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Add CondConv support for EfficientNet into WIP for GenEfficientNet Feature extraction setup

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pull/53/head
Ross Wightman 5 years ago
parent db04677c94
commit 506df0e3d0
4 changed files with 978 additions and 695 deletions
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120
timm/models/conv2d_helpers.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import math
def _is_static_pad(kernel_size, stride=1, dilation=1, **_):
return stride == 1 and (dilation * (kernel_size - 1)) % 2 == 0
def _get_padding(kernel_size, stride=1, dilation=1, **_):
padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
return padding
def _calc_same_pad(i, k, s, d):
return max((math.ceil(i / s) - 1) * s + (k - 1) * d + 1 - i, 0)
def _split_channels(num_chan, num_groups):
split = [num_chan // num_groups for _ in range(num_groups)]
split[0] += num_chan - sum(split)
return split
class Conv2dSame(nn.Conv2d):
""" Tensorflow like 'SAME' convolution wrapper for 2D convolutions
"""
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super(Conv2dSame, self).__init__(
in_channels, out_channels, kernel_size, stride, 0, dilation,
groups, bias)
def forward(self, x):
ih, iw = x.size()[-2:]
kh, kw = self.weight.size()[-2:]
pad_h = _calc_same_pad(ih, kh, self.stride[0], self.dilation[0])
pad_w = _calc_same_pad(iw, kw, self.stride[1], self.dilation[1])
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)
def conv2d_pad(in_chs, out_chs, kernel_size, **kwargs):
padding = kwargs.pop('padding', '')
kwargs.setdefault('bias', False)
if isinstance(padding, str):
# for any string padding, the padding will be calculated for you, one of three ways
padding = padding.lower()
if padding == 'same':
# TF compatible 'SAME' padding, has a performance and GPU memory allocation impact
if _is_static_pad(kernel_size, **kwargs):
# static case, no extra overhead
padding = _get_padding(kernel_size, **kwargs)
return nn.Conv2d(in_chs, out_chs, kernel_size, padding=padding, **kwargs)
else:
# dynamic padding
return Conv2dSame(in_chs, out_chs, kernel_size, **kwargs)
elif padding == 'valid':
# 'VALID' padding, same as padding=0
return nn.Conv2d(in_chs, out_chs, kernel_size, padding=0, **kwargs)
else:
# Default to PyTorch style 'same'-ish symmetric padding
padding = _get_padding(kernel_size, **kwargs)
return nn.Conv2d(in_chs, out_chs, kernel_size, padding=padding, **kwargs)
else:
# padding was specified as a number or pair
return nn.Conv2d(in_chs, out_chs, kernel_size, padding=padding, **kwargs)
class MixedConv2d(nn.Module):
""" Mixed Grouped Convolution
Based on MDConv and GroupedConv in MixNet impl:
https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mixnet/custom_layers.py
"""
def __init__(self, in_channels, out_channels, kernel_size=3,
stride=1, padding='', dilated=False, depthwise=False, **kwargs):
super(MixedConv2d, self).__init__()
kernel_size = kernel_size if isinstance(kernel_size, list) else [kernel_size]
num_groups = len(kernel_size)
in_splits = _split_channels(in_channels, num_groups)
out_splits = _split_channels(out_channels, num_groups)
for idx, (k, in_ch, out_ch) in enumerate(zip(kernel_size, in_splits, out_splits)):
d = 1
# FIXME make compat with non-square kernel/dilations/strides
if stride == 1 and dilated:
d, k = (k - 1) // 2, 3
conv_groups = out_ch if depthwise else 1
# use add_module to keep key space clean
self.add_module(
str(idx),
conv2d_pad(
in_ch, out_ch, k, stride=stride,
padding=padding, dilation=d, groups=conv_groups, **kwargs)
)
self.splits = in_splits
def forward(self, x):
x_split = torch.split(x, self.splits, 1)
x_out = [c(x) for x, c in zip(x_split, self._modules.values())]
x = torch.cat(x_out, 1)
return x
# helper method
def select_conv2d(in_chs, out_chs, kernel_size, **kwargs):
assert 'groups' not in kwargs # only use 'depthwise' bool arg
if isinstance(kernel_size, list):
# We're going to use only lists for defining the MixedConv2d kernel groups,
# ints, tuples, other iterables will continue to pass to normal conv and specify h, w.
return MixedConv2d(in_chs, out_chs, kernel_size, **kwargs)
else:
depthwise = kwargs.pop('depthwise', False)
groups = out_chs if depthwise else 1
return conv2d_pad(in_chs, out_chs, kernel_size, groups=groups, **kwargs)

255
timm/models/conv2d_layers.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch._six import container_abcs
from itertools import repeat
from functools import partial
import numpy as np
import math
def _ntuple(n):
def parse(x):
if isinstance(x, container_abcs.Iterable):
return x
return tuple(repeat(x, n))
return parse
_single = _ntuple(1)
_pair = _ntuple(2)
_triple = _ntuple(3)
_quadruple = _ntuple(4)
def _is_static_pad(kernel_size, stride=1, dilation=1, **_):
return stride == 1 and (dilation * (kernel_size - 1)) % 2 == 0
def _get_padding(kernel_size, stride=1, dilation=1, **_):
padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
return padding
def _calc_same_pad(i, k, s, d):
return max((math.ceil(i / s) - 1) * s + (k - 1) * d + 1 - i, 0)
def _split_channels(num_chan, num_groups):
split = [num_chan // num_groups for _ in range(num_groups)]
split[0] += num_chan - sum(split)
return split
# pylint: disable=unused-argument
def conv2d_same(x, weight, bias=None, stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1):
ih, iw = x.size()[-2:]
kh, kw = weight.size()[-2:]
pad_h = _calc_same_pad(ih, kh, stride[0], dilation[0])
pad_w = _calc_same_pad(iw, kw, stride[1], dilation[1])
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, weight, bias, stride, (0, 0), dilation, groups)
class Conv2dSame(nn.Conv2d):
""" Tensorflow like 'SAME' convolution wrapper for 2D convolutions
"""
# pylint: disable=unused-argument
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super(Conv2dSame, self).__init__(
in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias)
def forward(self, x):
return conv2d_same(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
def get_padding_value(padding, kernel_size, **kwargs):
dynamic = False
if isinstance(padding, str):
# for any string padding, the padding will be calculated for you, one of three ways
padding = padding.lower()
if padding == 'same':
# TF compatible 'SAME' padding, has a performance and GPU memory allocation impact
if _is_static_pad(kernel_size, **kwargs):
# static case, no extra overhead
padding = _get_padding(kernel_size, **kwargs)
else:
# dynamic padding
padding = 0
dynamic = True
elif padding == 'valid':
# 'VALID' padding, same as padding=0
padding = 0
else:
# Default to PyTorch style 'same'-ish symmetric padding
padding = _get_padding(kernel_size, **kwargs)
return padding, dynamic
def create_conv2d_pad(in_chs, out_chs, kernel_size, **kwargs):
padding = kwargs.pop('padding', '')
kwargs.setdefault('bias', False)
padding, is_dynamic = get_padding_value(padding, kernel_size, **kwargs)
if is_dynamic:
return Conv2dSame(in_chs, out_chs, kernel_size, **kwargs)
else:
return nn.Conv2d(in_chs, out_chs, kernel_size, padding=padding, **kwargs)
class MixedConv2d(nn.Module):
""" Mixed Grouped Convolution
Based on MDConv and GroupedConv in MixNet impl:
https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mixnet/custom_layers.py
"""
def __init__(self, in_channels, out_channels, kernel_size=3,
stride=1, padding='', dilation=1, mixed_dilated=False, depthwise=False, **kwargs):
super(MixedConv2d, self).__init__()
kernel_size = kernel_size if isinstance(kernel_size, list) else [kernel_size]
num_groups = len(kernel_size)
in_splits = _split_channels(in_channels, num_groups)
out_splits = _split_channels(out_channels, num_groups)
self.in_channels = sum(in_splits)
self.out_channels = sum(out_splits)
for idx, (k, in_ch, out_ch) in enumerate(zip(kernel_size, in_splits, out_splits)):
d = dilation
# FIXME make compat with non-square kernel/dilations/strides
if stride == 1 and mixed_dilated:
d, k = (k - 1) // 2, 3
conv_groups = out_ch if depthwise else 1
# use add_module to keep key space clean
self.add_module(
str(idx),
create_conv2d_pad(
in_ch, out_ch, k, stride=stride,
padding=padding, dilation=d, groups=conv_groups, **kwargs)
)
self.splits = in_splits
def forward(self, x):
x_split = torch.split(x, self.splits, 1)
x_out = [c(x) for x, c in zip(x_split, self._modules.values())]
x = torch.cat(x_out, 1)
return x
def get_condconv_initializer(initializer, num_experts, expert_shape):
def condconv_initializer(weight):
"""CondConv initializer function."""
num_params = np.prod(expert_shape)
if (len(weight.shape) != 2 or weight.shape[0] != num_experts or
weight.shape[1] != num_params):
raise (ValueError(
'CondConv variables must have shape [num_experts, num_params]'))
for i in range(num_experts):
initializer(weight[i].view(expert_shape))
return condconv_initializer
class CondConv2d(nn.Module):
""" Conditional Convolution
Inspired by: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/condconv/condconv_layers.py
"""
def __init__(self, in_channels, out_channels, kernel_size=3,
stride=1, padding='', dilation=1, groups=1, bias=False, num_experts=4):
super(CondConv2d, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = _pair(kernel_size)
self.stride = _pair(stride)
padding_val, is_padding_dynamic = get_padding_value(
padding, kernel_size, stride=stride, dilation=dilation)
self.conv_fn = conv2d_same if is_padding_dynamic else F.conv2d
self.padding = _pair(padding_val)
self.dilation = _pair(dilation)
self.transposed = False
self.output_padding = _pair(0)
self.groups = groups
self.padding_mode = 'zero'
self.num_experts = num_experts
self.weight_shape = (self.out_channels, self.in_channels // self.groups) + self.kernel_size
weight_num_param = 1
for wd in self.weight_shape:
weight_num_param *= wd
self.weight = torch.nn.Parameter(torch.Tensor(self.num_experts, weight_num_param))
# FIXME I haven't tested bias yet
if bias:
self.bias_shape = (self.out_channels,)
condconv_bias_shape = (self.num_experts, self.out_channels)
self.bias = torch.nn.Parameter(torch.Tensor(condconv_bias_shape))
else:
self.register_parameter('bias', None)
self.reset_parameters()
# FIXME once I'm satisfied this works, remove the looping path?
self._use_groups = True # use groups for parallel per-batch-element kernel convolution
def reset_parameters(self):
init_weight = get_condconv_initializer(
partial(nn.init.kaiming_uniform_, a=math.sqrt(5)), self.num_experts, self.weight_shape)
init_weight(self.weight)
if self.bias is not None:
# FIXME bias not tested
fan_in = np.prod(self.weight_shape[1:])
bound = 1 / math.sqrt(fan_in)
init_bias = get_condconv_initializer(
partial(nn.init.uniform_, a=-bound, b=bound), self.num_experts, self.bias_shape)
init_bias(self.bias)
def forward(self, x, routing_weights):
weight = torch.matmul(routing_weights, self.weight)
bias = torch.matmul(routing_weights, self.bias) if self.bias is not None else None
B, C, H, W = x.shape
if self._use_groups:
new_weight_shape = (B * self.out_channels, self.in_channels // self.groups) + self.kernel_size
weight = weight.view(new_weight_shape)
x = x.view(1, B * C, H, W)
out = self.conv_fn(
x, weight, bias, stride=self.stride, padding=self.padding,
dilation=self.dilation, groups=self.groups * B)
out = out.permute([1, 0, 2, 3]).view(B, self.out_channels, out.shape[-2], out.shape[-1])
else:
x = torch.split(x, 1, 0)
weight = torch.split(weight, 1, 0)
if self.bias is not None:
bias = torch.matmul(routing_weights, self.bias)
bias = torch.split(bias, 1, 0)
else:
bias = [None] * B
out = []
for xi, wi, bi in zip(x, weight, bias):
wi = wi.view(*self.weight_shape)
if bi is not None:
bi = bi.view(*self.bias_shape)
out.append(self.conv_fn(
xi, wi, bi, stride=self.stride, padding=self.padding,
dilation=self.dilation, groups=self.groups))
out = torch.cat(out, 0)
return out
# helper method
def select_conv2d(in_chs, out_chs, kernel_size, **kwargs):
assert 'groups' not in kwargs # only use 'depthwise' bool arg
if isinstance(kernel_size, list):
assert 'num_experts' not in kwargs # MixNet + CondConv combo not supported currently
# We're going to use only lists for defining the MixedConv2d kernel groups,
# ints, tuples, other iterables will continue to pass to normal conv and specify h, w.
return MixedConv2d(in_chs, out_chs, kernel_size, **kwargs)
else:
depthwise = kwargs.pop('depthwise', False)
groups = out_chs if depthwise else 1
if 'num_experts' in kwargs and kwargs['num_experts'] > 0:
create_fn = CondConv2d
else:
create_fn = create_conv2d_pad
return create_fn(in_chs, out_chs, kernel_size, groups=groups, **kwargs)

1295
timm/models/gen_efficientnet.py
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3
timm/models/helpers.py
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@ -57,7 +57,7 @@ def resume_checkpoint(model, checkpoint_path):
raise FileNotFoundError()
def load_pretrained(model, cfg=None, num_classes=1000, in_chans=3, filter_fn=None):
def load_pretrained(model, cfg=None, num_classes=1000, in_chans=3, filter_fn=None, strict=True):
if cfg is None:
cfg = getattr(model, 'default_cfg')
if cfg is None or 'url' not in cfg or not cfg['url']:
@ -74,7 +74,6 @@ def load_pretrained(model, cfg=None, num_classes=1000, in_chans=3, filter_fn=Non
elif in_chans != 3:
assert False, "Invalid in_chans for pretrained weights"
strict = True
classifier_name = cfg['classifier']
if num_classes == 1000 and cfg['num_classes'] == 1001:
# special case for imagenet trained models with extra background class in pretrained weights

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