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122 lines
5.0 KiB
122 lines
5.0 KiB
""" PyTorch Conditionally Parameterized Convolution (CondConv)
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Paper: CondConv: Conditionally Parameterized Convolutions for Efficient Inference
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(https://arxiv.org/abs/1904.04971)
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Hacked together by Ross Wightman
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"""
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import math
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from functools import partial
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import numpy as np
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import torch
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from torch import nn as nn
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from torch.nn import functional as F
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from .helpers import tup_pair
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from .conv2d_same import get_padding_value, conv2d_same
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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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""" Conditionally Parameterized 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 = tup_pair(kernel_size)
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self.stride = tup_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 = tup_pair(padding_val)
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self.dilation = tup_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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