Add shufflenetv2 1.5 and 2.0 weights

docs/source/models.rst

@@ -176,8 +176,10 @@ Densenet-201                      76.896          93.370
 Densenet-161                      77.138          93.560
 Inception v3                      77.294          93.450
 GoogleNet                         69.778          89.530
-ShuffleNet V2 x1.0                69.362          88.316
 ShuffleNet V2 x0.5                60.552          81.746
+ShuffleNet V2 x1.0                69.362          88.316
+ShuffleNet V2 x1.5                72.996          91.086
+ShuffleNet V2 x2.0                76.230          93.006
 MobileNet V2                      71.878          90.286
 MobileNet V3 Large                74.042          91.340
 MobileNet V3 Small                67.668          87.402
@@ -499,8 +501,10 @@ Model                             Acc@1          Acc@5
 ================================  =============  =============
 MobileNet V2                      71.658         90.150
 MobileNet V3 Large                73.004         90.858
-ShuffleNet V2 x1.0                68.360         87.582
 ShuffleNet V2 x0.5                57.972         79.780
+ShuffleNet V2 x1.0                68.360         87.582
+ShuffleNet V2 x1.5                72.052         90.700
+ShuffleNet V2 x2.0                75.354         92.488
 ResNet 18                         69.494         88.882
 ResNet 50                         75.920         92.814
 ResNext 101 32x8d                 78.986         94.480

hubconf.py

@@ -59,7 +59,12 @@
     deeplabv3_mobilenet_v3_large,
     lraspp_mobilenet_v3_large,
 )
-from torchvision.models.shufflenetv2 import shufflenet_v2_x0_5, shufflenet_v2_x1_0
+from torchvision.models.shufflenetv2 import (
+    shufflenet_v2_x0_5,
+    shufflenet_v2_x1_0,
+    shufflenet_v2_x1_5,
+    shufflenet_v2_x2_0,
+)
 from torchvision.models.squeezenet import squeezenet1_0, squeezenet1_1
 from torchvision.models.vgg import vgg11, vgg13, vgg16, vgg19, vgg11_bn, vgg13_bn, vgg16_bn, vgg19_bn
 from torchvision.models.vision_transformer import (

references/classification/README.md

@@ -236,6 +236,20 @@ torchrun --nproc_per_node=8 train.py\
 Note that `--val-resize-size` was optimized in a post-training step, see their `Weights` entry for the exact value.
 
 
+### ShuffleNet V2
+```
+torchrun --nproc_per_node=8 train.py \
+--batch-size=128 \
+--lr=0.5 --lr-scheduler=cosineannealinglr --lr-warmup-epochs=5 --lr-warmup-method=linear \
+--auto-augment=ta_wide --epochs=600 --random-erase=0.1 --weight-decay=0.00002 \
+--norm-weight-decay=0.0 --label-smoothing=0.1 --mixup-alpha=0.2 --cutmix-alpha=1.0 \
+--train-crop-size=176 --model-ema --val-resize-size=232 --ra-sampler --ra-reps=4
+```
+Here `$MODEL` is either `shufflenet_v2_x1_5` or `shufflenet_v2_x2_0`.
+
+For the model `shufflenet_v2_x0_5` and `shufflenet_v2_x1_0` was contributed [from community](https://github.com/barrh/Shufflenet-v2-Pytorch/tree/v0.1.0).
+
+
 ## Mixed precision training
 Automatic Mixed Precision (AMP) training on GPU for Pytorch can be enabled with the [torch.cuda.amp](https://pytorch.org/docs/stable/amp.html?highlight=amp#module-torch.cuda.amp).
 
@@ -263,6 +277,21 @@ python train_quantization.py --device='cpu' --post-training-quantize --backend='
 ```
 Here `$MODEL` is one of `googlenet`, `inception_v3`, `resnet18`, `resnet50`, `resnext101_32x8d`, `shufflenet_v2_x0_5` and `shufflenet_v2_x1_0`.
 
+### Quantized ShuffleNet V2
+
+Here are commands that we use to quantized the `shufflenet_v2_x1_5` and `shufflenet_v2_x2_0` models.
+```
+# For shufflenet_v2_x1_5
+python train_quantization.py --device='cpu' --post-training-quantize --backend='fbgemm' \
+    --model=shufflenet_v2_x1_5 --weights="ShuffleNet_V2_X1_5_Weights.IMAGENET1K_V1" \
+    --train-crop-size 176 --val-resize-size 232 --data-path /datasets01_ontap/imagenet_full_size/061417/
+
+# For shufflenet_v2_x2_0
+python train_quantization.py --device='cpu' --post-training-quantize --backend='fbgemm' \
+    --model=shufflenet_v2_x1_5 --weights="ShuffleNet_V2_X1_5_Weights.IMAGENET1K_V1" \
+    --train-crop-size 176 --val-resize-size 232 --data-path /datasets01_ontap/imagenet_full_size/061417/
+```
+
 ### QAT MobileNetV2
 
 For Mobilenet-v2, the model was trained with quantization aware training, the settings used are:

torchvision/models/quantization/shufflenetv2.py

@@ -10,16 +10,25 @@
 from .._api import WeightsEnum, Weights
 from .._meta import _IMAGENET_CATEGORIES
 from .._utils import handle_legacy_interface, _ovewrite_named_param
-from ..shufflenetv2 import ShuffleNet_V2_X0_5_Weights, ShuffleNet_V2_X1_0_Weights
+from ..shufflenetv2 import (
+    ShuffleNet_V2_X0_5_Weights,
+    ShuffleNet_V2_X1_0_Weights,
+    ShuffleNet_V2_X1_5_Weights,
+    ShuffleNet_V2_X2_0_Weights,
+)
 from .utils import _fuse_modules, _replace_relu, quantize_model
 
 
 __all__ = [
     "QuantizableShuffleNetV2",
     "ShuffleNet_V2_X0_5_QuantizedWeights",
     "ShuffleNet_V2_X1_0_QuantizedWeights",
+    "ShuffleNet_V2_X1_5_QuantizedWeights",
+    "ShuffleNet_V2_X2_0_QuantizedWeights",
     "shufflenet_v2_x0_5",
     "shufflenet_v2_x1_0",
+    "shufflenet_v2_x1_5",
+    "shufflenet_v2_x2_0",
 ]
 
 
@@ -143,6 +152,42 @@ class ShuffleNet_V2_X1_0_QuantizedWeights(WeightsEnum):
     DEFAULT = IMAGENET1K_FBGEMM_V1
 
 
+class ShuffleNet_V2_X1_5_QuantizedWeights(WeightsEnum):
+    IMAGENET1K_FBGEMM_V1 = Weights(
+        url="https://download.pytorch.org/models/quantized/shufflenetv2_x1_5_fbgemm-d7401f05.pth",
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
+        meta={
+            **_COMMON_META,
+            "recipe": "https://github.com/pytorch/vision/pull/5906",
+            "num_params": 3503624,
+            "unquantized": ShuffleNet_V2_X1_5_Weights.IMAGENET1K_V1,
+            "metrics": {
+                "acc@1": 72.052,
+                "acc@5": 90.700,
+            },
+        },
+    )
+    DEFAULT = IMAGENET1K_FBGEMM_V1
+
+
+class ShuffleNet_V2_X2_0_QuantizedWeights(WeightsEnum):
+    IMAGENET1K_FBGEMM_V1 = Weights(
+        url="https://download.pytorch.org/models/quantized/shufflenetv2_x2_0_fbgemm-5cac526c.pth",
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
+        meta={
+            **_COMMON_META,
+            "recipe": "https://github.com/pytorch/vision/pull/5906",
+            "num_params": 7393996,
+            "unquantized": ShuffleNet_V2_X2_0_Weights.IMAGENET1K_V1,
+            "metrics": {
+                "acc@1": 75.354,
+                "acc@5": 92.488,
+            },
+        },
+    )
+    DEFAULT = IMAGENET1K_FBGEMM_V1
+
+
 @handle_legacy_interface(
     weights=(
         "pretrained",
@@ -205,3 +250,51 @@ def shufflenet_v2_x1_0(
     return _shufflenetv2(
         [4, 8, 4], [24, 116, 232, 464, 1024], weights=weights, progress=progress, quantize=quantize, **kwargs
     )
+
+
+def shufflenet_v2_x1_5(
+    *,
+    weights: Optional[Union[ShuffleNet_V2_X1_5_QuantizedWeights, ShuffleNet_V2_X1_5_Weights]] = None,
+    progress: bool = True,
+    quantize: bool = False,
+    **kwargs: Any,
+) -> QuantizableShuffleNetV2:
+    """
+    Constructs a ShuffleNetV2 with 1.5x output channels, as described in
+    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
+    <https://arxiv.org/abs/1807.11164>`_.
+
+    Args:
+        weights (ShuffleNet_V2_X1_5_QuantizedWeights or ShuffleNet_V2_X1_5_Weights, optional): The pretrained
+            weights for the model
+        progress (bool): If True, displays a progress bar of the download to stderr
+        quantize (bool): If True, return a quantized version of the model
+    """
+    weights = (ShuffleNet_V2_X1_5_QuantizedWeights if quantize else ShuffleNet_V2_X1_5_Weights).verify(weights)
+    return _shufflenetv2(
+        [4, 8, 4], [24, 176, 352, 704, 1024], weights=weights, progress=progress, quantize=quantize, **kwargs
+    )
+
+
+def shufflenet_v2_x2_0(
+    *,
+    weights: Optional[Union[ShuffleNet_V2_X2_0_QuantizedWeights, ShuffleNet_V2_X2_0_Weights]] = None,
+    progress: bool = True,
+    quantize: bool = False,
+    **kwargs: Any,
+) -> QuantizableShuffleNetV2:
+    """
+    Constructs a ShuffleNetV2 with 2.0x output channels, as described in
+    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
+    <https://arxiv.org/abs/1807.11164>`_.
+
+    Args:
+        weights (ShuffleNet_V2_X2_0_QuantizedWeights or ShuffleNet_V2_X2_0_Weights, optional): The pretrained
+            weights for the model
+        progress (bool): If True, displays a progress bar of the download to stderr
+        quantize (bool): If True, return a quantized version of the model
+    """
+    weights = (ShuffleNet_V2_X2_0_QuantizedWeights if quantize else ShuffleNet_V2_X2_0_Weights).verify(weights)
+    return _shufflenetv2(
+        [4, 8, 4], [24, 244, 488, 976, 2048], weights=weights, progress=progress, quantize=quantize, **kwargs
+    )

torchvision/models/shufflenetv2.py

@@ -223,11 +223,37 @@ class ShuffleNet_V2_X1_0_Weights(WeightsEnum):
 
 
 class ShuffleNet_V2_X1_5_Weights(WeightsEnum):
-    pass
+    IMAGENET1K_V1 = Weights(
+        url="https://download.pytorch.org/models/shufflenetv2_x1_5-3c479a10.pth",
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
+        meta={
+            **_COMMON_META,
+            "recipe": "https://github.com/pytorch/vision/pull/5906",
+            "num_params": 3503624,
+            "metrics": {
+                "acc@1": 72.996,
+                "acc@5": 91.086,
+            },
+        },
+    )
+    DEFAULT = IMAGENET1K_V1
 
 
 class ShuffleNet_V2_X2_0_Weights(WeightsEnum):
-    pass
+    IMAGENET1K_V1 = Weights(
+        url="https://download.pytorch.org/models/shufflenetv2_x2_0-8be3c8ee.pth",
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
+        meta={
+            **_COMMON_META,
+            "recipe": "https://github.com/pytorch/vision/pull/5906",
+            "num_params": 7393996,
+            "metrics": {
+                "acc@1": 76.230,
+                "acc@5": 93.006,
+            },
+        },
+    )
+    DEFAULT = IMAGENET1K_V1
 
 
 @handle_legacy_interface(weights=("pretrained", ShuffleNet_V2_X0_5_Weights.IMAGENET1K_V1))