] * 3 + ["10"]
+    widths_table = " ".join(column_widths)
+
     table = tabulate(content, headers=column_names, tablefmt="rst")
 
     generated_dir = Path("generated")
     generated_dir.mkdir(exist_ok=True)
     with open(generated_dir / f"{table_name}_table.rst", "w+") as table_file:
         table_file.write(".. rst-class:: table-weights\n")  # Custom CSS class, see custom_torchvision.css
         table_file.write(".. table::\n")
-        table_file.write(f"    :widths: 100 {'20 ' * len(metrics_names)} 20 10\n\n")
+        table_file.write(f"    :widths: {widths_table} \n\n")
         table_file.write(f"{textwrap.indent(table, ' ' * 4)}\n\n")
 
 

test/test_extended_models.py

@@ -155,11 +155,13 @@ def test_schema_meta_validation(model_fn):
         "recipe",
         "unquantized",
         "_docs",
+        "_ops",
+        "_weight_size",
     }
     # mandatory fields for each computer vision task
     classification_fields = {"categories", ("_metrics", "ImageNet-1K", "acc@1"), ("_metrics", "ImageNet-1K", "acc@5")}
     defaults = {
-        "all": {"_metrics", "min_size", "num_params", "recipe", "_docs"},
+        "all": {"_metrics", "min_size", "num_params", "recipe", "_docs", "_weight_size", "_ops"},
         "models": classification_fields,
         "detection": {"categories", ("_metrics", "COCO-val2017", "box_map")},
         "quantization": classification_fields | {"backend", "unquantized"},

torchvision/models/alexnet.py

@@ -67,6 +67,8 @@ class AlexNet_Weights(WeightsEnum):
                     "acc@5": 79.066,
                 }
             },
+            "_ops": 0.714,
+            "_weight_size": 233.087,
             "_docs": """
                 These weights reproduce closely the results of the paper using a simplified training recipe.
             """,

torchvision/models/convnext.py

@@ -219,6 +219,8 @@ class ConvNeXt_Tiny_Weights(WeightsEnum):
                     "acc@5": 96.146,
                 }
             },
+            "_ops": 4.456,
+            "_weight_size": 109.119,
         },
     )
     DEFAULT = IMAGENET1K_V1
@@ -237,6 +239,8 @@ class ConvNeXt_Small_Weights(WeightsEnum):
                     "acc@5": 96.650,
                 }
             },
+            "_ops": 8.684,
+            "_weight_size": 191.703,
         },
     )
     DEFAULT = IMAGENET1K_V1
@@ -255,6 +259,8 @@ class ConvNeXt_Base_Weights(WeightsEnum):
                     "acc@5": 96.870,
                 }
             },
+            "_ops": 15.355,
+            "_weight_size": 338.064,
         },
     )
     DEFAULT = IMAGENET1K_V1
@@ -273,6 +279,8 @@ class ConvNeXt_Large_Weights(WeightsEnum):
                     "acc@5": 96.976,
                 }
             },
+            "_ops": 34.361,
+            "_weight_size": 754.537,
         },
     )
     DEFAULT = IMAGENET1K_V1

torchvision/models/densenet.py

@@ -15,7 +15,6 @@
 from ._meta import _IMAGENET_CATEGORIES
 from ._utils import _ovewrite_named_param, handle_legacy_interface
 
-
 __all__ = [
     "DenseNet",
     "DenseNet121_Weights",
@@ -278,6 +277,8 @@ class DenseNet121_Weights(WeightsEnum):
                     "acc@5": 91.972,
                 }
             },
+            "_ops": 2.834,
+            "_weight_size": 30.845,
         },
     )
     DEFAULT = IMAGENET1K_V1
@@ -296,6 +297,8 @@ class DenseNet161_Weights(WeightsEnum):
                     "acc@5": 93.560,
                 }
             },
+            "_ops": 7.728,
+            "_weight_size": 110.369,
         },
     )
     DEFAULT = IMAGENET1K_V1
@@ -314,6 +317,8 @@ class DenseNet169_Weights(WeightsEnum):
                     "acc@5": 92.806,
                 }
             },
+            "_ops": 3.36,
+            "_weight_size": 54.708,
         },
     )
     DEFAULT = IMAGENET1K_V1
@@ -332,6 +337,8 @@ class DenseNet201_Weights(WeightsEnum):
                     "acc@5": 93.370,
                 }
             },
+            "_ops": 4.291,
+            "_weight_size": 77.373,
         },
     )
     DEFAULT = IMAGENET1K_V1
@@ -444,7 +451,6 @@ def densenet201(*, weights: Optional[DenseNet201_Weights] = None, progress: bool
 # The dictionary below is internal implementation detail and will be removed in v0.15
 from ._utils import _ModelURLs
 
-
 model_urls = _ModelURLs(
     {
         "densenet121": DenseNet121_Weights.IMAGENET1K_V1.url,

torchvision/models/detection/faster_rcnn.py

@@ -388,6 +388,8 @@ class FasterRCNN_ResNet50_FPN_Weights(WeightsEnum):
                     "box_map": 37.0,
                 }
             },
+            "_ops": 134.38,
+            "_weight_size": 159.743,
             "_docs": """These weights were produced by following a similar training recipe as on the paper.""",
         },
     )
@@ -407,6 +409,8 @@ class FasterRCNN_ResNet50_FPN_V2_Weights(WeightsEnum):
                     "box_map": 46.7,
                 }
             },
+            "_ops": 280.371,
+            "_weight_size": 167.104,
             "_docs": """These weights were produced using an enhanced training recipe to boost the model accuracy.""",
         },
     )
@@ -426,6 +430,8 @@ class FasterRCNN_MobileNet_V3_Large_FPN_Weights(WeightsEnum):
                     "box_map": 32.8,
                 }
             },
+            "_ops": 4.494,
+            "_weight_size": 74.239,
             "_docs": """These weights were produced by following a similar training recipe as on the paper.""",
         },
     )
@@ -445,6 +451,8 @@ class FasterRCNN_MobileNet_V3_Large_320_FPN_Weights(WeightsEnum):
                     "box_map": 22.8,
                 }
             },
+            "_ops": 0.719,
+            "_weight_size": 74.239,
             "_docs": """These weights were produced by following a similar training recipe as on the paper.""",
         },
     )

torchvision/models/detection/fcos.py

@@ -662,6 +662,8 @@ class FCOS_ResNet50_FPN_Weights(WeightsEnum):
                     "box_map": 39.2,
                 }
             },
+            "_ops": 128.207,
+            "_weight_size": 123.608,
             "_docs": """These weights were produced by following a similar training recipe as on the paper.""",
         },
     )

torchvision/models/detection/keypoint_rcnn.py

@@ -328,6 +328,8 @@ class KeypointRCNN_ResNet50_FPN_Weights(WeightsEnum):
                     "kp_map": 61.1,
                 }
             },
+            "_ops": 133.924,
+            "_weight_size": 226.054,
             "_docs": """
                 These weights were produced by following a similar training recipe as on the paper but use a checkpoint
                 from an early epoch.
@@ -347,6 +349,8 @@ class KeypointRCNN_ResNet50_FPN_Weights(WeightsEnum):
                     "kp_map": 65.0,
                 }
             },
+            "_ops": 137.42,
+            "_weight_size": 226.054,
             "_docs": """These weights were produced by following a similar training recipe as on the paper.""",
         },
     )

torchvision/models/detection/mask_rcnn.py

@@ -370,6 +370,8 @@ class MaskRCNN_ResNet50_FPN_Weights(WeightsEnum):
                     "mask_map": 34.6,
                 }
             },
+            "_ops": 134.38,
+            "_weight_size": 169.84,
             "_docs": """These weights were produced by following a similar training recipe as on the paper.""",
         },
     )
@@ -390,6 +392,8 @@ class MaskRCNN_ResNet50_FPN_V2_Weights(WeightsEnum):
                     "mask_map": 41.8,
                 }
             },
+            "_ops": 333.577,
+            "_weight_size": 177.219,
             "_docs": """These weights were produced using an enhanced training recipe to boost the model accuracy.""",
         },
     )

torchvision/models/detection/retinanet.py

@@ -690,6 +690,8 @@ class RetinaNet_ResNet50_FPN_Weights(WeightsEnum):
                     "box_map": 36.4,
                 }
             },
+            "_ops": 151.54,
+            "_weight_size": 130.267,
             "_docs": """These weights were produced by following a similar training recipe as on the paper.""",
         },
     )
@@ -709,6 +711,8 @@ class RetinaNet_ResNet50_FPN_V2_Weights(WeightsEnum):
                     "box_map": 41.5,
                 }
             },
+            "_ops": 152.238,
+            "_weight_size": 146.037,
             "_docs": """These weights were produced using an enhanced training recipe to boost the model accuracy.""",
         },
     )

torchvision/models/detection/ssd.py

@@ -39,6 +39,8 @@ class SSD300_VGG16_Weights(WeightsEnum):
                     "box_map": 25.1,
                 }
             },
+            "_ops": 34.858,
+            "_weight_size": 135.988,
             "_docs": """These weights were produced by following a similar training recipe as on the paper.""",
         },
     )

torchvision/models/detection/ssdlite.py

@@ -198,6 +198,8 @@ class SSDLite320_MobileNet_V3_Large_Weights(WeightsEnum):
                     "box_map": 21.3,
                 }
             },
+            "_ops": 0.583,
+            "_weight_size": 13.418,
             "_docs": """These weights were produced by following a similar training recipe as on the paper.""",
         },
     )

torchvision/models/efficientnet.py

@@ -464,6 +464,8 @@ class EfficientNet_B0_Weights(WeightsEnum):
                     "acc@5": 93.532,
                 }
             },
+            "_ops": 0.386,
+            "_weight_size": 20.451,
             "_docs": """These weights are ported from the original paper.""",
         },
     )
@@ -486,6 +488,8 @@ class EfficientNet_B1_Weights(WeightsEnum):
                     "acc@5": 94.186,
                 }
             },
+            "_ops": 0.687,
+            "_weight_size": 30.134,
             "_docs": """These weights are ported from the original paper.""",
         },
     )
@@ -504,6 +508,8 @@ class EfficientNet_B1_Weights(WeightsEnum):
                     "acc@5": 94.934,
                 }
             },
+            "_ops": 0.687,
+            "_weight_size": 30.136,
             "_docs": """
                 These weights improve upon the results of the original paper by using a modified version of TorchVision's
                 `new training recipe
@@ -530,6 +536,8 @@ class EfficientNet_B2_Weights(WeightsEnum):
                     "acc@5": 95.310,
                 }
             },
+            "_ops": 1.088,
+            "_weight_size": 35.174,
             "_docs": """These weights are ported from the original paper.""",
         },
     )
@@ -552,6 +560,8 @@ class EfficientNet_B3_Weights(WeightsEnum):
                     "acc@5": 96.054,
                 }
             },
+            "_ops": 1.827,
+            "_weight_size": 47.184,
             "_docs": """These weights are ported from the original paper.""",
         },
     )
@@ -574,6 +584,8 @@ class EfficientNet_B4_Weights(WeightsEnum):
                     "acc@5": 96.594,
                 }
             },
+            "_ops": 4.394,
+            "_weight_size": 74.489,
             "_docs": """These weights are ported from the original paper.""",
         },
     )
@@ -596,6 +608,8 @@ class EfficientNet_B5_Weights(WeightsEnum):
                     "acc@5": 96.628,
                 }
             },
+            "_ops": 10.266,
+            "_weight_size": 116.864,
             "_docs": """These weights are ported from the original paper.""",
         },
     )
@@ -618,6 +632,8 @@ class EfficientNet_B6_Weights(WeightsEnum):
                     "acc@5": 96.916,
                 }
             },
+            "_ops": 19.068,
+            "_weight_size": 165.362,
             "_docs": """These weights are ported from the original paper.""",
         },
     )
@@ -640,6 +656,8 @@ class EfficientNet_B7_Weights(WeightsEnum):
                     "acc@5": 96.908,
                 }
             },
+            "_ops": 37.746,
+            "_weight_size": 254.675,
             "_docs": """These weights are ported from the original paper.""",
         },
     )
@@ -664,6 +682,8 @@ class EfficientNet_V2_S_Weights(WeightsEnum):
                     "acc@5": 96.878,
                 }
             },
+            "_ops": 8.366,
+            "_weight_size": 82.704,
             "_docs": """
                 These weights improve upon the results of the original paper by using a modified version of TorchVision's
                 `new training recipe
@@ -692,6 +712,8 @@ class EfficientNet_V2_M_Weights(WeightsEnum):
                     "acc@5": 97.156,
                 }
             },
+            "_ops": 24.582,
+            "_weight_size": 208.01,
             "_docs": """
                 These weights improve upon the results of the original paper by using a modified version of TorchVision's
                 `new training recipe
@@ -723,6 +745,8 @@ class EfficientNet_V2_L_Weights(WeightsEnum):
                     "acc@5": 97.788,
                 }
             },
+            "_ops": 56.08,
+            "_weight_size": 454.573,
             "_docs": """These weights are ported from the original paper.""",
         },
     )