Add standard resnet models (#405)

Summary:
Pull Request resolved: #405

It is annoying to configure the ResNet blocks all the time. Add the standard
models to the code so we can refer to them by name

Differential Revision: D20050757

fbshipit-source-id: 82ef26dc660f5ac3b9778560f40135810c5acdc4

classy_vision/models/resnext.py

@@ -9,7 +9,7 @@
 """
 
 import math
-from typing import Any, Dict
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import torch.nn as nn
 from classy_vision.generic.util import is_pos_int
@@ -228,13 +228,13 @@ class ResNeXt(ClassyModel):
  def __init__(
  self,
  num_blocks,
- init_planes,
- reduction,
- small_input,
- zero_init_bn_residuals,
- base_width_and_cardinality,
- basic_layer,
- final_bn_relu,
+ init_planes: int = 64,
+ reduction: int = 4,
+ small_input: bool = False,
+ zero_init_bn_residuals: bool = False,
+ base_width_and_cardinality: Optional[Union[Tuple, List]] = None,
+ basic_layer: bool = False,
+ final_bn_relu: bool = True,
  ):
  """
  Implementation of `ResNeXt <https://arxiv.org/pdf/1611.05431.pdf>`_.
@@ -414,3 +414,108 @@ def output_shape(self):
  @property
  def model_depth(self):
  return sum(self.num_blocks)
+
+
+@register_model("resnet18")
+class ResNet18(ResNeXt):
+ def __init__(self):
+ super().__init__(
+ num_blocks=[2, 2, 2, 2], basic_layer=True, zero_init_bn_residuals=True
+ )
+
+ @classmethod
+ def from_config(cls, config: Dict[str, Any]) -> "ResNeXt":
+ return cls()
+
+
+@register_model("resnet34")
+class ResNet34(ResNeXt):
+ def __init__(self):
+ super().__init__(
+ num_blocks=[3, 4, 6, 3], basic_layer=True, zero_init_bn_residuals=True
+ )
+
+ @classmethod
+ def from_config(cls, config: Dict[str, Any]) -> "ResNeXt":
+ return cls()
+
+
+@register_model("resnet50")
+class ResNet50(ResNeXt):
+ def __init__(self):
+ super().__init__(
+ num_blocks=[3, 4, 6, 3], basic_layer=False, zero_init_bn_residuals=True
+ )
+
+ @classmethod
+ def from_config(cls, config: Dict[str, Any]) -> "ResNeXt":
+ return cls()
+
+
+@register_model("resnet101")
+class ResNet101(ResNeXt):
+ def __init__(self):
+ super().__init__(
+ num_blocks=[3, 4, 23, 3], basic_layer=False, zero_init_bn_residuals=True
+ )
+
+ @classmethod
+ def from_config(cls, config: Dict[str, Any]) -> "ResNeXt":
+ return cls()
+
+
+@register_model("resnet152")
+class ResNet152(ResNeXt):
+ def __init__(self):
+ super().__init__(
+ num_blocks=[3, 8, 36, 3], basic_layer=False, zero_init_bn_residuals=True
+ )
+
+ @classmethod
+ def from_config(cls, config: Dict[str, Any]) -> "ResNeXt":
+ return cls()
+
+
+@register_model("resnext50_32x4d")
+class ResNeXt50(ResNeXt):
+ def __init__(self):
+ super().__init__(
+ num_blocks=[3, 4, 6, 3],
+ basic_layer=False,
+ zero_init_bn_residuals=True,
+ base_width_and_cardinality=(4, 32),
+ )
+
+ @classmethod
+ def from_config(cls, config: Dict[str, Any]) -> "ResNeXt":
+ return cls()
+
+
+@register_model("resnext101_32x4d")
+class ResNeXt101(ResNeXt):
+ def __init__(self):
+ super().__init__(
+ num_blocks=[3, 4, 23, 3],
+ basic_layer=False,
+ zero_init_bn_residuals=True,
+ base_width_and_cardinality=(4, 32),
+ )
+
+ @classmethod
+ def from_config(cls, config: Dict[str, Any]) -> "ResNeXt":
+ return cls()
+
+
+@register_model("resnext152_32x4d")
+class ResNeXt152(ResNeXt):
+ def __init__(self):
+ super().__init__(
+ num_blocks=[3, 8, 36, 3],
+ basic_layer=False,
+ zero_init_bn_residuals=True,
+ base_width_and_cardinality=(4, 32),
+ )
+
+ @classmethod
+ def from_config(cls, config: Dict[str, Any]) -> "ResNeXt":
+ return cls()