Adding more ConvNeXt variants + Speed optimizations

docs/source/models.rst

@@ -249,6 +249,9 @@ vit_b_32                          75.912          92.466
 vit_l_16                          79.662          94.638
 vit_l_32                          76.972          93.070
 convnext_tiny (prototype)         82.520          96.146
+convnext_small (prototype)        83.616          96.650
+convnext_base (prototype)         84.062          96.870
+convnext_large (prototype)        84.414          96.976
 ================================  =============   =============
 
 

references/classification/README.md

@@ -201,11 +201,12 @@ and `--batch_size 64`.
 ### ConvNeXt
 ```
 torchrun --nproc_per_node=8 train.py\ 
---model convnext_tiny --batch-size 128 --opt adamw --lr 1e-3 --lr-scheduler cosineannealinglr \ 
+--model $MODEL --batch-size 128 --opt adamw --lr 1e-3 --lr-scheduler cosineannealinglr \ 
 --lr-warmup-epochs 5 --lr-warmup-method linear --auto-augment ta_wide --epochs 600 --random-erase 0.1 \ 
 --label-smoothing 0.1 --mixup-alpha 0.2 --cutmix-alpha 1.0 --weight-decay 0.05 --norm-weight-decay 0.0 \
---train-crop-size 176 --model-ema --val-resize-size 236 --ra-sampler --ra-reps 4
+--train-crop-size 176 --model-ema --val-resize-size 232 --ra-sampler --ra-reps 4
 ```
+Here `$MODEL` is one of `convnext_tiny`, `convnext_small`, `convnext_base` and `convnext_large`. Note that each variant had its `--val-resize-size` optimized in a post-training step, see their `Weights` entry for their exact value.
 
 Note that the above command corresponds to training on a single node with 8 GPUs.
 For generatring the pre-trained weights, we trained with 2 nodes, each with 8 GPUs (for a total of 16 GPUs),

torchvision/prototype/models/convnext.py

@@ -15,47 +15,56 @@
 from ._utils import handle_legacy_interface, _ovewrite_named_param
 
 
-__all__ = ["ConvNeXt", "ConvNeXt_Tiny_Weights", "convnext_tiny"]
+__all__ = [
+    "ConvNeXt",
+    "ConvNeXt_Tiny_Weights",
+    "ConvNeXt_Small_Weights",
+    "ConvNeXt_Base_Weights",
+    "ConvNeXt_Large_Weights",
+    "convnext_tiny",
+    "convnext_small",
+    "convnext_base",
+    "convnext_large",
+]
 
 
 class LayerNorm2d(nn.LayerNorm):
-    def __init__(self, *args: Any, **kwargs: Any) -> None:
-        self.channels_last = kwargs.pop("channels_last", False)
-        super().__init__(*args, **kwargs)
-
     def forward(self, x: Tensor) -> Tensor:
-        #  TODO: Benchmark this against the approach described at https://github.com/pytorch/vision/pull/5197#discussion_r786251298
-        if not self.channels_last:
-            x = x.permute(0, 2, 3, 1)
+        x = x.permute(0, 2, 3, 1)
         x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
-        if not self.channels_last:
-            x = x.permute(0, 3, 1, 2)
+        x = x.permute(0, 3, 1, 2)
         return x
 
 
+class Permute(nn.Module):
+    def __init__(self, dims: List[int]):
+        super().__init__()
+        self.dims = dims
+
+    def forward(self, x):
+        return torch.permute(x, self.dims)
+
+
 class CNBlock(nn.Module):
     def __init__(
-        self, dim, layer_scale: float, stochastic_depth_prob: float, norm_layer: Callable[..., nn.Module]
+        self,
+        dim,
+        layer_scale: float,
+        stochastic_depth_prob: float,
+        norm_layer: Optional[Callable[..., nn.Module]] = None,
     ) -> None:
         super().__init__()
+        if norm_layer is None:
+            norm_layer = partial(nn.LayerNorm, eps=1e-6)
+
         self.block = nn.Sequential(
-            ConvNormActivation(
-                dim,
-                dim,
-                kernel_size=7,
-                groups=dim,
-                norm_layer=norm_layer,
-                activation_layer=None,
-                bias=True,
-            ),
-            ConvNormActivation(dim, 4 * dim, kernel_size=1, norm_layer=None, activation_layer=nn.GELU, inplace=None),
-            ConvNormActivation(
-                4 * dim,
-                dim,
-                kernel_size=1,
-                norm_layer=None,
-                activation_layer=None,
-            ),
+            nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim, bias=True),
+            Permute([0, 2, 3, 1]),
+            norm_layer(dim),
+            nn.Linear(in_features=dim, out_features=4 * dim, bias=True),
+            nn.GELU(),
+            nn.Linear(in_features=4 * dim, out_features=dim, bias=True),
+            Permute([0, 3, 1, 2]),
         )
         self.layer_scale = nn.Parameter(torch.ones(dim, 1, 1) * layer_scale)
         self.stochastic_depth = StochasticDepth(stochastic_depth_prob, "row")
@@ -138,7 +147,7 @@ def __init__(
             for _ in range(cnf.num_layers):
                 # adjust stochastic depth probability based on the depth of the stage block
                 sd_prob = stochastic_depth_prob * stage_block_id / (total_stage_blocks - 1.0)
-                stage.append(block(cnf.input_channels, layer_scale, sd_prob, norm_layer))
+                stage.append(block(cnf.input_channels, layer_scale, sd_prob))
                 stage_block_id += 1
             layers.append(nn.Sequential(*stage))
             if cnf.out_channels is not None:
@@ -177,30 +186,95 @@ def forward(self, x: Tensor) -> Tensor:
         return self._forward_impl(x)
 
 
+def _convnext(
+    block_setting: List[CNBlockConfig],
+    stochastic_depth_prob: float,
+    weights: Optional[WeightsEnum],
+    progress: bool,
+    **kwargs: Any,
+) -> ConvNeXt:
+    if weights is not None:
+        _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
+
+    model = ConvNeXt(block_setting, stochastic_depth_prob=stochastic_depth_prob, **kwargs)
+
+    if weights is not None:
+        model.load_state_dict(weights.get_state_dict(progress=progress))
+
+    return model
+
+
+_COMMON_META = {
+    "task": "image_classification",
+    "architecture": "ConvNeXt",
+    "publication_year": 2022,
+    "size": (224, 224),
+    "min_size": (32, 32),
+    "categories": _IMAGENET_CATEGORIES,
+    "interpolation": InterpolationMode.BILINEAR,
+    "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#convnext",
+}
+
+
 class ConvNeXt_Tiny_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
-        url="https://download.pytorch.org/models/convnext_tiny-47b116bd.pth",
+        url="https://download.pytorch.org/models/convnext_tiny-983f1562.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=236),
         meta={
-            "task": "image_classification",
-            "architecture": "ConvNeXt",
-            "publication_year": 2022,
+            **_COMMON_META,
             "num_params": 28589128,
-            "size": (224, 224),
-            "min_size": (32, 32),
-            "categories": _IMAGENET_CATEGORIES,
-            "interpolation": InterpolationMode.BILINEAR,
-            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#convnext",
             "acc@1": 82.520,
             "acc@5": 96.146,
         },
     )
     DEFAULT = IMAGENET1K_V1
 
 
+class ConvNeXt_Small_Weights(WeightsEnum):
+    IMAGENET1K_V1 = Weights(
+        url="https://download.pytorch.org/models/convnext_small-0c510722.pth",
+        transforms=partial(ImageNetEval, crop_size=224, resize_size=230),
+        meta={
+            **_COMMON_META,
+            "num_params": 50223688,
+            "acc@1": 83.616,
+            "acc@5": 96.650,
+        },
+    )
+    DEFAULT = IMAGENET1K_V1
+
+
+class ConvNeXt_Base_Weights(WeightsEnum):
+    IMAGENET1K_V1 = Weights(
+        url="https://download.pytorch.org/models/convnext_base-6075fbad.pth",
+        transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
+        meta={
+            **_COMMON_META,
+            "num_params": 88591464,
+            "acc@1": 84.062,
+            "acc@5": 96.870,
+        },
+    )
+    DEFAULT = IMAGENET1K_V1
+
+
+class ConvNeXt_Large_Weights(WeightsEnum):
+    IMAGENET1K_V1 = Weights(
+        url="https://download.pytorch.org/models/convnext_large-ea097f82.pth",
+        transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
+        meta={
+            **_COMMON_META,
+            "num_params": 197767336,
+            "acc@1": 84.414,
+            "acc@5": 96.976,
+        },
+    )
+    DEFAULT = IMAGENET1K_V1
+
+
 @handle_legacy_interface(weights=("pretrained", ConvNeXt_Tiny_Weights.IMAGENET1K_V1))
 def convnext_tiny(*, weights: Optional[ConvNeXt_Tiny_Weights] = None, progress: bool = True, **kwargs: Any) -> ConvNeXt:
-    r"""ConvNeXt model architecture from the
+    r"""ConvNeXt Tiny model architecture from the
     `"A ConvNet for the 2020s" <https://arxiv.org/abs/2201.03545>`_ paper.
 
     Args:
@@ -209,19 +283,57 @@ def convnext_tiny(*, weights: Optional[ConvNeXt_Tiny_Weights] = None, progress:
     """
     weights = ConvNeXt_Tiny_Weights.verify(weights)
 
-    if weights is not None:
-        _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
-
     block_setting = [
         CNBlockConfig(96, 192, 3),
         CNBlockConfig(192, 384, 3),
         CNBlockConfig(384, 768, 9),
         CNBlockConfig(768, None, 3),
     ]
     stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.1)
-    model = ConvNeXt(block_setting, stochastic_depth_prob=stochastic_depth_prob, **kwargs)
+    return _convnext(block_setting, stochastic_depth_prob, weights, progress, **kwargs)
 
-    if weights is not None:
-        model.load_state_dict(weights.get_state_dict(progress=progress))
 
-    return model
+@handle_legacy_interface(weights=("pretrained", ConvNeXt_Small_Weights.IMAGENET1K_V1))
+def convnext_small(
+    *, weights: Optional[ConvNeXt_Small_Weights] = None, progress: bool = True, **kwargs: Any
+) -> ConvNeXt:
+    weights = ConvNeXt_Small_Weights.verify(weights)
+
+    block_setting = [
+        CNBlockConfig(96, 192, 3),
+        CNBlockConfig(192, 384, 3),
+        CNBlockConfig(384, 768, 27),
+        CNBlockConfig(768, None, 3),
+    ]
+    stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.4)
+    return _convnext(block_setting, stochastic_depth_prob, weights, progress, **kwargs)
+
+
+@handle_legacy_interface(weights=("pretrained", ConvNeXt_Base_Weights.IMAGENET1K_V1))
+def convnext_base(*, weights: Optional[ConvNeXt_Base_Weights] = None, progress: bool = True, **kwargs: Any) -> ConvNeXt:
+    weights = ConvNeXt_Base_Weights.verify(weights)
+
+    block_setting = [
+        CNBlockConfig(128, 256, 3),
+        CNBlockConfig(256, 512, 3),
+        CNBlockConfig(512, 1024, 27),
+        CNBlockConfig(1024, None, 3),
+    ]
+    stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.5)
+    return _convnext(block_setting, stochastic_depth_prob, weights, progress, **kwargs)
+
+
+@handle_legacy_interface(weights=("pretrained", ConvNeXt_Large_Weights.IMAGENET1K_V1))
+def convnext_large(
+    *, weights: Optional[ConvNeXt_Large_Weights] = None, progress: bool = True, **kwargs: Any
+) -> ConvNeXt:
+    weights = ConvNeXt_Large_Weights.verify(weights)
+
+    block_setting = [
+        CNBlockConfig(192, 384, 3),
+        CNBlockConfig(384, 768, 3),
+        CNBlockConfig(768, 1536, 27),
+        CNBlockConfig(1536, None, 3),
+    ]
+    stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.5)
+    return _convnext(block_setting, stochastic_depth_prob, weights, progress, **kwargs)