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Files changed (44) hide show
  1. 04-18/exp_args.txt +60 -0
  2. README.md +1 -1
  3. app.py +177 -62
  4. data/masks/124084_0_label.png +0 -0
  5. libs/__pycache__/__init__.cpython-37.pyc +0 -0
  6. libs/__pycache__/__init__.cpython-38.pyc +0 -0
  7. libs/__pycache__/flow_transforms.cpython-37.pyc +0 -0
  8. libs/__pycache__/flow_transforms.cpython-38.pyc +0 -0
  9. libs/__pycache__/nnutils.cpython-37.pyc +0 -0
  10. libs/__pycache__/nnutils.cpython-38.pyc +0 -0
  11. libs/__pycache__/options.cpython-37.pyc +0 -0
  12. libs/__pycache__/options.cpython-38.pyc +0 -0
  13. libs/__pycache__/test_base.cpython-37.pyc +0 -0
  14. libs/__pycache__/test_base.cpython-38.pyc +0 -0
  15. libs/__pycache__/utils.cpython-37.pyc +0 -0
  16. libs/__pycache__/utils.cpython-38.pyc +0 -0
  17. models/week0417/__pycache__/loss.cpython-37.pyc +0 -0
  18. models/week0417/__pycache__/model.cpython-37.pyc +0 -0
  19. models/week0417/__pycache__/nnutils.cpython-37.pyc +0 -0
  20. models/week0417/__pycache__/taming_blocks.cpython-37.pyc +0 -0
  21. swapae/models/__pycache__/__init__.cpython-37.pyc +0 -0
  22. swapae/models/__pycache__/base_model.cpython-37.pyc +0 -0
  23. swapae/models/networks/__pycache__/__init__.cpython-37.pyc +0 -0
  24. swapae/models/networks/__pycache__/base_network.cpython-37.pyc +0 -0
  25. swapae/models/networks/__pycache__/stylegan2_layers.cpython-37.pyc +0 -0
  26. swapae/models/networks/stylegan2_op/__pycache__/__init__.cpython-37.pyc +0 -0
  27. swapae/models/networks/stylegan2_op/__pycache__/fused_act.cpython-37.pyc +0 -0
  28. swapae/models/networks/stylegan2_op/__pycache__/upfirdn2d.cpython-37.pyc +0 -0
  29. swapae/util/__pycache__/__init__.cpython-37.pyc +0 -0
  30. swapae/util/__pycache__/html.cpython-37.pyc +0 -0
  31. swapae/util/__pycache__/iter_counter.cpython-37.pyc +0 -0
  32. swapae/util/__pycache__/metric_tracker.cpython-37.pyc +0 -0
  33. swapae/util/__pycache__/util.cpython-37.pyc +0 -0
  34. swapae/util/__pycache__/visualizer.cpython-37.pyc +0 -0
  35. tmp/0.png +0 -0
  36. tmp/1.png +0 -0
  37. tmp/2.png +0 -0
  38. tmp/3.png +0 -0
  39. tmp/4.png +0 -0
  40. tmp/5.png +0 -0
  41. tmp/6.png +0 -0
  42. tmp/7.png +0 -0
  43. tmp/8.png +0 -0
  44. tmp/9.png +0 -0
04-18/exp_args.txt ADDED
@@ -0,0 +1,60 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ add_clustering_epoch: 0 [default: 1000]
2
+ add_gcn_epoch: 0 [default: None]
3
+ add_self_loops: 1
4
+ add_texture_epoch: 0 [default: 1000]
5
+ batch_size: 1
6
+ beta: 0.999
7
+ config_file: models/week0417/json/single_scale_grouping_ft.json
8
+ crop_size: 224
9
+ data_path: images
10
+ dataset: dataset [default: None]
11
+ dec_input_mode: sine_wave_noise [default: None]
12
+ display_freq: 100
13
+ exp_name: 04-18/ [default: None]
14
+ gumbel: 0
15
+ hidden_dim: 256 [default: None]
16
+ img_path: None
17
+ l1_loss_wt: 1.0
18
+ lambda_GAN: 1 [default: None]
19
+ lambda_L1: 1 [default: None]
20
+ lambda_style_loss: 1.0 [default: None]
21
+ local_rank: None
22
+ log_freq: 10
23
+ lr: 5e-05 [default: 0.1]
24
+ lr_decay_freq: 3000
25
+ maxIter: 1000
26
+ model_name: model [default: None]
27
+ momentum: 0.5
28
+ nChannel: 100
29
+ nConv: 2
30
+ n_cluster: 10 [default: None]
31
+ n_layers_D: 3 [default: None]
32
+ nepochs: 20 [default: None]
33
+ netE_nc_steepness: 2.0
34
+ netE_num_downsampling_gl: 2
35
+ netE_num_downsampling_sp: 4
36
+ netE_scale_capacity: 1.0
37
+ netG_num_base_resnet_layers: 2
38
+ netG_resnet_ch: 256
39
+ netG_scale_capacity: 1.0
40
+ no_ganFeat_loss: False
41
+ num_D: 2 [default: None]
42
+ num_classes: 0
43
+ out_dir: ./04-18/ [default: None]
44
+ patch_size: 40
45
+ perceptual_loss_wt: 1.0
46
+ pretrained_ae: /home/xli/WORKDIR/07-16/transformer/cpk.pth
47
+ pretrained_path: /home/xtli/WORKDIR/04-15/single_scale_grouping_resume/cpk.pth [default: None]
48
+ project_name: test_time
49
+ save_freq: 1000 [default: 2000]
50
+ sine_weight: 1 [default: None]
51
+ sp_num: None
52
+ spatial_code_ch: 8
53
+ spatial_code_dim: 32 [default: 256]
54
+ temperature: 23 [default: 1]
55
+ test_time: 0
56
+ texture_code_ch: 256
57
+ use_slic: True
58
+ use_wandb: False
59
+ work_dir: ./
60
+ workers: 4
README.md CHANGED
@@ -4,7 +4,7 @@ emoji: 🏢
4
  colorFrom: gray
5
  colorTo: purple
6
  sdk: streamlit
7
- sdk_version: 1.2.0
8
  app_file: app.py
9
  pinned: false
10
  ---
 
4
  colorFrom: gray
5
  colorTo: purple
6
  sdk: streamlit
7
+ sdk_version: 1.10.0
8
  app_file: app.py
9
  pinned: false
10
  ---
app.py CHANGED
@@ -69,10 +69,10 @@ class Tester(TesterBase):
69
  def to_pil(self, tensor):
70
  return transforms.ToPILImage()(tensor.cpu().squeeze().clamp(0.0, 1.0)).convert("RGB")
71
 
72
- def display(self):
73
  with st.spinner('Running...'):
74
  with torch.no_grad():
75
- grouping_mask = self.model_forward(self.data, self.slic, return_type = 'grouping')
76
 
77
  data = (self.data + 1) / 2.0
78
 
@@ -124,7 +124,7 @@ class Tester(TesterBase):
124
  tex_size = st.slider('', 0, 1000, 256)
125
  tex_size = (tex_size // 8) * 8
126
  with torch.no_grad():
127
- tex = self.model_forward(self.data, self.slic, tex_idx = tex_idx, tex_size = tex_size, return_type = 'tex')
128
  col1, col2, col3, col4 = st.columns([1, 1, 4, 1])
129
  with col1:
130
  st.markdown("")
@@ -140,33 +140,159 @@ class Tester(TesterBase):
140
  with col4:
141
  st.markdown("")
142
  st.markdown('<p class="big-font">You can choose another image from the examplar images on the top and start again!</p>', unsafe_allow_html=True)
143
- #torch.cuda.empty_cache()
144
 
145
- """
146
- st.markdown("#### Texture Editing")
147
- st.markdown("**Choose one texture segment to remove.**")
148
- remove_idx = clickable_images(
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
149
  tmp_img_list,
150
  titles=[f"Group #{str(i)}" for i in range(len(tmp_img_list))],
151
  div_style={"display": "flex", "justify-content": "center", "flex-wrap": "wrap"},
152
- img_style={"margin": "5px", "height": "120px"},
153
- key=1
154
  )
155
- st.markdown("**Choose one texture segment to fill in the missing pixels.**")
156
- fill_idx = clickable_images(
157
- tmp_img_list,
158
- titles=[f"Group #{str(i)}" for i in range(len(tmp_img_list))],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
159
  div_style={"display": "flex", "justify-content": "center", "flex-wrap": "wrap"},
160
- img_style={"margin": "5px", "height": "120px"},
161
- key=2
162
  )
163
- rec = self.model_forward(self.data, self.slic, return_type = 'editing', fill_idx = fill_idx, remove_idx = remove_idx)
164
- st.image(self.to_pil(rec))
165
- """
166
 
167
- def model_forward(self, rgb_img, slic, epoch = 1000, test_time = False,
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
168
  test = True, tex_idx = None, tex_size = 256,
169
- return_type = 'tex', fill_idx = None, remove_idx = None):
 
170
  args = self.args
171
  B, _, imgH, imgW = rgb_img.shape
172
 
@@ -185,47 +311,29 @@ class Tester(TesterBase):
185
  if return_type == 'grouping':
186
  return torch.argmax(sp_assign.cpu(), dim = 1)
187
 
188
-
189
  tex_seg = poolfeat(conv_feats, softmax, avg = True)
190
  seg = label2one_hot_torch(torch.argmax(softmax, dim = 1).unsqueeze(1), C = softmax.shape[1])
191
 
192
- if return_type == 'tex':
193
- sampled_code = tex_seg[:, tex_idx, :]
194
- rec_tex = sampled_code.view(1, -1, 1, 1).repeat(1, 1, tex_size, tex_size)
195
- sine_wave = self.model.get_sine_wave(rec_tex, 'rec')
196
- H = tex_size // 8; W = tex_size // 8
197
- noise = torch.randn(B, self.model.sine_wave_dim, H, W).to(tex_code.device)
198
- dec_input = torch.cat((sine_wave, noise), dim = 1)
199
-
200
- weight = self.model.ChannelWeight(rec_tex)
201
- weight = F.adaptive_avg_pool2d(weight, output_size = (1)).view(weight.shape[0], -1, 1, 1)
202
- weight = torch.sigmoid(weight)
203
- dec_input *= weight
204
-
205
- rep_rec = self.model.G(dec_input, rec_tex)
206
- rep_rec = (rep_rec + 1) / 2.0
207
- return rep_rec
208
- elif return_type == 'editing':
209
- remove_mask = 0
210
- fill_mask = 1
211
- rec_tex = upfeat(tex_seg, seg)
212
- remove_mask = seg[:, remove_idx:remove_idx+1]
213
- fill_tex = tex_seg[:, fill_idx, :].view(1, -1, 1, 1).repeat(1, 1, imgH, imgW)
214
- rec_tex = rec_tex * (1 - remove_mask) + fill_tex * remove_mask
215
-
216
- sine_wave = self.model.get_sine_wave(rec_tex, 'rec')
217
- H = imgH // 8; W = imgW // 8
218
- noise = torch.randn(B, self.model.sine_wave_dim, H, W).to(tex_code.device)
219
- dec_input = torch.cat((sine_wave, noise), dim = 1)
220
- weight = self.model.ChannelWeight(rec_tex)
221
- weight = F.adaptive_avg_pool2d(weight, output_size = (1)).view(weight.shape[0], -1, 1, 1)
222
- weight = torch.sigmoid(weight)
223
- dec_input *= weight
224
-
225
- rep_rec = self.model.G(dec_input, rec_tex)
226
- rep_rec = (rep_rec + 1) / 2.0
227
- return rep_rec
228
-
229
 
230
  def load_data(self, data_path):
231
  rgb_img = Image.open(data_path)
@@ -253,12 +361,12 @@ class Tester(TesterBase):
253
  self.model = self.model.module
254
  return
255
 
 
256
  def test(self):
257
- """ Test function
258
- """
259
  #for iteration in tqdm(range(args.nsamples)):
260
  self.test_step(0)
261
  self.display(0, 'train')
 
262
 
263
  def main():
264
  #torch.cuda.empty_cache()
@@ -300,7 +408,14 @@ def main():
300
  tester.define_model()
301
  tester.load_data(img_path)
302
  tester.load_model(args.pretrained_path)
303
- tester.display()
 
 
 
 
 
 
 
304
 
305
  if __name__ == '__main__':
306
  os.system("pip install torch-geometric==1.7.2")
 
69
  def to_pil(self, tensor):
70
  return transforms.ToPILImage()(tensor.cpu().squeeze().clamp(0.0, 1.0)).convert("RGB")
71
 
72
+ def display_synthesis(self):
73
  with st.spinner('Running...'):
74
  with torch.no_grad():
75
+ grouping_mask = self.model_forward_synthesis(self.data, self.slic, return_type = 'grouping')
76
 
77
  data = (self.data + 1) / 2.0
78
 
 
124
  tex_size = st.slider('', 0, 1000, 256)
125
  tex_size = (tex_size // 8) * 8
126
  with torch.no_grad():
127
+ tex = self.model_forward_synthesis(self.data, self.slic, tex_idx = tex_idx, tex_size = tex_size, return_type = 'tex')
128
  col1, col2, col3, col4 = st.columns([1, 1, 4, 1])
129
  with col1:
130
  st.markdown("")
 
140
  with col4:
141
  st.markdown("")
142
  st.markdown('<p class="big-font">You can choose another image from the examplar images on the top and start again!</p>', unsafe_allow_html=True)
 
143
 
144
+ def model_forward_synthesis(self, rgb_img, slic, epoch = 1000, test_time = False,
145
+ test = True, tex_idx = None, tex_size = 256,
146
+ return_type = 'tex', fill_idx = None, remove_idx = None):
147
+ args = self.args
148
+ B, _, imgH, imgW = rgb_img.shape
149
+
150
+ # Encoder: img (B, 3, H, W) -> feature (B, C, imgH//8, imgW//8)
151
+ conv_feat, _ = self.model.enc(rgb_img)
152
+ B, C, H, W = conv_feat.shape
153
+
154
+ # Texture code for each superpixel
155
+ tex_code = self.model.ToTexCode(conv_feat)
156
+
157
+ code = F.interpolate(tex_code, size = (imgH, imgW), mode = 'bilinear', align_corners = False)
158
+ pool_code = poolfeat(code, slic, avg = True)
159
+
160
+ prop_code, sp_assign, conv_feats = self.model.gcn(pool_code, slic, (args.add_clustering_epoch <= epoch))
161
+ softmax = F.softmax(sp_assign * args.temperature, dim = 1)
162
+ if return_type == 'grouping':
163
+ return torch.argmax(sp_assign.cpu(), dim = 1)
164
+
165
+ tex_seg = poolfeat(conv_feats, softmax, avg = True)
166
+ seg = label2one_hot_torch(torch.argmax(softmax, dim = 1).unsqueeze(1), C = softmax.shape[1])
167
+
168
+ sampled_code = tex_seg[:, tex_idx, :]
169
+ rec_tex = sampled_code.view(1, -1, 1, 1).repeat(1, 1, tex_size, tex_size)
170
+ sine_wave = self.model.get_sine_wave(rec_tex, 'rec')
171
+ H = tex_size // 8; W = tex_size // 8
172
+ noise = torch.randn(B, self.model.sine_wave_dim, H, W).to(tex_code.device)
173
+ dec_input = torch.cat((sine_wave, noise), dim = 1)
174
+
175
+ weight = self.model.ChannelWeight(rec_tex)
176
+ weight = F.adaptive_avg_pool2d(weight, output_size = (1)).view(weight.shape[0], -1, 1, 1)
177
+ weight = torch.sigmoid(weight)
178
+ dec_input *= weight
179
+
180
+ rep_rec = self.model.G(dec_input, rec_tex)
181
+ rep_rec = (rep_rec + 1) / 2.0
182
+ return rep_rec
183
+
184
+ def display_editing(self):
185
+ with st.spinner('Running...'):
186
+ with torch.no_grad():
187
+ grouping_mask = self.model_forward_editing(self.data, self.slic, return_type = 'grouping')
188
+
189
+ data = (self.data + 1) / 2.0
190
+
191
+ seg = grouping_mask.view(-1, 1, args.crop_size, args.crop_size)
192
+ color_vq = self.draw_color_seg(seg)
193
+ color_vq = color_vq * 0.8 + data.cpu() * 0.2
194
+
195
+ st.markdown('<p class="big-font">Given the image you chose, our model decomposes the image into ten texture segments, each depicts one kind of texture in the image.</p>', unsafe_allow_html=True)
196
+ col1, col2, col3, col4 = st.columns(4)
197
+ with col1:
198
+ st.markdown("")
199
+
200
+ with col2:
201
+ st.markdown("Chosen image")
202
+ st.image(self.to_pil(data))
203
+
204
+ with col3:
205
+ st.markdown("Grouping mask")
206
+ st.image(self.to_pil(color_vq))
207
+
208
+ with col4:
209
+ st.markdown("")
210
+
211
+ seg_onehot = label2one_hot_torch(seg, C = 10)
212
+ parts = data.cpu() * seg_onehot.squeeze().unsqueeze(1)
213
+
214
+ st.markdown('<p class="big-font">We show all texture segments below.</p>', unsafe_allow_html=True)
215
+ tmp_img_list = []
216
+ for i in range(parts.shape[0]):
217
+ part_img = self.to_pil(parts[i])
218
+ out_path = 'tmp/{}.png'.format(i)
219
+ part_img.save(out_path)
220
+
221
+ with open(out_path, "rb") as image:
222
+ encoded = base64.b64encode(image.read()).decode()
223
+ tmp_img_list.append(f"data:image/jpeg;base64,{encoded}")
224
+
225
+ tex_idx = clickable_images(
226
  tmp_img_list,
227
  titles=[f"Group #{str(i)}" for i in range(len(tmp_img_list))],
228
  div_style={"display": "flex", "justify-content": "center", "flex-wrap": "wrap"},
229
+ img_style={"margin": "5px", "height": "150px"},
230
+ key=2
231
  )
232
+
233
+ st.markdown('<p class="big-font">Choose the texture segment for each group in the given mask below.</p>', unsafe_allow_html=True)
234
+ given_mask = Image.open('data/masks/124084_0_label.png').convert("L")
235
+ given_mask = np.asarray(given_mask)
236
+ given_mask = torch.from_numpy(given_mask)
237
+ H, W = given_mask.shape[0], given_mask.shape[1]
238
+ given_mask = label2one_hot_torch(given_mask.view(1, 1, H, W), C = (given_mask.max()+1))
239
+ mask_img_list = []
240
+ for i in range(given_mask.shape[1]):
241
+ part_img = self.to_pil(given_mask[0, i])
242
+ out_path = 'tmp/{}.png'.format(i)
243
+ part_img.save(out_path)
244
+
245
+ with open(out_path, "rb") as image:
246
+ encoded = base64.b64encode(image.read()).decode()
247
+ mask_img_list.append(f"data:image/jpeg;base64,{encoded}")
248
+
249
+ part_idx = clickable_images(
250
+ mask_img_list,
251
  div_style={"display": "flex", "justify-content": "center", "flex-wrap": "wrap"},
252
+ img_style={"margin": "5px", "height": "150px"},
253
+ key=1
254
  )
 
 
 
255
 
256
+ cols = st.columns(len(mask_img_list))
257
+ options = []
258
+ for i, col in enumerate(cols):
259
+ with col:
260
+ option = st.selectbox(
261
+ "",
262
+ ([str(ii) for ii in range(10)]),
263
+ key = i)
264
+ options.append(int(option))
265
+ print(options)
266
+
267
+ if len(options) > 0:
268
+ with st.spinner('Running...'):
269
+ st.markdown('<p class="big-font">Edited image is shown below.</p>', unsafe_allow_html=True)
270
+ #tex_size = st.slider('', 0, 1000, 256)
271
+ #tex_size = (tex_size // 8) * 8
272
+ with torch.no_grad():
273
+ edited = self.model_forward_editing(self.data, self.slic, options=options, given_mask=given_mask, return_type = 'edited')
274
+ col1, col2, col3, col4 = st.columns([1, 1, 4, 1])
275
+ with col1:
276
+ st.markdown("")
277
+
278
+ with col2:
279
+ st.markdown("Input image")
280
+ img = F.interpolate(self.data, size = edited.shape[-2:], mode = 'bilinear', align_corners = False)
281
+ st.image(self.to_pil((img + 1) / 2.0))
282
+ print(img.shape, edited.shape)
283
+
284
+ with col3:
285
+ st.markdown("Synthesized texture image")
286
+ st.image(self.to_pil(edited))
287
+
288
+ with col4:
289
+ st.markdown("")
290
+ st.markdown('<p class="big-font">You can choose another image from the examplar images on the top and start again!</p>', unsafe_allow_html=True)
291
+
292
+ def model_forward_editing(self, rgb_img, slic, epoch = 1000, test_time = False,
293
  test = True, tex_idx = None, tex_size = 256,
294
+ return_type = 'edited', fill_idx = None, remove_idx = None,
295
+ options = None, given_mask = None):
296
  args = self.args
297
  B, _, imgH, imgW = rgb_img.shape
298
 
 
311
  if return_type == 'grouping':
312
  return torch.argmax(sp_assign.cpu(), dim = 1)
313
 
 
314
  tex_seg = poolfeat(conv_feats, softmax, avg = True)
315
  seg = label2one_hot_torch(torch.argmax(softmax, dim = 1).unsqueeze(1), C = softmax.shape[1])
316
 
317
+ given_mask = F.interpolate(given_mask, size = (512, 512), mode = 'bilinear', align_corners = False)
318
+ rec_tex = torch.zeros((1, tex_seg.shape[-1], 512, 512))
319
+ for i in range(given_mask.shape[1]):
320
+ label = options[i]
321
+ code = tex_seg[0, label, :].view(1, -1, 1, 1).repeat(1, 1, 512, 512)
322
+ rec_tex += code * given_mask[:, i:i+1]
323
+ tex_size = 512
324
+ sine_wave = self.model.get_sine_wave(rec_tex, 'rec')
325
+ H = tex_size // 8; W = tex_size // 8
326
+ noise = torch.randn(B, self.model.sine_wave_dim, H, W).to(tex_code.device)
327
+ dec_input = torch.cat((sine_wave, noise), dim = 1)
328
+
329
+ weight = self.model.ChannelWeight(rec_tex)
330
+ weight = F.adaptive_avg_pool2d(weight, output_size = (1)).view(weight.shape[0], -1, 1, 1)
331
+ weight = torch.sigmoid(weight)
332
+ dec_input *= weight
333
+
334
+ rep_rec = self.model.G(dec_input, rec_tex)
335
+ rep_rec = (rep_rec + 1) / 2.0
336
+ return rep_rec
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
337
 
338
  def load_data(self, data_path):
339
  rgb_img = Image.open(data_path)
 
361
  self.model = self.model.module
362
  return
363
 
364
+ """
365
  def test(self):
 
 
366
  #for iteration in tqdm(range(args.nsamples)):
367
  self.test_step(0)
368
  self.display(0, 'train')
369
+ """
370
 
371
  def main():
372
  #torch.cuda.empty_cache()
 
408
  tester.define_model()
409
  tester.load_data(img_path)
410
  tester.load_model(args.pretrained_path)
411
+ app_idx = st.selectbox('Please select between texture synthesis or editing',
412
+ ["Texture Synthesis", "Texture Editing"])
413
+ if app_idx == 'Texture Editing':
414
+ st.header("Texture Editing")
415
+ tester.display_editing()
416
+ else:
417
+ st.header("Texture Synthesis")
418
+ tester.display_synthesis()
419
 
420
  if __name__ == '__main__':
421
  os.system("pip install torch-geometric==1.7.2")
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