gen_score_seg.py

"""
Code has been built upon https://github.com/nv-tlabs/datasetGAN_release with
following copyright:

Copyright (C) 2021 NVIDIA Corporation.  All rights reserved.
Licensed under The MIT License (MIT)

Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
"""


import sys
sys.path.append('../')
import os
import argparse
import gc
import os
import torch
import torchvision
from torch.utils.data import Dataset, DataLoader
import glob
import json
import numpy as np
from data_loader.data_loader_celebamask import Data_Loader
from models.resnet import InsResNet50,InsResNet18,InsResNet34,InsResNet101,InsResNet152
from models.segmentor import fcn, UNet
from torchvision import transforms

import torch.nn.functional as F
import cv2


from PIL import Image

import scipy.misc


def process_image(images):
    drange = [-1, 1]
    scale = 255 / (drange[1] - drange[0])
    images = images * scale + (0.5 - drange[0] * scale)

    images = images.astype(int)
    images[images > 255] = 255
    images[images < 0] = 0

    return images.astype(int)

class ImageLabelDataset(Dataset):
    def __init__(
            self,
            img_path_list,
            label_path_list,
            img_size=(128, 128),
    ):
        self.img_path_list = img_path_list
        self.label_path_list = label_path_list
        self.img_size = img_size

    def __len__(self):
        return len(self.img_path_list)

    def __getitem__(self, index):
        im_path = self.img_path_list[index]
        lbl_path = self.label_path_list[index]
        im = Image.open(im_path)
        try:
            lbl = np.load(lbl_path)
        except:
            lbl = np.array(Image.open(lbl_path))
        if len(lbl.shape) == 3:
            lbl = lbl[:, :, 0]

        lbl = Image.fromarray(lbl.astype('uint8'))
        im, lbl = self.transform(im, lbl)

        return im, lbl, im_path

    def transform(self, img, lbl):
        img = img.resize((512, 512))
        lbl = lbl.resize((self.img_size[0], self.img_size[1]), resample=Image.NEAREST)
        lbl = torch.from_numpy(np.array(lbl)).long()
        img = transforms.ToTensor()(img)
        return img, lbl


def cross_validate(args):
    
    ignore_index = -1
    cp_path = args.resume
    base_path = os.path.join(cp_path, "cross_validation")
    if not os.path.exists(base_path):
        os.mkdir(base_path)

    cps_all = glob.glob(cp_path + "/*")
    cp_list = [data for data in cps_all if '.pth' in data and 'resnet' not in data]
    cp_list.sort()

    ids = range(34)

    data_all = glob.glob('./DatasetGAN_data/annotation/testing_data/face_34_class/' + "/*")
    images = [path for path in data_all if 'npy' not in path]
    labels = [path for path in data_all if 'npy' in path]
    images.sort()
    labels.sort()


    fold_num =int( len(images) / 5)
    resnet_transform = torchvision.transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225])

    input_size = 512 
    pool_size = int(input_size / 2**5)
    model = InsResNet50(pool_size=pool_size, pretrained=True)
    desc_dim = {1:64, 2:256, 3:512, 4:1024, 5:2048}

    feat_dim = 0
    for i in range(4):
        feat_dim += desc_dim[5-i]

    if args.distill == True:
        segmentor = torchvision.models.segmentation.deeplabv3_resnet101(pretrained=False, progress=False,                                                                      num_classes=34, aux_loss=None)
    elif args.model == "UNet":
        segmentor = UNet(feat_dim, n_classes=34)
    elif args.model == "fcn":
        segmentor = fcn(feat_dim, n_classes=34)

    cross_mIOU = []

    for i in range(5):
        val_image = images[fold_num * i: fold_num *i + fold_num]
        val_label = labels[fold_num * i: fold_num *i + fold_num]
        test_image = [img for img in images if img not in val_image]
        test_label =[label for label in labels if label not in val_label]
        print("Val Data length,", str(len(val_image)))
        print("Testing Data length,", str(len(test_image)))

        val_data = ImageLabelDataset(img_path_list=val_image,
                                      label_path_list=val_label,
                                      img_size=(512, 512))
        val_data = DataLoader(val_data, batch_size=1, shuffle=False, num_workers=0)

        test_data = ImageLabelDataset(img_path_list=test_image,
                                  label_path_list=test_label,
                                img_size=(512, 512))
        test_data = DataLoader(test_data, batch_size=1, shuffle=False, num_workers=0)

        best_miou = 0
        best_val_miou = 0

        for i_ckpt, ckpt_name in enumerate(cp_list):
                if i_ckpt==0 and args.distill==False:
                    ckpt = torch.load(os.path.join(cp_path, 'resnet50.pth'))
                    state_dict = ckpt['model']
                    model.load_state_dict(state_dict, strict=False)
                # import pdb;pdb.set_trace()
                print(args)
                if args.distill==False:
                    model.cuda()
                    model.eval()
                    ckpt_seg = torch.load(ckpt_name)
                    segmentor.load_state_dict(ckpt_seg['segmentor'])
                else:
                    ckpt_seg = torch.load(ckpt_name)
                    segmentor.load_state_dict(ckpt_seg['model_state_dict'])

                segmentor.cuda()
                segmentor.eval()

                unions = {}
                intersections = {}
                for target_num in ids:
                    unions[target_num] = 0
                    intersections[target_num] = 0

                with torch.no_grad():
                    for idxx, da, in enumerate(val_data):

                        img, mask = da[0], da[1]

                        if img.size(1) == 4:
                            img = img[:, :-1, :, :]

                        img = img.cuda()
                        mask = mask.cuda()
                        input_img_tensor = []
                        for b in range(img.size(0)):
                            input_img_tensor.append(resnet_transform(img[b]))
                        input_img_tensor = torch.stack(input_img_tensor)

                        if args.distill == False:
                            feat = model(input_img_tensor, 4, True, (512,512))
                            feat = feat.detach()
                            y_pred = segmentor(feat)
                        else:
                            y_pred = segmentor(input_img_tensor)['out']

                        y_pred = torch.log_softmax(y_pred, dim=1)
                        _, y_pred = torch.max(y_pred, dim=1)
                        y_pred = y_pred.cpu().detach().numpy()

                        mask = mask.cpu().detach().numpy()
                        bs = y_pred.shape[0]

                        curr_iou = []
                        if ignore_index > 0:
                            y_pred = y_pred * (mask != ignore_index)
                        for target_num in ids:
                            y_pred_tmp = (y_pred == target_num).astype(int)
                            mask_tmp = (mask == target_num).astype(int)

                            intersection = (y_pred_tmp & mask_tmp).sum()
                            union = (y_pred_tmp | mask_tmp).sum()

                            unions[target_num] += union
                            intersections[target_num] += intersection

                            if not union == 0:
                                curr_iou.append(intersection / union)
                    mean_ious = []

                    for target_num in ids:
                        mean_ious.append(intersections[target_num] / (1e-8 + unions[target_num]))
                    mean_iou_val = np.array(mean_ious).mean()

                    if mean_iou_val > best_val_miou:
                        best_val_miou = mean_iou_val
                        unions = {}
                        intersections = {}
                        for target_num in ids:
                            unions[target_num] = 0
                            intersections[target_num] = 0

                        with torch.no_grad():
                            testing_vis = []
                            for idxx, da, in enumerate(test_data):

                                img, mask = da[0], da[1]

                                if img.size(1) == 4:
                                    img = img[:, :-1, :, :]

                                img = img.cuda()
                                mask = mask.cuda()
                                input_img_tensor = []
                                for b in range(img.size(0)):
                                    input_img_tensor.append(resnet_transform(img[b]))
                                input_img_tensor = torch.stack(input_img_tensor)

                                if args.distill == False:
                                    feat = model(input_img_tensor, 4, True, (512,512))
                                    feat = feat.detach()
                                    y_pred = segmentor(feat)
                                else:
                                    y_pred = segmentor(input_img_tensor)['out']

                                y_pred = torch.log_softmax(y_pred, dim=1)
                                _, y_pred = torch.max(y_pred, dim=1)
                                y_pred = y_pred.cpu().detach().numpy()


                                mask = mask.cpu().detach().numpy()

                                curr_iou = []
                                if ignore_index > 0:
                                    y_pred = y_pred * (mask != ignore_index)
                                for target_num in ids:
                                    y_pred_tmp = (y_pred == target_num).astype(int)
                                    mask_tmp = (mask == target_num).astype(int)

                                    intersection = (y_pred_tmp & mask_tmp).sum()
                                    union = (y_pred_tmp | mask_tmp).sum()

                                    unions[target_num] += union
                                    intersections[target_num] += intersection

                                    if not union == 0:
                                        curr_iou.append(intersection / union)


                                img = img.cpu().numpy()
                                img =  img * 255.
                                img = np.transpose(img, (0, 2, 3, 1)).astype(np.uint8)

                            test_mean_ious = []

                            for target_num in ids:
                                test_mean_ious.append(intersections[target_num] / (1e-8 + unions[target_num]))

                            best_test_miou = np.array(test_mean_ious).mean()


                            print("Best IOU ,", str(best_test_miou), "CP: ", ckpt_name)

        cross_mIOU.append(best_test_miou)

    print(cross_mIOU)
    print(" cross validation mean:" , np.mean(cross_mIOU) )
    print(" cross validation std:", np.std(cross_mIOU))
    result = {"Cross validation mean": np.mean(cross_mIOU), "Cross validation std": np.std(cross_mIOU), "Cross validation":cross_mIOU }



if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--resume', type=str,  default="")
    parser.add_argument('--distill', action='store_true')
    parser.add_argument('--model', type=str,  default="UNet")

    args = parser.parse_args()
    # import pdb;pdb.set_trace()
    print(args)

    cross_validate(args)