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utils.py
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utils.py
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import pandas as pd
import numpy as np
import os, copy, sys
import shutil
from tqdm import tqdm
from PIL import Image, ImageDraw, ImageFont, ImageOps
# from skimage import feature
from plotHelper import plot_surgery_by_layer, plot_concept_importance, layerNames, scatterPlot, scatterPlot2, scatterPlotPair
import torch
import argparse
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib.image as mpimg
import random
import ipdb
def check_path(path):
sep_path = path.split('/')
test_path = sep_path[0]
# if input is a dir+file name, delete file name
if os.path.isfile(path):
del sep_path[-1]
for seg in sep_path[1:]:
test_path = test_path+f'/{seg}'
if not os.path.isdir(test_path):
os.mkdir(test_path)
# montage gradient plots
def montage_tv_train_grad(varies, model='ResNet18', img_path='result/grad_img',
montage_path='result/grad_img/montage'
):
files = os.listdir(f'result/grad_img/{model}_s')
files.sort()
for file in files:
os.system(
f"montage -pointsize 30 -label 'Original' result/grad_img/org/{file} "
f"-label 'Standard' {img_path}/{model}_s/{file} "
f"-label {varies[0]} {img_path}/{model}_{varies[0]}/{file} "
f"-label {varies[1]} {img_path}/{model}_{varies[1]}/{file} "
f"-label {varies[2]} {img_path}/{model}_{varies[2]}/{file} "
f"-label {varies[3]} {img_path}/{model}_{varies[3]}/{file} "
f"-label 'Robust' {img_path}/{model}_robust_eps_1/{file} "
f"-tile 7x1 -geometry +0+0 {montage_path}/{file}")
# plot gradient with chirag's colormap
def plot_grad(file_path, out_path):
check_path(out_path)
files = os.listdir(file_path)
if file_path.split('/')[-1] == 'org':
files.sort()
for file in files[:10]:
img = mpimg.imread(f'{file_path}/{file}')
plt.imshow(img)
plt.xticks([])
plt.yticks([])
plt.tight_layout()
plt.savefig(f'{out_path}/{file}')
else:
for idx, img in enumerate(files):
if idx > 10:
break
grad_img = np.load(f'{file_path}/{img}')
grad_img = np.mean(grad_img, axis=-1)
grad_img = grad_img / np.max(np.abs(grad_img))
# normalize
# for channel in range(3):
# if grad_img[:, :, channel].min() < 0:
# grad_img[:, :, channel] += abs(grad_img[:, :, channel].min())
# grad_img[:, :, channel] = grad_img[:, :, channel] / grad_img[:, :, channel].max()
# Creating colormap
uP = cm.get_cmap('Reds', 129)
dowN = cm.get_cmap('Blues_r', 128)
newcolors = np.vstack((
dowN(np.linspace(0, 1, 128)),
uP(np.linspace(0, 1, 129))
))
cMap = ListedColormap(newcolors, name='RedsBlues')
cMap.colors[257 // 2, :] = [1, 1, 1, 1]
plt.imshow(grad_img, interpolation='none', cmap=cMap)
plt.xticks([])
plt.yticks([])
plt.tight_layout()
plt.savefig(f'{out_path}/{img[:-4]}.jpg')
# find super intersect between different dataset
def find_super_inter_dataset():
clean_csv = pd.read_csv('dataset/correct_csv/alexnet_clean_inter.csv', index_col=0)
noisy_csv = pd.read_csv('dataset/correct_csv/alexnet_gaussian_inter.csv', index_col=0)
clean_list = []
noisy_list = []
for _, row in clean_csv.iterrows():
clean_list.append(row.File.split('/')[-1])
for _, row in noisy_csv.iterrows():
noisy_list.append(row.File.split('/')[-1])
clean_frame = pd.DataFrame(clean_list, columns=['File'])
noisy_frame = pd.DataFrame(noisy_list, columns=['File'])
inter_frame = pd.merge(clean_frame, noisy_frame, how='inner')
inter_list = inter_frame.File.to_list()
true_table = []
for _, row in clean_csv.iterrows():
if row.File.split('/')[-1] not in inter_list:
true_table.append(False)
else:
true_table.append(True)
clean_inter = clean_csv[true_table]
clean_inter.to_csv('dataset/correct_csv/alexnet_clean_noise_inter_cleanpath.csv')
true_table = []
for _, row in noisy_csv.iterrows():
if row.File.split('/')[-1] not in inter_list:
true_table.append(False)
else:
true_table.append(True)
noisy_inter = noisy_csv[true_table]
noisy_inter.to_csv('dataset/correct_csv/alexnet_clean_noise_inter_noisepath.csv')
# simple version of finding intersection
def find_intersect(path1, path2):
data1 = pd.read_csv(path1, index_col=0).File
data2 = pd.read_csv(path2, index_col=0).File
intersect = pd.merge(data1, data2, how='inner')
return intersect
# string to bool value
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
# find stats for each network
def dissect_label_stats(path, network):
files = os.listdir(path)
indexName = [a.replace('.csv', '') for a in files]
stats = pd.DataFrame(np.zeros((len(indexName), 1)), index=indexName, columns=['Init'])
for each_file in files:
index = each_file[:-4]
file_dir = os.path.join(path, each_file)
rawData = pd.read_csv(file_dir)
for idx, row in rawData.iterrows():
if stats.columns.isin([row.label]).any():
stats.loc[index, row.label] += 1
else:
stats[row.label] = 0
stats.loc[index, row.label] += 1
stats.drop(columns=['Init'], inplace=True)
stats.loc['Sum'] = stats.sum()
stats.sort_values('Sum', axis=1, ascending=False, inplace=True)
stats.to_csv("result/tally/"+network+"_summary.csv", index=True)
''' Copy images in a list to target folder'''
def copy_img(img_list, out_dir):
check_path(out_dir)
for each_file in img_list:
shutil.copy(each_file, out_dir)
''' Find interection of given two pandas dataframe
Input: pandas DataFrame *2
Output: pandas DataFrame
'''
def find_intersect_diff_stats(path1, path2, save_name=None):
# read and clean the data
data1 = pd.read_csv(path1, index_col=0)
data2 = pd.read_csv(path2, index_col=0)
obs_data = pd.DataFrame(columns=['File', 'Acc', 'Class'])
if len(data1) > len(data2):
data_org = data1
data_after = data2
else:
data_org = data2
data_after = data1
# find intersect
pbar = tqdm(total=len(data_after))
pbar.set_description('Finding intersection')
for idx, row in data_after.iterrows():
if data_org.File.isin([row.File]).any():
obs_data = obs_data.append(pd.Series([row.File, data_org.loc[idx, 'Confidence'] - row.Confidence, row.Class],
index= ['File', 'Acc', 'Class']), ignore_index=True)
pbar.update(1)
pbar.close()
obs_data.sort_values(by='Acc', inplace=True, ascending=False)
obs_data.reset_index(drop=True, inplace=True)
# save a bb for faster read
if save_name is not None:
obs_data.to_csv(save_name, index=True)
return obs_data
''' Function that plot images from image list and confidence list
Input : [ImageList, DataList] or (Pandas DataFrame), output path
Output: Single image
'''
def display_from_stats(dis_data, save_path, topk=100, tile=(10, 10), img_size=224):
# if input is a list
if isinstance(dis_data, list):
if len(dis_data[0]) < topk:
topk = len(dis_data)
imageList = dis_data[0]
confidence = dis_data[1]
else:
if len(dis_data) < topk:
topk = len(dis_data)
dis_data = dis_data[:topk]
imageList = dis_data.File.to_list()
confidence = dis_data.Confidence.to_list()
font_size = 30
new_img = Image.new('RGB', (img_size*tile[0], (img_size+font_size)*tile[1]))
for i, img_file in enumerate(imageList):
image = Image.open(img_file).convert('RGB')
image = image.resize((img_size, img_size))
# generate empty space for confidence socre
add_conf_space = Image.new('RGB', (img_size, img_size + font_size), (255, 255, 255))
font = ImageFont.truetype("/usr/share/fonts/truetype/freefont/FreeMono.ttf", font_size, encoding="unic")
add_conf_space.paste(image, (0, 0))
# put confidence score input the image
draw = ImageDraw.Draw(add_conf_space)
(x_msg, y_msg) = (30, 224)
if isinstance(confidence[i], str):
message = confidence[i]
else:
message = str(round(float(confidence[i]), 4))
draw.text((x_msg, y_msg), message, fill=0, font=font)
# put new image to proper location
y = (i//tile[0])*(img_size+font_size)
x = (i % tile[0])*img_size
new_img.paste(add_conf_space, (x, y))
new_img.save(save_path)
return save_path
''' Read labels in a layer'''
def layer_labels(file):
data = pd.read_csv(file, index_col=0)
data.sort_values(by='unit', inplace=True)
return data.label.to_list()
''' Calculate the mean of given npy file and return pandas DataFrame'''
def find_mean(file):
data = np.load(file)
mean = np.mean(data, axis=0)
layer_len, _, _ = mean.shape
difference = []
for unit in range(layer_len):
difference.append(mean[unit].sum())
diff_frame = pd.DataFrame(difference, columns=['Score'])
return diff_frame
''' Find statistical result after surgery'''
def find_stats_after_surgery(model_path, label_path, save_path):
stat_files = os.listdir(model_path)
for npy_file in stat_files:
layer_name = npy_file.split(".")[0]
score_frame = find_mean(f'{model_path}/{npy_file}')
test = layer_labels(f'{label_path}/tally{layer_name}.csv')
score_frame['Label'] = test
score_frame['Unit'] = range(len(score_frame))
score_frame.sort_values(by="Score", ascending=False, inplace=True, ignore_index=True)
score_frame.to_csv(f'{save_path}/{layer_name}.csv')
def rename_by_idx(dir, spilt='_'):
os.chdir(dir)
files = os.listdir()
for each_file in files:
name = each_file.split(spilt)
name[0] = name[0].zfill(3)
new_name = f'{name[0]}_{name[1]}'
os.rename(each_file, new_name)
''' Find the difference confidence '''
def find_confidence_difference(target_data, org_data, relative=False, full_path=False):
conf_diff = []
# keep name only
if full_path:
img_name_list = []
for idx, row in org_data.iterrows():
img_name_list.append(row.File.split('/')[-1])
org_data.File = img_name_list
# img_name_list = []
# for idx, row in target_data.iterrows():
# img_name_list.append(row.File.split('/')[-1])
# target_data.File = img_name_list
for _, row in target_data.iterrows():
if relative:
org = org_data[org_data.File == row.File].Confidence.values[0]
after = row.Confidence
changes = (org - after)/org
conf_diff.append(changes)
else:
if full_path:
img_name = row.File.split('/')[-1]
conf_diff.append((org_data[org_data.File == img_name].Confidence - row.Confidence).values[0])
else:
conf_diff.append((org_data[org_data.File == row.File].Confidence - row.Confidence).values[0])
return conf_diff
''' Find accuracy '''
def find_accuracy(target_folder, org_result, by='channelID', keep=False, orgSet=False):
file_list = os.listdir(target_folder)
org_length = len(pd.read_csv(org_result))
if not keep:
accuracy = pd.DataFrame(np.zeros((len(file_list), 2)), columns=['Target', 'Accuracy_Drop'])
else:
accuracy = pd.DataFrame(np.zeros((len(file_list), 2)), columns=['Target', 'Accuracy'])
common_channels = []
for idx, file in enumerate(file_list):
channel_id = file.split('_')[0]
if by == 'concept':
concept = file.split('_')
# concept.pop(0)
concept.pop(-1)
concept = "_".join(concept)
item_length = len(pd.read_csv(f'{target_folder}/{file}'))
# in original dataset
if orgSet:
acc_drop = org_length/50000 - (1-item_length/50000)
# in intersect dataset
else:
acc_drop = item_length/org_length
if by == 'channelID':
accuracy.iloc[idx] = channel_id, acc_drop
elif by == 'concept':
accuracy.iloc[idx] = concept, acc_drop
common_channels.append(int(file.split('_')[-1][:-4]))
if by == 'concept':
accuracy['Counts'] = common_channels
return accuracy
def find_accuracy_stats(networks, operation, index_by, layer='11', keep=False):
accuracy_stats = pd.DataFrame(columns=networks)
for network in networks:
net_stats = pd.read_csv(f'result/tally/{network}/tally{layer}.csv', index_col=False)
if not keep:
folder = f'result/wrong_csv_alblation/zero_out/{network}/{operation}'
org_path = 'dataset/correct_csv/alexnet_inter.csv'
accuracy_drop = find_accuracy(folder, org_path, index_by)
for idx, row in accuracy_drop.iterrows():
if len(row) == 2:
accuracy_stats.loc[row.Target, network] = row.Accuracy_Drop
elif len(row) == 3:
accuracy_stats.loc[row.Target, network] = row.Accuracy_Drop
accuracy_stats.loc[row.Target, 'Count'] = row.Counts
else:
folder = f'result/correct_csv_alblation/keep/{network}/{operation}'
org_path = 'dataset/correct_csv/alexnet_inter.csv'
accuracy = find_accuracy(folder, org_path, index_by, keep)
for idx, row in accuracy.iterrows():
if len(row) == 2:
accuracy_stats.loc[row.Target, network] = row.Accuracy
elif len(row) == 3:
accuracy_stats.loc[row.Target, network] = row.Accuracy
accuracy_stats.loc[row.Target, 'Count'] = row.Counts
if index_by == 'channelID':
net_stats.sort_values(by='unit', inplace=True)
net_stats.reset_index(drop=True, inplace=True)
accuracy_stats[f'Concept_{network}'] = net_stats.label
elif index_by == 'concept':
accuracy_stats[f'Concept_{network}'] = accuracy_stats.index
return accuracy_stats
# fix naming issue in alblation experiment
def fix_name(model, layer, patient_path, file_extension='csv'):
layer_stats = pd.read_csv(f'result/tally/{model}/tally{layer}.csv')
file_list = copy.deepcopy(os.listdir(patient_path))
os.chdir(patient_path)
for file in file_list:
channel_id = file.split('_')[0]
correct_label = layer_stats[layer_stats.unit == (int(channel_id) + 1)].label.values[0]
name = f'{channel_id}_{correct_label}.{file_extension}'
os.rename(file, name)
# plot output images after alblation
'''Display images after alblation
Input: file_path: csv file path
networks: network names
specification: folder name of specific operation
save_path: path that save results
'''
def show_images_after_alblation(alblation_type, networks, specification, reletive=False, abs=False, full_path=False, data_type=None):
if alblation_type == 'zero_out':
file_path = f'result/wrong_csv_alblation/{alblation_type}'
elif alblation_type == 'keep':
file_path = f'result/correct_csv_alblation/{alblation_type}'
for network in networks:
path = f'{file_path}/{network}/{specification}'
files = os.listdir(path)
if data_type is None:
comp_data = pd.read_csv(f'dataset/correct_csv/{network}.csv')
else:
comp_data = pd.read_csv(f'dataset/correct_csv/{network}_{data_type}.csv')
pbar = tqdm(total=len(files))
pbar.set_description(f"Ploting images for {network}")
for csv_file in files:
data_path = f'{path}/{csv_file}'
save_path = f'result/alblation_plots/{alblation_type}/{network}/{specification}/{csv_file[:-4]}.jpg'
check_path(save_path)
img_data = pd.read_csv(data_path, index_col=0)
# img_data.sort_values(by='Confidence', ascending=False, inplace=True)
file_list = img_data.File
if alblation_type == 'zero_out':
conf_list = find_confidence_difference(img_data, comp_data, reletive, full_path=full_path)
elif alblation_type == 'keep':
conf_list = img_data.Confidence
if abs:
conf_list = abs(conf_list)
# new image data with confidence difference
img_data = pd.DataFrame(file_list, columns=['File'])
img_data['Confidence'] = conf_list
img_data.sort_values(by='Confidence', ascending=False, inplace=True)
conf_list = img_data.Confidence.values
conf_list_in_percentage = []
for i in range(len(conf_list)):
drop = round(conf_list[i]*100, 2)
conf_list_in_percentage.append(f'{drop}%')
img_data['Confidence'] = conf_list_in_percentage
display_from_stats(img_data, save_path, 100, (10, 10))
pbar.update(1)
pbar.close()
# remove not intersect data
def cleanData():
layer_data_1 = pd.read_csv(f'result/tally/alexnet/tally11.csv')
layer_data_2 = pd.read_csv(f'result/tally/alexnet-r/tally11.csv')
labels1 = layer_data_1.label
labels2 = layer_data_2.label
labels = pd.merge(labels1, labels2, how='inner')
labels = labels.label.unique()
path1 = 'result/wrong_csv_alblation/zero_out/alexnet/Conv5_concept_max'
path2 = 'result/wrong_csv_alblation/zero_out/alexnet-r/Conv5_concept_max'
list1 = os.listdir(path1)
list2 = os.listdir(path2)
inter_table = []
filename = []
for file_name in list1:
concept = file_name.split('_')
concept.pop(0)
concept.pop(1)
concept = '_'.join(concept)
filename.append(file_name)
if concept in labels:
inter_table.append(True)
else:
inter_table.append(False)
inter_table = pd.DataFrame(inter_table, columns=['Intersect'])
inter_table['File'] = filename
for idx, row in inter_table.iterrows():
if not row.Intersect:
os.remove(f'{path1}/{row.File}')
os.remove(f'{path2}/{row.File}')
def moveFilesOut(folder, out_folder):
sub_folders = os.listdir(folder)
for sub_folder in sub_folders:
files = os.listdir(f'{folder}/{sub_folder}')
for file_name in files:
shutil.move(f'{folder}/{sub_folder}/{file_name}', out_folder)
def find_intersection_labels(file1, file2, out_file, by=None):
if by is None:
layer_data_1 = pd.read_csv(file1)
layer_data_2 = pd.read_csv(file2)
labels1 = layer_data_1.label
labels2 = layer_data_2.label
labels = pd.merge(labels1, labels2, how='inner')
labels = labels.label.unique()
pd.DataFrame(labels, columns=['Concept']).to_csv(out_file)
elif by == 'category':
pass
# create intersect dataste with imagenet layout
def createIntersectDataset(orgPath, out_path, correct_csv):
correct_list = pd.read_csv(correct_csv).File.to_list()
if orgPath is None:
path_in_list = correct_list[0].split("/")[:-2]
orgPath = '/'.join(path_in_list)
full_path = True
else:
full_path = False
imgnet_folders = os.listdir(orgPath)
for folder in tqdm(imgnet_folders):
class_folder = f'{out_path}/{folder}'
os.mkdir(class_folder)
img_list = os.listdir(f'{orgPath}/{folder}')
for img in img_list:
if full_path:
img = f'{orgPath}/{folder}/{img}'
if img in correct_list:
shutil.copy(f'{img}', class_folder)
else:
if img in correct_list:
shutil.copy(f'{orgPath}/{folder}/{img}', class_folder)
# plot ablation results when targeting on concepts
def abl_plot_concept(networks, alblation_type, layer_list, scale_list, plot_type, data_type="stander"):
plot_name = {networks[0]: "", networks[1]: ""}
for layer in layer_list:
layer_name = layerNames(networks[0])
if data_type == 'stander':
specification = f'{layer_name[layer]}_concept_same'
else:
specification = f'{layer_name[layer]}_{data_type}_concept_same'
by = 'concept'
if alblation_type == 'keep':
keep = True
else:
keep = False
accuracy_stats = find_accuracy_stats(networks, specification, by, layer, keep=keep)
# plot_type = 'max'
for network in networks:
concept_summary_path = f'result/tally/{network}_summary.csv'
concept_summary = pd.read_csv(concept_summary_path, index_col=0)
save_path = f'result/alblation_plots/{alblation_type}/{network}'
img_name = plot_concept_importance(concept_summary, accuracy_stats, network, specification,
compared_layer=layer, out_dir=save_path, plotby=plot_type,
alblation_type=alblation_type, sortby='concept', scale=scale_list[layer])
plot_name[network] = img_name
layerwise_path = f'result/alblation_plots/{alblation_type}'
os.system(f"montage -quiet {plot_name[networks[0]]} {plot_name[networks[1]]} -tile 1x2 -geometry +0+0 "
f"{layerwise_path}/{networks[0]}_{specification}_{plot_type}.jpg")
os.system(f"rm {plot_name[networks[0]]} {plot_name[networks[1]]}")
# compute bb box range of the target images
def bb_range_count(file_path, full_path=True):
target_file = pd.read_csv(file_path, index_col=0)
img_name_list = []
for idx, row in target_file.iterrows():
img_name = row.File.split('/')[-1]
if img_name[-3:] == 'png':
img_name = f'{img_name[:-3]}JPEG'
img_name_list.append(img_name)
target_file.File = img_name_list
# images in bb range
bb_folders = os.listdir('imagenet_texture_bb')
bb_folders.sort()
bb_dict = {}
bb_count = {}
for folder in bb_folders:
bb_dict[folder] = os.listdir(f'imagenet_texture_bb/{folder}')
bb_count[folder] = 0
# count bb range
img_counter = 0
bb_counter = 0
for img in img_name_list:
img_counter += 1
for key, img_list in bb_dict.items():
if img in img_list:
bb_count[key] += 1
bb_counter += 1
continue
print(f"miss count {img_counter - bb_counter}")
return bb_count
def scrimble_img(null_img):
patches = []
grid_size = 112
for ii in range(0, 229 - grid_size, grid_size):
for jj in range(0, 229 - grid_size, grid_size):
patches.append(null_img[:, :, ii:ii + grid_size, jj:jj + grid_size])
np.random.seed(seed=212)
randomize_patch = np.random.permutation(len(patches))
patch_ind = 0
texture_img = torch.zeros_like(null_img)
for ii in range(0, 229 - grid_size, grid_size):
for jj in range(0, 229 - grid_size, grid_size):
texture_img[0, :, ii:ii + grid_size, jj:jj + grid_size] = patches[randomize_patch[patch_ind]]
patch_ind += 1
img = texture_img.clone()
return img
def image_name_to_netid():
# Map imagenet names to their netids
input_f = open("ILSVRC2012/imagenet_validation_imagename_labels.txt")
label_map = {}
netid_map = {}
for line in input_f:
parts = line.strip().split(" ")
label_map[parts[0]] = parts[1]
netid_map[parts[1]] = parts[2]
return label_map, netid_map
if __name__ == '__main__':
'''Save intersect'''
# intersect = np.load(f'/home/chirag/convergent_learning/interesection_resnet50_resnet50_r.npy')
# target_path = 'dataset/correct/resnet50_inter'
# check_path(target_path)
# for img in intersect:
# img_path = f'ILSVRC2012/ILSVRC2012_img_val/{img}'
# shutil.copy(img_path, target_path)
""" Rename by index"""
# rename_path = 'dataset/wrong_csv_alblation/alexnet-r/Conv5'
# rename_by_idx(rename_path)
'''find stats for tally files'''
# models = ['alexnet', 'alexnet-r', 'googlenet', 'googlenet-r', 'resnet50', 'resnet50-r']
# for model in models:
# dissect_label_stats(f'result/tally/{model}', model)
'''Copy intersect datas to form a new dataset'''
# model = 'alexnet'
# new_path = f'dataset/correct/{model}_inter'
# check_path(new_path)
# img_list = pd.read_csv(f'dataset/correct_csv/{model}_inter.csv', index_col=0).index.values.tolist()
# copy_img(img_list, new_path)
''' Plot from surgery csv files'''
# networks = ['alexnet', 'alexnet-r']
# topk = 20
# for network in networks:
# file_path = f'result/observe_surgery/stats/{network}'
# save_path = f'result/observe_surgery/plots/{network}/orange_1'
# check_path(save_path)
# stat_files = os.listdir(file_path)
# for file_name in stat_files:
# plot_title = f'{network} layer {file_name[:-4]}'
# plot_surgery_by_layer(f'{file_path}/{file_name}', topk, plot_title, f'{save_path}/{file_name[:-4].zfill(2)}.jpg')
# os.system(f'montage {save_path}/*.jpg -tile 4x1 -geometry +40+0 total.jpg')
""" The following is alblation experiments """
''' Fix Naming issue'''
# fix_name('alexnet-r', '11', 'result/correct_csv_alblation/keep/alexnet-r/Conv5', 'csv')
'''Display images after alblation'''
# alblation_type = 'zero_out'
# networks = ['resnet50', 'resnet50-r']
# specification = 'chequered_stylized_same'
# show_images_after_alblation(alblation_type, networks, specification, False, full_path=True, data_type='stylized')
''' Plot histogram of accuracy after ablation --- zero out by concepts'''
# networks = ['alexnet', 'alexnet-r']
# alblation_type = 'zero_out'
# layer_list = ['1', '4', '7', '9', '11']
# # normal imagenet scale
# scale_list = {'1': (0, 50), '4': (0, 25), '7': (0, 12), '9': (0, 10), '11': (0, 5)}
# # Gaussian imagenet scale
# # scale_list = {'1': (0, 74), '4': (0, 42), '7': (0, 30), '9': (0, 30), '11': (0, 30)}
# plot_type = 'max'
# data_type = "stander"
# abl_plot_concept(networks, alblation_type, layer_list, scale_list, plot_type, data_type)
''' Plot histogram of accuracy after ablation --- zero out by channels'''
'''Find intersect concepts in each layer '''
# network = ['resnet50', 'resnet50-r']
# out_dir = f'result/tally/inter_concepts'
# check_path(out_dir)
# for layer in ['conv1', 'layer1', 'layer2', 'layer3', 'layer4']:
# find_intersection_labels(f'result/tally/{network[0]}/tally{layer}.csv', f'result/tally/{network[1]}/tally{layer}.csv', f'result/tally/inter_concepts/{network[0]}_{network[1]}_{layer}.csv')
''' create intetsect dataset'''
# data_path = ['dataset/gaussian_noise', '/home/chirag/convergent_learning/scrambling_dataset_112/', '/home/chirag/stylized_imagenet/val']
# for network in ['alexnet']:
# for version in ['gaussian', 'scramble', 'stylized']:
# # find inter
# inter_data = find_intersect(f'dataset/correct_csv/{network}_{version}.csv', f'dataset/correct_csv/{network}-r_{version}.csv')
# inter_data.to_csv(f'dataset/correct_csv/{network}_{version}_inter.csv')
#
# target = f'{network}_{version}_inter'
# # org_dataset = '/home/chirag/convergent_learning/scrambling_dataset_112/'
# out_dir = f'dataset/correct/{target}'
# check_path(out_dir)
# csv = f'dataset/correct_csv/{target}.csv'
# createIntersectDataset(None, out_dir, csv)
''' Count bonding box range'''
# target_path = 'result/wrong_csv_alblation/zero_out/resnet50'
# tested_concepts = ['chequered_same', 'chequered_stylized_same', 'striped_same', 'striped_stylized_same', 'zigzagged_same', 'zigzagged_stylized_same']
# bb_range_frame = pd.DataFrame(index=['0.0_0.1', '0.1_0.2', '0.2_0.3', '0.3_0.4', '0.4_0.5', '0.5_0.6', '0.6_0.7', '0.7_0.8', '0.8_0.9', '0.9_1.0'], columns=tested_concepts)
# for folder in tested_concepts:
# print(f'Counting {folder}:')
# file_name = os.listdir(f'{target_path}/{folder}')[0]
# target_file = f'{target_path}/{folder}/{file_name}'
# bb_range_dict = bb_range_count(target_file)
# bb_range_frame[folder] = pd.DataFrame(bb_range_dict, index=[folder]).values[0]
# out_dir = 'result/count_bb_range/zero_out/resnet50'
# check_path(out_dir)
# name = 'three_special_concepts.csv'
# out_file = f"{out_dir}/{name}"
# bb_range_frame.T.to_csv(out_file)
""" find accuracy after ablation"""
# networks = ['resnet50', 'resnet50-r']
# surgery = 'org'
# org_result = f'dataset/correct_csv/{networks[0]}_inter.csv'
# for network in networks:
# out_dir = f'result/zero_out_acc{network}'
# target_folder = f'result/wrong_csv_alblation/zero_out/{network}/concepts_{surgery}'
# acc_stats = find_accuracy(target_folder, org_result, by='concept')
# # filter non-intersect conepts
# acc_stats = acc_stats[~np.array(acc_stats.Counts == 0)]
# acc_stats.sort_values(by='Counts', inplace=True, ascending=False)
# acc_stats.to_csv(f'{out_dir}/{network}_{surgery}.csv')
"""Acc drop"""
# surgery_list = ['org', 'stylized', 'scramble']
# networks = ['resnet', 'resnet-r']
# stats = []
# column = []
# for surgery in surgery_list:
# for network in networks:
# stats.append(pd.read_csv(f'result/zero_out_acc/{network}_{surgery}.csv', index_col=0))
# column.append(f'{network}_{surgery}')
#
# all_in_one = pd.DataFrame(index=stats[0].index, columns=column)
# for data, col_name in zip(stats, column):
# all_in_one[col_name] = data.Accuracy_Drop*100
# all_in_one['Count'] = data.Counts
# all_in_one.set_index(stats[0].Target, drop=True, inplace=True)
# all_in_one.round(decimals=2).to_csv('result/zero_out_acc/resnet_acc_drop.csv')
''' Acc drop layer-wise Alexnet'''
# networks = ['alexnet', 'alexnet-r']
# datasets = ['org', 'gaussian', 'scramble', 'stylized']
# titles = ['Clean ImageNet', 'Gaussian ImageNet', 'Scrambled ImageNet', 'Stylized ImageNet']
# # datasets = ['gaussian', 'scramble', 'stylized']
# # datasets = ['org', 'gaussian']
# target_path = 'result/wrong_csv_alblation/zero_out'
# out_path = 'result/alblation_plots/zero_out'
# compare_path = 'dataset/correct_csv'
# # layers = ['Conv2', 'Conv3', 'Conv4', 'Conv5']
# layers = ['Conv5']
# scale = None
#
# for layer in layers:
# all_stats = []
# for data_type in datasets:
# network_acc_stats = []
# target_folder = f'{layer}_{data_type}_concept_same'
# for network in networks:
# layer_data = find_accuracy(f'{target_path}/{network}/{target_folder}', f'{compare_path}/alexnet_{data_type}_inter.csv', by='concept').sort_values(by='Target')
# network_acc_stats.append(layer_data)
# all_stats.append(network_acc_stats)
#
# # sort according to R net
# stander = all_stats[0][1].sort_values(by='Accuracy_Drop', ascending=False)
# stander.index = stander.Target
# sorted_all = []
# for data_pair in all_stats:
# sorted_pair = []
# for data in data_pair:
# data.index = data.Target
# _, sorted_data = stander.align(data, join='left')
# sorted_data.reset_index(drop=True, inplace=True)
# sorted_pair.append(sorted_data)
# sorted_all.append(sorted_pair)
#
# # single layer plot
# global_stats = []
# for idx, pair_stats in enumerate(sorted_all):
# stats = []
# concepts = pair_stats[0].Target.to_list()
# xtick = []
# for concept in concepts:
# name = '_'.join(concept.split('_')[1:])
# name = '-'.join(name.split('-')[:1])
# xtick.append(name)
# out_dir = f'{out_path}/{networks[0]}'
# check_path(out_dir)
# # plot_name = f'Ablation_{layer}_{str(idx).zfill(2)}{datasets[idx]}.pdf'
# plot_name = f'Ablation_{layer}_{datasets[idx]}.pdf'
# stats.append(pair_stats[0].Accuracy_Drop.values*100)
# stats.append(pair_stats[1].Accuracy_Drop.values*100)
# global_stats.append(stats)
#
# scatterPlot(stats, xtick, out_dir, plot_name, img_size='auto', pltheight=3, fontsize=12, scale=scale)
'''Global accuracy drop plot '''
# networks = ['alexnet', 'alexnet-r']
# datasets = ['org', 'gaussian', 'scramble', 'stylized']
# # datasets = ['gaussian', 'scramble', 'stylized']
# # datasets = ['org']
# target_path = 'result/correct_csv_alblation/zero_out'
# out_path = 'result/alblation_plots/zero_out'
# compare_path = 'dataset/correct_csv'
#
#
# all_stats = []
# for data_type in datasets:
# network_acc_stats = []
# # target_folder = f'{data_type}_concept_same_orgSet'
# target_folder = f'Conv5_concept_same_orgSet'
# for network in networks:
# # for intersent
# # layer_data = find_accuracy(f'{target_path}/{network}/{target_folder}', f'{compare_path}/alexnet_{data_type}_inter.csv', by='concept').sort_values(by='Target')
# # for org dataset
# layer_data = find_accuracy(f'{target_path}/{network}/{target_folder}',
# f'{compare_path}/{network}.csv', by='concept', orgSet=True).sort_values(by='Target')
# network_acc_stats.append(layer_data)
# all_stats.append(network_acc_stats)
#
# # sort according to R net
# stander = all_stats[0][1].sort_values(by='Accuracy_Drop', ascending=False)
# stander.index = stander.Target
# sorted_all = []
# for data_pair in all_stats:
# sorted_pair = []
# for data in data_pair:
# data.index = data.Target
# _, sorted_data = stander.align(data, join='left')
# sorted_data.reset_index(drop=True, inplace=True)
# sorted_pair.append(sorted_data)
# sorted_all.append(sorted_pair)
#
# # single layer plot
# global_stats = []
# for idx, pair_stats in enumerate(sorted_all):
# stats = []
# concepts = pair_stats[0].Target.to_list()
# xtick = concepts
# # for concept in concepts:
# # xtick.append('_'.join(concept.split('_')[1:]))
# out_dir = f'{out_path}/{networks[0]}'
# check_path(out_dir)
# plot_name = f'Ablation_Conv5_{datasets[idx]}_50k.pdf'
# stats.append(pair_stats[0].Accuracy_Drop.values*100)
# stats.append(pair_stats[1].Accuracy_Drop.values*100)
# global_stats.append(stats)
# scatterPlot(stats, xtick, out_dir, plot_name, title=datasets[idx])
# plot all dataset together
# xtick = datasets
# out_dir = f'{out_path}/{networks[0]}'
# check_path(out_dir)
# plot_name = f'Conv5_all_dataset.pdf'
# scatterPlot(global_stats, xtick, out_dir, plot_name, img_size=(6, 4))
''' Acc drop layer-wise all in one'''
# networks = ['alexnet', 'alexnet-r']
# ablation_type = 'keep'
# datasets = ['org', 'gaussian', 'scramble', 'stylized']
# titles = ['Clean ImageNet', 'Gaussian ImageNet', 'Scrambled ImageNet', 'Stylized ImageNet']
# target_path = 'result/wrong_csv_alblation/zero_out'
# out_path = 'result/alblation_plots/zero_out'
# compare_path = 'dataset/correct_csv'
# layers = ['Conv5']
#
#
# for layer in layers:
# all_stats = []
# for data_type in datasets:
# network_acc_stats = []
# target_folder = f'{layer}_{data_type}_concept_same'
# for network in networks:
# layer_data = find_accuracy(f'{target_path}/{network}/{target_folder}', f'{compare_path}/alexnet_{data_type}_inter.csv', by='concept').sort_values(by='Target')
# network_acc_stats.append(layer_data)
# all_stats.append(network_acc_stats)
#
# # sort according to R net
# stander = all_stats[0][1].sort_values(by='Accuracy_Drop', ascending=False)
# stander.index = stander.Target
# sorted_all = []
# for data_pair in all_stats:
# sorted_pair = []
# for data in data_pair:
# data.index = data.Target
# _, sorted_data = stander.align(data, join='left')
# sorted_data.reset_index(drop=True, inplace=True)
# sorted_pair.append(sorted_data)
# sorted_all.append(sorted_pair)
#
# # use same xtick for all plots
# xtick = []
# out_dir = f'{out_path}/{networks[0]}'
# for concept in sorted_all[0][0].Target.to_list():
# name = '_'.join(concept.split('_')[1:])
# name = '-'.join(name.split('-')[:1])
# xtick.append(name)
# # single layer plot for all dataset
# stats = []
# plot_name = f'Ablation_{layer}_4_dataset.pdf'
# scale = (0, 33)
# for idx, pair_stats in enumerate(sorted_all):
# stats.append([pair_stats[0].Accuracy_Drop.values*100, pair_stats[1].Accuracy_Drop.values*100])
# scatterPlot2(stats, xtick, out_dir, plot_name, img_size=(12,4), pltheight=4, fontsize=12, scale=scale, topk=10, title=None)
''' Find intersect images from clean and noisy dataset'''
# out_path = 'dataset/correct/alexnet_noise_super_inter'
# check_path(out_path)
# csv_file = 'dataset/correct_csv/alexnet_clean_noise_inter_noisepath.csv'
# createIntersectDataset(None, out_path, csv_file)
''' Montage channel acitvations'''
# result_path = 'result/channel_activation'
# networks = ['alexnet', 'alexnet-r']
# layers = ['Conv1', 'Conv2', 'Conv3', 'Conv4', 'Conv5']
# data_type = ['clean', 'noise']
# # for network in networks:
# # for img in range(10):
# # for layer in layers:
# # type_plot_list = []
# # for d_type in data_type:
# # type_plot_name = f"{result_path}/{network}/img{img:02d}_{layer}_{d_type}.jpg"
# # os.system(f"montage -quiet {result_path}/{network}/{d_type}/img{img:02d}_{layer}*.jpg -tile 16x -geometry +1+1 {type_plot_name}")
# # type_plot_list.append(type_plot_name)
# # # montage clean and noise version side by side
# # side_by_side_plot_name = f"{result_path}/{network}/img{img:02d}_{layer}.jpg"
# # os.system(f"montage -quiet {type_plot_list[0]} {type_plot_list[1]} -tile 1x2 -geometry +0+10 {side_by_side_plot_name}")
# # os.system(f"rm {type_plot_list[0]} {type_plot_list[1]}")
#
# # montage with origin image
# for i in range(10):
# for layer in layers:
# os.system(f"montage {result_path}/org_img/img_org_{i:02d}.jpg "
# f"{result_path}/{networks[0]}/img{i:02d}_{layer}.jpg "
# f"{result_path}/{networks[1]}/img{i:02d}_{layer}.jpg -tile 3x1 -geometry +5+0 "
# f"{result_path}/result/{layer}_img{i:02d}.jpg")
# path = 'result/channel_activation/clean_img'
# out_path = 'result/channel_activation/clean_img_resize'
# check_path(out_path)
# files = os.listdir(path)
# for file in files:
# full_path = os.path.join(path, file)
# img = Image.open(full_path)
# width, height = img.size
# if width >= height:
# size = (int(width*(256/height)), 256)
# else:
# size = (256, int(256/width)*height)
# img = img.resize(size)
# width, height = img.size
# img = img.crop(((width-224)/2, (height-224)/2, (width+224)/2, (height+224)/2))
# out_full_path = os.path.join(out_path, file)
# img.save(out_full_path)
# tested = ['alpha_100M', 'alpha_1B', 'alpha_10B', 'alpha_100B']
# model = 'ResNet18'
# path = 'result/grad_data'
# out_path = 'result/grad_img'
# for test in tested:
# file_path = f'{path}/{model}_{test}'
# img_path = f'{out_path}/{model}_{test}'
# plot_grad(file_path, img_path)
# montage results
# montage_tv_train_grad(tested)
''' Find intersection of correctly classified images'''
# net1 = 'googlenet'
# net2 = 'googlenet-r'
# snet = np.load(f'data/{net1}.npy')
# rnet = np.load(f'data/{net2}.npy')
# intersect = np.intersect1d(snet, rnet)
# print(len(intersect))
# np.save(f'data/intersection_{net1}_{net2}.npy', intersect)
''' Copy images to according to ImageNet-CL'''
# networks = ['alexnet', 'googlenet', 'resnet50']
# for network in networks:
# file_name = f'intersection_{network}_{network}-r.npy'
# save_path = f'dataset/{network}_silhouette'
# os.makedirs(save_path, exist_ok=True)
# image_list = np.load(f'data/{file_name}')
# silouette_path = 'dataset/silhouette_imagenet_val_opencv'