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test_phosnet.py
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test_phosnet.py
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# Library imports
import os
import argparse
import itertools
import numpy as np
from numpy import linalg as LA
import pandas as pd
import tensorflow as tf
from tensorflow.keras.preprocessing.image import img_to_array,load_img
import matplotlib.pyplot as plt
from tensorflow_addons.layers import SpatialPyramidPooling2D
from phos_label_generator import gen_label
# Uncomment the following line and set appropriate GPU if you want to set up/assign a GPU device to run this code
# os.environ["CUDA_VISIBLE_DEVICES"]="1"
# Argument parser variables
ap = argparse.ArgumentParser()
ap.add_argument("-model", type=str,required=False,
help="Pretrained Model")
ap.add_argument("-test", type=str,required=True,
help="Folder having unseen words")
ap.add_argument("-mp", type=str,required=True,
help="CSV file for Test Images to Class Label map")
ap.add_argument("-stf", type=str,default=None,required=False,
help="Folder for seen Words (Gen. ZSl Setting)")
ap.add_argument("-smap", type=str,default=None,required=False,
help="CSV file for Seen Images to Class Label map (Gen. ZSL Setting)")
ap.add_argument("-train", type=str,default=None,required=False,
help="CSV file for Train Images to Class Label map (Gen. ZSL Setting)")
ap.add_argument("-idn", type=str,required=False, default='',
help="Identifier for saving image files")
args = vars(ap.parse_args())
# Input: Two vectors x and y
# Output: Similarity index = Cosine simialirity * 1000
def similarity(x,y):
return 1000*np.dot(x,y)/(LA.norm(x)*LA.norm(y))
# Input: Confusion matrix, true and predicted class names, plot title, color map and normalization parameter(bool)
# Output: Plots and saves confusion matrix
def plot_confusion_matrix(cm,target_names_true,target_names_pred,title='Confusion matrix',cmap=None,normalize=True):
#accuracy = np.trace(cm) / float(np.sum(cm))
if cmap is None:
cmap = plt.get_cmap('Blues')
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
cm = cm * 100
cm[np.isnan(cm)] = 0
plt.figure(figsize=(12, 9))
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks_true = np.arange(len(target_names_true))
tick_marks_pred = np.arange(len(target_names_pred))
plt.xticks(tick_marks_pred, target_names_pred)
plt.yticks(tick_marks_true, target_names_true)
thresh = cm.max() / 1.5 if normalize else cm.max() / 2
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
if normalize:
plt.text(j, i, "{:0.2f}".format(cm[i, j]),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
else:
plt.text(j, i, "{:,}".format(cm[i, j]),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True Word Class Label Length')
plt.savefig("Test_Plots/"+title+".png")
plt.xlabel('Predicted Word Class Label Length')
plt.show()
# Input: Model, dataframes for test set samples, dictionary for test set words and label(PHOC vector)
# Output: Similarity index = Cosine simialirity * 1000
def accuracy_test(model,df_test,test_word_label,name):
cnt=0
no_of_images=len(df_test)
acc_by_len=dict()
word_count_by_len=dict()
for k in df_test['Word'].tolist():
acc_by_len[len(k)]=0
word_count_by_len[len(k)]=0
lengths_true=sorted(acc_by_len.keys())
lengths_pred=list(set(len(x) for x in test_word_label))
l=len(lengths_true)
m=len(lengths_pred)
idx_true=dict()
idx_pred=dict()
for k in range(l):
idx_true[lengths_true[k]]=k
for k in range(m):
idx_pred[lengths_pred[k]]=k
conf_matrix=np.zeros(shape=(l,m))
Predictions=[]
# Finding predictions for test set word images
for i in range(len(df_test)):
x=img_to_array(load_img(df_test['Image'].iloc[i]))
word=df_test['Word'].iloc[i]
word_count_by_len[len(word)]+=1
x = np.expand_dims(x, axis=0)
y_pred=np.squeeze(model.predict(x))
mx=0
for k in test_word_label:
temp=similarity(y_pred,test_word_label[k])
if temp>mx:
mx=temp
op=k
conf_matrix[idx_true[len(word)]][idx_pred[len(op)]]+=1
Predictions.append((df_test['Image'].iloc[i],word,op))
if op==word:
cnt+=1
acc_by_len[len(word)]+=1
for k in acc_by_len:
if acc_by_len[k]!=0:
acc_by_len[k]=acc_by_len[k]/word_count_by_len[k] * 100
# Storing true and predicted labels for each image sample
df=pd.DataFrame(Predictions,columns=["Image","True Label","Predicted Label"])
df.set_index('Image', inplace=True)
df.to_csv("Test_Results/"+name+".csv")
print("Correct predictions:",cnt," Accuracy=",cnt/no_of_images)
# Plotting length-wise correct predictions
plt.figure(figsize=(10,6))
plt.bar(*zip(*acc_by_len.items()))
plt.title('Acc:'+str(cnt)+'/'+str(no_of_images)+' Correct predictions lengthwise')
plt.xticks(lengths_true)
plt.xlabel('Word Length')
plt.ylabel('Percentage of correct predictions')
plt.savefig("Test_Plots/"+name+"_ZSL_acc.png")
plt.show()
# Plotting length-wise confusion matrix
plot_confusion_matrix(conf_matrix,lengths_true,lengths_pred,title=name+"_confmat")
return cnt/no_of_images
# Input: model, folder names for samples, CSV files having sample to label mapping(Train and test set), and name(identifier in plot names)
# Output: Prediction accuracy (Also calls functions for plotting)
def zsl_test(model,test_folder,test_csv_file,seen_word_folder,seen_word_map,train_csv_file,name):
df_test=pd.read_csv(test_csv_file)
test_word_label=gen_label(list(set(df_test['Word'])))
df_test['Image']=test_folder+"/"+df_test['Image']
acc_unseen=accuracy_test(model, df_test, test_word_label, name+"_conv")
print("Conventional ZSL Accuracy = ", acc_unseen)
if seen_word_folder!=None and train_csv_file!=None:
df_train=pd.read_csv(seen_word_map)
df_lex=pd.read_csv(train_csv_file)
train_word_label=gen_label(list(set(df_lex['Word'])))
df_train['Image']=seen_word_folder+"/"+df_train['Image']
test_word_label={**test_word_label,**train_word_label}
acc_unseen=accuracy_test(model, df_test, test_word_label, name+"_gen_unseen")
acc_seen=accuracy_test(model, df_train, test_word_label, name+"_gen_seen")
print("Accuracy with Unseen Words = ", acc_unseen)
print("Accuracy with Seen Words = ", acc_seen)
gen_zsl_acc=2*acc_unseen*acc_seen/(acc_unseen+acc_seen)
print("Generalized ZSL Accuracy = ",gen_zsl_acc)
MODEL=args['model']
test_folder=args['test']
test_map=args['mp']
seen_word_map=args['smap']
seen_words_folder=args['stf']
train_map=args['train']
name=MODEL+"_"+args['idn']
# Create directories for storing test results and plots
if not os.path.exists("Test_Plots"):
os.makedirs("Test_Plots")
if not os.path.exists("Test_Results"):
os.makedirs("Test_Results")
# Load model from filename and print model name(if successfully loaded)
model=tf.keras.models.load_model(MODEL+".h5")
print(MODEL)
# Function called for test set prediction and result plotting
zsl_test(model,test_folder,test_map,seen_words_folder,seen_word_map,train_map,name)