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utils.py
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utils.py
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import re
import math
import collections
from functools import partial
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils import model_zoo
########################################################################
############### HELPERS FUNCTIONS FOR MODEL ARCHITECTURE ###############
########################################################################
# Parameters for the entire model (stem, all blocks, and head)
GlobalParams = collections.namedtuple('GlobalParams', [
'batch_norm_momentum', 'batch_norm_epsilon', 'dropout_rate',
'num_classes', 'width_coefficient', 'depth_coefficient',
'depth_divisor', 'min_depth', 'drop_connect_rate', 'image_size'])
# Parameters for an individual model block
BlockArgs = collections.namedtuple('BlockArgs', [
'kernel_size', 'num_repeat', 'input_filters', 'output_filters',
'expand_ratio', 'id_skip', 'stride', 'se_ratio'])
# Change namedtuple defaults
GlobalParams.__new__.__defaults__ = (None,) * len(GlobalParams._fields)
BlockArgs.__new__.__defaults__ = (None,) * len(BlockArgs._fields)
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_variables[0]
sigmoid_i = torch.sigmoid(i)
return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i)))
class MemoryEfficientSwish(nn.Module):
def forward(self, x):
return SwishImplementation.apply(x)
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 depth multiplier. """
multiplier = global_params.width_coefficient
if not multiplier:
return filters
divisor = global_params.depth_divisor
min_depth = global_params.min_depth
filters *= multiplier
min_depth = min_depth or divisor
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):
""" Round number of filters based on depth multiplier. """
multiplier = global_params.depth_coefficient
if not multiplier:
return repeats
return int(math.ceil(multiplier * repeats))
def drop_connect(inputs, p, training):
""" Drop connect. """
if not training:
return inputs
batch_size = inputs.shape[0]
keep_prob = 1 - p
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. """
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 """
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)
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, for a fixed image size"""
def __init__(self, in_channels, out_channels, kernel_size, image_size=None, **kwargs):
super().__init__(in_channels, out_channels, kernel_size, **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 if type(image_size) == list else [
image_size, 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 = 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
class Identity(nn.Module):
def __init__(self, ):
super(Identity, self).__init__()
def forward(self, input):
return input
########################################################################
############## HELPERS FUNCTIONS FOR LOADING MODEL PARAMS ##############
########################################################################
def efficientnet_params(model_name):
""" Map EfficientNet model name to parameter coefficients. """
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),
}
return params_dict[model_name]
class BlockDecoder(object):
""" Block Decoder for readability, straight from the official TensorFlow repository """
@staticmethod
def _decode_block_string(block_string):
""" Gets a block through a string notation of arguments. """
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(
kernel_size=int(options['k']),
num_repeat=int(options['r']),
input_filters=int(options['i']),
output_filters=int(options['o']),
expand_ratio=int(options['e']),
id_skip=('noskip' not in block_string),
se_ratio=float(options['se']) if 'se' in options else None,
stride=[int(options['s'][0])])
@staticmethod
def _encode_block_string(block):
"""Encodes a block to a string."""
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):
"""
Decodes a list of string notations to specify blocks inside the network.
:param string_list: a list of strings, each string is a notation of block
:return: 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):
"""
Encodes a list of BlockArgs to a list of strings.
:param blocks_args: a list of BlockArgs namedtuples of block args
:return: 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
def efficientnet(width_coefficient=None, depth_coefficient=None, dropout_rate=0.2,
drop_connect_rate=0.2, image_size=None, num_classes=1000):
""" Creates a efficientnet 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_s22_e6_i80_o112_se0.25', 'r4_k5_s22_e6_i112_o192_se0.25',
'r1_k3_s22_e6_i192_o320_se0.25',
]
blocks_args = BlockDecoder.decode(blocks_args)
global_params = GlobalParams(
batch_norm_momentum=0.99,
batch_norm_epsilon=1e-3,
dropout_rate=dropout_rate,
drop_connect_rate=drop_connect_rate,
# data_format='channels_last', # removed, this is always true in PyTorch
num_classes=num_classes,
width_coefficient=width_coefficient,
depth_coefficient=depth_coefficient,
depth_divisor=8,
min_depth=None,
image_size=image_size,
)
return blocks_args, global_params
def get_model_params(model_name, override_params):
""" Get the block args and global params for a given model """
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: %s' % 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
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, load_fc=True):
""" Loads pretrained weights, and downloads if loading for the first time. """
state_dict = model_zoo.load_url(url_map[model_name])
if load_fc:
model.load_state_dict(state_dict)
else:
state_dict.pop('_fc.weight')
state_dict.pop('_fc.bias')
res = model.load_state_dict(state_dict, strict=False)
assert set(res.missing_keys) == set(
['_fc.weight', '_fc.bias']), 'issue loading pretrained weights'
print('Loaded pretrained weights for {}'.format(model_name))