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Beta b #101

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Jan 10, 2024
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Beta b #101

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b6db1d4
modified: BETA_E_Model_T&T.ipynb
Aydinhamedi Jan 10, 2024
9967deb
new file: Interface/CLI/Run_CLI.py
Aydinhamedi Jan 10, 2024
87909fa
Merge pull request #100 from Aydinhamedi/Alpha-b
Aydinhamedi Jan 10, 2024
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50 changes: 3 additions & 47 deletions BETA_E_Model_T&T.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -2771,8 +2771,8 @@
"for i in range(10):\n",
" plt.subplot(2, 5, i+1)\n",
" img = x_val[i]\n",
" heatmap = make_gradcam_heatmap(img[np.newaxis, ...], model, 'top_activation', second_last_conv_layer_name = 'top_conv', sensitivity_map = 2) \n",
" heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))\n",
" heatmap = make_gradcam_heatmap(img[np.newaxis, ...], model, 'top_activation', second_last_conv_layer_name = 'top_conv', sensitivity_map = 1) \n",
" heatmap = cv2.resize(np.clip(heatmap, 0, 1), (img.shape[1], img.shape[0]))\n",
" heatmap = np.uint8(255 * heatmap)\n",
" # Apply Adaptive Histogram Equalization\n",
" clahe = cv2.createCLAHE(clipLimit=1, tileGridSize=(8,8)) # Create CLAHE object\n",
Expand Down Expand Up @@ -2854,51 +2854,7 @@
"plt.yticks(np.arange(0, max(incorrect_predictions) + 5, 3))\n",
"\n",
"plt.title('Number of Incorrect Predictions vs. Number of Data Points')\n",
"plt.show()\n",
"# Deprecated⚠️------------------------------>>>\n",
"# prob_L = 0.9995\n",
"# # Define the range of test data sizes to use\n",
"# data_sizes = range(1, len(x_test), 1) \n",
"\n",
"# # Calculate the probability of a wrong prediction based on test accuracy\n",
"# prob_wrong = 1 - test_accuracy\n",
"\n",
"# # Create a list to store the probability of getting at least one wrong answer for each test data size\n",
"# probabilities = []\n",
"\n",
"# # Calculate the probability of getting at least one wrong answer for each data size\n",
"# for size in data_sizes:\n",
"# # Calculate the cumulative distribution function (CDF) of the binomial distribution at 0\n",
"# cdf = binom.cdf(0, size, prob_wrong)\n",
"# # Subtract the CDF from 1 to get the probability of getting at least one wrong answer\n",
"# prob = 1 - cdf\n",
"# probabilities.append(prob)\n",
"\n",
"# # Find the index of the first data point that has a probability greater than prob_L%\n",
"# index = next((i for i, p in enumerate(probabilities) if p > prob_L), len(probabilities))\n",
"\n",
"# # Limit the x-axis to the first data point that has a probability greater than prob_L%\n",
"# data_sizes = data_sizes[:index+1]\n",
"# probabilities = probabilities[:index+1]\n",
"\n",
"# # Plot the probability vs. the number of data points\n",
"# plt.figure(figsize=(10, 6))\n",
"# plt.plot(data_sizes, probabilities)\n",
"# plt.xlabel('Number of Data Points')\n",
"# plt.ylabel('Probability')\n",
"\n",
"# # Add gridlines for the x and y axes\n",
"# plt.grid(True)\n",
"\n",
"# # Change the tick spacing for the x and y axes\n",
"# plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, 5 + 10))\n",
"# plt.yticks(np.arange(0, max(probabilities)+0.1, 5 / 100))\n",
"\n",
"# plt.ylim(top=1.01)\n",
"\n",
"# plt.title('Probability of Getting at Least One Wrong Answer vs. Number of Data Points')\n",
"# plt.show()\n",
"# Deprecated⚠️------------------------------<<<"
"plt.show()"
]
}
],
Expand Down
2 changes: 1 addition & 1 deletion Interface/CLI/Data/CLI_main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -432,7 +432,7 @@ def CI_pwai(Auto: bool = False):
model, 'top_activation',
second_last_conv_layer_name = 'top_conv',
sensitivity_map = 2, pred_index=tf.argmax(model_prediction_ORG[0]))
Grad_cam_heatmap = cv2.resize(Grad_cam_heatmap, (img_array.shape[1], img_array.shape[2]))
Grad_cam_heatmap = cv2.resize(np.clip(Grad_cam_heatmap, 0, 1), (img_array.shape[1], img_array.shape[2]))
Grad_cam_heatmap = np.uint8(255 * Grad_cam_heatmap)
Grad_cam_heatmap = cv2.applyColorMap(Grad_cam_heatmap, cv2.COLORMAP_VIRIDIS)
Grad_cam_heatmap = np.clip(np.uint8((Grad_cam_heatmap * 0.3) + ((img_array * 255) * 0.7)), 0, 255)
Expand Down
10 changes: 3 additions & 7 deletions Interface/CLI/Data/Utils/Grad_cam.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,9 @@
import os
import glob
import numpy as np
import tensorflow as tf
# Other
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
tf.get_logger().setLevel('ERROR')
physical_devices = tf.config.list_physical_devices('GPU')
for gpu_instance in physical_devices:
Expand Down Expand Up @@ -37,7 +39,7 @@ def make_gradcam_heatmap(img_array,
model,
last_conv_layer_name,
second_last_conv_layer_name=None,
pred_index=None, threshold=0,
pred_index=None,
sensitivity_map=1.0):
"""
Function to compute the Grad-CAM heatmap for a specific class, given an input image.
Expand All @@ -48,17 +50,11 @@ def make_gradcam_heatmap(img_array,

# Compute heatmap for the last convolutional layer
heatmap = _compute_heatmap(model, img_array, last_conv_layer_name, pred_index)

# Apply threshold and adjust sensitivity
heatmap = np.where(heatmap > threshold, heatmap, 0)
heatmap = heatmap ** sensitivity_map

if second_last_conv_layer_name is not None:
# Compute heatmap for the second last convolutional layer
heatmap_second = _compute_heatmap(model, img_array, second_last_conv_layer_name, pred_index)

# Apply threshold and adjust sensitivity
heatmap_second = np.where(heatmap_second > threshold, heatmap_second, 0)
heatmap_second = heatmap_second ** sensitivity_map

# Average the two heatmaps
Expand Down
33 changes: 33 additions & 0 deletions Interface/CLI/Run_CLI.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,33 @@
import subprocess
import traceback
import sys
import os
# Other
from Data.Utils.print_color_V1_OLD import print_Color
def run_program(file_path):
while True:
try:
try:
# Run the other Python program using subprocess
subprocess.run(["python", file_path], check=True)
except subprocess.CalledProcessError as ERROR_Py:
print_Color("~*An error occurred: \nERROR: ~*" + str(ERROR_Py), ['yellow', 'red'], advanced_mode=True)
print_Color('~*Do you want to see the detailed error message? ~*[~*Y~*/~*n~*]: ',
['yellow', 'normal', 'green', 'normal', 'red', 'normal'],
advanced_mode = True,
print_END='')
show_detailed_error = input('')
if show_detailed_error.lower() == 'y':
print_Color('detailed error message:', ['yellow'])
#print_Color('1th ERROR (FILE ERROR) [!MAIN!]:', ['red'])
print_Color('2th ERROR (subprocess.run ERROR):', ['red'])
traceback.print_exc()
choice = input("Do you want to restart the program? (y/n): ")
if choice.lower() != "y":
break
os.system('cls' if os.name == 'nt' else 'clear')
else:
break
except OSError:
break
run_program('Data\CLI_main.py')
50 changes: 3 additions & 47 deletions Model_T&T.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2771,8 +2771,8 @@
"for i in range(10):\n",
" plt.subplot(2, 5, i+1)\n",
" img = x_val[i]\n",
" heatmap = make_gradcam_heatmap(img[np.newaxis, ...], model, 'top_activation', second_last_conv_layer_name = 'top_conv', sensitivity_map = 2) \n",
" heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))\n",
" heatmap = make_gradcam_heatmap(img[np.newaxis, ...], model, 'top_activation', second_last_conv_layer_name = 'top_conv', sensitivity_map = 1) \n",
" heatmap = cv2.resize(np.clip(heatmap, 0, 1), (img.shape[1], img.shape[0]))\n",
" heatmap = np.uint8(255 * heatmap)\n",
" # Apply Adaptive Histogram Equalization\n",
" clahe = cv2.createCLAHE(clipLimit=1, tileGridSize=(8,8)) # Create CLAHE object\n",
Expand Down Expand Up @@ -2854,51 +2854,7 @@
"plt.yticks(np.arange(0, max(incorrect_predictions) + 5, 3))\n",
"\n",
"plt.title('Number of Incorrect Predictions vs. Number of Data Points')\n",
"plt.show()\n",
"# Deprecated⚠️------------------------------>>>\n",
"# prob_L = 0.9995\n",
"# # Define the range of test data sizes to use\n",
"# data_sizes = range(1, len(x_test), 1) \n",
"\n",
"# # Calculate the probability of a wrong prediction based on test accuracy\n",
"# prob_wrong = 1 - test_accuracy\n",
"\n",
"# # Create a list to store the probability of getting at least one wrong answer for each test data size\n",
"# probabilities = []\n",
"\n",
"# # Calculate the probability of getting at least one wrong answer for each data size\n",
"# for size in data_sizes:\n",
"# # Calculate the cumulative distribution function (CDF) of the binomial distribution at 0\n",
"# cdf = binom.cdf(0, size, prob_wrong)\n",
"# # Subtract the CDF from 1 to get the probability of getting at least one wrong answer\n",
"# prob = 1 - cdf\n",
"# probabilities.append(prob)\n",
"\n",
"# # Find the index of the first data point that has a probability greater than prob_L%\n",
"# index = next((i for i, p in enumerate(probabilities) if p > prob_L), len(probabilities))\n",
"\n",
"# # Limit the x-axis to the first data point that has a probability greater than prob_L%\n",
"# data_sizes = data_sizes[:index+1]\n",
"# probabilities = probabilities[:index+1]\n",
"\n",
"# # Plot the probability vs. the number of data points\n",
"# plt.figure(figsize=(10, 6))\n",
"# plt.plot(data_sizes, probabilities)\n",
"# plt.xlabel('Number of Data Points')\n",
"# plt.ylabel('Probability')\n",
"\n",
"# # Add gridlines for the x and y axes\n",
"# plt.grid(True)\n",
"\n",
"# # Change the tick spacing for the x and y axes\n",
"# plt.xticks(np.arange(min(data_sizes), max(data_sizes)+1, 5 + 10))\n",
"# plt.yticks(np.arange(0, max(probabilities)+0.1, 5 / 100))\n",
"\n",
"# plt.ylim(top=1.01)\n",
"\n",
"# plt.title('Probability of Getting at Least One Wrong Answer vs. Number of Data Points')\n",
"# plt.show()\n",
"# Deprecated⚠️------------------------------<<<"
"plt.show()"
]
}
],
Expand Down
10 changes: 3 additions & 7 deletions Utils/Grad_cam.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,7 +1,9 @@
import os
import glob
import numpy as np
import tensorflow as tf
# Other
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
tf.get_logger().setLevel('ERROR')
physical_devices = tf.config.list_physical_devices('GPU')
for gpu_instance in physical_devices:
Expand Down Expand Up @@ -37,7 +39,7 @@ def make_gradcam_heatmap(img_array,
model,
last_conv_layer_name,
second_last_conv_layer_name=None,
pred_index=None, threshold=0,
pred_index=None,
sensitivity_map=1.0):
"""
Function to compute the Grad-CAM heatmap for a specific class, given an input image.
Expand All @@ -48,17 +50,11 @@ def make_gradcam_heatmap(img_array,

# Compute heatmap for the last convolutional layer
heatmap = _compute_heatmap(model, img_array, last_conv_layer_name, pred_index)

# Apply threshold and adjust sensitivity
heatmap = np.where(heatmap > threshold, heatmap, 0)
heatmap = heatmap ** sensitivity_map

if second_last_conv_layer_name is not None:
# Compute heatmap for the second last convolutional layer
heatmap_second = _compute_heatmap(model, img_array, second_last_conv_layer_name, pred_index)

# Apply threshold and adjust sensitivity
heatmap_second = np.where(heatmap_second > threshold, heatmap_second, 0)
heatmap_second = heatmap_second ** sensitivity_map

# Average the two heatmaps
Expand Down