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eval.py.REMOTE.8280.py
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eval.py.REMOTE.8280.py
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#! /usr/bin/env python
from sklearn.metrics import precision_recall_curve
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
import os
import time
import datetime
#from tensorflow.contrib import learn
from eval_helper import InputHelper, compute_distance,plot_precision_recall
from scipy import misc
# Parameters
# ==================================================
# Eval Parameters
tf.flags.DEFINE_integer("batch_size", 4, "Batch Size (default: 4)")
tf.flags.DEFINE_string("checkpoint_dir", "", "Checkpoint directory from training run")
tf.flags.DEFINE_string("model", "/home/tushar/codes/rnn-cnn/runs/lucia_amos_scratch/checkpoints/model-2811", "Load trained model checkpoint (Default: None)")
tf.flags.DEFINE_string("eval_filepath", "/home/tushar/Heavy_dataset/gta_data/final/", "testing folder (default: /home/halwai/gta/final)")
tf.flags.DEFINE_string("ann_filepath", "./annotation_files4/", "testing folde")
tf.flags.DEFINE_integer("max_frames", 20, "Maximum Number of frame (default: 20)")
tf.flags.DEFINE_string("loss", "contrastive", "Type of Loss functions:: contrastive/AAAI(default: contrastive)")
tf.flags.DEFINE_string("name","result" ,"name for saving" )
# Misc Parameters
tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")
tf.flags.DEFINE_boolean("get_stats", False, "get stats")
#tf.flags.DEFINE_string("ann_filepath", "./annotation_files/", "testing folde")
tf.flags.DEFINE_string("pos_file", "./annotation_files4/", "testing folde")
tf.flags.DEFINE_string("neg_file", "./annotation_files4/", "testing folde")
FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
print("{}={}".format(attr.upper(), value))
print("")
if FLAGS.eval_filepath==None or FLAGS.model==None :
print("Eval or Vocab filepaths are empty.")
exit()
# load data and map id-transform based on training time vocabulary
inpH = InputHelper()
x1_test,x2_test,y_test,video_lengths_test,pairData = inpH.getTestDataSet(FLAGS.pos_file,FLAGS.neg_file,FLAGS.ann_filepath, FLAGS.eval_filepath, FLAGS.max_frames)
print("\nEvaluating...\n")
# Evaluation
# ==================================================
checkpoint_file = FLAGS.model
print(checkpoint_file)
graph = tf.Graph()
with graph.as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
sess = tf.Session(config=session_conf)
with sess.as_default():
# Load the saved meta graph and restore variables
saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
sess.run(tf.global_variables_initializer())
saver.restore(sess, checkpoint_file)
# Get the placeholders from the graph by name
input_imgs = graph.get_operation_by_name("input_imgs").outputs[0]
input_x1 = graph.get_operation_by_name("input_x1").outputs[0]
input_x2 = graph.get_operation_by_name("input_x2").outputs[0]
input_y = graph.get_operation_by_name("input_y").outputs[0]
video_lengths = graph.get_operation_by_name("video_lengths").outputs[0]
dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0]
# Tensors we want to evaluate
conv_output = graph.get_operation_by_name("conv/output").outputs[0]
predictions = graph.get_tensor_by_name("distance:0")
print(conv_output, predictions)
# Generate batches for one epoch
batches = inpH.batch_iter(x1_test,x2_test,y_test,video_lengths_test,pairData, 1, 1, [[104, 114, 124], (227, 227)] ,shuffle=False, is_train=False)
# Collect the predictions here
all_predictions = []
all_dist=[]
true_pos=[]
true_neg=[]
false_pos=[]
false_neg=[]
all_labels=[]
sum_neg_correct=0.0
sum_pos_correct=0.0
for (x1_dev_b,x2_dev_b,y_dev_b,v_len_b,pair_data) in batches: #change
misc.imsave('temp.png', np.vstack([np.hstack(x1_dev_b),np.hstack(x2_dev_b)]))
#print(x1_dev_b)
[x1] = sess.run([conv_output], {input_imgs: x1_dev_b})
[x2] = sess.run([conv_output], {input_imgs: x2_dev_b})
[dist] = sess.run([predictions], {input_x1: x1, input_x2: x2, input_y:y_dev_b, dropout_keep_prob: 1.0, video_lengths: v_len_b})
d = compute_distance(dist, FLAGS.loss)
correct = np.sum(y_dev_b==d)
correctarr=(y_dev_b==d)
print(dist, y_dev_b, d)
for j in range(len(y_dev_b)):
if(correctarr[j]):
if(y_dev_b[j]):
true_pos.append(pair_data[j])
else:
true_neg.append(pair_data[j])
else:
if(y_dev_b[j]):
false_neg.append(pair_data[j])
else:
false_pos.append(pair_data[j])
num_pos_correct=np.sum(d*correct)
num_neg_correct=np.sum(correct)-num_pos_correct
sum_pos_correct=sum_pos_correct+num_pos_correct
sum_neg_correct=sum_neg_correct+num_neg_correct
all_dist.append(dist)
all_predictions.append(correct)
all_labels.append(y_dev_b)
#for ex in all_predictions:
# print(ex)
correct_predictions = np.sum(all_predictions)*1.0/ len(all_predictions)
print("Accuracy: {:g} ".format(correct_predictions))
total_pos=np.sum(all_labels)
total_neg=(len(all_labels)-np.sum(all_labels))
positive_accuracy=sum_pos_correct*1.0/total_pos
negative_accuracy=sum_neg_correct*1.0/total_neg
print('total positives:')
print(total_pos)
print('positive accuracy')
print(positive_accuracy)
print('total negatives:')
print(total_neg)
print('negative accuracy')
print(negative_accuracy)
#invert dist also
dist2 = [1-x for x in all_dist]
precision, recall, _ = precision_recall_curve(all_labels,all_dist)
precision2,recall2,_=precision_recall_curve(all_labels,dist2)
plot_precision_recall(recall,precision,'0','./pr_0'+FLAGS.name)
plot_precision_recall(recall2,precision2,'1','./pr_1'+FLAGS.name)
print('false neg')
for i in range(len(false_neg)):
print(str(false_neg[i][0]))
print(str(false_pos[i][1]))
print('')
print('false pos')
for i in range(len(false_pos)):
print(str(false_pos[i][0]))
print(str(false_pos[i][1]))
print('')