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tester.py
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tester.py
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from __future__ import print_function
from trainer import *
class DPIG_FourNets_testOnly(DPIG_Encoder_GAN_BodyROI):
def _define_input(self):
self.train_LuNet = tf.Variable(False, name='phase')
self.is_train_tensor = tf.Variable(self.is_train, name='phase')
self.Generator_encoder_fn = models.GeneratorCNN_ID_Encoder
self.Generator_fn = models.GeneratorCNN_ID_UAEAfterResidual
self.wgan_gp = WGAN_GP(DATA_DIR='', MODE='dcgan', DIM=64, BATCH_SIZE=self.batch_size, ITERS=200000,
LAMBDA=10, G_OUTPUT_DIM=self.img_H*self.img_W*3)
self.Discriminator_fn = self._getDiscriminator(self.wgan_gp, arch='DCGAN')
def init_net(self):
self.build_model()
if self.pretrained_path is not None:
var1 = tf.get_collection(tf.GraphKeys.VARIABLES, scope='Encoder')
var2 = tf.get_collection(tf.GraphKeys.VARIABLES, scope='ID_AE')
self.saverPart = tf.train.Saver(var1+var2, max_to_keep=20)
if self.pretrained_appSample_path is not None:
var = tf.get_collection(tf.GraphKeys.VARIABLES, scope='Gaussian_FC')
self.saverAppSamplePart = tf.train.Saver(var, max_to_keep=20)
if self.pretrained_poseAE_path is not None:
var = tf.get_collection(tf.GraphKeys.VARIABLES, scope='PoseAE')
self.saverPosePart = tf.train.Saver(var, max_to_keep=20)
if self.pretrained_poseSample_path is not None:
var = tf.get_collection(tf.GraphKeys.VARIABLES, scope='PoseGaussian')
self.saverPoseSamplePart = tf.train.Saver(var, max_to_keep=20)
self.saver = tf.train.Saver(max_to_keep=20)
self.summary_writer = tf.summary.FileWriter(self.model_dir)
sv = tf.train.Supervisor(logdir=self.model_dir,
is_chief=True,
saver=None,
summary_op=None,
summary_writer=self.summary_writer,
global_step=self.step,
save_model_secs=0,
ready_for_local_init_op=None)
gpu_options = tf.GPUOptions(allow_growth=True)
sess_config = tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options)
self.sess = sv.prepare_or_wait_for_session(config=sess_config)
if self.pretrained_path is not None:
self.saverPart.restore(self.sess, self.pretrained_path)
print('restored from pretrained_path:', self.pretrained_path)
if self.pretrained_appSample_path is not None:
self.saverAppSamplePart.restore(self.sess, self.pretrained_appSample_path)
print('restored from pretrained_appSample_path:', self.pretrained_appSample_path)
if self.pretrained_poseAE_path is not None:
self.saverPosePart.restore(self.sess, self.pretrained_poseAE_path)
print('restored from pretrained_poseAE_path:', self.pretrained_poseAE_path)
if self.pretrained_poseSample_path is not None:
self.saverPoseSamplePart.restore(self.sess, self.pretrained_poseSample_path)
print('restored from pretrained_poseSample_path:', self.pretrained_poseSample_path)
if self.ckpt_path is not None:
self.saver.restore(self.sess, self.ckpt_path)
print('restored from ckpt_path:', self.ckpt_path)
# self.test_batch_num = 751
self.test_batch_num = 24000
# self.test_dir_name = 'test_result_SampleApp'
# self.test_dir_name = 'test_result_SamplePose'
# self.test_dir_name = 'test_result_RandomPose'
# self.test_dir_name = 'test_result_SampleAppSamplePose'
self.test_dir_name = 'test_result_SampleAppRandomPose_%dx%d'%(self.test_batch_num, self.batch_size)
def build_model(self):
self._define_input()
# self.pose_rcv = tf.Print(self.pose_rcv, [self.pose_rcv], 'self.pose_rcv = ', summarize=30)
################################### Pose ###################################
with tf.variable_scope("PoseAE") as vs:
## Norm to [-1, 1]
pose_rcv_norm = tf.reshape(self.pose_rcv, [self.batch_size, self.keypoint_num, 3])
R = tf.cast(tf.slice(pose_rcv_norm, [0,0,0], [-1,-1,1]), tf.float32)/float(self.img_H)*2.0 - 1
C = tf.cast(tf.slice(pose_rcv_norm, [0,0,1], [-1,-1,1]), tf.float32)/float(self.img_W)*2.0 - 1
V = tf.cast(tf.slice(pose_rcv_norm, [0,0,2], [-1,-1,1]), tf.float32)
pose_rcv_norm = tf.concat([R,C,V], axis=-1)
self.pose_embs, self.G_var_encoder = models.PoseEncoderFCRes(tf.reshape(pose_rcv_norm, [self.batch_size,-1]),
z_num=100, repeat_num=4, hidden_num=512, data_format=self.data_format, activation_fn=LeakyReLU, reuse=False)
pose_embs_shape = self.pose_embs.get_shape().as_list()
with tf.variable_scope("PoseGaussian") as vs:
self.G_pose_embs, self.G_var_embs = models.GaussianFCRes(pose_embs_shape, pose_embs_shape[-1], repeat_num=4, hidden_num=512, data_format=self.data_format, activation_fn=LeakyReLU)
with tf.variable_scope("PoseAE") as vs:
G_pose_coord, G_pose_visible, self.G_var_decoder = models.PoseDecoderFCRes(self.pose_embs, self.keypoint_num,
repeat_num=4, hidden_num=512, data_format=self.data_format, activation_fn=LeakyReLU, reuse=False)
self.G_var_pose = self.G_var_encoder + self.G_var_decoder
if self.sample_pose: ## Sampling new poses during testing
self.G_pose_rcv = tf.concat([tf.reshape(G_pose_coord, [self.batch_size,self.keypoint_num,2]), tf.expand_dims(G_pose_visible,-1)], axis=-1)
G_pose = coord2channel_simple_rcv(self.G_pose_rcv, self.keypoint_num, is_normalized=True, img_H=self.img_H, img_W=self.img_W)
self.G_pose = denorm_img(tf.tile(tf.reduce_max(G_pose, axis=-1, keep_dims=True), [1,1,1,3]), self.data_format)
else:
self.G_pose_rcv = pose_rcv_norm
G_pose = coord2channel_simple_rcv(self.G_pose_rcv, self.keypoint_num, is_normalized=True, img_H=self.img_H, img_W=self.img_W)
self.reconstruct_loss = tf.reduce_mean(tf.square(pose_rcv_norm - self.G_pose_rcv))
################################### Appearance ###################################
## Use the pose to generate person with pretrained generator
with tf.variable_scope("Encoder") as vs:
self.embs, _, self.Encoder_var = GeneratorCNN_ID_Encoder_BodyROI(self.x, self.part_bbox, 7, 32,
self.repeat_num, self.conv_hidden_num, self.data_format, activation_fn=tf.nn.relu, reuse=False)
embs_shape = self.embs.get_shape().as_list()
tf.set_random_seed(0)
with tf.variable_scope("Gaussian_FC") as vs:
embs_random, self.G_var_app_embs = models.GaussianFCRes(embs_shape, embs_shape[-1], repeat_num=4, hidden_num=512, data_format=self.data_format, activation_fn=LeakyReLU)
if self.one_app_per_batch:
embs_random = tf.tile(tf.slice(embs_random, [0,0], [1,-1]), [self.batch_size,1])
if self.sample_app:
self.embs = embs_random
self.embs_rep = tf.tile(tf.expand_dims(self.embs,-1), [1, 1, self.img_H*self.img_W])
self.embs_rep = tf.reshape(self.embs_rep, [self.batch_size, -1, self.img_H, self.img_W])
self.embs_rep = nchw_to_nhwc(self.embs_rep)
## Use py code to get G_pose_inflated, so the op is out of the graph
self.G_pose_inflated = tf.placeholder(tf.float32, shape=G_pose.get_shape())
with tf.variable_scope("ID_AE") as vs:
G, _, _ = self.Generator_fn(
self.embs_rep, self.G_pose_inflated,
self.channel, self.z_num, self.repeat_num, self.conv_hidden_num, self.data_format, activation_fn=tf.nn.relu, reuse=False)
self.G = denorm_img(G, self.data_format)
self.G_dis_score = self.Discriminator_fn(tf.transpose( G, [0,3,1,2] ), input_dim=3)
## For PatchDis, we need average
shape = self.G_dis_score.get_shape().as_list()
self.G_dis_score = tf.reduce_mean(self.G_dis_score, range(1,len(shape)))
def test(self):
test_result_dir = os.path.join(self.model_dir, self.test_dir_name)
test_result_dir_x = os.path.join(test_result_dir, 'x')
test_result_dir_x_target = os.path.join(test_result_dir, 'x_target')
test_result_dir_G = os.path.join(test_result_dir, 'G')
test_result_dir_pose = os.path.join(test_result_dir, 'pose')
test_result_dir_pose_target = os.path.join(test_result_dir, 'pose_target')
test_result_dir_G_pose = os.path.join(test_result_dir, 'G_pose')
test_result_dir_mask = os.path.join(test_result_dir, 'mask')
test_result_dir_mask_target = os.path.join(test_result_dir, 'mask_target')
if not os.path.exists(test_result_dir):
os.makedirs(test_result_dir)
if not os.path.exists(test_result_dir_x):
os.makedirs(test_result_dir_x)
if not os.path.exists(test_result_dir_x_target):
os.makedirs(test_result_dir_x_target)
if not os.path.exists(test_result_dir_G):
os.makedirs(test_result_dir_G)
if not os.path.exists(test_result_dir_pose):
os.makedirs(test_result_dir_pose)
if not os.path.exists(test_result_dir_pose_target):
os.makedirs(test_result_dir_pose_target)
if not os.path.exists(test_result_dir_G_pose):
os.makedirs(test_result_dir_G_pose)
if not os.path.exists(test_result_dir_mask):
os.makedirs(test_result_dir_mask)
if not os.path.exists(test_result_dir_mask_target):
os.makedirs(test_result_dir_mask_target)
for i in xrange(self.test_batch_num):
x_fixed, x_target_fixed, pose_fixed, pose_target_fixed, pose_rcv_fixed, pose_rcv_target_fixed, \
mask_fixed, mask_target_fixed, part_bbox_fixed, part_bbox_target_fixed, part_vis_fixed, part_vis_target_fixed = self.get_image_from_loader()
x = process_image(x_fixed, 127.5, 127.5)
x_target = process_image(x_target_fixed, 127.5, 127.5)
if i<4:
x_fake, G_pose, G_dis_score = self.generate(x, pose_fixed, pose_rcv_fixed, part_bbox_fixed, part_vis_fixed, test_result_dir, idx=i, save=True)
else:
x_fake, G_pose, G_dis_score = self.generate(x, pose_fixed, pose_rcv_fixed, part_bbox_fixed, part_vis_fixed, test_result_dir, idx=i, save=False)
p = (np.amax(pose_fixed, axis=-1, keepdims=False)+1.0)*127.5
pt = (np.amax(pose_target_fixed, axis=-1, keepdims=False)+1.0)*127.5
for j in xrange(self.batch_size):
idx = i*self.batch_size+j
im = Image.fromarray(x_fixed[j,:].astype(np.uint8))
im.save('%s/%05d.png'%(test_result_dir_x, idx))
im = Image.fromarray(x_target_fixed[j,:].astype(np.uint8))
im.save('%s/%05d.png'%(test_result_dir_x_target, idx))
im = Image.fromarray(x_fake[j,:].astype(np.uint8))
im.save('%s/%04d_c1s1_%06d_%05d_%f.png'%(test_result_dir_G, i, j, idx, G_dis_score[j]))
im = Image.fromarray(p[j,:].astype(np.uint8))
im.save('%s/%05d.png'%(test_result_dir_pose, idx))
im = Image.fromarray(pt[j,:].astype(np.uint8))
im.save('%s/%05d.png'%(test_result_dir_pose_target, idx))
im = Image.fromarray(G_pose[j,:].astype(np.uint8))
im.save('%s/%04d_%04d.png'%(test_result_dir_G_pose, i, j))
im = Image.fromarray(mask_fixed[j,:].squeeze().astype(np.uint8))
im.save('%s/%05d.png'%(test_result_dir_mask, idx))
im = Image.fromarray(mask_target_fixed[j,:].squeeze().astype(np.uint8))
im.save('%s/%05d.png'%(test_result_dir_mask_target, idx))
if 0==i:
save_image(x_fixed, '{}/x_fixed.png'.format(test_result_dir))
save_image(x_target_fixed, '{}/x_target_fixed.png'.format(test_result_dir))
save_image(mask_fixed, '{}/mask_fixed.png'.format(test_result_dir))
save_image(mask_target_fixed, '{}/mask_target_fixed.png'.format(test_result_dir))
save_image((np.amax(pose_fixed, axis=-1, keepdims=True)+1.0)*127.5, '{}/pose_fixed.png'.format(test_result_dir))
save_image((np.amax(pose_target_fixed, axis=-1, keepdims=True)+1.0)*127.5, '{}/pose_target_fixed.png'.format(test_result_dir))
def get_image_from_loader(self):
x, x_target, pose, pose_target, pose_rcv, pose_rcv_target, mask, mask_target, part_bbox, part_bbox_target, part_vis, part_vis_target = self.sess.run([self.x, self.x_target, self.pose, self.pose_target,
self.pose_rcv, self.pose_rcv_target, self.mask_r6, self.mask_r6_target, self.part_bbox, self.part_bbox_target, self.part_vis, self.part_vis_target])
x = unprocess_image(x, 127.5, 127.5)
x_target = unprocess_image(x_target, 127.5, 127.5)
mask = mask*255
mask_target = mask_target*255
return x, x_target, pose, pose_target, pose_rcv, pose_rcv_target, mask, mask_target, part_bbox, part_bbox_target, part_vis, part_vis_target
def generate(self, x_fixed, pose_fixed, pose_rcv_fixed, part_bbox_fixed, part_vis_fixed, root_path=None, path=None, idx=None, save=True):
if self.sample_pose:
G_pose_rcv, G_pose, reconstruct_loss = self.sess.run([self.G_pose_rcv, self.G_pose, self.reconstruct_loss], \
{self.pose: pose_fixed, self.part_bbox: part_bbox_fixed, self.part_vis: part_vis_fixed})
is_normalized=True
else:
## Use reconstructed Pose
# G_pose_rcv, G_pose, reconstruct_loss = self.sess.run([self.G_pose_rcv, self.G_pose, self.reconstruct_loss], {self.pose: pose_fixed})
# is_normalized=True
## Use random Pose
G_pose_rcv, G_pose, reconstruct_loss = self.sess.run([self.pose_rcv, self.pose, self.reconstruct_loss])
G_pose_rcv = np.reshape(G_pose_rcv, [self.batch_size, self.keypoint_num, 3])
G_pose = np.tile(np.amax((G_pose+1)*127.5, axis=-1, keepdims=True), [1,1,1,3])
is_normalized=False
## Use fixed Pose
# G_pose_rcv = np.reshape(pose_rcv_fixed, [self.batch_size, self.keypoint_num, 3])
# G_pose = np.tile(np.amax((pose_rcv_fixed+1)*127.5, axis=-1, keepdims=True), [1,1,1,3])
# reconstruct_loss = 0.0
# is_normalized=False
G_pose_inflated = py_poseInflate(G_pose_rcv, is_normalized=is_normalized, radius=4, img_H=128, img_W=64)
G, G_dis_score = self.sess.run([self.G, self.G_dis_score], {self.x: x_fixed, self.G_pose_inflated: G_pose_inflated, self.part_bbox: part_bbox_fixed})
G_pose_inflated_img = (np.tile(np.amax(G_pose_inflated, axis=-1, keepdims=True), [1,1,1,3])+1)*127.5
ssim_G_x_list = []
for i in xrange(G.shape[0]):
G_gray = rgb2gray((G[i,:]).clip(min=0,max=255).astype(np.uint8))
x_gray = rgb2gray(((x_fixed[i,:]+1)*127.5).clip(min=0,max=255).astype(np.uint8))
ssim_G_x_list.append(ssim(G_gray, x_gray, data_range=x_gray.max() - x_gray.min(), multichannel=False))
ssim_G_x_mean = np.mean(ssim_G_x_list)
if path is None and save:
path = os.path.join(root_path, '{}_G_ssim{}.png'.format(idx,ssim_G_x_mean))
save_image(G, path)
print("[*] Samples saved: {}".format(path))
path = os.path.join(root_path, '{}_G_pose_reLoss{}.png'.format(idx,reconstruct_loss))
save_image(G_pose, path)
print("[*] Samples saved: {}".format(path))
path = os.path.join(root_path, '{}_G_pose_inflated_reLoss{}.png'.format(idx,reconstruct_loss))
save_image(G_pose_inflated_img, path)
print("[*] Samples saved: {}".format(path))
return G, G_pose_inflated_img, G_dis_score
class DPIG_FourNetsFgBg_testOnly(DPIG_FourNets_testOnly):
def init_net(self):
self.build_model()
if self.pretrained_path is not None:
var1 = tf.get_collection(tf.GraphKeys.VARIABLES, scope='Encoder')
var2 = tf.get_collection(tf.GraphKeys.VARIABLES, scope='ID_AE')
var3 = lib.params_with_name('Discriminator.')
self.saverPart = tf.train.Saver(var1+var2+var3, max_to_keep=20)
if self.pretrained_appSample_path is not None:
var_fg = tf.get_collection(tf.GraphKeys.VARIABLES, scope='Gaussian_FC_Fg')
var_bg = tf.get_collection(tf.GraphKeys.VARIABLES, scope='Gaussian_FC_Bg')
self.saverAppSamplePart = tf.train.Saver(var_fg+var_bg, max_to_keep=20)
if self.pretrained_poseAE_path is not None:
var = tf.get_collection(tf.GraphKeys.VARIABLES, scope='PoseAE')
self.saverPosePart = tf.train.Saver(var, max_to_keep=20)
if self.pretrained_poseSample_path is not None:
var = tf.get_collection(tf.GraphKeys.VARIABLES, scope='PoseGaussian')
self.saverPoseSamplePart = tf.train.Saver(var, max_to_keep=20)
self.saver = tf.train.Saver(max_to_keep=20)
self.summary_writer = tf.summary.FileWriter(self.model_dir)
sv = tf.train.Supervisor(logdir=self.model_dir,
is_chief=True,
saver=None,
summary_op=None,
summary_writer=self.summary_writer,
global_step=self.step,
save_model_secs=0,
ready_for_local_init_op=None)
gpu_options = tf.GPUOptions(allow_growth=True)
sess_config = tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options)
self.sess = sv.prepare_or_wait_for_session(config=sess_config)
if self.pretrained_path is not None:
self.saverPart.restore(self.sess, self.pretrained_path)
print('restored from pretrained_path:', self.pretrained_path)
if self.pretrained_appSample_path is not None:
self.saverAppSamplePart.restore(self.sess, self.pretrained_appSample_path)
print('restored from pretrained_appSample_path:', self.pretrained_appSample_path)
if self.pretrained_poseAE_path is not None:
self.saverPosePart.restore(self.sess, self.pretrained_poseAE_path)
print('restored from pretrained_poseAE_path:', self.pretrained_poseAE_path)
if self.pretrained_poseSample_path is not None:
self.saverPoseSamplePart.restore(self.sess, self.pretrained_poseSample_path)
print('restored from pretrained_poseSample_path:', self.pretrained_poseSample_path)
if self.ckpt_path is not None:
self.saver.restore(self.sess, self.ckpt_path)
print('restored from ckpt_path:', self.ckpt_path)
self.test_batch_num = 751
# self.test_batch_num = 1000
# self.test_batch_num = 24000
# self.test_dir_name = 'test_result_SampleApp_FgBg'
# self.test_dir_name = 'test_result_SamplePose_FgBg'
# self.test_dir_name = 'test_result_RandomPose_FgBg'
# self.test_dir_name = 'test_result_SampleAppSamplePose_FgBg'
# self.test_dir_name = 'test_result_ROI7_PatchDis28x28_RealFgBgRandomPose_FixFgRandBg_pretrain_%dx%d'%(self.test_batch_num, self.batch_size)
self.test_dir_name = 'test_result_ROI7_RealFgBgRandomPose_FixFgRandBg_pretrain_%dx%d'%(self.test_batch_num, self.batch_size)
def build_model(self):
self._define_input()
# self.pose_rcv = tf.Print(self.pose_rcv, [self.pose_rcv], 'self.pose_rcv = ', summarize=30)
################################### Pose ###################################
with tf.variable_scope("PoseAE") as vs:
## Norm to [-1, 1]
pose_rcv_norm = tf.reshape(self.pose_rcv, [self.batch_size, self.keypoint_num, 3])
R = tf.cast(tf.slice(pose_rcv_norm, [0,0,0], [-1,-1,1]), tf.float32)/float(self.img_H)*2.0 - 1
C = tf.cast(tf.slice(pose_rcv_norm, [0,0,1], [-1,-1,1]), tf.float32)/float(self.img_W)*2.0 - 1
V = tf.cast(tf.slice(pose_rcv_norm, [0,0,2], [-1,-1,1]), tf.float32)
pose_rcv_norm = tf.concat([R,C,V], axis=-1)
self.pose_embs, self.G_var_encoder = models.PoseEncoderFCRes(tf.reshape(pose_rcv_norm, [self.batch_size,-1]),
z_num=32, repeat_num=4, hidden_num=512, data_format=self.data_format, activation_fn=LeakyReLU, reuse=False)
pose_embs_shape = self.pose_embs.get_shape().as_list()
with tf.variable_scope("PoseGaussian") as vs:
self.G_pose_embs, self.G_var_embs = models.GaussianFCRes(pose_embs_shape, pose_embs_shape[-1], repeat_num=4, hidden_num=512, data_format=self.data_format, activation_fn=LeakyReLU)
with tf.variable_scope("PoseAE") as vs:
G_pose_coord, G_pose_visible, self.G_var_decoder = models.PoseDecoderFCRes(self.pose_embs, self.keypoint_num,
repeat_num=4, hidden_num=512, data_format=self.data_format, activation_fn=LeakyReLU, reuse=False)
self.G_var_pose = self.G_var_encoder + self.G_var_decoder
if self.sample_pose: ## Sampling new poses during testing
self.G_pose_rcv = tf.concat([tf.reshape(G_pose_coord, [self.batch_size,self.keypoint_num,2]), tf.expand_dims(G_pose_visible,-1)], axis=-1)
G_pose = coord2channel_simple_rcv(self.G_pose_rcv, self.keypoint_num, is_normalized=True, img_H=self.img_H, img_W=self.img_W)
self.G_pose = denorm_img(tf.tile(tf.reduce_max(G_pose, axis=-1, keep_dims=True), [1,1,1,3]), self.data_format)
else:
self.G_pose_rcv = pose_rcv_norm
G_pose = coord2channel_simple_rcv(self.G_pose_rcv, self.keypoint_num, is_normalized=True, img_H=self.img_H, img_W=self.img_W)
self.reconstruct_loss = tf.reduce_mean(tf.square(pose_rcv_norm - self.G_pose_rcv))
################################### Appearance ###################################
## Use the pose to generate person with pretrained generator
self.roi_emb_dim = 32
with tf.variable_scope("Encoder") as vs:
pb_list = tf.split(self.part_bbox, self.part_num, axis=1)
pv_list = tf.split(self.part_vis, self.part_num, axis=1)
## Part 1-7 (totally 8)
indices = range(7)
## Part 1,8-16 (totally 10)
# indices = [0] + range(7,16)
select_part_bbox = tf.concat([pb_list[i] for i in indices], axis=1)
select_part_vis = tf.cast(tf.concat([pv_list[i] for i in indices], axis=1), tf.float32)
self.embs, _, _, self.Encoder_var = models.GeneratorCNN_ID_Encoder_BodyROIVis_FgBgFeaTwoBranch(self.x, self.mask_r6, select_part_bbox, select_part_vis, len(indices), self.roi_emb_dim,
self.repeat_num, self.conv_hidden_num, self.data_format, activation_fn=tf.nn.relu, keep_part_prob=1.0, reuse=False)
self.fg_embs = tf.slice(self.embs, [0,0], [-1,len(indices)*self.roi_emb_dim])
self.bg_embs = tf.slice(self.embs, [0,len(indices)*self.roi_emb_dim], [-1,-1])
tf.set_random_seed(0)
with tf.variable_scope("Gaussian_FC_Fg") as vs:
embs_shape = self.fg_embs.get_shape().as_list()
self.app_embs_fg, self.G_var_app_embs_fg = models.GaussianFCRes(embs_shape, embs_shape[-1], repeat_num=4, hidden_num=512, data_format=self.data_format, activation_fn=LeakyReLU)
with tf.variable_scope("Gaussian_FC_Bg") as vs:
embs_shape = self.bg_embs.get_shape().as_list()
self.app_embs_bg, self.G_var_app_embs_bg = models.GaussianFCRes(embs_shape, embs_shape[-1], repeat_num=4, hidden_num=256, data_format=self.data_format, activation_fn=LeakyReLU)
if self.one_app_per_batch:
## Fix fg and random bg for one person
fg_one = tf.tile(tf.slice(self.app_embs_fg, [0,0], [1,-1]), [self.batch_size,1])
embs_random = tf.concat([fg_one, self.app_embs_bg], axis=-1)
## Fix fg and bg for one person
# embs_random = tf.concat([self.app_embs_fg, self.app_embs_bg], axis=-1)
# embs_random = tf.tile(tf.slice(embs_random, [0,0], [1,-1]), [self.batch_size,1])
else:
embs_random = tf.concat([self.app_embs_fg, self.app_embs_bg], axis=-1)
if self.sample_app:
self.embs = embs_random
else:
if self.one_app_per_batch:
## Fix fg and random bg for one person
fg_one = tf.tile(tf.slice(self.fg_embs, [0,0], [1,-1]), [self.batch_size,1])
embs_random = tf.concat([fg_one, self.bg_embs], axis=-1)
self.embs = embs_random
self.embs_rep = tf.tile(tf.expand_dims(self.embs,-1), [1, 1, self.img_H*self.img_W])
self.embs_rep = tf.reshape(self.embs_rep, [self.batch_size, -1, self.img_H, self.img_W])
self.embs_rep = nchw_to_nhwc(self.embs_rep)
## Use py code to get G_pose_inflated, so the op is out of the graph
self.G_pose_inflated = tf.placeholder(tf.float32, shape=G_pose.get_shape())
with tf.variable_scope("ID_AE") as vs:
G, _, _ = self.Generator_fn(
self.embs_rep, self.G_pose_inflated,
self.channel, self.z_num, self.repeat_num, self.conv_hidden_num, self.data_format, activation_fn=tf.nn.relu, reuse=False)
self.G = denorm_img(G, self.data_format)
self.G_dis_score = self.Discriminator_fn(tf.transpose( G, [0,3,1,2] ), input_dim=3)
## For PatchDis, we need average
shape = self.G_dis_score.get_shape().as_list()
self.G_dis_score = tf.reduce_mean(self.G_dis_score, range(1,len(shape)))
class DPIG_FourNetsFgBg_testOnlySampleFactor(DPIG_FourNets_testOnly):
def init_net(self):
self.build_model()
if self.pretrained_path is not None:
var1 = tf.get_collection(tf.GraphKeys.VARIABLES, scope='Encoder')
var2 = tf.get_collection(tf.GraphKeys.VARIABLES, scope='ID_AE')
var3 = lib.params_with_name('Discriminator.')
self.saverPart = tf.train.Saver(var1+var2+var3, max_to_keep=20)
if self.pretrained_appSample_path is not None:
var_fg = tf.get_collection(tf.GraphKeys.VARIABLES, scope='Gaussian_FC_Fg')
var_bg = tf.get_collection(tf.GraphKeys.VARIABLES, scope='Gaussian_FC_Bg')
self.saverAppSamplePart = tf.train.Saver(var_fg+var_bg, max_to_keep=20)
if self.pretrained_poseAE_path is not None:
var = tf.get_collection(tf.GraphKeys.VARIABLES, scope='PoseAE')
self.saverPosePart = tf.train.Saver(var, max_to_keep=20)
if self.pretrained_poseSample_path is not None:
var = tf.get_collection(tf.GraphKeys.VARIABLES, scope='PoseGaussian')
self.saverPoseSamplePart = tf.train.Saver(var, max_to_keep=20)
self.saver = tf.train.Saver(max_to_keep=20)
self.summary_writer = tf.summary.FileWriter(self.model_dir)
sv = tf.train.Supervisor(logdir=self.model_dir,
is_chief=True,
saver=None,
summary_op=None,
summary_writer=self.summary_writer,
global_step=self.step,
save_model_secs=0,
ready_for_local_init_op=None)
gpu_options = tf.GPUOptions(allow_growth=True)
sess_config = tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options)
self.sess = sv.prepare_or_wait_for_session(config=sess_config)
if self.pretrained_path is not None:
self.saverPart.restore(self.sess, self.pretrained_path)
print('restored from pretrained_path:', self.pretrained_path)
if self.pretrained_appSample_path is not None:
self.saverAppSamplePart.restore(self.sess, self.pretrained_appSample_path)
print('restored from pretrained_appSample_path:', self.pretrained_appSample_path)
if self.pretrained_poseAE_path is not None:
self.saverPosePart.restore(self.sess, self.pretrained_poseAE_path)
print('restored from pretrained_poseAE_path:', self.pretrained_poseAE_path)
if self.pretrained_poseSample_path is not None:
self.saverPoseSamplePart.restore(self.sess, self.pretrained_poseSample_path)
print('restored from pretrained_poseSample_path:', self.pretrained_poseSample_path)
if self.ckpt_path is not None:
self.saver.restore(self.sess, self.ckpt_path)
print('restored from ckpt_path:', self.ckpt_path)
# self.test_batch_num = 20 ## For test
self.test_batch_num = 400 ## For IS score
self.test_dir_name = 'test_result_ROI7_SampleFg%rSampleBg%rSamplePose%r_pretrain_%dx%d'%(self.sample_fg,self.sample_bg,self.sample_pose, self.test_batch_num, self.batch_size)
# self.test_dir_name = 'test_result_ROI7_SampleFg%rSampleBg%rRealPose%r_pretrain_%dx%d'%(self.sample_fg,self.sample_bg,self.sample_pose, self.test_batch_num, self.batch_size)
def build_model(self):
self._define_input()
# self.pose_rcv = tf.Print(self.pose_rcv, [self.pose_rcv], 'self.pose_rcv = ', summarize=30)
################################### Pose ###################################
with tf.variable_scope("PoseAE") as vs:
## Norm to [-1, 1]
pose_rcv_norm = tf.reshape(self.pose_rcv, [self.batch_size, self.keypoint_num, 3])
R = tf.cast(tf.slice(pose_rcv_norm, [0,0,0], [-1,-1,1]), tf.float32)/float(self.img_H)*2.0 - 1
C = tf.cast(tf.slice(pose_rcv_norm, [0,0,1], [-1,-1,1]), tf.float32)/float(self.img_W)*2.0 - 1
V = tf.cast(tf.slice(pose_rcv_norm, [0,0,2], [-1,-1,1]), tf.float32)
pose_rcv_norm = tf.concat([R,C,V], axis=-1)
self.pose_embs, self.G_var_encoder = models.PoseEncoderFCRes(tf.reshape(pose_rcv_norm, [self.batch_size,-1]),
z_num=32, repeat_num=4, hidden_num=512, data_format=self.data_format, activation_fn=LeakyReLU, reuse=False)
pose_embs_shape = self.pose_embs.get_shape().as_list()
with tf.variable_scope("PoseGaussian") as vs:
self.G_pose_embs, self.G_var_embs = models.GaussianFCRes(pose_embs_shape, pose_embs_shape[-1], repeat_num=4, hidden_num=512, data_format=self.data_format, activation_fn=LeakyReLU)
with tf.variable_scope("PoseAE") as vs:
G_pose_coord, G_pose_visible, self.G_var_decoder = models.PoseDecoderFCRes(self.pose_embs, self.keypoint_num,
repeat_num=4, hidden_num=512, data_format=self.data_format, activation_fn=LeakyReLU, reuse=False)
self.G_var_pose = self.G_var_encoder + self.G_var_decoder
if self.sample_pose: ## Sampling new poses during testing
self.G_pose_rcv = tf.concat([tf.reshape(G_pose_coord, [self.batch_size,self.keypoint_num,2]), tf.expand_dims(G_pose_visible,-1)], axis=-1)
G_pose = coord2channel_simple_rcv(self.G_pose_rcv, self.keypoint_num, is_normalized=True, img_H=self.img_H, img_W=self.img_W)
self.G_pose = denorm_img(tf.tile(tf.reduce_max(G_pose, axis=-1, keep_dims=True), [1,1,1,3]), self.data_format)
else:
pose_rcv_norm_one = tf.tile(tf.slice(pose_rcv_norm, [0,0,0], [1,-1,-1]), [self.batch_size,1,1])
self.G_pose_rcv = pose_rcv_norm_one
# self.G_pose_rcv = pose_rcv_norm
G_pose = coord2channel_simple_rcv(self.G_pose_rcv, self.keypoint_num, is_normalized=True, img_H=self.img_H, img_W=self.img_W)
self.G_pose = denorm_img(tf.tile(tf.reduce_max(G_pose, axis=-1, keep_dims=True), [1,1,1,3]), self.data_format)
self.reconstruct_loss = tf.reduce_mean(tf.square(pose_rcv_norm - self.G_pose_rcv))
################################### Appearance ###################################
## Use the pose to generate person with pretrained generator
self.roi_emb_dim = 32
with tf.variable_scope("Encoder") as vs:
pb_list = tf.split(self.part_bbox, self.part_num, axis=1)
pv_list = tf.split(self.part_vis, self.part_num, axis=1)
## Part 1-7 (totally 8)
indices = range(7)
## Part 1,8-16 (totally 10)
# indices = [0] + range(7,16)
select_part_bbox = tf.concat([pb_list[i] for i in indices], axis=1)
select_part_vis = tf.cast(tf.concat([pv_list[i] for i in indices], axis=1), tf.float32)
self.embs, _, _, self.Encoder_var = models.GeneratorCNN_ID_Encoder_BodyROIVis_FgBgFeaTwoBranch(self.x, self.mask_r6, select_part_bbox, select_part_vis, len(indices), self.roi_emb_dim,
self.repeat_num, self.conv_hidden_num, self.data_format, activation_fn=tf.nn.relu, keep_part_prob=1.0, reuse=False)
self.fg_embs = tf.slice(self.embs, [0,0], [-1,len(indices)*self.roi_emb_dim])
self.bg_embs = tf.slice(self.embs, [0,len(indices)*self.roi_emb_dim], [-1,-1])
## FG
tf.set_random_seed(0)
with tf.variable_scope("Gaussian_FC_Fg") as vs:
embs_shape = self.fg_embs.get_shape().as_list()
self.app_embs_fg, self.G_var_app_embs_fg = models.GaussianFCRes(embs_shape, embs_shape[-1], repeat_num=4, hidden_num=512, data_format=self.data_format, activation_fn=LeakyReLU)
if self.sample_fg:
self.embs_fg = self.app_embs_fg
else:
fg_one = tf.tile(tf.slice(self.fg_embs, [0,0], [1,-1]), [self.batch_size,1])
self.embs_fg = fg_one
## BG
with tf.variable_scope("Gaussian_FC_Bg") as vs:
embs_shape = self.bg_embs.get_shape().as_list()
self.app_embs_bg, self.G_var_app_embs_bg = models.GaussianFCRes(embs_shape, embs_shape[-1], repeat_num=4, hidden_num=256, data_format=self.data_format, activation_fn=LeakyReLU)
if self.sample_bg:
self.embs_bg = self.app_embs_bg
else:
bg_one = tf.tile(tf.slice(self.bg_embs, [0,0], [1,-1]), [self.batch_size,1])
self.embs_bg = bg_one
self.embs = tf.concat([self.embs_fg, self.embs_bg], axis=-1)
self.embs_rep = tf.tile(tf.expand_dims(self.embs,-1), [1, 1, self.img_H*self.img_W])
self.embs_rep = tf.reshape(self.embs_rep, [self.batch_size, -1, self.img_H, self.img_W])
self.embs_rep = nchw_to_nhwc(self.embs_rep)
## Use py code to get G_pose_inflated, so the op is out of the graph
# self.G_pose_inflated = tf.placeholder(tf.float32, shape=G_pose.get_shape())
self.G_pose_inflated = tf_poseInflate(G_pose, self.keypoint_num, radius=4, img_H=128, img_W=64)
with tf.variable_scope("ID_AE") as vs:
G, _, _ = self.Generator_fn(
self.embs_rep, self.G_pose_inflated,
self.channel, self.z_num, self.repeat_num, self.conv_hidden_num, self.data_format, activation_fn=tf.nn.relu, reuse=False)
self.G = denorm_img(G, self.data_format)
self.G_dis_score = self.Discriminator_fn(tf.transpose( G, [0,3,1,2] ), input_dim=3)
## For PatchDis, we need average
shape = self.G_dis_score.get_shape().as_list()
self.G_dis_score = tf.reduce_mean(self.G_dis_score, range(1,len(shape)))
def generate(self, x_fixed, pose_fixed, pose_rcv_fixed, part_bbox_fixed, part_vis_fixed, root_path=None, path=None, idx=None, save=True):
if self.sample_pose:
G_pose_rcv, G_pose, reconstruct_loss = self.sess.run([self.G_pose_rcv, self.G_pose, self.reconstruct_loss], \
{self.pose: pose_fixed, self.part_bbox: part_bbox_fixed, self.part_vis: part_vis_fixed})
is_normalized=True
else:
## Use reconstructed Pose
# G_pose_rcv, G_pose, reconstruct_loss = self.sess.run([self.G_pose_rcv, self.G_pose, self.reconstruct_loss], {self.pose: pose_fixed})
# is_normalized=True
## Use random Pose
G_pose_rcv, G_pose, reconstruct_loss = self.sess.run([self.pose_rcv, self.pose, self.reconstruct_loss])
G_pose_rcv = np.reshape(G_pose_rcv, [self.batch_size, self.keypoint_num, 3])
G_pose = np.tile(np.amax((G_pose+1)*127.5, axis=-1, keepdims=True), [1,1,1,3])
is_normalized=False
## Use fixed Pose
# G_pose_rcv = np.reshape(pose_rcv_fixed, [self.batch_size, self.keypoint_num, 3])
# G_pose = np.tile(np.amax((pose_rcv_fixed+1)*127.5, axis=-1, keepdims=True), [1,1,1,3])
# reconstruct_loss = 0.0
# is_normalized=False
# G_pose_inflated = py_poseInflate(G_pose_rcv, is_normalized=is_normalized, radius=4, img_H=128, img_W=64)
G, G_dis_score, G_pose_inflated = self.sess.run([self.G, self.G_dis_score, self.G_pose_inflated], {self.x: x_fixed, self.part_bbox: part_bbox_fixed})
G_pose_inflated_img = (np.tile(np.amax(G_pose_inflated, axis=-1, keepdims=True), [1,1,1,3])+1)*127.5
ssim_G_x_list = []
for i in xrange(G.shape[0]):
G_gray = rgb2gray((G[i,:]).clip(min=0,max=255).astype(np.uint8))
x_gray = rgb2gray(((x_fixed[i,:]+1)*127.5).clip(min=0,max=255).astype(np.uint8))
ssim_G_x_list.append(ssim(G_gray, x_gray, data_range=x_gray.max() - x_gray.min(), multichannel=False))
ssim_G_x_mean = np.mean(ssim_G_x_list)
if path is None and save:
path = os.path.join(root_path, '{}_G_ssim{}.png'.format(idx,ssim_G_x_mean))
save_image(G, path)
print("[*] Samples saved: {}".format(path))
path = os.path.join(root_path, '{}_G_pose_reLoss{}.png'.format(idx,reconstruct_loss))
save_image(G_pose, path)
print("[*] Samples saved: {}".format(path))
path = os.path.join(root_path, '{}_G_pose_inflated_reLoss{}.png'.format(idx,reconstruct_loss))
save_image(G_pose_inflated_img, path)
print("[*] Samples saved: {}".format(path))
return G, G_pose_inflated_img, G_dis_score
class DPIG_FourNetsFgBg_testOnlyCondition(DPIG_FourNets_testOnly):
def init_net(self):
self.build_model()
if self.pretrained_path is not None:
var1 = tf.get_collection(tf.GraphKeys.VARIABLES, scope='Encoder')
var2 = tf.get_collection(tf.GraphKeys.VARIABLES, scope='ID_AE')
var3 = tf.get_collection(tf.GraphKeys.VARIABLES, scope='Discriminator.')
self.saverPart = tf.train.Saver(var1+var2+var3, max_to_keep=20)
self.saver = tf.train.Saver(max_to_keep=20)
self.summary_writer = tf.summary.FileWriter(self.model_dir)
sv = tf.train.Supervisor(logdir=self.model_dir,
is_chief=True,
saver=None,
summary_op=None,
summary_writer=self.summary_writer,
global_step=self.step,
save_model_secs=0,
ready_for_local_init_op=None)
gpu_options = tf.GPUOptions(allow_growth=True)
sess_config = tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options)
self.sess = sv.prepare_or_wait_for_session(config=sess_config)
if self.pretrained_path is not None:
self.saverPart.restore(self.sess, self.pretrained_path)
print('restored from pretrained_path:', self.pretrained_path)
if self.ckpt_path is not None:
self.saver.restore(self.sess, self.ckpt_path)
print('restored from ckpt_path:', self.ckpt_path)
# self.test_batch_num = 10 ## for test_samples
self.test_batch_num = 600 ## for test
if self.sample_pose:
self.test_dir_name = 'test_samples_result_ROI7_Condition_TargetPose_%dx%d'%(self.test_batch_num, self.batch_size)
else:
self.test_dir_name = 'test_samples_result_ROI7_Condition_TargetPose_%dx%d'%(self.test_batch_num, self.batch_size)
def build_model(self):
self._define_input()
################################### Appearance ###################################
## Use the pose to generate person with pretrained generator
with tf.variable_scope("Encoder") as vs:
pb_list = tf.split(self.part_bbox, self.part_num, axis=1)
pv_list = tf.split(self.part_vis, self.part_num, axis=1)
## Part 1-7 (totally 8)
indices = range(7)
## Part 1,8-16 (totally 10)
# indices = [0] + range(7,16)
select_part_bbox = tf.concat([pb_list[i] for i in indices], axis=1)
select_part_vis = tf.cast(tf.concat([pv_list[i] for i in indices], axis=1), tf.float32)
self.embs, _, _, self.Encoder_var = models.GeneratorCNN_ID_Encoder_BodyROIVis_FgBgFeaTwoBranch(self.x, self.mask_r6, select_part_bbox, select_part_vis, len(indices), 32,
self.repeat_num, self.conv_hidden_num, self.data_format, activation_fn=tf.nn.relu, keep_part_prob=1.0, reuse=False)
self.embs_rep = tf.tile(tf.expand_dims(self.embs,-1), [1, 1, self.img_H*self.img_W])
self.embs_rep = tf.reshape(self.embs_rep, [self.batch_size, -1, self.img_H, self.img_W])
self.embs_rep = nchw_to_nhwc(self.embs_rep)
with tf.variable_scope("ID_AE") as vs:
G, _, _ = self.Generator_fn(
self.embs_rep, self.pose_target,
self.channel, self.z_num, self.repeat_num, self.conv_hidden_num, self.data_format, activation_fn=tf.nn.relu, reuse=False)
self.G = denorm_img(G, self.data_format)
self.G_dis_score = self.Discriminator_fn(tf.transpose( G, [0,3,1,2] ), input_dim=3)
## For PatchDis, we need average
shape = self.G_dis_score.get_shape().as_list()
self.G_dis_score = tf.reduce_mean(self.G_dis_score, range(1,len(shape)))
def generate(self, x_fixed, x_target_fixed, pose_target_fixed, mask_fixed, part_bbox_fixed, part_vis_fixed, root_path=None, path=None, idx=None, save=True):
G, G_dis_score = self.sess.run([self.G, self.G_dis_score], {self.x: x_fixed, self.pose_target: pose_target_fixed, \
self.mask_r6: mask_fixed, self.part_bbox: part_bbox_fixed, self.part_vis: part_vis_fixed})
ssim_G_x_list = []
for i in xrange(G.shape[0]):
G_gray = rgb2gray((G[i,:]).clip(min=0,max=255).astype(np.uint8))
x_target_gray = rgb2gray(((x_target_fixed[i,:]+1)*127.5).clip(min=0,max=255).astype(np.uint8))
ssim_G_x_list.append(ssim(G_gray, x_target_gray, data_range=x_target_gray.max() - x_target_gray.min(), multichannel=False))
ssim_G_x_mean = np.mean(ssim_G_x_list)
if path is None and save:
path = os.path.join(root_path, '{}_G_ssim{}.png'.format(idx,ssim_G_x_mean))
save_image(G, path)
print("[*] Samples saved: {}".format(path))
return G, G_dis_score
def test(self):
test_result_dir = os.path.join(self.model_dir, self.test_dir_name)
test_result_dir_x = os.path.join(test_result_dir, 'x')
test_result_dir_x_target = os.path.join(test_result_dir, 'x_target')
test_result_dir_G = os.path.join(test_result_dir, 'G')
test_result_dir_pose = os.path.join(test_result_dir, 'pose')
test_result_dir_pose_target = os.path.join(test_result_dir, 'pose_target')
test_result_dir_mask = os.path.join(test_result_dir, 'mask')
test_result_dir_mask_target = os.path.join(test_result_dir, 'mask_target')
if not os.path.exists(test_result_dir):
os.makedirs(test_result_dir)
if not os.path.exists(test_result_dir_x):
os.makedirs(test_result_dir_x)
if not os.path.exists(test_result_dir_x_target):
os.makedirs(test_result_dir_x_target)
if not os.path.exists(test_result_dir_G):
os.makedirs(test_result_dir_G)
if not os.path.exists(test_result_dir_pose):
os.makedirs(test_result_dir_pose)
if not os.path.exists(test_result_dir_pose_target):
os.makedirs(test_result_dir_pose_target)
if not os.path.exists(test_result_dir_mask):
os.makedirs(test_result_dir_mask)
if not os.path.exists(test_result_dir_mask_target):
os.makedirs(test_result_dir_mask_target)
for i in xrange(self.test_batch_num):
x_fixed, x_target_fixed, pose_fixed, pose_target_fixed, pose_rcv_fixed, pose_rcv_target_fixed, \
mask_fixed, mask_target_fixed, part_bbox_fixed, part_bbox_target_fixed, part_vis_fixed, part_vis_target_fixed = self.get_image_from_loader()
x = process_image(x_fixed, 127.5, 127.5)
x_target = process_image(x_target_fixed, 127.5, 127.5)
mask = mask_fixed/255
if 0==i:
x_fake, G_dis_score = self.generate(x, x_target, pose_target_fixed, mask, part_bbox_fixed, part_vis_fixed, test_result_dir, idx=i, save=True)
else:
x_fake, G_dis_score = self.generate(x, x_target, pose_target_fixed, mask, part_bbox_fixed, part_vis_fixed, test_result_dir, idx=i, save=False)
p = (np.amax(pose_fixed, axis=-1, keepdims=False)+1.0)*127.5
pt = (np.amax(pose_target_fixed, axis=-1, keepdims=False)+1.0)*127.5
for j in xrange(self.batch_size):
idx = i*self.batch_size+j
im = Image.fromarray(x_fixed[j,:].astype(np.uint8))
im.save('%s/%05d.png'%(test_result_dir_x, idx))
im = Image.fromarray(x_target_fixed[j,:].astype(np.uint8))
im.save('%s/%05d.png'%(test_result_dir_x_target, idx))
im = Image.fromarray(x_fake[j,:].astype(np.uint8))
im.save('%s/%05d.png'%(test_result_dir_G, i*self.batch_size+j))
# im.save('%s/%04d_c1s1_%06d_%05d_%f.png'%(test_result_dir_G, i, j, idx, G_dis_score[j]))
im = Image.fromarray(p[j,:].astype(np.uint8))
im.save('%s/%05d.png'%(test_result_dir_pose, idx))
im = Image.fromarray(pt[j,:].astype(np.uint8))
im.save('%s/%05d.png'%(test_result_dir_pose_target, idx))
# pdb.set_trace()
im = Image.fromarray(mask_fixed[j,:].squeeze().astype(np.uint8))
im.save('%s/%05d.png'%(test_result_dir_mask, idx))
im = Image.fromarray(mask_target_fixed[j,:].squeeze().astype(np.uint8))
im.save('%s/%05d.png'%(test_result_dir_mask_target, idx))
# pdb.set_trace()
if 0==i:
save_image(x_fixed, '{}/x_fixed.png'.format(test_result_dir))
save_image(x_target_fixed, '{}/x_target_fixed.png'.format(test_result_dir))
save_image(mask_fixed, '{}/mask_fixed.png'.format(test_result_dir))
save_image(mask_target_fixed, '{}/mask_target_fixed.png'.format(test_result_dir))
save_image((np.amax(pose_fixed, axis=-1, keepdims=True)+1.0)*127.5, '{}/pose_fixed.png'.format(test_result_dir))
save_image((np.amax(pose_target_fixed, axis=-1, keepdims=True)+1.0)*127.5, '{}/pose_target_fixed.png'.format(test_result_dir))
################################################################################################
####################################### DF test models #########################################
class DPIG_ThreeNetsApp_testOnlyCondition_256(DPIG_Encoder_GAN_BodyROI_256):
def init_net(self):
self.build_model()
if self.pretrained_path is not None:
var1 = tf.get_collection(tf.GraphKeys.VARIABLES, scope='Encoder')
var2 = tf.get_collection(tf.GraphKeys.VARIABLES, scope='ID_AE')
self.saverPart = tf.train.Saver(var1+var2, max_to_keep=20)
self.saver = tf.train.Saver(max_to_keep=20)
self.summary_writer = tf.summary.FileWriter(self.model_dir)
sv = tf.train.Supervisor(logdir=self.model_dir,
is_chief=True,
saver=None,
summary_op=None,
summary_writer=self.summary_writer,
global_step=self.step,
save_model_secs=0,
ready_for_local_init_op=None)
gpu_options = tf.GPUOptions(allow_growth=True)
sess_config = tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options)
self.sess = sv.prepare_or_wait_for_session(config=sess_config)
if self.pretrained_path is not None:
self.saverPart.restore(self.sess, self.pretrained_path)
print('restored from pretrained_path:', self.pretrained_path)
if self.ckpt_path is not None:
self.saver.restore(self.sess, self.ckpt_path)
print('restored from ckpt_path:', self.ckpt_path)
# self.test_batch_num = 30 ## for test_samples
self.test_batch_num = 400 ## for test
if self.sample_pose:
self.test_dir_name = 'test_result_ROI7_Condition_TargetPose_%dx%d'%(self.test_batch_num, self.batch_size)
else:
self.test_dir_name = 'test_result_ROI7_Condition_TargetPose_%dx%d'%(self.test_batch_num, self.batch_size)
def build_model(self):
self._define_input()
with tf.variable_scope("Encoder") as vs:
pb_list = tf.split(self.part_bbox, self.part_num, axis=1)
pv_list = tf.split(self.part_vis, self.part_num, axis=1)
## Part 1-7 (totally 7)
indices = range(7)
select_part_bbox = tf.concat([pb_list[i] for i in indices], axis=1)
select_part_vis = tf.cast(tf.concat([pv_list[i] for i in indices], axis=1), tf.float32)
self.embs, _, self.Encoder_var = models.GeneratorCNN_ID_Encoder_BodyROIVis(self.x,select_part_bbox, select_part_vis, len(indices), 32,
self.repeat_num+1, self.conv_hidden_num, self.data_format, activation_fn=tf.nn.relu, keep_part_prob=1.0, roi_size=64, reuse=False)
self.embs_rep = tf.tile(tf.expand_dims(self.embs,-1), [1, 1, self.img_H*self.img_W])
self.embs_rep = tf.reshape(self.embs_rep, [self.batch_size, -1, self.img_H, self.img_W])
self.embs_rep = nchw_to_nhwc(self.embs_rep)
with tf.variable_scope("ID_AE") as vs:
G, _, _ = self.Generator_fn(
self.embs_rep, self.pose_target,
self.channel, self.z_num, self.repeat_num-1, self.conv_hidden_num, self.data_format, activation_fn=tf.nn.relu, reuse=False)
self.G = denorm_img(G, self.data_format)
def test(self):
test_result_dir = os.path.join(self.model_dir, self.test_dir_name)
test_result_dir_x = os.path.join(test_result_dir, 'x')
test_result_dir_x_target = os.path.join(test_result_dir, 'x_target')
test_result_dir_G = os.path.join(test_result_dir, 'G')
test_result_dir_pose = os.path.join(test_result_dir, 'pose')
test_result_dir_pose_target = os.path.join(test_result_dir, 'pose_target')
test_result_dir_mask = os.path.join(test_result_dir, 'mask')
test_result_dir_mask_target = os.path.join(test_result_dir, 'mask_target')
if not os.path.exists(test_result_dir):
os.makedirs(test_result_dir)
if not os.path.exists(test_result_dir_x):
os.makedirs(test_result_dir_x)
if not os.path.exists(test_result_dir_x_target):
os.makedirs(test_result_dir_x_target)
if not os.path.exists(test_result_dir_G):
os.makedirs(test_result_dir_G)
if not os.path.exists(test_result_dir_pose):
os.makedirs(test_result_dir_pose)
if not os.path.exists(test_result_dir_pose_target):
os.makedirs(test_result_dir_pose_target)
if not os.path.exists(test_result_dir_mask):
os.makedirs(test_result_dir_mask)
if not os.path.exists(test_result_dir_mask_target):
os.makedirs(test_result_dir_mask_target)
for i in xrange(self.test_batch_num):
x_fixed, x_target_fixed, pose_fixed, pose_target_fixed, pose_rcv_fixed, pose_rcv_target_fixed, mask_fixed, mask_target_fixed, \
part_bbox_fixed, part_bbox_target_fixed, part_vis_fixed, part_vis_target_fixed = self.get_image_from_loader()
x = process_image(x_fixed, 127.5, 127.5)
x_target = process_image(x_target_fixed, 127.5, 127.5)
if 0==i:
x_fake = self.generate(x, x_target, pose_target_fixed, part_bbox_fixed, part_vis_fixed, test_result_dir, idx=i, save=True)
else:
x_fake = self.generate(x, x_target, pose_target_fixed, part_bbox_fixed, part_vis_fixed, test_result_dir, idx=i, save=False)
p = (np.amax(pose_fixed, axis=-1, keepdims=False)+1.0)*127.5
pt = (np.amax(pose_target_fixed, axis=-1, keepdims=False)+1.0)*127.5
for j in xrange(self.batch_size):
idx = i*self.batch_size+j
im = Image.fromarray(x_fixed[j,:].astype(np.uint8))
im.save('%s/%05d.png'%(test_result_dir_x, idx))
im = Image.fromarray(x_target_fixed[j,:].astype(np.uint8))
im.save('%s/%05d.png'%(test_result_dir_x_target, idx))
im = Image.fromarray(x_fake[j,:].astype(np.uint8))
im.save('%s/%05d.png'%(test_result_dir_G, i*self.batch_size+j))
im = Image.fromarray(p[j,:].astype(np.uint8))
im.save('%s/%05d.png'%(test_result_dir_pose, idx))
im = Image.fromarray(pt[j,:].astype(np.uint8))
im.save('%s/%05d.png'%(test_result_dir_pose_target, idx))
# pdb.set_trace()
im = Image.fromarray(mask_fixed[j,:].squeeze().astype(np.uint8))
im.save('%s/%05d.png'%(test_result_dir_mask, idx))
im = Image.fromarray(mask_target_fixed[j,:].squeeze().astype(np.uint8))
im.save('%s/%05d.png'%(test_result_dir_mask_target, idx))
# pdb.set_trace()
if 0==i:
save_image(x_fixed, '{}/x_fixed.png'.format(test_result_dir))
save_image(x_target_fixed, '{}/x_target_fixed.png'.format(test_result_dir))
save_image(mask_fixed, '{}/mask_fixed.png'.format(test_result_dir))
save_image(mask_target_fixed, '{}/mask_target_fixed.png'.format(test_result_dir))
save_image((np.amax(pose_fixed, axis=-1, keepdims=True)+1.0)*127.5, '{}/pose_fixed.png'.format(test_result_dir))
save_image((np.amax(pose_target_fixed, axis=-1, keepdims=True)+1.0)*127.5, '{}/pose_target_fixed.png'.format(test_result_dir))
def generate(self, x_fixed, x_target_fixed, pose_target_fixed, part_bbox_fixed, part_vis_fixed, root_path=None, path=None, idx=None, save=True):
G = self.sess.run(self.G, {self.x: x_fixed, self.pose_target: pose_target_fixed, \
self.part_bbox: part_bbox_fixed, self.part_vis: part_vis_fixed})
ssim_G_x_list = []
for i in xrange(G.shape[0]):
G_gray = rgb2gray((G[i,:]).clip(min=0,max=255).astype(np.uint8))
x_target_gray = rgb2gray(((x_target_fixed[i,:]+1)*127.5).clip(min=0,max=255).astype(np.uint8))
ssim_G_x_list.append(ssim(G_gray, x_target_gray, data_range=x_target_gray.max() - x_target_gray.min(), multichannel=False))
ssim_G_x_mean = np.mean(ssim_G_x_list)
if path is None and save:
path = os.path.join(root_path, '{}_G_ssim{}.png'.format(idx,ssim_G_x_mean))
save_image(G, path)
print("[*] Samples saved: {}".format(path))
return G
class DPIG_ThreeNetsApp_testOnlySampleFactor_256(DPIG_Encoder_GAN_BodyROI_256):
def _define_input(self):
self.train_LuNet = tf.Variable(False, name='phase')
self.is_train_tensor = tf.Variable(self.is_train, name='phase')
self.Generator_encoder_fn = models.GeneratorCNN_ID_Encoder
self.Generator_fn = models.GeneratorCNN_ID_UAEAfterResidual
# self.wgan_gp = WGAN_GP(DATA_DIR='', MODE='dcgan', DIM=64, BATCH_SIZE=self.batch_size, ITERS=200000,
# LAMBDA=10, G_OUTPUT_DIM=self.img_H*self.img_W*3)
# self.Discriminator_fn = self._getDiscriminator(self.wgan_gp, arch='DCGAN')
def init_net(self):
self.build_model()
if self.pretrained_path is not None:
var1 = tf.get_collection(tf.GraphKeys.VARIABLES, scope='Encoder')
var2 = tf.get_collection(tf.GraphKeys.VARIABLES, scope='ID_AE')
# var3 = lib.params_with_name('Discriminator.')
self.saverPart = tf.train.Saver(var1+var2, max_to_keep=20)
if self.pretrained_appSample_path is not None:
var = tf.get_collection(tf.GraphKeys.VARIABLES, scope='Gaussian_FC')
self.saverAppSamplePart = tf.train.Saver(var, max_to_keep=20)
if self.pretrained_poseAE_path is not None:
var = tf.get_collection(tf.GraphKeys.VARIABLES, scope='PoseAE')
self.saverPosePart = tf.train.Saver(var, max_to_keep=20)
if self.pretrained_poseSample_path is not None:
var = tf.get_collection(tf.GraphKeys.VARIABLES, scope='PoseGaussian')
self.saverPoseSamplePart = tf.train.Saver(var, max_to_keep=20)
self.saver = tf.train.Saver(max_to_keep=20)
self.summary_writer = tf.summary.FileWriter(self.model_dir)
sv = tf.train.Supervisor(logdir=self.model_dir,
is_chief=True,
saver=None,
summary_op=None,
summary_writer=self.summary_writer,
global_step=self.step,
save_model_secs=0,
ready_for_local_init_op=None)
gpu_options = tf.GPUOptions(allow_growth=True)
sess_config = tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options)
self.sess = sv.prepare_or_wait_for_session(config=sess_config)
if self.pretrained_path is not None:
self.saverPart.restore(self.sess, self.pretrained_path)
print('restored from pretrained_path:', self.pretrained_path)
if self.pretrained_appSample_path is not None:
self.saverAppSamplePart.restore(self.sess, self.pretrained_appSample_path)
print('restored from pretrained_appSample_path:', self.pretrained_appSample_path)
if self.pretrained_poseAE_path is not None:
self.saverPosePart.restore(self.sess, self.pretrained_poseAE_path)
print('restored from pretrained_poseAE_path:', self.pretrained_poseAE_path)
if self.pretrained_poseSample_path is not None:
self.saverPoseSamplePart.restore(self.sess, self.pretrained_poseSample_path)
print('restored from pretrained_poseSample_path:', self.pretrained_poseSample_path)
if self.ckpt_path is not None:
self.saver.restore(self.sess, self.ckpt_path)
print('restored from ckpt_path:', self.ckpt_path)
self.test_batch_num = 100 ## For test
# self.test_batch_num = 400 ## For IS score
self.test_dir_name = 'test_result_ROI7_SampleApp%rSamplePose%r_pretrain_%dx%d'%(self.sample_app,self.sample_pose, self.test_batch_num, self.batch_size)
# self.test_dir_name = 'test_result_ROI7_SampleFg%rSampleBg%rRealPose%r_pretrain_%dx%d'%(self.sample_fg,self.sample_bg,self.sample_pose, self.test_batch_num, self.batch_size)
def build_model(self):
self._define_input()
################################### Pose ###################################
with tf.variable_scope("PoseAE") as vs:
## Norm to [-1, 1]
pose_rcv_norm = tf.reshape(self.pose_rcv, [self.batch_size, self.keypoint_num, 3])
R = tf.cast(tf.slice(pose_rcv_norm, [0,0,0], [-1,-1,1]), tf.float32)/float(self.img_H)*2.0 - 1
C = tf.cast(tf.slice(pose_rcv_norm, [0,0,1], [-1,-1,1]), tf.float32)/float(self.img_W)*2.0 - 1
V = tf.cast(tf.slice(pose_rcv_norm, [0,0,2], [-1,-1,1]), tf.float32)
pose_rcv_norm = tf.concat([R,C,V], axis=-1)
self.pose_embs, self.G_var_encoder = models.PoseEncoderFCRes(tf.reshape(pose_rcv_norm, [self.batch_size,-1]),
z_num=32, repeat_num=4, hidden_num=512, data_format=self.data_format, activation_fn=LeakyReLU, reuse=False)
pose_embs_shape = self.pose_embs.get_shape().as_list()
with tf.variable_scope("PoseGaussian") as vs:
self.G_pose_embs, self.G_var_embs = models.GaussianFCRes(pose_embs_shape, pose_embs_shape[-1], repeat_num=4, hidden_num=512, data_format=self.data_format, activation_fn=LeakyReLU)