Use black for lib/models
This commit is contained in:
D-X-Y
@@ -4,390 +4,616 @@
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import math, torch
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import torch.nn as nn
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from ..initialization import initialize_resnet
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from ..SharedUtils import additive_func
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from .SoftSelect import select2withP, ChannelWiseInter
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from .SoftSelect import linear_forward
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from .SoftSelect import get_width_choices as get_choices
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from ..SharedUtils import additive_func
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from .SoftSelect import select2withP, ChannelWiseInter
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from .SoftSelect import linear_forward
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from .SoftSelect import get_width_choices as get_choices
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def conv_forward(inputs, conv, choices):
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iC = conv.in_channels
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fill_size = list(inputs.size())
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fill_size[1] = iC - fill_size[1]
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filled = torch.zeros(fill_size, device=inputs.device)
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xinputs = torch.cat((inputs, filled), dim=1)
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outputs = conv(xinputs)
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selecteds = [outputs[:,:oC] for oC in choices]
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return selecteds
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iC = conv.in_channels
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fill_size = list(inputs.size())
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fill_size[1] = iC - fill_size[1]
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filled = torch.zeros(fill_size, device=inputs.device)
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xinputs = torch.cat((inputs, filled), dim=1)
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outputs = conv(xinputs)
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selecteds = [outputs[:, :oC] for oC in choices]
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return selecteds
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class ConvBNReLU(nn.Module):
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num_conv = 1
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def __init__(self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu):
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super(ConvBNReLU, self).__init__()
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self.InShape = None
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self.OutShape = None
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self.choices = get_choices(nOut)
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self.register_buffer('choices_tensor', torch.Tensor( self.choices ))
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num_conv = 1
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if has_avg : self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
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else : self.avg = None
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self.conv = nn.Conv2d(nIn, nOut, kernel_size=kernel, stride=stride, padding=padding, dilation=1, groups=1, bias=bias)
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#if has_bn : self.bn = nn.BatchNorm2d(nOut)
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#else : self.bn = None
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self.has_bn = has_bn
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self.BNs = nn.ModuleList()
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for i, _out in enumerate(self.choices):
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self.BNs.append(nn.BatchNorm2d(_out))
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if has_relu: self.relu = nn.ReLU(inplace=True)
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else : self.relu = None
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self.in_dim = nIn
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self.out_dim = nOut
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self.search_mode = 'basic'
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def __init__(
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self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu
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):
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super(ConvBNReLU, self).__init__()
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self.InShape = None
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self.OutShape = None
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self.choices = get_choices(nOut)
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self.register_buffer("choices_tensor", torch.Tensor(self.choices))
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def get_flops(self, channels, check_range=True, divide=1):
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iC, oC = channels
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if check_range: assert iC <= self.conv.in_channels and oC <= self.conv.out_channels, '{:} vs {:} | {:} vs {:}'.format(iC, self.conv.in_channels, oC, self.conv.out_channels)
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assert isinstance(self.InShape, tuple) and len(self.InShape) == 2, 'invalid in-shape : {:}'.format(self.InShape)
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assert isinstance(self.OutShape, tuple) and len(self.OutShape) == 2, 'invalid out-shape : {:}'.format(self.OutShape)
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#conv_per_position_flops = self.conv.kernel_size[0] * self.conv.kernel_size[1] * iC * oC / self.conv.groups
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conv_per_position_flops = (self.conv.kernel_size[0] * self.conv.kernel_size[1] * 1.0 / self.conv.groups)
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all_positions = self.OutShape[0] * self.OutShape[1]
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flops = (conv_per_position_flops * all_positions / divide) * iC * oC
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if self.conv.bias is not None: flops += all_positions / divide
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return flops
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if has_avg:
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self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
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else:
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self.avg = None
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self.conv = nn.Conv2d(
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nIn,
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nOut,
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kernel_size=kernel,
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stride=stride,
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padding=padding,
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dilation=1,
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groups=1,
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bias=bias,
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)
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# if has_bn : self.bn = nn.BatchNorm2d(nOut)
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# else : self.bn = None
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self.has_bn = has_bn
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self.BNs = nn.ModuleList()
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for i, _out in enumerate(self.choices):
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self.BNs.append(nn.BatchNorm2d(_out))
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if has_relu:
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self.relu = nn.ReLU(inplace=True)
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else:
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self.relu = None
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self.in_dim = nIn
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self.out_dim = nOut
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self.search_mode = "basic"
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def get_range(self):
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return [self.choices]
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def get_flops(self, channels, check_range=True, divide=1):
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iC, oC = channels
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if check_range:
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assert (
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iC <= self.conv.in_channels and oC <= self.conv.out_channels
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), "{:} vs {:} | {:} vs {:}".format(
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iC, self.conv.in_channels, oC, self.conv.out_channels
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)
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assert (
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isinstance(self.InShape, tuple) and len(self.InShape) == 2
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), "invalid in-shape : {:}".format(self.InShape)
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assert (
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isinstance(self.OutShape, tuple) and len(self.OutShape) == 2
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), "invalid out-shape : {:}".format(self.OutShape)
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# conv_per_position_flops = self.conv.kernel_size[0] * self.conv.kernel_size[1] * iC * oC / self.conv.groups
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conv_per_position_flops = (
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self.conv.kernel_size[0] * self.conv.kernel_size[1] * 1.0 / self.conv.groups
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)
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all_positions = self.OutShape[0] * self.OutShape[1]
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flops = (conv_per_position_flops * all_positions / divide) * iC * oC
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if self.conv.bias is not None:
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flops += all_positions / divide
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return flops
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def forward(self, inputs):
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if self.search_mode == 'basic':
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return self.basic_forward(inputs)
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elif self.search_mode == 'search':
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return self.search_forward(inputs)
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else:
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raise ValueError('invalid search_mode = {:}'.format(self.search_mode))
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def get_range(self):
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return [self.choices]
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def search_forward(self, tuple_inputs):
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assert isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5, 'invalid type input : {:}'.format( type(tuple_inputs) )
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inputs, expected_inC, probability, index, prob = tuple_inputs
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index, prob = torch.squeeze(index).tolist(), torch.squeeze(prob)
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probability = torch.squeeze(probability)
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assert len(index) == 2, 'invalid length : {:}'.format(index)
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# compute expected flop
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#coordinates = torch.arange(self.x_range[0], self.x_range[1]+1).type_as(probability)
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expected_outC = (self.choices_tensor * probability).sum()
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expected_flop = self.get_flops([expected_inC, expected_outC], False, 1e6)
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if self.avg : out = self.avg( inputs )
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else : out = inputs
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# convolutional layer
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out_convs = conv_forward(out, self.conv, [self.choices[i] for i in index])
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out_bns = [self.BNs[idx](out_conv) for idx, out_conv in zip(index, out_convs)]
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# merge
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out_channel = max([x.size(1) for x in out_bns])
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outA = ChannelWiseInter(out_bns[0], out_channel)
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outB = ChannelWiseInter(out_bns[1], out_channel)
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out = outA * prob[0] + outB * prob[1]
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#out = additive_func(out_bns[0]*prob[0], out_bns[1]*prob[1])
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def forward(self, inputs):
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if self.search_mode == "basic":
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return self.basic_forward(inputs)
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elif self.search_mode == "search":
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return self.search_forward(inputs)
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else:
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raise ValueError("invalid search_mode = {:}".format(self.search_mode))
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if self.relu: out = self.relu( out )
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else : out = out
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return out, expected_outC, expected_flop
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def search_forward(self, tuple_inputs):
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assert (
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isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5
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), "invalid type input : {:}".format(type(tuple_inputs))
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inputs, expected_inC, probability, index, prob = tuple_inputs
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index, prob = torch.squeeze(index).tolist(), torch.squeeze(prob)
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probability = torch.squeeze(probability)
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assert len(index) == 2, "invalid length : {:}".format(index)
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# compute expected flop
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# coordinates = torch.arange(self.x_range[0], self.x_range[1]+1).type_as(probability)
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expected_outC = (self.choices_tensor * probability).sum()
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expected_flop = self.get_flops([expected_inC, expected_outC], False, 1e6)
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if self.avg:
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out = self.avg(inputs)
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else:
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out = inputs
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# convolutional layer
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out_convs = conv_forward(out, self.conv, [self.choices[i] for i in index])
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out_bns = [self.BNs[idx](out_conv) for idx, out_conv in zip(index, out_convs)]
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# merge
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out_channel = max([x.size(1) for x in out_bns])
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outA = ChannelWiseInter(out_bns[0], out_channel)
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outB = ChannelWiseInter(out_bns[1], out_channel)
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out = outA * prob[0] + outB * prob[1]
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# out = additive_func(out_bns[0]*prob[0], out_bns[1]*prob[1])
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def basic_forward(self, inputs):
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if self.avg : out = self.avg( inputs )
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else : out = inputs
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conv = self.conv( out )
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if self.has_bn:out= self.BNs[-1]( conv )
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else : out = conv
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if self.relu: out = self.relu( out )
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else : out = out
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if self.InShape is None:
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self.InShape = (inputs.size(-2), inputs.size(-1))
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self.OutShape = (out.size(-2) , out.size(-1))
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return out
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if self.relu:
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out = self.relu(out)
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else:
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out = out
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return out, expected_outC, expected_flop
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def basic_forward(self, inputs):
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if self.avg:
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out = self.avg(inputs)
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else:
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out = inputs
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conv = self.conv(out)
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if self.has_bn:
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out = self.BNs[-1](conv)
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else:
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out = conv
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if self.relu:
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out = self.relu(out)
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else:
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out = out
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if self.InShape is None:
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self.InShape = (inputs.size(-2), inputs.size(-1))
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self.OutShape = (out.size(-2), out.size(-1))
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return out
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class ResNetBasicblock(nn.Module):
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expansion = 1
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num_conv = 2
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def __init__(self, inplanes, planes, stride):
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super(ResNetBasicblock, self).__init__()
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assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
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self.conv_a = ConvBNReLU(inplanes, planes, 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
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self.conv_b = ConvBNReLU( planes, planes, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False)
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if stride == 2:
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self.downsample = ConvBNReLU(inplanes, planes, 1, 1, 0, False, has_avg=True, has_bn=False, has_relu=False)
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elif inplanes != planes:
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self.downsample = ConvBNReLU(inplanes, planes, 1, 1, 0, False, has_avg=False,has_bn=True , has_relu=False)
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else:
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self.downsample = None
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self.out_dim = planes
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self.search_mode = 'basic'
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expansion = 1
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num_conv = 2
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def get_range(self):
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return self.conv_a.get_range() + self.conv_b.get_range()
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def __init__(self, inplanes, planes, stride):
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super(ResNetBasicblock, self).__init__()
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assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
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self.conv_a = ConvBNReLU(
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inplanes,
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planes,
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3,
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stride,
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1,
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False,
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has_avg=False,
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has_bn=True,
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has_relu=True,
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)
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self.conv_b = ConvBNReLU(
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planes, planes, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=False
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)
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if stride == 2:
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self.downsample = ConvBNReLU(
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inplanes,
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planes,
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1,
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1,
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0,
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False,
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has_avg=True,
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has_bn=False,
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has_relu=False,
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)
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elif inplanes != planes:
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self.downsample = ConvBNReLU(
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inplanes,
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planes,
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1,
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1,
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0,
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False,
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has_avg=False,
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has_bn=True,
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has_relu=False,
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)
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else:
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self.downsample = None
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self.out_dim = planes
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self.search_mode = "basic"
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def get_flops(self, channels):
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assert len(channels) == 3, 'invalid channels : {:}'.format(channels)
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flop_A = self.conv_a.get_flops([channels[0], channels[1]])
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flop_B = self.conv_b.get_flops([channels[1], channels[2]])
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if hasattr(self.downsample, 'get_flops'):
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flop_C = self.downsample.get_flops([channels[0], channels[-1]])
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else:
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flop_C = 0
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if channels[0] != channels[-1] and self.downsample is None: # this short-cut will be added during the infer-train
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flop_C = channels[0] * channels[-1] * self.conv_b.OutShape[0] * self.conv_b.OutShape[1]
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return flop_A + flop_B + flop_C
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def get_range(self):
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return self.conv_a.get_range() + self.conv_b.get_range()
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def forward(self, inputs):
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if self.search_mode == 'basic' : return self.basic_forward(inputs)
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elif self.search_mode == 'search': return self.search_forward(inputs)
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else: raise ValueError('invalid search_mode = {:}'.format(self.search_mode))
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def get_flops(self, channels):
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assert len(channels) == 3, "invalid channels : {:}".format(channels)
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flop_A = self.conv_a.get_flops([channels[0], channels[1]])
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flop_B = self.conv_b.get_flops([channels[1], channels[2]])
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if hasattr(self.downsample, "get_flops"):
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flop_C = self.downsample.get_flops([channels[0], channels[-1]])
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else:
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flop_C = 0
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if (
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channels[0] != channels[-1] and self.downsample is None
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): # this short-cut will be added during the infer-train
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flop_C = (
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channels[0]
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* channels[-1]
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* self.conv_b.OutShape[0]
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* self.conv_b.OutShape[1]
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)
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return flop_A + flop_B + flop_C
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def search_forward(self, tuple_inputs):
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assert isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5, 'invalid type input : {:}'.format( type(tuple_inputs) )
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inputs, expected_inC, probability, indexes, probs = tuple_inputs
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assert indexes.size(0) == 2 and probs.size(0) == 2 and probability.size(0) == 2
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out_a, expected_inC_a, expected_flop_a = self.conv_a( (inputs, expected_inC , probability[0], indexes[0], probs[0]) )
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out_b, expected_inC_b, expected_flop_b = self.conv_b( (out_a , expected_inC_a, probability[1], indexes[1], probs[1]) )
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if self.downsample is not None:
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residual, _, expected_flop_c = self.downsample( (inputs, expected_inC , probability[1], indexes[1], probs[1]) )
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else:
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residual, expected_flop_c = inputs, 0
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out = additive_func(residual, out_b)
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return nn.functional.relu(out, inplace=True), expected_inC_b, sum([expected_flop_a, expected_flop_b, expected_flop_c])
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def forward(self, inputs):
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if self.search_mode == "basic":
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return self.basic_forward(inputs)
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elif self.search_mode == "search":
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return self.search_forward(inputs)
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else:
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raise ValueError("invalid search_mode = {:}".format(self.search_mode))
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def basic_forward(self, inputs):
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basicblock = self.conv_a(inputs)
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basicblock = self.conv_b(basicblock)
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if self.downsample is not None: residual = self.downsample(inputs)
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else : residual = inputs
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out = additive_func(residual, basicblock)
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return nn.functional.relu(out, inplace=True)
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def search_forward(self, tuple_inputs):
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assert (
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isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5
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), "invalid type input : {:}".format(type(tuple_inputs))
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inputs, expected_inC, probability, indexes, probs = tuple_inputs
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assert indexes.size(0) == 2 and probs.size(0) == 2 and probability.size(0) == 2
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out_a, expected_inC_a, expected_flop_a = self.conv_a(
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(inputs, expected_inC, probability[0], indexes[0], probs[0])
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)
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out_b, expected_inC_b, expected_flop_b = self.conv_b(
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(out_a, expected_inC_a, probability[1], indexes[1], probs[1])
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)
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if self.downsample is not None:
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residual, _, expected_flop_c = self.downsample(
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(inputs, expected_inC, probability[1], indexes[1], probs[1])
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)
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else:
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residual, expected_flop_c = inputs, 0
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out = additive_func(residual, out_b)
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return (
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nn.functional.relu(out, inplace=True),
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expected_inC_b,
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sum([expected_flop_a, expected_flop_b, expected_flop_c]),
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)
|
||||
|
||||
def basic_forward(self, inputs):
|
||||
basicblock = self.conv_a(inputs)
|
||||
basicblock = self.conv_b(basicblock)
|
||||
if self.downsample is not None:
|
||||
residual = self.downsample(inputs)
|
||||
else:
|
||||
residual = inputs
|
||||
out = additive_func(residual, basicblock)
|
||||
return nn.functional.relu(out, inplace=True)
|
||||
|
||||
|
||||
class ResNetBottleneck(nn.Module):
|
||||
expansion = 4
|
||||
num_conv = 3
|
||||
def __init__(self, inplanes, planes, stride):
|
||||
super(ResNetBottleneck, self).__init__()
|
||||
assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
|
||||
self.conv_1x1 = ConvBNReLU(inplanes, planes, 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True)
|
||||
self.conv_3x3 = ConvBNReLU( planes, planes, 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
|
||||
self.conv_1x4 = ConvBNReLU(planes, planes*self.expansion, 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=False)
|
||||
if stride == 2:
|
||||
self.downsample = ConvBNReLU(inplanes, planes*self.expansion, 1, 1, 0, False, has_avg=True, has_bn=False, has_relu=False)
|
||||
elif inplanes != planes*self.expansion:
|
||||
self.downsample = ConvBNReLU(inplanes, planes*self.expansion, 1, 1, 0, False, has_avg=False,has_bn=True , has_relu=False)
|
||||
else:
|
||||
self.downsample = None
|
||||
self.out_dim = planes * self.expansion
|
||||
self.search_mode = 'basic'
|
||||
expansion = 4
|
||||
num_conv = 3
|
||||
|
||||
def get_range(self):
|
||||
return self.conv_1x1.get_range() + self.conv_3x3.get_range() + self.conv_1x4.get_range()
|
||||
def __init__(self, inplanes, planes, stride):
|
||||
super(ResNetBottleneck, self).__init__()
|
||||
assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
|
||||
self.conv_1x1 = ConvBNReLU(
|
||||
inplanes, planes, 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=True
|
||||
)
|
||||
self.conv_3x3 = ConvBNReLU(
|
||||
planes,
|
||||
planes,
|
||||
3,
|
||||
stride,
|
||||
1,
|
||||
False,
|
||||
has_avg=False,
|
||||
has_bn=True,
|
||||
has_relu=True,
|
||||
)
|
||||
self.conv_1x4 = ConvBNReLU(
|
||||
planes,
|
||||
planes * self.expansion,
|
||||
1,
|
||||
1,
|
||||
0,
|
||||
False,
|
||||
has_avg=False,
|
||||
has_bn=True,
|
||||
has_relu=False,
|
||||
)
|
||||
if stride == 2:
|
||||
self.downsample = ConvBNReLU(
|
||||
inplanes,
|
||||
planes * self.expansion,
|
||||
1,
|
||||
1,
|
||||
0,
|
||||
False,
|
||||
has_avg=True,
|
||||
has_bn=False,
|
||||
has_relu=False,
|
||||
)
|
||||
elif inplanes != planes * self.expansion:
|
||||
self.downsample = ConvBNReLU(
|
||||
inplanes,
|
||||
planes * self.expansion,
|
||||
1,
|
||||
1,
|
||||
0,
|
||||
False,
|
||||
has_avg=False,
|
||||
has_bn=True,
|
||||
has_relu=False,
|
||||
)
|
||||
else:
|
||||
self.downsample = None
|
||||
self.out_dim = planes * self.expansion
|
||||
self.search_mode = "basic"
|
||||
|
||||
def get_flops(self, channels):
|
||||
assert len(channels) == 4, 'invalid channels : {:}'.format(channels)
|
||||
flop_A = self.conv_1x1.get_flops([channels[0], channels[1]])
|
||||
flop_B = self.conv_3x3.get_flops([channels[1], channels[2]])
|
||||
flop_C = self.conv_1x4.get_flops([channels[2], channels[3]])
|
||||
if hasattr(self.downsample, 'get_flops'):
|
||||
flop_D = self.downsample.get_flops([channels[0], channels[-1]])
|
||||
else:
|
||||
flop_D = 0
|
||||
if channels[0] != channels[-1] and self.downsample is None: # this short-cut will be added during the infer-train
|
||||
flop_D = channels[0] * channels[-1] * self.conv_1x4.OutShape[0] * self.conv_1x4.OutShape[1]
|
||||
return flop_A + flop_B + flop_C + flop_D
|
||||
def get_range(self):
|
||||
return (
|
||||
self.conv_1x1.get_range()
|
||||
+ self.conv_3x3.get_range()
|
||||
+ self.conv_1x4.get_range()
|
||||
)
|
||||
|
||||
def forward(self, inputs):
|
||||
if self.search_mode == 'basic' : return self.basic_forward(inputs)
|
||||
elif self.search_mode == 'search': return self.search_forward(inputs)
|
||||
else: raise ValueError('invalid search_mode = {:}'.format(self.search_mode))
|
||||
def get_flops(self, channels):
|
||||
assert len(channels) == 4, "invalid channels : {:}".format(channels)
|
||||
flop_A = self.conv_1x1.get_flops([channels[0], channels[1]])
|
||||
flop_B = self.conv_3x3.get_flops([channels[1], channels[2]])
|
||||
flop_C = self.conv_1x4.get_flops([channels[2], channels[3]])
|
||||
if hasattr(self.downsample, "get_flops"):
|
||||
flop_D = self.downsample.get_flops([channels[0], channels[-1]])
|
||||
else:
|
||||
flop_D = 0
|
||||
if (
|
||||
channels[0] != channels[-1] and self.downsample is None
|
||||
): # this short-cut will be added during the infer-train
|
||||
flop_D = (
|
||||
channels[0]
|
||||
* channels[-1]
|
||||
* self.conv_1x4.OutShape[0]
|
||||
* self.conv_1x4.OutShape[1]
|
||||
)
|
||||
return flop_A + flop_B + flop_C + flop_D
|
||||
|
||||
def basic_forward(self, inputs):
|
||||
bottleneck = self.conv_1x1(inputs)
|
||||
bottleneck = self.conv_3x3(bottleneck)
|
||||
bottleneck = self.conv_1x4(bottleneck)
|
||||
if self.downsample is not None: residual = self.downsample(inputs)
|
||||
else : residual = inputs
|
||||
out = additive_func(residual, bottleneck)
|
||||
return nn.functional.relu(out, inplace=True)
|
||||
def forward(self, inputs):
|
||||
if self.search_mode == "basic":
|
||||
return self.basic_forward(inputs)
|
||||
elif self.search_mode == "search":
|
||||
return self.search_forward(inputs)
|
||||
else:
|
||||
raise ValueError("invalid search_mode = {:}".format(self.search_mode))
|
||||
|
||||
def search_forward(self, tuple_inputs):
|
||||
assert isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5, 'invalid type input : {:}'.format( type(tuple_inputs) )
|
||||
inputs, expected_inC, probability, indexes, probs = tuple_inputs
|
||||
assert indexes.size(0) == 3 and probs.size(0) == 3 and probability.size(0) == 3
|
||||
out_1x1, expected_inC_1x1, expected_flop_1x1 = self.conv_1x1( (inputs, expected_inC , probability[0], indexes[0], probs[0]) )
|
||||
out_3x3, expected_inC_3x3, expected_flop_3x3 = self.conv_3x3( (out_1x1,expected_inC_1x1, probability[1], indexes[1], probs[1]) )
|
||||
out_1x4, expected_inC_1x4, expected_flop_1x4 = self.conv_1x4( (out_3x3,expected_inC_3x3, probability[2], indexes[2], probs[2]) )
|
||||
if self.downsample is not None:
|
||||
residual, _, expected_flop_c = self.downsample( (inputs, expected_inC , probability[2], indexes[2], probs[2]) )
|
||||
else:
|
||||
residual, expected_flop_c = inputs, 0
|
||||
out = additive_func(residual, out_1x4)
|
||||
return nn.functional.relu(out, inplace=True), expected_inC_1x4, sum([expected_flop_1x1, expected_flop_3x3, expected_flop_1x4, expected_flop_c])
|
||||
def basic_forward(self, inputs):
|
||||
bottleneck = self.conv_1x1(inputs)
|
||||
bottleneck = self.conv_3x3(bottleneck)
|
||||
bottleneck = self.conv_1x4(bottleneck)
|
||||
if self.downsample is not None:
|
||||
residual = self.downsample(inputs)
|
||||
else:
|
||||
residual = inputs
|
||||
out = additive_func(residual, bottleneck)
|
||||
return nn.functional.relu(out, inplace=True)
|
||||
|
||||
def search_forward(self, tuple_inputs):
|
||||
assert (
|
||||
isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5
|
||||
), "invalid type input : {:}".format(type(tuple_inputs))
|
||||
inputs, expected_inC, probability, indexes, probs = tuple_inputs
|
||||
assert indexes.size(0) == 3 and probs.size(0) == 3 and probability.size(0) == 3
|
||||
out_1x1, expected_inC_1x1, expected_flop_1x1 = self.conv_1x1(
|
||||
(inputs, expected_inC, probability[0], indexes[0], probs[0])
|
||||
)
|
||||
out_3x3, expected_inC_3x3, expected_flop_3x3 = self.conv_3x3(
|
||||
(out_1x1, expected_inC_1x1, probability[1], indexes[1], probs[1])
|
||||
)
|
||||
out_1x4, expected_inC_1x4, expected_flop_1x4 = self.conv_1x4(
|
||||
(out_3x3, expected_inC_3x3, probability[2], indexes[2], probs[2])
|
||||
)
|
||||
if self.downsample is not None:
|
||||
residual, _, expected_flop_c = self.downsample(
|
||||
(inputs, expected_inC, probability[2], indexes[2], probs[2])
|
||||
)
|
||||
else:
|
||||
residual, expected_flop_c = inputs, 0
|
||||
out = additive_func(residual, out_1x4)
|
||||
return (
|
||||
nn.functional.relu(out, inplace=True),
|
||||
expected_inC_1x4,
|
||||
sum(
|
||||
[
|
||||
expected_flop_1x1,
|
||||
expected_flop_3x3,
|
||||
expected_flop_1x4,
|
||||
expected_flop_c,
|
||||
]
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
class SearchWidthCifarResNet(nn.Module):
|
||||
def __init__(self, block_name, depth, num_classes):
|
||||
super(SearchWidthCifarResNet, self).__init__()
|
||||
|
||||
def __init__(self, block_name, depth, num_classes):
|
||||
super(SearchWidthCifarResNet, self).__init__()
|
||||
# Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
|
||||
if block_name == "ResNetBasicblock":
|
||||
block = ResNetBasicblock
|
||||
assert (depth - 2) % 6 == 0, "depth should be one of 20, 32, 44, 56, 110"
|
||||
layer_blocks = (depth - 2) // 6
|
||||
elif block_name == "ResNetBottleneck":
|
||||
block = ResNetBottleneck
|
||||
assert (depth - 2) % 9 == 0, "depth should be one of 164"
|
||||
layer_blocks = (depth - 2) // 9
|
||||
else:
|
||||
raise ValueError("invalid block : {:}".format(block_name))
|
||||
|
||||
#Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
|
||||
if block_name == 'ResNetBasicblock':
|
||||
block = ResNetBasicblock
|
||||
assert (depth - 2) % 6 == 0, 'depth should be one of 20, 32, 44, 56, 110'
|
||||
layer_blocks = (depth - 2) // 6
|
||||
elif block_name == 'ResNetBottleneck':
|
||||
block = ResNetBottleneck
|
||||
assert (depth - 2) % 9 == 0, 'depth should be one of 164'
|
||||
layer_blocks = (depth - 2) // 9
|
||||
else:
|
||||
raise ValueError('invalid block : {:}'.format(block_name))
|
||||
self.message = (
|
||||
"SearchWidthCifarResNet : Depth : {:} , Layers for each block : {:}".format(
|
||||
depth, layer_blocks
|
||||
)
|
||||
)
|
||||
self.num_classes = num_classes
|
||||
self.channels = [16]
|
||||
self.layers = nn.ModuleList(
|
||||
[
|
||||
ConvBNReLU(
|
||||
3, 16, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True
|
||||
)
|
||||
]
|
||||
)
|
||||
self.InShape = None
|
||||
for stage in range(3):
|
||||
for iL in range(layer_blocks):
|
||||
iC = self.channels[-1]
|
||||
planes = 16 * (2 ** stage)
|
||||
stride = 2 if stage > 0 and iL == 0 else 1
|
||||
module = block(iC, planes, stride)
|
||||
self.channels.append(module.out_dim)
|
||||
self.layers.append(module)
|
||||
self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:3d}, oC={:3d}, stride={:}".format(
|
||||
stage,
|
||||
iL,
|
||||
layer_blocks,
|
||||
len(self.layers) - 1,
|
||||
iC,
|
||||
module.out_dim,
|
||||
stride,
|
||||
)
|
||||
|
||||
self.message = 'SearchWidthCifarResNet : Depth : {:} , Layers for each block : {:}'.format(depth, layer_blocks)
|
||||
self.num_classes = num_classes
|
||||
self.channels = [16]
|
||||
self.layers = nn.ModuleList( [ ConvBNReLU(3, 16, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True) ] )
|
||||
self.InShape = None
|
||||
for stage in range(3):
|
||||
for iL in range(layer_blocks):
|
||||
iC = self.channels[-1]
|
||||
planes = 16 * (2**stage)
|
||||
stride = 2 if stage > 0 and iL == 0 else 1
|
||||
module = block(iC, planes, stride)
|
||||
self.channels.append( module.out_dim )
|
||||
self.layers.append ( module )
|
||||
self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:3d}, oC={:3d}, stride={:}".format(stage, iL, layer_blocks, len(self.layers)-1, iC, module.out_dim, stride)
|
||||
|
||||
self.avgpool = nn.AvgPool2d(8)
|
||||
self.classifier = nn.Linear(module.out_dim, num_classes)
|
||||
self.InShape = None
|
||||
self.tau = -1
|
||||
self.search_mode = 'basic'
|
||||
#assert sum(x.num_conv for x in self.layers) + 1 == depth, 'invalid depth check {:} vs {:}'.format(sum(x.num_conv for x in self.layers)+1, depth)
|
||||
|
||||
# parameters for width
|
||||
self.Ranges = []
|
||||
self.layer2indexRange = []
|
||||
for i, layer in enumerate(self.layers):
|
||||
start_index = len(self.Ranges)
|
||||
self.Ranges += layer.get_range()
|
||||
self.layer2indexRange.append( (start_index, len(self.Ranges)) )
|
||||
assert len(self.Ranges) + 1 == depth, 'invalid depth check {:} vs {:}'.format(len(self.Ranges) + 1, depth)
|
||||
self.avgpool = nn.AvgPool2d(8)
|
||||
self.classifier = nn.Linear(module.out_dim, num_classes)
|
||||
self.InShape = None
|
||||
self.tau = -1
|
||||
self.search_mode = "basic"
|
||||
# assert sum(x.num_conv for x in self.layers) + 1 == depth, 'invalid depth check {:} vs {:}'.format(sum(x.num_conv for x in self.layers)+1, depth)
|
||||
|
||||
self.register_parameter('width_attentions', nn.Parameter(torch.Tensor(len(self.Ranges), get_choices(None))))
|
||||
nn.init.normal_(self.width_attentions, 0, 0.01)
|
||||
self.apply(initialize_resnet)
|
||||
# parameters for width
|
||||
self.Ranges = []
|
||||
self.layer2indexRange = []
|
||||
for i, layer in enumerate(self.layers):
|
||||
start_index = len(self.Ranges)
|
||||
self.Ranges += layer.get_range()
|
||||
self.layer2indexRange.append((start_index, len(self.Ranges)))
|
||||
assert len(self.Ranges) + 1 == depth, "invalid depth check {:} vs {:}".format(
|
||||
len(self.Ranges) + 1, depth
|
||||
)
|
||||
|
||||
def arch_parameters(self):
|
||||
return [self.width_attentions]
|
||||
self.register_parameter(
|
||||
"width_attentions",
|
||||
nn.Parameter(torch.Tensor(len(self.Ranges), get_choices(None))),
|
||||
)
|
||||
nn.init.normal_(self.width_attentions, 0, 0.01)
|
||||
self.apply(initialize_resnet)
|
||||
|
||||
def base_parameters(self):
|
||||
return list(self.layers.parameters()) + list(self.avgpool.parameters()) + list(self.classifier.parameters())
|
||||
def arch_parameters(self):
|
||||
return [self.width_attentions]
|
||||
|
||||
def get_flop(self, mode, config_dict, extra_info):
|
||||
if config_dict is not None: config_dict = config_dict.copy()
|
||||
#weights = [F.softmax(x, dim=0) for x in self.width_attentions]
|
||||
channels = [3]
|
||||
for i, weight in enumerate(self.width_attentions):
|
||||
if mode == 'genotype':
|
||||
def base_parameters(self):
|
||||
return (
|
||||
list(self.layers.parameters())
|
||||
+ list(self.avgpool.parameters())
|
||||
+ list(self.classifier.parameters())
|
||||
)
|
||||
|
||||
def get_flop(self, mode, config_dict, extra_info):
|
||||
if config_dict is not None:
|
||||
config_dict = config_dict.copy()
|
||||
# weights = [F.softmax(x, dim=0) for x in self.width_attentions]
|
||||
channels = [3]
|
||||
for i, weight in enumerate(self.width_attentions):
|
||||
if mode == "genotype":
|
||||
with torch.no_grad():
|
||||
probe = nn.functional.softmax(weight, dim=0)
|
||||
C = self.Ranges[i][torch.argmax(probe).item()]
|
||||
elif mode == "max":
|
||||
C = self.Ranges[i][-1]
|
||||
elif mode == "fix":
|
||||
C = int(math.sqrt(extra_info) * self.Ranges[i][-1])
|
||||
elif mode == "random":
|
||||
assert isinstance(extra_info, float), "invalid extra_info : {:}".format(
|
||||
extra_info
|
||||
)
|
||||
with torch.no_grad():
|
||||
prob = nn.functional.softmax(weight, dim=0)
|
||||
approximate_C = int(math.sqrt(extra_info) * self.Ranges[i][-1])
|
||||
for j in range(prob.size(0)):
|
||||
prob[j] = 1 / (
|
||||
abs(j - (approximate_C - self.Ranges[i][j])) + 0.2
|
||||
)
|
||||
C = self.Ranges[i][torch.multinomial(prob, 1, False).item()]
|
||||
else:
|
||||
raise ValueError("invalid mode : {:}".format(mode))
|
||||
channels.append(C)
|
||||
flop = 0
|
||||
for i, layer in enumerate(self.layers):
|
||||
s, e = self.layer2indexRange[i]
|
||||
xchl = tuple(channels[s : e + 1])
|
||||
flop += layer.get_flops(xchl)
|
||||
# the last fc layer
|
||||
flop += channels[-1] * self.classifier.out_features
|
||||
if config_dict is None:
|
||||
return flop / 1e6
|
||||
else:
|
||||
config_dict["xchannels"] = channels
|
||||
config_dict["super_type"] = "infer-width"
|
||||
config_dict["estimated_FLOP"] = flop / 1e6
|
||||
return flop / 1e6, config_dict
|
||||
|
||||
def get_arch_info(self):
|
||||
string = "for width, there are {:} attention probabilities.".format(
|
||||
len(self.width_attentions)
|
||||
)
|
||||
discrepancy = []
|
||||
with torch.no_grad():
|
||||
probe = nn.functional.softmax(weight, dim=0)
|
||||
C = self.Ranges[i][ torch.argmax(probe).item() ]
|
||||
elif mode == 'max':
|
||||
C = self.Ranges[i][-1]
|
||||
elif mode == 'fix':
|
||||
C = int( math.sqrt( extra_info ) * self.Ranges[i][-1] )
|
||||
elif mode == 'random':
|
||||
assert isinstance(extra_info, float), 'invalid extra_info : {:}'.format(extra_info)
|
||||
for i, att in enumerate(self.width_attentions):
|
||||
prob = nn.functional.softmax(att, dim=0)
|
||||
prob = prob.cpu()
|
||||
selc = prob.argmax().item()
|
||||
prob = prob.tolist()
|
||||
prob = ["{:.3f}".format(x) for x in prob]
|
||||
xstring = "{:03d}/{:03d}-th : {:}".format(
|
||||
i, len(self.width_attentions), " ".join(prob)
|
||||
)
|
||||
logt = ["{:.3f}".format(x) for x in att.cpu().tolist()]
|
||||
xstring += " || {:52s}".format(" ".join(logt))
|
||||
prob = sorted([float(x) for x in prob])
|
||||
disc = prob[-1] - prob[-2]
|
||||
xstring += " || dis={:.2f} || select={:}/{:}".format(
|
||||
disc, selc, len(prob)
|
||||
)
|
||||
discrepancy.append(disc)
|
||||
string += "\n{:}".format(xstring)
|
||||
return string, discrepancy
|
||||
|
||||
def set_tau(self, tau_max, tau_min, epoch_ratio):
|
||||
assert (
|
||||
epoch_ratio >= 0 and epoch_ratio <= 1
|
||||
), "invalid epoch-ratio : {:}".format(epoch_ratio)
|
||||
tau = tau_min + (tau_max - tau_min) * (1 + math.cos(math.pi * epoch_ratio)) / 2
|
||||
self.tau = tau
|
||||
|
||||
def get_message(self):
|
||||
return self.message
|
||||
|
||||
def forward(self, inputs):
|
||||
if self.search_mode == "basic":
|
||||
return self.basic_forward(inputs)
|
||||
elif self.search_mode == "search":
|
||||
return self.search_forward(inputs)
|
||||
else:
|
||||
raise ValueError("invalid search_mode = {:}".format(self.search_mode))
|
||||
|
||||
def search_forward(self, inputs):
|
||||
flop_probs = nn.functional.softmax(self.width_attentions, dim=1)
|
||||
selected_widths, selected_probs = select2withP(self.width_attentions, self.tau)
|
||||
with torch.no_grad():
|
||||
prob = nn.functional.softmax(weight, dim=0)
|
||||
approximate_C = int( math.sqrt( extra_info ) * self.Ranges[i][-1] )
|
||||
for j in range(prob.size(0)):
|
||||
prob[j] = 1 / (abs(j - (approximate_C-self.Ranges[i][j])) + 0.2)
|
||||
C = self.Ranges[i][ torch.multinomial(prob, 1, False).item() ]
|
||||
else:
|
||||
raise ValueError('invalid mode : {:}'.format(mode))
|
||||
channels.append( C )
|
||||
flop = 0
|
||||
for i, layer in enumerate(self.layers):
|
||||
s, e = self.layer2indexRange[i]
|
||||
xchl = tuple( channels[s:e+1] )
|
||||
flop+= layer.get_flops(xchl)
|
||||
# the last fc layer
|
||||
flop += channels[-1] * self.classifier.out_features
|
||||
if config_dict is None:
|
||||
return flop / 1e6
|
||||
else:
|
||||
config_dict['xchannels'] = channels
|
||||
config_dict['super_type'] = 'infer-width'
|
||||
config_dict['estimated_FLOP'] = flop / 1e6
|
||||
return flop / 1e6, config_dict
|
||||
selected_widths = selected_widths.cpu()
|
||||
|
||||
def get_arch_info(self):
|
||||
string = "for width, there are {:} attention probabilities.".format(len(self.width_attentions))
|
||||
discrepancy = []
|
||||
with torch.no_grad():
|
||||
for i, att in enumerate(self.width_attentions):
|
||||
prob = nn.functional.softmax(att, dim=0)
|
||||
prob = prob.cpu() ; selc = prob.argmax().item() ; prob = prob.tolist()
|
||||
prob = ['{:.3f}'.format(x) for x in prob]
|
||||
xstring = '{:03d}/{:03d}-th : {:}'.format(i, len(self.width_attentions), ' '.join(prob))
|
||||
logt = ['{:.3f}'.format(x) for x in att.cpu().tolist()]
|
||||
xstring += ' || {:52s}'.format(' '.join(logt))
|
||||
prob = sorted( [float(x) for x in prob] )
|
||||
disc = prob[-1] - prob[-2]
|
||||
xstring += ' || dis={:.2f} || select={:}/{:}'.format(disc, selc, len(prob))
|
||||
discrepancy.append( disc )
|
||||
string += '\n{:}'.format(xstring)
|
||||
return string, discrepancy
|
||||
x, last_channel_idx, expected_inC, flops = inputs, 0, 3, []
|
||||
for i, layer in enumerate(self.layers):
|
||||
selected_w_index = selected_widths[
|
||||
last_channel_idx : last_channel_idx + layer.num_conv
|
||||
]
|
||||
selected_w_probs = selected_probs[
|
||||
last_channel_idx : last_channel_idx + layer.num_conv
|
||||
]
|
||||
layer_prob = flop_probs[
|
||||
last_channel_idx : last_channel_idx + layer.num_conv
|
||||
]
|
||||
x, expected_inC, expected_flop = layer(
|
||||
(x, expected_inC, layer_prob, selected_w_index, selected_w_probs)
|
||||
)
|
||||
last_channel_idx += layer.num_conv
|
||||
flops.append(expected_flop)
|
||||
flops.append(expected_inC * (self.classifier.out_features * 1.0 / 1e6))
|
||||
features = self.avgpool(x)
|
||||
features = features.view(features.size(0), -1)
|
||||
logits = linear_forward(features, self.classifier)
|
||||
return logits, torch.stack([sum(flops)])
|
||||
|
||||
def set_tau(self, tau_max, tau_min, epoch_ratio):
|
||||
assert epoch_ratio >= 0 and epoch_ratio <= 1, 'invalid epoch-ratio : {:}'.format(epoch_ratio)
|
||||
tau = tau_min + (tau_max-tau_min) * (1 + math.cos(math.pi * epoch_ratio)) / 2
|
||||
self.tau = tau
|
||||
|
||||
def get_message(self):
|
||||
return self.message
|
||||
|
||||
def forward(self, inputs):
|
||||
if self.search_mode == 'basic':
|
||||
return self.basic_forward(inputs)
|
||||
elif self.search_mode == 'search':
|
||||
return self.search_forward(inputs)
|
||||
else:
|
||||
raise ValueError('invalid search_mode = {:}'.format(self.search_mode))
|
||||
|
||||
def search_forward(self, inputs):
|
||||
flop_probs = nn.functional.softmax(self.width_attentions, dim=1)
|
||||
selected_widths, selected_probs = select2withP(self.width_attentions, self.tau)
|
||||
with torch.no_grad():
|
||||
selected_widths = selected_widths.cpu()
|
||||
|
||||
x, last_channel_idx, expected_inC, flops = inputs, 0, 3, []
|
||||
for i, layer in enumerate(self.layers):
|
||||
selected_w_index = selected_widths[last_channel_idx: last_channel_idx+layer.num_conv]
|
||||
selected_w_probs = selected_probs[last_channel_idx: last_channel_idx+layer.num_conv]
|
||||
layer_prob = flop_probs[last_channel_idx: last_channel_idx+layer.num_conv]
|
||||
x, expected_inC, expected_flop = layer( (x, expected_inC, layer_prob, selected_w_index, selected_w_probs) )
|
||||
last_channel_idx += layer.num_conv
|
||||
flops.append( expected_flop )
|
||||
flops.append( expected_inC * (self.classifier.out_features*1.0/1e6) )
|
||||
features = self.avgpool(x)
|
||||
features = features.view(features.size(0), -1)
|
||||
logits = linear_forward(features, self.classifier)
|
||||
return logits, torch.stack( [sum(flops)] )
|
||||
|
||||
def basic_forward(self, inputs):
|
||||
if self.InShape is None: self.InShape = (inputs.size(-2), inputs.size(-1))
|
||||
x = inputs
|
||||
for i, layer in enumerate(self.layers):
|
||||
x = layer( x )
|
||||
features = self.avgpool(x)
|
||||
features = features.view(features.size(0), -1)
|
||||
logits = self.classifier(features)
|
||||
return features, logits
|
||||
def basic_forward(self, inputs):
|
||||
if self.InShape is None:
|
||||
self.InShape = (inputs.size(-2), inputs.size(-1))
|
||||
x = inputs
|
||||
for i, layer in enumerate(self.layers):
|
||||
x = layer(x)
|
||||
features = self.avgpool(x)
|
||||
features = features.view(features.size(0), -1)
|
||||
logits = self.classifier(features)
|
||||
return features, logits
|
||||
|
||||
Reference in New Issue
Block a user