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slowfast_nfnet.py
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slowfast_nfnet.py
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# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Slowfast Norm-Free Nets (https://arxiv.org/abs/2111.12124).
This class of network contains a fast and a slow branch, both based on the NFNet
backbone (https://arxiv.org/abs/2102.06171). The is also a fushion layer mixing
fast features to the slow branch.
The NFNet backbone follows the original implementation:
https://github.com/deepmind/deepmind-research/blob/master/nfnets/nfnet.py
with changes to allow for customized kernel and stride patterns.
"""
from typing import Text, Optional, Sequence, Any
import chex
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
from slowfast_nfnets import base
class NFNet(hk.Module):
"""Normalizer-Free networks, The Next Generation."""
variant_dict = base.slowfast_nfnet_params
def __init__(self,
variant: Text = 'F0',
width: float = 1.0,
se_ratio: float = 0.5,
alpha: float = 0.2,
stochdepth_rate: float = 0.1,
drop_rate: Optional[float] = None,
activation: Text = 'gelu',
# Multiplier for the final conv channel count
final_conv_mult: int = 2,
final_conv_ch: Optional[int] = None,
use_two_convs: bool = True,
name: Optional[Text] = 'NFNet'):
super().__init__(name=name)
self.variant = variant
self.width = width
self.se_ratio = se_ratio
# Get variant info
block_params = self.variant_dict[self.variant]
self.width_pattern = block_params['width']
self.depth_pattern = block_params['depth']
self.bneck_pattern = block_params['expansion']
self.group_pattern = block_params['group_width']
self.big_pattern = block_params['big_width']
stem_kernel_pattern = block_params['stem_kernel_pattern']
stem_stride_pattern = block_params['stem_stride_pattern']
kernel_pattern = block_params['kernel_pattern']
stride_pattern = block_params['stride_pattern']
self.activation = base.nonlinearities[activation]
if drop_rate is None:
self.drop_rate = block_params['drop_rate']
else:
self.drop_rate = drop_rate
self.which_conv = base.WSConv2D
# Stem
ch = self.width_pattern[0] // 2
self.stem = hk.Sequential([
self.which_conv(ch // 8, kernel_shape=stem_kernel_pattern[0],
stride=stem_stride_pattern[0], padding='SAME',
name='stem_conv0'),
self.activation,
self.which_conv(ch // 4, kernel_shape=stem_kernel_pattern[1],
stride=stem_stride_pattern[1], padding='SAME',
name='stem_conv1'),
self.activation,
self.which_conv(ch // 2, kernel_shape=stem_kernel_pattern[2],
stride=stem_stride_pattern[2], padding='SAME',
name='stem_conv2'),
self.activation,
self.which_conv(ch, kernel_shape=stem_kernel_pattern[3],
stride=stem_stride_pattern[3], padding='SAME',
name='stem_conv3'),
])
# Body
self.blocks = []
expected_std = 1.0
num_blocks = sum(self.depth_pattern)
index = 0 # Overall block index
block_args = zip(self.width_pattern, self.depth_pattern, self.bneck_pattern,
self.group_pattern, self.big_pattern, kernel_pattern,
stride_pattern)
for (block_width, stage_depth, expand_ratio,
group_size, big_width, kernel, stride) in block_args:
for block_index in range(stage_depth):
# Scalar pre-multiplier so each block sees an N(0,1) input at init
beta = 1./ expected_std
# Block stochastic depth drop-rate
block_stochdepth_rate = stochdepth_rate * index / num_blocks
out_ch = (int(block_width * self.width))
self.blocks += [NFBlock(ch,
out_ch,
expansion=expand_ratio,
se_ratio=se_ratio,
group_size=group_size,
kernel_shape=kernel,
stride=stride if block_index == 0 else (1, 1),
beta=beta,
alpha=alpha,
activation=self.activation,
which_conv=self.which_conv,
stochdepth_rate=block_stochdepth_rate,
big_width=big_width,
use_two_convs=use_two_convs,
)]
ch = out_ch
index += 1
# Reset expected std but still give it 1 block of growth
if block_index == 0:
expected_std = 1.0
expected_std = (expected_std **2 + alpha**2)**0.5
# Head
if final_conv_mult is None:
if final_conv_ch is None:
raise ValueError('Must provide one of final_conv_mult or final_conv_ch')
ch = final_conv_ch
else:
ch = int(final_conv_mult * ch)
self.final_conv = self.which_conv(ch, kernel_shape=1,
padding='SAME', name='final_conv')
def __call__(self, x: chex.Array, is_training: bool) -> chex.Array:
"""Return the output of the final layer without any [log-]softmax."""
# Stem
out = self.stem(x)
# Blocks
for block in self.blocks:
out, _ = block(out, is_training=is_training)
# Final-conv->activation, pool, dropout, classify
out = self.activation(self.final_conv(out))
out = jnp.mean(out, [1, 2])
return out
class NFBlock(hk.Module):
"""Normalizer-Free Net Block."""
def __init__(self,
in_ch: int,
out_ch: int,
expansion: float = 0.5,
se_ratio: float = 0.5,
kernel_shape: Sequence[int] = (3, 1),
second_conv_kernel_shape: Sequence[int] = (1, 3),
group_size: int = 128,
stride: Sequence[int] = (1, 1),
beta: float = 1.0,
alpha: float = 0.2,
which_conv: Any = base.WSConv2D,
activation: Any = jax.nn.gelu,
big_width: bool = True,
use_two_convs: bool = True,
stochdepth_rate: Optional[float] = None,
name: Optional[Text] = None):
super().__init__(name=name)
self.in_ch, self.out_ch = in_ch, out_ch
self.expansion = expansion
self.se_ratio = se_ratio
self.kernel_shape = kernel_shape
self.activation = activation
self.beta, self.alpha = beta, alpha
# Mimic resnet style bigwidth scaling?
width = int((self.out_ch if big_width else self.in_ch) * expansion)
# Round expanded with based on group count
self.groups = width // group_size
self.width = group_size * self.groups
self.stride = stride
self.use_two_convs = use_two_convs
# Conv 0 (typically expansion conv)
self.conv0 = which_conv(self.width, kernel_shape=1, padding='SAME',
name='conv0')
# Grouped NxN conv
self.conv1 = which_conv(self.width, kernel_shape=kernel_shape,
stride=stride, padding='SAME',
feature_group_count=self.groups, name='conv1')
if self.use_two_convs:
self.conv1b = which_conv(self.width,
kernel_shape=second_conv_kernel_shape,
stride=1, padding='SAME',
feature_group_count=self.groups, name='conv1b')
# Conv 2, typically projection conv
self.conv2 = which_conv(self.out_ch, kernel_shape=1, padding='SAME',
name='conv2')
# Use shortcut conv on channel change or downsample.
self.use_projection = np.prod(stride) > 1 or self.in_ch != self.out_ch
if self.use_projection:
self.conv_shortcut = which_conv(self.out_ch, kernel_shape=1,
padding='SAME', name='conv_shortcut')
# Squeeze + Excite Module
self.se = base.SqueezeExcite(self.out_ch, self.out_ch, self.se_ratio)
# Are we using stochastic depth?
self._has_stochdepth = (stochdepth_rate is not None and
stochdepth_rate > 0. and stochdepth_rate < 1.0)
if self._has_stochdepth:
self.stoch_depth = base.StochDepth(stochdepth_rate)
def __call__(self, x: chex.Array, is_training: bool) -> chex.Array:
out = self.activation(x) * self.beta
if np.prod(self.stride) > 1: # Average-pool downsample.
pool_size = [1] + list(self.stride) + [1]
shortcut = hk.avg_pool(out, window_shape=pool_size,
strides=pool_size, padding='SAME')
if self.use_projection:
shortcut = self.conv_shortcut(shortcut)
elif self.use_projection:
shortcut = self.conv_shortcut(out)
else:
shortcut = x
out = self.conv0(out)
out = self.conv1(self.activation(out))
if self.use_two_convs:
out = self.conv1b(self.activation(out))
out = self.conv2(self.activation(out))
out = (self.se(out) * 2) * out # Multiply by 2 for rescaling
# Get average residual standard deviation for reporting metrics.
res_avg_var = jnp.mean(jnp.var(out, axis=[0, 1, 2]))
# Apply stochdepth if applicable.
if self._has_stochdepth:
out = self.stoch_depth(out, is_training)
# SkipInit Gain
out = out * hk.get_parameter('skip_gain', (), out.dtype, init=jnp.zeros)
return out * self.alpha + shortcut, res_avg_var
class FuseFast2Slow(hk.Module):
"""Fuses the information from the Fast pathway to the Slow pathway."""
def __init__(self,
channels: int,
kernel_size: Sequence[int] = (7, 1),
stride: Sequence[int] = (4, 1),
which_conv: Any = base.WSConv2D,
activation: Any = jax.nn.gelu,
name: Optional[Text] = None):
super().__init__(name=name)
self._activation = activation
self._which_conv = which_conv
self._conv_f2s = which_conv(channels, kernel_shape=kernel_size,
stride=stride, padding='SAME',
with_bias=True, name='conv_f2s')
def __call__(self, x_f: chex.Array, x_s: chex.Array,
is_training: bool) -> chex.Array:
fuse = self._conv_f2s(x_f)
fuse = self._activation(fuse)
x_s_fuse = jnp.concatenate([x_s, fuse], axis=-1)
return x_s_fuse
class SlowFastNFNet(hk.Module):
"""Slow fast NFNet-F0."""
def __init__(self,
variant: Text = 'F0',
time_ratio: int = 4,
activation: Any = jax.nn.gelu,
fusion_conv_channel_ratio: int = 2,
name: Optional[Text] = None):
super().__init__(name=name)
self._time_ratio = time_ratio
self._activation = activation
self._fast_net = NFNet(variant=f'{variant}-fast')
self._slow_net = NFNet(variant=f'{variant}-slow')
self._depth_pattern = self._slow_net.depth_pattern
self._fast2slow = []
for channels in self._fast_net.width_pattern[0] * np.array([1, 4, 8, 16]):
self._fast2slow.append(
FuseFast2Slow(
channels=channels * fusion_conv_channel_ratio))
def __call__(self, x: chex.Array, is_training: bool) -> chex.Array:
h_s = hk.max_pool(
x, window_shape=(1, self._time_ratio, 1, 1),
strides=(1, self._time_ratio, 1, 1),
padding='SAME')
h_f = x
h_s = self._slow_net.stem(h_s)
h_f = self._fast_net.stem(h_f)
depth = 0
for block_group, num_blocks in enumerate(self._depth_pattern):
f2s_block = self._fast2slow[block_group]
h_s = f2s_block(h_f, h_s, is_training)
for _ in range(num_blocks):
fast_block = self._fast_net.blocks[depth]
slow_block = self._slow_net.blocks[depth]
h_f, _ = fast_block(h_f, is_training=is_training)
h_s, _ = slow_block(h_s, is_training=is_training)
depth += 1
assert depth == len(self._fast_net.blocks)
h_f = self._activation(self._fast_net.final_conv(h_f))
h_s = self._activation(self._slow_net.final_conv(h_s))
h_f = jnp.mean(h_f, [1, 2])
h_s = jnp.mean(h_s, [1, 2])
out = jnp.concatenate([h_f, h_s], axis=-1)
return out