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create_dump.py
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import argparse
import scipy
from scipy import ndimage
import numpy as np
import sys
import torch
from torch.autograd import Variable
import torchvision.models as models
import torch.nn.functional as F
from torch.utils import data, model_zoo
from model.deeplab_multi import Res_Deeplab
from model.deeplab_multi import getVGG
from dataset.cityscapes_dataset import cityscapesDataSet
from dataset.gta5_dataset import GTA5DataSet
from collections import OrderedDict
import os
from PIL import Image
import matplotlib.pyplot as plt
import torch.nn as nn
IMG_MEAN = np.array((104.00698793,116.66876762,122.67891434), dtype=np.float32)
DATA_DIRECTORY = './GTA5'#'./Cityscape'#
DATA_LIST_PATH = "./dataset/gta5_list/train.txt"#"./dataset/cityscapes_list/train.txt"#
SAVE_PATH = './result/cityscapes_vgg'
IGNORE_LABEL = 255
NUM_CLASSES = 19
NUM_STEPS = 500 # Number of images in the validation set.
RESTORE_FROM = "./weights/GTA2Cityscapes_multi-ed35151c.pth"
#RESTORE_FROM = "./snapshots_vgg/GTA5_VGG_35000.pth"
SET = 'train'
palette = [128, 64, 128, 244, 35, 232, 70, 70, 70, 102, 102, 156, 190, 153, 153, 153, 153, 153, 250, 170, 30,
220, 220, 0, 107, 142, 35, 152, 251, 152, 70, 130, 180, 220, 20, 60, 255, 0, 0, 0, 0, 142, 0, 0, 70,
0, 60, 100, 0, 80, 100, 0, 0, 230, 119, 11, 32]
zero_pad = 256 * 3 - len(palette)
for i in range(zero_pad):
palette.append(0)
def colorize_mask(mask):
# mask: numpy array of the mask
new_mask = Image.fromarray(mask.astype(np.uint8)).convert('P')
new_mask.putpalette(palette)
return new_mask
def get_arguments():
"""Parse all the arguments provided from the CLI.
Returns:
A list of parsed arguments.
"""
parser = argparse.ArgumentParser(description="DeepLab-ResNet Network")
parser.add_argument("--data-dir", type=str, default=DATA_DIRECTORY,
help="Path to the directory containing the Cityscapes dataset.")
parser.add_argument("--data-list", type=str, default=DATA_LIST_PATH,
help="Path to the file listing the images in the dataset.")
parser.add_argument("--ignore-label", type=int, default=IGNORE_LABEL,
help="The index of the label to ignore during the training.")
parser.add_argument("--num-classes", type=int, default=NUM_CLASSES,
help="Number of classes to predict (including background).")
parser.add_argument("--restore-from", type=str, default=RESTORE_FROM,
help="Where restore model parameters from.")
parser.add_argument("--gpu", type=int, default=0,
help="choose gpu device.")
parser.add_argument("--set", type=str, default=SET,
help="choose evaluation set.")
parser.add_argument("--save", type=str, default=SAVE_PATH,
help="Path to save result.")
return parser.parse_args()
def main():
city = np.load("dump_cityscape5.npy")
gta = np.load("dump_gta5.npy")
city_scape = city[:1000,:]
gta5 = gta[:1000,:]
combined = np.concatenate((city[1000:,:], gta[1000:,:]))
np.save('source.npy', gta5)
np.save('target.npy', city_scape)
np.save('mixed.npy', combined)
print(city_scape.shape)
print(gta5.shape)
print(combined.shape)
exit()
print(type(dump))
print(dump.shape)
b = dump
print(type(dump))
print(dump.shape)
import random
a = np.stack(random.sample(a, 500))
b = np.stack(random.sample(b, 500))
dump = np.concatenate((a,b))
print(dump.shape)
arr = np.arange(10)
#print(dump)
exit()
"""Create the model and start the evaluation process."""
args = get_arguments()
gpu0 = args.gpu
if not os.path.exists(args.save):
os.makedirs(args.save)
model = Res_Deeplab(num_classes=args.num_classes)
#model = getVGG(num_classes=args.num_classes)
if args.restore_from[:4] == 'http' :
saved_state_dict = model_zoo.load_url(args.restore_from)
else:
saved_state_dict = torch.load(args.restore_from)
model.load_state_dict(saved_state_dict)
model.eval()
model.cuda(gpu0)
#trainloader = data.DataLoader(cityscapesDataSet(args.data_dir, args.data_list, crop_size=(1024, 512), mean=IMG_MEAN, scale=False, mirror=False, set=args.set), batch_size=1, shuffle=False, pin_memory=True)
trainloader = data.DataLoader(GTA5DataSet(args.data_dir, args.data_list, crop_size=(1024, 512), mean=IMG_MEAN, scale=False, mirror=False), batch_size=1, shuffle=False, pin_memory=True)
trainloader_iter = enumerate(trainloader)
interp = nn.Upsample(size=(1024, 2048), mode='bilinear')
dump_array = np.array((1,2))
for itr in xrange(2000):
print(itr)
_, batch = trainloader_iter.next()
images, labels, _, _ = batch
#images, _, _ = batch
output1, output2 = model(Variable(images, volatile=True).cuda(gpu0))
import torch.nn.functional as F
output2 = F.avg_pool2d(output2, (4, 4))
output2 = output2.data.cpu().numpy()
output2 = np.reshape(output2, (1, -1))
if dump_array.shape == (2,):
dump_array = output2
else:
dump_array = np.concatenate((dump_array, output2))
np.save('dump_gta5.npy', dump_array)
if __name__ == '__main__':
main()