models.py

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):
  filters: int
  conv: ModuleDef
  norm: ModuleDef
  act: Callable
  strides: Tuple[int, int] = (1, 1)

  @nn.compact
  def __call__(self, x):
    residual = x
    y = self.conv(self.filters, (3, 3), self.strides)(x)
    y = self.norm()(y)
    y = self.act(y)
    y = self.conv(self.filters, (3, 3))(y)
    y = self.norm(scale_init=nn.initializers.zeros)(y)

    if residual.shape != y.shape:
      residual = self.conv(self.filters, (1, 1),
                           self.strides, name='conv_proj')(residual)
      residual = self.norm(name='norm_proj')(residual)

    return self.act(residual + y)


class BottleneckResNetBlock(nn.Module):
  filters: int
  conv: ModuleDef
  norm: ModuleDef
  act: Callable
  strides: Tuple[int, int] = (1, 1)

  @nn.compact
  def __call__(self, x):
    residual = x
    y = self.conv(self.filters, (1, 1))(x)
    y = self.norm()(y)
    y = self.act(y)
    y = self.conv(self.filters, (3, 3), self.strides)(y)
    y = self.norm()(y)
    y = self.act(y)
    y = self.conv(self.filters * 4, (1, 1))(y)
    y = self.norm(scale_init=nn.initializers.zeros)(y)

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

    return self.act(residual + y)


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

  @nn.compact
  def __call__(self, x, train: bool = True):
    conv = partial(nn.Conv, use_bias=False, dtype=self.dtype)
    norm = partial(nn.BatchNorm,
                   use_running_average=not train,
                   momentum=0.8,
                   epsilon=1e-5,
                   dtype=self.dtype)
    x = conv(self.num_filters, (7, 7), (2, 2),
             padding=[(3, 3), (3, 3)],
             name='conv_init')(x)
    x = norm(name='bn_init')(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,
                           norm=norm,
                           act=self.act)(x)
    x = jnp.mean(x, axis=(1, 2))
    x = nn.Dense(self.num_classes, dtype=self.dtype)(x)
    x = jnp.asarray(x, self.dtype)
    return x


ResNet50 = partial(ResNet, stage_sizes=[3, 4, 6, 3],
                   block_cls=BottleneckResNetBlock)