def geneator_handler():
print('start request!')
zvector = None
batchSize = 1
# Upload a serialized Zvector
if request.method == 'POST':
print('POST!')
# DO things
# check if the post request has the file part
if 'file' not in request.files:
return BadRequest("File not present in request")
file = request.files['file']
if file.filename == '':
return BadRequest("File name is not present in request")
if not allowed_file(file.filename):
return BadRequest("Invalid file type")
filename = secure_filename(file.filename)
# input_filepath = os.path.join('./', filename)
file.save(filename)
# Load a Z vector and Retrieve the N of samples to generate
zvector = torch.load(filename)
batchSize = zvector.size()[0]
checkpoint = request.form.get("ckp") or "netG_epoch_99.pth"
# Check for cuda availability
if torch.cuda.is_available():
# GPU and cuda
Generator = DCGAN(netG=os.path.join(MODEL_PATH, checkpoint), zvector=zvector, batchSize=batchSize, ngpu=1, cuda=True, outf="./")
else:
# CPU
Generator = DCGAN(netG=os.path.join(MODEL_PATH, checkpoint), zvector=zvector, batchSize=batchSize, ngpu=0, outf="./")
Generator.build_model()
Generator.generate()
return send_file(OUTPUT_PATH, mimetype='image/png')
from dcgan import DCGAN lr = 0.0001 root = '/Users/mac/Documents/GitHub/GAN/google/data/' batch_size = 64 model = DCGAN() model.load_dataset(root = root, batch_size = batch_size) model.build_model(lr = lr, Epoch = 50) model.train()
parser = argparse.ArgumentParser()
parser.add_argument('--netG', required=True, default='', help="path to netG (for generating images)")
parser.add_argument('--outf', default='/output', help='folder to output images')
parser.add_argument('--Zvector', help="path to Serialized Z vector")
parser.add_argument('--cuda', action='store_true', help='enables cuda')
parser.add_argument('--ngpu', type=int, default=0, help='number of GPUs to use')
opt = parser.parse_args()
print(opt)
zvector = None
batchSize = 1
# Load a Z vector and Retrieve the N of samples to generate
if opt.Zvector:
zvector = torch.load(opt.Zvector)
batchSize = zvector.size()[0]
outf = "/output"
if opt.outf:
outf = opt.outf
# GPU and CUDA
cuda = None
if opt.cuda:
cuda = opt.cuda
ngpu = int(opt.ngpu)
# Generate An Image from input json or default parameters
Generator = DCGAN(netG=opt.netG, zvector=zvector, batchSize=batchSize, outf=outf, cuda=cuda, ngpu=ngpu)
Generator.build_model()
Generator.generate()
