##################################################### # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 # ##################################################### from typing import List, Text, Any import torch.nn as nn from models.cell_operations import ResNetBasicblock from models.cell_infers.cells import InferCell class DynamicShapeTinyNet(nn.Module): def __init__(self, channels: List[int], genotype: Any, num_classes: int): super(DynamicShapeTinyNet, self).__init__() self._channels = channels if len(channels) % 3 != 2: raise ValueError("invalid number of layers : {:}".format(len(channels))) self._num_stage = N = len(channels) // 3 self.stem = nn.Sequential( nn.Conv2d(3, channels[0], kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(channels[0]), ) # layer_channels = [C ] * N + [C*2 ] + [C*2 ] * N + [C*4 ] + [C*4 ] * N layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N c_prev = channels[0] self.cells = nn.ModuleList() for index, (c_curr, reduction) in enumerate(zip(channels, layer_reductions)): if reduction: cell = ResNetBasicblock(c_prev, c_curr, 2, True) else: cell = InferCell(genotype, c_prev, c_curr, 1) self.cells.append(cell) c_prev = cell.out_dim self._num_layer = len(self.cells) self.lastact = nn.Sequential(nn.BatchNorm2d(c_prev), nn.ReLU(inplace=True)) self.global_pooling = nn.AdaptiveAvgPool2d(1) self.classifier = nn.Linear(c_prev, num_classes) def get_message(self) -> Text: string = self.extra_repr() for i, cell in enumerate(self.cells): string += "\n {:02d}/{:02d} :: {:}".format( i, len(self.cells), cell.extra_repr() ) return string def extra_repr(self): return "{name}(C={_channels}, N={_num_stage}, L={_num_layer})".format( name=self.__class__.__name__, **self.__dict__ ) def forward(self, inputs): feature = self.stem(inputs) for i, cell in enumerate(self.cells): feature = cell(feature) out = self.lastact(feature) out = self.global_pooling(out) out = out.view(out.size(0), -1) logits = self.classifier(out) return out, logits