deux modeles debut projet
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alexnet_pytorch.py
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alexnet_pytorch.py
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# -*- coding: utf-8 -*-
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"""AlexNet_pytorch.ipynb
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Automatically generated by Colaboratory.
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Original file is located at
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https://colab.research.google.com/drive/1iafZNxt1THDq6WRYvmYPwGbDmSQFfj9m
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# Dataset creation section
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"""
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from __future__ import print_function, division
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import os
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import torch
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import pandas as pd
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from skimage import io, transform
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import numpy as np
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import matplotlib.pyplot as plt
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from torch.utils.data import Dataset, DataLoader
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import torchvision.transforms as transforms
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import torchvision
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import cv2
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from sklearn.model_selection import train_test_split
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# Ignore warnings
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import warnings
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warnings.filterwarnings("ignore")
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plt.ion() # interactive mode
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from google.colab import drive
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drive.mount('/content/drive')
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data = pd.read_csv('/content/drive/MyDrive/insa 5/Datasets/celebA/labels.csv',nrows=4999)
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data = data[["image_id","Smiling"]]
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data= data.replace(-1,0)
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pd.set_option("display.max_rows", 20, "display.max_columns", 20)
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train_set,test_set=train_test_split(data,test_size=0.25)
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print(train_set)
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class ImageDataset(Dataset):
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def __init__(self,csv,img_folder,transform):
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self.csv=csv
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self.transform=transform
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self.img_folder=img_folder
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self.image_names=self.csv[:]['image_id']
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self.labels=np.array(self.csv.drop(['image_id'], axis=1))
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#The __len__ function returns the number of samples in our dataset.
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def __len__(self):
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return len(self.image_names)
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def __getitem__(self,index):
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image=cv2.imread(self.img_folder+self.image_names.iloc[index])
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image=cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
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image=self.transform(image)
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targets=self.labels[index]
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sample = {'image': image,'labels':targets}
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#return sample
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return image,targets
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train_transform = transforms.Compose([
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transforms.ToPILImage(),
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transforms.Resize((224, 224)),
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transforms.ToTensor()])
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#,transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
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test_transform =transforms.Compose([
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transforms.ToPILImage(),
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transforms.Resize((224, 224)),
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transforms.ToTensor()])
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train_dataset=ImageDataset(train_set,'/content/drive/MyDrive/insa 5/Datasets/celebA/images/',train_transform)
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test_dataset=ImageDataset(test_set,'/content/drive/MyDrive/insa 5/Datasets/celebA/images/',test_transform)
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BATCH_SIZE=16
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train_dataloader = DataLoader(
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train_dataset,
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batch_size=BATCH_SIZE,
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shuffle=True
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)
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test_dataloader = DataLoader(
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test_dataset,
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batch_size=BATCH_SIZE,
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shuffle=True
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)
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sample = next(iter(train_dataloader))
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input, label = sample
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input = input.view(BATCH_SIZE, -1)
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#print(sample[input])
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print(input.size())
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def imshow(inp, title=None):
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"""imshow for Tensor."""
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inp = inp.numpy().transpose((1, 2, 0))
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inp = np.clip(inp, 0, 1)
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plt.imshow(inp)
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# Get a batch of training data
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images, labels = next(iter(test_dataloader))
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# Make a grid from batch
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output = torchvision.utils.make_grid(images)
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imshow(output)
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"""# Training section"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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AlexNet_model=torch.hub.load('pytorch/vision:v0.6.0', 'alexnet', pretrained=True)
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AlexNet_model.eval()
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print(AlexNet_model)
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AlexNet_model.classifier[4]=nn.Linear(4096,1024)
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AlexNet_model.classifier[6]=nn.Linear(1024,2)
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AlexNet_model.eval()
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print(AlexNet_model)
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class Net(nn.Module):
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def __init__(self):
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super().__init__()
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self.conv1 = nn.Conv2d(3, 96, 11, stride=4)
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self.pool = nn.MaxPool2d(3, stride = 2)
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self.conv2 = nn.Conv2d(96, 256, 5, padding=2)
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self.conv3 = nn.Conv2d(256, 384, 3, padding=1)
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self.conv4 = nn.Conv2d(384, 384, 3, padding=1)
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self.conv5 = nn.Conv2d(384, 256, 3, padding=1)
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self.fc1 = nn.Linear(6400, 4096)
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self.fc2 = nn.Linear(4096, 4096)
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self.fc3 = nn.Linear(4096, 1000)
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self.fc4 = nn.Linear(1000, 2)
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self.dropout = nn.Dropout(0.5)
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def forward(self, x):
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x = self.pool(F.relu(self.conv1(x)))
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x = self.pool(F.relu(self.conv2(x)))
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x = F.relu(self.conv3(x))
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x = F.relu(self.conv4(x))
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x = F.relu(self.conv5(x))
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x = self.pool(x)
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x = torch.flatten(x, 1) # flatten all dimensions except batch
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x = self.dropout(F.relu(self.fc1(x)))
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x = self.dropout(F.relu(self.fc2(x)))
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x = self.dropout(F.relu(self.fc3(x)))
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x = self.fc4(x)
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return x
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import torch
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#print(torch.cuda.get_device_name(0))
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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print(device)
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net = AlexNet_model
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net.to(device)
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import torch.optim as optim
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#criterion = nn.BCELoss()
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
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for epoch in range(10): # loop over the dataset multiple times
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#print(epoch)
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running_loss = 0.0
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for i, data in enumerate(train_dataloader, 0):
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#print(i)
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# get the inputs; data is a list of [inputs, labels]
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inputs, labels = data
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inputs, labels = inputs.cuda(), labels.cuda()
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#print(inputs.size())
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#print(labels.flatten())
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# zero the parameter gradients
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optimizer.zero_grad()
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outputs = net(inputs)
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loss = criterion(outputs, labels.flatten())
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loss.backward()
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optimizer.step()
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#predicted=[]
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# forward + backward + optimize
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#outputs = net(inputs)
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#predicted = torch.nn.functional.softmax(outputs, dim=0)
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#outputs=outputs.float()
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#predicted=torch.tensor(predicted)
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#predicted=predicted.float()
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#labels=labels.float()
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#print(predicted.flatten())
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#loss = criterion(predicted.flatten(), labels.flatten())
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#loss.backward()
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#optimizer.step()
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# print statistics
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running_loss += loss.item()
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if i % 10 == 9: # print every 2000 mini-batches
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print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss/10 ))
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running_loss = 0.0
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print('Finished Training')
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PATH = '/content/drive/MyDrive/Models/saved_AlexNet.pth'
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torch.save(net.state_dict(), PATH)
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"""# Testing section"""
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dataiter = iter(train_dataloader)
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images, labels = dataiter.next()
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classes = ("Not Smiling", "Smiling")
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# print images
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imshow(torchvision.utils.make_grid(images))
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print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(16)))
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PATH = '/content/drive/MyDrive/insa 5/Models/saved_AlexNet.pth'
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net.load_state_dict(torch.load(PATH))
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outputs = net(images.cuda())
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print(outputs)
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_, predicted = torch.max(outputs, 1)
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print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]
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for j in range(16)))
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correct = 0
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total = 0
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predicted = 0
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# since we're not training, we don't need to calculate the gradients for our outputs
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with torch.no_grad():
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for data in test_dataloader:
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images, labels = data
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# calculate outputs by running images through the network
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outputs = net(images.cuda())
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# the class with the highest energy is what we choose as prediction
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_, predicted = torch.max(outputs.data, 1)
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#probabilities = torch.nn.functional.softmax(outputs[0], dim=0)
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#print(probabilities)
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#print("labels.size(0): ",labels.size(0))
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#print("Batch size: ",BATCH_SIZE)
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total += labels.size(0)
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#if probabilities >= 0.5:
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# predicted = 1
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#else:
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# predicted = 0
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correct += (predicted == labels.flatten().cuda()).sum().item()
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#print("Predicted: ", predicted)
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#print("Labels: ", labels.flatten())
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#print("Correct: ", correct)
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print(correct)
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print(total)
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print('Accuracy of the network on the 750 test images: %d %%' % (
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100 * correct / total))
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121
hog&svm.py
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hog&svm.py
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# -*- coding: utf-8 -*-
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"""HOG&SVM.ipynb
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Automatically generated by Colaboratory.
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Original file is located at
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https://colab.research.google.com/drive/128Eq-6Qnnv2Q7qW3cs8YlUe_f2EdGps5
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"""
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from __future__ import print_function, division
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import os
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import torch
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import pandas as pd
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from skimage import io, transform
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import numpy as np
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import matplotlib.pyplot as plt
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from torch.utils.data import Dataset, DataLoader
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import torchvision.transforms as transforms
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import torchvision
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import cv2
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from sklearn.model_selection import train_test_split
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import numpy as np # linear algebra
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import json
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from matplotlib import pyplot as plt
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from skimage import color
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from skimage.feature import hog
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from sklearn import svm
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from sklearn.metrics import classification_report,accuracy_score
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# Ignore warnings
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import warnings
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warnings.filterwarnings("ignore")
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plt.ion() # interactive mode
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from google.colab import drive
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drive.mount('/content/drive')
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from numpy import genfromtxt
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labels = genfromtxt('/content/drive/MyDrive/insa 5/Datasets/celebA/labels.csv', delimiter=',')
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labels=np.where(labels==-1, 0, labels)
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print(labels)
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labels=np.delete(labels,0,0)
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labels=np.delete(labels,0,1)
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print(labels)
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labels_smil=[]
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for i in range(5002):
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labels_smil.append(labels[i,31])
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print(labels_smil)
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import os
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#1)recuperation du contenu du dossier c:\dossier\
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contenu=os.listdir('/content/drive/MyDrive/insa 5/Datasets/celebA/images/')
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#ça te donnera une liste du chemin complet de chaques fichiers du dossier, par exemple pour le fichier 001.jpg, ça ressemblerait à ça : 'c:/dossier/001.jpg'
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#2)recuperation des noms de fichiers (sans le chemin) qui sont des .jpg:
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contenu=[x.split('/')[-1] for x in contenu if '.jpg' in x.split('/')[-1]]
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#3)Maintenant on trie la liste
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contenu.sort()
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print(contenu)
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images=[]
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for i in range(len(contenu)):
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#print('/content/drive/MyDrive/insa 5/Datasets/celebA/images/'+str(contenu[i]))
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image=cv2.imread('/content/drive/MyDrive/insa 5/Datasets/celebA/images/'+str(contenu[i]))
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#print(image)
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images.append(image)
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hog_images=[]
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hog_features=[]
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for i in range(len(contenu)):
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fd, hog_img = hog(images[i], orientations=8, pixels_per_cell=(16, 16),
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cells_per_block=(4, 4), visualize=True)
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hog_images.append(hog_img)
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hog_features.append(fd)
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print(hog_features)
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print(hog_images)
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print(str(len(hog_features)))
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print(hog_images[0])
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import matplotlib.pyplot as plt
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from skimage.feature import hog
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from skimage import data, exposure
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), sharex=True, sharey=True)
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ax1.axis('off')
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ax1.imshow(images[53], cmap=plt.cm.gray)
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ax1.set_title('Input image')
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# Rescale histogram for better display
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hog_image_rescaled = exposure.rescale_intensity(hog_images[53], in_range=(0, 10))
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ax2.axis('off')
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ax2.imshow(hog_image_rescaled, cmap=plt.cm.gray)
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ax2.set_title('Histogram of Oriented Gradients')
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plt.show()
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clf = svm.SVC()
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x_train, x_test = hog_features[:4000], hog_features[4000:]
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y_train, y_test = labels_smil[:4000] , labels_smil[4000:]
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print(x_train)
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print(y_train)
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clf.fit(x_train,y_train)
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y_pred = clf.predict(x_test)
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print("Accuracy: "+str(accuracy_score(y_test, y_pred)))
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print('\n')
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print(classification_report(y_test, y_pred))
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