Hugging Face's logo

Spaces:
markytools
/
strexp
Build error

App Files Community
strexp
File size: 6,804 Bytes 
5f5c8d7
7978529
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5f5c8d7
7978529
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
 
import streamlit as st
from PIL import Image
import settings
import captum
import numpy as np
import torch
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
from utils import get_args
from utils import CTCLabelConverter, AttnLabelConverter, Averager, TokenLabelConverter
import string
import time
import sys
from dataset import hierarchical_dataset, AlignCollate
import validators
from model import Model, STRScore
from PIL import Image
from lime.wrappers.scikit_image import SegmentationAlgorithm
from captum._utils.models.linear_model import SkLearnLinearModel, SkLearnRidge
import random
import os
from skimage.color import gray2rgb
import pickle
from train_shap_corr import getPredAndConf
import re
from captum_test import acquire_average_auc, saveAttrData
import copy
from skimage.color import gray2rgb
from matplotlib import pyplot as plt
from torchvision import transforms

device = torch.device('cpu')
opt = get_args(is_train=False)

""" vocab / character number configuration """
if opt.sensitive:
    opt.character = string.printable[:-6]  # same with ASTER setting (use 94 char).

cudnn.benchmark = True
cudnn.deterministic = True
# opt.num_gpu = torch.cuda.device_count()

# combineBestDataXAI(opt)
# acquire_average_auc(opt)
# acquireSingleCharAttrAve(opt)
modelName = "parseq"
opt.modelName = modelName
# opt.eval_data = "datasets/data_lmdb_release/evaluation"

if modelName=="vitstr":
    opt.benchmark_all_eval = True
    opt.Transformation = "None"
    opt.FeatureExtraction = "None"
    opt.SequenceModeling = "None"
    opt.Prediction = "None"
    opt.Transformer = True
    opt.sensitive = True
    opt.imgH = 224
    opt.imgW = 224
    opt.data_filtering_off = True
    opt.TransformerModel= "vitstr_base_patch16_224"
    opt.saved_model = "pretrained/vitstr_base_patch16_224_aug.pth"
    opt.batch_size = 1
    opt.workers = 0
    opt.scorer = "mean"
    opt.blackbg = True
elif modelName=="parseq":
    opt.benchmark_all_eval = True
    opt.Transformation = "None"
    opt.FeatureExtraction = "None"
    opt.SequenceModeling = "None"
    opt.Prediction = "None"
    opt.Transformer = True
    opt.sensitive = True
    opt.imgH = 32
    opt.imgW = 128
    opt.data_filtering_off = True
    opt.batch_size = 1
    opt.workers = 0
    opt.scorer = "mean"
    opt.blackbg = True

# x = st.slider('Select a value')
# st.write(x, 'squared is', x * x)

image = Image.open('demo_image/demo_ballys.jpg') #Brand logo image (optional)
#Create two columns with different width
col1, col2 = st.columns( [0.8, 0.2])
with col1:               # To display the header text using css style
    st.markdown(""" <style> .font {
    font-size:35px ; font-family: 'Cooper Black'; color: #FF9633;}
    </style> """, unsafe_allow_html=True)
    st.markdown('<p class="font">Upload your photo here...</p>', unsafe_allow_html=True)
with col2:               # To display brand logo
    st.image(image,  width=150)

uploaded_file = st.file_uploader("Choose a file", type=["png", "jpg"])
if uploaded_file is not None:
    # To read file as bytes:
    bytes_data = uploaded_file.getvalue()
    pilImg = Image.open(uploaded_file)

    orig_img_tensors = transforms.ToTensor()(pilImg).unsqueeze(0)
    img1 = orig_img_tensors.to(device)
    # image_tensors = ((torch.clone(orig_img_tensors) + 1.0) / 2.0) * 255.0
    image_tensors = torch.mean(orig_img_tensors, dim=1).unsqueeze(0).unsqueeze(0)
    imgDataDict = {}
    img_numpy = image_tensors.cpu().detach().numpy()[0] ### Need to set batch size to 1 only
    if img_numpy.shape[0] == 1:
        img_numpy = gray2rgb(img_numpy[0])
    # print("img_numpy shape: ", img_numpy.shape) # (1, 32, 128, 3)
    segmOutput = segmentation_fn(img_numpy[0])

    results_dict = {}
    aveAttr = []
    aveAttr_charContrib = []
    target = converter.encode([labels])

    # labels: RONALDO
    segmDataNP = segmOutput
    img1.requires_grad = True
    bgImg = torch.zeros(img1.shape).to(device)

    # preds = model(img1, seqlen=converter.batch_max_length)
    input = img1
    origImgNP = torch.clone(orig_img_tensors).detach().cpu().numpy()[0][0] # (1, 1, 224, 224)
    origImgNP = gray2rgb(origImgNP)
    charOffset = 0
    img1 = transforms.Normalize(0.5, 0.5)(img1) # Between -1 to 1
    target = converter.encode([labels])

    ### Local explanations only
    collectedAttributions = []
    for charIdx in range(0, len(labels)):
        scoring_singlechar.setSingleCharOutput(charIdx + charOffset)
        gtClassNum = target[0][charIdx + charOffset]

        gs = GradientShap(super_pixel_model_singlechar)
        baseline_dist = torch.zeros((1, 3, opt.imgH, opt.imgW))
        baseline_dist = baseline_dist.to(device)
        attributions = gs.attribute(input, baselines=baseline_dist, target=0)
        collectedAttributions.append(attributions)
    aveAttributions = torch.mean(torch.cat(collectedAttributions,dim=0), dim=0).unsqueeze(0)
    # if not torch.isnan(aveAttributions).any():
    #     rankedAttr = rankedAttributionsBySegm(aveAttributions, segmDataNP)
    #     rankedAttr = rankedAttr.detach().cpu().numpy()[0][0]
    #     rankedAttr = gray2rgb(rankedAttr)
    #     mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map', cmap='RdYlGn')
    #     mplotfig.savefig(outputDir + '{}_shapley_l.png'.format(nameNoExt))
    #     mplotfig.clear()
    #     plt.close(mplotfig)

    ### Local Sampling
    gs = GradientShap(super_pixel_model)
    baseline_dist = torch.zeros((1, 3, opt.imgH, opt.imgW))
    baseline_dist = baseline_dist.to(device)
    attributions = gs.attribute(input, baselines=baseline_dist, target=0)
    # if not torch.isnan(attributions).any():
    #     collectedAttributions.append(attributions)
    #     rankedAttr = rankedAttributionsBySegm(attributions, segmDataNP)
    #     rankedAttr = rankedAttr.detach().cpu().numpy()[0][0]
    #     rankedAttr = gray2rgb(rankedAttr)
    #     mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map', cmap='RdYlGn')
    #     mplotfig.savefig(outputDir + '{}_shapley.png'.format(nameNoExt))
    #     mplotfig.clear()
    #     plt.close(mplotfig)

    ### Global + Local context
    aveAttributions = torch.mean(torch.cat(collectedAttributions,dim=0), dim=0).unsqueeze(0)
    if not torch.isnan(aveAttributions).any():
        rankedAttr = rankedAttributionsBySegm(aveAttributions, segmDataNP)
        rankedAttr = rankedAttr.detach().cpu().numpy()[0][0]
        rankedAttr = gray2rgb(rankedAttr)
        mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map', cmap='RdYlGn')
        fig = mplotfig.figure(figsize=(8,8))
        st.pyplot(fig)
        # mplotfig.savefig(outputDir + '{}_shapley_gl.png'.format(nameNoExt))
        # mplotfig.clear()
        # plt.close(mplotfig)