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# Copyright 2024 Big Vision 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.
"""ResNet V1 with GroupNorm."""
from typing import Optional, Sequence, Union
from big_vision import utils
from big_vision.models import common
import flax
import flax.linen as nn
import flax.training.checkpoints
import jax.numpy as jnp
import numpy as np
def weight_standardize(w, axis, eps):
w = w - jnp.mean(w, axis=axis)
w = w / (jnp.std(w, axis=axis) + eps)
return w
class StdConv(nn.Conv):
def param(self, name, *a, **kw):
param = super().param(name, *a, **kw)
if name == "kernel":
param = weight_standardize(param, axis=[0, 1, 2], eps=1e-5)
return param
class ResidualUnit(nn.Module):
"""Bottleneck ResNet block."""
nmid: Optional[int] = None
strides: Sequence[int] = (1, 1)
@nn.compact
def __call__(self, x):
nmid = self.nmid or x.shape[-1] // 4
nout = nmid * 4
residual = x
if x.shape[-1] != nout or self.strides != (1, 1):
residual = StdConv(nout, (1, 1), self.strides, use_bias=False,
name="conv_proj")(residual)
residual = nn.GroupNorm(name="gn_proj")(residual)
y = StdConv(nmid, (1, 1), use_bias=False, name="conv1")(x)
y = nn.GroupNorm(name="gn1")(y)
y = nn.relu(y)
y = StdConv(nmid, (3, 3), self.strides, use_bias=False, name="conv2")(y)
y = nn.GroupNorm(name="gn2")(y)
y = nn.relu(y)
y = StdConv(nout, (1, 1), use_bias=False, name="conv3")(y)
y = nn.GroupNorm(name="gn3", scale_init=nn.initializers.zeros)(y)
y = nn.relu(residual + y)
return y
class ResNetStage(nn.Module):
"""One stage of ResNet."""
block_size: int
first_stride: Sequence[int] = (1, 1)
nmid: Optional[int] = None
@nn.compact
def __call__(self, x):
x = ResidualUnit(self.nmid, strides=self.first_stride, name="unit1")(x)
for i in range(1, self.block_size):
x = ResidualUnit(self.nmid, name=f"unit{i + 1}")(x)
return x
class Model(nn.Module):
"""ResNetV1."""
num_classes: Optional[int] = None
width: float = 1
depth: Union[int, Sequence[int]] = 50
@nn.compact
def __call__(self, image, *, train=False):
del train # Unused
blocks = get_block_desc(self.depth)
width = int(64 * self.width)
out = {}
# Root block
x = StdConv(width, (7, 7), (2, 2), use_bias=False, name="conv_root")(image)
x = nn.GroupNorm(name="gn_root")(x)
x = nn.relu(x)
x = nn.max_pool(x, (3, 3), strides=(2, 2), padding="SAME")
out["stem"] = x
# Stages
x = ResNetStage(blocks[0], nmid=width, name="block1")(x)
out["stage1"] = x
for i, block_size in enumerate(blocks[1:], 1):
x = ResNetStage(block_size, nmid=width * 2 ** i,
first_stride=(2, 2), name=f"block{i + 1}")(x)
out[f"stage{i + 1}"] = x
out["pre_logits_2d"] = x
# Head
x = out["pre_logits"] = jnp.mean(x, axis=(1, 2))
if self.num_classes:
head = nn.Dense(self.num_classes, name="head",
kernel_init=nn.initializers.zeros)
out["logits_2d"] = head(out["pre_logits_2d"])
x = out["logits"] = head(out["pre_logits"])
return x, out
# A dictionary mapping the number of layers in a resnet to the number of
# blocks in each stage of the model.
# NOTE: Does not include 18/34 as they also need non-bottleneck block!
def get_block_desc(depth):
if isinstance(depth, list): # Be robust to silly mistakes.
depth = tuple(depth)
return {
26: [2, 2, 2, 2], # From timm, gets ~75% on ImageNet.
50: [3, 4, 6, 3],
101: [3, 4, 23, 3],
152: [3, 8, 36, 3],
200: [3, 24, 36, 3]
}.get(depth, depth)
def fix_old_checkpoints(params):
"""Modifies params from old checkpoints to run with current implementation."""
params = flax.core.unfreeze(
flax.training.checkpoints.convert_pre_linen(params))
# Old linen used to store non-squeezed GN params.
params = flax.traverse_util.unflatten_dict({
k: np.squeeze(v) if (set(k)
& {"gn_root", "gn_proj", "gn1", "gn2", "gn3"}) else v
for k, v in flax.traverse_util.flatten_dict(params).items()
})
return params
def load(init_params, init_file, model_cfg, dont_load=()):
"""Load init from checkpoint."""
del model_cfg # Unused
params = utils.load_params(init_file)
params = common.merge_params(params, init_params, dont_load)
params = fix_old_checkpoints(params)
return params
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