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framework.py
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framework.py
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#!/usr/bin/env python
# _*_coding:utf-8_*_
"""
@Time : 2020/8/26 22:44
@Author: sml2h3
@File: framework
@Software: PyCharm
"""
from torch import optim
from torch.autograd import Variable
import torch
import torch.nn as nn
import torch.nn.functional as F
from utils.constants import *
from utils.exception import *
class FrameWork(nn.Module):
def __init__(self, config):
super(FrameWork, self).__init__()
self.inputs = None
self.labels = None
self.config = config
if torch.cuda.is_available() and self.config['System']['GPU'] and int(self.config['System']['GPU_ID']) > -1:
self.device = torch.device('cuda:{}'.format(self.config['System']['GPU_ID']))
else:
self.device = torch.device('cpu')
self.image_channel = self.config['Model']['ImageChannel']
self.resize = self.config['Model']['RESIZE']
self.cnn_type = self.config['Train']['CNN']['NAME']
self.paramters = []
if self.cnn_type == CNN.MobileNetV2.value:
from torchvision.models import mobilenet_v2
self.cnn = mobilenet_v2().features
self.cnn[0][0] = torch.nn.Conv2d(int(self.image_channel), 32, (3, 3), stride=(2, 2), padding=(1, 1),
bias=False)
self.cnn.to(device=self.device)
self.paramters.append({'params': self.cnn.parameters()})
self.out_size = 1280
elif self.cnn_type == CNN.EfficientNetb0.value:
from efficientnet_pytorch import EfficientNet
self.cnn = EfficientNet.from_name('efficientnet-b0')
self.cnn._conv_stem = torch.nn.Conv2d(int(self.image_channel), 32, kernel_size=3, stride=2, bias=False)
self.cnn.to(device=self.device)
self.paramters.append({'params': self.cnn.parameters()})
self.out_size = 1280
else:
raise CnnNotFoundError("CNN Name not found!")
rnn = self.config['Train']['RNN']['NAME']
self.hidden_num = int(self.config['Train']['LSTM']['HIDDEN_NUM'])
dropout = int(self.config['Train']['LSTM']['DROPOUT'])
if rnn == RNN.LSTM.value:
self.lstm = nn.LSTM(input_size=self.out_size, hidden_size=self.hidden_num, num_layers=2, bidirectional=True,
dropout=dropout)
self.lstm.to(device=self.device)
self.paramters.append({'params': self.lstm.parameters()})
else:
raise RnnNotFoundError("RNN Name not found!")
self.charset = self.config['Model']['CharSet']
self.charset = json.loads(self.charset)
self.charset_len = len(self.charset)
self.fc = nn.Linear(in_features=self.hidden_num * 2, out_features=self.charset_len)
self.fc.to(device=self.device)
self.paramters.append({'params': self.fc.parameters()})
self.ctc_loss = nn.CTCLoss(blank=0, reduction='mean')
self.ctc_loss.to(device=self.device)
self.paramters.append({'params': self.ctc_loss.parameters()})
optimizer = self.config['Train']['OPTIMIZER']
self.lr = self.config['Train']['LR']
if optimizer == OPTIMIZER.Momentum.value:
self.optimizer = optim.SGD(self.paramters, lr=self.lr, momentum=0.9)
elif optimizer == OPTIMIZER.Adma.value:
self.optimizer = optim.Adam(self.paramters, lr=self.lr, betas=(0.9, 0.99))
else:
raise OptimizerNotFoundError("Optimizer Name not found!")
self.scheduler = optim.lr_scheduler.ExponentialLR(self.optimizer, gamma=0.98)
def forward(self, inputs):
self.cnn.train()
self.lstm.train()
self.fc.train()
predict = self.get_feature(inputs)
outputs = predict.max(2)[1].transpose(0, 1)
return outputs
def train_op(self, inputs, labels, label_length):
self.cnn.train()
self.lstm.train()
self.fc.train()
self.ctc_loss.train()
predict = self.get_feature(inputs)
loss, lr = self.get_loss(predict, labels, label_length)
return loss, lr
def test_op(self, inputs, labels, label_length):
self.cnn.eval()
self.lstm.eval()
self.fc.eval()
self.ctc_loss.eval()
predict = self.get_feature(inputs)
outputs = predict.max(2)[1].transpose(0, 1)
pred_decode_labels = []
for pred_labels in outputs:
decoded = []
for item in pred_labels:
if item != 0:
decoded.append(item.item())
pred_decode_labels.append(decoded)
labels_list = []
labels = labels.tolist()
i = 0
for idx in label_length.tolist():
labels_list.append(labels[i: i + idx])
i += idx
if len(labels_list) != len(pred_decode_labels):
raise PredictLabelLengthIsNotMatch("origin labels length is {}, but pred labels length is {}".format(
len(labels_list, len(pred_decode_labels))))
correct_list = []
error_list = []
for ids in range(len(labels_list)):
if labels_list[ids] == pred_decode_labels[ids]:
correct_list.append(ids)
else:
error_list.append(ids)
return pred_decode_labels, labels_list, correct_list, error_list
def get_feature(self, inputs):
inputs = Variable(inputs).to(device=self.device)
if self.cnn_type == CNN.EfficientNetb0.value:
x = self.cnn.extract_features(inputs)
else:
x = self.cnn(inputs)
x = torch.reshape(x, (x.shape[0], x.shape[2] * x.shape[3], x.shape[1])).to(device=self.device)
x, _ = self.lstm(x)
x = torch.reshape(x, (-1, self.hidden_num * 2)).to(device=self.device)
x = self.fc(x)
x = torch.reshape(x, [inputs.shape[0], -1, self.charset_len]).to(device=self.device)
predict = torch.transpose(x, 1, 0).to(device=self.device)
return predict
def get_loss(self, pred, labels, label_length):
labels = Variable(labels).to(device=self.device)
log_pred = pred.log_softmax(2)
seq_len = torch.IntTensor([log_pred.shape[0]] * log_pred.shape[1]).to(device=self.device)
loss = self.ctc_loss(log_pred.cpu(), labels, seq_len, label_length)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return loss.item(), self.scheduler.state_dict()['_last_lr'][-1]
def save_model(self, PATH, net):
torch.save(net.state_dict(), PATH)