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370 lines
14 KiB
370 lines
14 KiB
from collections import OrderedDict
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch._six import container_abcs
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from itertools import repeat
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from functools import partial
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import numpy as np
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import math
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# Tuple helpers ripped from PyTorch
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def _ntuple(n):
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def parse(x):
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if isinstance(x, container_abcs.Iterable):
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return x
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return tuple(repeat(x, n))
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return parse
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_single = _ntuple(1)
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_pair = _ntuple(2)
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_triple = _ntuple(3)
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_quadruple = _ntuple(4)
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def _is_static_pad(kernel_size, stride=1, dilation=1, **_):
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return stride == 1 and (dilation * (kernel_size - 1)) % 2 == 0
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def _get_padding(kernel_size, stride=1, dilation=1, **_):
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padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
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return padding
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def _calc_same_pad(i, k, s, d):
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return max((math.ceil(i / s) - 1) * s + (k - 1) * d + 1 - i, 0)
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def _split_channels(num_chan, num_groups):
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split = [num_chan // num_groups for _ in range(num_groups)]
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split[0] += num_chan - sum(split)
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return split
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# pylint: disable=unused-argument
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def conv2d_same(x, weight, bias=None, stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1):
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ih, iw = x.size()[-2:]
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kh, kw = weight.size()[-2:]
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pad_h = _calc_same_pad(ih, kh, stride[0], dilation[0])
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pad_w = _calc_same_pad(iw, kw, stride[1], dilation[1])
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if pad_h > 0 or pad_w > 0:
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x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2])
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return F.conv2d(x, weight, bias, stride, (0, 0), dilation, groups)
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class Conv2dSame(nn.Conv2d):
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""" Tensorflow like 'SAME' convolution wrapper for 2D convolutions
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"""
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# pylint: disable=unused-argument
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def __init__(self, in_channels, out_channels, kernel_size, stride=1,
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padding=0, dilation=1, groups=1, bias=True):
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super(Conv2dSame, self).__init__(
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in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias)
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def forward(self, x):
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return conv2d_same(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
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def get_padding_value(padding, kernel_size, **kwargs):
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dynamic = False
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if isinstance(padding, str):
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# for any string padding, the padding will be calculated for you, one of three ways
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padding = padding.lower()
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if padding == 'same':
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# TF compatible 'SAME' padding, has a performance and GPU memory allocation impact
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if _is_static_pad(kernel_size, **kwargs):
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# static case, no extra overhead
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padding = _get_padding(kernel_size, **kwargs)
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else:
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# dynamic 'SAME' padding, has runtime/GPU memory overhead
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padding = 0
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dynamic = True
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elif padding == 'valid':
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# 'VALID' padding, same as padding=0
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padding = 0
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else:
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# Default to PyTorch style 'same'-ish symmetric padding
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padding = _get_padding(kernel_size, **kwargs)
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return padding, dynamic
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def create_conv2d_pad(in_chs, out_chs, kernel_size, **kwargs):
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padding = kwargs.pop('padding', '')
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kwargs.setdefault('bias', False)
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padding, is_dynamic = get_padding_value(padding, kernel_size, **kwargs)
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if is_dynamic:
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return Conv2dSame(in_chs, out_chs, kernel_size, **kwargs)
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else:
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return nn.Conv2d(in_chs, out_chs, kernel_size, padding=padding, **kwargs)
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class MixedConv2d(nn.ModuleDict):
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""" Mixed Grouped Convolution
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Based on MDConv and GroupedConv in MixNet impl:
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https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mixnet/custom_layers.py
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"""
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def __init__(self, in_channels, out_channels, kernel_size=3,
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stride=1, padding='', dilation=1, depthwise=False, **kwargs):
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super(MixedConv2d, self).__init__()
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kernel_size = kernel_size if isinstance(kernel_size, list) else [kernel_size]
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num_groups = len(kernel_size)
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in_splits = _split_channels(in_channels, num_groups)
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out_splits = _split_channels(out_channels, num_groups)
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self.in_channels = sum(in_splits)
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self.out_channels = sum(out_splits)
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for idx, (k, in_ch, out_ch) in enumerate(zip(kernel_size, in_splits, out_splits)):
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conv_groups = out_ch if depthwise else 1
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# use add_module to keep key space clean
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self.add_module(
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str(idx),
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create_conv2d_pad(
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in_ch, out_ch, k, stride=stride,
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padding=padding, dilation=dilation, groups=conv_groups, **kwargs)
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)
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self.splits = in_splits
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def forward(self, x):
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x_split = torch.split(x, self.splits, 1)
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x_out = [c(x_split[i]) for i, c in enumerate(self.values())]
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x = torch.cat(x_out, 1)
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return x
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def get_condconv_initializer(initializer, num_experts, expert_shape):
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def condconv_initializer(weight):
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"""CondConv initializer function."""
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num_params = np.prod(expert_shape)
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if (len(weight.shape) != 2 or weight.shape[0] != num_experts or
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weight.shape[1] != num_params):
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raise (ValueError(
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'CondConv variables must have shape [num_experts, num_params]'))
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for i in range(num_experts):
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initializer(weight[i].view(expert_shape))
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return condconv_initializer
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class CondConv2d(nn.Module):
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""" Conditional Convolution
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Inspired by: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/condconv/condconv_layers.py
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Grouped convolution hackery for parallel execution of the per-sample kernel filters inspired by this discussion:
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https://github.com/pytorch/pytorch/issues/17983
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"""
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__constants__ = ['bias', 'in_channels', 'out_channels', 'dynamic_padding']
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def __init__(self, in_channels, out_channels, kernel_size=3,
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stride=1, padding='', dilation=1, groups=1, bias=False, num_experts=4):
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super(CondConv2d, self).__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.kernel_size = _pair(kernel_size)
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self.stride = _pair(stride)
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padding_val, is_padding_dynamic = get_padding_value(
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padding, kernel_size, stride=stride, dilation=dilation)
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self.dynamic_padding = is_padding_dynamic # if in forward to work with torchscript
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self.padding = _pair(padding_val)
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self.dilation = _pair(dilation)
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self.groups = groups
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self.num_experts = num_experts
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self.weight_shape = (self.out_channels, self.in_channels // self.groups) + self.kernel_size
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weight_num_param = 1
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for wd in self.weight_shape:
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weight_num_param *= wd
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self.weight = torch.nn.Parameter(torch.Tensor(self.num_experts, weight_num_param))
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if bias:
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self.bias_shape = (self.out_channels,)
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self.bias = torch.nn.Parameter(torch.Tensor(self.num_experts, self.out_channels))
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else:
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self.register_parameter('bias', None)
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self.reset_parameters()
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def reset_parameters(self):
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init_weight = get_condconv_initializer(
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partial(nn.init.kaiming_uniform_, a=math.sqrt(5)), self.num_experts, self.weight_shape)
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init_weight(self.weight)
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if self.bias is not None:
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fan_in = np.prod(self.weight_shape[1:])
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bound = 1 / math.sqrt(fan_in)
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init_bias = get_condconv_initializer(
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partial(nn.init.uniform_, a=-bound, b=bound), self.num_experts, self.bias_shape)
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init_bias(self.bias)
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def forward(self, x, routing_weights):
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B, C, H, W = x.shape
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weight = torch.matmul(routing_weights, self.weight)
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new_weight_shape = (B * self.out_channels, self.in_channels // self.groups) + self.kernel_size
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weight = weight.view(new_weight_shape)
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bias = None
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if self.bias is not None:
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bias = torch.matmul(routing_weights, self.bias)
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bias = bias.view(B * self.out_channels)
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# move batch elements with channels so each batch element can be efficiently convolved with separate kernel
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x = x.view(1, B * C, H, W)
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if self.dynamic_padding:
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out = conv2d_same(
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x, weight, bias, stride=self.stride, padding=self.padding,
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dilation=self.dilation, groups=self.groups * B)
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else:
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out = F.conv2d(
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x, weight, bias, stride=self.stride, padding=self.padding,
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dilation=self.dilation, groups=self.groups * B)
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out = out.permute([1, 0, 2, 3]).view(B, self.out_channels, out.shape[-2], out.shape[-1])
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# Literal port (from TF definition)
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# x = torch.split(x, 1, 0)
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# weight = torch.split(weight, 1, 0)
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# if self.bias is not None:
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# bias = torch.matmul(routing_weights, self.bias)
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# bias = torch.split(bias, 1, 0)
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# else:
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# bias = [None] * B
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# out = []
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# for xi, wi, bi in zip(x, weight, bias):
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# wi = wi.view(*self.weight_shape)
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# if bi is not None:
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# bi = bi.view(*self.bias_shape)
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# out.append(self.conv_fn(
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# xi, wi, bi, stride=self.stride, padding=self.padding,
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# dilation=self.dilation, groups=self.groups))
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# out = torch.cat(out, 0)
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return out
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class SelectiveKernelAttn(nn.Module):
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def __init__(self, channels, num_paths=2, attn_channels=32,
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act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d):
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super(SelectiveKernelAttn, self).__init__()
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self.num_paths = num_paths
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self.pool = nn.AdaptiveAvgPool2d(1)
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self.fc_reduce = nn.Conv2d(channels, attn_channels, kernel_size=1, bias=False)
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self.bn = norm_layer(attn_channels)
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self.act = act_layer(inplace=True)
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self.fc_select = nn.Conv2d(attn_channels, channels * num_paths, kernel_size=1, bias=False)
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def forward(self, x):
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assert x.shape[1] == self.num_paths
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x = torch.sum(x, dim=1)
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x = self.pool(x)
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x = self.fc_reduce(x)
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x = self.bn(x)
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x = self.act(x)
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x = self.fc_select(x)
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B, C, H, W = x.shape
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x = x.view(B, self.num_paths, C // self.num_paths, H, W)
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x = torch.softmax(x, dim=1)
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return x
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def _kernel_valid(k):
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if isinstance(k, (list, tuple)):
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for ki in k:
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return _kernel_valid(ki)
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assert k >= 3 and k % 2
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class ConvBnAct(nn.Module):
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def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, dilation=1, groups=1,
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drop_block=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d):
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super(ConvBnAct, self).__init__()
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padding = _get_padding(kernel_size, stride, dilation) # assuming PyTorch style padding for this block
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self.conv = nn.Conv2d(
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in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride,
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padding=padding, dilation=dilation, groups=groups, bias=False)
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self.bn = norm_layer(out_channels)
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self.drop_block = drop_block
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if act_layer is not None:
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self.act = act_layer(inplace=True)
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else:
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self.act = None
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def forward(self, x):
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x = self.conv(x)
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x = self.bn(x)
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if self.drop_block is not None:
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x = self.drop_block(x)
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if self.act is not None:
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x = self.act(x)
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return x
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class SelectiveKernelConv(nn.Module):
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def __init__(self, in_channels, out_channels, kernel_size=None, stride=1, dilation=1, groups=1,
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attn_reduction=16, min_attn_channels=32, keep_3x3=True, split_input=False,
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drop_block=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d):
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super(SelectiveKernelConv, self).__init__()
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kernel_size = kernel_size or [3, 5]
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_kernel_valid(kernel_size)
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if not isinstance(kernel_size, list):
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kernel_size = [kernel_size] * 2
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if keep_3x3:
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dilation = [dilation * (k - 1) // 2 for k in kernel_size]
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kernel_size = [3] * len(kernel_size)
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else:
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dilation = [dilation] * len(kernel_size)
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num_paths = len(kernel_size)
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self.num_paths = num_paths
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self.split_input = split_input
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self.in_channels = in_channels
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self.out_channels = out_channels
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if split_input:
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assert in_channels % num_paths == 0 and out_channels % num_paths == 0
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in_channels = in_channels // num_paths
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out_channels = out_channels // num_paths
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groups = min(out_channels, groups)
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conv_kwargs = dict(
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stride=stride, groups=groups, drop_block=drop_block, act_layer=act_layer, norm_layer=norm_layer)
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self.paths = nn.ModuleList([
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ConvBnAct(in_channels, out_channels, kernel_size=k, dilation=d, **conv_kwargs)
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for k, d in zip(kernel_size, dilation)])
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attn_channels = max(int(out_channels / attn_reduction), min_attn_channels)
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self.attn = SelectiveKernelAttn(out_channels, num_paths, attn_channels)
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self.drop_block = drop_block
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def forward(self, x):
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if self.split_input:
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x_split = torch.split(x, self.in_channels // self.num_paths, 1)
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x_paths = [op(x_split[i]) for i, op in enumerate(self.paths)]
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else:
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x_paths = [op(x) for op in self.paths]
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x = torch.stack(x_paths, dim=1)
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x_attn = self.attn(x)
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x = x * x_attn
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if self.split_input:
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B, N, C, H, W = x.shape
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x = x.reshape(B, N * C, H, W)
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else:
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x = torch.sum(x, dim=1)
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return x
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# helper method
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def select_conv2d(in_chs, out_chs, kernel_size, **kwargs):
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assert 'groups' not in kwargs # only use 'depthwise' bool arg
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if isinstance(kernel_size, list):
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assert 'num_experts' not in kwargs # MixNet + CondConv combo not supported currently
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# We're going to use only lists for defining the MixedConv2d kernel groups,
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# ints, tuples, other iterables will continue to pass to normal conv and specify h, w.
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m = MixedConv2d(in_chs, out_chs, kernel_size, **kwargs)
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else:
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depthwise = kwargs.pop('depthwise', False)
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groups = out_chs if depthwise else 1
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if 'num_experts' in kwargs and kwargs['num_experts'] > 0:
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m = CondConv2d(in_chs, out_chs, kernel_size, groups=groups, **kwargs)
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else:
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m = create_conv2d_pad(in_chs, out_chs, kernel_size, groups=groups, **kwargs)
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return m
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