File size: 19,913 Bytes
e7824d0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 |
#-*- coding: utf-8 -*-
import math
import re
import collections
from functools import partial
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.utils import model_zoo
# 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',
'include_hm_decoder', 'head_conv', 'heads', 'num_layers', 'INIT_WEIGHTS',
'use_c2', 'use_c3', 'use_c4', 'use_c51', 'efpn', 'se_layer', 'tfpn'])
# 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'])
# Set GlobalParams and BlockArgs's defaults
GlobalParams.__new__.__defaults__ = (None,) * len(GlobalParams._fields)
BlockArgs.__new__.__defaults__ = (None,) * len(BlockArgs._fields)
# Swish activation function
if hasattr(nn, 'SiLU'):
Swish = nn.SiLU
else:
# For compatibility with old PyTorch versions
class Swish(nn.Module):
def forward(self, x):
return x * torch.sigmoid(x)
def round_filters(filters, global_params):
"""Calculate and round number of filters based on width multiplier.
Use width_coefficient, depth_divisor and min_depth of global_params.
Args:
filters (int): Filters number to be calculated.
global_params (namedtuple): Global params of the model.
Returns:
new_filters: New filters number after calculating.
"""
multiplier = global_params.width_coefficient
if not multiplier:
return filters
# TODO: modify the params names.
# maybe the names (width_divisor,min_width)
# are more suitable than (depth_divisor,min_depth).
divisor = global_params.depth_divisor
min_depth = global_params.min_depth
filters *= multiplier
min_depth = min_depth or divisor # pay attention to this line when using min_depth
# follow the formula transferred from official TensorFlow implementation
new_filters = max(min_depth, int(filters + divisor / 2) // divisor * divisor)
if new_filters < 0.9 * filters: # prevent rounding by more than 10%
new_filters += divisor
return int(new_filters)
def round_repeats(repeats, global_params):
"""Calculate module's repeat number of a block based on depth multiplier.
Use depth_coefficient of global_params.
Args:
repeats (int): num_repeat to be calculated.
global_params (namedtuple): Global params of the model.
Returns:
new repeat: New repeat number after calculating.
"""
multiplier = global_params.depth_coefficient
if not multiplier:
return repeats
# follow the formula transferred from official TensorFlow implementation
return int(math.ceil(multiplier * repeats))
def drop_connect(inputs, p, training):
"""Drop connect.
Args:
input (tensor: BCWH): Input of this structure.
p (float: 0.0~1.0): Probability of drop connection.
training (bool): The running mode.
Returns:
output: Output after drop connection.
"""
assert 0 <= p <= 1, 'p must be in range of [0,1]'
if not training:
return inputs
batch_size = inputs.shape[0]
keep_prob = 1 - p
# generate binary_tensor mask according to probability (p for 0, 1-p for 1)
random_tensor = keep_prob
random_tensor += torch.rand([batch_size, 1, 1, 1], dtype=inputs.dtype, device=inputs.device)
binary_tensor = torch.floor(random_tensor)
output = inputs / keep_prob * binary_tensor
return output
def get_same_padding_conv2d(image_size=None):
"""Chooses static padding if you have specified an image size, and dynamic padding otherwise.
Static padding is necessary for ONNX exporting of models.
Args:
image_size (int or tuple): Size of the image.
Returns:
Conv2dDynamicSamePadding or Conv2dStaticSamePadding.
"""
if image_size is None:
return Conv2dDynamicSamePadding
else:
return partial(Conv2dStaticSamePadding, image_size=image_size)
class Conv2dDynamicSamePadding(nn.Conv2d):
"""2D Convolutions like TensorFlow, for a dynamic image size.
The padding is operated in forward function by calculating dynamically.
"""
# Tips for 'SAME' mode padding.
# Given the following:
# i: width or height
# s: stride
# k: kernel size
# d: dilation
# p: padding
# Output after Conv2d:
# o = floor((i+p-((k-1)*d+1))/s+1)
# If o equals i, i = floor((i+p-((k-1)*d+1))/s+1),
# => p = (i-1)*s+((k-1)*d+1)-i
def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, groups=1, bias=True):
super().__init__(in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias)
self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]] * 2
def forward(self, x):
ih, iw = x.size()[-2:]
kh, kw = self.weight.size()[-2:]
sh, sw = self.stride
oh, ow = math.ceil(ih / sh), math.ceil(iw / sw) # change the output size according to stride ! ! !
pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)
pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)
if pad_h > 0 or pad_w > 0:
x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2])
return F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
class Conv2dStaticSamePadding(nn.Conv2d):
"""2D Convolutions like TensorFlow's 'SAME' mode, with the given input image size.
The padding mudule is calculated in construction function, then used in forward.
"""
# With the same calculation as Conv2dDynamicSamePadding
def __init__(self, in_channels, out_channels, kernel_size, stride=1, image_size=None, **kwargs):
super().__init__(in_channels, out_channels, kernel_size, stride, **kwargs)
self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]] * 2
# Calculate padding based on image size and save it
assert image_size is not None
ih, iw = (image_size, image_size) if isinstance(image_size, int) else image_size
kh, kw = self.weight.size()[-2:]
sh, sw = self.stride
oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)
pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)
if pad_h > 0 or pad_w > 0:
self.static_padding = nn.ZeroPad2d((pad_w // 2, pad_w - pad_w // 2,
pad_h // 2, pad_h - pad_h // 2))
else:
self.static_padding = nn.Identity()
def forward(self, x):
x = self.static_padding(x)
x = F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
return x
def get_model_params(model_name, override_params):
"""Get the block args and global params for a given model name.
Args:
model_name (str): Model's name.
override_params (dict): A dict to modify global_params.
Returns:
blocks_args, global_params
"""
if model_name.startswith('efficientnet'):
w, d, s, p = efficientnet_params(model_name)
# note: all models have drop connect rate = 0.2
blocks_args, global_params = efficientnet(
width_coefficient=w, depth_coefficient=d, dropout_rate=p, image_size=s)
else:
raise NotImplementedError('model name is not pre-defined: {}'.format(model_name))
if override_params:
# ValueError will be raised here if override_params has fields not included in global_params.
global_params = global_params._replace(**override_params)
return blocks_args, global_params
def efficientnet_params(model_name):
"""Map EfficientNet model name to parameter coefficients.
Args:
model_name (str): Model name to be queried.
Returns:
params_dict[model_name]: A (width,depth,res,dropout) tuple.
"""
params_dict = {
# Coefficients: width,depth,res,dropout
'efficientnet-b0': (1.0, 1.0, 224, 0.2),
'efficientnet-b1': (1.0, 1.1, 240, 0.2),
'efficientnet-b2': (1.1, 1.2, 260, 0.3),
'efficientnet-b3': (1.2, 1.4, 300, 0.3),
'efficientnet-b4': (1.4, 1.8, 380, 0.4),
'efficientnet-b5': (1.6, 2.2, 456, 0.4),
'efficientnet-b6': (1.8, 2.6, 528, 0.5),
'efficientnet-b7': (2.0, 3.1, 600, 0.5),
'efficientnet-b8': (2.2, 3.6, 672, 0.5),
'efficientnet-l2': (4.3, 5.3, 800, 0.5),
}
return params_dict[model_name]
def efficientnet(width_coefficient=None, depth_coefficient=None, image_size=None,
dropout_rate=0.2, drop_connect_rate=0.2, num_classes=1000,
include_top=True, include_hm_decoder=False, head_conv=None,
heads=None, use_c2=False, use_c3=False, use_c4=False, use_c51=False,
num_layers=None, INIT_WEIGHTS=None, efpn=False, se_layer=False, tfpn=False):
"""Create BlockArgs and GlobalParams for efficientnet model.
Args:
width_coefficient (float)
depth_coefficient (float)
image_size (int)
dropout_rate (float)
drop_connect_rate (float)
num_classes (int)
Meaning as the name suggests.
Returns:
blocks_args, global_params.
"""
# Blocks args for the whole model(efficientnet-b0 by default)
# It will be modified in the construction of EfficientNet Class according to model
blocks_args = [
'r1_k3_s11_e1_i32_o16_se0.25',
'r2_k3_s22_e6_i16_o24_se0.25',
'r2_k5_s22_e6_i24_o40_se0.25',
'r3_k3_s22_e6_i40_o80_se0.25',
'r3_k5_s11_e6_i80_o112_se0.25',
'r4_k5_s22_e6_i112_o192_se0.25',
'r1_k3_s11_e6_i192_o320_se0.25',
]
blocks_args = BlockDecoder.decode(blocks_args)
global_params = GlobalParams(
width_coefficient=width_coefficient,
depth_coefficient=depth_coefficient,
image_size=image_size,
dropout_rate=dropout_rate,
num_classes=num_classes,
batch_norm_momentum=0.99,
batch_norm_epsilon=1e-3,
drop_connect_rate=drop_connect_rate,
depth_divisor=8,
min_depth=None,
include_top=include_top,
include_hm_decoder=include_hm_decoder,
head_conv=head_conv,
heads=heads,
use_c2=use_c2,
use_c3=use_c3,
use_c4=use_c4,
use_c51=use_c51,
efpn=efpn,
tfpn=tfpn,
se_layer=se_layer,
num_layers=num_layers,
INIT_WEIGHTS=INIT_WEIGHTS
)
return blocks_args, global_params
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,
id_skip=('noskip' not in block_string))
@staticmethod
def _encode_block_string(block):
"""Encode a block to a string.
Args:
block (namedtuple): A BlockArgs type argument.
Returns:
block_string: A String form of BlockArgs.
"""
args = [
'r%d' % block.num_repeat,
'k%d' % block.kernel_size,
's%d%d' % (block.strides[0], block.strides[1]),
'e%s' % block.expand_ratio,
'i%d' % block.input_filters,
'o%d' % block.output_filters
]
if 0 < block.se_ratio <= 1:
args.append('se%s' % block.se_ratio)
if block.id_skip is False:
args.append('noskip')
return '_'.join(args)
@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
@staticmethod
def encode(blocks_args):
"""Encode a list of BlockArgs to a list of strings.
Args:
blocks_args (list[namedtuples]): A list of BlockArgs namedtuples of block args.
Returns:
block_strings: A list of strings, each string is a notation of block.
"""
block_strings = []
for block in blocks_args:
block_strings.append(BlockDecoder._encode_block_string(block))
return block_strings
class SwishImplementation(torch.autograd.Function):
@staticmethod
def forward(ctx, i):
result = i * torch.sigmoid(i)
ctx.save_for_backward(i)
return result
@staticmethod
def backward(ctx, grad_output):
i = ctx.saved_tensors[0]
sigmoid_i = torch.sigmoid(i)
return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i)))
def get_width_and_height_from_size(x):
"""Obtain height and width from x.
Args:
x (int, tuple or list): Data size.
Returns:
size: A tuple or list (H,W).
"""
if isinstance(x, int):
return x, x
if isinstance(x, list) or isinstance(x, tuple):
return x
else:
raise TypeError()
def calculate_output_image_size(input_image_size, stride):
"""Calculates the output image size when using Conv2dSamePadding with a stride.
Necessary for static padding. Thanks to mannatsingh for pointing this out.
Args:
input_image_size (int, tuple or list): Size of input image.
stride (int, tuple or list): Conv2d operation's stride.
Returns:
output_image_size: A list [H,W].
"""
if input_image_size is None:
return None
image_height, image_width = get_width_and_height_from_size(input_image_size)
stride = stride if isinstance(stride, int) else stride[0]
image_height = int(math.ceil(image_height / stride))
image_width = int(math.ceil(image_width / stride))
return [image_height, image_width]
class MemoryEfficientSwish(nn.Module):
def forward(self, x):
return SwishImplementation.apply(x)
url_map_advprop = {
'efficientnet-b0': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b0-b64d5a18.pth',
'efficientnet-b1': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b1-0f3ce85a.pth',
'efficientnet-b2': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b2-6e9d97e5.pth',
'efficientnet-b3': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b3-cdd7c0f4.pth',
'efficientnet-b4': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b4-44fb3a87.pth',
'efficientnet-b5': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b5-86493f6b.pth',
'efficientnet-b6': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b6-ac80338e.pth',
'efficientnet-b7': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b7-4652b6dd.pth',
'efficientnet-b8': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b8-22a8fe65.pth',
}
url_map = {
'efficientnet-b0': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth',
'efficientnet-b1': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b1-f1951068.pth',
'efficientnet-b2': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b2-8bb594d6.pth',
'efficientnet-b3': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b3-5fb5a3c3.pth',
'efficientnet-b4': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b4-6ed6700e.pth',
'efficientnet-b5': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b5-b6417697.pth',
'efficientnet-b6': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b6-c76e70fd.pth',
'efficientnet-b7': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b7-dcc49843.pth',
}
def load_pretrained_weights(model, model_name, weights_path=None, load_fc=True, advprop=False, verbose=True):
"""Loads pretrained weights from weights path or download using url.
Args:
model (Module): The whole model of efficientnet.
model_name (str): Model name of efficientnet.
weights_path (None or str):
str: path to pretrained weights file on the local disk.
None: use pretrained weights downloaded from the Internet.
load_fc (bool): Whether to load pretrained weights for fc layer at the end of the model.
advprop (bool): Whether to load pretrained weights
trained with advprop (valid when weights_path is None).
"""
if isinstance(weights_path, str):
state_dict = torch.load(weights_path)
else:
# AutoAugment or Advprop (different preprocessing)
url_map_ = url_map_advprop if advprop else url_map
state_dict = model_zoo.load_url(url_map_[model_name])
if load_fc:
ret = model.load_state_dict(state_dict, strict=False)
assert not ret.missing_keys, 'Missing keys when loading pretrained weights: {}'.format(ret.missing_keys)
else:
state_dict.pop('_fc.weight')
state_dict.pop('_fc.bias')
ret = model.load_state_dict(state_dict, strict=False)
# if len(ret.missing_keys):
# assert set(ret.missing_keys) == set(
# ['_fc.weight', '_fc.bias']), 'Missing keys when loading pretrained weights: {}'.format(ret.missing_keys)
assert not ret.unexpected_keys, 'Missing keys when loading pretrained weights: {}'.format(ret.unexpected_keys)
if verbose:
print('Loaded pretrained weights for {}'.format(model_name))
|