Switched conv partial impl
This commit is contained in:
James Betker
parent
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commit
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torchvision import datasets, models, transforms
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import tqdm
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from torch.utils.tensorboard import SummaryWriter
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import torch.nn.init as init
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import torch.nn.functional as F
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def initialize_weights(net_l, scale=1):
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if not isinstance(net_l, list):
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net_l = [net_l]
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for net in net_l:
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for m in net.modules():
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if isinstance(m, nn.Conv2d):
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init.kaiming_normal_(m.weight, a=0, mode='fan_in')
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m.weight.data *= scale # for residual block
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if m.bias is not None:
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m.bias.data.zero_()
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elif isinstance(m, nn.Linear):
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init.kaiming_normal_(m.weight, a=0, mode='fan_in')
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m.weight.data *= scale
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if m.bias is not None:
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m.bias.data.zero_()
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elif isinstance(m, nn.BatchNorm2d):
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init.constant_(m.weight, 1)
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init.constant_(m.bias.data, 0.0)
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class ConvSwitch(nn.Module):
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'''
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Initializes the ConvSwitch.
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nf_attention_basis: Number of filters provided to the attention_basis input of forward(). Must be divisible by two
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and nf_attention_basis/2 >= num_convs.
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num_convs: Number of elements that will appear in the conv_group() input. The attention mechanism will
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select across this number.
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att_kernel_size: The size of the attention mechanisms convolutional kernels.
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att_stride: The stride of the attention mechanisms conv blocks.
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att_pads: The padding of the attention mechanisms conv blocks.
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att_interpolate_scale_factor:
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The scale factor applied to the attention mechanism's outputs.
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*** NOTE ***: Between stride, pads, and interpolation_scale_factor, the output of the attention mechanism MUST
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have the same width/height as the conv_group inputs.
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initial_temperature: The initial softmax temperature of the attention mechanism. For training from scratch, this
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should be set to a high number, for example 30.
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'''
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def __init__(self, nf_attention_basis, num_convs=8, att_kernel_size=5, att_stride=1, att_pads=2,
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att_interpolate_scale_factor=1, initial_temperature=1):
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super(ConvSwitch, self).__init__()
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# Requirements: input filter count is even, and there are more filters than there are sequences to attend to.
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assert nf_attention_basis % 2 == 0
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assert nf_attention_basis / 2 >= num_convs
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self.num_convs = num_convs
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self.interpolate_scale_factor = att_interpolate_scale_factor
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self.attention_conv1 = nn.Conv2d(nf_attention_basis, int(nf_attention_basis / 2), att_kernel_size, att_stride, att_pads, bias=True)
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self.att_bn1 = nn.BatchNorm2d(int(nf_attention_basis / 2))
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self.attention_conv2 = nn.Conv2d(int(nf_attention_basis / 2), num_convs, att_kernel_size, 1, att_pads, bias=True)
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self.softmax = nn.Softmax(dim=-1)
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self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
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self.temperature = initial_temperature
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def set_attention_temperature(self, temp):
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self.temperature = temp
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# SwitchedConv.forward takes these arguments;
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# attention_basis: The tensor to compute attention vectors from. Generally this should be the original inputs of
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# your conv group.
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# conv_group: A list of output tensors from the convolutional groups that the attention mechanism is selecting
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# from. Each tensor in this list is expected to have the shape (batch, filters, width, height)
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def forward(self, attention_basis, conv_group, output_attention_weights=False):
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assert self.num_convs == len(conv_group)
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# Stack up the conv_group input first and permute it to (batch, width, height, filter, groups)
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conv_outputs = torch.stack(conv_outputs, dim=0).permute(1, 3, 4, 2, 0)
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# Now calculate the attention across those convs.
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conv_attention = self.lrelu(self.att_bn1(self.attention_conv1(attention_basis)))
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conv_attention = self.attention_conv2(conv_attention).permute(0, 2, 3, 1)
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conv_attention = self.softmax(conv_attention / self.temperature)
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# conv_outputs shape: (batch, width, height, filters, groups)
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# conv_attention shape: (batch, width, height, groups)
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# We want to format them so that we can matmul them together to produce:
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# desired shape: (batch, width, height, filters)
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# Note: conv_attention will generally be cast to float32 regardless of the input type, so cast conv_outputs to
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# float32 as well to match it.
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attention_result = torch.einsum("...ij,...j->...i", [conv_outputs.to(dtype=torch.float32), conv_attention])
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# Remember to shift the filters back into the expected slot.
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if output_attention_weights:
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return attention_result.permute(0, 3, 1, 2), conv_attention
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else:
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return attention_result.permute(0, 3, 1, 2)
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class SwitchedConv2d(nn.Module):
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def __init__(self, nf_in_per_conv, nf_out_per_conv, num_convs=8, att_kernel_size=5, att_stride=1, att_pads=2,
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initial_temperature=1):
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super(ConvSwitch, self).__init__()
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# Requirements: input filter count is even, and there are more filters than there are sequences to attend to.
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assert nf_in_per_conv % 2 == 0
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assert nf_in_per_conv / 2 >= num_convs
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self.nf = nf_out_per_conv
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self.num_convs = num_convs
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self.conv_list = nn.ModuleList([nn.Conv2d(nf_in_per_conv, nf_out_per_conv, kernel_size, att_stride, pads, bias=has_bias) for _ in range(num_convs)])
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self.attention_conv1 = nn.Conv2d(nf_in_per_conv, int(nf_in_per_conv/2), att_kernel_size, att_stride, att_pads, bias=True)
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self.att_bn1 = nn.BatchNorm2d(int(nf_in_per_conv/2))
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self.attention_conv2 = nn.Conv2d(int(nf_in_per_conv/2), num_convs, att_kernel_size, 1, att_pads, bias=True)
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self.softmax = nn.Softmax(dim=-1)
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self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
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self.temperature = initial_temperature
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def set_attention_temperature(self, temp):
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self.temperature = temp
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def forward(self, x, output_attention_weights=False):
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# Build up the individual conv components first.
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conv_outputs = []
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for conv in self.conv_list:
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conv_outputs.append(conv.forward(x))
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conv_outputs = torch.stack(conv_outputs, dim=0).permute(1, 3, 4, 2, 0)
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# Now calculate the attention across those convs.
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conv_attention = self.lrelu(self.att_bn1(self.attention_conv1(x)))
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conv_attention = self.attention_conv2(conv_attention).permute(0, 2, 3, 1)
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conv_attention = self.softmax(conv_attention / self.temperature)
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# conv_outputs shape: (batch, width, height, filters, sequences)
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# conv_attention shape: (batch, width, height, sequences)
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# We want to format them so that we can matmul them together to produce:
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# desired shape: (batch, width, height, filters)
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# Note: conv_attention will generally be cast to float32 regardless of the input type, so cast conv_outputs to
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# float32 as well to match it.
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attention_result = torch.einsum("...ij,...j->...i", [conv_outputs.to(dtype=torch.float32), conv_attention])
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# Remember to shift the filters back into the expected slot.
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if output_attention_weights:
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return attention_result.permute(0, 3, 1, 2), conv_attention
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else:
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return attention_result.permute(0, 3, 1, 2)
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def compute_attention_specificity(att_weights, topk=3):
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att = att_weights.detach()
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vals, indices = torch.topk(att, topk, dim=-1)
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avg = torch.sum(vals, dim=-1)
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avg = avg.flatten().mean()
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return avg.item(), indices.flatten().detach()
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class DynamicConvTestModule(nn.Module):
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def __init__(self):
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super(DynamicConvTestModule, self).__init__()
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self.init_conv = nn.Conv2d(3, 16, 3, 1, 1, bias=True)
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self.conv1 = ConvSwitch(16, 32, 3, att_stride=2, pads=1, num_convs=4, initial_temperature=10)
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self.bn1 = nn.BatchNorm2d(32)
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self.conv2 = ConvSwitch(32, 64, 3, att_stride=2, pads=1, att_kernel_size=3, att_pads=1, num_convs=8, initial_temperature=10)
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self.bn2 = nn.BatchNorm2d(64)
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self.conv3 = ConvSwitch(64, 128, 3, att_stride=2, pads=1, att_kernel_size=3, att_pads=1, num_convs=16, initial_temperature=10)
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self.bn3 = nn.BatchNorm2d(128)
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self.relu = nn.ReLU()
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self.dense1 = nn.Linear(128 * 4 * 4, 256)
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self.dense2 = nn.Linear(256, 100)
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self.softmax = nn.Softmax(-1)
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def set_temp(self, temp):
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self.conv1.set_attention_temperature(temp)
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self.conv2.set_attention_temperature(temp)
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self.conv3.set_attention_temperature(temp)
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def forward(self, x):
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x = self.init_conv(x)
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x, att1 = self.conv1(x, output_attention_weights=True)
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x = self.relu(self.bn1(x))
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x, att2 = self.conv2(x, output_attention_weights=True)
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x = self.relu(self.bn2(x))
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x, att3 = self.conv3(x, output_attention_weights=True)
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x = self.relu(self.bn3(x))
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atts = [att1, att2, att3]
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usage_hists = []
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mean = 0
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for a in atts:
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m, u = compute_attention_specificity(a)
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mean += m
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usage_hists.append(u)
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mean /= 3
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x = x.flatten(1)
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x = self.relu(self.dense1(x))
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x = self.dense2(x)
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# Compute metrics across attention weights.
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return self.softmax(x), mean, usage_hists
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def test_dynamic_conv():
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writer = SummaryWriter()
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dataset = datasets.ImageFolder("C:\\data\\cifar-100-python\\images\\train", transforms.Compose([
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transforms.Resize(32, 32),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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]))
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batch_size = 256
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temperature = 30
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loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=4)
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device = torch.device("cuda:0")
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net = DynamicConvTestModule()
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net = net.to(device)
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net.set_temp(temperature)
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initialize_weights(net)
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optimizer = optim.Adam(net.parameters(), lr=1e-3)
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# Load state, where necessary.
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'''
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netstate, optimstate = torch.load("test_net.pth")
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net.load_state_dict(netstate)
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optimizer.load_state_dict(optimstate)
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'''
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criterion = nn.CrossEntropyLoss()
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step = 0
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running_corrects = 0
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running_att_mean = 0
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running_att_hist = None
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for e in range(300):
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tq = tqdm.tqdm(loader)
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for batch, labels in tq:
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batch = batch.to(device)
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labels = labels.to(device)
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optimizer.zero_grad()
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logits, att_mean, att_usage_hist = net.forward(batch)
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running_att_mean += att_mean
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if running_att_hist is None:
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running_att_hist = att_usage_hist
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else:
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for i in range(len(att_usage_hist)):
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running_att_hist[i] = torch.cat([running_att_hist[i], att_usage_hist[i]]).flatten()
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loss = criterion(logits, labels)
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loss.backward()
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if step % 50 == 0:
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c1_grad_avg = sum([m.weight.grad.abs().mean().item() for m in net.conv1.conv_list._modules.values()]) / len(net.conv1.conv_list._modules)
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c1a_grad_avg = (net.conv1.attention_conv1.weight.grad.abs().mean() + net.conv1.attention_conv2.weight.grad.abs().mean()) / 2
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c2_grad_avg = sum([m.weight.grad.abs().mean().item() for m in net.conv2.conv_list._modules.values()]) / len(net.conv2.conv_list._modules)
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c2a_grad_avg = (net.conv2.attention_conv1.weight.grad.abs().mean() + net.conv2.attention_conv2.weight.grad.abs().mean()) / 2
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c3_grad_avg = sum([m.weight.grad.abs().mean().item() for m in net.conv3.conv_list._modules.values()]) / len(net.conv3.conv_list._modules)
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c3a_grad_avg = (net.conv3.attention_conv1.weight.grad.abs().mean() + net.conv3.attention_conv2.weight.grad.abs().mean()) / 2
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writer.add_scalar("c1_grad_avg", c1_grad_avg, global_step=step)
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writer.add_scalar("c2_grad_avg", c2_grad_avg, global_step=step)
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writer.add_scalar("c3_grad_avg", c3_grad_avg, global_step=step)
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writer.add_scalar("c1a_grad_avg", c1a_grad_avg, global_step=step)
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writer.add_scalar("c2a_grad_avg", c2a_grad_avg, global_step=step)
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writer.add_scalar("c3a_grad_avg", c3a_grad_avg, global_step=step)
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optimizer.step()
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_, preds = torch.max(logits, 1)
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running_corrects += torch.sum(preds == labels.data)
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if step % 50 == 0:
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print("Step: %i, Loss: %f, acc: %f, att_mean: %f" % (step, loss.item(), running_corrects / (50.0 * batch_size),
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running_att_mean / 50.0))
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writer.add_scalar("Loss", loss.item(), global_step=step)
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writer.add_scalar("Accuracy", running_corrects / (50.0 * batch_size), global_step=step)
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writer.add_scalar("Att Mean", running_att_mean / 50, global_step=step)
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for i in range(len(running_att_hist)):
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writer.add_histogram("Att Hist %i" % (i,), running_att_hist[i], global_step=step)
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writer.flush()
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running_corrects = 0
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running_att_mean = 0
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running_att_hist = None
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if step % 1000 == 0:
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temperature = max(temperature-1, 1)
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net.set_temp(temperature)
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print("Temperature drop. Now: %i" % (temperature,))
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step += 1
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torch.save((net.state_dict(), optimizer.state_dict()), "test_net.pth")
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if __name__ == '__main__':
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test_dynamic_conv()
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