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61669ea702
...
2523df5a50
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@ -1,178 +1,3 @@
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assets/*
|
||||
*.jpg
|
||||
beerds.json
|
||||
|
||||
# Byte-compiled / optimized / DLL files
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
*$py.class
|
||||
|
||||
# C extensions
|
||||
*.so
|
||||
|
||||
# Distribution / packaging
|
||||
.Python
|
||||
build/
|
||||
develop-eggs/
|
||||
dist/
|
||||
downloads/
|
||||
eggs/
|
||||
.eggs/
|
||||
lib/
|
||||
lib64/
|
||||
parts/
|
||||
sdist/
|
||||
var/
|
||||
wheels/
|
||||
share/python-wheels/
|
||||
*.egg-info/
|
||||
.installed.cfg
|
||||
*.egg
|
||||
MANIFEST
|
||||
|
||||
# PyInstaller
|
||||
# Usually these files are written by a python script from a template
|
||||
# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
||||
*.manifest
|
||||
*.spec
|
||||
|
||||
# Installer logs
|
||||
pip-log.txt
|
||||
pip-delete-this-directory.txt
|
||||
|
||||
# Unit test / coverage reports
|
||||
htmlcov/
|
||||
.tox/
|
||||
.nox/
|
||||
.coverage
|
||||
.coverage.*
|
||||
.cache
|
||||
nosetests.xml
|
||||
coverage.xml
|
||||
*.cover
|
||||
*.py,cover
|
||||
.hypothesis/
|
||||
.pytest_cache/
|
||||
cover/
|
||||
|
||||
# Translations
|
||||
*.mo
|
||||
*.pot
|
||||
|
||||
# Django stuff:
|
||||
*.log
|
||||
local_settings.py
|
||||
db.sqlite3
|
||||
db.sqlite3-journal
|
||||
|
||||
# Flask stuff:
|
||||
instance/
|
||||
.webassets-cache
|
||||
|
||||
# Scrapy stuff:
|
||||
.scrapy
|
||||
|
||||
# Sphinx documentation
|
||||
docs/_build/
|
||||
|
||||
# PyBuilder
|
||||
.pybuilder/
|
||||
target/
|
||||
|
||||
# Jupyter Notebook
|
||||
.ipynb_checkpoints
|
||||
|
||||
# IPython
|
||||
profile_default/
|
||||
ipython_config.py
|
||||
|
||||
# pyenv
|
||||
# For a library or package, you might want to ignore these files since the code is
|
||||
# intended to run in multiple environments; otherwise, check them in:
|
||||
# .python-version
|
||||
|
||||
# pipenv
|
||||
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
|
||||
# However, in case of collaboration, if having platform-specific dependencies or dependencies
|
||||
# having no cross-platform support, pipenv may install dependencies that don't work, or not
|
||||
# install all needed dependencies.
|
||||
#Pipfile.lock
|
||||
|
||||
# UV
|
||||
# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
|
||||
# This is especially recommended for binary packages to ensure reproducibility, and is more
|
||||
# commonly ignored for libraries.
|
||||
#uv.lock
|
||||
|
||||
# poetry
|
||||
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
|
||||
# This is especially recommended for binary packages to ensure reproducibility, and is more
|
||||
# commonly ignored for libraries.
|
||||
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
|
||||
#poetry.lock
|
||||
|
||||
# pdm
|
||||
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
|
||||
#pdm.lock
|
||||
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
|
||||
# in version control.
|
||||
# https://pdm.fming.dev/latest/usage/project/#working-with-version-control
|
||||
.pdm.toml
|
||||
.pdm-python
|
||||
.pdm-build/
|
||||
|
||||
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
|
||||
__pypackages__/
|
||||
|
||||
# Celery stuff
|
||||
celerybeat-schedule
|
||||
celerybeat.pid
|
||||
|
||||
# SageMath parsed files
|
||||
*.sage.py
|
||||
|
||||
# Environments
|
||||
.env
|
||||
.venv
|
||||
env/
|
||||
venv/
|
||||
ENV/
|
||||
env.bak/
|
||||
venv.bak/
|
||||
|
||||
# Spyder project settings
|
||||
.spyderproject
|
||||
.spyproject
|
||||
|
||||
# Rope project settings
|
||||
.ropeproject
|
||||
|
||||
# mkdocs documentation
|
||||
/site
|
||||
|
||||
# mypy
|
||||
.mypy_cache/
|
||||
.dmypy.json
|
||||
dmypy.json
|
||||
|
||||
# Pyre type checker
|
||||
.pyre/
|
||||
|
||||
# pytype static type analyzer
|
||||
.pytype/
|
||||
|
||||
# Cython debug symbols
|
||||
cython_debug/
|
||||
|
||||
# PyCharm
|
||||
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
|
||||
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
|
||||
# and can be added to the global gitignore or merged into this file. For a more nuclear
|
||||
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
|
||||
#.idea/
|
||||
|
||||
# Ruff stuff:
|
||||
.ruff_cache/
|
||||
|
||||
# PyPI configuration file
|
||||
.pypirc
|
||||
|
|
@ -1 +0,0 @@
|
|||
3.11
|
||||
12
Makefile
12
Makefile
|
|
@ -1,12 +0,0 @@
|
|||
api:
|
||||
uv run granian --interface asgi server.main:app
|
||||
|
||||
runml:
|
||||
uv run ml/beerds.py
|
||||
|
||||
format:
|
||||
uv run ruff format app
|
||||
|
||||
lint:
|
||||
uv run mypy ./ --explicit-package-bases;
|
||||
ruff check --fix
|
||||
|
|
@ -1,5 +0,0 @@
|
|||
Нужно установить драйвер Nvindia + Cuda
|
||||
|
||||
```
|
||||
sudo apt install nvidia-cuda-toolkit
|
||||
```
|
||||
|
|
@ -1,3 +0,0 @@
|
|||
Можно скачать отсюда блог и перевести
|
||||
|
||||
https://dogtime.com/dog-breeds/afador
|
||||
Binary file not shown.
Binary file not shown.
|
|
@ -0,0 +1,107 @@
|
|||
|
||||
import os
|
||||
import random
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import tensorflow as tf
|
||||
from tensorflow import keras
|
||||
from tensorflow.keras import layers
|
||||
from tensorflow.keras.utils import image_dataset_from_directory, split_dataset
|
||||
|
||||
|
||||
img_size = (200, 200)
|
||||
|
||||
|
||||
# обогащение выборки
|
||||
data_augmentation = keras.Sequential(
|
||||
[
|
||||
layers.RandomFlip("horizontal"),
|
||||
layers.RandomRotation(0.1),
|
||||
layers.RandomZoom(0.2),
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
|
||||
input_dir = "assets/dog"
|
||||
|
||||
labels_dict = {}
|
||||
for fname in os.listdir(input_dir):
|
||||
if fname in labels_dict:
|
||||
continue
|
||||
labels_dict[fname] = len(labels_dict)
|
||||
|
||||
model_name = "beerd_25_04_2023.keras"
|
||||
train_dataset, val_ds = image_dataset_from_directory(
|
||||
input_dir,
|
||||
labels="inferred",
|
||||
label_mode="categorical",
|
||||
class_names=None,
|
||||
color_mode="rgb",
|
||||
batch_size=32,
|
||||
seed=12,
|
||||
image_size=img_size,
|
||||
shuffle=True,
|
||||
validation_split=0.1,
|
||||
subset="both",
|
||||
interpolation="bilinear",
|
||||
follow_links=False,
|
||||
crop_to_aspect_ratio=False
|
||||
)
|
||||
|
||||
validation_dataset, test_dataset = split_dataset(val_ds, left_size=0.8)
|
||||
|
||||
inputs = keras.Input(shape=img_size + (3,))
|
||||
x = data_augmentation(inputs)
|
||||
x = layers.Rescaling(1./255)(x)
|
||||
x = layers.Conv2D(filters=32, kernel_size=5, use_bias=False)(x)
|
||||
for size in [32, 64, 128, 256, 512, 1024]:
|
||||
residual = x
|
||||
x = layers.BatchNormalization()(x)
|
||||
x = layers.Activation("relu")(x)
|
||||
x = layers.SeparableConv2D(size, 3, padding="same", use_bias=False)(x)
|
||||
x = layers.BatchNormalization()(x)
|
||||
x = layers.Activation("relu")(x)
|
||||
x = layers.SeparableConv2D(size, 3, padding="same", use_bias=False)(x)
|
||||
x = layers.MaxPooling2D(3, strides=2, padding="same")(x)
|
||||
residual = layers.Conv2D(
|
||||
size, 1, strides=2, padding="same", use_bias=False)(residual)
|
||||
x = layers.add([x, residual])
|
||||
|
||||
x = layers.GlobalAveragePooling2D()(x)
|
||||
x = layers.Dropout(0.5)(x)
|
||||
outputs = layers.Dense(len(labels_dict), activation="softmax")(x)
|
||||
model = keras.Model(inputs, outputs)
|
||||
|
||||
model.compile(optimizer="rmsprop",
|
||||
loss="categorical_crossentropy", metrics=['accuracy'])
|
||||
callbacks = [
|
||||
keras.callbacks.ModelCheckpoint(model_name,
|
||||
save_best_only=True)
|
||||
]
|
||||
history = model.fit(train_dataset,
|
||||
epochs=200,
|
||||
callbacks=callbacks,
|
||||
validation_data=validation_dataset,)
|
||||
|
||||
epochs = range(1, len(history.history["loss"]) + 1)
|
||||
loss = history.history["loss"]
|
||||
val_loss = history.history["val_loss"]
|
||||
acc = history.history["accuracy"]
|
||||
val_acc = history.history["val_accuracy"]
|
||||
plt.plot(epochs, acc, "bo", label="Точность на этапе обучения")
|
||||
plt.plot(epochs, val_acc, "b", label="Точность на этапе проверки")
|
||||
plt.title("Точность на этапах обучения и проверки")
|
||||
plt.legend()
|
||||
plt.figure()
|
||||
plt.plot(epochs, loss, "bo", label="Потери на этапе обучения")
|
||||
plt.plot(epochs, val_loss, "b", label="Потери на этапе проверки")
|
||||
plt.title("Потери на этапах обучения и проверки")
|
||||
plt.legend()
|
||||
plt.show()
|
||||
|
||||
|
||||
test_model = keras.models.load_model(model_name)
|
||||
test_loss, test_acc = test_model.evaluate(test_dataset)
|
||||
print(f"Test accuracy: {test_acc:.3f}")
|
||||
|
|
@ -0,0 +1,100 @@
|
|||
|
||||
import os
|
||||
import random
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import tensorflow as tf
|
||||
from tensorflow import keras
|
||||
from tensorflow.keras import layers
|
||||
from tensorflow.keras.utils import image_dataset_from_directory, split_dataset
|
||||
|
||||
|
||||
img_size = (180, 180)
|
||||
|
||||
conv_base = keras.applications.vgg16.VGG16(
|
||||
weights="imagenet",
|
||||
include_top=False,
|
||||
input_shape=(180, 180, 3))
|
||||
conv_base.trainable = False
|
||||
|
||||
|
||||
# обогащение выборки
|
||||
data_augmentation = keras.Sequential(
|
||||
[
|
||||
layers.RandomFlip("horizontal"),
|
||||
layers.RandomRotation(0.1),
|
||||
layers.RandomZoom(0.2),
|
||||
])
|
||||
|
||||
|
||||
|
||||
input_dir = "assets/dog"
|
||||
|
||||
labels_dict = {}
|
||||
for fname in os.listdir(input_dir):
|
||||
if fname in labels_dict:
|
||||
continue
|
||||
labels_dict[fname] = len(labels_dict)
|
||||
|
||||
model_name = "beerd_imagenet_25_04_2023.keras"
|
||||
train_dataset, val_ds = image_dataset_from_directory(
|
||||
input_dir,
|
||||
labels="inferred",
|
||||
label_mode="categorical",
|
||||
class_names=None,
|
||||
color_mode="rgb",
|
||||
batch_size=32,
|
||||
seed=12,
|
||||
image_size=img_size,
|
||||
shuffle=True,
|
||||
validation_split=0.1,
|
||||
subset="both",
|
||||
interpolation="bilinear",
|
||||
follow_links=False,
|
||||
crop_to_aspect_ratio=False
|
||||
)
|
||||
|
||||
validation_dataset, test_dataset = split_dataset(val_ds, left_size=0.8)
|
||||
|
||||
inputs = keras.Input(shape=(180, 180, 3))
|
||||
x = data_augmentation(inputs)
|
||||
x = keras.applications.vgg16.preprocess_input(x)
|
||||
x = conv_base(x)
|
||||
x = layers.Flatten()(x)
|
||||
x = layers.Dense(512)(x)
|
||||
x = layers.Dropout(0.5)(x)
|
||||
outputs = layers.Dense(len(labels_dict), activation="softmax")(x)
|
||||
model = keras.Model(inputs, outputs)
|
||||
|
||||
model.compile(optimizer="rmsprop",
|
||||
loss="categorical_crossentropy", metrics=['accuracy'])
|
||||
callbacks = [
|
||||
keras.callbacks.ModelCheckpoint(model_name,
|
||||
save_best_only=True)
|
||||
]
|
||||
history = model.fit(train_dataset,
|
||||
epochs=100,
|
||||
callbacks=callbacks,
|
||||
validation_data=validation_dataset,)
|
||||
|
||||
epochs = range(1, len(history.history["loss"]) + 1)
|
||||
loss = history.history["loss"]
|
||||
val_loss = history.history["val_loss"]
|
||||
acc = history.history["accuracy"]
|
||||
val_acc = history.history["val_accuracy"]
|
||||
plt.plot(epochs, acc, "bo", label="Точность на этапе обучения")
|
||||
plt.plot(epochs, val_acc, "b", label="Точность на этапе проверки")
|
||||
plt.title("Точность на этапах обучения и проверки")
|
||||
plt.legend()
|
||||
plt.figure()
|
||||
plt.plot(epochs, loss, "bo", label="Потери на этапе обучения")
|
||||
plt.plot(epochs, val_loss, "b", label="Потери на этапе проверки")
|
||||
plt.title("Потери на этапах обучения и проверки")
|
||||
plt.legend()
|
||||
plt.show()
|
||||
|
||||
|
||||
test_model = keras.models.load_model(model_name)
|
||||
test_loss, test_acc = test_model.evaluate(test_dataset)
|
||||
print(f"Test accuracy: {test_acc:.3f}")
|
||||
|
|
@ -0,0 +1,44 @@
|
|||
|
||||
import os
|
||||
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
|
||||
import json
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import tensorflow as tf
|
||||
from tensorflow import keras
|
||||
from tensorflow.keras import layers
|
||||
from tensorflow.keras.utils import load_img, img_to_array
|
||||
from tensorflow.keras.utils import image_dataset_from_directory
|
||||
|
||||
|
||||
# model_name = "beerd_25_04_2023.keras"
|
||||
model_name = "beerd_imagenet_25_04_2023.keras"
|
||||
img = load_img("photo_2023-04-25_10-02-25.jpg", color_mode="rgb")
|
||||
img = tf.image.resize(img, (180, 180, ), "bilinear")
|
||||
img_array = img_to_array(img)
|
||||
|
||||
test_model = keras.models.load_model(model_name)
|
||||
test_loss = test_model.predict(np.expand_dims(img_array, 0))
|
||||
|
||||
|
||||
list_labels = [fname for fname in os.listdir("assets/dog")]
|
||||
list_labels.sort()
|
||||
dict_names = {}
|
||||
for i, label in enumerate(list_labels):
|
||||
dict_names[i] = label
|
||||
|
||||
with open("beerds.json", "w") as f:
|
||||
f.write(json.dumps(dict_names))
|
||||
|
||||
max_val = 0
|
||||
max_num = 0
|
||||
for i, val in enumerate(test_loss[0]):
|
||||
if val < max_val:
|
||||
continue
|
||||
max_val = val
|
||||
max_num = i
|
||||
|
||||
print("-----------------------")
|
||||
print(list_labels)
|
||||
print(test_loss)
|
||||
print(max_num, max_val, dict_names[max_num])
|
||||
165
ml/dogs.py
165
ml/dogs.py
|
|
@ -1,165 +0,0 @@
|
|||
import os
|
||||
import matplotlib.pyplot as plt
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torchvision.datasets import ImageFolder # type: ignore
|
||||
from torch.utils.data import Dataset, DataLoader, random_split
|
||||
from torchvision import transforms # type: ignore
|
||||
import torchvision
|
||||
from typing import Tuple
|
||||
|
||||
# Настройка устройства для вычислений
|
||||
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
print(f"Using device: {DEVICE}")
|
||||
IMG_SIZE = (200, 200)
|
||||
INPUT_DIR = "assets/dog"
|
||||
NUM_EPOCHS = 90
|
||||
|
||||
def get_labels(input_dir, img_size):
|
||||
# Преобразования изображений
|
||||
transform = transforms.Compose([
|
||||
transforms.Resize(img_size),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
|
||||
])
|
||||
dataset = ImageFolder(root=input_dir, transform=transform)
|
||||
|
||||
# Создание labels_dict для соответствия классов и индексов
|
||||
labels_dict = {idx: class_name for idx, class_name in enumerate(dataset.classes)}
|
||||
return labels_dict, dataset
|
||||
|
||||
def get_loaders(dataset: Dataset) -> Tuple[DataLoader, DataLoader]:
|
||||
# Разделение данных на тренировочные и валидационные
|
||||
train_size = int(0.8 * len(dataset))
|
||||
val_size = len(dataset) - train_size
|
||||
train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
|
||||
|
||||
# Загрузчики данных
|
||||
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
|
||||
val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)
|
||||
return train_loader, val_loader
|
||||
|
||||
|
||||
def load_model(model_path: str, device: str = 'cuda') -> nn.Module:
|
||||
if not os.path.isfile(model_path):
|
||||
print("Start new model")
|
||||
model = torchvision.models.resnet50(pretrained=True)
|
||||
model.fc = torch.nn.Linear(model.fc.in_features, len(labels_dict))
|
||||
return model
|
||||
model = torch.load(model_path, map_location=device, weights_only=False)
|
||||
model.eval()
|
||||
return model
|
||||
|
||||
def train(num_epochs: int, model: nn.Module, train_loader: DataLoader, val_loader: DataLoader) -> Tuple[list[float], list[float], list[float], list[float]]:
|
||||
criterion = torch.nn.CrossEntropyLoss()
|
||||
optimizer = torch.optim.Adam(model.fc.parameters(), lr=0.001, weight_decay=0.001)
|
||||
# История метрик
|
||||
train_loss_history = []
|
||||
train_acc_history = []
|
||||
val_loss_history = []
|
||||
val_acc_history = []
|
||||
# Обучение с проверкой и сохранением лучшей модели
|
||||
best_val_loss = float('inf')
|
||||
for epoch in range(num_epochs):
|
||||
model.train()
|
||||
running_loss = 0.0
|
||||
correct = 0
|
||||
total = 0
|
||||
|
||||
# Обучение на тренировочных данных
|
||||
for inputs, labels in train_loader:
|
||||
inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
|
||||
|
||||
optimizer.zero_grad()
|
||||
outputs = model(inputs)
|
||||
loss = criterion(outputs, labels)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
running_loss += loss.item()
|
||||
_, predicted = outputs.max(1)
|
||||
total += labels.size(0)
|
||||
correct += predicted.eq(labels).sum().item()
|
||||
|
||||
train_loss = running_loss / len(train_loader)
|
||||
train_acc = 100. * correct / total
|
||||
train_loss_history.append(train_loss)
|
||||
train_acc_history.append(train_acc)
|
||||
print(f"Epoch {epoch+1}/{num_epochs}, Train Loss: {train_loss:.4f}, Train Accuracy: {train_acc:.2f}%")
|
||||
|
||||
# Оценка на валидационных данных
|
||||
model.eval()
|
||||
val_loss = 0.0
|
||||
correct = 0
|
||||
total = 0
|
||||
with torch.no_grad():
|
||||
for inputs, labels in val_loader:
|
||||
inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
|
||||
outputs = model(inputs)
|
||||
loss = criterion(outputs, labels)
|
||||
val_loss += loss.item()
|
||||
_, predicted = outputs.max(1)
|
||||
total += labels.size(0)
|
||||
correct += predicted.eq(labels).sum().item()
|
||||
|
||||
val_loss /= len(val_loader)
|
||||
val_acc = 100. * correct / total
|
||||
val_loss_history.append(val_loss)
|
||||
val_acc_history.append(val_acc)
|
||||
if val_loss < best_val_loss:
|
||||
best_val_loss = val_loss
|
||||
print("save model")
|
||||
torch.save(model, "full_model.pth")
|
||||
print(f"Validation Loss: {val_loss:.4f}, Validation Accuracy: {val_acc:.2f}%")
|
||||
return val_acc_history, train_acc_history, val_loss_history, train_loss_history
|
||||
|
||||
|
||||
def show(num_epochs: int,
|
||||
val_acc_history: list[float],
|
||||
train_acc_history: list[float],
|
||||
val_loss_history: list[float],
|
||||
train_loss_history: list[float]):
|
||||
|
||||
# Построение графиков
|
||||
epochs = range(1, num_epochs + 1)
|
||||
|
||||
# График точности
|
||||
plt.figure(figsize=(10, 5))
|
||||
plt.plot(epochs, train_acc_history, "bo-", label="Точность на обучении")
|
||||
plt.plot(epochs, val_acc_history, "ro-", label="Точность на валидации")
|
||||
plt.title("Точность на этапах обучения и проверки")
|
||||
plt.xlabel("Эпохи")
|
||||
plt.ylabel("Точность (%)")
|
||||
plt.legend()
|
||||
plt.grid()
|
||||
plt.show()
|
||||
|
||||
# График потерь
|
||||
plt.figure(figsize=(10, 5))
|
||||
plt.plot(epochs, train_loss_history, "bo-", label="Потери на обучении")
|
||||
plt.plot(epochs, val_loss_history, "ro-", label="Потери на валидации")
|
||||
plt.title("Потери на этапах обучения и проверки")
|
||||
plt.xlabel("Эпохи")
|
||||
plt.ylabel("Потери")
|
||||
plt.legend()
|
||||
plt.grid()
|
||||
plt.show()
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Инициализация данных и модели
|
||||
labels_dict: dict[int, str]
|
||||
dataset: ImageFolder
|
||||
labels_dict, dataset = get_labels(INPUT_DIR, IMG_SIZE)
|
||||
|
||||
model: nn.Module = load_model("full_model.pth").to(DEVICE)
|
||||
|
||||
# Подготовка данных
|
||||
train_loader: DataLoader
|
||||
val_loader: DataLoader
|
||||
train_loader, val_loader = get_loaders(dataset)
|
||||
|
||||
# Обучение модели
|
||||
val_acc_history, train_acc_history, val_loss_history, train_loss_history = train(NUM_EPOCHS, model, train_loader, val_loader)
|
||||
|
||||
# Визуализация результатов
|
||||
show(NUM_EPOCHS, val_acc_history, train_acc_history, val_loss_history, train_loss_history)
|
||||
|
|
@ -1,52 +0,0 @@
|
|||
|
||||
import torch
|
||||
from torchvision import transforms # type: ignore
|
||||
import torch.nn.functional as F
|
||||
from torchvision import transforms
|
||||
from PIL import Image
|
||||
import json
|
||||
|
||||
|
||||
# Создание labels_dict для соответствия классов и индексов
|
||||
with open("labels.json", "r") as f:
|
||||
data_labels = f.read()
|
||||
labels_dict = json.loads(data_labels)
|
||||
|
||||
|
||||
def load_model(model_path,device='cuda'):
|
||||
model = torch.load(model_path, map_location=device, weights_only=False)
|
||||
model.eval()
|
||||
return model
|
||||
|
||||
# Инициализация
|
||||
device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
||||
model = load_model('full_model.pth', device=device)
|
||||
|
||||
# Преобразования для изображения (адаптируйте под ваш случай)
|
||||
# Преобразования изображений
|
||||
def predict_image(image_path, model, device='cuda'):
|
||||
img_size = (200, 200)
|
||||
preprocess = transforms.Compose([
|
||||
transforms.Resize(img_size),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
|
||||
])
|
||||
image = Image.open(image_path).convert('RGB')
|
||||
input_tensor = preprocess(image)
|
||||
input_batch = input_tensor.unsqueeze(0).to(device) # Добавляем dimension для батча
|
||||
|
||||
with torch.no_grad():
|
||||
output = model(input_batch)
|
||||
|
||||
probabilities = F.softmax(output[0], dim=0)
|
||||
|
||||
_, predicted_idx = torch.max(probabilities, 0)
|
||||
return predicted_idx.item(), probabilities.cpu().numpy()
|
||||
|
||||
# Пример использования
|
||||
image_path = 'assets/test/photo_2023-04-25_10-02-25.jpg'
|
||||
predicted_idx, probabilities = predict_image(image_path, model, device)
|
||||
|
||||
# Предполагая, что labels_dict - словарь вида {индекс: 'название_класса'}
|
||||
predicted_label = labels_dict[str(predicted_idx)]
|
||||
print(f'Predicted class: {predicted_label} (prob: {probabilities[predicted_idx]:.2f})')
|
||||
|
|
@ -1,18 +0,0 @@
|
|||
[project]
|
||||
name = "ai"
|
||||
version = "0.1.0"
|
||||
description = "Add your description here"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.11"
|
||||
dependencies = [
|
||||
"granian>=2.2.4",
|
||||
"jinja2>=3.1.6",
|
||||
"matplotlib>=3.10.1",
|
||||
"mypy>=1.15.0",
|
||||
"numpy==1.23.5",
|
||||
"pyqt5>=5.15.11",
|
||||
"ruff>=0.11.5",
|
||||
"starlite>=1.51.16",
|
||||
"torch>=2.6.0",
|
||||
"torchvision>=0.21.0",
|
||||
]
|
||||
|
|
@ -4,8 +4,6 @@
|
|||
<meta charset="UTF-8">
|
||||
<meta name="yandex-verification" content="2d4efced567f0f7f" />
|
||||
<meta name="google-site-verification" content="gKPSnPZ1ULUF9amD0vw_JQqkS5GLqc937UxayaN_s-I" />
|
||||
<meta name="description" content="Опередление породы собаки по фото. Определение породы происходит при помощи нейронной сети - точность опеределения составляет 60%." />
|
||||
<link rel="icon" type="image/x-icon" href="static/favicon.ico">
|
||||
<title>Определение породы собаки по фото</title>
|
||||
<link rel="stylesheet" href="static/styles.css">
|
||||
<!-- Yandex.Metrika counter -->
|
||||
|
|
@ -28,20 +26,15 @@
|
|||
<body>
|
||||
<section id="main">
|
||||
<h1>Определить породу собаки по фото</h1>
|
||||
<p>Загрузите фото, чтобы опеределить породу собаки или щенка. Если порода смешанная (или порода определена неточно), после загрузки будет показана вероятность породы животного.</p>
|
||||
<p>Определение породы происходит при помощи нейронной сети - точность опеределения составляет 60%, сеть обучена на <a href="https://vk.com/albums-220240483" target="_blank">125 породах</a>. Если на фото будет неизвестная порода или не собака - сеть не сможет правильно опеределить, что это.</p>
|
||||
<p>Загрузите фото, чтобы опеределить породу собаки или щенка. Если порода смешанная, после загрузки будет показана вероятность породы животного.</p>
|
||||
<p>Опредление породы происходит при помощи нейронной сети - точность опеределения составляет 60%, сеть обучена на 125 породах. Если на фото будет неизвестная порода или не собака - сеть не сможет правильно опеределить, что это.</p>
|
||||
<p>Для распознования все фото отправляются на сервер, но там не сохраняются</p>
|
||||
<form enctype="multipart/form-data" method="post" action="/breeds" onsubmit="SavePhoto();return false">
|
||||
<form enctype="multipart/form-data" method="post" action="/beeds" onsubmit="SavePhoto();return false">
|
||||
<p><input type="file" name="f" id="file-input">
|
||||
<input type="submit" value="Определить"></p>
|
||||
<input type="submit" value="Отправить"></p>
|
||||
</form>
|
||||
<div>
|
||||
<div id="upload-image">
|
||||
<div id="upload-image-text"></div>
|
||||
<img id="image" style="max-width: 200px;"/>
|
||||
</div>
|
||||
<div id="result"></div>
|
||||
</div>
|
||||
<img id="image" style="max-width: 200px;"/>
|
||||
</body>
|
||||
</section>
|
||||
<script src="static/scripts.js"></script>
|
||||
127
server/main.py
127
server/main.py
|
|
@ -1,60 +1,34 @@
|
|||
|
||||
from pathlib import Path
|
||||
|
||||
from PIL import Image
|
||||
from starlite import Starlite, Controller, StaticFilesConfig, get, post, Body, MediaType, RequestEncodingType, Starlite, UploadFile, Template, TemplateConfig
|
||||
from starlite.contrib.jinja import JinjaTemplateEngine
|
||||
from sanic import Sanic
|
||||
from sanic.response import json as json_answer
|
||||
import numpy as np
|
||||
from tensorflow import keras
|
||||
from tensorflow.keras.utils import img_to_array
|
||||
import io
|
||||
import os
|
||||
import json
|
||||
import requests
|
||||
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
|
||||
|
||||
|
||||
model_name = "models/beerd_imagenet_02_05_2023.keras"
|
||||
app = Sanic("Ai")
|
||||
model_name = "../beerd_imagenet_25_04_2023.keras"
|
||||
test_model_imagenet = keras.models.load_model(model_name)
|
||||
|
||||
model_name = "./models/beerd_25_04_2023.keras"
|
||||
model_name = "../beerd_25_04_2023.keras"
|
||||
test_model = keras.models.load_model(model_name)
|
||||
|
||||
dict_names = {}
|
||||
with open("beerds.json", "r") as f:
|
||||
dict_names = json.loads(f.read())
|
||||
for key in dict_names:
|
||||
dict_names[key] = dict_names[key].replace("_", " ")
|
||||
|
||||
VK_URL = "https://api.vk.com/method/"
|
||||
TOKEN = ""
|
||||
headers = {"Authorization": f"Bearer {TOKEN}"}
|
||||
group_id = 220240483
|
||||
postfix = "?v=5.131"
|
||||
IMAGES = {}
|
||||
app.static("/", "index.html", name="main")
|
||||
app.static("/static/", "static/", name="static")
|
||||
|
||||
|
||||
def get_images():
|
||||
global IMAGES
|
||||
|
||||
r = requests.get(
|
||||
f"{VK_URL}photos.getAll{postfix}&access_token={TOKEN}&owner_id=-{group_id}&count=200")
|
||||
items = r.json().get("response").get("items")
|
||||
for item in items:
|
||||
for s in item.get("sizes"):
|
||||
if s.get("type") != "x":
|
||||
continue
|
||||
IMAGES[item.get("text")] = s.get("url")
|
||||
break
|
||||
|
||||
|
||||
get_images()
|
||||
|
||||
|
||||
class BeerdsController(Controller):
|
||||
path = "/breeds"
|
||||
|
||||
@post("/", media_type=MediaType.TEXT)
|
||||
async def beeds(self, data: UploadFile = Body(media_type=RequestEncodingType.MULTI_PART)) -> dict:
|
||||
body = await data.read()
|
||||
@app.post("/beeds")
|
||||
async def beeds(request):
|
||||
body = request.files.get("f").body
|
||||
|
||||
img = Image.open(io.BytesIO(body))
|
||||
img = img.convert('RGB')
|
||||
|
|
@ -79,76 +53,11 @@ class BeerdsController(Controller):
|
|||
if val <= 0.09:
|
||||
continue
|
||||
result_net[val] = dict_names[str(i)]
|
||||
items_one = dict(sorted(result.items(), reverse=True))
|
||||
items_two = dict(sorted(result_net.items(), reverse=True))
|
||||
images = []
|
||||
for item in items_one:
|
||||
name = items_one[item].replace("_", " ")
|
||||
if name not in IMAGES:
|
||||
continue
|
||||
images.append({"name": name, "url": IMAGES[name]})
|
||||
for item in items_two:
|
||||
name = items_two[item].replace("_", " ")
|
||||
if name not in IMAGES:
|
||||
continue
|
||||
images.append({"name": name, "url": IMAGES[name]})
|
||||
return {
|
||||
"results": items_one,
|
||||
"results_net": items_two,
|
||||
"images": images,
|
||||
}
|
||||
|
||||
return json_answer({
|
||||
"results": dict(sorted(result.items(), reverse=True)),
|
||||
"results_net": dict(sorted(result_net.items(), reverse=True)),
|
||||
})
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
class BaseController(Controller):
|
||||
path = "/"
|
||||
|
||||
@get("/")
|
||||
async def main(self) -> Template:
|
||||
return Template(name="index.html")
|
||||
|
||||
@get("/sitemap.xml", media_type=MediaType.XML)
|
||||
async def sitemaps(self) -> bytes:
|
||||
return '''<?xml version="1.0" encoding="UTF-8"?>
|
||||
<urlset
|
||||
xmlns="http://www.sitemaps.org/schemas/sitemap/0.9"
|
||||
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
|
||||
xsi:schemaLocation="http://www.sitemaps.org/schemas/sitemap/0.9
|
||||
http://www.sitemaps.org/schemas/sitemap/0.9/sitemap.xsd">
|
||||
<!-- created with Free Online Sitemap Generator www.xml-sitemaps.com -->
|
||||
|
||||
|
||||
<url>
|
||||
<loc>https://xn-----6kcp3cadbabfh8a0a.xn--p1ai/</loc>
|
||||
<lastmod>2023-05-01T19:01:03+00:00</lastmod>
|
||||
</url>
|
||||
|
||||
|
||||
</urlset>
|
||||
'''.encode()
|
||||
|
||||
|
||||
@get("/robots.txt", media_type=MediaType.TEXT)
|
||||
async def robots(self) -> str:
|
||||
return '''
|
||||
User-agent: *
|
||||
Allow: /
|
||||
|
||||
Sitemap: https://xn-----6kcp3cadbabfh8a0a.xn--p1ai/sitemap.xml
|
||||
'''
|
||||
|
||||
|
||||
app = Starlite(
|
||||
route_handlers=[BeerdsController, BaseController],
|
||||
static_files_config=[
|
||||
StaticFilesConfig(directories=["static"], path="/static"),
|
||||
|
||||
],
|
||||
template_config=TemplateConfig(
|
||||
directory=Path("templates"),
|
||||
engine=JinjaTemplateEngine,
|
||||
),
|
||||
)
|
||||
if __name__ == "__main__":
|
||||
app.run(auto_reload=True, port=4003, host="0.0.0.0")
|
||||
|
|
|
|||
Binary file not shown.
|
Before Width: | Height: | Size: 7.2 KiB |
|
|
@ -1,728 +0,0 @@
|
|||
[
|
||||
{
|
||||
"name_en": "beagle",
|
||||
"name": "бигль",
|
||||
"photo": "https://sun9-46.userapi.com/impg/B48745e4fBGwvuPzG-Oj0tIADbP_huU4Y49pIQ/nfmchsz5Fug.jpg?size=960x640&quality=95&sign=5f9bb57ea668a7d0a65a3542fa4382b7&type=album"
|
||||
|
||||
},
|
||||
{
|
||||
"name_en": "australian kelpie",
|
||||
"name": "австралийский келпи",
|
||||
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||||
}
|
||||
,
|
||||
{
|
||||
"name_en": "",
|
||||
"name": "",
|
||||
"photo": ""
|
||||
}
|
||||
,
|
||||
{
|
||||
"name_en": "",
|
||||
"name": "",
|
||||
"photo": ""
|
||||
}
|
||||
,
|
||||
{
|
||||
"name_en": "",
|
||||
"name": "",
|
||||
"photo": ""
|
||||
}
|
||||
,
|
||||
{
|
||||
"name_en": "",
|
||||
"name": "",
|
||||
"photo": ""
|
||||
}
|
||||
,
|
||||
{
|
||||
"name_en": "",
|
||||
"name": "",
|
||||
"photo": ""
|
||||
}
|
||||
,
|
||||
{
|
||||
"name_en": "",
|
||||
"name": "",
|
||||
"photo": ""
|
||||
}
|
||||
,
|
||||
{
|
||||
"name_en": "",
|
||||
"name": "",
|
||||
"photo": ""
|
||||
}
|
||||
,
|
||||
{
|
||||
"name_en": "",
|
||||
"name": "",
|
||||
"photo": ""
|
||||
}
|
||||
,
|
||||
{
|
||||
"name_en": "",
|
||||
"name": "",
|
||||
"photo": ""
|
||||
}
|
||||
,
|
||||
{
|
||||
"name_en": "",
|
||||
"name": "",
|
||||
"photo": ""
|
||||
}
|
||||
|
||||
|
||||
|
||||
]
|
||||
|
|
@ -1,48 +1,27 @@
|
|||
|
||||
let urlCreator = window.URL || window.webkitURL;
|
||||
|
||||
async function SavePhoto() {
|
||||
async function SavePhoto()
|
||||
{
|
||||
document.getElementById("result").innerHTML = "";
|
||||
let photo = document.getElementById("file-input").files[0];
|
||||
let formData = new FormData();
|
||||
// TODO: пройтись по всем результатм - если совпадают дважды - поднять вверх
|
||||
|
||||
formData.append("f", photo);
|
||||
let response = await fetch('/beeds', {method: "POST", body: formData});
|
||||
if (response.ok) {
|
||||
let json = await response.json();
|
||||
let text = "<h3 class='image-results'>Результаты</h3>";
|
||||
let uniqChecker = {};
|
||||
for (let key in json.results) {
|
||||
text += "<div class='image-block'><div class='image-text'>" + json.results[key] + " (вероятность: " + Math.round(parseFloat(key)*10)/10 + ")</div>";
|
||||
for (let imgKey in json.images) {
|
||||
if (json.images[imgKey].name != json.results[key]) {
|
||||
continue;
|
||||
}
|
||||
if (json.images[imgKey] == undefined) {
|
||||
continue;
|
||||
}
|
||||
text += "<div class='image-container'><img src='" + json.images[imgKey].url + "'/></div>";
|
||||
break;
|
||||
}
|
||||
text += "</div>";
|
||||
uniqChecker[json.results[key]] = key;
|
||||
}
|
||||
let text = ""
|
||||
let uniqChecker = {}
|
||||
for (let key in json.results_net) {
|
||||
if (uniqChecker[json.results_net[key]] != undefined) {
|
||||
text += "<div>" + json.results_net[key] + ": " + key + "</div>";
|
||||
uniqChecker[json.results_net[key]] = key;
|
||||
}
|
||||
for (let key in json.results) {
|
||||
if (uniqChecker[json.results[key]] != undefined) {
|
||||
continue
|
||||
}
|
||||
text += "<div class='image-block'><div class='image-text'>" + json.results_net[key] + "</div>";
|
||||
for (let imgKey in json.images) {
|
||||
if (json.images[imgKey].name != json.results_net[key]) {
|
||||
continue;
|
||||
}
|
||||
if (json.images[imgKey] == undefined) {
|
||||
continue;
|
||||
}
|
||||
text += "<div class='image-container'><img src='" + json.images[imgKey].url + "'/></div>";
|
||||
break;
|
||||
}
|
||||
text += "</div>";
|
||||
text += "<div>" + json.results[key] + ": " + key + "</div>";
|
||||
}
|
||||
document.getElementById("result").innerHTML = text;
|
||||
} else {
|
||||
|
|
@ -54,6 +33,5 @@ document.getElementById("file-input").addEventListener("change", function () {
|
|||
let photo = document.getElementById("file-input").files[0];
|
||||
let imageUrl = urlCreator.createObjectURL(photo);
|
||||
document.getElementById("image").src = imageUrl;
|
||||
document.getElementById("upload-image-text").innerHTML = "Ваше изображение:";
|
||||
|
||||
});
|
||||
|
|
@ -2,17 +2,8 @@ body {
|
|||
background: #fff;
|
||||
margin: 0;
|
||||
border: none;
|
||||
color: #696969;
|
||||
font-family: Arial, Helvetica, sans-serif;
|
||||
line-height: 180%;
|
||||
}
|
||||
|
||||
a {
|
||||
color: black;
|
||||
text-decoration: underline;
|
||||
}
|
||||
|
||||
|
||||
img {
|
||||
margin: 0;
|
||||
border: none;
|
||||
|
|
@ -20,38 +11,9 @@ img {
|
|||
|
||||
h1 {
|
||||
text-align: center;
|
||||
color: #696969;
|
||||
}
|
||||
|
||||
form {
|
||||
border: 1px solid #696969;
|
||||
padding: 0 10px 0 10px;
|
||||
}
|
||||
|
||||
#main {
|
||||
max-width: 1024px;
|
||||
margin: auto;
|
||||
padding: 10px;
|
||||
min-width: 360px;
|
||||
}
|
||||
|
||||
.image-block {
|
||||
display: flex;
|
||||
}
|
||||
|
||||
.image-block div {
|
||||
width: 50%;
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.image-block img {
|
||||
max-width: 200px;
|
||||
}
|
||||
|
||||
.upload-image-text {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.image-results {
|
||||
text-align: center;
|
||||
}
|
||||
|
|
@ -1,10 +1,9 @@
|
|||
import os
|
||||
import time
|
||||
import requests # type: ignore
|
||||
import requests
|
||||
|
||||
# Получить токен чтобы:
|
||||
# https://oauth.vk.com/oauth/authorize?client_id=51534014&display=page&scope=photos,offline&response_type=token&v=5.131&slogin_h=4984535b54c59e09ca.f1e0b6dce0d0cc82e7&__q_hash=618f24fbac4bc34edbf09b8bc503e923
|
||||
TOKEN = ""
|
||||
#TOKEN = "vk1.a.mf4KFdN9gC14SSGDFHVwFRTpzBKBeNxkdlEe0IFlZqU5a5rHH5PwiPn5ekWnDhc94lEI5d2vtXzfxvjXRPapsQZCCt89YUwCIQB1alo06A0Iup9PCWbd6F5GayBn0TS_26N5BTQ1B7deFzi25BV3LKimP9g5ZkeoY0xhNfQ7XawPnBhhK0a2ipL5zZxygYgf"
|
||||
TOKEN = "vk1.a.m92hxDp9fUi06dr424RERKOsika5s5WTNZM64XxTZbU_feIuIRQs72UH_WpeZBr0f_av3V68MvADcNyRSwPMl015aWW0EZjc0d50b8my4_w88BELd1BVT2p9o_7W0YyedfnYGGylyQzfj0jGy2Ufuc3CGFQXJjGJrnINmMSjNps84jhMk0LDqTVFG0a6VlgJ"
|
||||
VK_URL = "https://api.vk.com/method/"
|
||||
headers = {"Authorization": f"Bearer {TOKEN}"}
|
||||
postfix = "?v=5.131&state=123456"
|
||||
|
|
@ -12,24 +11,7 @@ group_id = 220240483
|
|||
dir ="../assets/dog"
|
||||
list_labels = [fname for fname in os.listdir(dir)]
|
||||
|
||||
r = requests.get(
|
||||
f"{VK_URL}photos.getAll{postfix}&access_token={TOKEN}&owner_id=-{group_id}&count=200")
|
||||
if "error" in r.json():
|
||||
print("error", r.json())
|
||||
exit()
|
||||
items = r.json().get("response").get("items")
|
||||
names = {}
|
||||
for item in items:
|
||||
if item.get("text") in names:
|
||||
print("Doubles: ", item.get("text"))
|
||||
names[item.get("text")] = True
|
||||
for name in list_labels:
|
||||
if name.replace("_", " ") in names:
|
||||
print(f"Continue: {name}")
|
||||
continue
|
||||
if name in names:
|
||||
print(f"Continue: {name}")
|
||||
continue
|
||||
max_size = 0
|
||||
max_index = 0
|
||||
list_data = os.listdir(os.path.join(dir, name))
|
||||
|
|
@ -40,25 +22,17 @@ for name in list_labels:
|
|||
max_index = i
|
||||
image_name = list_data[max_index]
|
||||
file_stats = os.stat(os.path.join(dir, name, image_name))
|
||||
r = requests.post(f"{VK_URL}photos.createAlbum{postfix}", data={
|
||||
"title": name.replace("_", " "), "group_id": group_id}, headers=headers)
|
||||
r = requests.post(f"{VK_URL}photos.createAlbum{postfix}", data={"title": name, "group_id": group_id}, headers=headers)
|
||||
if "error" in r.json():
|
||||
print("error", r.json())
|
||||
break
|
||||
album_id = r.json().get("response").get("id")
|
||||
r = requests.get(
|
||||
f"{VK_URL}photos.getUploadServer{postfix}&album_id={album_id}&access_token={TOKEN}&group_id={group_id}")
|
||||
r = requests.get(f"{VK_URL}photos.getUploadServer{postfix}&album_id={album_id}&access_token={TOKEN}&group_id={group_id}")
|
||||
url = r.json().get("response").get("upload_url")
|
||||
files = {'file1': open(os.path.join(dir, name, image_name),'rb')}
|
||||
r = requests.post(url, files=files)
|
||||
server = r.json().get("server")
|
||||
photos_list = r.json().get("photos_list")
|
||||
hash_data = r.json().get("hash")
|
||||
aid = r.json().get("aid")
|
||||
r = requests.post(f"{VK_URL}photos.save{postfix}&hash={hash_data}", data={"album_id": aid, "server": server,
|
||||
"photos_list": photos_list, "caption": name.replace("_", " "), "group_id": group_id}, headers=headers)
|
||||
if "error" in r.json():
|
||||
print("error", r.json())
|
||||
break
|
||||
print(f"Created: {name}")
|
||||
r = requests.post(f"{VK_URL}photos.save{postfix}", data={"album_id": album_id, "server": server, "photos_list": photos_list, "hash": hash_data}, headers=headers)
|
||||
time.sleep(1)
|
||||
Loading…
Reference in New Issue