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import os
import sys
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/../')
from net.component_i_2d import RfbfBlock2d, ResBasicBlock2d
from net.component_i_2d import create_conv_block_k3
from net.component_c import init_modules_2d
#用于测试
from cls_utils.data import test_save_ckpt
class FPN(nn.Module):
def __init__(self, n_channels, n_base_filters, groups=1):
super(FPN, self).__init__()
self.down_level1 = nn.Sequential(
RfbfBlock2d(n_channels, n_base_filters, groups=groups)
)
self.down_level2 = nn.Sequential(
ResBasicBlock2d(1 * n_base_filters, 2 * n_base_filters, stride=(2, 2), groups=groups),
ResBasicBlock2d(2 * n_base_filters, 2 * n_base_filters, groups=groups)
)
self.down_level3 = nn.Sequential(
ResBasicBlock2d(2 * n_base_filters, 4 * n_base_filters, stride=(2, 2), groups=groups),
ResBasicBlock2d(4 * n_base_filters, 4 * n_base_filters, groups=groups)
)
self.down_level4 = nn.Sequential(
ResBasicBlock2d(4 * n_base_filters, 8 * n_base_filters, stride=2, groups=groups, se=True),
ResBasicBlock2d(8 * n_base_filters, 8 * n_base_filters, groups=groups, se=True),
ResBasicBlock2d(8 * n_base_filters, 8 * n_base_filters, groups=groups, se=True)
)
self.down_level5 = nn.Sequential(
ResBasicBlock2d(8 * n_base_filters, 16 * n_base_filters, stride=2, groups=groups, se=True),
ResBasicBlock2d(16 * n_base_filters, 8 * n_base_filters, groups=groups, se=True),
ResBasicBlock2d(8 * n_base_filters, 16 * n_base_filters, groups=groups, se=True)
)
self.down_level6 = nn.Sequential(
ResBasicBlock2d(16 * n_base_filters, 16 * n_base_filters, stride=2, groups=groups, se=True)
)
self.down_level7 = nn.Sequential(
ResBasicBlock2d(16 * n_base_filters, 16 * n_base_filters, stride=2, groups=groups, se=True)
)
self.down_level8 = nn.Sequential(
create_conv_block_k3(16 * n_base_filters, 16 * n_base_filters, padding=0, bn=False)
)
def forward(self, x):
down_out1 = self.down_level1(x)
down_out2 = self.down_level2(down_out1)
down_out3 = self.down_level3(down_out2)
down_out4 = self.down_level4(down_out3)
down_out5 = self.down_level5(down_out4)
down_out6 = self.down_level6(down_out5)
down_out7 = self.down_level7(down_out6)
down_out8 = self.down_level8(down_out7)
return down_out8
class Net(nn.Module):
def __init__(self, n_channels=1, n_diff_classes=1, n_base_filters=8):
super(Net, self).__init__()
self.fpn = FPN(8 * n_channels, n_base_filters)
self.diff_classifier = nn.Linear(16 * n_base_filters, n_diff_classes)
#初始化模型参数
init_modules_2d(self.modules())
def forward(self, data):
out = self.fpn(data)
out_avg = F.adaptive_avg_pool2d(out, (1, 1))
out = torch.flatten(out_avg, 1)
diff_output = self.diff_classifier(out)
return diff_output
def net_test():
cfg = dict()
cfg['n_channels'] = 1
cfg['n_diff_classes'] = 1
cfg['training_crop_size'] = [128, 128]
cfg['pretrain_ckpt'] = ''
batch_size = 1
x = torch.rand(batch_size, cfg['n_channels'] * 8,
cfg['training_crop_size'][0], cfg['training_crop_size'][1])
print('x.shape:', x.shape)
model = Net(n_channels=cfg.get('n_channels'), n_diff_classes=cfg.get('n_diff_classes'))
print('模型结构：')
print(model)
print('------------------------------------------')
"""
print(type(model))
for layer in model:
#X = layer(x)
print(layer.__class__.__name__, f'output size: ')"""
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(device)
x = x.to(device)
print(x.shape, x.device)
#加载模型参数
model = model.to(device)
#print('参数加载成功')
model.eval()
with torch.no_grad():
diff_output = model(x)
print(diff_output.shape)
#test_save_ckpt(model=model)
if __name__ == '__main__':
net_test()