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inference_cvc.py
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import torch
import torch.nn as nn
import argparse
import torch.nn.functional as F
import torchvision.transforms.functional as tf
from torchvision import transforms as T
from PIL import Image, ImageOps, ImageFilter
import numpy as np
import os
import cv2
import genotypes
from models import get_models,models_dict
from nas_search_unet_prune import BuildNasUnetPrune
from utils import BinaryIndicatorsMetric
# Validation and visualization
Image_dir=r'E:\datasets\CVC-ClinicDB\valid'
Mask_dir=r'E:\datasets\CVC-ClinicDB\valid_GT'
# model
parse=argparse.ArgumentParser("BaseLine Model Inference !")
parse.add_argument('--image',type=str,default=Image_dir)
parse.add_argument('--mask',type=str,default=Mask_dir)
parse.add_argument('--im_channel', type=int, default=3, help="input image channel ")
parse.add_argument('--class_num', type=int, default=1, help="output feature channel")
parse.add_argument('--init_weight_type', type=str, choices=["kaiming", 'normal', 'xavier', 'orthogonal'],
default="kaiming", help=" model init mode")
parse.add_argument('--deepsupervision', action='store_true', help=" deepsupervision for unet++")
parse.add_argument('--time_step', type=int, default=3, help=" r2unet use time step !")
parse.add_argument('--alpha', type=float, default=1.67, help=" multires unet channel changg ")
args=parse.parse_args()
def create_dir(root,dir_name):
path=os.path.join(root,dir_name)
if not os.path.exists(path):
os.mkdir(path)
res_dir=r'C:\Users\rileyliu\Desktop\images_res\cvc'
model_name_list = ['unet', 'unet++', 'multires_unet', 'attention_unet_v1',"nas_search"]
create_dir(res_dir,'images')
create_dir(res_dir,'mask')
for name in model_name_list:
create_dir(res_dir, name)
# def show_images(images,masks,o1,o2,o3,o4,filename):
# image = Image.fromarray(images)
# mask = Image.fromarray(masks)
#
# unet = Image.fromarray(o1)
# unetpp = Image.fromarray(o2)
# multires_unet = Image.fromarray(o3)
# attention_unet_v1 = Image.fromarray(o4)
#
# image.save(os.path.join(os.path.join(res_dir,'images'), '{}.png'.format(filename)))
# mask.save(os.path.join(os.path.join(res_dir,'mask'), '{}.png'.format(filename)))
# unet.save(os.path.join(os.path.join(res_dir,'unet'), '{}.png'.format(filename)))
# unetpp.save(os.path.join(os.path.join(res_dir,'unet++'), '{}.png'.format(filename)))
# multires_unet.save(os.path.join(os.path.join(res_dir,'multires_unet'), '{}.png'.format(filename)))
# attention_unet_v1.save(os.path.join(os.path.join(res_dir,'attention_unet_v1'), '{}.png'.format(filename)))
def show_images(images,masks,o1,o2,filename):
image = Image.fromarray(images)
mask = Image.fromarray(masks)
unet = Image.fromarray(o1)
nas_search = Image.fromarray(o2)
image.save(os.path.join(os.path.join(res_dir,'images'), '{}.png'.format(filename)))
mask.save(os.path.join(os.path.join(res_dir,'mask'), '{}.png'.format(filename)))
unet.save(os.path.join(os.path.join(res_dir,'unet'), '{}.png'.format(filename)))
nas_search.save(os.path.join(os.path.join(res_dir,'nas_search'), '{}.png'.format(filename)))
def isic_transform(image_dir,mask_dir):
'''
:param image: PIL.Image
:return:
'''
# _img=cv2.imread(image_dir,1)
# _target=cv2.imread(mask_dir,0)
# _img=cv2.resize(_img,(256,256),interpolation=cv2.INTER_LINEAR)
# _target=cv2.resize(_target,(256,256),interpolation=cv2.INTER_NEAREST)
_img = cv2.imread(image_dir, cv2.IMREAD_COLOR)
_img = _img[:, :, [2, 1, 0]]
_img = Image.fromarray(_img).convert("RGB")
_target = Image.open(mask_dir)
_img = _img.resize((256, 192), Image.BILINEAR)
_target = _target.resize((256, 192), Image.NEAREST)
img = tf.to_tensor(_img)
img = tf.normalize(img, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)).unsqueeze(0)
return img,_img,_target
def inference_isic(models_list,img_dir,mask_dir):
OtherVal = BinaryIndicatorsMetric()
for model in models_list:
model.eval()
filenames = os.listdir(img_dir)
data_list = []
gt_list = []
img_ids = []
for filename in filenames:
data_list.append(filename)
gt_list.append(filename)
img_ids.append(filename)
assert os.path.splitext(filename)[-1] == '.tif'
assert (len(data_list) == len(gt_list))
data_list = [os.path.join(img_dir, i) for i in data_list]
gt_list = [os.path.join(mask_dir, i) for i in gt_list]
hard_filenames=[]
better_filenames=[]
easy_filenames=[]
dataset_wrong_case=[]
all_bad_case=[]
model_name_list=['unet','unet++','multires_unet','attention_unet_v1','nas_search']
for i in range(len(data_list)):
file_name=img_ids[i].split('.')[0]
print("Filename:{}".format(file_name))
img,original_img,mask=isic_transform(data_list[i],gt_list[i])
outputs_original=[model(img) for model in models_list]
nas_output=outputs_original[-1][-1].clone()
nas_output=nas_output.view(nas_output.size(0),-1)
target=torch.from_numpy(np.asarray(mask))
target=target.unsqueeze(0).unsqueeze(0)
target=target.view(target.size(0), -1)
# OtherVal.update(labels=target, preds=nas_output, n=1)
OtherVal.update(labels=target, preds=outputs_original[0].view(outputs_original[0].size(0),-1), n=1)
# outputs=[]
# for index,output in enumerate(outputs_original):
# if isinstance(output,list):
# print("Index:{} is nas search mmodel !".format(index))
# outputs.append(torch.sigmoid(output[-1]).data.cpu().numpy()[0,0,:,:])
# else:
# outputs.append(torch.sigmoid(output).data.cpu().numpy()[0,0,:,:])
# outputs=[(output>0.5).astype(np.uint8) for output in outputs]
# for output in outputs:
# output[output>=1]=255
# #flip_output=model1(flipimg)
# # flip_output=torch.sigmoid(flip_output).data.cpu().numpy()[0,0,:,:]
# # flip_output=np.fliplr(flip_output)
# # 可视化
# oimage=np.asarray(original_img).astype(np.uint8)
# mask=np.asarray(mask).astype(np.uint8)
# # flip_output=(flip_output>0.5).astype(np.uint8)
# mask[mask>=1]=255
#
# # flip_output[flip_output>=1]=255
# #img[..., 2] = np.where(mask == 1, 255, img[..., 2])
# unet=outputs[0]
# # unetpp=outputs[1]
# # multires_unet=outputs[2]
# # attention_unet_v1=outputs[3]
# nas_search_output=outputs[-1]
#
# # rgb
# contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# cv2.drawContours(oimage, contours, -1, (0, 0,255), 1,lineType=cv2.LINE_AA)
# output_contours, _ = cv2.findContours(unet, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# cv2.drawContours(oimage, output_contours, -1, (0, 255,0), 1,lineType=cv2.LINE_AA)
#
# # output_contours, _ = cv2.findContours(unetpp, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# # cv2.drawContours(oimage, output_contours, -1, (0, 0,255), 1,lineType=cv2.LINE_AA)
#
# # output_contours, _ = cv2.findContours(multires_unet, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# # cv2.drawContours(oimage, output_contours, -1, (0, 255,255), 1,lineType=cv2.LINE_AA)
# #
# #
# # output_contours, _ = cv2.findContours(attention_unet_v1, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# # cv2.drawContours(oimage, output_contours, -1, (255, 0,255), 1,lineType=cv2.LINE_AA)
#
#
# output_contours, _ = cv2.findContours(nas_search_output, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# cv2.drawContours(oimage, output_contours, -1, (255, 0,0), 1,lineType=cv2.LINE_AA)
#
# # #show_images(oimage.copy(),mask.copy(),unet.copy(),unetpp.copy(),multires_unet.copy(),attention_unet_v1.copy(),file_name)
# show_images(oimage.copy(), mask.copy(), unet.copy(),nas_search_output.copy(), file_name)
value=OtherVal.get_avg
mr, ms, mp, mf, mjc, md, macc = value
print("Acc:{:.3f} Dice:{:.3f} Jc:{:.3f}".format(macc, md, mjc))
def main(args):
# 0.762
# args.model='unet'
# model1=get_models(args)
# model1.load_state_dict(torch.load(r'E:\segmentation\Image_Segmentation\logs\cvc_logs\unet_ep1600\cvc\20200312-143050\model_best.pth.tar',map_location='cpu')['state_dict'])
# # 0.766/0.773
# args.model='unet++'
# model2=get_models(args)
# model2.load_state_dict(torch.load(r'E:\segmentation\Image_Segmentation\logs\cvc_logs\unet++_nodeep_ep800\cvc\no_deep\model_best.pth.tar',map_location='cpu')['state_dict'])
#
# # mutilres 0.695
# args.model='multires_unet'
# model3=get_models(args)
# model3.load_state_dict(torch.load(r'E:\segmentation\Image_Segmentation\logs\cvc_logs\multires_unet_800\cvc\20200310-172036\checkpoint.pth.tar',map_location='cpu')['state_dict'])
#
#
# attention_unet 0.778
args.model = 'attention_unet_v1'
model4 = get_models(args)
model4.load_state_dict(torch.load(r'E:\segmentation\Image_Segmentation\logs\cvc_logs\attention_unet_v1_ep1600\cvc\20200312-143413\model_best.pth.tar',map_location='cpu')['state_dict'])
genotype = eval('genotypes.%s' % 'layer7_double_deep')
#BuildNasUnetPrune
model5=BuildNasUnetPrune(
genotype=genotype,
input_c=3,
c=16,
num_classes=1,
meta_node_num=4,
layers=9,
dp=0,
use_sharing=True,
double_down_channel=True,
aux=True,
)
model5.load_state_dict(torch.load(r'E:\segmentation\Image_Segmentation\nas_search_unet\logs\cvc\layer7_double_deep_ep1600_20200320-200539\model_best.pth.tar',map_location='cpu')['state_dict'])
models_list=[model4,model5]
inference_isic(models_list,args.image,args.mask)
if __name__=="__main__":
main(args)
# def regist_hook_outfeature(self, model):
# """
# 创建hook获取每层的结果
# """
# out_feat = OrderedDict()
# hooks = []
# # print('layer num:', self.layers_num)
# all_op_type = self._all_op_type
#
# def _make_hook(m):
#
# def _hook(m, input, output):
# class_name = str(m.__class__).split(".")[-1].split("'")[0]
# layer_type = type(m).__name__
# idx = len(out_feat) % (int(self.layers_num + 1))
# if (idx == 0):
# out_feat.clear()
# out_feat['image'] = input[0].detach().cpu().numpy()
# idx = len(out_feat)
# name_keys = "%s_%i" % (class_name, idx)
# # name_keys = "%s_%i" % (class_name, len(out_feat))
# out_feat[name_keys] = output.detach().cpu().numpy()
#
# if (type(m).__name__ in all_op_type):
# hooks.append(m.register_forward_hook(_hook))
#
# # 对模型中的每个模块的输出均执行_make_hook操作，本质商也就是保存每一层的输出特征图
# # 后面我们使用的时候，要有针对性的修改，具体来说就是只保存有意义的特征图的修改
#
# model.apply(_make_hook)
# # register hook
#
# return out_feat, hooks