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Tree_single_inference.py
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Tree_single_inference.py
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from LEARNTOBRANCH import LEARNTOBRANCH
import paddle.nn as nn
import paddle
import paddle.nn.initializer as init
from coder import *
"""
返回一个单分支前向预测的森林
"""
class ForestNet(LEARNTOBRANCH):
def __init__(self, dataset, num_attributes, num_channels=64):
super(ForestNet, self).__init__()
self.avgpool = nn.AdaptiveAvgPool2D((1, 1))
fc_channel = 1
self.dataset = dataset
if dataset == 'CIFAR10':
self.num_children = [1, 2, 4, 8, 8]
self.num_in_channels = [3, num_channels // 4, num_channels // 2, num_channels, num_channels]
self.num_out_channels = [num_channels // 4, num_channels // 2, num_channels, num_channels]
self.cardinality = [1, 1, 1, 1, 1]
# self.ds = [level1, level2, level3, level4]
self.output_channels = [num_attributes] * 8
self.classifier_nodes = [[1], [2], [4, 4], [8, 8, 8, 8]]
self.fc_num = 8
elif dataset == 'CIFAR100':
# self.num_children = [1, 2, 4, 8, 8]
# self.num_in_channels = [3, num_channels, num_channels, num_channels, num_channels]
# self.num_out_channels = [num_channels, num_channels, num_channels, num_channels]
# self.cardinality = [1, 1, 1, 1, 1]
# self.output_channels = [num_attributes]*8
# self.classifier_nodes = [[1], [2], [4, 4], [8, 8, 8, 8]]
# self.num_children = [1, 2, 4, 8, 8]
# self.num_in_channels = [3, num_channels, num_channels, num_channels, num_channels]
# self.num_out_channels = [num_channels, num_channels, num_channels, num_channels]
# self.cardinality = [1, 1, 1, 1, 1]
# self.output_channels = [num_attributes] * 8
# self.classifier_nodes = [[1], [2], [4, 4], [8]*4]
self.fc_num = 4
self.num_children = [1, 1, 1, 4, 4]
self.ds = [level1, level2, level3, level4]
self.num_in_channels = [3, num_channels // 4, num_channels // 2, num_channels, num_channels]
self.num_out_channels = [num_channels // 4, num_channels // 2, num_channels, num_channels]
self.cardinality = [1, 1, 1, 1, 1]
self.output_channels = [num_attributes] * self.fc_num
self.classifier_nodes = [[1], [1], [1], [4]]
elif dataset == 'TINY-IMAGENET':
self.fc_num = 8
self.num_children = [1, 2, 4, 8, 8]
self.num_in_channels = [3, num_channels // 4, num_channels // 2, num_channels, num_channels]
self.num_out_channels = [num_channels // 4, num_channels // 2, num_channels, num_channels]
self.cardinality = [1, 1, 1, 1, 1]
self.output_channels = [num_attributes] * self.fc_num
self.classifier_nodes = [[1], [2], [4, 4], [8] * 4]
self.branches = []
for layer in range(len(self.num_children) - 1):
layer_child = self.num_children[layer]
for i in range(layer_child): # block
setattr(self, 'conv{}_{}'.format(str(layer + 2), str(i)),
nn.Sequential(*[
nn.Conv2D(self.num_in_channels[layer], self.num_out_channels[layer], kernel_size=3,
padding=1),
nn.BatchNorm2D(self.num_out_channels[layer]),
nn.ReLU(True),
nn.Conv2D(self.num_out_channels[layer], self.num_in_channels[layer + 1], kernel_size=3,
padding=1),
nn.BatchNorm2D(self.num_in_channels[layer + 1]),
nn.ReLU(True),
nn.MaxPool2D(kernel_size=2)]))
if layer != len(self.num_children) - 2 and self.classifier_nodes[layer + 1][i] > 1:
setattr(self, 'router{}_{}'.format(str(layer + 2), str(i)),
nn.Sequential(*[
nn.Conv2D(self.num_in_channels[layer + 1], num_channels // 2, kernel_size=3, padding=1),
nn.ReLU(True),
nn.Conv2D(num_channels // 2, num_channels // 2, kernel_size=3, padding=1),
nn.ReLU(True),
nn.AdaptiveAvgPool2D((3, 3))
]))
setattr(self, 'router_classifier{}_{}'.format(str(layer + 2), str(i)),
nn.Sequential(*[
nn.Linear(9 * num_channels // 2, self.classifier_nodes[layer + 1][i]),
nn.Softmax()
]))
for i in range(self.fc_num):
if self.output_channels[i] > 1:
setattr(self, 'fc1_' + str(i),
nn.Sequential(*[
nn.Linear(fc_channel * num_channels, self.output_channels[i]),
nn.Softmax()
]))
self.num_attributes = num_attributes
self._initialize_weights()
def forward(self, x, t=10, training=True):
bs = x.shape[0]
xs = [] # 用于生成下一层的张量，single-path状态下则只有一个
x_branches = [x] # 当前层的输入张量，single-path状态下则只有一个
pro = paddle.ones([bs, 1]).cuda() # 当前层每个learner的输出，single-path状态下则只有一个
pros = []
tool = 0 # 当前概率最大的孩子节点标号
for layer in range(len(self.num_children) - 1):
layer_child = self.num_children[layer]
conv = getattr(self, 'conv{}_{}'.format(str(layer + 2), str(tool))) # 获得概率最大的learner卷积结果
# print(layer, i)
after_conv = conv(x_branches[0])
xs.append(after_conv) # 生成传到下一层learner的输出
if layer != len(self.num_children) - 2 and self.classifier_nodes[layer + 1][
tool] > 1: # 当前learner的后代如果不止一个才需要sender
router = getattr(self, 'router{}_{}'.format(str(layer + 2), str(tool)))
classifier = getattr(self, 'router_classifier{}_{}'.format(str(layer + 2), str(tool)))
pro = router(after_conv)
pro = paddle.reshape(pro, [bs, -1])
pro = classifier(pro)
pro = paddle.clip(pro, 0.001, 0.999) # paddle.clip==pytorch.clamp
# pro = (pro >= torch.max(pro, 1)[0].view(bs, -1)).float() * pro
# pro = pro * (paddle.reshape(pre_pro[i], [bs, -1]))
# pro = paddle.clip(pro, 0.001, 0.999)
pros.append(pro[0]) # 存储learner输出经过sender处理后的结果，因为预测时是单样本输入，取CHW出来
else:
pros.append(paddle.to_tensor([1]))
# print(pros)
if layer != len(self.num_children) - 2:
now_max = 0 # 表示当前最大概率
for i in range(self.num_children[layer + 1]): # child
# print(pros[0][i])
pro = pros[0][i]
if pro > now_max:
now_max = pro
tool = i # 更新概率最大的孩子节点标号
x_branches = [xs[0]]
xs = []
pros = []
outputs = 0
tx = xs[0]
tx = self.avgpool(tx)
# outputs+=pro
tx = paddle.reshape(tx, [paddle.shape(x)[0], -1])
if self.output_channels[tool] > 1:
fc1 = getattr(self, 'fc1_' + str(tool))
out = fc1(tx)
out = paddle.clip(out, 0.001, 0.999)
# pro = torch.clamp(pro, 0.001, 0.999)
outputs = out
# print(torch.sum(outputs, 1))
outputs = paddle.log(outputs)
return outputs
"""
from thop import profile
from thop import clever_format
from torchvision import models
# model = ForestNet('TINY-IMAGENET', num_attributes=200, num_channels=256).cuda()
# model = ForestNet('CIFAR100', 100, num_channels=288).cuda()
model = ForestNet('CIFAR100', 100, num_channels=288)
input = torch.randn(1, 3, 64, 64)
# macs, params = profile(model, inputs=(input,))
# macs, params = clever_format([macs, params], "%.3f")
# print(macs, params)
out = model.forward(input)
print('???output:', out.shape)
# forest 279.831 4.742M
# forest imitate adaptive neural network num_channel=64 1.439M
# forest .... num_channel=96 2.879M
"""