| import sys |
| sys.path.insert(0, "../mxnet/python") |
| |
| import mxnet as mx |
| import numpy as np |
| |
| #import basic |
| import data_processing |
| import gen_v3 |
| import gen_v4 |
| |
| dshape = (1, 3, 480, 640) |
| clip_norm = 1.0 * np.prod(dshape) |
| model_prefix = "./model/" |
| ctx = mx.gpu(0) |
| |
| |
| |
| # generator |
| gens = [gen_v4.get_module("g0", dshape, ctx), |
| gen_v3.get_module("g1", dshape, ctx), |
| gen_v3.get_module("g2", dshape, ctx), |
| gen_v4.get_module("g3", dshape, ctx)] |
| for i in range(len(gens)): |
| gens[i].load_params("./model/%d/v3_0002-0026000.params" % i) |
| |
| content_np = data_processing.PreprocessContentImage("../IMG_4343.jpg", min(dshape[2:]), dshape) |
| data = [mx.nd.array(content_np)] |
| for i in range(len(gens)): |
| gens[i].forward(mx.io.DataBatch([data[-1]], [0]), is_train=False) |
| new_img = gens[i].get_outputs()[0] |
| data.append(new_img.copyto(mx.cpu())) |
| data_processing.SaveImage(new_img.asnumpy(), "out_%d.jpg" % i) |
| |
| |
| import os |
| os.system("rm -rf out.zip") |
| os.system("zip out.zip out_*") |
