Python util.util.util() Examples
The following are 30
code examples of util.util.util().
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Example #1
| Source File: ui_model.py From EverybodyDanceNow_reproduce_pytorch with MIT License | 6 votes |
|
def add_objects(self, click_src, label_tgt, mask, style_id=0):
y, x = click_src[0], click_src[1]
mask = np.transpose(mask, (2, 0, 1))[np.newaxis,...]
idx_src = torch.from_numpy(mask).cuda().nonzero()
idx_src[:,2] += y
idx_src[:,3] += x
# backup current maps
self.backup_current_state()
# update label map
self.label_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
for k in range(self.opt.label_nc):
self.net_input[idx_src[:,0], idx_src[:,1] + k, idx_src[:,2], idx_src[:,3]] = 0
self.net_input[idx_src[:,0], idx_src[:,1] + label_tgt, idx_src[:,2], idx_src[:,3]] = 1
# update instance map
self.inst_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
self.net_input[:,-1,:,:] = self.get_edges(self.inst_map)
# update feature map
self.set_features(idx_src, self.feat, style_id)
self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
Example #2
| Source File: ui_model.py From deep-learning-for-document-dewarping with MIT License | 6 votes |
|
def add_objects(self, click_src, label_tgt, mask, style_id=0):
y, x = click_src[0], click_src[1]
mask = np.transpose(mask, (2, 0, 1))[np.newaxis,...]
idx_src = torch.from_numpy(mask).cuda().nonzero()
idx_src[:,2] += y
idx_src[:,3] += x
# backup current maps
self.backup_current_state()
# update label map
self.label_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
for k in range(self.opt.label_nc):
self.net_input[idx_src[:,0], idx_src[:,1] + k, idx_src[:,2], idx_src[:,3]] = 0
self.net_input[idx_src[:,0], idx_src[:,1] + label_tgt, idx_src[:,2], idx_src[:,3]] = 1
# update instance map
self.inst_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
self.net_input[:,-1,:,:] = self.get_edges(self.inst_map)
# update feature map
self.set_features(idx_src, self.feat, style_id)
self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
Example #3
| Source File: innerResShiftTriple.py From Shift-Net_pytorch with MIT License | 6 votes |
|
def forward(self, input):
#print(input.shape)
_, self.c, self.h, self.w = input.size()
self.flag = util.cal_flag_given_mask_thred(self.mask, self.shift_sz, self.stride, self.mask_thred)
shift_out = InnerShiftTripleFunction.apply(input, self.shift_sz, self.stride, self.triple_weight, self.flag, self.show_flow)
c_out = shift_out.size(1)
# get F_c, F_s, F_shift
F_c = shift_out.narrow(1, 0, c_out//3)
F_s = shift_out.narrow(1, c_out//3, c_out//3)
F_shift = shift_out.narrow(1, c_out*2//3, c_out//3)
F_fuse = F_c * F_shift
F_com = torch.cat([F_c, F_fuse], dim=1)
res_out = self.res_net(F_com)
F_c = F_c + res_out
final_out = torch.cat([F_c, F_s], dim=1)
if self.show_flow:
self.flow_srcs = InnerShiftTripleFunction.get_flow_src()
return final_out
Example #4
| Source File: test_model.py From non-stationary_texture_syn with MIT License | 6 votes |
|
def recurrent_test(self, step=5):
input_size = self.input_A.cpu().shape
width,height = input_size[3], input_size[2]
results = []
self.real_A = Variable(self.input_A, volatile=True)
self.fake_B = self.netG.forward(self.real_A)
real_A = util.tensor2im(self.real_A.data)
fake_B = util.tensor2im(self.fake_B.data)
results.append(('real_{}_A'.format(0), real_A))
results.append(('fake_{}_B'.format(0), fake_B))
for i in range(1, step):
# rw = random.randint(0, width)
# rh = random.randint(0, height)
rw = int(width/2)
rh = int(height/2)
self.real_A = Variable(self.fake_B.data[:, :, rh:rh + height, rw:rw + width], volatile=True)
self.fake_B = self.netG.forward(self.real_A)
real_A = util.tensor2im(self.real_A.data)
fake_B = util.tensor2im(self.fake_B.data)
results.append(('real_{}_A'.format(i), real_A))
results.append(('fake_{}_B'.format(i), fake_B))
return OrderedDict(results)
Example #5
| Source File: ui_model.py From everybody_dance_now_pytorch with GNU Affero General Public License v3.0 | 6 votes |
|
def add_objects(self, click_src, label_tgt, mask, style_id=0):
y, x = click_src[0], click_src[1]
mask = np.transpose(mask, (2, 0, 1))[np.newaxis,...]
idx_src = torch.from_numpy(mask).cuda().nonzero()
idx_src[:,2] += y
idx_src[:,3] += x
# backup current maps
self.backup_current_state()
# update label map
self.label_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
for k in range(self.opt.label_nc):
self.net_input[idx_src[:,0], idx_src[:,1] + k, idx_src[:,2], idx_src[:,3]] = 0
self.net_input[idx_src[:,0], idx_src[:,1] + label_tgt, idx_src[:,2], idx_src[:,3]] = 1
# update instance map
self.inst_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
self.net_input[:,-1,:,:] = self.get_edges(self.inst_map)
# update feature map
self.set_features(idx_src, self.feat, style_id)
self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
Example #6
| Source File: test_model.py From non-stationary_texture_syn with MIT License | 6 votes |
|
def recurrent_test_l2_searching(self, step=5):
input_size = self.input_A.cpu().shape
width,height = input_size[3], input_size[2]
results = []
self.real_A = Variable(self.input_A, volatile=True)
self.fake_B = self.netG.forward(self.real_A)
real_A = util.tensor2im(self.real_A.data)
fake_B = util.tensor2im(self.fake_B.data)
results.append(('l2_search_real_{}_A'.format(0), real_A))
results.append(('l2_search_fake_{}_B'.format(0), fake_B))
for i in range(1, step):
# rw = random.randint(0, width)
# rh = random.randint(0, height)
rw, rh = self.l2_searching(self.real_A.clone(), self.fake_B.clone())
print("end selection: ", rw, rh)
self.real_A = Variable(self.fake_B.data[:, :, rh:rh + height, rw:rw + width], volatile=True)
self.fake_B = self.netG.forward(self.real_A)
real_A = util.tensor2im(self.real_A.data)
fake_B = util.tensor2im(self.fake_B.data)
results.append(('l2_search_real_{}_{}_{}_A'.format(i, rw, rh), real_A))
results.append(('l2_search_fake_{}_B'.format(i), fake_B))
return OrderedDict(results)
Example #7
| Source File: test_model.py From non-stationary_texture_syn with MIT License | 6 votes |
|
def random_crop(self, crop_patch=6):
input_size = self.input_A.cpu().shape
width, height = input_size[3], input_size[2]
results = []
self.real_A = Variable(self.input_A, volatile=True)
self.fake_B = self.netG.forward(self.real_A)
src_fake_B = self.fake_B.clone()
real_A = util.tensor2im(self.real_A.data)
fake_B = util.tensor2im(self.fake_B.data)
results.append(('real_A', real_A))
results.append(('fake_{}_B'.format('src'), fake_B))
for i in range(0, crop_patch):
rw = random.randint(0, width)
rh = random.randint(0, height)
self.real_A = Variable(src_fake_B.data[:, :, rh:rh + height, rw:rw + width], volatile=True)
self.fake_B = self.netG.forward(self.real_A)
real_A = util.tensor2im(self.real_A.data)
fake_B = util.tensor2im(self.fake_B.data)
results.append(('real_{}_{}_{}_A'.format(i, rw, rh), real_A))
results.append(('fake_{}_B'.format(i), fake_B))
return OrderedDict(results)
Example #8
| Source File: test_model.py From non-stationary_texture_syn with MIT License | 6 votes |
|
def random_crop_256x256(self, crop_patch=6):
input_size = self.input_A.cpu().shape
width, height = input_size[3], input_size[2]
results = []
self.real_A = Variable(self.input_A, volatile=True)
real_A_src = self.real_A.clone()
# self.fake_B = self.netG.forward(self.real_A)
# src_fake_B = self.fake_B.clone()
real_A = util.tensor2im(self.real_A.data)
# fake_B = util.tensor2im(self.fake_B.data)
results.append(('real_A', real_A))
# results.append(('fake_{}_B'.format('src'), fake_B))
for i in range(0, crop_patch):
rw = random.randint(0, width - 256)
rh = random.randint(0, height - 256)
self.real_A = Variable(real_A_src.data[:, :, rh:rh + 256, rw:rw + 256], volatile=True)
self.fake_B = self.netG.forward(self.real_A)
real_A = util.tensor2im(self.real_A.data)
fake_B = util.tensor2im(self.fake_B.data)
results.append(('256_real_{}_{}_{}_A'.format(i, rw, rh), real_A))
results.append(('512_fake_{}_B'.format(i), fake_B))
return OrderedDict(results)
Example #9
| Source File: sparse_wgangp_pix2pix_model.py From iSketchNFill with GNU General Public License v3.0 | 6 votes |
|
def get_latent_space_visualization(self,num_interpolate=20,label_1=-1,label_2=-1):
rand_perm = np.random.permutation( self.opt.n_classes )
if label_1 == -1:
label_1 = self.label[0] #rand_perm[0]
if label_2 == -1:
label_2 = self.opt.target_label #rand_perm[1]
alpha_blends = np.linspace(0,1,num_interpolate)
self.label[0] = label_1
output_gate_1 = self.netG.forward_gate(self.label)
self.label[0] = label_2
output_gate_2 = self.netG.forward_gate(self.label)
results={}
results['latent_real_A']=util.tensor2im(self.real_A.data)
results['latent_real_B']=util.tensor2im(self.real_B.data)
for i in range(num_interpolate):
alpha_blend = alpha_blends[i]
output_gate = output_gate_1*alpha_blend + output_gate_2*(1-alpha_blend)
self.fake_B = self.netG.forward_main( self.real_A,output_gate)
results['%d_L_fake_B_inter'%(i)]=util.tensor2im(self.fake_B.data)
return OrderedDict(results)
Example #10
| Source File: InnerFaceShiftTriple.py From Shift-Net_pytorch with MIT License | 6 votes |
|
def forward(self, input, flip_feat=None):
self.bz, self.c, self.h, self.w = input.size()
if self.device != 'cpu':
self._split_mask(self.bz)
else:
self.cur_mask = self.mask_all
self.mask = self.cur_mask
self.mask_flip = torch.flip(self.mask, [3])
self.flag = util.cal_flag_given_mask_thred(self.mask, self.shift_sz, self.stride, self.mask_thred)
self.flag_flip = util.cal_flag_given_mask_thred(self.mask_flip, self.shift_sz, self.stride, self.mask_thred)
final_out = InnerFaceShiftTripleFunction.apply(input, self.shift_sz, self.stride, self.triple_weight, self.flag, self.flag_flip, self.show_flow, flip_feat)
if self.show_flow:
self.flow_srcs = InnerFaceShiftTripleFunction.get_flow_src()
innerFeat = input.clone().narrow(1, self.c//2, self.c//2)
return final_out, innerFeat
Example #11
| Source File: ui_model.py From EverybodyDanceNow-Temporal-FaceGAN with MIT License | 6 votes |
|
def add_objects(self, click_src, label_tgt, mask, style_id=0):
y, x = click_src[0], click_src[1]
mask = np.transpose(mask, (2, 0, 1))[np.newaxis,...]
idx_src = torch.from_numpy(mask).cuda().nonzero()
idx_src[:,2] += y
idx_src[:,3] += x
# backup current maps
self.backup_current_state()
# update label map
self.label_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
for k in range(self.opt.label_nc):
self.net_input[idx_src[:,0], idx_src[:,1] + k, idx_src[:,2], idx_src[:,3]] = 0
self.net_input[idx_src[:,0], idx_src[:,1] + label_tgt, idx_src[:,2], idx_src[:,3]] = 1
# update instance map
self.inst_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
self.net_input[:,-1,:,:] = self.get_edges(self.inst_map)
# update feature map
self.set_features(idx_src, self.feat, style_id)
self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
Example #12
| Source File: CycleGAN.py From Bayesian-CycleGAN with MIT License | 6 votes |
|
def get_current_visuals(self):
real_A = util.tensor2im(self.input_A)
fake_B = util.tensor2im(self.fake_B)
rec_A = util.tensor2im(self.rec_A)
real_B = util.tensor2im(self.input_B)
fake_A = util.tensor2im(self.fake_A)
rec_B = util.tensor2im(self.rec_B)
visuals = OrderedDict([
('real_A', real_A),
('fake_B', fake_B),
('rec_A', rec_A),
('real_B', real_B),
('fake_A', fake_A),
('rec_B', rec_B)
])
return visuals
Example #13
| Source File: label_channel_gated_pix2pix_model.py From iSketchNFill with GNU General Public License v3.0 | 6 votes |
|
def get_latent_space_visualization(self,num_interpolate=20,label_1=-1,label_2=-1):
rand_perm = np.random.permutation( self.opt.n_classes )
if label_1 == -1:
label_1 = self.label[0] #rand_perm[0]
if label_2 == -1:
label_2 = self.opt.target_label #rand_perm[1]
alpha_blends = np.linspace(0,1,num_interpolate)
self.label[0] = label_1
output_gate_1 = self.netG.forward_gate(self.label)
self.label[0] = label_2
output_gate_2 = self.netG.forward_gate(self.label)
results={}
results['latent_real_A']=util.tensor2im(self.real_A.data)
results['latent_real_B']=util.tensor2im(self.real_B.data)
for i in range(num_interpolate):
alpha_blend = alpha_blends[i]
output_gate = output_gate_1*alpha_blend + output_gate_2*(1-alpha_blend)
self.fake_B = self.netG.forward_main( self.real_A,output_gate)
results['%d_L_fake_B_inter'%(i)]=util.tensor2im(self.fake_B.data)
return OrderedDict(results)
Example #14
| Source File: CycleGAN_bayes_z.py From Bayesian-CycleGAN with MIT License | 6 votes |
|
def get_current_visuals(self):
real_A = util.tensor2im(self.input_A)
fake_B = util.tensor2im(self.fake_B)
rec_A = util.tensor2im(self.rec_A)
real_B = util.tensor2im(self.input_B)
fake_A = util.tensor2im(self.fake_A)
rec_B = util.tensor2im(self.rec_B)
visuals = OrderedDict([
('real_A', real_A),
('fake_B', fake_B),
('rec_A', rec_A),
('real_B', real_B),
('fake_A', fake_A),
('rec_B', rec_B)
])
return visuals
Example #15
| Source File: CycleGAN_bayes.py From Bayesian-CycleGAN with MIT License | 6 votes |
|
def get_current_visuals(self):
real_A = util.tensor2im(self.input_A)
fake_B = util.tensor2im(self.fake_B)
rec_A = util.tensor2im(self.rec_A)
real_B = util.tensor2im(self.input_B)
fake_A = util.tensor2im(self.fake_A)
rec_B = util.tensor2im(self.rec_B)
visuals = OrderedDict([
('real_A', real_A),
('fake_B', fake_B),
('rec_A', rec_A),
('real_B', real_B),
('fake_A', fake_A),
('rec_B', rec_B)
])
return visuals
Example #16
| Source File: Gen_final_v1.py From Talking-Face-Generation-DAVS with MIT License | 6 votes |
|
def get_current_visuals(self):
fake_B_audio = self.audio_gen_fakes.view(-1, self.opt.sequence_length, self.opt.image_channel_size, self.opt.image_size, self.opt.image_size)
fake_B_image = self.image_gen_fakes.view(-1, self.opt.sequence_length, self.opt.image_channel_size, self.opt.image_size, self.opt.image_size)
real_A = util.tensor2im(self.real_A.data)
oderdict = OrderedDict([('real_A', real_A)])
fake_audio_B = {}
fake_image_B = {}
real_B = {}
for i in range(self.opt.sequence_length):
fake_audio_B[i] = util.tensor2im(fake_B_audio[:, i, :, :, :].data)
fake_image_B[i] = util.tensor2im(fake_B_image[:, i, :, :, :].data)
real_B[i] = util.tensor2im(self.real_videos[:, i, :, :, :].data)
oderdict['real_B_' + str(i)] = real_B[i]
oderdict['fake_audio_B_' + str(i)] = fake_audio_B[i]
oderdict['fake_image_B_' + str(i)] = fake_image_B[i]
return oderdict
Example #17
| Source File: pix2pixHD_condImg_model.py From neurips18_hierchical_image_manipulation with MIT License | 5 votes |
|
def get_current_visuals(self):
return OrderedDict([
('input_label', util.tensor2label(self.input_label, self.opt.label_nc)),
('input_image', util.tensor2im(self.input_image)),
('real_image', util.tensor2im(self.real_image)),
('synthesized_image', util.tensor2im(self.fake_image))
])
Example #18
| Source File: pix2pixHD_condImgColor_model.py From neurips18_hierchical_image_manipulation with MIT License | 5 votes |
|
def get_current_visuals(self):
return OrderedDict([
('input_label', util.tensor2label(self.input_label, self.opt.label_nc)),
('input_image', util.tensor2im(self.input_image)),
('real_image', util.tensor2im(self.real_image)),
('synthesized_image', util.tensor2im(self.fake_image))
])
Example #19
| Source File: innerPatchSoftShiftTriple.py From Shift-Net_pytorch with MIT License | 5 votes |
|
def set_mask(self, mask_global):
mask = util.cal_feat_mask(mask_global, self.layer_to_last)
self.mask = mask
return self.mask
# If mask changes, then need to set cal_fix_flag true each iteration.
Example #20
| Source File: combogan_model.py From ToDayGAN with BSD 2-Clause "Simplified" License | 5 votes |
|
def get_current_visuals(self, testing=False):
if not testing:
self.visuals = [self.real_A, self.fake_B, self.rec_A, self.real_B, self.fake_A, self.rec_B]
self.labels = ['real_A', 'fake_B', 'rec_A', 'real_B', 'fake_A', 'rec_B']
images = [util.tensor2im(v.data) for v in self.visuals]
return OrderedDict(zip(self.labels, images))
Example #21
| Source File: face_shiftnet_model.py From Shift-Net_pytorch with MIT License | 5 votes |
|
def backward_D(self):
fake_B = self.fake_B
# Real
real_B = self.real_B # GroundTruth
# Has been verfied, for square mask, let D discrinate masked patch, improves the results.
if self.opt.mask_type == 'center' or self.opt.mask_sub_type == 'rect':
# Using the cropped fake_B as the input of D.
fake_B = self.fake_B[:, :, self.rand_t:self.rand_t+self.opt.fineSize//2-2*self.opt.overlap, \
self.rand_l:self.rand_l+self.opt.fineSize//2-2*self.opt.overlap]
real_B = self.real_B[:, :, self.rand_t:self.rand_t+self.opt.fineSize//2-2*self.opt.overlap, \
self.rand_l:self.rand_l+self.opt.fineSize//2-2*self.opt.overlap]
self.pred_fake = self.netD(fake_B.detach())
self.pred_real = self.netD(real_B)
if self.opt.gan_type == 'wgan_gp':
gradient_penalty, _ = util.cal_gradient_penalty(self.netD, real_B, fake_B.detach(), self.device, constant=1, lambda_gp=self.opt.gp_lambda)
self.loss_D_fake = torch.mean(self.pred_fake)
self.loss_D_real = -torch.mean(self.pred_real)
self.loss_D = self.loss_D_fake + self.loss_D_real + gradient_penalty
else:
if self.opt.gan_type in ['vanilla', 'lsgan']:
self.loss_D_fake = self.criterionGAN(self.pred_fake, False)
self.loss_D_real = self.criterionGAN (self.pred_real, True)
self.loss_D = (self.loss_D_fake + self.loss_D_real) * 0.5
elif self.opt.gan_type == 're_s_gan':
self.loss_D = self.criterionGAN(self.pred_real - self.pred_fake, True)
elif self.opt.gan_type == 're_avg_gan':
self.loss_D = (self.criterionGAN (self.pred_real - torch.mean(self.pred_fake), True) \
+ self.criterionGAN (self.pred_fake - torch.mean(self.pred_real), False)) / 2.
# for `re_avg_gan`, need to retain graph of D.
if self.opt.gan_type == 're_avg_gan':
self.loss_D.backward(retain_graph=True)
else:
self.loss_D.backward()
Example #22
| Source File: InnerFaceShiftTriple.py From Shift-Net_pytorch with MIT License | 5 votes |
|
def set_mask(self, mask_global):
self.mask_all = util.cal_feat_mask(mask_global, self.layer_to_last)
Example #23
| Source File: combogan_model.py From ComboGAN with BSD 2-Clause "Simplified" License | 5 votes |
|
def get_current_visuals(self, testing=False):
if not testing:
self.visuals = [self.real_A, self.fake_B, self.rec_A, self.real_B, self.fake_A, self.rec_B]
self.labels = ['real_A', 'fake_B', 'rec_A', 'real_B', 'fake_A', 'rec_B']
images = [util.tensor2im(v.data) for v in self.visuals]
return OrderedDict(zip(self.labels, images))
Example #24
| Source File: InnerCos.py From Shift-Net_pytorch with MIT License | 5 votes |
|
def set_mask(self, mask_global):
mask_all = util.cal_feat_mask(mask_global, self.layer_to_last)
self.mask_all = mask_all.float()
Example #25
| Source File: ui_model.py From deep-learning-for-document-dewarping with MIT License | 5 votes |
|
def add_strokes(self, click_src, label_tgt, bw, save):
# get the region of the new strokes (bw is the brush width)
size = self.net_input.size()
h, w = size[2], size[3]
idx_src = torch.LongTensor(bw**2, 4).fill_(0)
for i in range(bw):
idx_src[i*bw:(i+1)*bw, 2] = min(h-1, max(0, click_src[0]-bw//2 + i))
for j in range(bw):
idx_src[i*bw+j, 3] = min(w-1, max(0, click_src[1]-bw//2 + j))
idx_src = idx_src.cuda()
# again, need to update 3 things
if idx_src.shape:
# backup current maps
if save:
self.backup_current_state()
# update the label map (and the network input) in the stroke region
self.label_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
for k in range(self.opt.label_nc):
self.net_input[idx_src[:,0], idx_src[:,1] + k, idx_src[:,2], idx_src[:,3]] = 0
self.net_input[idx_src[:,0], idx_src[:,1] + label_tgt, idx_src[:,2], idx_src[:,3]] = 1
# update the instance map (and the network input)
self.inst_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
self.net_input[:,-1,:,:] = self.get_edges(self.inst_map)
# also update the features if available
if self.opt.instance_feat:
feat = self.features_clustered[label_tgt]
#np.random.seed(label_tgt+1)
#cluster_idx = np.random.randint(0, feat.shape[0])
cluster_idx = self.cluster_indices[label_tgt]
self.set_features(idx_src, feat, cluster_idx)
self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
# add an object to the clicked position with selected style
Example #26
| Source File: ui_model.py From deep-learning-for-document-dewarping with MIT License | 5 votes |
|
def change_labels(self, click_src, click_tgt):
y_src, x_src = click_src[0], click_src[1]
y_tgt, x_tgt = click_tgt[0], click_tgt[1]
label_src = int(self.label_map[0, 0, y_src, x_src])
inst_src = self.inst_map[0, 0, y_src, x_src]
label_tgt = int(self.label_map[0, 0, y_tgt, x_tgt])
inst_tgt = self.inst_map[0, 0, y_tgt, x_tgt]
idx_src = (self.inst_map == inst_src).nonzero()
# need to change 3 things: label map, instance map, and feature map
if idx_src.shape:
# backup current maps
self.backup_current_state()
# change both the label map and the network input
self.label_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
self.net_input[idx_src[:,0], idx_src[:,1] + label_src, idx_src[:,2], idx_src[:,3]] = 0
self.net_input[idx_src[:,0], idx_src[:,1] + label_tgt, idx_src[:,2], idx_src[:,3]] = 1
# update the instance map (and the network input)
if inst_tgt > 1000:
# if different instances have different ids, give the new object a new id
tgt_indices = (self.inst_map > label_tgt * 1000) & (self.inst_map < (label_tgt+1) * 1000)
inst_tgt = self.inst_map[tgt_indices].max() + 1
self.inst_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = inst_tgt
self.net_input[:,-1,:,:] = self.get_edges(self.inst_map)
# also copy the source features to the target position
idx_tgt = (self.inst_map == inst_tgt).nonzero()
if idx_tgt.shape:
self.copy_features(idx_src, idx_tgt[0,:])
self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
# add strokes of target label in the image
Example #27
| Source File: ui_model.py From EverybodyDanceNow_reproduce_pytorch with MIT License | 5 votes |
|
def get_current_visuals(self, getLabel=False):
mask = self.mask
if self.mask is not None:
mask = np.transpose(self.mask[0].cpu().float().numpy(), (1,2,0)).astype(np.uint8)
dict_list = [('fake_image', self.fake_image), ('mask', mask)]
if getLabel: # only output label map if needed to save bandwidth
label = util.tensor2label(self.net_input.data[0], self.opt.label_nc)
dict_list += [('label', label)]
return OrderedDict(dict_list)
Example #28
| Source File: ui_model.py From EverybodyDanceNow-Temporal-FaceGAN with MIT License | 5 votes |
|
def change_labels(self, click_src, click_tgt):
y_src, x_src = click_src[0], click_src[1]
y_tgt, x_tgt = click_tgt[0], click_tgt[1]
label_src = int(self.label_map[0, 0, y_src, x_src])
inst_src = self.inst_map[0, 0, y_src, x_src]
label_tgt = int(self.label_map[0, 0, y_tgt, x_tgt])
inst_tgt = self.inst_map[0, 0, y_tgt, x_tgt]
idx_src = (self.inst_map == inst_src).nonzero()
# need to change 3 things: label map, instance map, and feature map
if idx_src.shape:
# backup current maps
self.backup_current_state()
# change both the label map and the network input
self.label_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
self.net_input[idx_src[:,0], idx_src[:,1] + label_src, idx_src[:,2], idx_src[:,3]] = 0
self.net_input[idx_src[:,0], idx_src[:,1] + label_tgt, idx_src[:,2], idx_src[:,3]] = 1
# update the instance map (and the network input)
if inst_tgt > 1000:
# if different instances have different ids, give the new object a new id
tgt_indices = (self.inst_map > label_tgt * 1000) & (self.inst_map < (label_tgt+1) * 1000)
inst_tgt = self.inst_map[tgt_indices].max() + 1
self.inst_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = inst_tgt
self.net_input[:,-1,:,:] = self.get_edges(self.inst_map)
# also copy the source features to the target position
idx_tgt = (self.inst_map == inst_tgt).nonzero()
if idx_tgt.shape:
self.copy_features(idx_src, idx_tgt[0,:])
self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
# add strokes of target label in the image
Example #29
| Source File: ui_model.py From EverybodyDanceNow-Temporal-FaceGAN with MIT License | 5 votes |
|
def add_strokes(self, click_src, label_tgt, bw, save):
# get the region of the new strokes (bw is the brush width)
size = self.net_input.size()
h, w = size[2], size[3]
idx_src = torch.LongTensor(bw**2, 4).fill_(0)
for i in range(bw):
idx_src[i*bw:(i+1)*bw, 2] = min(h-1, max(0, click_src[0]-bw//2 + i))
for j in range(bw):
idx_src[i*bw+j, 3] = min(w-1, max(0, click_src[1]-bw//2 + j))
idx_src = idx_src.cuda()
# again, need to update 3 things
if idx_src.shape:
# backup current maps
if save:
self.backup_current_state()
# update the label map (and the network input) in the stroke region
self.label_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
for k in range(self.opt.label_nc):
self.net_input[idx_src[:,0], idx_src[:,1] + k, idx_src[:,2], idx_src[:,3]] = 0
self.net_input[idx_src[:,0], idx_src[:,1] + label_tgt, idx_src[:,2], idx_src[:,3]] = 1
# update the instance map (and the network input)
self.inst_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
self.net_input[:,-1,:,:] = self.get_edges(self.inst_map)
# also update the features if available
if self.opt.instance_feat:
feat = self.features_clustered[label_tgt]
#np.random.seed(label_tgt+1)
#cluster_idx = np.random.randint(0, feat.shape[0])
cluster_idx = self.cluster_indices[label_tgt]
self.set_features(idx_src, feat, cluster_idx)
self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
# add an object to the clicked position with selected style
Example #30
| Source File: ui_model.py From EverybodyDanceNow-Temporal-FaceGAN with MIT License | 5 votes |
|
def get_current_visuals(self, getLabel=False):
mask = self.mask
if self.mask is not None:
mask = np.transpose(self.mask[0].cpu().float().numpy(), (1,2,0)).astype(np.uint8)
dict_list = [('fake_image', self.fake_image), ('mask', mask)]
if getLabel: # only output label map if needed to save bandwidth
label = util.tensor2label(self.net_input.data[0], self.opt.label_nc)
dict_list += [('label', label)]
return OrderedDict(dict_list)
