resnet_models.py

# Copyright 2022 The Flax Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Flax implementation of ResNet V1."""

# See issue #620.
# pytype: disable=wrong-arg-count

from functools import partial
from typing import Any, Callable, Sequence, Tuple

from flax import linen as nn
import jax.numpy as jnp

ModuleDef = Any


class ResNetBlock(nn.Module):
  """ResNet block."""
  filters: int
  conv: ModuleDef
  norm: ModuleDef
  act: Callable
  strides: Tuple[int, int] = (1, 1)
  dtype: Any = jnp.float32

  @nn.compact
  def __call__(self, x,):
    norm_kwargs = {"num_groups":32, "dtype":self.dtype}
    residual = x
    y = self.conv(self.filters, (3, 3), self.strides, kernel_dilation=(2,2))(x)
    y = nn.GroupNorm(**norm_kwargs)(y)
    y = self.act(y)
    y = self.conv(self.filters, (3, 3), kernel_dilation=(2,2))(y)
    y = nn.GroupNorm(scale_init=nn.initializers.zeros_init(), **norm_kwargs)(y)

    if residual.shape != y.shape:
      residual = self.conv(self.filters, (1, 1),
                           self.strides, kernel_dilation=(2,2), 
                           name='conv_proj')(residual)
      residual = nn.GroupNorm(**norm_kwargs)(residual)

    return self.act(residual + y)


class BottleneckResNetBlock(nn.Module):
  """Bottleneck ResNet block."""
  filters: int
  conv: ModuleDef
  act: Callable
  strides: Tuple[int, int] = (1, 1)
  dtype: Any = jnp.float32

  @nn.compact
  def __call__(self, x):
    norm_kwargs = {"num_groups":32, "dtype":self.dtype}
    residual = x
    y = self.conv(self.filters, (1, 1))(x)
    y = nn.GroupNorm(**norm_kwargs)(y)
    y = self.act(y)
    y = self.conv(self.filters, (3, 3), self.strides, kernel_dilation=(2,2))(y)
    y = nn.GroupNorm(**norm_kwargs)(y)
    y = self.act(y)
    y = self.conv(self.filters * 4, (1, 1))(y)
    y = nn.GroupNorm(scale_init=nn.initializers.zeros_init(), **norm_kwargs)(y)

    if residual.shape != y.shape:
      residual = self.conv(self.filters * 4, (1, 1),
                           self.strides, name='conv_proj')(residual)
      residual = nn.GroupNorm(**norm_kwargs)(residual)

    return self.act(residual + y)


class ResNet(nn.Module):
  """ResNetV1."""
  stage_sizes: Sequence[int]
  block_cls: ModuleDef
  num_filters: int = 64
  dtype: Any = jnp.float32
  act: Callable = nn.relu
  conv: ModuleDef = nn.Conv

  @nn.compact
  def __call__(self, x, train: bool = True):
    conv = partial(self.conv, use_bias=False, dtype=self.dtype)
    norm_kwargs = {"num_groups":32, "dtype":self.dtype}

    x = conv(self.num_filters, (7, 7), (2, 2),
             padding=[(3, 3), (3, 3)],
             name='conv_init')(x)
    x = nn.GroupNorm(**norm_kwargs)(x)
    x = nn.relu(x)
    x = nn.max_pool(x, (3, 3), strides=(2, 2), padding='SAME')
    for i, block_size in enumerate(self.stage_sizes):
      for j in range(block_size):
        strides = (2, 2) if i > 0 and j == 0 else (1, 1)
        x = self.block_cls(self.num_filters * 2 ** i,
                           strides=strides,
                           conv=conv,
                           act=self.act)(x)
    x = jnp.asarray(x, self.dtype)
    return x


ResNet18 = partial(ResNet, stage_sizes=[2, 2, 2, 2],
                   block_cls=ResNetBlock)
ResNet34 = partial(ResNet, stage_sizes=[3, 4, 6, 3],
                   block_cls=ResNetBlock)
ResNet50 = partial(ResNet, stage_sizes=[3, 4, 6, 3],
                   block_cls=BottleneckResNetBlock)
ResNet101 = partial(ResNet, stage_sizes=[3, 4, 23, 3],
                    block_cls=BottleneckResNetBlock)
ResNet152 = partial(ResNet, stage_sizes=[3, 8, 36, 3],
                    block_cls=BottleneckResNetBlock)
ResNet200 = partial(ResNet, stage_sizes=[3, 24, 36, 3],
                    block_cls=BottleneckResNetBlock)


ResNet18Local = partial(ResNet, stage_sizes=[2, 2, 2, 2],
                        block_cls=ResNetBlock, conv=nn.ConvLocal)