dual_GAN实现 (使用tensorflow)
具体实现地址：https://github.com/codehxj/DualGAN
以下是改编成notebook版本
import sys; sys.path.append("/home/hxj/anaconda3/lib/python3.6/site-packages") from __future__ import division import math import json import random import pprint import scipy.misc import numpy as np import os from time import gmtime, strftime #pp = pprint.PrettyPrinter() #get_stddev = lambda x, k_h, k_w: 1/math.sqrt(k_w*k_h*x.get_shape()[-1]) def load_data(image_path, flip=False, is_test=False, image_size = 128): img = load_image(image_path) img = preprocess_img(img, img_size=image_size, flip=flip, is_test=is_test) img = img/127.5 - 1. if len(img.shape)<3: img = np.expand_dims(img, axis=2) return img def load_image(image_path): img = imread(image_path) return img def preprocess_img(img, img_size=128, flip=False, is_test=False): img = scipy.misc.imresize(img, [img_size, img_size]) if (not is_test) and flip and np.random.random() > 0.5: img = np.fliplr(img) return img def get_image(image_path, image_size, is_crop=True, resize_w=64, is_grayscale = False): return transform(imread(image_path, is_grayscale), image_size, is_crop, resize_w) def save_images(images, size, image_path): dir = os.path.dirname(image_path) if not os.path.exists(dir): os.makedirs(dir) return imsave(inverse_transform(images), size, image_path) def imread(path, is_grayscale = False): if (is_grayscale): return scipy.misc.imread(path, flatten = True)#.astype(np.float) else: return scipy.misc.imread(path)#.astype(np.float) def merge_images(images, size): return inverse_transform(images) def merge(images, size): h, w = images.shape[1], images.shape[2] if len(images.shape) < 4: img = np.zeros((h * size[0], w * size[1], 1)) images = np.expand_dims(images, axis = 3) else: img = np.zeros((h * size[0], w * size[1], images.shape[3])) for idx, image in enumerate(images): i = idx % size[1] j = idx // size[1] img[j*h:j*h+h, i*w:i*w+w, :] = image if images.shape[3] ==1: return np.concatenate([img,img,img],axis=2) else: return img.astype(np.uint8) def imsave(images, size, path): return scipy.misc.imsave(path, merge(images, size)) def transform(image, npx=64, is_crop=True, resize_w=64): # npx : # of pixels width/height of image if is_crop: cropped_image = center_crop(image, npx, resize_w=resize_w) else: cropped_image = image return np.array(cropped_image)/127.5 - 1. def inverse_transform(images): return ((images+1.)*127.5)
import tensorflow as tf from tensorflow.python.framework import ops from utils import * def batch_norm(x, name="batch_norm"): eps = 1e-6 with tf.variable_scope(name): nchannels = x.get_shape()[3] scale = tf.get_variable("scale", [nchannels], initializer=tf.random_normal_initializer(1.0, 0.02, dtype=tf.float32)) center = tf.get_variable("center", [nchannels], initializer=tf.constant_initializer(0.0, dtype = tf.float32)) ave, dev = tf.nn.moments(x, axes=[1,2], keep_dims=True) inv_dev = tf.rsqrt(dev + eps) normalized = (x-ave)*inv_dev * scale + center return normalized def conv2d(input_, output_dim, k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02, name="conv2d"): with tf.variable_scope(name): w = tf.get_variable('w', [k_h, k_w, input_.get_shape()[-1], output_dim], initializer=tf.truncated_normal_initializer(stddev=stddev)) conv = tf.nn.conv2d(input_, w, strides=[1, d_h, d_w, 1], padding='SAME') biases = tf.get_variable('biases', [output_dim], initializer=tf.constant_initializer(0.0)) conv = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape()) return conv def deconv2d(input_, output_shape, k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02, name="deconv2d", with_w=False): with tf.variable_scope(name): # filter : [height, width, output_channels, in_channels] w = tf.get_variable('w', [k_h, k_w, output_shape[-1], input_.get_shape()[-1]], initializer=tf.random_normal_initializer(stddev=stddev)) try: deconv = tf.nn.conv2d_transpose(input_, w, output_shape=output_shape, strides=[1, d_h, d_w, 1]) # Support for verisons of TensorFlow before 0.7.0 except AttributeError: deconv = tf.nn.deconv2d(input_, w, output_shape=output_shape, strides=[1, d_h, d_w, 1]) biases = tf.get_variable('biases', [output_shape[-1]], initializer=tf.constant_initializer(0.0)) deconv = tf.reshape(tf.nn.bias_add(deconv, biases), deconv.get_shape()) if with_w: return deconv, w, biases else: return deconv def lrelu(x, leak=0.2, name="lrelu"): return tf.maximum(x, leak*x) def celoss(logits, labels): return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels))
from __future__ import division import os import time from glob import glob import tensorflow as tf import numpy as np from six.moves import xrange #from ops import * #from utils import * class DualNet(object): def __init__(self, sess, image_size=256, batch_size=1,fcn_filter_dim = 64, \ A_channels = 3, B_channels = 3, dataset_name='', \ checkpoint_dir=None, lambda_A = 20., lambda_B = 20., \ sample_dir=None, loss_metric = 'L1', flip = False): self.df_dim = fcn_filter_dim self.flip = flip self.lambda_A = lambda_A self.lambda_B = lambda_B self.sess = sess self.is_grayscale_A = (A_channels == 1) self.is_grayscale_B = (B_channels == 1) self.batch_size = batch_size self.image_size = image_size self.fcn_filter_dim = fcn_filter_dim self.A_channels = A_channels self.B_channels = B_channels self.loss_metric = loss_metric self.dataset_name = dataset_name self.checkpoint_dir = checkpoint_dir #directory name for output and logs saving self.dir_name = "%s-img_sz_%s-fltr_dim_%d-%s-lambda_AB_%s_%s" % ( self.dataset_name, self.image_size, self.fcn_filter_dim, self.loss_metric, self.lambda_A, self.lambda_B ) self.build_model() def build_model(self): ### define place holders self.real_A = tf.placeholder(tf.float32,[self.batch_size, self.image_size, self.image_size, self.A_channels ],name='real_A') self.real_B = tf.placeholder(tf.float32, [self.batch_size, self.image_size, self.image_size, self.B_channels ], name='real_B') ### define graphs self.A2B = self.A_g_net(self.real_A, reuse = False) self.B2A = self.B_g_net(self.real_B, reuse = False) self.A2B2A = self.B_g_net(self.A2B, reuse = True) self.B2A2B = self.A_g_net(self.B2A, reuse = True) if self.loss_metric == 'L1': self.A_loss = tf.reduce_mean(tf.abs(self.A2B2A - self.real_A)) self.B_loss = tf.reduce_mean(tf.abs(self.B2A2B - self.real_B)) elif self.loss_metric == 'L2': self.A_loss = tf.reduce_mean(tf.square(self.A2B2A - self.real_A)) self.B_loss = tf.reduce_mean(tf.square(self.B2A2B - self.real_B)) self.Ad_logits_fake = self.A_d_net(self.A2B, reuse = False) self.Ad_logits_real = self.A_d_net(self.real_B, reuse = True) self.Ad_loss_real = celoss(self.Ad_logits_real, tf.ones_like(self.Ad_logits_real)) self.Ad_loss_fake = celoss(self.Ad_logits_fake, tf.zeros_like(self.Ad_logits_fake)) self.Ad_loss = self.Ad_loss_fake + self.Ad_loss_real self.Ag_loss = celoss(self.Ad_logits_fake, labels=tf.ones_like(self.Ad_logits_fake))+self.lambda_B * (self.B_loss ) self.Bd_logits_fake = self.B_d_net(self.B2A, reuse = False) self.Bd_logits_real = self.B_d_net(self.real_A, reuse = True) self.Bd_loss_real = celoss(self.Bd_logits_real, tf.ones_like(self.Bd_logits_real)) self.Bd_loss_fake = celoss(self.Bd_logits_fake, tf.zeros_like(self.Bd_logits_fake)) self.Bd_loss = self.Bd_loss_fake + self.Bd_loss_real self.Bg_loss = celoss(self.Bd_logits_fake, tf.ones_like(self.Bd_logits_fake))+self.lambda_A * (self.A_loss) self.d_loss = self.Ad_loss + self.Bd_loss self.g_loss = self.Ag_loss + self.Bg_loss ## define trainable variables t_vars = tf.trainable_variables() self.A_d_vars = [var for var in t_vars if 'A_d_' in var.name] self.B_d_vars = [var for var in t_vars if 'B_d_' in var.name] self.A_g_vars = [var for var in t_vars if 'A_g_' in var.name] self.B_g_vars = [var for var in t_vars if 'B_g_' in var.name] self.d_vars = self.A_d_vars + self.B_d_vars self.g_vars = self.A_g_vars + self.B_g_vars self.saver = tf.train.Saver() def clip_trainable_vars(self, var_list): for var in var_list: self.sess.run(var.assign(tf.clip_by_value(var, -self.c, self.c))) def load_random_samples(self): #np.random.choice( sample_files =np.random.choice(glob('./datasets/{}/val/A/*.jpg'.format(self.dataset_name)),self.batch_size) sample_A_imgs = [load_data(f, image_size =self.image_size, flip = False) for f in sample_files] sample_files = np.random.choice(glob('./datasets/{}/val/B/*.jpg'.format(self.dataset_name)),self.batch_size) sample_B_imgs = [load_data(f, image_size =self.image_size, flip = False) for f in sample_files] sample_A_imgs = np.reshape(np.array(sample_A_imgs).astype(np.float32),(self.batch_size,self.image_size, self.image_size,-1)) sample_B_imgs = np.reshape(np.array(sample_B_imgs).astype(np.float32),(self.batch_size,self.image_size, self.image_size,-1)) return sample_A_imgs, sample_B_imgs def sample_shotcut(self, sample_dir, epoch_idx, batch_idx): sample_A_imgs,sample_B_imgs = self.load_random_samples() Ag, A2B2A_imgs, A2B_imgs = self.sess.run([self.A_loss, self.A2B2A, self.A2B], feed_dict={self.real_A: sample_A_imgs, self.real_B: sample_B_imgs}) Bg, B2A2B_imgs, B2A_imgs = self.sess.run([self.B_loss, self.B2A2B, self.B2A], feed_dict={self.real_A: sample_A_imgs, self.real_B: sample_B_imgs}) save_images(A2B_imgs, [self.batch_size,1], './{}/{}/{:06d}_{:04d}_A2B.jpg'.format(sample_dir,self.dir_name , epoch_idx, batch_idx)) save_images(A2B2A_imgs, [self.batch_size,1], './{}/{}/{:06d}_{:04d}_A2B2A.jpg'.format(sample_dir,self.dir_name, epoch_idx, batch_idx)) save_images(B2A_imgs, [self.batch_size,1], './{}/{}/{:06d}_{:04d}_B2A.jpg'.format(sample_dir,self.dir_name, epoch_idx, batch_idx)) save_images(B2A2B_imgs, [self.batch_size,1], './{}/{}/{:06d}_{:04d}_B2A2B.jpg'.format(sample_dir,self.dir_name, epoch_idx, batch_idx)) print("[Sample] A_loss: {:.8f}, B_loss: {:.8f}".format(Ag, Bg)) def train(self, args): """Train Dual GAN""" decay = 0.9 self.d_optim = tf.train.RMSPropOptimizer(args.lr, decay=decay) \ .minimize(self.d_loss, var_list=self.d_vars) self.g_optim = tf.train.RMSPropOptimizer(args.lr, decay=decay) \ .minimize(self.g_loss, var_list=self.g_vars) tf.global_variables_initializer().run() self.writer = tf.summary.FileWriter("./logs/"+self.dir_name, self.sess.graph) step = 1 start_time = time.time() if self.load(self.checkpoint_dir): print(" [*] Load SUCCESS") else: print(" Load failed...ignored...") print(" start training...") for epoch_idx in xrange(args.epoch): data_A = glob('./datasets/{}/train/A/*.jpg'.format(self.dataset_name)) data_B = glob('./datasets/{}/train/B/*.jpg'.format(self.dataset_name)) np.random.shuffle(data_A) np.random.shuffle(data_B) epoch_size = min(len(data_A), len(data_B)) // (self.batch_size) print('[*] training data loaded successfully') print("#data_A: %d #data_B:%d" %(len(data_A),len(data_B))) print('[*] run optimizor...') for batch_idx in xrange(0, epoch_size): imgA_batch = self.load_training_imgs(data_A, batch_idx) imgB_batch = self.load_training_imgs(data_B, batch_idx) print("Epoch: [%2d] [%4d/%4d]"%(epoch_idx, batch_idx, epoch_size)) step = step + 1 self.run_optim(imgA_batch, imgB_batch, step, start_time) if np.mod(step, 100) == 1: self.sample_shotcut(args.sample_dir, epoch_idx, batch_idx) if np.mod(step, args.save_freq) == 2: self.save(args.checkpoint_dir, step) def load_training_imgs(self, files, idx): batch_files = files[idx*self.batch_size:(idx+1)*self.batch_size] batch_imgs = [load_data(f, image_size =self.image_size, flip = self.flip) for f in batch_files] batch_imgs = np.reshape(np.array(batch_imgs).astype(np.float32),(self.batch_size,self.image_size, self.image_size,-1)) return batch_imgs def run_optim(self,batch_A_imgs, batch_B_imgs, counter, start_time): _, Adfake,Adreal,Bdfake,Bdreal, Ad, Bd = self.sess.run( [self.d_optim, self.Ad_loss_fake, self.Ad_loss_real, self.Bd_loss_fake, self.Bd_loss_real, self.Ad_loss, self.Bd_loss], feed_dict = {self.real_A: batch_A_imgs, self.real_B: batch_B_imgs}) _, Ag, Bg, Aloss, Bloss = self.sess.run( [self.g_optim, self.Ag_loss, self.Bg_loss, self.A_loss, self.B_loss], feed_dict={ self.real_A: batch_A_imgs, self.real_B: batch_B_imgs}) _, Ag, Bg, Aloss, Bloss = self.sess.run( [self.g_optim, self.Ag_loss, self.Bg_loss, self.A_loss, self.B_loss], feed_dict={ self.real_A: batch_A_imgs, self.real_B: batch_B_imgs}) print("time: %4.4f, Ad: %.2f, Ag: %.2f, Bd: %.2f, Bg: %.2f, U_diff: %.5f, V_diff: %.5f" \ % (time.time() - start_time, Ad,Ag,Bd,Bg, Aloss, Bloss)) print("Ad_fake: %.2f, Ad_real: %.2f, Bd_fake: %.2f, Bg_real: %.2f" % (Adfake,Adreal,Bdfake,Bdreal)) def A_d_net(self, imgs, y = None, reuse = False): return self.discriminator(imgs, prefix = 'A_d_', reuse = reuse) def B_d_net(self, imgs, y = None, reuse = False): return self.discriminator(imgs, prefix = 'B_d_', reuse = reuse) def discriminator(self, image, y=None, prefix='A_d_', reuse=False): # image is 256 x 256 x (input_c_dim + output_c_dim) with tf.variable_scope(tf.get_variable_scope()) as scope: if reuse: scope.reuse_variables() else: assert scope.reuse == False h0 = lrelu(conv2d(image, self.df_dim, name=prefix+'h0_conv')) # h0 is (128 x 128 x self.df_dim) h1 = lrelu(batch_norm(conv2d(h0, self.df_dim*2, name=prefix+'h1_conv'), name = prefix+'bn1')) # h1 is (64 x 64 x self.df_dim*2) h2 = lrelu(batch_norm(conv2d(h1, self.df_dim*4, name=prefix+'h2_conv'), name = prefix+ 'bn2')) # h2 is (32x 32 x self.df_dim*4) h3 = lrelu(batch_norm(conv2d(h2, self.df_dim*8, d_h=1, d_w=1, name=prefix+'h3_conv'), name = prefix+ 'bn3')) # h3 is (32 x 32 x self.df_dim*8) h4 = conv2d(h3, 1, d_h=1, d_w=1, name =prefix+'h4') return h4 def A_g_net(self, imgs, reuse=False): return self.fcn(imgs, prefix='A_g_', reuse = reuse) def B_g_net(self, imgs, reuse=False): return self.fcn(imgs, prefix = 'B_g_', reuse = reuse) def fcn(self, imgs, prefix=None, reuse = False): with tf.variable_scope(tf.get_variable_scope()) as scope: if reuse: scope.reuse_variables() else: assert scope.reuse == False s = self.image_size s2, s4, s8, s16, s32, s64, s128 = int(s/2), int(s/4), int(s/8), int(s/16), int(s/32), int(s/64), int(s/128) # imgs is (256 x 256 x input_c_dim) e1 = conv2d(imgs, self.fcn_filter_dim, name=prefix+'e1_conv') # e1 is (128 x 128 x self.fcn_filter_dim) e2 = batch_norm(conv2d(lrelu(e1), self.fcn_filter_dim*2, name=prefix+'e2_conv'), name = prefix+'bn_e2') # e2 is (64 x 64 x self.fcn_filter_dim*2) e3 = batch_norm(conv2d(lrelu(e2), self.fcn_filter_dim*4, name=prefix+'e3_conv'), name = prefix+'bn_e3') # e3 is (32 x 32 x self.fcn_filter_dim*4) e4 = batch_norm(conv2d(lrelu(e3), self.fcn_filter_dim*8, name=prefix+'e4_conv'), name = prefix+'bn_e4') # e4 is (16 x 16 x self.fcn_filter_dim*8) e5 = batch_norm(conv2d(lrelu(e4), self.fcn_filter_dim*8, name=prefix+'e5_conv'), name = prefix+'bn_e5') # e5 is (8 x 8 x self.fcn_filter_dim*8) e6 = batch_norm(conv2d(lrelu(e5), self.fcn_filter_dim*8, name=prefix+'e6_conv'), name = prefix+'bn_e6') # e6 is (4 x 4 x self.fcn_filter_dim*8) e7 = batch_norm(conv2d(lrelu(e6), self.fcn_filter_dim*8, name=prefix+'e7_conv'), name = prefix+'bn_e7') # e7 is (2 x 2 x self.fcn_filter_dim*8) e8 = batch_norm(conv2d(lrelu(e7), self.fcn_filter_dim*8, name=prefix+'e8_conv'), name = prefix+'bn_e8') # e8 is (1 x 1 x self.fcn_filter_dim*8) self.d1, self.d1_w, self.d1_b = deconv2d(tf.nn.relu(e8), [self.batch_size, s128, s128, self.fcn_filter_dim*8], name=prefix+'d1', with_w=True) d1 = tf.nn.dropout(batch_norm(self.d1, name = prefix+'bn_d1'), 0.5) d1 = tf.concat([d1, e7],3) # d1 is (2 x 2 x self.fcn_filter_dim*8*2) self.d2, self.d2_w, self.d2_b = deconv2d(tf.nn.relu(d1), [self.batch_size, s64, s64, self.fcn_filter_dim*8], name=prefix+'d2', with_w=True) d2 = tf.nn.dropout(batch_norm(self.d2, name = prefix+'bn_d2'), 0.5) d2 = tf.concat([d2, e6],3) # d2 is (4 x 4 x self.fcn_filter_dim*8*2) self.d3, self.d3_w, self.d3_b = deconv2d(tf.nn.relu(d2), [self.batch_size, s32, s32, self.fcn_filter_dim*8], name=prefix+'d3', with_w=True) d3 = tf.nn.dropout(batch_norm(self.d3, name = prefix+'bn_d3'), 0.5) d3 = tf.concat([d3, e5],3) # d3 is (8 x 8 x self.fcn_filter_dim*8*2) self.d4, self.d4_w, self.d4_b = deconv2d(tf.nn.relu(d3), [self.batch_size, s16, s16, self.fcn_filter_dim*8], name=prefix+'d4', with_w=True) d4 = batch_norm(self.d4, name = prefix+'bn_d4') d4 = tf.concat([d4, e4],3) # d4 is (16 x 16 x self.fcn_filter_dim*8*2) self.d5, self.d5_w, self.d5_b = deconv2d(tf.nn.relu(d4), [self.batch_size, s8, s8, self.fcn_filter_dim*4], name=prefix+'d5', with_w=True) d5 = batch_norm(self.d5, name = prefix+'bn_d5') d5 = tf.concat([d5, e3],3) # d5 is (32 x 32 x self.fcn_filter_dim*4*2) self.d6, self.d6_w, self.d6_b = deconv2d(tf.nn.relu(d5), [self.batch_size, s4, s4, self.fcn_filter_dim*2], name=prefix+'d6', with_w=True) d6 = batch_norm(self.d6, name = prefix+'bn_d6') d6 = tf.concat([d6, e2],3) # d6 is (64 x 64 x self.fcn_filter_dim*2*2) self.d7, self.d7_w, self.d7_b = deconv2d(tf.nn.relu(d6), [self.batch_size, s2, s2, self.fcn_filter_dim], name=prefix+'d7', with_w=True) d7 = batch_norm(self.d7, name = prefix+'bn_d7') d7 = tf.concat([d7, e1],3) # d7 is (128 x 128 x self.fcn_filter_dim*1*2) if prefix == 'B_g_': self.d8, self.d8_w, self.d8_b = deconv2d(tf.nn.relu(d7),[self.batch_size, s, s, self.A_channels], name=prefix+'d8', with_w=True) elif prefix == 'A_g_': self.d8, self.d8_w, self.d8_b = deconv2d(tf.nn.relu(d7),[self.batch_size, s, s, self.B_channels], name=prefix+'d8', with_w=True) # d8 is (256 x 256 x output_c_dim) return tf.nn.tanh(self.d8) def save(self, checkpoint_dir, step): model_name = "DualNet.model" model_dir = self.dir_name checkpoint_dir = os.path.join(checkpoint_dir, model_dir) if not os.path.exists(checkpoint_dir): os.makedirs(checkpoint_dir) self.saver.save(self.sess, os.path.join(checkpoint_dir, model_name), global_step=step) def load(self, checkpoint_dir): print(" [*] Reading checkpoint...") model_dir = self.dir_name checkpoint_dir = os.path.join(checkpoint_dir, model_dir) ckpt = tf.train.get_checkpoint_state(checkpoint_dir) if ckpt and ckpt.model_checkpoint_path: ckpt_name = os.path.basename(ckpt.model_checkpoint_path) self.saver.restore(self.sess, os.path.join(checkpoint_dir, ckpt_name)) return True else: return False def test(self, args): """Test DualNet""" start_time = time.time() tf.global_variables_initializer().run() if self.load(self.checkpoint_dir): print(" [*] Load SUCCESS") test_dir = './{}/{}'.format(args.test_dir, self.dir_name) if not os.path.exists(test_dir): os.makedirs(test_dir) test_log = open(test_dir+'evaluation.txt','a') test_log.write(self.dir_name) self.test_domain(args, test_log, type = 'A') self.test_domain(args, test_log, type = 'B') test_log.close() def test_domain(self, args, test_log, type = 'A'): test_files = glob('./datasets/{}/val/{}/*.jpg'.format(self.dataset_name,type)) # load testing input print("Loading testing images ...") test_imgs = [load_data(f, is_test=True, image_size =self.image_size, flip = args.flip) for f in test_files] print("#images loaded: %d"%(len(test_imgs))) test_imgs = np.reshape(np.asarray(test_imgs).astype(np.float32),(len(test_files),self.image_size, self.image_size,-1)) test_imgs = [test_imgs[i*self.batch_size:(i+1)*self.batch_size] for i in xrange(0, len(test_imgs)//self.batch_size)] test_imgs = np.asarray(test_imgs) test_path = './{}/{}/'.format(args.test_dir, self.dir_name) # test input samples if type == 'A': for i in xrange(0, len(test_files)//self.batch_size): filename_o = test_files[i*self.batch_size].split('/')[-1].split('.')[0] print(filename_o) idx = i+1 A_imgs = np.reshape(np.array(test_imgs[i]), (self.batch_size,self.image_size, self.image_size,-1)) print("testing A image %d"%(idx)) print(A_imgs.shape) A2B_imgs, A2B2A_imgs = self.sess.run( [self.A2B, self.A2B2A], feed_dict={self.real_A: A_imgs} ) save_images(A_imgs, [self.batch_size, 1], test_path+filename_o+'_realA.jpg') save_images(A2B_imgs, [self.batch_size, 1], test_path+filename_o+'_A2B.jpg') save_images(A2B2A_imgs, [self.batch_size, 1], test_path+filename_o+'_A2B2A.jpg') elif type=='B': for i in xrange(0, len(test_files)//self.batch_size): filename_o = test_files[i*self.batch_size].split('/')[-1].split('.')[0] idx = i+1 B_imgs = np.reshape(np.array(test_imgs[i]), (self.batch_size,self.image_size, self.image_size,-1)) print("testing B image %d"%(idx)) B2A_imgs, B2A2B_imgs = self.sess.run( [self.B2A, self.B2A2B], feed_dict={self.real_B:B_imgs} ) save_images(B_imgs, [self.batch_size, 1],test_path+filename_o+'_realB.jpg') save_images(B2A_imgs, [self.batch_size, 1],test_path+filename_o+'_B2A.jpg') save_images(B2A2B_imgs, [self.batch_size, 1],test_path+filename_o+'_B2A2B.jpg')
import argparse #from model import DualNet import tensorflow as tf parser = argparse.ArgumentParser(description='Argument parser') """ Arguments related to network architecture""" #parser.add_argument('--network_type', dest='network_type', default='fcn_4', help='fcn_1,fcn_2,fcn_4,fcn_8, fcn_16, fcn_32, fcn_64, fcn_128') parser.add_argument('--image_size', dest='image_size', type=int, default=256, help='size of input images (applicable to both A images and B images)') parser.add_argument('--fcn_filter_dim', dest='fcn_filter_dim', type=int, default=64, help='# of fcn filters in first conv layer') parser.add_argument('--A_channels', dest='A_channels', type=int, default=1, help='# of channels of image A') parser.add_argument('--B_channels', dest='B_channels', type=int, default=1, help='# of channels of image B') """Arguments related to run mode""" parser.add_argument('--phase', dest='phase', default='train', help='train, test') """Arguments related to training""" parser.add_argument('--loss_metric', dest='loss_metric', default='L1', help='L1, or L2') parser.add_argument('--niter', dest='niter', type=int, default=30, help='# of iter at starting learning rate') parser.add_argument('--lr', dest='lr', type=float, default=0.00005, help='initial learning rate for adam')#0.0002 parser.add_argument('--beta1', dest='beta1', type=float, default=0.5, help='momentum term of adam') parser.add_argument('--flip', dest='flip', type=bool, default=True, help='if flip the images for data argumentation') parser.add_argument('--dataset_name', dest='dataset_name', default='sketch-photo', help='name of the dataset') parser.add_argument('--epoch', dest='epoch', type=int, default=50, help='# of epoch') parser.add_argument('--batch_size', dest='batch_size', type=int, default=1, help='# images in batch') parser.add_argument('--lambda_A', dest='lambda_A', type=float, default=20.0, help='# weights of A recovery loss') parser.add_argument('--lambda_B', dest='lambda_B', type=float, default=20.0, help='# weights of B recovery loss') """Arguments related to monitoring and outputs""" parser.add_argument('--save_freq', dest='save_freq', type=int, default=50, help='save the model every save_freq sgd iterations') parser.add_argument('--checkpoint_dir', dest='checkpoint_dir', default='./checkpoint', help='models are saved here') parser.add_argument('--sample_dir', dest='sample_dir', default='./sample', help='sample are saved here') parser.add_argument('--test_dir', dest='test_dir', default='./test', help='test sample are saved here') args=parser.parse_args([]) #这里是关键，使用notebook的必须加上[]，如果在命令行使用 args=parser.parse_args() def main(_): if not os.path.exists(args.checkpoint_dir): os.makedirs(args.checkpoint_dir) if not os.path.exists(args.sample_dir): os.makedirs(args.sample_dir) if not os.path.exists(args.test_dir): os.makedirs(args.test_dir) with tf.Session() as sess: model = DualNet(sess, image_size=args.image_size, batch_size=args.batch_size,\ dataset_name=args.dataset_name,A_channels = args.A_channels, \ B_channels = args.B_channels, flip = (args.flip == 'True'),\ checkpoint_dir=args.checkpoint_dir, sample_dir=args.sample_dir,\ fcn_filter_dim = args.fcn_filter_dim,\ loss_metric=args.loss_metric, lambda_B=args.lambda_B, \ lambda_A= args.lambda_A) if args.phase == 'train': model.train(args) else: model.test(args) if __name__ == '__main__': tf.app.run()
