174 lines
6.2 KiB
Python
174 lines
6.2 KiB
Python
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import math
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from copy import deepcopy
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from .construct_utils import drop_path
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from ..operations import OPS, Identity, FactorizedReduce, ReLUConvBN
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class MixedOp(nn.Module):
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def __init__(self, C, stride, PRIMITIVES):
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super(MixedOp, self).__init__()
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self._ops = nn.ModuleList()
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self.name2idx = {}
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for idx, primitive in enumerate(PRIMITIVES):
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op = OPS[primitive](C, C, stride, False)
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self._ops.append(op)
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assert primitive not in self.name2idx, '{:} has already in'.format(primitive)
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self.name2idx[primitive] = idx
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def forward(self, x, weights, op_name):
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if op_name is None:
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if weights is None:
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return [op(x) for op in self._ops]
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else:
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return sum(w * op(x) for w, op in zip(weights, self._ops))
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else:
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op_index = self.name2idx[op_name]
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return self._ops[op_index](x)
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class SearchCell(nn.Module):
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def __init__(self, steps, multiplier, C_prev_prev, C_prev, C, reduction, reduction_prev, PRIMITIVES, use_residual):
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super(SearchCell, self).__init__()
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self.reduction = reduction
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self.PRIMITIVES = deepcopy(PRIMITIVES)
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if reduction_prev:
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self.preprocess0 = FactorizedReduce(C_prev_prev, C, 2, affine=False)
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else:
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self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0, affine=False)
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self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0, affine=False)
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self._steps = steps
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self._multiplier = multiplier
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self._use_residual = use_residual
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self._ops = nn.ModuleList()
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for i in range(self._steps):
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for j in range(2+i):
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stride = 2 if reduction and j < 2 else 1
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op = MixedOp(C, stride, self.PRIMITIVES)
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self._ops.append(op)
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def extra_repr(self):
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return ('{name}(residual={_use_residual}, steps={_steps}, multiplier={_multiplier})'.format(name=self.__class__.__name__, **self.__dict__))
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def forward(self, S0, S1, weights, connect, adjacency, drop_prob, modes):
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if modes[0] is None:
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if modes[1] == 'normal':
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output = self.__forwardBoth(S0, S1, weights, connect, adjacency, drop_prob)
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elif modes[1] == 'only_W':
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output = self.__forwardOnlyW(S0, S1, drop_prob)
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else:
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test_genotype = modes[0]
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if self.reduction: operations, concats = test_genotype.reduce, test_genotype.reduce_concat
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else : operations, concats = test_genotype.normal, test_genotype.normal_concat
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s0, s1 = self.preprocess0(S0), self.preprocess1(S1)
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states, offset = [s0, s1], 0
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assert self._steps == len(operations), '{:} vs. {:}'.format(self._steps, len(operations))
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for i, (opA, opB) in enumerate(operations):
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A = self._ops[offset + opA[1]](states[opA[1]], None, opA[0])
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B = self._ops[offset + opB[1]](states[opB[1]], None, opB[0])
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state = A + B
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offset += len(states)
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states.append(state)
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output = torch.cat([states[i] for i in concats], dim=1)
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if self._use_residual and S1.size() == output.size():
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return S1 + output
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else: return output
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def __forwardBoth(self, S0, S1, weights, connect, adjacency, drop_prob):
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s0, s1 = self.preprocess0(S0), self.preprocess1(S1)
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states, offset = [s0, s1], 0
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for i in range(self._steps):
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clist = []
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for j, h in enumerate(states):
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x = self._ops[offset+j](h, weights[offset+j], None)
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if self.training and drop_prob > 0.:
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x = drop_path(x, math.pow(drop_prob, 1./len(states)))
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clist.append( x )
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connection = torch.mm(connect['{:}'.format(i)], adjacency[i]).squeeze(0)
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state = sum(w * node for w, node in zip(connection, clist))
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offset += len(states)
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states.append(state)
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return torch.cat(states[-self._multiplier:], dim=1)
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def __forwardOnlyW(self, S0, S1, drop_prob):
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s0, s1 = self.preprocess0(S0), self.preprocess1(S1)
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states, offset = [s0, s1], 0
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for i in range(self._steps):
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clist = []
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for j, h in enumerate(states):
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xs = self._ops[offset+j](h, None, None)
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clist += xs
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if self.training and drop_prob > 0.:
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xlist = [drop_path(x, math.pow(drop_prob, 1./len(states))) for x in clist]
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else: xlist = clist
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state = sum(xlist) * 2 / len(xlist)
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offset += len(states)
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states.append(state)
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return torch.cat(states[-self._multiplier:], dim=1)
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class InferCell(nn.Module):
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def __init__(self, genotype, C_prev_prev, C_prev, C, reduction, reduction_prev):
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super(InferCell, self).__init__()
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print(C_prev_prev, C_prev, C)
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if reduction_prev is None:
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self.preprocess0 = Identity()
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elif reduction_prev:
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self.preprocess0 = FactorizedReduce(C_prev_prev, C, 2)
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else:
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self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0)
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self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0)
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if reduction: step_ops, concat = genotype.reduce, genotype.reduce_concat
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else : step_ops, concat = genotype.normal, genotype.normal_concat
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self._steps = len(step_ops)
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self._concat = concat
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self._multiplier = len(concat)
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self._ops = nn.ModuleList()
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self._indices = []
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for operations in step_ops:
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for name, index in operations:
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stride = 2 if reduction and index < 2 else 1
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if reduction_prev is None and index == 0:
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op = OPS[name](C_prev_prev, C, stride, True)
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else:
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op = OPS[name](C , C, stride, True)
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self._ops.append( op )
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self._indices.append( index )
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def extra_repr(self):
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return ('{name}(steps={_steps}, concat={_concat})'.format(name=self.__class__.__name__, **self.__dict__))
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def forward(self, S0, S1, drop_prob):
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s0 = self.preprocess0(S0)
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s1 = self.preprocess1(S1)
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states = [s0, s1]
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for i in range(self._steps):
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h1 = states[self._indices[2*i]]
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h2 = states[self._indices[2*i+1]]
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op1 = self._ops[2*i]
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op2 = self._ops[2*i+1]
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h1 = op1(h1)
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h2 = op2(h2)
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if self.training and drop_prob > 0.:
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if not isinstance(op1, Identity):
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h1 = drop_path(h1, drop_prob)
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if not isinstance(op2, Identity):
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h2 = drop_path(h2, drop_prob)
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state = h1 + h2
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states += [state]
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output = torch.cat([states[i] for i in self._concat], dim=1)
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return output
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