import os
import random
import time
import numpy as np
import torch
import torchvision.transforms as transforms
from PIL import Image
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from torch.utils.data import DataLoader, Dataset
import dataset
import utils
from cnn import resnet50
device = utils.selectDevice()
# -----------------------------------------------------------------
# To save the numpy array into the file, there are several options
# Machine readable:
# - ndarray.dump(), ndarray.dumps(), pickle.dump(), pickle.dumps():
# Generate .pkl file.
# - np.save(), np.savez(), np.savez_compressed()
# Generate .npy file
# - np.savetxt()
# Generate .txt file.
# -----------------------------------------------------------------
def video_to_features(data_path):
""" Transfer the training set and validation set videos into features """
Synopsis [ Generate images from GAN / ACGAN. ]
"""
import argparse
import os
import numpy as np
import torch
import torch.nn as nn
import torchvision
from torchvision.utils import save_image
import utils
from GAN.model import DCGAN_Generator
DEVICE = utils.selectDevice()
class ACGAN_Generator(nn.Module):
def __init__(self):
super(ACGAN_Generator, self).__init__()
self.linear = nn.Linear(102, 512 * 4 * 4)
self.bn0 = nn.BatchNorm2d(512)
self.relu0 = nn.ReLU(inplace=True)
self.conv_blocks = nn.Sequential(
nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(256, 128, 4, 2, 1, bias=False),
def main():
transform = transforms.Compose([
transforms.Resize((448, 448)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
grid_num = 7 if args.command == "basic" else 14
trainset = dataset.MyDataset(root="hw2_train_val/train15000",
grid_num=grid_num,
train=args.augment,
transform=transform)
testset = dataset.MyDataset(grid_num=grid_num,
root="hw2_train_val/val1500",
train=False,
transform=transform)
trainLoader = DataLoader(trainset,
batch_size=args.batchs,
shuffle=True,
num_workers=args.worker)
testLoader = DataLoader(testset,
batch_size=1,
shuffle=False,
num_workers=args.worker)
device = utils.selectDevice(show=True)
if args.command == "basic":
model = models.Yolov1_vgg16bn(pretrained=True).to(device)
criterion = models.YoloLoss(7., 2., 5., 0.5, device).to(device)
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
weight_decay=1e-4)
scheduler = optim.lr_scheduler.MultiStepLR(optimizer, [20, 45, 55],
gamma=0.1)
start_epoch = 0
if args.load:
model, optimizer, start_epoch, scheduler = utils.loadCheckpoint(
args.load, model, optimizer, scheduler)
model = train(model,
criterion,
optimizer,
scheduler,
trainLoader,
testLoader,
start_epoch,
args.epochs,
device,
lr=args.lr,
grid_num=7)
elif args.command == "improve":
model_improve = models.Yolov1_vgg16bn_Improve(
pretrained=True).to(device)
criterion = models.YoloLoss(14., 2., 5, 0.5, device).to(device)
optimizer = optim.SGD(model_improve.parameters(),
lr=args.lr,
weight_decay=1e-4)
scheduler = optim.lr_scheduler.MultiStepLR(optimizer, [20, 40, 70],
gamma=0.1)
start_epoch = 0
if args.load:
model_improve, optimizer, start_epoch, scheduler = utils.loadCheckpoint(
args.load, model, optimizer, scheduler)
model_improve = train(model_improve,
criterion,
optimizer,
scheduler,
trainLoader,
testLoader,
start_epoch,
args.epochs,
device,
lr=args.lr,
grid_num=7,
save_name="Yolov1-Improve")