Clean unnecessary files

others/GDAS/lib/nas_rnn/model_search.py

@@ -1,104 +0,0 @@
-import copy, torch
-import torch.nn as nn
-import torch.nn.functional as F
-from collections import namedtuple
-from .genotypes import PRIMITIVES, STEPS, CONCAT, Genotype
-from .basemodel import DARTSCell, RNNModel
-
-
-class DARTSCellSearch(DARTSCell):
-
-  def __init__(self, ninp, nhid, dropouth, dropoutx):
-    super(DARTSCellSearch, self).__init__(ninp, nhid, dropouth, dropoutx, genotype=None)
-    self.bn = nn.BatchNorm1d(nhid, affine=False)
-    self.check_zero = False
-
-  def set_check(self, check_zero):
-    self.check_zero = check_zero
-
-  def cell(self, x, h_prev, x_mask, h_mask, arch_probs):
-    s0 = self._compute_init_state(x, h_prev, x_mask, h_mask)
-    s0 = self.bn(s0)
-    if self.check_zero:
-      arch_probs_cpu = arch_probs.cpu().tolist()
-    #arch_probs = F.softmax(self.weights, dim=-1)
-
-    offset = 0
-    states = s0.unsqueeze(0)
-    for i in range(STEPS):
-      if self.training:
-        masked_states = states * h_mask.unsqueeze(0)
-      else:
-        masked_states = states
-      ch = masked_states.view(-1, self.nhid).mm(self._Ws[i]).view(i+1, -1, 2*self.nhid)
-      c, h = torch.split(ch, self.nhid, dim=-1)
-      c = c.sigmoid()
-
-      s = torch.zeros_like(s0)
-      for k, name in enumerate(PRIMITIVES):
-        if name == 'none':
-          continue
-        fn = self._get_activation(name)
-        unweighted = states + c * (fn(h) - states)
-        if self.check_zero:
-          INDEX, INDDX = [], []
-          for jj in range(offset, offset+i+1):
-            if arch_probs_cpu[jj][k] > 0:
-              INDEX.append(jj)
-              INDDX.append(jj-offset)
-          if len(INDEX) == 0: continue
-          s += torch.sum(arch_probs[INDEX, k].unsqueeze(-1).unsqueeze(-1) * unweighted[INDDX, :, :], dim=0)
-        else:
-          s += torch.sum(arch_probs[offset:offset+i+1, k].unsqueeze(-1).unsqueeze(-1) * unweighted, dim=0)
-      s = self.bn(s)
-      states = torch.cat([states, s.unsqueeze(0)], 0)
-      offset += i+1
-    output = torch.mean(states[-CONCAT:], dim=0)
-    return output
-
-
-class RNNModelSearch(RNNModel):
-
-  def __init__(self, *args):
-    super(RNNModelSearch, self).__init__(*args)
-    self._args = copy.deepcopy( args )
-
-    k = sum(i for i in range(1, STEPS+1))
-    self.arch_weights = nn.Parameter(torch.Tensor(k, len(PRIMITIVES)))
-    nn.init.normal_(self.arch_weights, 0, 0.001)
-
-  def base_parameters(self):
-    lists  = list(self.lockdrop.parameters())
-    lists += list(self.encoder.parameters())
-    lists += list(self.rnns.parameters())
-    lists += list(self.decoder.parameters())
-    return lists
-
-  def arch_parameters(self):
-    return [self.arch_weights]
-
-  def genotype(self):
-
-    def _parse(probs):
-      gene = []
-      start = 0
-      for i in range(STEPS):
-        end = start + i + 1
-        W = probs[start:end].copy()
-        #j = sorted(range(i + 1), key=lambda x: -max(W[x][k] for k in range(len(W[x])) if k != PRIMITIVES.index('none')))[0]
-        j = sorted(range(i + 1), key=lambda x: -max(W[x][k] for k in range(len(W[x])) ))[0]
-        k_best = None
-        for k in range(len(W[j])):
-          #if k != PRIMITIVES.index('none'):
-          #  if k_best is None or W[j][k] > W[j][k_best]:
-          #    k_best = k
-          if k_best is None or W[j][k] > W[j][k_best]:
-            k_best = k
-        gene.append((PRIMITIVES[k_best], j))
-        start = end
-      return gene
-
-    with torch.no_grad():
-      gene = _parse(F.softmax(self.arch_weights, dim=-1).cpu().numpy())
-    genotype = Genotype(recurrent=gene, concat=list(range(STEPS+1)[-CONCAT:]))
-    return genotype