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predict.py
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predict.py
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import os
import sys
import random
import numpy as np
import matplotlib as m
import matplotlib.pyplot as plt
from sklearn.utils import shuffle
import tensorflow as tf
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Model, load_model, save_model
from tensorflow.keras.layers import Input,Dropout,BatchNormalization,Activation,Add,Flatten,Dense,Reshape
from tensorflow.keras.layers import Conv2D, Conv2DTranspose, LeakyReLU, UpSampling2D, Input, Cropping2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import Concatenate
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from tensorflow.keras import backend as K
from tensorflow.keras import optimizers
import time
import pandas as pd
import gc
def plot1x2Array(image, mask1, v_min, v_max, k, plot_save_dict):
#invoke matplotlib!
f, ax = plt.subplots(1,2)
im1 = ax[0].imshow(image, vmin=v_min, vmax=v_max, aspect='auto')
im = ax[1].imshow(mask1, vmin=v_min, vmax=v_max, aspect='auto')
f.colorbar(im1)
ax[0].grid()
ax[1].grid()
ax[0].set_title('Model number {}'.format(k))
ax[1].set_title('Predicted Model')
plt.savefig(plot_save_dict + str(k) + '.png')
#plt.show()
def vellog_1x2Array(image, mask1, k, plot_save_dict):
Y = np.linspace(0, 256, num=256)
Y=Y.reshape(Y.shape[0],1)
plt.rcParams['xtick.bottom'] = plt.rcParams['xtick.labelbottom'] = False
plt.rcParams['xtick.top'] = plt.rcParams['xtick.labeltop'] = True
vel11 = image[:, 49]
vel12 = image[:, 124]
vel13 = image[:, 199]
vel21 = mask1[:, 49]
vel22 = mask1[:, 124]
vel23 = mask1[:, 199]
f, ax = plt.subplots(1,3)
ax[0].plot(vel11[:],Y[:], "-b", label="Ground Truth")
ax[0].plot(vel21[:],Y[:], "-r", label="Predicted")
ax[0].set_ylim([256,0])
ax[0].legend(loc="upper right")
ax[1].plot(vel12[:],Y[:], "-b", label="Ground Truth")
ax[1].plot(vel22[:],Y[:], "-r", label="Predicted")
ax[1].set_ylim([256,0])
ax[1].legend(loc="upper right")
ax[2].plot(vel13[:],Y[:], "-b", label="Ground Truth")
ax[2].plot(vel23[:],Y[:], "-r", label="Predicted")
ax[2].set_ylim([256,0])
ax[2].legend(loc="upper right")
ax[0].grid()
ax[1].grid()
ax[2].grid()
ax[0].set_title('Log at 500 m from left (49 pixel mark)')
ax[1].set_title('Log at 1.25 km from left (124 pixel mark)')
ax[2].set_title('Log at 2 km from left (199 pixel mark)')
plt.savefig(plot_save_dict + str(k) + '_vellogs.png')
#plt.show()
def plot1x3Array(image, mask1, mask2, v_min, v_max, k, plot_save_dict):
#invoke matplotlib!
f, ax = plt.subplots(1,3)
im1 = ax[0].imshow(image, vmin=v_min, vmax=v_max, aspect='auto')
im = ax[1].imshow(mask1, vmin=v_min, vmax=v_max, aspect='auto')
im = ax[2].imshow(mask2, vmin=v_min, vmax=v_max, aspect='auto')
f.colorbar(im1)
ax[0].grid()
ax[1].grid()
ax[2].grid()
ax[0].set_title('Model number {}'.format(k))
ax[1].set_title('Predicted from CNN fresh_training')
ax[2].set_title('Predicted from CNN fine_tuning')
plt.savefig(plot_save_dict + str(k) + '_models.png')
#plt.show()
def vellog_1x3Array(image, mask1, mask2, k, plot_save_dict):
Y = np.linspace(0, 256, num=256)
Y=Y.reshape(Y.shape[0],1)
plt.rcParams['xtick.bottom'] = plt.rcParams['xtick.labelbottom'] = False
plt.rcParams['xtick.top'] = plt.rcParams['xtick.labeltop'] = True
vel11 = image[:, 49]
vel12 = image[:, 124]
vel13 = image[:, 199]
vel21 = mask1[:, 49]
vel22 = mask1[:, 124]
vel23 = mask1[:, 199]
vel31 = mask2[:, 49]
vel32 = mask2[:, 124]
vel33 = mask2[:, 199]
f, ax = plt.subplots(1,3)
ax[0].plot(vel11[:],Y[:], "-b", label="Ground Truth")
ax[0].plot(vel21[:],Y[:], "-r", label="Predicted from CNN fresh_training")
ax[0].plot(vel31[:],Y[:], "-g", label="Predicted from CNN fine_tuning")
ax[0].set_ylim([256,0])
ax[0].legend(loc="upper right")
ax[1].plot(vel12[:],Y[:], "-b", label="Ground Truth")
ax[1].plot(vel22[:],Y[:], "-r", label="Predicted from CNN fresh_training")
ax[1].plot(vel32[:],Y[:], "-g", label="Predicted from CNN fine_tuning")
ax[1].set_ylim([256,0])
ax[1].legend(loc="upper right")
ax[2].plot(vel13[:],Y[:], "-b", label="Ground Truth")
ax[2].plot(vel23[:],Y[:], "-r", label="Predicted from CNN fresh_training")
ax[2].plot(vel33[:],Y[:], "-g", label="Predicted from CNN fine_tuning")
ax[2].set_ylim([256,0])
ax[2].legend(loc="upper right")
ax[0].grid()
ax[1].grid()
ax[2].grid()
ax[0].set_title('Log at 500 m from left (49 pixel mark)')
ax[1].set_title('Log at 1.25 km from left (124 pixel mark)')
ax[2].set_title('Log at 2 km from left (199 pixel mark)')
plt.savefig(plot_save_dict + str(k) + '_vellogs.png')
#plt.show()
plt.rcParams['figure.figsize'] = [20, 5]
if __name__ == '__main__':
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
# print(tf.__version__)
physical_devices = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)
#gpu_options = tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=0.8)
#sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options))
dataset_type = 'SEGSALT' # Enter DUTCH or SEGSALT
if dataset_type == 'DUTCH':
set_num = 4 # Select set_num from 1 to 4
#Loading dataset
X_test = np.load('D:/WORK/SVMB/train_test/DI_DC/test/traces.npz')
X_test = X_test['data']
y_test = np.load('D:/WORK/SVMB/train_test/DI_DC/test/tiles.npz')
y_test = y_test['data']
y_test = y_test.reshape(y_test.shape[0], y_test.shape[1], y_test.shape[2], 1)
model = load_model("D:/WORK/SVMB/trained_nets/DI_DC/NN3_set{}_16bit.h5".format(set_num))
start_time = time.time()
y_pred = model.predict(X_test, batch_size=16)
train_time = time.time() - start_time
metrics = model.evaluate(X_test, y_test, batch_size=16)
with open("D:/WORK/SVMB/train_test_Time.txt", "a") as text_file:
print("Time taken to predict {} for set_num{}: {} seconds\n Evaluation metrics :{}".format(dataset_type, set_num, train_time, metrics), file=text_file)
print("Test MSE : ", metrics)
metrics_df = pd.DataFrame(metrics)
metrics_json_file = "D:/WORK/SVMB/test_results/DI_DC/set{}/metrics.json".format(set_num)
with open(metrics_json_file, mode='w') as f:
metrics_df.to_json(f)
for i in range(25):
k = np.random.randint(X_test.shape[0])
image = y_test[k, :, :, 0]
mask1 = y_pred[k, :, :, 0]
plot1x2Array(image, mask1, v_min = np.min(image), v_max = np.max(image), k=k,
plot_save_dict="D:/WORK/SVMB/test_results/DI_DC/set{}/".format(set_num))
vellog_1x2Array(image, mask1, k=k, plot_save_dict="D:/WORK/SVMB/test_results/DI_DC/set{}/".format(set_num))
elif dataset_type == 'SEGSALT':
allPred = False # True if DI_DC trained network is used to test all segsalt models
if allPred == True:
set_num = 4
#Loading dataset
X_test = np.load('D:/WORK/SVMB/segSaltData/traces.npz')
X_test = X_test['data']
y_test = np.load('D:/WORK/SVMB/segSaltData/tiles.npz')
y_test = y_test['data']
y_test = y_test.reshape(y_test.shape[0], y_test.shape[1], y_test.shape[2], 1)
model = load_model("D:/WORK/SVMB/trained_nets/DI_DC/NN3_set{}_16bit.h5".format(set_num))
start_time = time.time()
y_pred = model.predict(X_test, batch_size=16)
train_time = time.time() - start_time
metrics = model.evaluate(X_test, y_test, batch_size=16)
with open("D:/WORK/SVMB/train_test_Time.txt", "a") as text_file:
print("Time taken to predict {} using network trained on DI_DC set_num{}: {} seconds\n Evaluation metrics :{}".format(dataset_type, set_num, train_time, metrics), file=text_file)
print("Test MSE : ", metrics)
metrics_df = pd.DataFrame(metrics)
metrics_json_file = "D:/WORK/SVMB/test_results/segsalt/allPred/metrics.json"
with open(metrics_json_file, mode='w') as f:
metrics_df.to_json(f)
for i in range(25):
k = np.random.randint(X_test.shape[0])
image = y_test[k, :, :, 0]
mask1 = y_pred[k, :, :, 0]
plot1x2Array(image, mask1, v_min = np.min(image), v_max = np.max(image), k=k,
plot_save_dict="D:/WORK/SVMB/test_results/segsalt/allPred/")
vellog_1x2Array(image, mask1, k=k, plot_save_dict="D:/WORK/SVMB/test_results/segsalt/allPred/")
else:
lst = [304, 608, 1014]
for item in lst:
gc.collect()
#Loading dataset
test_data = np.load('D:/WORK/SVMB/train_test/segsalt/test/set{}_test.npz'.format(item))
X_test = test_data['tr']
y_test = test_data['ti']
y_test = y_test.reshape(y_test.shape[0], y_test.shape[1], y_test.shape[2], 1)
model0 = load_model("D:/WORK/SVMB/trained_nets/segsalt/fine_tuning/NN3_set{}_16bit.h5".format(item))
model1 = load_model("D:/WORK/SVMB/trained_nets/segsalt/fresh_training/NN3_set{}_16bit.h5".format(item))
start_time = time.time()
y_pred0 = model0.predict(X_test, batch_size=16)
y_pred1 = model1.predict(X_test, batch_size=16)
train_time = time.time() - start_time
metrics0 = model0.evaluate(X_test, y_test, batch_size=16)
metrics1 = model1.evaluate(X_test, y_test, batch_size=16)
with open("D:/WORK/SVMB/train_test_Time.txt", "a") as text_file:
print("Time taken to predict fine_tuning and fresh_training {} for set_num{}: {} seconds\n Evaluation metrics fine_tuning:{}\n Evaluation metrics fresh_training:{}\n".format(dataset_type, item, train_time, metrics0, metrics1), file=text_file)
print("Test MSE fine_tuning: ", metrics0)
print("Test MSE fresh_training: ", metrics1)
metrics0_df = pd.DataFrame(metrics0)
metrics0_json_file = "D:/WORK/SVMB/test_results/segsalt/set{}/metrics_fine_tuning.json".format(item)
with open(metrics0_json_file, mode='w') as f:
metrics0_df.to_json(f)
metrics1_df = pd.DataFrame(metrics1)
metrics1_json_file = "D:/WORK/SVMB/test_results/segsalt/set{}/metrics_fresh_training.json".format(item)
with open(metrics1_json_file, mode='w') as f:
metrics1_df.to_json(f)
for i in range(25):
k = np.random.randint(X_test.shape[0])
image = y_test[k, :, :, 0]
mask1 = y_pred0[k, :, :, 0]
mask2 = y_pred1[k, :, :, 0]
plot1x3Array(image, mask2, mask1, v_min = np.min(image), v_max = np.max(image), k=k,
plot_save_dict='D:/WORK/SVMB/test_results/segsalt/set{}/'.format(item))
vellog_1x3Array(image, mask2, mask1, k=k,
plot_save_dict='D:/WORK/SVMB/test_results/segsalt/set{}/'.format(item))