Add generate script.

generate.py

@@ -0,0 +1,62 @@
+from __future__ import print_function
+import argparse
+import random
+import torch
+import torch.nn as nn
+import torch.nn.parallel
+import torch.backends.cudnn as cudnn
+import torch.optim as optim
+import torch.utils.data
+import torchvision.datasets as dset
+import torchvision.transforms as transforms
+import torchvision.utils as vutils
+from torch.autograd import Variable
+import os
+import json
+
+import models.dcgan as dcgan
+import models.mlp as mlp
+
+if __name__=="__main__":
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-c', '--config', required=True, type=str, help='path to generator config .json file')
+    parser.add_argument('-w', '--weights', required=True, type=str, help='path to generator weights .pth file')
+    parser.add_argument('-o', '--output_dir', required=True, type=str, help="path to to output directory")
+    parser.add_argument('-n', '--nimages', required=True, type=int, help="number of images to generate", default=1)
+    parser.add_argument('--cuda', action='store_true', help='enables cuda')
+    opt = parser.parse_args()
+
+    with open(opt.config, 'r') as gencfg:
+        generator_config = json.loads(gencfg.read())
+    
+    imageSize = generator_config["imageSize"]
+    nz = generator_config["nz"]
+    nc = generator_config["nc"]
+    ngf = generator_config["ngf"]
+    noBN = generator_config["noBN"]
+    ngpu = generator_config["ngpu"]
+    mlp_G = generator_config["mlp_G"]
+    n_extra_layers = generator_config["n_extra_layers"]
+
+    if noBN:
+        netG = dcgan.DCGAN_G_nobn(imageSize, nz, nc, ngf, ngpu, n_extra_layers)
+    elif mlp_G:
+        netG = mlp.MLP_G(imageSize, nz, nc, ngf, ngpu)
+    else:
+        netG = dcgan.DCGAN_G(imageSize, nz, nc, ngf, ngpu, n_extra_layers)
+
+    # load weights
+    netG.load_state_dict(torch.load(opt.weights))
+
+    # initialize noise
+    fixed_noise = torch.FloatTensor(opt.nimages, nz, 1, 1).normal_(0, 1)
+
+    if opt.cuda:
+        netG.cuda()
+        fixed_noise = fixed_noise.cuda()
+
+    fake = netG(Variable(fixed_noise, volatile=True))
+    fake.data = fake.data.mul(0.5).add(0.5)
+
+    vutils.save_image(fake.data, os.path.join(opt.output_dir, "generated.png"))