mirror of
https://github.com/ROCm/jax.git
synced 2025-04-27 11:56:07 +00:00
146 lines
4.5 KiB
Python
146 lines
4.5 KiB
Python
# Copyright 2022 The JAX 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
|
|
#
|
|
# https://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.
|
|
"""ResNet from Flax example imagenet, see:
|
|
|
|
https://github.com/google/flax/tree/main/examples/imagenet
|
|
"""
|
|
|
|
# See issue #620.
|
|
# pytype: disable=wrong-arg-count
|
|
|
|
from collections.abc import Callable, Sequence
|
|
from functools import partial
|
|
from typing import Any
|
|
|
|
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)
|
|
|
|
@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):
|
|
"""Bottleneck ResNet block."""
|
|
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):
|
|
"""ResNetV1."""
|
|
stage_sizes: Sequence[int]
|
|
block_cls: ModuleDef
|
|
num_classes: int
|
|
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 = partial(nn.BatchNorm,
|
|
use_running_average=not train,
|
|
momentum=0.9,
|
|
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
|
|
|
|
|
|
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)
|
|
|
|
|
|
# Used for testing only.
|
|
_ResNet1 = partial(ResNet, stage_sizes=[1], block_cls=ResNetBlock)
|
|
_ResNet1Local = partial(ResNet, stage_sizes=[1], block_cls=ResNetBlock,
|
|
conv=nn.ConvLocal)
|