import find_mxnet import mxnet as mx import numpy as np import importlib import logging logging.basicConfig(level=logging.DEBUG) import argparse from collections import namedtuple from skimage import io, transform from skimage.restoration import denoise_tv_chambolle CallbackData = namedtuple('CallbackData', field_names=['eps','epoch','img','filename']) def get_args(arglist=None): parser = argparse.ArgumentParser(description='neural style') parser.add_argument('--model', type=str, default='vgg19', choices = ['vgg'], help = 'the pretrained model to use') parser.add_argument('--content-image', type=str, default='input/IMG_4343.jpg', help='the content image') parser.add_argument('--style-image', type=str, default='input/starry_night.jpg', help='the style image') parser.add_argument('--stop-eps', type=float, default=.005, help='stop if the relative chanage is less than eps') parser.add_argument('--content-weight', type=float, default=10, help='the weight for the content image') parser.add_argument('--style-weight', type=float, default=1, help='the weight for the style image') parser.add_argument('--tv-weight', type=float, default=1e-2, help='the magtitute on TV loss') parser.add_argument('--max-num-epochs', type=int, default=1000, help='the maximal number of training epochs') parser.add_argument('--max-long-edge', type=int, default=600, help='resize the content image') parser.add_argument('--lr', type=float, default=.001, help='the initial learning rate') parser.add_argument('--gpu', type=int, default=0, help='which gpu card to use, -1 means using cpu') parser.add_argument('--output_dir', type=str, default='output/', help='the output image') parser.add_argument('--save-epochs', type=int, default=50, help='save the output every n epochs') parser.add_argument('--remove-noise', type=float, default=.02, help='the magtitute to remove noise') parser.add_argument('--lr-sched-delay', type=int, default=75, help='how many epochs between decreasing learning rate') parser.add_argument('--lr-sched-factor', type=int, default=0.9, help='factor to decrease learning rate on schedule') if arglist is None: return parser.parse_args() else: return parser.parse_args(arglist) def PreprocessContentImage(path, long_edge): img = io.imread(path) logging.info("load the content image, size = %s", img.shape[:2]) factor = float(long_edge) / max(img.shape[:2]) new_size = (int(img.shape[0] * factor), int(img.shape[1] * factor)) resized_img = transform.resize(img, new_size) sample = np.asarray(resized_img) * 256 # swap axes to make image from (224, 224, 3) to (3, 224, 224) sample = np.swapaxes(sample, 0, 2) sample = np.swapaxes(sample, 1, 2) # sub mean sample[0, :] -= 123.68 sample[1, :] -= 116.779 sample[2, :] -= 103.939 logging.info("resize the content image to %s", new_size) return np.resize(sample, (1, 3, sample.shape[1], sample.shape[2])) def PreprocessStyleImage(path, shape): img = io.imread(path) resized_img = transform.resize(img, (shape[2], shape[3])) sample = np.asarray(resized_img) * 256 sample = np.swapaxes(sample, 0, 2) sample = np.swapaxes(sample, 1, 2) sample[0, :] -= 123.68 sample[1, :] -= 116.779 sample[2, :] -= 103.939 return np.resize(sample, (1, 3, sample.shape[1], sample.shape[2])) def PostprocessImage(img): img = np.resize(img, (3, img.shape[2], img.shape[3])) img[0, :] += 123.68 img[1, :] += 116.779 img[2, :] += 103.939 img = np.swapaxes(img, 1, 2) img = np.swapaxes(img, 0, 2) img = np.clip(img, 0, 255) return img.astype('uint8') def SaveImage(img, filename, remove_noise=0.): logging.info('save output to %s', filename) out = PostprocessImage(img) if remove_noise != 0.0: out = denoise_tv_chambolle(out, weight=remove_noise, multichannel=True) io.imsave(filename, out) def style_gram_symbol(input_size, style): _, output_shapes, _ = style.infer_shape(data=(1, 3, input_size[0], input_size[1])) gram_list = [] grad_scale = [] for i in range(len(style.list_outputs())): shape = output_shapes[i] x = mx.sym.Reshape(style[i], target_shape=(int(shape[1]), int(np.prod(shape[2:])))) # use fully connected to quickly do dot(x, x^T) gram = mx.sym.FullyConnected(x, x, no_bias=True, num_hidden=shape[1]) gram_list.append(gram) grad_scale.append(np.prod(shape[1:]) * shape[1]) return mx.sym.Group(gram_list), grad_scale def get_loss(gram, content): gram_loss = [] for i in range(len(gram.list_outputs())): gvar = mx.sym.Variable("target_gram_%d" % i) gram_loss.append(mx.sym.sum(mx.sym.square(gvar - gram[i]))) cvar = mx.sym.Variable("target_content") content_loss = mx.sym.sum(mx.sym.square(cvar - content)) return mx.sym.Group(gram_loss), content_loss def get_tv_grad_executor(img, ctx, tv_weight): """create TV gradient executor with input binded on img """ if tv_weight <= 0.0: return None nchannel = img.shape[1] simg = mx.sym.Variable("img") skernel = mx.sym.Variable("kernel") channels = mx.sym.SliceChannel(simg, num_outputs=nchannel) out = mx.sym.Concat(*[ mx.sym.Convolution(data=channels[i], weight=skernel, num_filter=1, kernel=(3, 3), pad=(1,1), no_bias=True, stride=(1,1)) for i in range(nchannel)]) kernel = mx.nd.array(np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]]) .reshape((1, 1, 3, 3)), ctx) / 8.0 out = out * tv_weight return out.bind(ctx, args={"img": img, "kernel": kernel}) def train_nstyle(args, callback=None): """Train a neural style network. Args are from argparse and control input, output, hyper-parameters. callback allows for display of training progress. """ # input dev = mx.gpu(args.gpu) if args.gpu >= 0 else mx.cpu() content_np = PreprocessContentImage(args.content_image, args.max_long_edge) style_np = PreprocessStyleImage(args.style_image, shape=content_np.shape) size = content_np.shape[2:] # model Executor = namedtuple('Executor', ['executor', 'data', 'data_grad']) model_module = importlib.import_module('model_' + args.model) style, content = model_module.get_symbol() gram, gscale = style_gram_symbol(size, style) model_executor = model_module.get_executor(gram, content, size, dev) model_executor.data[:] = style_np model_executor.executor.forward() style_array = [] for i in range(len(model_executor.style)): style_array.append(model_executor.style[i].copyto(mx.cpu())) model_executor.data[:] = content_np model_executor.executor.forward() content_array = model_executor.content.copyto(mx.cpu()) # delete the executor del model_executor style_loss, content_loss = get_loss(gram, content) model_executor = model_module.get_executor( style_loss, content_loss, size, dev) grad_array = [] for i in range(len(style_array)): style_array[i].copyto(model_executor.arg_dict["target_gram_%d" % i]) grad_array.append(mx.nd.ones((1,), dev) * (float(args.style_weight) / gscale[i])) grad_array.append(mx.nd.ones((1,), dev) * (float(args.content_weight))) print([x.asscalar() for x in grad_array]) content_array.copyto(model_executor.arg_dict["target_content"]) # train # initialize img with random noise img = mx.nd.zeros(content_np.shape, ctx=dev) img[:] = mx.rnd.uniform(-0.1, 0.1, img.shape) lr = mx.lr_scheduler.FactorScheduler(step=args.lr_sched_delay, factor=args.lr_sched_factor) optimizer = mx.optimizer.NAG( learning_rate = args.lr, wd = 0.0001, momentum=0.95, lr_scheduler = lr) optim_state = optimizer.create_state(0, img) logging.info('start training arguments %s', args) old_img = img.copyto(dev) clip_norm = 1 * np.prod(img.shape) tv_grad_executor = get_tv_grad_executor(img, dev, args.tv_weight) for e in range(args.max_num_epochs): img.copyto(model_executor.data) model_executor.executor.forward() model_executor.executor.backward(grad_array) gnorm = mx.nd.norm(model_executor.data_grad).asscalar() if gnorm > clip_norm: model_executor.data_grad[:] *= clip_norm / gnorm if tv_grad_executor is not None: tv_grad_executor.forward() optimizer.update(0, img, model_executor.data_grad + tv_grad_executor.outputs[0], optim_state) else: optimizer.update(0, img, model_executor.data_grad, optim_state) new_img = img eps = (mx.nd.norm(old_img - new_img) / mx.nd.norm(new_img)).asscalar() old_img = new_img.copyto(dev) logging.info('epoch %d, relative change %f', e, eps) if eps < args.stop_eps: logging.info('eps < args.stop_eps, training finished') break if callback: cbdata = { 'eps': eps, 'epoch': e+1, } if (e+1) % args.save_epochs == 0: outfn = args.output_dir + 'e_'+str(e+1)+'.jpg' npimg = new_img.asnumpy() SaveImage(npimg, outfn, args.remove_noise) if callback: cbdata['filename'] = outfn cbdata['img'] = npimg if callback: callback(cbdata) final_fn = args.output_dir + '/final.jpg' SaveImage(new_img.asnumpy(), final_fn) if __name__ == "__main__": args = get_args() train_nstyle(args)