################################################## # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # ################################################## import math, torch import torch.nn as nn from ..initialization import initialize_resnet from ..SharedUtils import additive_func from .SoftSelect import select2withP, ChannelWiseInter from .SoftSelect import linear_forward from .SoftSelect import get_width_choices as get_choices def conv_forward(inputs, conv, choices): iC = conv.in_channels fill_size = list(inputs.size()) fill_size[1] = iC - fill_size[1] filled = torch.zeros(fill_size, device=inputs.device) xinputs = torch.cat((inputs, filled), dim=1) outputs = conv(xinputs) selecteds = [outputs[:,:oC] for oC in choices] return selecteds class ConvBNReLU(nn.Module): num_conv = 1 def __init__(self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu): super(ConvBNReLU, self).__init__() self.InShape = None self.OutShape = None self.choices = get_choices(nOut) self.register_buffer('choices_tensor', torch.Tensor( self.choices )) if has_avg : self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0) else : self.avg = None self.conv = nn.Conv2d(nIn, nOut, kernel_size=kernel, stride=stride, padding=padding, dilation=1, groups=1, bias=bias) #if has_bn : self.bn = nn.BatchNorm2d(nOut) #else : self.bn = None self.has_bn = has_bn self.BNs = nn.ModuleList() for i, _out in enumerate(self.choices): self.BNs.append(nn.BatchNorm2d(_out)) if has_relu: self.relu = nn.ReLU(inplace=True) else : self.relu = None self.in_dim = nIn self.out_dim = nOut self.search_mode = 'basic' def get_flops(self, channels, check_range=True, divide=1): iC, oC = channels 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) assert isinstance(self.InShape, tuple) and len(self.InShape) == 2, 'invalid in-shape : {:}'.format(self.InShape) assert isinstance(self.OutShape, tuple) and len(self.OutShape) == 2, 'invalid out-shape : {:}'.format(self.OutShape) #conv_per_position_flops = self.conv.kernel_size[0] * self.conv.kernel_size[1] * iC * oC / self.conv.groups conv_per_position_flops = (self.conv.kernel_size[0] * self.conv.kernel_size[1] * 1.0 / self.conv.groups) all_positions = self.OutShape[0] * self.OutShape[1] flops = (conv_per_position_flops * all_positions / divide) * iC * oC if self.conv.bias is not None: flops += all_positions / divide return flops def get_range(self): return [self.choices] 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, index, prob = tuple_inputs index, prob = torch.squeeze(index).tolist(), torch.squeeze(prob) probability = torch.squeeze(probability) assert len(index) == 2, 'invalid length : {:}'.format(index) # compute expected flop #coordinates = torch.arange(self.x_range[0], self.x_range[1]+1).type_as(probability) expected_outC = (self.choices_tensor * probability).sum() expected_flop = self.get_flops([expected_inC, expected_outC], False, 1e6) if self.avg : out = self.avg( inputs ) else : out = inputs # convolutional layer out_convs = conv_forward(out, self.conv, [self.choices[i] for i in index]) out_bns = [self.BNs[idx](out_conv) for idx, out_conv in zip(index, out_convs)] # merge out_channel = max([x.size(1) for x in out_bns]) outA = ChannelWiseInter(out_bns[0], out_channel) outB = ChannelWiseInter(out_bns[1], out_channel) out = outA * prob[0] + outB * prob[1] #out = additive_func(out_bns[0]*prob[0], out_bns[1]*prob[1]) if self.relu: out = self.relu( out ) else : out = out return out, expected_outC, expected_flop def basic_forward(self, inputs): if self.avg : out = self.avg( inputs ) else : out = inputs conv = self.conv( out ) if self.has_bn:out= self.BNs[-1]( conv ) else : out = conv if self.relu: out = self.relu( out ) else : out = out if self.InShape is None: self.InShape = (inputs.size(-2), inputs.size(-1)) self.OutShape = (out.size(-2) , out.size(-1)) return out class SimBlock(nn.Module): expansion = 1 num_conv = 1 def __init__(self, inplanes, planes, stride): super(SimBlock, self).__init__() assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride) self.conv = ConvBNReLU(inplanes, planes, 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True) if stride == 2: self.downsample = ConvBNReLU(inplanes, planes, 1, 1, 0, False, has_avg=True, has_bn=False, has_relu=False) elif inplanes != planes: self.downsample = ConvBNReLU(inplanes, planes, 1, 1, 0, False, has_avg=False,has_bn=True , has_relu=False) else: self.downsample = None self.out_dim = planes self.search_mode = 'basic' def get_range(self): return self.conv.get_range() def get_flops(self, channels): assert len(channels) == 2, 'invalid channels : {:}'.format(channels) flop_A = self.conv.get_flops([channels[0], channels[1]]) if hasattr(self.downsample, 'get_flops'): flop_C = self.downsample.get_flops([channels[0], channels[-1]]) else: flop_C = 0 if channels[0] != channels[-1] and self.downsample is None: # this short-cut will be added during the infer-train flop_C = channels[0] * channels[-1] * self.conv.OutShape[0] * self.conv.OutShape[1] return flop_A + flop_C 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) == 1 and probs.size(0) == 1 and probability.size(0) == 1, 'invalid size : {:}, {:}, {:}'.format(indexes.size(), probs.size(), probability.size()) out, expected_next_inC, expected_flop = self.conv( (inputs, expected_inC , probability[0], indexes[0], probs[0]) ) if self.downsample is not None: residual, _, expected_flop_c = self.downsample( (inputs, expected_inC , probability[-1], indexes[-1], probs[-1]) ) else: residual, expected_flop_c = inputs, 0 out = additive_func(residual, out) return nn.functional.relu(out, inplace=True), expected_next_inC, sum([expected_flop, expected_flop_c]) def basic_forward(self, inputs): basicblock = self.conv(inputs) 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 SearchWidthSimResNet(nn.Module): def __init__(self, depth, num_classes): super(SearchWidthSimResNet, self).__init__() assert (depth - 2) % 3 == 0, 'depth should be one of 5, 8, 11, 14, ... instead of {:}'.format(depth) layer_blocks = (depth - 2) // 3 self.message = 'SearchWidthSimResNet : 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 = SimBlock(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.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 arch_parameters(self): return [self.width_attentions] 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(): 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(): 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