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train.py
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train.py
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import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import torch
from torchvision import models, transforms
from torch.autograd import Variable
import datetime
import cv2
import glob
import os
from torch.utils.data.dataset import Dataset
from torch.utils.data import DataLoader
from torch.utils.data.sampler import RandomSampler
from network import Net
import torch.optim as optim
from logger import Logger
import torch.optim.lr_scheduler as lr_scheduler
def tensor_to_img(img, mean=0, std=1):
img = img.numpy()[0]
img = (img*std+ mean)
img = img.astype(np.uint8)
#img = cv2.cvtColor(img , cv2.COLOR_BGR2RGB)
return img
class Solver:
def __init__(self, batch_size, epoch_num, net):
self.batch_size = batch_size
self.epoch_num = epoch_num
self.net = net
class Rand_num(Dataset):
def __init__(self, csv_path, img_path, img_size, transform=None):
self.csv_paths = csv_path
self.img_paths = img_path
self.file_count = sum(len(files) for _, _, files in os.walk(img_path))
self.num_classes = 0
self.num_cells = 7
self.transform = transform
#self.labels=image_labels
def __getitem__(self, index):
image_addr = self.img_paths+'/'+str(index)+'.jpg'
label_addr = self.csv_paths+'/'+str(index)+'.csv'
img = np.expand_dims(cv2.imread(image_addr,0), 0)
#label = self.labels[index]
image_labels = np.genfromtxt(label_addr, delimiter=',')
image_labels.flatten()
image_labels = np.reshape(image_labels, [self.num_cells, self.num_cells, self.num_classes+5])
if self.transform is not None:
img = self.transform(img)
return img, image_labels
def __len__(self):
# print ('\tcalling Dataset:__len__')
return self.file_count
if __name__ == '__main__':
SAVE_PATH = './checkpoint/cp.pth'
torch.set_default_tensor_type('torch.cuda.FloatTensor')
torch.backends.cudnn.benchmark = True
logger = Logger('./logs')
batch_size = 20
load_checkpoint= False
print (datetime.datetime.now())
print( '%s: calling main function ... ' % os.path.basename(__file__))
csv_path = 'data_eq_label'
img_path = 'data_eq'
validation_label = 'validation_eq_label'
validation_data = 'validation_eq'
dataset = Rand_num(csv_path, img_path, 224, None)
validationset = Rand_num(validation_label, validation_data, 224, None)
sampler = RandomSampler(dataset)
val_sampler = RandomSampler(validationset)
loader = DataLoader(dataset, batch_size = batch_size, sampler = sampler, shuffle = False, num_workers=2)
val_loader = DataLoader(validationset, batch_size = batch_size, sampler = val_sampler, shuffle = False, num_workers=2)
print (datetime.datetime.now())
print ('dataset comp')
# dataiter = iter(loader)
# images, labels = dataiter.next()
# print (images)
# images=tensor_to_img(images)
# print (labels)
# print (images)
net = Net(batch_size)
if load_checkpoint:
net.load_state_dict(torch.load(SAVE_PATH))
print('network loaded')
net.cuda()
optimizer = optim.Adam(net.parameters(), lr=0.001)
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, 'min', verbose=True)
for epoch in range(2000):
for i, data in enumerate(loader, 0):
# get the inputs
inputs, labels = data
inputs, labels = inputs.float()/256, labels.float()
# wrap them in Variable
inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
#print (inputs)
net.train()
outputs = net.forward(inputs)
loss, _ = net.loss_function_vec(outputs, labels, 0.5)
loss.backward()
optimizer.step()
# print statistics
#running_loss += loss.data[0]
if epoch % 1 == 0 and i == 0:
# net.eval()
# outputs = net.forward(inputs)
# loss, accu = net.loss_function_vec(outputs, labels, 0.2, cal_accuracy=True)
print (datetime.datetime.now())
print ('Epoch %g'%(epoch))
print(loss.data.cpu().numpy())
logger.scalar_summary('training loss', loss.data.cpu().numpy(), epoch)
if epoch % 1 == 0 and i==0:
torch.save(net.state_dict(), SAVE_PATH)
total_loss=[]
for i, data in enumerate(val_loader, 0):
inputs, labels = data
inputs, labels = inputs.float()/256, labels.float()
inputs, labels = Variable(inputs.cuda(), volatile=True), Variable(labels.cuda(), volatile = True)
net.eval()
outputs = net.forward(inputs)
loss, _ = net.loss_function_vec(outputs, labels, 0.2)
total_loss.append(loss.data.cpu().numpy())
mean_loss = np.mean(total_loss)
print (datetime.datetime.now())
print('val loss is %g'%(mean_loss))
logger.scalar_summary('validation loss', mean_loss, epoch)
scheduler.step(mean_loss)
torch.save(net.state_dict(), SAVE_PATH)
print('Finished Training')