Use black for lib/models
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D-X-Y
@@ -11,111 +11,145 @@ from models.cell_operations import OPS
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# Cell for NAS-Bench-201
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class InferCell(nn.Module):
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def __init__(
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self, genotype, C_in, C_out, stride, affine=True, track_running_stats=True
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):
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super(InferCell, self).__init__()
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def __init__(self, genotype, C_in, C_out, stride, affine=True, track_running_stats=True):
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super(InferCell, self).__init__()
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self.layers = nn.ModuleList()
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self.node_IN = []
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self.node_IX = []
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self.genotype = deepcopy(genotype)
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for i in range(1, len(genotype)):
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node_info = genotype[i - 1]
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cur_index = []
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cur_innod = []
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for (op_name, op_in) in node_info:
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if op_in == 0:
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layer = OPS[op_name](
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C_in, C_out, stride, affine, track_running_stats
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)
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else:
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layer = OPS[op_name](C_out, C_out, 1, affine, track_running_stats)
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cur_index.append(len(self.layers))
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cur_innod.append(op_in)
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self.layers.append(layer)
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self.node_IX.append(cur_index)
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self.node_IN.append(cur_innod)
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self.nodes = len(genotype)
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self.in_dim = C_in
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self.out_dim = C_out
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self.layers = nn.ModuleList()
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self.node_IN = []
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self.node_IX = []
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self.genotype = deepcopy(genotype)
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for i in range(1, len(genotype)):
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node_info = genotype[i-1]
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cur_index = []
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cur_innod = []
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for (op_name, op_in) in node_info:
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if op_in == 0:
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layer = OPS[op_name](C_in , C_out, stride, affine, track_running_stats)
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else:
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layer = OPS[op_name](C_out, C_out, 1, affine, track_running_stats)
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cur_index.append( len(self.layers) )
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cur_innod.append( op_in )
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self.layers.append( layer )
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self.node_IX.append( cur_index )
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self.node_IN.append( cur_innod )
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self.nodes = len(genotype)
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self.in_dim = C_in
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self.out_dim = C_out
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def extra_repr(self):
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string = 'info :: nodes={nodes}, inC={in_dim}, outC={out_dim}'.format(**self.__dict__)
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laystr = []
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for i, (node_layers, node_innods) in enumerate(zip(self.node_IX,self.node_IN)):
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y = ['I{:}-L{:}'.format(_ii, _il) for _il, _ii in zip(node_layers, node_innods)]
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x = '{:}<-({:})'.format(i+1, ','.join(y))
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laystr.append( x )
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return string + ', [{:}]'.format( ' | '.join(laystr) ) + ', {:}'.format(self.genotype.tostr())
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def forward(self, inputs):
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nodes = [inputs]
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for i, (node_layers, node_innods) in enumerate(zip(self.node_IX,self.node_IN)):
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node_feature = sum( self.layers[_il](nodes[_ii]) for _il, _ii in zip(node_layers, node_innods) )
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nodes.append( node_feature )
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return nodes[-1]
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def extra_repr(self):
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string = "info :: nodes={nodes}, inC={in_dim}, outC={out_dim}".format(
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**self.__dict__
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)
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laystr = []
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for i, (node_layers, node_innods) in enumerate(zip(self.node_IX, self.node_IN)):
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y = [
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"I{:}-L{:}".format(_ii, _il)
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for _il, _ii in zip(node_layers, node_innods)
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]
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x = "{:}<-({:})".format(i + 1, ",".join(y))
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laystr.append(x)
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return (
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string
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+ ", [{:}]".format(" | ".join(laystr))
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+ ", {:}".format(self.genotype.tostr())
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)
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def forward(self, inputs):
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nodes = [inputs]
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for i, (node_layers, node_innods) in enumerate(zip(self.node_IX, self.node_IN)):
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node_feature = sum(
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self.layers[_il](nodes[_ii])
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for _il, _ii in zip(node_layers, node_innods)
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)
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nodes.append(node_feature)
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return nodes[-1]
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# Learning Transferable Architectures for Scalable Image Recognition, CVPR 2018
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class NASNetInferCell(nn.Module):
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def __init__(
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self,
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genotype,
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C_prev_prev,
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C_prev,
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C,
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reduction,
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reduction_prev,
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affine,
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track_running_stats,
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):
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super(NASNetInferCell, self).__init__()
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self.reduction = reduction
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if reduction_prev:
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self.preprocess0 = OPS["skip_connect"](
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C_prev_prev, C, 2, affine, track_running_stats
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)
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else:
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self.preprocess0 = OPS["nor_conv_1x1"](
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C_prev_prev, C, 1, affine, track_running_stats
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)
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self.preprocess1 = OPS["nor_conv_1x1"](
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C_prev, C, 1, affine, track_running_stats
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)
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def __init__(self, genotype, C_prev_prev, C_prev, C, reduction, reduction_prev, affine, track_running_stats):
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super(NASNetInferCell, self).__init__()
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self.reduction = reduction
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if reduction_prev: self.preprocess0 = OPS['skip_connect'](C_prev_prev, C, 2, affine, track_running_stats)
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else : self.preprocess0 = OPS['nor_conv_1x1'](C_prev_prev, C, 1, affine, track_running_stats)
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self.preprocess1 = OPS['nor_conv_1x1'](C_prev, C, 1, affine, track_running_stats)
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if not reduction:
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nodes, concats = genotype["normal"], genotype["normal_concat"]
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else:
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nodes, concats = genotype["reduce"], genotype["reduce_concat"]
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self._multiplier = len(concats)
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self._concats = concats
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self._steps = len(nodes)
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self._nodes = nodes
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self.edges = nn.ModuleDict()
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for i, node in enumerate(nodes):
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for in_node in node:
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name, j = in_node[0], in_node[1]
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stride = 2 if reduction and j < 2 else 1
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node_str = "{:}<-{:}".format(i + 2, j)
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self.edges[node_str] = OPS[name](
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C, C, stride, affine, track_running_stats
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)
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if not reduction:
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nodes, concats = genotype['normal'], genotype['normal_concat']
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else:
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nodes, concats = genotype['reduce'], genotype['reduce_concat']
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self._multiplier = len(concats)
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self._concats = concats
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self._steps = len(nodes)
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self._nodes = nodes
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self.edges = nn.ModuleDict()
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for i, node in enumerate(nodes):
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for in_node in node:
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name, j = in_node[0], in_node[1]
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stride = 2 if reduction and j < 2 else 1
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node_str = '{:}<-{:}'.format(i+2, j)
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self.edges[node_str] = OPS[name](C, C, stride, affine, track_running_stats)
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# [TODO] to support drop_prob in this function..
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def forward(self, s0, s1, unused_drop_prob):
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s0 = self.preprocess0(s0)
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s1 = self.preprocess1(s1)
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# [TODO] to support drop_prob in this function..
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def forward(self, s0, s1, unused_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, node in enumerate(self._nodes):
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clist = []
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for in_node in node:
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name, j = in_node[0], in_node[1]
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node_str = '{:}<-{:}'.format(i+2, j)
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op = self.edges[ node_str ]
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clist.append( op(states[j]) )
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states.append( sum(clist) )
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return torch.cat([states[x] for x in self._concats], dim=1)
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states = [s0, s1]
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for i, node in enumerate(self._nodes):
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clist = []
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for in_node in node:
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name, j = in_node[0], in_node[1]
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node_str = "{:}<-{:}".format(i + 2, j)
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op = self.edges[node_str]
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clist.append(op(states[j]))
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states.append(sum(clist))
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return torch.cat([states[x] for x in self._concats], dim=1)
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class AuxiliaryHeadCIFAR(nn.Module):
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def __init__(self, C, num_classes):
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"""assuming input size 8x8"""
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super(AuxiliaryHeadCIFAR, self).__init__()
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self.features = nn.Sequential(
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nn.ReLU(inplace=True),
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nn.AvgPool2d(
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5, stride=3, padding=0, count_include_pad=False
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), # image size = 2 x 2
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nn.Conv2d(C, 128, 1, bias=False),
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nn.BatchNorm2d(128),
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nn.ReLU(inplace=True),
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nn.Conv2d(128, 768, 2, bias=False),
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nn.BatchNorm2d(768),
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nn.ReLU(inplace=True),
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)
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self.classifier = nn.Linear(768, num_classes)
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def __init__(self, C, num_classes):
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"""assuming input size 8x8"""
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super(AuxiliaryHeadCIFAR, self).__init__()
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self.features = nn.Sequential(
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nn.ReLU(inplace=True),
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nn.AvgPool2d(5, stride=3, padding=0, count_include_pad=False), # image size = 2 x 2
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nn.Conv2d(C, 128, 1, bias=False),
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nn.BatchNorm2d(128),
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nn.ReLU(inplace=True),
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nn.Conv2d(128, 768, 2, bias=False),
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nn.BatchNorm2d(768),
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nn.ReLU(inplace=True)
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)
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self.classifier = nn.Linear(768, num_classes)
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def forward(self, x):
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x = self.features(x)
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x = self.classifier(x.view(x.size(0),-1))
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return x
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def forward(self, x):
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x = self.features(x)
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x = self.classifier(x.view(x.size(0), -1))
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return x
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