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Unverified Commit 2ec0e847 authored by Nicolas Hug's avatar Nicolas Hug Committed by GitHub
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New schema for metrics in weights meta-data (#6047) 

* Classif models

* Detection

* Segmentation

* quantization

* Video

* optical flow

* tests

* Fix docs

* Fix Video dataset

* Consistency for RAFT dataset names

* use ImageNet-1K

* Use COCO-val2017-VOC-labels for segmentation

* formatting
parent 2a35dde3
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Showing with 297 additions and 186 deletions
docs/source/conf.py
View file @ 2ec0e847
......@@ -334,25 +334,22 @@ def inject_weight_metadata(app, what, name, obj, options, lines):
lines.append("")
for field in obj:
lines += [f"**{str(field)}**:", ""]
table = []
# the `meta` dict contains another embedded `metrics` dict. To
# simplify the table generation below, we create the
# `meta_with_metrics` dict, where the metrics dict has been "flattened"
meta = copy(field.meta)
metrics = meta.pop("metrics", {})
meta_with_metrics = dict(meta, **metrics)
lines += [meta_with_metrics.pop("_docs")]
lines += [f"**{str(field)}**:", ""]
lines += [meta.pop("_docs")]
if field == obj.DEFAULT:
lines += [f"Also available as ``{obj.__name__}.DEFAULT``."]
lines += [""]
for k, v in meta_with_metrics.items():
table = []
metrics = meta.pop("_metrics")
for dataset, dataset_metrics in metrics.items():
for metric_name, metric_value in dataset_metrics.items():
table.append((f"{metric_name} (on {dataset})", str(metric_value)))
for k, v in meta.items():
if k in {"recipe", "license"}:
v = f"`link <{v}>`__"
elif k == "min_size":
......@@ -374,7 +371,7 @@ def inject_weight_metadata(app, what, name, obj, options, lines):
lines.append("")
def generate_weights_table(module, table_name, metrics, include_patterns=None, exclude_patterns=None):
def generate_weights_table(module, table_name, metrics, dataset, include_patterns=None, exclude_patterns=None):
weights_endswith = "_QuantizedWeights" if module.__name__.split(".")[-1] == "quantization" else "_Weights"
weight_enums = [getattr(module, name) for name in dir(module) if name.endswith(weights_endswith)]
weights = [w for weight_enum in weight_enums for w in weight_enum]
......@@ -391,7 +388,7 @@ def generate_weights_table(module, table_name, metrics, include_patterns=None, e
content = [
(
f":class:`{w} <{type(w).__name__}>`",
*(w.meta["metrics"][metric] for metric in metrics_keys),
*(w.meta["_metrics"][dataset][metric] for metric in metrics_keys),
f"{w.meta['num_params']/1e6:.1f}M",
f"`link <{w.meta['recipe']}>`__",
)
......@@ -408,29 +405,45 @@ def generate_weights_table(module, table_name, metrics, include_patterns=None, e
table_file.write(f"{textwrap.indent(table, ' ' * 4)}\n\n")
generate_weights_table(module=M, table_name="classification", metrics=[("acc@1", "Acc@1"), ("acc@5", "Acc@5")])
generate_weights_table(
module=M.quantization, table_name="classification_quant", metrics=[("acc@1", "Acc@1"), ("acc@5", "Acc@5")]
module=M, table_name="classification", metrics=[("acc@1", "Acc@1"), ("acc@5", "Acc@5")], dataset="ImageNet-1K"
)
generate_weights_table(
module=M.quantization,
table_name="classification_quant",
metrics=[("acc@1", "Acc@1"), ("acc@5", "Acc@5")],
dataset="ImageNet-1K",
)
generate_weights_table(
module=M.detection, table_name="detection", metrics=[("box_map", "Box MAP")], exclude_patterns=["Mask", "Keypoint"]
module=M.detection,
table_name="detection",
metrics=[("box_map", "Box MAP")],
exclude_patterns=["Mask", "Keypoint"],
dataset="COCO-val2017",
)
generate_weights_table(
module=M.detection,
table_name="instance_segmentation",
metrics=[("box_map", "Box MAP"), ("mask_map", "Mask MAP")],
dataset="COCO-val2017",
include_patterns=["Mask"],
)
generate_weights_table(
module=M.detection,
table_name="detection_keypoint",
metrics=[("box_map", "Box MAP"), ("kp_map", "Keypoint MAP")],
dataset="COCO-val2017",
include_patterns=["Keypoint"],
)
generate_weights_table(
module=M.segmentation, table_name="segmentation", metrics=[("miou", "Mean IoU"), ("pixel_acc", "pixelwise Acc")]
module=M.segmentation,
table_name="segmentation",
metrics=[("miou", "Mean IoU"), ("pixel_acc", "pixelwise Acc")],
dataset="COCO-val2017-VOC-labels",
)
generate_weights_table(
module=M.video, table_name="video", metrics=[("acc@1", "Acc@1"), ("acc@5", "Acc@5")], dataset="Kinetics-400"
)
generate_weights_table(module=M.video, table_name="video", metrics=[("acc@1", "Acc@1"), ("acc@5", "Acc@5")])
def setup(app):
......
test/test_extended_models.py
View file @ 2ec0e847
......@@ -85,7 +85,7 @@ def test_schema_meta_validation(model_fn):
"categories",
"keypoint_names",
"license",
"metrics",
"_metrics",
"min_size",
"num_params",
"recipe",
......@@ -93,19 +93,23 @@ def test_schema_meta_validation(model_fn):
"_docs",
}
# mandatory fields for each computer vision task
classification_fields = {"categories", ("metrics", "acc@1"), ("metrics", "acc@5")}
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"},
"models": classification_fields,
"detection": {"categories", ("metrics", "box_map")},
"detection": {"categories", ("_metrics", "COCO-val2017", "box_map")},
"quantization": classification_fields | {"backend", "unquantized"},
"segmentation": {"categories", ("metrics", "miou"), ("metrics", "pixel_acc")},
"video": classification_fields,
"segmentation": {
"categories",
("_metrics", "COCO-val2017-VOC-labels", "miou"),
("_metrics", "COCO-val2017-VOC-labels", "pixel_acc"),
},
"video": {"categories", ("_metrics", "Kinetics-400", "acc@1"), ("_metrics", "Kinetics-400", "acc@5")},
"optical_flow": set(),
}
model_name = model_fn.__name__
module_name = model_fn.__module__.split(".")[-2]
fields = defaults["all"] | defaults[module_name]
expected_fields = defaults["all"] | defaults[module_name]
weights_enum = _get_model_weights(model_fn)
if len(weights_enum) == 0:
......@@ -115,7 +119,13 @@ def test_schema_meta_validation(model_fn):
incorrect_params = []
bad_names = []
for w in weights_enum:
missing_fields = fields - (set(w.meta.keys()) | set(("metrics", x) for x in w.meta.get("metrics", {}).keys()))
actual_fields = set(w.meta.keys())
actual_fields |= set(
("_metrics", dataset, metric_key)
for dataset in w.meta.get("_metrics", {}).keys()
for metric_key in w.meta.get("_metrics", {}).get(dataset, {}).keys()
)
missing_fields = expected_fields - actual_fields
unsupported_fields = set(w.meta.keys()) - permitted_fields
if missing_fields or unsupported_fields:
problematic_weights[w] = {"missing": missing_fields, "unsupported": unsupported_fields}
......
torchvision/models/alexnet.py
View file @ 2ec0e847
......@@ -61,9 +61,11 @@ class AlexNet_Weights(WeightsEnum):
"min_size": (63, 63),
"categories": _IMAGENET_CATEGORIES,
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg",
"metrics": {
"acc@1": 56.522,
"acc@5": 79.066,
"_metrics": {
"ImageNet-1K": {
"acc@1": 56.522,
"acc@5": 79.066,
}
},
"_docs": """
These weights reproduce closely the results of the paper using a simplified training recipe.
......
torchvision/models/convnext.py
View file @ 2ec0e847
......@@ -222,9 +222,11 @@ class ConvNeXt_Tiny_Weights(WeightsEnum):
meta={
**_COMMON_META,
"num_params": 28589128,
"metrics": {
"acc@1": 82.520,
"acc@5": 96.146,
"_metrics": {
"ImageNet-1K": {
"acc@1": 82.520,
"acc@5": 96.146,
}
},
},
)
......@@ -238,9 +240,11 @@ class ConvNeXt_Small_Weights(WeightsEnum):
meta={
**_COMMON_META,
"num_params": 50223688,
"metrics": {
"acc@1": 83.616,
"acc@5": 96.650,
"_metrics": {
"ImageNet-1K": {
"acc@1": 83.616,
"acc@5": 96.650,
}
},
},
)
......@@ -254,9 +258,11 @@ class ConvNeXt_Base_Weights(WeightsEnum):
meta={
**_COMMON_META,
"num_params": 88591464,
"metrics": {
"acc@1": 84.062,
"acc@5": 96.870,
"_metrics": {
"ImageNet-1K": {
"acc@1": 84.062,
"acc@5": 96.870,
}
},
},
)
......@@ -270,9 +276,11 @@ class ConvNeXt_Large_Weights(WeightsEnum):
meta={
**_COMMON_META,
"num_params": 197767336,
"metrics": {
"acc@1": 84.414,
"acc@5": 96.976,
"_metrics": {
"ImageNet-1K": {
"acc@1": 84.414,
"acc@5": 96.976,
}
},
},
)
......
torchvision/models/densenet.py
View file @ 2ec0e847
......@@ -272,9 +272,11 @@ class DenseNet121_Weights(WeightsEnum):
meta={
**_COMMON_META,
"num_params": 7978856,
"metrics": {
"acc@1": 74.434,
"acc@5": 91.972,
"_metrics": {
"ImageNet-1K": {
"acc@1": 74.434,
"acc@5": 91.972,
}
},
},
)
......@@ -288,9 +290,11 @@ class DenseNet161_Weights(WeightsEnum):
meta={
**_COMMON_META,
"num_params": 28681000,
"metrics": {
"acc@1": 77.138,
"acc@5": 93.560,
"_metrics": {
"ImageNet-1K": {
"acc@1": 77.138,
"acc@5": 93.560,
}
},
},
)
......@@ -304,9 +308,11 @@ class DenseNet169_Weights(WeightsEnum):
meta={
**_COMMON_META,
"num_params": 14149480,
"metrics": {
"acc@1": 75.600,
"acc@5": 92.806,
"_metrics": {
"ImageNet-1K": {
"acc@1": 75.600,
"acc@5": 92.806,
}
},
},
)
......@@ -320,9 +326,11 @@ class DenseNet201_Weights(WeightsEnum):
meta={
**_COMMON_META,
"num_params": 20013928,
"metrics": {
"acc@1": 76.896,
"acc@5": 93.370,
"_metrics": {
"ImageNet-1K": {
"acc@1": 76.896,
"acc@5": 93.370,
}
},
},
)
......
torchvision/models/detection/faster_rcnn.py
View file @ 2ec0e847
......@@ -383,8 +383,10 @@ class FasterRCNN_ResNet50_FPN_Weights(WeightsEnum):
**_COMMON_META,
"num_params": 41755286,
"recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-resnet-50-fpn",
"metrics": {
"box_map": 37.0,
"_metrics": {
"COCO-val2017": {
"box_map": 37.0,
}
},
"_docs": """These weights were produced by following a similar training recipe as on the paper.""",
},
......@@ -400,8 +402,10 @@ class FasterRCNN_ResNet50_FPN_V2_Weights(WeightsEnum):
**_COMMON_META,
"num_params": 43712278,
"recipe": "https://github.com/pytorch/vision/pull/5763",
"metrics": {
"box_map": 46.7,
"_metrics": {
"COCO-val2017": {
"box_map": 46.7,
}
},
"_docs": """These weights were produced using an enhanced training recipe to boost the model accuracy.""",
},
......@@ -417,8 +421,10 @@ class FasterRCNN_MobileNet_V3_Large_FPN_Weights(WeightsEnum):
**_COMMON_META,
"num_params": 19386354,
"recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-mobilenetv3-large-fpn",
"metrics": {
"box_map": 32.8,
"_metrics": {
"COCO-val2017": {
"box_map": 32.8,
}
},
"_docs": """These weights were produced by following a similar training recipe as on the paper.""",
},
......@@ -434,8 +440,10 @@ class FasterRCNN_MobileNet_V3_Large_320_FPN_Weights(WeightsEnum):
**_COMMON_META,
"num_params": 19386354,
"recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-mobilenetv3-large-320-fpn",
"metrics": {
"box_map": 22.8,
"_metrics": {
"COCO-val2017": {
"box_map": 22.8,
}
},
"_docs": """These weights were produced by following a similar training recipe as on the paper.""",
},
......
torchvision/models/detection/fcos.py
View file @ 2ec0e847
......@@ -658,8 +658,10 @@ class FCOS_ResNet50_FPN_Weights(WeightsEnum):
"categories": _COCO_CATEGORIES,
"min_size": (1, 1),
"recipe": "https://github.com/pytorch/vision/tree/main/references/detection#fcos-resnet-50-fpn",
"metrics": {
"box_map": 39.2,
"_metrics": {
"COCO-val2017": {
"box_map": 39.2,
}
},
"_docs": """These weights were produced by following a similar training recipe as on the paper.""",
},
......
torchvision/models/detection/keypoint_rcnn.py
View file @ 2ec0e847
......@@ -322,9 +322,11 @@ class KeypointRCNN_ResNet50_FPN_Weights(WeightsEnum):
**_COMMON_META,
"num_params": 59137258,
"recipe": "https://github.com/pytorch/vision/issues/1606",
"metrics": {
"box_map": 50.6,
"kp_map": 61.1,
"_metrics": {
"COCO-val2017": {
"box_map": 50.6,
"kp_map": 61.1,
}
},
"_docs": """
These weights were produced by following a similar training recipe as on the paper but use a checkpoint
......@@ -339,9 +341,11 @@ class KeypointRCNN_ResNet50_FPN_Weights(WeightsEnum):
**_COMMON_META,
"num_params": 59137258,
"recipe": "https://github.com/pytorch/vision/tree/main/references/detection#keypoint-r-cnn",
"metrics": {
"box_map": 54.6,
"kp_map": 65.0,
"_metrics": {
"COCO-val2017": {
"box_map": 54.6,
"kp_map": 65.0,
}
},
"_docs": """These weights were produced by following a similar training recipe as on the paper.""",
},
......
torchvision/models/detection/mask_rcnn.py
View file @ 2ec0e847
......@@ -364,9 +364,11 @@ class MaskRCNN_ResNet50_FPN_Weights(WeightsEnum):
**_COMMON_META,
"num_params": 44401393,
"recipe": "https://github.com/pytorch/vision/tree/main/references/detection#mask-r-cnn",
"metrics": {
"box_map": 37.9,
"mask_map": 34.6,
"_metrics": {
"COCO-val2017": {
"box_map": 37.9,
"mask_map": 34.6,
}
},
"_docs": """These weights were produced by following a similar training recipe as on the paper.""",
},
......@@ -382,9 +384,11 @@ class MaskRCNN_ResNet50_FPN_V2_Weights(WeightsEnum):
**_COMMON_META,
"num_params": 46359409,
"recipe": "https://github.com/pytorch/vision/pull/5773",
"metrics": {
"box_map": 47.4,
"mask_map": 41.8,
"_metrics": {
"COCO-val2017": {
"box_map": 47.4,
"mask_map": 41.8,
}
},
"_docs": """These weights were produced using an enhanced training recipe to boost the model accuracy.""",
},
......
torchvision/models/detection/retinanet.py
View file @ 2ec0e847
......@@ -687,8 +687,10 @@ class RetinaNet_ResNet50_FPN_Weights(WeightsEnum):
**_COMMON_META,
"num_params": 34014999,
"recipe": "https://github.com/pytorch/vision/tree/main/references/detection#retinanet",
"metrics": {
"box_map": 36.4,
"_metrics": {
"COCO-val2017": {
"box_map": 36.4,
}
},
"_docs": """These weights were produced by following a similar training recipe as on the paper.""",
},
......@@ -704,8 +706,10 @@ class RetinaNet_ResNet50_FPN_V2_Weights(WeightsEnum):
**_COMMON_META,
"num_params": 38198935,
"recipe": "https://github.com/pytorch/vision/pull/5756",
"metrics": {
"box_map": 41.5,
"_metrics": {
"COCO-val2017": {
"box_map": 41.5,
}
},
"_docs": """These weights were produced using an enhanced training recipe to boost the model accuracy.""",
},
......
torchvision/models/detection/ssd.py
View file @ 2ec0e847
......@@ -34,8 +34,10 @@ class SSD300_VGG16_Weights(WeightsEnum):
"categories": _COCO_CATEGORIES,
"min_size": (1, 1),
"recipe": "https://github.com/pytorch/vision/tree/main/references/detection#ssd300-vgg16",
"metrics": {
"box_map": 25.1,
"_metrics": {
"COCO-val2017": {
"box_map": 25.1,
}
},
"_docs": """These weights were produced by following a similar training recipe as on the paper.""",
},
......
torchvision/models/detection/ssdlite.py
View file @ 2ec0e847
......@@ -193,8 +193,10 @@ class SSDLite320_MobileNet_V3_Large_Weights(WeightsEnum):
"categories": _COCO_CATEGORIES,
"min_size": (1, 1),
"recipe": "https://github.com/pytorch/vision/tree/main/references/detection#ssdlite320-mobilenetv3-large",
"metrics": {
"box_map": 21.3,
"_metrics": {
"COCO-val2017": {
"box_map": 21.3,
}
},
"_docs": """These weights were produced by following a similar training recipe as on the paper.""",
},
......
torchvision/models/efficientnet.py
View file @ 2ec0e847
......@@ -458,9 +458,11 @@ class EfficientNet_B0_Weights(WeightsEnum):
meta={
**_COMMON_META_V1,
"num_params": 5288548,
"metrics": {
"acc@1": 77.692,
"acc@5": 93.532,
"_metrics": {
"ImageNet-1K": {
"acc@1": 77.692,
"acc@5": 93.532,
}
},
"_docs": """These weights are ported from the original paper.""",
},
......@@ -478,9 +480,11 @@ class EfficientNet_B1_Weights(WeightsEnum):
meta={
**_COMMON_META_V1,
"num_params": 7794184,
"metrics": {
"acc@1": 78.642,
"acc@5": 94.186,
"_metrics": {
"ImageNet-1K": {
"acc@1": 78.642,
"acc@5": 94.186,
}
},
"_docs": """These weights are ported from the original paper.""",
},
......@@ -494,9 +498,11 @@ class EfficientNet_B1_Weights(WeightsEnum):
**_COMMON_META_V1,
"num_params": 7794184,
"recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-lr-wd-crop-tuning",
"metrics": {
"acc@1": 79.838,
"acc@5": 94.934,
"_metrics": {
"ImageNet-1K": {
"acc@1": 79.838,
"acc@5": 94.934,
}
},
"_docs": """
These weights improve upon the results of the original paper by using a modified version of TorchVision's
......@@ -518,9 +524,11 @@ class EfficientNet_B2_Weights(WeightsEnum):
meta={
**_COMMON_META_V1,
"num_params": 9109994,
"metrics": {
"acc@1": 80.608,
"acc@5": 95.310,
"_metrics": {
"ImageNet-1K": {
"acc@1": 80.608,
"acc@5": 95.310,
}
},
"_docs": """These weights are ported from the original paper.""",
},
......@@ -538,9 +546,11 @@ class EfficientNet_B3_Weights(WeightsEnum):
meta={
**_COMMON_META_V1,
"num_params": 12233232,
"metrics": {
"acc@1": 82.008,
"acc@5": 96.054,
"_metrics": {
"ImageNet-1K": {
"acc@1": 82.008,
"acc@5": 96.054,
}
},
"_docs": """These weights are ported from the original paper.""",
},
......@@ -558,9 +568,11 @@ class EfficientNet_B4_Weights(WeightsEnum):
meta={
**_COMMON_META_V1,
"num_params": 19341616,
"metrics": {
"acc@1": 83.384,
"acc@5": 96.594,
"_metrics": {
"ImageNet-1K": {
"acc@1": 83.384,
"acc@5": 96.594,
}
},
"_docs": """These weights are ported from the original paper.""",
},
......@@ -578,9 +590,11 @@ class EfficientNet_B5_Weights(WeightsEnum):
meta={
**_COMMON_META_V1,
"num_params": 30389784,
"metrics": {
"acc@1": 83.444,
"acc@5": 96.628,
"_metrics": {
"ImageNet-1K": {
"acc@1": 83.444,
"acc@5": 96.628,
}
},
"_docs": """These weights are ported from the original paper.""",
},
......@@ -598,9 +612,11 @@ class EfficientNet_B6_Weights(WeightsEnum):
meta={
**_COMMON_META_V1,
"num_params": 43040704,
"metrics": {
"acc@1": 84.008,
"acc@5": 96.916,
"_metrics": {
"ImageNet-1K": {
"acc@1": 84.008,
"acc@5": 96.916,
}
},
"_docs": """These weights are ported from the original paper.""",
},
......@@ -618,9 +634,11 @@ class EfficientNet_B7_Weights(WeightsEnum):
meta={
**_COMMON_META_V1,
"num_params": 66347960,
"metrics": {
"acc@1": 84.122,
"acc@5": 96.908,
"_metrics": {
"ImageNet-1K": {
"acc@1": 84.122,
"acc@5": 96.908,
}
},
"_docs": """These weights are ported from the original paper.""",
},
......@@ -640,9 +658,11 @@ class EfficientNet_V2_S_Weights(WeightsEnum):
meta={
**_COMMON_META_V2,
"num_params": 21458488,
"metrics": {
"acc@1": 84.228,
"acc@5": 96.878,
"_metrics": {
"ImageNet-1K": {
"acc@1": 84.228,
"acc@5": 96.878,
}
},
"_docs": """
These weights improve upon the results of the original paper by using a modified version of TorchVision's
......@@ -666,9 +686,11 @@ class EfficientNet_V2_M_Weights(WeightsEnum):
meta={
**_COMMON_META_V2,
"num_params": 54139356,
"metrics": {
"acc@1": 85.112,
"acc@5": 97.156,
"_metrics": {
"ImageNet-1K": {
"acc@1": 85.112,
"acc@5": 97.156,
}
},
"_docs": """
These weights improve upon the results of the original paper by using a modified version of TorchVision's
......@@ -695,9 +717,11 @@ class EfficientNet_V2_L_Weights(WeightsEnum):
meta={
**_COMMON_META_V2,
"num_params": 118515272,
"metrics": {
"acc@1": 85.808,
"acc@5": 97.788,
"_metrics": {
"ImageNet-1K": {
"acc@1": 85.808,
"acc@5": 97.788,
}
},
"_docs": """These weights are ported from the original paper.""",
},
......
torchvision/models/googlenet.py
View file @ 2ec0e847
......@@ -284,9 +284,11 @@ class GoogLeNet_Weights(WeightsEnum):
"min_size": (15, 15),
"categories": _IMAGENET_CATEGORIES,
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#googlenet",
"metrics": {
"acc@1": 69.778,
"acc@5": 89.530,
"_metrics": {
"ImageNet-1K": {
"acc@1": 69.778,
"acc@5": 89.530,
}
},
"_docs": """These weights are ported from the original paper.""",
},
......
torchvision/models/inception.py
View file @ 2ec0e847
......@@ -416,9 +416,11 @@ class Inception_V3_Weights(WeightsEnum):
"min_size": (75, 75),
"categories": _IMAGENET_CATEGORIES,
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#inception-v3",
"metrics": {
"acc@1": 77.294,
"acc@5": 93.450,
"_metrics": {
"ImageNet-1K": {
"acc@1": 77.294,
"acc@5": 93.450,
}
},
"_docs": """These weights are ported from the original paper.""",
},
......
torchvision/models/mnasnet.py
View file @ 2ec0e847
......@@ -225,9 +225,11 @@ class MNASNet0_5_Weights(WeightsEnum):
meta={
**_COMMON_META,
"num_params": 2218512,
"metrics": {
"acc@1": 67.734,
"acc@5": 87.490,
"_metrics": {
"ImageNet-1K": {
"acc@1": 67.734,
"acc@5": 87.490,
}
},
"_docs": """These weights reproduce closely the results of the paper.""",
},
......@@ -243,9 +245,11 @@ class MNASNet0_75_Weights(WeightsEnum):
**_COMMON_META,
"recipe": "https://github.com/pytorch/vision/pull/6019",
"num_params": 3170208,
"metrics": {
"acc@1": 71.180,
"acc@5": 90.496,
"_metrics": {
"ImageNet-1K": {
"acc@1": 71.180,
"acc@5": 90.496,
}
},
"_docs": """
These weights were trained from scratch by using TorchVision's `new training recipe
......@@ -263,9 +267,11 @@ class MNASNet1_0_Weights(WeightsEnum):
meta={
**_COMMON_META,
"num_params": 4383312,
"metrics": {
"acc@1": 73.456,
"acc@5": 91.510,
"_metrics": {
"ImageNet-1K": {
"acc@1": 73.456,
"acc@5": 91.510,
}
},
"_docs": """These weights reproduce closely the results of the paper.""",
},
......@@ -281,9 +287,11 @@ class MNASNet1_3_Weights(WeightsEnum):
**_COMMON_META,
"recipe": "https://github.com/pytorch/vision/pull/6019",
"num_params": 6282256,
"metrics": {
"acc@1": 76.506,
"acc@5": 93.522,
"_metrics": {
"ImageNet-1K": {
"acc@1": 76.506,
"acc@5": 93.522,
}
},
"_docs": """
These weights were trained from scratch by using TorchVision's `new training recipe
......
torchvision/models/mobilenetv2.py
View file @ 2ec0e847
......@@ -208,9 +208,11 @@ class MobileNet_V2_Weights(WeightsEnum):
meta={
**_COMMON_META,
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv2",
"metrics": {
"acc@1": 71.878,
"acc@5": 90.286,
"_metrics": {
"ImageNet-1K": {
"acc@1": 71.878,
"acc@5": 90.286,
}
},
"_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""",
},
......@@ -221,9 +223,11 @@ class MobileNet_V2_Weights(WeightsEnum):
meta={
**_COMMON_META,
"recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-reg-tuning",
"metrics": {
"acc@1": 72.154,
"acc@5": 90.822,
"_metrics": {
"ImageNet-1K": {
"acc@1": 72.154,
"acc@5": 90.822,
}
},
"_docs": """
These weights improve upon the results of the original paper by using a modified version of TorchVision's
......
torchvision/models/mobilenetv3.py
View file @ 2ec0e847
......@@ -317,9 +317,11 @@ class MobileNet_V3_Large_Weights(WeightsEnum):
**_COMMON_META,
"num_params": 5483032,
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv3-large--small",
"metrics": {
"acc@1": 74.042,
"acc@5": 91.340,
"_metrics": {
"ImageNet-1K": {
"acc@1": 74.042,
"acc@5": 91.340,
}
},
"_docs": """These weights were trained from scratch by using a simple training recipe.""",
},
......@@ -331,9 +333,11 @@ class MobileNet_V3_Large_Weights(WeightsEnum):
**_COMMON_META,
"num_params": 5483032,
"recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-reg-tuning",
"metrics": {
"acc@1": 75.274,
"acc@5": 92.566,
"_metrics": {
"ImageNet-1K": {
"acc@1": 75.274,
"acc@5": 92.566,
}
},
"_docs": """
These weights improve marginally upon the results of the original paper by using a modified version of
......@@ -353,9 +357,11 @@ class MobileNet_V3_Small_Weights(WeightsEnum):
**_COMMON_META,
"num_params": 2542856,
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv3-large--small",
"metrics": {
"acc@1": 67.668,
"acc@5": 87.402,
"_metrics": {
"ImageNet-1K": {
"acc@1": 67.668,
"acc@5": 87.402,
}
},
"_docs": """
These weights improve upon the results of the original paper by using a simple training recipe.
......
torchvision/models/optical_flow/raft.py
View file @ 2ec0e847
......@@ -525,11 +525,10 @@ class Raft_Large_Weights(WeightsEnum):
**_COMMON_META,
"num_params": 5257536,
"recipe": "https://github.com/princeton-vl/RAFT",
"metrics": {
"sintel_train_cleanpass_epe": 1.4411,
"sintel_train_finalpass_epe": 2.7894,
"kitti_train_per_image_epe": 5.0172,
"kitti_train_fl_all": 17.4506,
"_metrics": {
"Sintel-Train-Cleanpass": {"epe": 1.4411},
"Sintel-Train-Finalpass": {"epe": 2.7894},
"Kitti-Train": {"per_image_epe": 5.0172, "fl_all": 17.4506},
},
"_docs": """These weights were ported from the original paper. They are trained on Chairs + Things.""",
},
......@@ -542,11 +541,10 @@ class Raft_Large_Weights(WeightsEnum):
**_COMMON_META,
"num_params": 5257536,
"recipe": "https://github.com/pytorch/vision/tree/main/references/optical_flow",
"metrics": {
"sintel_train_cleanpass_epe": 1.3822,
"sintel_train_finalpass_epe": 2.7161,
"kitti_train_per_image_epe": 4.5118,
"kitti_train_fl_all": 16.0679,
"_metrics": {
"Sintel-Train-Cleanpass": {"epe": 1.3822},
"Sintel-Train-Finalpass": {"epe": 2.7161},
"Kitti-Train": {"per_image_epe": 4.5118, "fl_all": 16.0679},
},
"_docs": """These weights were trained from scratch on Chairs + Things.""",
},
......@@ -560,9 +558,9 @@ class Raft_Large_Weights(WeightsEnum):
**_COMMON_META,
"num_params": 5257536,
"recipe": "https://github.com/princeton-vl/RAFT",
"metrics": {
"sintel_test_cleanpass_epe": 1.94,
"sintel_test_finalpass_epe": 3.18,
"_metrics": {
"Sintel-Test-Cleanpass": {"epe": 1.94},
"Sintel-Test-Finalpass": {"epe": 3.18},
},
"_docs": """
These weights were ported from the original paper. They are trained on Chairs + Things and fine-tuned on
......@@ -578,9 +576,9 @@ class Raft_Large_Weights(WeightsEnum):
**_COMMON_META,
"num_params": 5257536,
"recipe": "https://github.com/pytorch/vision/tree/main/references/optical_flow",
"metrics": {
"sintel_test_cleanpass_epe": 1.819,
"sintel_test_finalpass_epe": 3.067,
"_metrics": {
"Sintel-Test-Cleanpass": {"epe": 1.819},
"Sintel-Test-Finalpass": {"epe": 3.067},
},
"_docs": """
These weights were trained from scratch on Chairs + Things and fine-tuned on Sintel (C+T+S+K+H).
......@@ -596,8 +594,8 @@ class Raft_Large_Weights(WeightsEnum):
**_COMMON_META,
"num_params": 5257536,
"recipe": "https://github.com/princeton-vl/RAFT",
"metrics": {
"kitti_test_fl_all": 5.10,
"_metrics": {
"Kitti-Test": {"fl_all": 5.10},
},
"_docs": """
These weights were ported from the original paper. They are trained on Chairs + Things, fine-tuned on
......@@ -613,8 +611,8 @@ class Raft_Large_Weights(WeightsEnum):
**_COMMON_META,
"num_params": 5257536,
"recipe": "https://github.com/pytorch/vision/tree/main/references/optical_flow",
"metrics": {
"kitti_test_fl_all": 5.19,
"_metrics": {
"Kitti-Test": {"fl_all": 5.19},
},
"_docs": """
These weights were trained from scratch on Chairs + Things, fine-tuned on Sintel and then on Kitti.
......@@ -634,11 +632,10 @@ class Raft_Small_Weights(WeightsEnum):
**_COMMON_META,
"num_params": 990162,
"recipe": "https://github.com/princeton-vl/RAFT",
"metrics": {
"sintel_train_cleanpass_epe": 2.1231,
"sintel_train_finalpass_epe": 3.2790,
"kitti_train_per_image_epe": 7.6557,
"kitti_train_fl_all": 25.2801,
"_metrics": {
"Sintel-Train-Cleanpass": {"epe": 2.1231},
"Sintel-Train-Finalpass": {"epe": 3.2790},
"Kitti-Train": {"per_image_epe": 7.6557, "fl_all": 25.2801},
},
"_docs": """These weights were ported from the original paper. They are trained on Chairs + Things.""",
},
......@@ -650,11 +647,10 @@ class Raft_Small_Weights(WeightsEnum):
**_COMMON_META,
"num_params": 990162,
"recipe": "https://github.com/pytorch/vision/tree/main/references/optical_flow",
"metrics": {
"sintel_train_cleanpass_epe": 1.9901,
"sintel_train_finalpass_epe": 3.2831,
"kitti_train_per_image_epe": 7.5978,
"kitti_train_fl_all": 25.2369,
"_metrics": {
"Sintel-Train-Cleanpass": {"epe": 1.9901},
"Sintel-Train-Finalpass": {"epe": 3.2831},
"Kitti-Train": {"per_image_epe": 7.5978, "fl_all": 25.2369},
},
"_docs": """These weights were trained from scratch on Chairs + Things.""",
},
......
torchvision/models/quantization/googlenet.py
View file @ 2ec0e847
......@@ -117,9 +117,11 @@ class GoogLeNet_QuantizedWeights(WeightsEnum):
"backend": "fbgemm",
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-models",
"unquantized": GoogLeNet_Weights.IMAGENET1K_V1,
"metrics": {
"acc@1": 69.826,
"acc@5": 89.404,
"_metrics": {
"ImageNet-1K": {
"acc@1": 69.826,
"acc@5": 89.404,
}
},
"_docs": """
These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized
......
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