autodl-projects/lib/models/shape_infers/InferTinyCellNet.py
2021-05-12 16:28:05 +08:00

65 lines
2.4 KiB
Python

#####################################################
# 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