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utils.py
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utils.py
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# coding: utf-8
# Author: lingff (ling@stu.pku.edu.cn)
# Description: For EfficientNet V2 utils.
# Create: 2021-12-2
import re
import collections
################################################################################
# Helper functions for loading model params
################################################################################
# BlockDecoder: A Class for encoding and decoding BlockArgs
# efficientnet_params: A function to query compound coefficient
# get_model_params and efficientnet:
# Functions to get BlockArgs and GlobalParams for efficientnet
# url_map and url_map_advprop: Dicts of url_map for pretrained weights
# load_pretrained_weights: A function to load pretrained weights
# Parameters for the entire model (stem, all blocks, and head)
GlobalParams = collections.namedtuple('GlobalParams', [
'width_coefficient', 'depth_coefficient', 'image_size', 'dropout_rate',
'num_classes', 'batch_norm_momentum', 'batch_norm_epsilon',
'drop_connect_rate', 'depth_divisor', 'min_depth', 'include_top'])
# Parameters for an individual model block
BlockArgs = collections.namedtuple('BlockArgs', [
'num_repeat', 'kernel_size', 'stride', 'expand_ratio',
'input_filters', 'output_filters', 'se_ratio', 'id_skip', 'fused'])
class BlockDecoder(object):
"""Block Decoder for readability,
straight from the official TensorFlow repository.
"""
@staticmethod
def _decode_block_string(block_string):
"""Get a block through a string notation of arguments.
Args:
block_string (str): A string notation of arguments.
Examples: 'r1_k3_s11_e1_i32_o16_se0.25_noskip'.
Returns:
BlockArgs: The namedtuple defined at the top of this file.
"""
assert isinstance(block_string, str)
ops = block_string.split('_')
options = {}
for op in ops:
splits = re.split(r'(\d.*)', op)
if len(splits) >= 2:
key, value = splits[:2]
options[key] = value
# Check stride
assert (('s' in options and len(options['s']) == 1) or
(len(options['s']) == 2 and options['s'][0] == options['s'][1]))
return BlockArgs(
num_repeat=int(options['r']),
kernel_size=int(options['k']),
stride=[int(options['s'][0])],
expand_ratio=int(options['e']),
input_filters=int(options['i']),
output_filters=int(options['o']),
se_ratio=float(options['se']) if 'se' in options else None,
fused=('f' in block_string),
id_skip=('noskip' not in block_string))
@staticmethod
def decode(string_list):
"""Decode a list of string notations to specify blocks inside the network.
Args:
string_list (list[str]): A list of strings, each string is a notation of block.
Returns:
blocks_args: A list of BlockArgs namedtuples of block args.
"""
assert isinstance(string_list, list)
blocks_args = []
for block_string in string_list:
blocks_args.append(BlockDecoder._decode_block_string(block_string))
return blocks_args
def get_efficientnetv2_params(model_name, num_classes):
#################### EfficientNet V2 configs ####################
v2_base_block = [ # The baseline config for v2 models.
'r1_k3_s1_e1_i32_o16_f',
'r2_k3_s2_e4_i16_o32_f',
'r2_k3_s2_e4_i32_o48_f',
'r3_k3_s2_e4_i48_o96_se0.25',
'r5_k3_s1_e6_i96_o112_se0.25',
'r8_k3_s2_e6_i112_o192_se0.25',
]
v2_s_block = [ # about base * (width1.4, depth1.8)
'r2_k3_s1_e1_i24_o24_f',
'r4_k3_s2_e4_i24_o48_f',
'r4_k3_s2_e4_i48_o64_f',
'r6_k3_s2_e4_i64_o128_se0.25',
'r9_k3_s1_e6_i128_o160_se0.25',
'r15_k3_s2_e6_i160_o256_se0.25',
]
v2_m_block = [ # about base * (width1.6, depth2.2)
'r3_k3_s1_e1_i24_o24_f',
'r5_k3_s2_e4_i24_o48_f',
'r5_k3_s2_e4_i48_o80_f',
'r7_k3_s2_e4_i80_o160_se0.25',
'r14_k3_s1_e6_i160_o176_se0.25',
'r18_k3_s2_e6_i176_o304_se0.25',
'r5_k3_s1_e6_i304_o512_se0.25',
]
v2_l_block = [ # about base * (width2.0, depth3.1)
'r4_k3_s1_e1_i32_o32_f',
'r7_k3_s2_e4_i32_o64_f',
'r7_k3_s2_e4_i64_o96_f',
'r10_k3_s2_e4_i96_o192_se0.25',
'r19_k3_s1_e6_i192_o224_se0.25',
'r25_k3_s2_e6_i224_o384_se0.25',
'r7_k3_s1_e6_i384_o640_se0.25',
]
v2_xl_block = [ # only for 21k pretraining.
'r4_k3_s1_e1_i32_o32_f',
'r8_k3_s2_e4_i32_o64_f',
'r8_k3_s2_e4_i64_o96_f',
'r16_k3_s2_e4_i96_o192_se0.25',
'r24_k3_s1_e6_i192_o256_se0.25',
'r32_k3_s2_e6_i256_o512_se0.25',
'r8_k3_s1_e6_i512_o640_se0.25',
]
efficientnetv2_params = {
# (block, width, depth, train_size, eval_size, dropout, randaug, mixup, aug)
'efficientnetv2-s': # 83.9% @ 22M
(v2_s_block, 1.0, 1.0, 300, 384, 0.2, 10, 0, 'randaug'),
'efficientnetv2-m': # 85.2% @ 54M
(v2_m_block, 1.0, 1.0, 384, 480, 0.3, 15, 0.2, 'randaug'),
'efficientnetv2-l': # 85.7% @ 120M
(v2_l_block, 1.0, 1.0, 384, 480, 0.4, 20, 0.5, 'randaug'),
'efficientnetv2-xl':
(v2_xl_block, 1.0, 1.0, 384, 512, 0.4, 20, 0.5, 'randaug'),
# For fair comparison to EfficientNetV1, using the same scaling and autoaug.
'efficientnetv2-b0': # 78.7% @ 7M params
(v2_base_block, 1.0, 1.0, 192, 224, 0.2, 0, 0, 'effnetv1_autoaug'),
'efficientnetv2-b1': # 79.8% @ 8M params
(v2_base_block, 1.0, 1.1, 192, 240, 0.2, 0, 0, 'effnetv1_autoaug'),
'efficientnetv2-b2': # 80.5% @ 10M params
(v2_base_block, 1.1, 1.2, 208, 260, 0.3, 0, 0, 'effnetv1_autoaug'),
'efficientnetv2-b3': # 82.1% @ 14M params
(v2_base_block, 1.2, 1.4, 240, 300, 0.3, 0, 0, 'effnetv1_autoaug'),
}
assert model_name in list(efficientnetv2_params.keys()), "Wrong model name."
all_params = efficientnetv2_params[model_name]
blocks_args = BlockDecoder.decode(all_params[0])
global_params = GlobalParams(
width_coefficient=all_params[1],
depth_coefficient=all_params[2],
image_size=all_params[3],
dropout_rate=all_params[5],
num_classes=num_classes,
batch_norm_momentum=None, #0.99,
batch_norm_epsilon=None, #1e-3,
drop_connect_rate=None, #drop_connect_rate,
depth_divisor=None, #8,
min_depth=None, #None,
include_top=None, #include_top,
)
return blocks_args, global_params
class AverageMeter(object):
"""Computes and stores the average and current value
Imported from https://github.com/pytorch/examples/blob/master/imagenet/main.py#L247-L262
"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
# # Myself, not good
# def accuracy(preds, targets):
# with torch.no_grad():
# batch_size = targets.size(0)
# pred_index = torch.argmax(preds, dim=1)
# correct = pred_index.eq(targets.view(1, -1))
# acc1 = correct.sum().float().mul_(100.0 / batch_size)
# return acc1
# good
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
if __name__ == '__main__':
b, g = get_efficientnetv2_params("efficientnetv2-s", 10)
print(b[0].input_filters)