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Beta b #188

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Mar 14, 2024
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Beta b #188

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06536a1
Merge pull request #177 from Aydinhamedi/Alpha-b
Aydinhamedi Mar 8, 2024
b9c881d
Merge pull request #178 from Aydinhamedi/Alpha-b
Aydinhamedi Mar 12, 2024
81cf3b2
Merge pull request #179 from Aydinhamedi/Alpha-b
Aydinhamedi Mar 12, 2024
bf708b0
Merge pull request #180 from Aydinhamedi/Alpha-b
Aydinhamedi Mar 13, 2024
3174f59
Merge pull request #182 from Aydinhamedi/Alpha-b
Aydinhamedi Mar 13, 2024
b1d79d1
Merge pull request #184 from Aydinhamedi/main
Aydinhamedi Mar 13, 2024
a73a30b
Merge pull request #185 from Aydinhamedi/Alpha-b
Aydinhamedi Mar 13, 2024
c234c8a
modified: Interface/GUI/Data/GUI_main.py
Aydinhamedi Mar 14, 2024
a9fa699
modified: Archive/keras_applications_mod/models/convnext.py
Aydinhamedi Mar 14, 2024
bea0952
Merge pull request #187 from Aydinhamedi/Alpha-b
Aydinhamedi Mar 14, 2024
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10 changes: 4 additions & 6 deletions Archive/keras_applications_mod/models/convnext.py
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Original file line number Diff line number Diff line change
Expand Up @@ -228,12 +228,10 @@ def call(self, x):

def get_config(self):
config = super().get_config()
config.update(
{
"init_values": self.init_values,
"projection_dim": self.projection_dim,
}
)
config.update({
"init_values": self.init_values,
"projection_dim": self.projection_dim,
})
return config


Expand Down
46 changes: 20 additions & 26 deletions Archive/keras_applications_mod/models/imagenet_utils_test.py
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Expand Up @@ -74,13 +74,11 @@ def test_preprocess_input(self):
self.assertAllClose(x, x2[..., ::-1])
self.assertNotEqual(xint.astype("float").max(), xint2.max())

@parameterized.named_parameters(
[
{"testcase_name": "mode_torch", "mode": "torch"},
{"testcase_name": "mode_tf", "mode": "tf"},
{"testcase_name": "mode_caffe", "mode": "caffe"},
]
)
@parameterized.named_parameters([
{"testcase_name": "mode_torch", "mode": "torch"},
{"testcase_name": "mode_tf", "mode": "tf"},
{"testcase_name": "mode_caffe", "mode": "caffe"},
])
def test_preprocess_input_symbolic(self, mode):
# Test image batch
x = np.random.uniform(0, 255, (2, 10, 10, 3))
Expand Down Expand Up @@ -131,13 +129,11 @@ def test_preprocess_input_symbolic(self, mode):
out2 = model2.predict(x2[np.newaxis])[0]
self.assertAllClose(out1, out2.transpose(1, 2, 0))

@parameterized.named_parameters(
[
{"testcase_name": "mode_torch", "mode": "torch"},
{"testcase_name": "mode_tf", "mode": "tf"},
{"testcase_name": "mode_caffe", "mode": "caffe"},
]
)
@parameterized.named_parameters([
{"testcase_name": "mode_torch", "mode": "torch"},
{"testcase_name": "mode_tf", "mode": "tf"},
{"testcase_name": "mode_caffe", "mode": "caffe"},
])
def test_preprocess_input_symbolic_mixed_precision(self, mode):
if not tf.__internal__.tf2.enabled():
self.skipTest("The global policy can only be tested in TensorFlow 2")
Expand All @@ -152,18 +148,16 @@ def test_preprocess_input_symbolic_mixed_precision(self, mode):
finally:
set_global_policy("float32")

@parameterized.named_parameters(
[
{
"testcase_name": "channels_last_format",
"data_format": "channels_last",
},
{
"testcase_name": "channels_first_format",
"data_format": "channels_first",
},
]
)
@parameterized.named_parameters([
{
"testcase_name": "channels_last_format",
"data_format": "channels_last",
},
{
"testcase_name": "channels_first_format",
"data_format": "channels_first",
},
])
def test_obtain_input_shape(self, data_format):
# input_shape and default_size are not identical.
with self.assertRaises(ValueError):
Expand Down
34 changes: 15 additions & 19 deletions BETA_E_Model_T&T.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -622,7 +622,7 @@
" # ┌-----------------┬--┬-------------------┐\n",
" # │ 𝑦=2/(𝑥+1)^(𝑥+𝑉) ├OR┤ 𝑦=2/(𝑥+1)^(𝑥+1.4) │\n",
" # └-----------------┴--┴-------------------┘\n",
" print(f\"> Generating ADB[{i+1}/{ADBD}]...\")\n",
" print(f\"> Generating ADB[{i + 1}/{ADBD}]...\")\n",
" # prepare an iterators to scale images\n",
" train_iterator = train_datagen.flow(x_train, y_train, batch_size=len(x_train))\n",
"\n",
Expand Down Expand Up @@ -3214,12 +3214,10 @@
" layer.trainable = True\n",
"\n",
" # Combine the output of the two base models\n",
" combined = concatenate(\n",
" [\n",
" Dense(512, activation=\"relu\", kernel_regularizer=l2(0.01))(GlobalAveragePooling2D()(base_model1_out)),\n",
" Dense(512, activation=\"relu\", kernel_regularizer=l2(0.01))(GlobalAveragePooling2D()(base_model2_out)),\n",
" ]\n",
" )\n",
" combined = concatenate([\n",
" Dense(512, activation=\"relu\", kernel_regularizer=l2(0.01))(GlobalAveragePooling2D()(base_model1_out)),\n",
" Dense(512, activation=\"relu\", kernel_regularizer=l2(0.01))(GlobalAveragePooling2D()(base_model2_out)),\n",
" ])\n",
"\n",
" # adding CDL\n",
" Dense_L1 = Dense(1024, activation=\"relu\", kernel_regularizer=l2(0.02))(combined)\n",
Expand Down Expand Up @@ -9958,12 +9956,10 @@
"\n",
" old_weights, old_biases = layer.get_weights()\n",
"\n",
" layer.set_weights(\n",
" [\n",
" weight_initializer(shape=old_weights.shape),\n",
" bias_initializer(shape=len(old_biases)),\n",
" ]\n",
" )"
" layer.set_weights([\n",
" weight_initializer(shape=old_weights.shape),\n",
" bias_initializer(shape=len(old_biases)),\n",
" ])"
]
},
{
Expand Down Expand Up @@ -11032,18 +11028,18 @@
" optimizer = self.model.optimizer\n",
" if hasattr(optimizer, \"lr\"):\n",
" lr = tf.keras.backend.get_value(optimizer.lr)\n",
" print(f\"\\nLearning rate for epoch {epoch+1} is {lr}\")\n",
" print(f\"\\nLearning rate for epoch {epoch + 1} is {lr}\")\n",
" if hasattr(optimizer, \"momentum\"):\n",
" momentum = tf.keras.backend.get_value(optimizer.momentum)\n",
" print(f\"Momentum for epoch {epoch+1} is {momentum}\")\n",
" print(f\"Momentum for epoch {epoch + 1} is {momentum}\")\n",
" if logs:\n",
" val_loss = logs.get(\"val_loss\")\n",
" val_acc = logs.get(\"val_accuracy\")\n",
" print(f\"Validation loss for epoch {epoch+1} is {val_loss}\")\n",
" print(f\"Validation accuracy for epoch {epoch+1} is {val_acc}\")\n",
" print(f\"Validation loss for epoch {epoch + 1} is {val_loss}\")\n",
" print(f\"Validation accuracy for epoch {epoch + 1} is {val_acc}\")\n",
"\n",
" print_Color_V2(\n",
" f\"`red`<!--------------------------------------|Epoch`yellow` [{epoch+1}]`red` End|--------------------------------------!> `green`PBE↓\",\n",
" f\"`red`<!--------------------------------------|Epoch`yellow` [{epoch + 1}]`red` End|--------------------------------------!> `green`PBE↓\",\n",
" start_char=\"`\",\n",
" end_char=\"`\",\n",
" )\n",
Expand Down Expand Up @@ -11538,7 +11534,7 @@
" plt.xlabel(\"Epoch (within chunk)\")\n",
" plt.ylabel(\"Average Value\")\n",
" plt.title(\"Average Validation Accuracy and Loss for Each Epoch Across Chunks (Ignoring First Chunk)\")\n",
" plt.xticks(epoch_indices, [f\"Epoch {i+1}\" for i in epoch_indices]) # Set x-tick labels to epoch numbers\n",
" plt.xticks(epoch_indices, [f\"Epoch {i + 1}\" for i in epoch_indices]) # Set x-tick labels to epoch numbers\n",
" plt.legend()\n",
"\n",
" plt.tight_layout()\n",
Expand Down
40 changes: 16 additions & 24 deletions Exports/V5/Python_EPO.py
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Original file line number Diff line number Diff line change
Expand Up @@ -595,18 +595,14 @@ def Debug_img_Save(img, id="DEF"):
mean_intensity_mean = reshaped_data_mean.mean(axis=-1)

# Stack the mean intensity with the reshaped data
data_with_mean_pixels = np.hstack(
[
reshaped_data_pixels,
mean_intensity_pixels.reshape(-1, 1),
]
)
data_with_mean_mean = np.hstack(
[
reshaped_data_mean,
mean_intensity_mean.reshape(-1, 1),
]
)
data_with_mean_pixels = np.hstack([
reshaped_data_pixels,
mean_intensity_pixels.reshape(-1, 1),
])
data_with_mean_mean = np.hstack([
reshaped_data_mean,
mean_intensity_mean.reshape(-1, 1),
])

# Calculate Z-scores
z_scores_pixels = np.abs(zscore(data_with_mean_pixels, axis=0))
Expand Down Expand Up @@ -855,12 +851,10 @@ def Combo_Model(freeze_layers1, freeze_layers2):
layer.trainable = True

# Combine the output of the two base models
combined = concatenate(
[
GlobalAveragePooling2D()(base_model1_out),
GlobalAveragePooling2D()(base_model2_out),
]
)
combined = concatenate([
GlobalAveragePooling2D()(base_model1_out),
GlobalAveragePooling2D()(base_model2_out),
])

# adding CDL
Dense_L1 = Dense(1024, activation="relu", kernel_regularizer=l2(0.03))(combined)
Expand Down Expand Up @@ -1153,12 +1147,10 @@ def Eff_B7_NS():

old_weights, old_biases = layer.get_weights()

layer.set_weights(
[
weight_initializer(shape=old_weights.shape),
bias_initializer(shape=len(old_biases)),
]
)
layer.set_weights([
weight_initializer(shape=old_weights.shape),
bias_initializer(shape=len(old_biases)),
])


# %% [markdown]
Expand Down
40 changes: 16 additions & 24 deletions Exports/V7/Python_EPO.py
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Original file line number Diff line number Diff line change
Expand Up @@ -592,18 +592,14 @@ def Debug_img_Save(img, id="DEF"):
mean_intensity_mean = reshaped_data_mean.mean(axis=-1)

# Stack the mean intensity with the reshaped data
data_with_mean_pixels = np.hstack(
[
reshaped_data_pixels,
mean_intensity_pixels.reshape(-1, 1),
]
)
data_with_mean_mean = np.hstack(
[
reshaped_data_mean,
mean_intensity_mean.reshape(-1, 1),
]
)
data_with_mean_pixels = np.hstack([
reshaped_data_pixels,
mean_intensity_pixels.reshape(-1, 1),
])
data_with_mean_mean = np.hstack([
reshaped_data_mean,
mean_intensity_mean.reshape(-1, 1),
])

# Calculate Z-scores
z_scores_pixels = np.abs(zscore(data_with_mean_pixels, axis=0))
Expand Down Expand Up @@ -860,12 +856,10 @@ def Combo_Model(freeze_layers1, freeze_layers2):
layer.trainable = True

# Combine the output of the two base models
combined = concatenate(
[
Dense(512, activation="relu", kernel_regularizer=l2(0.02))(GlobalAveragePooling2D()(base_model1_out)),
Dense(512, activation="relu", kernel_regularizer=l2(0.02))(GlobalAveragePooling2D()(base_model2_out)),
]
)
combined = concatenate([
Dense(512, activation="relu", kernel_regularizer=l2(0.02))(GlobalAveragePooling2D()(base_model1_out)),
Dense(512, activation="relu", kernel_regularizer=l2(0.02))(GlobalAveragePooling2D()(base_model2_out)),
])

# adding CDL
Dense_L1 = Dense(1024, activation="relu", kernel_regularizer=l2(0.03))(combined)
Expand Down Expand Up @@ -1411,12 +1405,10 @@ def load_model(filename):

old_weights, old_biases = layer.get_weights()

layer.set_weights(
[
weight_initializer(shape=old_weights.shape),
bias_initializer(shape=len(old_biases)),
]
)
layer.set_weights([
weight_initializer(shape=old_weights.shape),
bias_initializer(shape=len(old_biases)),
])


# %% [markdown]
Expand Down
25 changes: 14 additions & 11 deletions Interface/GUI/Data/GUI_main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,7 @@
sg.popup_auto_close("Loading GUI...", non_blocking=True, auto_close_duration=2, no_titlebar=False)
# pylib
try:
import re
# import re # noqa: F401
import time
import cv2
import json
Expand All @@ -38,14 +38,17 @@
import hashlib
import pydicom
import cpuinfo
import difflib
import inspect

# import difflib # noqa: F401
# import inspect # noqa: F401
import traceback
import subprocess
import threading
import requests
import numpy as np
from tqdm import tqdm
from time import sleep

# from time import sleep # noqa: F401
from loguru import logger
from tkinter import filedialog
from datetime import datetime
Expand All @@ -57,10 +60,10 @@
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
# Utils
from Utils.Grad_cam import make_gradcam_heatmap
from Utils.print_color_V2_NEW import print_Color_V2
from Utils.print_color_V2_NEW import print_Color_V2 # noqa: F401
from Utils.print_color_V1_OLD import print_Color
from Utils.FixedDropout import FixedDropout
from Utils.Other import *
from Utils.Other import * # noqa: F403
except (ImportError, NameError):
print("Failed to load the GUI libs")
print("detailed error message:")
Expand Down Expand Up @@ -134,9 +137,8 @@ def is_updated(self):
GUI_layout_Tab_main = [
[sg.Text("Enter the image dir:", font=(None, 10, "bold"))],
[
sg.Input(key="-INPUT_IMG_dir-"),
sg.Input(key="-INPUT_IMG_dir-", enable_events=True, size=(48, 1)),
sg.Button("Browse", key="-BUTTON_BROWSE_IMG_dir-"),
sg.Button("Ok", key="-BUTTON_OK_IMG_dir-"),
],
[sg.Text("Log:", font=(None, 10, "bold"))],
[sg.Multiline(key="-OUTPUT_ST-", size=(54, 6), autoscroll=True)],
Expand Down Expand Up @@ -538,7 +540,8 @@ def CI_liid(img_dir, Show_DICOM_INFO: bool = True) -> str:
try:
_, file_extension = os.path.splitext(img_dir)
except TypeError:
file_extension = "TEMP FILE EXTENSION"
logger.warning("CI_liid>>ERROR: Invalid file format. Please provide an image file. (Extension Extractiion Failed)")
return "ERROR: Invalid file format. Please provide an image file. (Extension Extractiion Failed)"
if file_extension.upper()[1:] not in IMG_AF:
logger.warning("CI_liid>>ERROR: Invalid file format. Please provide an image file.")
return "ERROR: Invalid file format. Please provide an image file."
Expand Down Expand Up @@ -567,7 +570,7 @@ def CI_liid(img_dir, Show_DICOM_INFO: bool = True) -> str:
GUI_layout_DICOM_Info_Window.finalize()
else:
img = Image.open(img_dir).resize((IMG_RES[1], IMG_RES[0]))
except NameError:
except (NameError, FileNotFoundError):
logger.warning("CI_liid>>ERROR: Invalid file dir. Please provide an image file.")
return "ERROR: Invalid file dir. Please provide an image file."
else:
Expand Down Expand Up @@ -777,7 +780,7 @@ def main() -> None:
GUI_window["-INPUT_IMG_dir-"].update(IMG_dir)

# Handle event for confirming the selected image directory
if event == "-BUTTON_OK_IMG_dir-":
if event == "-INPUT_IMG_dir-":
# Retrieve the image directory from the input field and update the display
IMG_dir = GUI_window["-INPUT_IMG_dir-"].get()
GUI_window["-INPUT_IMG_dir-"].update(IMG_dir)
Expand Down
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