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perform_test_2.py
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perform_test_2.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 2 14:16:53 2020
@author: dipu
"""
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 20 11:01:07 2019
same as perform_tests.py
but removes the images with no components and images with number of components greater than 100
@author: dipu
"""
import os
import torch
from torchvision import transforms
import glob
import json
from collections import defaultdict
#from utils import extract_features, compute_iou
import _init_paths
from BoundingBox import BoundingBox
from BoundingBoxes import BoundingBoxes
from PIL import Image
import pickle
from scipy.spatial.distance import cdist
import numpy as np
from eval_metrics.get_overall_IOU import get_overall_IOU
from eval_metrics.get_overall_Classwise_IOU import get_overall_Classwise_IOU
from eval_metrics.get_overall_pix_acc import get_overall_pix_acc
from RICO_Dataset import RICO_Dataset
#from models.model_CAE_emb512 import ConvAutoEncoder
from models.model_upsample_emb512 import CAE_upsample_dim512
#from models.model_CAE_emb512_25ChannelOut import ConvAutoEncoder
from utils import mkdir_if_missing, load_checkpoint
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
def extract_features(data_loader, model):
model.eval()
torch.set_grad_enabled(False)
features = []
labels = []
for i, (imgs, im_fn) in enumerate(data_loader):
#for i, (imgs, im_fn, img25Chan) in enumerate(data_loader):
imgs = imgs.cuda()
x_enc = model(imgs, training=False)
outputs = x_enc.detach().cpu().numpy()
features.append(outputs)
labels += list(im_fn)
# print(i)
return features, labels
def remove_null_and_large_comp_images(img_list):
nocomp_imlist = pickle.load(open('/home/dipu/codes/GraphEncoding-RICO/data/no_component_imglist.pkl', 'rb'))
ncomp_g100_imglist = pickle.load(open('/home/dipu/codes/GraphEncoding-RICO/data/ncomponents_g100_imglist.pkl', 'rb'))
nocomp_imlist = [x +'.png' for x in nocomp_imlist]
ncomp_g100_imglist = [x + '.png' for x in ncomp_g100_imglist]
img_list = list(set(img_list) - set(nocomp_imlist))
img_list = list(set(img_list) - set(ncomp_g100_imglist))
return img_list
data_dir = '/mnt/amber/scratch/Dipu/RICO/semantic_annotations/'
UI_data = pickle.load(open("/mnt/amber/scratch/Dipu/RICO/UI_data.p", "rb"))
UI_test_data = pickle.load(open("/mnt/amber/scratch/Dipu/RICO/UI_test_data.p", "rb"))
train_uis = UI_data['train_uis']
query_uis = UI_test_data['query_uis']
gallery_uis = UI_test_data['gallery_uis']
train_uis = remove_null_and_large_comp_images(train_uis)
query_uis = remove_null_and_large_comp_images(query_uis)
gallery_uis = remove_null_and_large_comp_images(gallery_uis)
data_transform = transforms.Compose([
transforms.Resize([255,127]),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
BATCH_SIZE = 128
train_dataset = RICO_Dataset(train_uis, data_dir, transform= data_transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size= BATCH_SIZE,
drop_last = True, pin_memory=True, num_workers=16)
query_dataset = RICO_Dataset(query_uis, data_dir, transform= data_transform)
query_loader = torch.utils.data.DataLoader(query_dataset, batch_size= BATCH_SIZE, shuffle=False,
drop_last = False, pin_memory=True, num_workers=16)
gallery_dataset = RICO_Dataset(gallery_uis, data_dir, transform= data_transform)
gallery_loader = torch.utils.data.DataLoader(gallery_dataset, batch_size= BATCH_SIZE, shuffle=False,
drop_last = False, pin_memory=True, num_workers=16)
def getBoundingBoxes(data_dir = '/mnt/scratch/Dipu/RICO/semantic_annotations/'):
allBoundingBoxes = BoundingBoxes()
files = glob.glob(data_dir+ "*.json")
for file in files:
imageName = os.path.split(file)[1]
imageName = imageName.replace(".json", "")
# print(imageName)
with open(file, "r") as f:
sui = json.load(f) # sui = semantic ui annotation.
elements, count = parse_ui_elements(sui)
for i in range(count):
box = elements[i]
bb = BoundingBox(
imageName,
box['component_Label'],
box['x'],
box['y'],
box['w'],
box['h'],
iconClass=box['iconClass'],
textButtonClass=box['textButtonClass'])
allBoundingBoxes.addBoundingBox(bb)
# testBoundingBoxes(allBoundingBoxes)
print('Collected {} bounding boxes from {} images'. format(allBoundingBoxes.count(), len(files) ))
return allBoundingBoxes
def main():
boundingBoxes = getBoundingBoxes()
#models = ['model_CAE_emb2688', 'model_CAE_emb512', 'model_CAE2_OnlyConv_emb2912']
models = ['model_CAE_emb512'] # ['modelCAE_emb512_25Channel_out']
for model_name in models:
onlyGallery = True
model = CAE_upsample_dim512()
model_path = '/home/dipu/codes/AutoEnconder_RicoDataset/runs/{}/ckp_ep20.pth.tar'.format(model_name)
resume = load_checkpoint(model_path)
model.load_state_dict(resume['state_dict'])
model = model.cuda()
model.eval()
q_feat, q_fnames = extract_features(query_loader, model)
g_feat, g_fnames = extract_features(gallery_loader, model)
print('extracted features from {} query images'.format(len(q_fnames)))
print('extracted features from {} gallery images'.format(len(g_fnames)))
#t_feat, t_fnames = extract_features(train_loader, model)
q_feat = np.concatenate(q_feat)
g_feat = np.concatenate(g_feat)
#t_feat = np.concatenate(t_feat)
if not(onlyGallery):
g_feat = np.vstack((g_feat,t_feat))
g_fnames = g_fnames + t_fnames
distances = cdist(q_feat, g_feat, metric= 'euclidean')
sort_inds = np.argsort(distances)
overallMeanClassIou, overallMeanWeightedClassIou, classwiseClassIoU = get_overall_Classwise_IOU(boundingBoxes,sort_inds,g_fnames,q_fnames, topk = [1,5,10])
overallMeanAvgPixAcc, overallMeanWeightedPixAcc, classPixAcc = get_overall_pix_acc(boundingBoxes,sort_inds,g_fnames,q_fnames, topk = [1,5,10])
print(model_name)
print('GAlleryOnly Flag:', onlyGallery)
print('The overallMeanClassIou = ' + str([ '{:.3f}'.format(x) for x in overallMeanClassIou]) + '\n')
print('The overallMeanWeightedClassIou = ' + str([ '{:.3f}'.format(x) for x in overallMeanWeightedClassIou]) + '\n')
print('The overallMeanAvgPixAcc = ' + str([ '{:.3f}'.format(x) for x in overallMeanAvgPixAcc]) + '\n')
print('The overallMeanWeightedPixAcc = ' + str([ '{:.3f}'.format(x) for x in overallMeanWeightedPixAcc]) + '\n')
# overallMeanIou, overallMeanWeightedIou, classIoU = get_overall_IOU(boundingBoxes,sort_inds,g_fnames,q_fnames)
# overallMeanClassIou, overallMeanWeightedClassIou, classwiseClassIoU = get_overall_Classwise_IOU(boundingBoxes,sort_inds,g_fnames,q_fnames)
# overallMeanAvgPixAcc, overallMeanWeightedPixAcc, classPixAcc = get_overall_pix_acc(boundingBoxes,sort_inds,g_fnames,q_fnames)
##
# print('\n\IOU Values:')
# print(model_name)
# print('GAlleryOnly Flag:', onlyGallery)
# print('The overallMeanIou = {:.3f} '.format(overallMeanIou))
# print('The overallMeanWeightedIou = {:.3f}'.format(overallMeanWeightedIou))
# print('The overallMeanClassIou = {:.3f})'.format(overallMeanClassIou))
# print('The overallMeanWeightedClassIou = {:.3f})'.format(overallMeanWeightedClassIou))
# print('The overallMeanAvgPixAcc = {:.3f}'.format(overallMeanAvgPixAcc))
# print('The overallMeanWeightedPixAcc = {:.3f} '.format(overallMeanWeightedPixAcc))
#
# #Save results
# savefile = model_name + '_results.p'
# results = {'overallMeanIou': overallMeanIou, 'overallMeanWeightedIou': overallMeanWeightedIou, 'classIoU': classIoU, \
# 'overallMeanClassIou': overallMeanClassIou, 'overallMeanWeightedClassIou': overallMeanWeightedClassIou, 'classwiseClassIoU': classwiseClassIoU, \
# 'overallMeanAvgPixAcc': overallMeanAvgPixAcc, 'overallMeanWeightedPixAcc': overallMeanWeightedPixAcc, 'classPixAcc':classPixAcc \
# }
#
# pickle.dump(results, open(savefile, "wb"))
#
# plot_classwiseResults(classIoU, model_name + 'classIoU' )
# plot_classwiseResults(classwiseClassIoU, model_name + 'classwiseClassIoU' )
# plot_classwiseResults(classPixAcc, model_name + 'classPixAcc' )
#%% Preparing the dataset
def parse_ui_elements(sui):
"""
Parse the json file iteratively using recursion,, un winding all the nested chilfre
returns the dictionay of elements
"""
global counter
counter = 0
elements = defaultdict(dict)
def recurse(sui):
global counter
n_uis = len(sui['children'])
for i in range(n_uis):
[x1, y1, x2, y2] = sui['children'][i]['bounds']
elements[counter]['component_Label'] = sui['children'][i]['componentLabel']
elements[counter]['x'] = x1
elements[counter]['y'] = y1
elements[counter]['w'] = x2-x1
elements[counter]['h'] = y2-y1
elements[counter]['iconClass'] = sui['children'][i].get('iconClass')
elements[counter]['textButtonClass'] = sui['children'][i].get('textButtonClass')
counter +=1
if sui['children'][i].get('children') != None:
recurse(sui['children'][i])
recurse(sui)
return elements, counter
def getBoundingBoxes(data_dir = '/mnt/scratch/Dipu/RICO/semantic_annotations/'):
allBoundingBoxes = BoundingBoxes()
files = glob.glob(data_dir+ "*.json")
for file in files:
imageName = os.path.split(file)[1]
imageName = imageName.replace(".json", "")
# print(imageName)
with open(file, "r") as f:
sui = json.load(f) # sui = semantic ui annotation.
elements, count = parse_ui_elements(sui)
for i in range(count):
box = elements[i]
bb = BoundingBox(
imageName,
box['component_Label'],
box['x'],
box['y'],
box['w'],
box['h'],
iconClass=box['iconClass'],
textButtonClass=box['textButtonClass'])
allBoundingBoxes.addBoundingBox(bb)
# testBoundingBoxes(allBoundingBoxes)
print('Collected {} bounding boxes from {} images'. format(allBoundingBoxes.count(), len(files) ))
return allBoundingBoxes
def testBoundingBoxes(boundingBoxes, samples = ['28970', '62918']):
#Visualize if every colored element is plotted or not.
from matplotlib import pyplot as plt
from PIL import Image
import matplotlib.patches as patches
samples = ['28970', '62918']
base_ui_path = '/mnt/scratch/Dipu/RICO/semantic_annotations/'
base_im_path = '/mnt/scratch/Dipu/RICO/combined/'
for sample in samples:
img = base_ui_path + sample + '.png'
img = Image.open(img).convert('RGB')
img2 = base_im_path + sample + '.jpg'
img2 = Image.open(img2).convert('RGB')
fig, ax = plt.subplots(1,2)
plt.setp(ax, xticklabels=[], yticklabels=[])
ax[0].imshow(img2)
ax[1].imshow(img)
bbs = boundingBoxes.getBoundingBoxesByImageName(sample)
for bb in bbs:
bb_cordinates = bb.getBoundingBox()
bb_class = bb.classId
# if bb_cordinates[2] < 0:
rect = patches.Rectangle((bb_cordinates[0], bb_cordinates[1]), bb_cordinates[2], bb_cordinates[3], linewidth=2, edgecolor='r', facecolor= 'none')
ax[1].add_patch(rect)
ax[1].text(bb_cordinates[0], bb_cordinates[1], bb_class, fontsize=8, color= 'r', verticalalignment='top')
plt.show()
def plot_classwiseResults(classwiseResult, name):
from matplotlib import pyplot as plt
import collections
D = classwiseResult
for k, v in D.items():
if D[k] == []:
D[k]= [0,0]
else:
D[k] = [np.mean(v), len(v)]
D = sorted(D.items(), key=lambda kv: kv[1][0], reverse=False)
D = collections.OrderedDict(D)
fig, ax = plt.subplots()
for i, (k,v) in enumerate(D.items()):
ax.text( v[0]+0.001, i+0.25 , '{:.2f} ({})'.format(v[0], v[1]), fontsize=10, fontweight='bold', color= 'b', verticalalignment='top')
# print (i ,k ,v)
# fig.title(name)
fig.set_size_inches(7, 5)
ax.barh(range(len(D)), [x[0] for x in D.values()], align='center')
plt.yticks(range(len(D)), list(D.keys()), rotation='horizontal')
plt.subplots_adjust(bottom=0.3)
plt.tight_layout(h_pad=1)
plt.show()
plt.savefig('Results/Result_Figures/{}.png'.format(name), dpi = 500)
if __name__ == '__main__':
main()