149 lines
5.7 KiB
Python
149 lines
5.7 KiB
Python
##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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import math, random, torch
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import warnings
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import torch.nn as nn
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import torch.nn.functional as F
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from copy import deepcopy
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from ..cell_operations import OPS
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class SearchCell(nn.Module):
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def __init__(self, C_in, C_out, stride, max_nodes, op_names, n_piece):
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super(SearchCell, self).__init__()
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self.op_names = deepcopy(op_names)
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self.max_nodes = max_nodes
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self.in_dim = C_in
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self.out_dim = C_out
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self.n_piece = n_piece
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self.multi_edges = nn.ModuleList()
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for i_piece in range(n_piece):
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edges = nn.ModuleDict()
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for i in range(1, max_nodes):
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for j in range(i):
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node_str = '{:}<-{:}'.format(i, j)
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if j == 0: xlists = [OPS[op_name](C_in , C_out, stride) for op_name in op_names]
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else : xlists = [OPS[op_name](C_in , C_out, 1) for op_name in op_names]
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edges[ node_str ] = nn.ModuleList( xlists )
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self.multi_edges.append( edges )
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self.edge_keys = sorted(list(edges.keys()))
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self.edge2index = {key:i for i, key in enumerate(self.edge_keys)}
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self.num_edges = len(edges)
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def extra_repr(self):
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string = 'info :: {max_nodes} nodes, inC={in_dim}, outC={out_dim}, nP={n_piece}'.format(**self.__dict__)
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return string
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def forward(self, inputs, weightss):
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nodes = [inputs]
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with torch.no_grad():
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xmod, xid, argmax = 1, 0, weightss.argmax(dim=1).cpu().tolist()
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for i, x in enumerate(argmax):
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xid += x * (xmod % self.n_piece)
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xmod = (xmod * len(self.op_names)) % self.n_piece
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xid = xid % self.n_piece
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edges = self.multi_edges[xid]
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for i in range(1, self.max_nodes):
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inter_nodes = []
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for j in range(i):
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node_str = '{:}<-{:}'.format(i, j)
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weights = weightss[ self.edge2index[node_str] ]
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inter_nodes.append( sum( layer(nodes[j]) * w for layer, w in zip(edges[node_str], weights) ) )
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nodes.append( sum(inter_nodes) )
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return nodes[-1]
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# GDAS
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def forward_gdas(self, inputs, alphas, _tau):
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avoid_zero = 0
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while True:
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gumbels = -torch.empty_like(alphas).exponential_().log()
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logits = (alphas.log_softmax(dim=1) + gumbels) / _tau
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probs = nn.functional.softmax(logits, dim=1)
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index = probs.max(-1, keepdim=True)[1]
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one_h = torch.zeros_like(logits).scatter_(-1, index, 1.0)
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hardwts = one_h - probs.detach() + probs
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if (torch.isinf(gumbels).any()) or (torch.isinf(probs).any()) or (torch.isnan(probs).any()):
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continue # avoid the numerical error
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nodes = [inputs]
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for i in range(1, self.max_nodes):
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inter_nodes = []
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for j in range(i):
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node_str = '{:}<-{:}'.format(i, j)
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weights = hardwts[ self.edge2index[node_str] ]
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argmaxs = index[ self.edge2index[node_str] ].item()
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weigsum = sum( weights[_ie] * edge(nodes[j]) if _ie == argmaxs else weights[_ie] for _ie, edge in enumerate(self.edges[node_str]) )
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inter_nodes.append( weigsum )
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nodes.append( sum(inter_nodes) )
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avoid_zero += 1
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if nodes[-1].sum().item() == 0:
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if avoid_zero < 10: continue
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else:
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warnings.warn('get zero outputs with avoid_zero={:}'.format(avoid_zero))
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break
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else:
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break
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return nodes[-1]
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# joint
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def forward_joint(self, inputs, weightss):
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nodes = [inputs]
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for i in range(1, self.max_nodes):
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inter_nodes = []
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for j in range(i):
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node_str = '{:}<-{:}'.format(i, j)
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weights = weightss[ self.edge2index[node_str] ]
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#aggregation = sum( layer(nodes[j]) * w for layer, w in zip(self.edges[node_str], weights) ) / weights.numel()
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aggregation = sum( layer(nodes[j]) * w for layer, w in zip(self.edges[node_str], weights) )
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inter_nodes.append( aggregation )
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nodes.append( sum(inter_nodes) )
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return nodes[-1]
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# uniform random sampling per iteration
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def forward_urs(self, inputs):
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nodes = [inputs]
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for i in range(1, self.max_nodes):
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while True: # to avoid select zero for all ops
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sops, has_non_zero = [], False
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for j in range(i):
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node_str = '{:}<-{:}'.format(i, j)
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candidates = self.edges[node_str]
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select_op = random.choice(candidates)
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sops.append( select_op )
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if not hasattr(select_op, 'is_zero') or select_op.is_zero == False: has_non_zero=True
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if has_non_zero: break
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inter_nodes = []
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for j, select_op in enumerate(sops):
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inter_nodes.append( select_op(nodes[j]) )
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nodes.append( sum(inter_nodes) )
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return nodes[-1]
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# select the argmax
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def forward_select(self, inputs, weightss):
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nodes = [inputs]
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for i in range(1, self.max_nodes):
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inter_nodes = []
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for j in range(i):
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node_str = '{:}<-{:}'.format(i, j)
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weights = weightss[ self.edge2index[node_str] ]
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inter_nodes.append( self.edges[node_str][ weights.argmax().item() ]( nodes[j] ) )
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#inter_nodes.append( sum( layer(nodes[j]) * w for layer, w in zip(self.edges[node_str], weights) ) )
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nodes.append( sum(inter_nodes) )
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return nodes[-1]
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# forward with a specific structure
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def forward_dynamic(self, inputs, structure):
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nodes = [inputs]
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for i in range(1, self.max_nodes):
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cur_op_node = structure.nodes[i-1]
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inter_nodes = []
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for op_name, j in cur_op_node:
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node_str = '{:}<-{:}'.format(i, j)
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op_index = self.op_names.index( op_name )
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inter_nodes.append( self.edges[node_str][op_index]( nodes[j] ) )
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nodes.append( sum(inter_nodes) )
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return nodes[-1]
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