aug.py

@@ -1,33 +0,0 @@
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-from os import listdir as ls
-from sys import argv, exit
-from tensorflow.keras.utils import array_to_img as img
-from tensorflow.keras import layers as kl
-
-if len(argv) != 3:
-    exit(1)
-
-files = ls(argv[1])
-
-for v, fn in enumerate(files):
-    i = np.array(Image.open(f"{argv[1]}/{fn}").convert('RGB').resize((300,300))) / 255.0
-    print(f"Processing {v+1:03d}/{len(files)}: {fn}")
-
-    for x1 in range(3):
-        i1 = tf.image.random_brightness(i, 0.6)
-        for x2 in range(3):
-            i2 = tf.image.random_saturation(i1, 0.1, 2.0)
-            for x3 in range(3):
-                i3 = tf.image.random_contrast(i2, 0.2, 1.9)
-                img(i3).save(f"{argv[2]}/{fn.rsplit('.', 1)[0]}_{x1}_{x2}_{x3}_1.png")
-
-                i3 = tf.image.flip_left_right(i3)
-                img(i3).save(f"{argv[2]}/{fn.rsplit('.', 1)[0]}_{x1}_{x2}_{x3}_2.png")
-
-                i3 = tf.image.flip_up_down(i3)
-                img(i3).save(f"{argv[2]}/{fn.rsplit('.', 1)[0]}_{x1}_{x2}_{x3}_3.png")
-
-                i3 = tf.image.flip_left_right(i3)
-                img(i3).save(f"{argv[2]}/{fn.rsplit('.', 1)[0]}_{x1}_{x2}_{x3}_4.png")

ident-live.py

@@ -1,45 +0,0 @@
-#from PIL import Image
-#from sys import argv, exit
-import numpy as np
-import cv2 as cv
-
-from tensorflow.keras.utils import array_to_img as img
-
-from matplotlib import pyplot as plt
-
-#if len(argv) != 2:
-#    exit(1)
-
-#i = np.array(Image.open(argv[1]).convert('RGB').resize((300,300)))
-
-from m import *
-mod.load_weights("model3-val-acc-0.74.model.keras")
-
-c = cv.VideoCapture(0)
-
-while True:
-    ret, frame = c.read()
-
-    pi = np.array(img(cv.cvtColor(frame, cv.COLOR_BGR2RGB)).resize((300, 300)).convert("RGB"))
-    #plt.imshow(pi)
-    #plt.show()
-
-    #print(pi.astype(float))
-
-    r = mod.predict(np.array([pi.astype(float)]))
-
-    cv.putText(frame, f"Y: {r[0][1]:.9f}", (10, 40), cv.FONT_HERSHEY_SIMPLEX, 1, (200,255,200), 2)
-    cv.putText(frame, f"N: {r[0][0]:.9f}", (10, 70), cv.FONT_HERSHEY_SIMPLEX, 1, (200,200,255), 2)
-
-    cv.imshow('me', frame)
-
-    if cv.waitKey(1) == ord('q'):
-        break
-
-#print(frame)
-
-c.release()
-cv.destroyAllWindows()
-
-#r = mod.predict(np.array([i]))
-#print(r)

ident.py

@@ -1,15 +0,0 @@
-from m import *
-from PIL import Image
-from sys import argv, exit
-import numpy as np
-
-
-if len(argv) != 2:
-    exit(1)
-
-i = np.array(Image.open(argv[1]).convert('RGB').resize((300,300)))
-
-mod.load_weights("model3.model.keras")
-
-r = mod.predict(np.array([i]))
-print(r)

m.py

@@ -1,160 +0,0 @@
-import tensorflow as tf
-import numpy as np
-
-from tensorflow import keras as k
-from tensorflow.keras import layers as kl
-from tensorflow.keras import models as km
-from tensorflow.keras import optimizers as ko
-from tensorflow.keras import callbacks as kc
-
-CONV_SIZE = 96
-MPPT_SIZE = 256
-DRPT_RATE = 0.3
-TOUT_AMNT = 2
-
-def conv(i, n, s, t = (1, 1)):
-    c = kl.Conv2D(n, s, padding = 'same', strides = t)(i)
-    b = kl.BatchNormalization()(c)
-    return kl.Activation('relu')(b)
-
-def avg(i, s):
-    return kl.AveragePooling2D(s, padding = 'same', strides = (1, 1))(i)
-
-def max(i, s):
-    return kl.MaxPooling2D(s, padding = 'same', strides = (1, 1))(i)
-
-def generate_start(i):
-    s = kl.Rescaling(1./255.)(i)
-    r1 = conv(s, CONV_SIZE, (3, 3))
-    r2 = conv(r1, CONV_SIZE, (3, 3))
-    r3 = conv(r2, CONV_SIZE, (3, 3))
-
-    m1 = kl.MaxPooling2D((3, 3))(r3)
-
-    r4 = conv(m1, CONV_SIZE, (1, 1))
-    r5 = conv(r4, CONV_SIZE, (3, 3))
-
-    return kl.MaxPooling2D((3, 3))(r5)
-
-def generate_type_a(i):
-    a11 = conv(i, CONV_SIZE, (1, 1))
-
-    a21 = avg(i, (3, 3))
-    a22 = conv(a21, CONV_SIZE, (1, 1))
-
-    a31 = conv(i, CONV_SIZE, (1, 1))
-    a32 = conv(a31, CONV_SIZE, (3, 3))
-
-    a41 = conv(i, CONV_SIZE, (1, 1))
-    a42 = conv(a41, CONV_SIZE, (3, 3))
-    a43 = conv(a42, CONV_SIZE, (3, 3))
-
-    return kl.Concatenate()([a11, a22, a32, a43])
-
-def generate_ab_bridge(i):
-    ab11 = conv(i, CONV_SIZE, (1, 1))
-    ab12 = conv(ab11, CONV_SIZE, (3, 3))
-    ab13 = conv(ab12, CONV_SIZE, (3, 3))
-
-    ab21 = conv(i, CONV_SIZE, (3, 3))
-
-    ab31 = max(i, (3, 3))
-
-    return kl.Concatenate()([ab13, ab21, ab31])
-
-def generate_type_b(i):
-    b11 = conv(i, CONV_SIZE, (1, 1))
-    b12 = conv(b11, CONV_SIZE, (7, 1))
-    b13 = conv(b12, CONV_SIZE, (1, 7))
-    b14 = conv(b13, CONV_SIZE, (7, 1))
-    b15 = conv(b14, CONV_SIZE, (1, 7))
-
-    b21 = conv(i, CONV_SIZE, (1, 1))
-    b22 = conv(b21, CONV_SIZE, (1, 7))
-    b23 = conv(b22, CONV_SIZE, (7, 1))
-
-    b31 = conv(i, CONV_SIZE, (1, 1))
-
-    b41 = avg(i, (3, 3))
-    b42 = conv(b41, CONV_SIZE, (1, 1))
-
-    return kl.Concatenate()([b15, b23, b31, b42])
-
-def generate_bc_bridge(i):
-    bc11 = conv(i, CONV_SIZE, (1, 1))
-    bc12 = conv(bc11, CONV_SIZE, (7, 1))
-    bc13 = conv(bc12, CONV_SIZE, (1, 7))
-    bc14 = conv(bc13, CONV_SIZE, (3, 3))
-
-    bc21 = conv(i, CONV_SIZE, (1, 1))
-    bc22 = conv(bc21, CONV_SIZE, (1, 1))
-
-    bc31 = max(i, (3, 3))
-
-    return kl.Concatenate()([bc14, bc22, bc31])
-
-def generate_aux(i):
-    u1 = kl.AveragePooling2D((5, 5))(i)
-    u2 = conv(u1, CONV_SIZE, (1, 1))
-    u3 = kl.Flatten()(u2)
-    u4 = kl.Dropout(DRPT_RATE)(u3)
-    u5 = kl.Dense(MPPT_SIZE)(u4)
-    u6 = kl.Dense(TOUT_AMNT)(u5)
-
-    return kl.Activation('softmax')(u6)
-
-def generate_type_c(i):
-    c11 = conv(i, CONV_SIZE, (1, 1))
-    c12 = conv(c11, CONV_SIZE, (3, 3))
-    c13 = conv(c12, CONV_SIZE, (1, 3))
-    c14 = conv(c12, CONV_SIZE, (3, 1))
-
-    c21 = conv(i, CONV_SIZE, (1, 1))
-    c22 = conv(c21, CONV_SIZE, (1, 3))
-    c23 = conv(c21, CONV_SIZE, (3, 1))
-
-    c31 = max(i, (3, 3))
-    c32 = conv(c31, CONV_SIZE, (1, 1))
-
-    c41 = conv(i, CONV_SIZE, (1, 1))
-
-    return kl.Concatenate()([c14, c23, c32, c41])
-
-def generate_finish(i):
-    f1 = kl.AveragePooling2D((5, 5))(i)
-    f2 = conv(f1, CONV_SIZE, (1, 1))
-    f3 = kl.Flatten()(f2)
-    f4 = kl.Dropout(DRPT_RATE)(f3)
-    f5 = kl.Dense(MPPT_SIZE)(f4)
-    f6 = kl.Dense(TOUT_AMNT)(f5)
-
-    return kl.Activation('softmax')(f6)
-
-
-gi = kl.Input((300, 300, 3))
-
-ds = generate_start(gi)
-
-for _ in range(3):
-    ds = generate_type_a(ds)
-
-ds = generate_ab_bridge(ds)
-
-for _ in range(4):
-    ds = generate_type_b(ds)
-
-uo = generate_aux(ds)
-
-go = generate_bc_bridge(ds)
-
-for _ in range(2):
-    go = generate_type_c(go)
-
-go = generate_finish(go)
-
-mod = k.Model(inputs = gi, outputs = go)
-
-mod.summary()
-mod.compile(optimizer = ko.Lion(learning_rate = 0.0001),
-            loss = 'categorical_crossentropy',
-            metrics = ['accuracy'])

stats-cm.py

@@ -1,47 +0,0 @@
-from m import *
-
-from matplotlib import pyplot as plt
-
-def __prep_conf_matr(data):
-    output_matrix = np.zeros([2, 2])
-
-    for p, r in zip(*data):
-        print(p, r)
-        output_matrix[np.argmax(p)][np.argmax(r)] += 1
-
-    return output_matrix
-
-
-def __plot_conf_matr(data):
-    matr = __prep_conf_matr(data)
-
-    _, ax = plt.subplots()
-
-    ax.matshow(matr, cmap = plt.cm.Blues)
-
-    for i, x in enumerate(matr):
-        for j, y in enumerate(x):
-            ax.text(i,
-                    j,
-                    str(round(y)),
-                    va = "center",
-                    ha = "center")
-
-    plt.show()
-
-ds = tf.keras.preprocessing.image_dataset_from_directory(
-    '../dataset-orig-aug-1-mini-1/',
-    labels = 'inferred',
-    label_mode = 'categorical',
-    color_mode = 'rgb',
-    image_size = (300, 300),
-    batch_size = 32,
-    verbose = True
-)
-
-#ds_short = ds.take(1)
-
-p = mod.predict(ds)
-r = np.concatenate([y for x, y in ds])
-
-__plot_conf_matr([p, r])

stats-generic.py

@@ -1,20 +0,0 @@
-from m import *
-
-from matplotlib import pyplot as plt
-
-ds = tf.keras.preprocessing.image_dataset_from_directory(
-    '../dataset-orig-aug-1-mini-1/',
-    labels = 'inferred',
-    label_mode = 'categorical',
-    color_mode = 'rgb',
-    image_size = (300, 300),
-    batch_size = 32,
-    verbose = True
-)
-
-p = mod.predict(ds)
-r = np.concatenate([y for x, y in ds])
-
-with open('results.txt', 'w') as f:
-    for i in zip(p, r):
-        f.write(f"{i[0][0]} {i[0][1]} {i[1][0]} {i[1][1]}\n")

stats-other.py

@@ -1,30 +0,0 @@
-from m import *
-from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score
-
-
-ds = tf.keras.preprocessing.image_dataset_from_directory(
-    '../dataset-orig-aug-1-mini-1/',
-    labels = 'inferred',
-    label_mode = 'categorical',
-    color_mode = 'rgb',
-    image_size = (300, 300),
-    batch_size = 32,
-    verbose = True
-)
-
-p = []
-r = []
-
-with open('results.txt', 'r') as f:
-    for i in f.read().split('\n'):
-        if i == '':
-            continue
-        
-        res = tuple(map(float, i.split()))
-        p.append([res[0] > 0.5, res[1] > 0.5])
-        r.append([res[2], res[3]])
-
-print(f"Accuracy  : {accuracy_score(p, r)}")
-print(f"Precision : {precision_score(p, r, average = 'micro')}")
-print(f"Recall    : {recall_score(p, r, average = 'micro')}")
-print(f"F1 Score  : {f1_score(p, r, average = 'micro')}")

stats-plot-1.py

@@ -1,13 +0,0 @@
-from matplotlib import pyplot as plt
-
-ta = [0.5396, 0.6266, 0.7137, 0.6768, 0.7114, 0.7821, 0.7373, 0.6866, 0.5987, 0.6861, 0.7236, 0.7147]
-va = [0.4273, 0.4714, 0.4273, 0.4229, 0.4317, 0.4229, 0.5022, 0.4449, 0.5947, 0.7489, 0.5815, 0.7048]
-
-x = [i+1 for i in range(12)]
-
-plt.plot(x, ta)
-plt.plot(x, va)
-
-plt.legend(["train_acc", "valid_acc"])
-
-plt.show()

test.py

@@ -0,0 +1,116 @@
+import tensorflow as tf
+import numpy as np
+
+from tensorflow.keras import layers as kl
+from tensorflow.keras import models as km
+from tensorflow.keras import optimizers as ko
+
+def generate_inception_model(a = 5, b = 4, c = 2):
+    # start
+    i = kl.Input(shape = (300, 300, 3))
+
+    r1 = kl.Conv2D(1024, (3, 3), padding = 'same', activation = 'relu')(i)
+    r2 = kl.Conv2D(1024, (5, 5), padding = 'same', activation = 'relu')(r1)
+    r3 = kl.Conv2D(1024, (7, 7), padding = 'same', activation = 'relu')(r2)
+
+    m1 = kl.MaxPooling2D((3, 3))(r3)
+
+    r4 = kl.Conv2D(1024, (3, 3), padding = 'same', activation = 'relu')(m1)
+    r5 = kl.Conv2D(1024, (5, 5), padding = 'same', activation = 'relu')(r4)
+
+    ia0 = kl.MaxPooling2D((2, 2))(r5)
+
+    # a types
+    for k in range(a):
+        exec(f"ia{k}_1_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(ia{k})")
+
+        exec(f"ia{k}_3_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(ia{k})")
+        exec(f"ia{k}_3_2 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(ia{k}_3_1)")
+
+        exec(f"ia{k}_5_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(ia{k})")
+        exec(f"ia{k}_5_2 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(ia{k}_5_1)")
+        exec(f"ia{k}_5_3 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(ia{k}_5_2)")
+
+        exec(f"ia{k}_7_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(ia{k})")
+        exec(f"ia{k}_7_2 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(ia{k}_7_1)")
+        exec(f"ia{k}_7_3 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(ia{k}_7_2)")
+        exec(f"ia{k}_7_4 = kl.Conv2D(1024, (7, 1), padding = 'same', activation = 'relu')(ia{k}_7_3)")
+
+        exec(f"ia{k+1} = kl.Concatenate()([ia{k}_1_1, ia{k}_3_2, ia{k}_5_3, ia{k}_7_4])")
+
+    # grid size reductor 1
+    iab_1 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(eval(f"ia{a}"))
+
+    iab_2 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(eval(f"ia{a}"))
+    iab_3 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(iab_2)
+    iab_4 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(iab_3)
+
+    iab_6 = kl.Concatenate()([iab_1, iab_4, iab_5])
+
+    # b types
+    for k in range(b):
+        exec(f"ib{k}_1_1 = kl.MaxPooling2D((2, 2), padding = 'same')(iab_6)")
+        exec(f"ib{k}_1_2 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(i)")
+
+        exec(f"ib{k}_3_1 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(iab_6)")
+
+        exec(f"ib{k}_5_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(iab_6)")
+        exec(f"ib{k}_5_2 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(ib{k}_5_1)")
+        exec(f"ib{k}_5_3 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(ib{k}_5_2)")
+
+        exec(f"ib{k}_7_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(iab_6)")
+        exec(f"ib{k}_7_2 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(ib{k}_7_1)")
+        exec(f"ib{k}_7_3 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(ib{k}_7_2)")
+        exec(f"ib{k}_7_4 = kl.Conv2D(1024, (7, 1), padding = 'same', activation = 'relu')(ib{k}_7_3)")
+
+        exec(f"ib{k+1} = kl.Concatenate()([ib{k}_1_2, ib{k}_3_2, ib{k}_5_3, ib{k}_7_4])")
+
+    # grid size reductor 2
+
+    # c types
+    for k in range(c):
+        exec(f"ic{k}_1_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(i{'b' if k else 'a'}{k if k else a})")
+
+        exec(f"ic{k}_3_1 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(i{'b' if k else 'a'}{k if k else a})")
+
+        exec(f"ic{k}_5_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(i{'b' if k else 'a'}{k if k else a})")
+        exec(f"ic{k}_5_2 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(ib{k}_5_1)")
+        exec(f"ic{k}_5_3 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(ib{k}_5_2)")
+
+        exec(f"ic{k}_7_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(i{'b' if k else 'a'}{k if k else a})")
+        exec(f"ic{k}_7_2 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(ib{k}_7_1)")
+        exec(f"ic{k}_7_3 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(ib{k}_7_2)")
+        exec(f"ic{k}_7_4 = kl.Conv2D(1024, (7, 1), padding = 'same', activation = 'relu')(ib{k}_7_3)")
+
+        exec(f"ib{k+1} = kl.Concatenate()([ib{k}_1_1, ib{k}_3_2, ib{k}_5_3, ib{k}_7_4])")
+
+    # mppt
+
+    o = eval(f"ic{c}")
+    return tf.keras.Model(inputs = i, outputs = o)
+
+
+'''
+a_i = l.Input(shape = (300, 300, 3))
+
+a_1_1 = l.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(a_i)
+
+a_3_1 = l.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(a_i)
+a_3_2 = l.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(a_3_1)
+
+a_5_1 = l.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(a_i)
+a_5_2 = l.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(a_5_1)
+
+a_7_1 = l.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(a_i)
+a_7_2 = l.Conv2D(1024, (7, 1), padding = 'same', activation = 'relu')(a_7_1)
+
+a_o = l.Concatenate()([a_1_1, a_3_2, a_5_2, a_7_2])
+
+inception_type_a = [a_i, a_o]
+
+tf.keras.Model(*inception_type_a)
+'''
+
+mod = generate_inception_model()
+mod.summary()
+mod.compile(optimizer = ko.Lion(learning_rate = 0.001))

train_l.py

@@ -1,15 +0,0 @@
-import tensorflow as tf
-
-ds_train, ds_valid = tf.keras.preprocessing.image_dataset_from_directory(
-    '../dataset-orig',
-    labels = 'inferred',
-    label_mode = 'categorical',
-    color_mode = 'rgb',
-    image_size = (300, 300),
-    shuffle = False,
-    validation_split = 0.05,
-    subset = 'both',
-    verbose = True
-)
-
-from m import mod

train_l_v2.py

@@ -1,25 +0,0 @@
-import tensorflow as tf
-
-ds_train, ds_valid = tf.keras.preprocessing.image_dataset_from_directory(
-    '/mnt/tmpfs1/ds-mini-1',
-    labels = 'inferred',
-    label_mode = 'categorical',
-    color_mode = 'rgb',
-    batch_size = 16,
-    image_size = (300, 300),
-    shuffle = False,
-    validation_split = 0.05,
-    subset = 'both',
-    verbose = True
-)
-
-from m import *
-
-ckpt = kc.ModelCheckpoint("model3.model.keras",
-                          monitor = 'val_accuracy',
-                          save_best_only = True)
-
-h = mod.fit(ds_train,
-            epochs = 9,
-            validation_data = ds_valid,
-            callbacks = [ckpt])