compute_CD.py

import os
import torch
import argparse
import itertools
import numpy as np
import pandas as pd
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import os.path as osp

from PIL import Image
from tqdm import tqdm
from torch.utils import data
from torchvision import transforms

from eval_utils.oracle_metrics import OracleMetrics


def arguments():
    parser = argparse.ArgumentParser(description='FVA arguments.')
    parser.add_argument('--gpu', default='0', type=str,
                        help='GPU id')
    parser.add_argument('--oracle-path', default='models/oracle.pth', type=str,
                        help='Oracle path')
    parser.add_argument('--exp-name', required=True, type=str,
                        help='Experiment Name')
    parser.add_argument('--output-path', required=True, type=str,
                        help='Results Path')
    parser.add_argument('--celeba-path', required=True, type=str,
                        help='CelebA path')

    return parser.parse_args()


args = arguments()

device = torch.device('cuda:' + args.gpu)

# # load oracle
oracle_metrics = OracleMetrics(weights_path=args.oracle_path,
                               device=device)
oracle_metrics.eval()

# create dataset to read the counterfactual results images
class CFDataset():
    def __init__(self, path, exp_name):

        self.images = []
        self.path = path
        self.exp_name = exp_name
        self.transform = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize([0.5, 0.5, 0.5],
                                 [0.5, 0.5, 0.5])
        ])

        for CL, CF in itertools.product(['CC', 'IC'], ['CCF']):
            self.images += [(CL, CF, I) for I in os.listdir(osp.join(path, 'Results', self.exp_name, CL, CF, 'CF'))]

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

    def switch(self, partition):
        if partition == 'C':
            LCF = ['CCF']
        elif partition == 'I':
            LCF = ['ICF']
        else:
            LCF = ['CCF', 'ICF']

        self.images = []

        for CL, CF in itertools.product(['CC', 'IC'], LCF):
            self.images += [(CL, CF, I) for I in os.listdir(osp.join(self.path, 'Results', self.exp_name, CL, CF, 'CF'))]

    def __getitem__(self, idx):
        CL, CF, I = self.images[idx]
        # get paths
        cl_path = osp.join(self.path, 'Original', 'Correct' if CL == 'CC' else 'Incorrect', I)
        cf_path = osp.join(self.path, 'Results', self.exp_name, CL, CF, 'CF', I)

        cl = self.load_img(cl_path)
        cf = self.load_img(cf_path)

        return cl, cf

    def load_img(self, path):
        with open(path, "rb") as f:
            img = Image.open(f)
            img = img.convert('RGB')
        return self.transform(img)



CELEBAPATH = os.path.join(args.celeba_path, 'list_attr_celeba.csv')
CELEBAPATHP = os.path.join(args.celeba_path, 'list_eval_partition.csv')
# extract the names of the labels

df = pd.read_csv(CELEBAPATH)
p = pd.read_csv(CELEBAPATHP)
labels = list(df.columns[1:])

df = df[p['partition'] == 0]  # 1 is val, 0 train
df.replace(-1, 0, inplace=True)

corrs = np.zeros(40)

for i in range(40):
    corrs[i] = np.corrcoef(df['Smiling'].to_numpy(), df.iloc[:, i + 1].to_numpy())[0, 1]

df = pd.read_csv(CELEBAPATH)
p = pd.read_csv(CELEBAPATHP)
labels = list(df.columns[1:])

df = df[p['partition'] == 1]  # 1 is val, 0 train
df.replace(-1, 0, inplace=True)

corrs2 = np.zeros(40)

for i in range(40):
    corrs2[i] = np.corrcoef(df['Smiling'].to_numpy(), df.iloc[:, i + 1].to_numpy())[0, 1]

diffs = np.zeros((2, 40))

for i in range(40):
    diffs[0, i] = df[df['Smiling'] == 1].iloc[:, i + 1].mean()
    diffs[1, i] = df[df['Smiling'] == 0].iloc[:, i + 1].mean()

maindiff = np.abs(diffs[0] - diffs[1])


@torch.no_grad()
def get_attrs_and_target_from_ds(path, exp_name,
                                 oracle,
                                 device):

    print('=' * 70)
    print('Evaluating data from:', path)
    print('          Experiment:', exp_name)
    dataset = CFDataset(path, exp_name)
    loader = data.DataLoader(dataset, batch_size=15,
                             shuffle=False,
                             num_workers=4, pin_memory=True)

    oracle_preds = {'cf': {'dist': [],
                           'pred': []},
                    'cl': {'dist': [],
                           'pred': []}}

    for cl, cf in tqdm(loader):
        cl = cl.to(device, dtype=torch.float)
        cf = cf.to(device, dtype=torch.float)

        cl_o_dist = torch.sigmoid(oracle.oracle(cl)[1])
        cf_o_dist = torch.sigmoid(oracle.oracle(cf)[1])

        oracle_preds['cl']['dist'].append(cl_o_dist.cpu().numpy())
        oracle_preds['cl']['pred'].append((cl_o_dist > 0.5).cpu().numpy())
        oracle_preds['cf']['dist'].append(cf_o_dist.cpu().numpy())
        oracle_preds['cf']['pred'].append((cf_o_dist > 0.5).cpu().numpy())

    oracle_preds['cl']['dist'] = np.concatenate(oracle_preds['cl']['dist'])
    oracle_preds['cf']['dist'] = np.concatenate(oracle_preds['cf']['dist'])
    oracle_preds['cl']['pred'] = np.concatenate(oracle_preds['cl']['pred'])
    oracle_preds['cf']['pred'] = np.concatenate(oracle_preds['cf']['pred'])

    return oracle_preds


def compute_CorrMetric(path,
                       exp_name,
                       oracle,
                       device,
                       query_label,
                       corr,
                       top=40,
                       sorted=None,
                       show=False,
                       diff=True,
                       remove_unchanged_oracle=False):
    
    oracle_preds = get_attrs_and_target_from_ds(path, exp_name, oracle, device)

    cf_pred = oracle_preds['cf']['pred'].astype('float')
    cl_pred = oracle_preds['cl']['pred'].astype('float')

    if diff:
        delta_query = cf_pred[:, query_label] - cl_pred[:, query_label]
        deltas = cf_pred - cl_pred
    else:
        delta_query = cf_pred[:, query_label]
        deltas = cf_pred

    if remove_unchanged_oracle:
        to_remove = cf_pred[:, query_label] != cl_pred[:, query_label]
        deltas = deltas[to_remove, :]
        delta_query = delta_query[to_remove]
        del to_remove

    print('Lenght:', len(deltas))

    our_corrs = np.zeros(40)

    for i in range(40):
        our_corrs[i] = np.corrcoef(deltas[:, i], delta_query)[0, 1]

    if show:
        if sorted is None:
            plt.bar(np.arange(len(our_corrs))[:top] - 0.15, corr[:top], width=0.3, label='Correlations')
            plt.bar(np.arange(len(our_corrs))[:top] + 0.15, metric[:top], width=0.3, label='Metric')
            plt.xticks(np.arange(len(our_corrs))[:top], our_corrs[:top], rotation=90)
        else:
            plt.bar(np.arange(len(our_corrs))[:top] - 0.15, corr[sorted][:top], width=0.3, label='Correlations')
            plt.bar(np.arange(len(our_corrs))[:top] + 0.15, our_corrs[sorted][:top], width=0.3, label='Metric')
            plt.xticks(np.arange(len(our_corrs))[:top], [our_corrs[i] for i in sorted][:top], rotation=90)

        plt.legend()
        plt.show()

    return our_corrs


def plot_bar(data, labs, top, sorted):

    r = 90
    f = 15
    n_items = len(data)
    eps = 1e-1
    x_base = np.arange(40)
    step = (1 - 2 * eps) / (2 * n_items + 1)
    width = 2 * step
    cmap = cm.get_cmap('viridis', 512)(np.linspace(0, 1, n_items))

    def plot(x, d, l, c):
        plt.bar(x, d, width=width, label=l, color=c)

    for i, (d, l) in enumerate(zip(data, labs)):
        c_x = x_base - 0.5 + eps + step * (2 * i + 1)
        c = [p.item() for p in cmap[i]]

        if sorted is not None:
            d = d[sorted]

        plot(c_x[:top], d[:top], l, c[:top])

    plt.legend()
    plt.tight_layout()

    if sorted is None:
        plt.xticks(x_base[:top], labels[:top], rotation=r, fontsize=f)
    else:
        plt.xticks(x_base[:top], [labels[i] for i in sorted][:top], rotation=r, fontsize=f)

    plt.show()


# get results from dataset

sorted = np.argsort(np.abs(corrs))[::-1]

results = compute_CorrMetric(args.output_path,
                             args.exp_name,
                             oracle_metrics,
                             device,
                             31,  # smile attribute
                             corrs,
                             top=40,
                             sorted=sorted,
                             show=False,
                             diff=True,
                             remove_unchanged_oracle=False)

print('CD Result:', np.sum(np.abs(results[sorted] - corrs[sorted])))

plot_bar([corrs, results],
         ['Correlation', 'Method'],
         40, sorted)