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inference_weak.py
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inference_weak.py
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import torch
import argparse
import torch.nn.functional as F
import statistics
import utils
from loaders.mms_dataloader_dg_aug_test import get_dg_data_loaders
from torch.utils.data import DataLoader
import models
from metrics.dice_loss import dice_coeff
from metrics.hausdorff import hausdorff_distance
from torch.utils.tensorboard import SummaryWriter
import torchvision.transforms.functional as TVF
from torchvision.transforms import InterpolationMode
# python inference_weak.py -bs 1 -c cp_compcsd2_tvA_weak/ -enc cp_unet_100_tvA/UNet.pth -t A -g 0
def get_args():
usage_text = (
"CompCSD Pytorch Implementation"
"Usage: python train.py [options],"
" with [options]:"
)
parser = argparse.ArgumentParser(description=usage_text)
#training details
parser.add_argument('-e','--epochs', type= int, default=50, help='Number of epochs')
parser.add_argument('-bs','--batch_size', type= int, default=4, help='Number of inputs per batch')
parser.add_argument('-c', '--cp', type=str, default='checkpoints', help='The name of the checkpoints.')
parser.add_argument('-t', '--tv', type=str, default='D', help='The name of the checkpoints.')
parser.add_argument('-w', '--wc', type=str, default='SDNet_LR00002_nB_FT', help='The name of the checkpoints.')
parser.add_argument('-n','--name', type=str, default='default_name', help='The name of this train/test. Used when storing information.')
parser.add_argument('-enc', '--encoder_dir', type=str, default='cp_unet_100_tvA/', help='The name of the pretrained encoder checkpoints.')
parser.add_argument('-mn','--model_name', type=str, default='compcsd2weak', help='Name of the model architecture to be used for training/testing.')
parser.add_argument('-lr','--learning_rate', type=float, default='0.0001', help='The learning rate for model training')
parser.add_argument('-wi','--weight_init', type=str, default="xavier", help='Weight initialization method, or path to weights file (for fine-tuning or continuing training)')
parser.add_argument('--save_path', type=str, default='checkpoints', help= 'Path to save model checkpoints')
#hardware
parser.add_argument('-g','--gpu', type=str, default='0', help='The ids of the GPU(s) that will be utilized. (e.g. 0 or 0,1, or 0,2). Use -1 for CPU.')
parser.add_argument('--num_workers' ,type= int, default = 0, help='Number of workers to use for dataload')
return parser.parse_args()
args = get_args()
device = torch.device('cuda:'+str(args.gpu) if torch.cuda.is_available() else 'cpu')
torch.manual_seed(14)
if device.type == 'cuda':
torch.cuda.manual_seed(14)
batch_size = args.batch_size
dir_checkpoint = args.cp
test_vendor = args.tv
wc = args.wc
model_name = args.model_name
enc_dir = args.encoder_dir
layer = 8
vc_num = 12 # kernel numbers
# Model selection and initialization
model_params = {
'image_channels': 1,
'layer': layer,
'vc_numbers': vc_num,
'num_classes': 3,
'anatomy_out_channels': 4,
'z_length': 8,
'vMF_kappa': 30
}
model = models.get_model(args.model_name, model_params)
num_params = utils.count_parameters(model)
# print(model)
print('Model Parameters: ', num_params)
model.to(device)
model.load_encoder_weights(enc_dir, device)
if layer == 6:
kernels_save_dir = test_vendor + '8_12kernels/'
elif layer == 7:
kernels_save_dir = test_vendor + '4_12kernels/'
elif layer == 8:
kernels_save_dir = test_vendor + '2_12kernels/'
else:
kernels_save_dir = test_vendor + '_12kernels/'
init_path = kernels_save_dir + 'init/'
kernel_save_name = 'dictionary_12.pickle'
dict_dir = init_path + 'dictionary/' + kernel_save_name
model.load_vmf_kernels(dict_dir)
model.load_state_dict(torch.load(dir_checkpoint+'CP_epoch.pth', map_location=device))
_, _, _, _, test_loader_A, test_dataset_A = get_dg_data_loaders(
args.batch_size, test_vendor='A', image_size=224)
_, _, _, _, test_loader_B, test_dataset_B = get_dg_data_loaders(
args.batch_size, test_vendor='B', image_size=224)
_, _, _, _, test_loader_C, test_dataset_C = get_dg_data_loaders(
args.batch_size, test_vendor='C', image_size=224)
_, _, _, _, test_loader_D, test_dataset_D = get_dg_data_loaders(
args.batch_size, test_vendor='D', image_size=224)
test_dataset_BCD = torch.utils.data.ConcatDataset([test_dataset_B, test_dataset_C, test_dataset_D])
test_loader_BCD = DataLoader(dataset=test_dataset_BCD, batch_size=batch_size, shuffle=False, drop_last=True, num_workers=4)
thresh_factor = 0.9
step = 0
tot = []
tot_sub = []
tot_hsd = []
tot_sub_hsd = []
flag = '000'
# i = 0
for imgs, true_masks, path_img in test_loader_A:
model.eval()
imgs = imgs.to(device=device, dtype=torch.float32)
mask_type = torch.float32
true_masks = true_masks.to(device=device, dtype=mask_type)
# print(flag)
if path_img[0][-10: -7] != flag:
# if i > 10:
# break
# i += 1
flag = path_img[0][-10: -7]
tot.append(sum(tot_sub)/len(tot_sub))
tot_sub = []
tot_hsd.append(sum(tot_sub_hsd)/len(tot_sub_hsd))
tot_sub_hsd = []
with torch.no_grad():
rec, pre_seg, content, features, kernels, L_visuals, _ = model(imgs,layer=layer)
pre_seg = TVF.resize(L_visuals, ((2**(9-layer))*L_visuals.size(2), (2**(9-layer))*L_visuals.size(3)), interpolation=InterpolationMode.NEAREST)
# pre_seg[:, 1, :, :] +
pred = ((pre_seg[:,8,:,:]) > thresh_factor).float()
dice = dice_coeff(pred, true_masks[:, 0, :, :]+true_masks[:, 1, :, :]+true_masks[:, 2, :, :], device).item()
hsd = hausdorff_distance(pred, true_masks[:, 0, :, :]+true_masks[:, 1, :, :]+true_masks[:, 2, :, :])
tot_sub.append(dice)
tot_sub_hsd.append(hsd)
# print(step)
step += 1
print('+'*10)
print('Test results')
print(tot)
print(sum(tot)/len(tot))
print(statistics.stdev(tot))
print(tot_hsd)
print(sum(tot_hsd)/len(tot_hsd))
print(statistics.stdev(tot_hsd))
step = 0
tot = []
tot_sub = []
tot_hsd = []
tot_sub_hsd = []
flag = '000'
# i = 0
for imgs, true_masks, path_img in test_loader_BCD:
model.eval()
imgs = imgs.to(device=device, dtype=torch.float32)
mask_type = torch.float32
true_masks = true_masks.to(device=device, dtype=mask_type)
# print(flag)
if path_img[0][-10: -7] != flag:
# if i > 10:
# break
# i += 1
flag = path_img[0][-10: -7]
tot.append(sum(tot_sub)/len(tot_sub))
tot_sub = []
tot_hsd.append(sum(tot_sub_hsd)/len(tot_sub_hsd))
tot_sub_hsd = []
with torch.no_grad():
rec, pre_seg, content, features, kernels, L_visuals, _ = model(imgs,layer=layer)
pre_seg = TVF.resize(L_visuals, ((2**(9-layer))*L_visuals.size(2), (2**(9-layer))*L_visuals.size(3)), interpolation=InterpolationMode.NEAREST)
# pre_seg[:, 1, :, :] +
pred = ((pre_seg[:,8,:,:]) > thresh_factor).float()
dice = dice_coeff(pred, true_masks[:, 0, :, :]+true_masks[:, 1, :, :]+true_masks[:, 2, :, :], device).item()
hsd = hausdorff_distance(pred, true_masks[:, 0, :, :]+true_masks[:, 1, :, :]+true_masks[:, 2, :, :])
tot_sub.append(dice)
tot_sub_hsd.append(hsd)
# print(step)
step += 1
print('+'*10)
print('Training results')
print(tot)
print(sum(tot)/len(tot))
print(statistics.stdev(tot))
print(tot_hsd)
print(sum(tot_hsd)/len(tot_hsd))
print(statistics.stdev(tot_hsd))