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Former-commit-id: 31ef92a7704222f12bc3f092fdaadc194dbb3413

model_execute.py

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+import pickle
+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+import torchvision
+
+def check_photo(name, photo):
+    preprocess = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+    input_tensor = preprocess(photo)
+    input_batch = input_tensor.unsqueeze(0)  # create a mini-batch as expected by the model
+
+    # move the input and model to GPU for speed if available
+    if torch.cuda.is_available():
+        input_batch = input_batch.to('cuda')
+        model.to('cuda')
+
+    with torch.no_grad():
+        output = model(input_batch)
+    # Tensor of shape 1000, with confidence scores over ImageNet's 1000 classes
+    print(name, output[0])
+    # The output has unnormalized scores. To get probabilities, you can run a softmax on it.
+    probabilities = torch.nn.functional.softmax(output[0], dim=0)
+    print(name, probabilities)
+
+pkl_filename = "pickle_model.pkl"
+with open(pkl_filename, 'rb') as file:
+    model = pickle.load(file)
+
+model.eval()
+# sample execution (requires torchvision)
+
+gates_photo = Image.open("gates500.jpg")
+musk_photo = Image.open("mask.jpg")
+bezos_photo = Image.open("bezos500.jpg")
+zuker_photo = Image.open("zuckerberg500.jpg")
+jobs_photo = Image.open("jobs500.jpg")
+test_photos_dict = {'gates':gates_photo, 'musk':musk_photo, 'bezos':bezos_photo,'zuker': zuker_photo,'jobs': jobs_photo}
+for name in test_photos_dict:
+    check_photo(name, test_photos_dict[name])
+# preprocess = transforms.Compose([
+#     transforms.Resize(256),
+#     transforms.CenterCrop(224),
+#     transforms.ToTensor(),
+#     transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+# ])
+# input_tensor = preprocess(test_photos_list)
+# input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
+#
+# # move the input and model to GPU for speed if available
+# if torch.cuda.is_available():
+#     input_batch = input_batch.to('cuda')
+#     model.to('cuda')
+#
+# with torch.no_grad():
+#     output = model(input_batch)
+# # Tensor of shape 1000, with confidence scores over ImageNet's 1000 classes
+# print(output[0])
+# print(model)
+
+# print(probabilities)

model_retrain.py

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+import os
+import torch
+import random
+import numpy as np
+import torchvision
+import matplotlib.pyplot as plt
+import torchvision.transforms as transforms
+import shutil
+import time
+import xml.etree.ElementTree as et
+import pickle
+
+from tqdm import tqdm
+from PIL import Image
+from torchvision import models
+from torch.utils.data import DataLoader
+from torchvision.datasets import ImageFolder
+BATCH_SIZE = 32
+
+use_gpu = torch.cuda.is_available()
+device = 'cuda' if use_gpu else 'cpu'
+print('Connected device:', device)
+
+musk = 'https://drive.google.com/uc?export=download&id=1BOuq35QzO1YtKQkYGfj_vtBj3Ps5xyBN'
+gates ='https://drive.google.com/uc?export=download&id=1jgHQF_NMpH9uMTvic9rGnURu_8UOGdiz'
+bezos = 'https://drive.google.com/uc?export=download&id=1n5UaLL-TAkjIeBbTNcn-Czkp_A3Eslhj'
+zuker = 'https://drive.google.com/uc?export=download&id=1ncPmYTg6EPHlUFdcjl_bXTbtWRLv2DXy'
+jobs = 'https://drive.google.com/uc?export=download&id=1TX3hiRyvSYiYVZUFrbAhN3Jpp9cd0Q9s'
+
+
+# Метки
+face_lst=[
+    ["Bill Gates",'people/gates500.jpg'],
+    ["Jeff Besoz",'people/bezos500.jpg'],
+    ["Mark Zuckerberg", 'people/zuckerberg500.jpg'],
+    ["Steve Jobs",'people/jobs500.jpg']
+]
+
+# Датасет скачивается во время выполнения программы
+import wget
+from zipfile import ZipFile
+
+# os.mkdir('data')
+
+# url = 'https://drive.google.com/uc?export=download&id=120xqh0mYtYZ1Qh7vr-XFzjPbSKivLJjA'
+# file_name = wget.download(url, 'data/')
+#
+# with ZipFile(file_name, 'r') as zip_file:
+#     zip_file.extractall()
+#
+# link_lst = [musk, gates, bezos, zuker, jobs]
+# for link in link_lst:
+#     wget.download(link, 'data/')
+
+# Датасет для тренировки
+train_dataset = ImageFolder(
+    root='people/train'
+)
+# Датасет для проверки
+valid_dataset = ImageFolder(
+    root='people/valid'
+)
+
+# augmentations (ухудшение качество чтобы не было переобучения)
+normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                                 std=[0.229, 0.224, 0.225])
+train_dataset.transform = transforms.Compose([
+    transforms.Resize([70, 70]),
+    transforms.RandomHorizontalFlip(),
+    transforms.RandomAutocontrast(),
+    transforms.RandomEqualize(),
+    transforms.ToTensor(),
+    normalize
+])
+
+valid_dataset.transform = transforms.Compose([
+    transforms.Resize([70, 70]),
+    transforms.ToTensor(),
+    normalize
+])
+
+# Размер каждого пакета при обучение
+# Training data loaders.
+train_loader = DataLoader(
+    train_dataset, batch_size=BATCH_SIZE,
+    shuffle=True
+)
+# Размер каждого пакета при проверке
+# Validation data loaders.
+valid_loader = DataLoader(
+    valid_dataset, batch_size=BATCH_SIZE,
+    shuffle=False
+)
+
+# Указание на используемую модель
+def google(): # pretrained=True для tensorflow
+    model = models.googlenet(weights=models.GoogLeNet_Weights.IMAGENET1K_V1)
+    # Добавление линейного (выходного) слоя на основании которого идет дообучение
+    model.fc = torch.nn.Linear(1024, len(train_dataset.classes))
+    for param in model.parameters():
+        param.requires_grad = True
+    # Заморозка весов т.к. при переобучении модели они должны быть постоянны, а меняться будет только последний слой
+    model.inception3a.requires_grad = False
+    model.inception3b.requires_grad = False
+    model.inception4a.requires_grad = False
+    model.inception4b.requires_grad = False
+    model.inception4c.requires_grad = False
+    model.inception4d.requires_grad = False
+    model.inception4e.requires_grad = False
+    return model
+
+# Функция обучения модели. Epoch - количество итераций обучения (прогонов по нейросети)
+def train(model, optimizer, train_loader, val_loader, epoch=10):
+    loss_train, acc_train = [], []
+    loss_valid, acc_valid = [], []
+    # tqdm - прогресс бар
+    for epoch in tqdm(range(epoch)):
+        # Ошибки
+        losses, equals = [], []
+        torch.set_grad_enabled(True)
+
+        # Train. Обучение. В цикле проходится по картинкам и оптимизируются потери
+        model.train()
+        for i, (image, target) in enumerate(train_loader):
+            image = image.to(device)
+            target = target.to(device)
+            output = model(image)
+            loss = criterion(output,target)
+
+            losses.append(loss.item())
+            equals.extend(
+                [x.item() for x in torch.argmax(output, 1) == target])
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+        # Метрики отображающие резултитаты обучения модели
+        loss_train.append(np.mean(losses))
+        acc_train.append(np.mean(equals))
+        losses, equals = [], []
+        torch.set_grad_enabled(False)
+
+        # Validate. Оценка качества обучения
+        model.eval()
+        for i , (image, target) in enumerate(valid_loader):
+            image = image.to(device)
+            target = target.to(device)
+
+            output = model(image)
+            loss = criterion(output,target)
+
+            losses.append(loss.item())
+            equals.extend(
+                [y.item() for y in torch.argmax(output, 1) == target])
+
+        loss_valid.append(np.mean(losses))
+        acc_valid.append(np.mean(equals))
+
+    return loss_train, acc_train, loss_valid, acc_valid
+
+criterion = torch.nn.CrossEntropyLoss()
+criterion = criterion.to(device)
+
+model = google() # здесь можете заменить на VGG
+print('Model: GoogLeNet\n')
+
+# оптимайзер - отвечает за поиск и подбор оптимальных весов
+optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
+model = model.to(device)
+
+loss_train, acc_train, loss_valid, acc_valid = train(
+model, optimizer, train_loader, valid_loader, 30)
+print('acc_train:', acc_train, '\nacc_valid:', acc_valid)
+
+# Сохранение модели в текущую рабочую директорию
+pkl_filename = "pickle_model.pkl"
+with open(pkl_filename, 'wb') as file:
+    pickle.dump(model, file)
+

pickle_model.pkl.REMOVED.git-id

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+7f6f655ec6c2e6c5e3909cb0b10f718b1e648be5