python util

获取文件夹下所有文件：

def get_all(cwd, result):get_dir = os.listdir(cwd)for i in get_dir:sub_dir = os.path.join(cwd,i)if os.path.isdir(sub_dir):get_all(sub_dir, result)else:result.append(cwd+"\\"+i)return result

保存数据为二进制：

data = []for name, param in net.named_parameters():print("name:", name, " param:", param.size())pa = param.reshape(-1).to(device_cpu)numpy_param = pa.detach().numpy()print("param:", len(numpy_param))data += list(numpy_param)
print("len data:", len(data))
f = open('net.bin','wb+')
data_struct = struct.pack(('%df' % len(data)), *data)
f.write(data_struct)
f.close()

list 数据写入文件注：line结尾加 "\n"

def write_line(file_name, lines):if file_name == "":returnwith open(file_name, "a+") as fs:for line in lines:fs.writelines(line)fs.close()

读取数据到list

def read_file(data_file, mode='more'):try:with open(data_file, 'r') as f:if mode == 'one':output = f.read()return outputelif mode == 'more':output = f.readlines()# return map(str.strip, output)return outputelse:return list()except IOError:return list()

rect 缩放：

def get_zoom_rect(rect, width, height, zoom):x, y = rect[0], rect[1]w = rect[2] - rect[0]h = rect[3] - rect[1]w1 = min(w*zoom, width-x)h1 = min(h*zoom, height-y)x1 = min(max(x - (w1 - w)/2, 0), width-w1)y1 = min(max(y - (h1 - h)/2, 0), height-h1)#print("rect:", rect, " new:", [x1, y1, x1+w1, y1+h1])return [x1, y1, x1+w1, y1+h1]

fuse BatchNorm 网络

import torch
import torch.nn as nn
import torchvision as tvclass DummyModule(nn.Module):def __init__(self):super(DummyModule, self).__init__()def forward(self, x):# print("Dummy, Dummy.")return xdef fuse(conv, bn):w = conv.weightmean = bn.running_meanvar_sqrt = torch.sqrt(bn.running_var + bn.eps)beta = bn.weightgamma = bn.biasif conv.bias is not None:b = conv.biaselse:b = mean.new_zeros(mean.shape)w = w * (beta / var_sqrt).reshape([conv.out_channels, 1, 1, 1])b = (b - mean)/var_sqrt * beta + gammafused_conv = nn.Conv2d(conv.in_channels,conv.out_channels,conv.kernel_size,conv.stride,conv.padding,bias=True)fused_conv.weight = nn.Parameter(w)fused_conv.bias = nn.Parameter(b)return fused_convdef fuse_module(m):children = list(m.named_children())c = Nonecn = Nonefor name, child in children:if isinstance(child, nn.BatchNorm2d):bc = fuse(c, child)m._modules[cn] = bcm._modules[name] = DummyModule()c = Noneelif isinstance(child, nn.Conv2d):c = childcn = nameelse:fuse_module(child)def test_net(m, input):import times = time.time()o_output = m(input)print("Original time: ", time.time() - s)fuse_module(m)s = time.time()f_output = m(input)print("Fused time: ", time.time() - s)print("Max abs diff: ", (o_output - f_output).abs().max().item())assert(o_output.argmax() == f_output.argmax())# print(o_output[0][0].item(), f_output[0][0].item())print("MSE diff: ", nn.MSELoss()(o_output, f_output).item())

使用方法：

use_cuda = True
device_cpu = torch.device('cpu')
net = FCNETCTC(len(CHARS)).to(device_cpu)
net.load_state_dict(torch.load("weights/best_net_ctc_params_opt1.pkl", map_location=device_cpu))
net.eval()example = torch.rand(1, 3, 24, 94)
test_net(net, example)

均值归一化：

img_tensor = img_tensor.permute({0, 3, 1, 2});   //翻转让通道是第二个维度//均值归一化img_tensor[0][0] = img_tensor[0][0].sub_(0.485).div_(0.229);img_tensor[0][1] = img_tensor[0][1].sub_(0.456).div_(0.224);img_tensor[0][2] = img_tensor[0][2].sub_(0.406).div_(0.225);

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