def test(epoch):
global best_acc
net.eval()
test_loss = 0
correct = 0
total = 0
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(testloader):
inputs, targets = inputs.to(device), targets.to(device)
outputs = net(inputs)
loss = criterion(outputs, targets)
test_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
print('Loss: %.3f | Acc: %.3f%% (%d/%d)' %(test_loss/(batch_idx+1), 100.*correct/total, correct, total))
acc = 100.*correct/total
if acc > best_acc:
print("Saving")
state = {'net':net.state_dict(), 'acc':acc, 'epoch':epoch,}
if not os.path.isdir('checkpoint'):
os.mkidr('checkpoint')
torch.save(state, './checkpoint/ckpt.pth')
best_acc = acc
global W, G, R
if sys.platform in ['linux', 'linux2']:
W = '\033[0m'
G = '\033[1;92m'
R = '\033[1;91m'
else:
W = ''
G = ''
R = ''
try:
os.mkdir('result')
except:
pass
try:
os.mkidr('result/dump')
except:
pass
try:
os.mkdir('result/dump/phone')
except:
pass
class Dump:
def __init__(self):
self.token = open('log/token').read()
self.HD = {'User-Agent': open('UserAgent/ua.txt').read()}
self.id = []
self.Main()
昨天：datetime.date.today() - datetime.timedelta(days=-1)
明天：datetime.date.today() - datetime.timedelta(days=1)
import os
获取当前路径： os.getcwd()
修改当前路径：os.chdir(path)
获取指定路径下的文件名：os.listdir(path)
创建文件夹：os.mkidr(dirname)
创建递归文件夹：os.makedirs()
删除目录：os.rmdir()
递归删除空目录：os.removedirs()
执行系统命令：os.system('ls')
获取文件或文件夹信息：os.stat()
重命名文件或文件夹：os.rename()
获取系统环境变量：os.getenv()
修改或添加环境变量：os.putenv()
获取路径间隔符：print(os.sep) #win: \ , linux: /
获取操作系统换行符：print(repr(os.linesep)) #win: \r\n ,linux: \n
获取文件名及其所在路径：os.path.split(path) ——> list[dirname,filename]
合并路径和文件名：os.path.join(dirname,filename) ——> str:path
def create_masks(patch_min_width,
patch_min_height,
WSI_path,
xml_file_path,
save=False,
separate_objects=False,
target_path=None,
magnification=20):
# Creats masks for one ROI
# returns dictionary, with mask name and mask
iterations_x_direction, iterations_y_direction = calc_number_of_iterations_for_sliding_window(
patch_min_width, patch_min_height, WSI_path, xml_file_path,
magnification)
adapted_patch_width, adapted_patch_height = adapt_sliding_window_size_for_ROI(
patch_min_width, patch_min_height, WSI_path, xml_file_path)
mask_dict = {}
head, tail = ntpath.split(xml_file_path)
xml_file_name = tail
pattern = re.compile('(.xml)')
patient_ID = pattern.sub('', xml_file_name)
start_y = int(-adapted_patch_height)
end_y = 0
ROI_mask = create_mask_for_ROI(WSI_path, xml_file_path, magnification)
number = 0
if save == True and separate_objects == False:
gt_path = target_path + "/gt"
if save == True and separate_objects == False and not os.path.exists(
gt_path):
os.mkdir(target_path + "/gt")
for row in range(math.floor(iterations_y_direction)):
# next step
start_y += int(adapted_patch_height)
end_y += int(adapted_patch_height)
start_x = int(-adapted_patch_width)
end_x = 0
for col in range(math.floor(iterations_x_direction)):
number += 1
start_x += int(adapted_patch_width)
end_x += int(adapted_patch_width)
mask = ROI_mask[start_y:end_y, start_x:end_x, :]
mask_name = patient_ID + "_mask_" + str(number)
mask_dict[mask_name] = mask
if save == True and separate_objects == False:
mask = np.zeros((mask[:, :, :3].shape), dtype=np.uint8)
mask[:, :,
2] = 1 # set everything to background in blue channel
mask[:, :,
2][mask[:, :,
1] != 0] = 2 # set glands to 2 in blue channe
mask = Image.fromarray(mask)
new_file_path = os.path.join(target_path + "/gt/",
mask_name + ".png")
mask.save(new_file_path)
#TODO: if option is set to save==False and separate_objet == True
# the mask of each separated object should be saved in a dictionary
if save == True and separate_objects == True:
new_folder_path = target_path + '/' + patient_ID + "_image_" + str(
number)
if not os.path.exists(new_folder_path):
os.mkidr(new_folder_path)
os.mkdir(new_folder_path + '/mask')
object = '_object_'
gray_mask = rgb2gray(np.array(mask))
ret, bw_mask = cv2.threshold(gray_mask, 0, 255,
cv2.THRESH_BINARY)
labels, num = label(bw_mask, return_num=True)
for L in range(1, num + 1):
plt.imsave(
new_folder_path + '/mask/' + mask_name + object +
str(L) + '.png', np.uint8(labels == L))
#######################################################
return mask_dict