import torch
from dataset import YelpDataSet
import configure as cf
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import dataLoader as load
if __name__ == "__main__":
train, val = load.get_train_valid_loader(cf.photo_url, 50, 32, 'food')
print train.__len__()
print val.__len__()
import torchvision.models as models
from PIL import Image
import dataset
import dataLoader
import configure as cf
import plot_utils as utils
import train_function as train
import resnet as modified_resnet
imgTransform = transforms.Compose([transforms.Scale(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))])
trainLoader, valLoader = dataLoader.get_train_valid_loader(cf.photo_url,1,32,'all',imgTransform,0.1,-1)
# define classification network
classification = modified_resnet.resnet50(pretrained = True)
classification.fc = nn.Linear(512*4, 5)
classification.load_state_dict(torch.load('./res_clas_do7'))
#define regression_food
regress_food = modified_resnet.resnet50(pretrained = True)
regress_food.fc = nn.Linear(512*4, 1)
regress_food.load_state_dict(torch.load('./test_FOOD_ResNet_Final'))
#define regression_drink
regress_drink = models.resnet50(pretrained = False)
regress_drink.fc = nn.Linear(512*4, 1)
regress_drink.load_state_dict(torch.load('./test_DRINK_ResNet_Final'))
#define regression_inside
import dataLoader
import configure as cf
import train_function as train
import plot_utils as utils
# define transform function, define trainset and valset
# VGG-16 requires the input size of 224*224*3
imgTransform = transforms.Compose(
[transforms.Scale(224),
transforms.CenterCrop(224),
transforms.ToTensor()])
trainLoader, valLoader = \
dataLoader.get_train_valid_loader(cf.photo_url, 50, 32, 'food', imgTransform, 0.1, -1)
# define learningRate
learningRate = 5 * 1e-4
# Definition of our network.
network = models.vgg16(pretrained=True)
network.classifier = nn.Sequential(
nn.Linear(512 * 7 * 7, 4096),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(4096, 1),
)