Merge pull request #12 from dribnet/generic_seeds

Move generation of seeds out of training network.

enhance.py

@@ -130,7 +130,10 @@ class DataLoader(threading.Thread):
         self.data_copied = threading.Event()
 
         self.resolution = args.batch_resolution
+        self.seed_resolution = int(args.batch_resolution / 2**args.scales)
+
         self.buffer = np.zeros((args.buffer_size, 3, self.resolution, self.resolution), dtype=np.float32)
+        self.seed_buffer = np.zeros((args.buffer_size, 3, self.seed_resolution, self.seed_resolution), dtype=np.float32)
         self.files = glob.glob(args.train)
         if len(self.files) == 0:
             error("There were no files found to train from searching for `{}`".format(args.train),
@@ -168,17 +171,23 @@ class DataLoader(threading.Thread):
 
                     i = self.available.pop()
                     self.buffer[i] = np.transpose(copy / 255.0 - 0.5, (2, 0, 1))
+                    seed_copy = scipy.misc.imresize(copy,
+                        size=(self.seed_resolution, self.seed_resolution),
+                        interp='bilinear')
+                    self.seed_buffer[i] = np.transpose(seed_copy / 255.0 - 0.5, (2, 0, 1))
                     self.ready.add(i)
 
                     if len(self.ready) >= args.batch_size:
                         self.data_ready.set()
 
-    def copy(self, output):
+    def copy(self, images_out, seeds_out):
         self.data_ready.wait()
         self.data_ready.clear()
 
         for i, j in enumerate(random.sample(self.ready, args.batch_size)):
-            output[i] = self.buffer[j]
+            images_out[i] = self.buffer[j]
+            seeds_out[i] = self.seed_buffer[j]
+
             self.available.add(j)
 
         self.data_copied.set()
@@ -212,12 +221,8 @@ class Model(object):
 
     def __init__(self):
         self.network = collections.OrderedDict()
-        if args.train:
+        self.network['img'] = InputLayer((None, 3, None, None))
-            self.network['img'] = InputLayer((None, 3, None, None))
+        self.network['seed'] = InputLayer((None, 3, None, None))
-            self.network['seed'] = PoolLayer(self.network['img'], pool_size=2**args.scales, mode='average_exc_pad')
-        else:
-            self.network['img'] = InputLayer((None, 3, None, None))
-            self.network['seed'] = self.network['img']
 
         config, params = self.load_model()
         self.setup_generator(self.last_layer(), config)
@@ -380,9 +385,10 @@ class Model(object):
     def compile(self):
         # Helper function for rendering test images during training, or standalone non-training mode.
         input_tensor = T.tensor4()
-        input_layers = {self.network['img']: input_tensor}
+        seed_tensor = T.tensor4()
+        input_layers = {self.network['img']: input_tensor, self.network['seed']: seed_tensor}
         output = lasagne.layers.get_output([self.network[k] for k in ['img', 'seed', 'out']], input_layers, deterministic=True)
-        self.predict = theano.function([input_tensor], output)
+        self.predict = theano.function([input_tensor, seed_tensor], output)
 
         if not args.train: return
 
@@ -408,7 +414,7 @@ class Model(object):
 
         # Combined Theano function for updating both generator and discriminator at the same time.
         updates = collections.OrderedDict(list(gen_updates.items()) + list(disc_updates.items()))
-        self.fit = theano.function([input_tensor], gen_losses + [disc_out.mean(axis=(1,2,3))], updates=updates)
+        self.fit = theano.function([input_tensor, seed_tensor], gen_losses + [disc_out.mean(axis=(1,2,3))], updates=updates)
 
 
 
@@ -449,7 +455,9 @@ class NeuralEnhancer(object):
             if t_cur % args.learning_period == 0: l_r *= args.learning_decay
 
     def train(self):
+        seed_size = int(args.batch_resolution / 2**args.scales)
         images = np.zeros((args.batch_size, 3, args.batch_resolution, args.batch_resolution), dtype=np.float32)
+        seeds = np.zeros((args.batch_size, 3, seed_size, seed_size), dtype=np.float32)
         learning_rate = self.decay_learning_rate()
         try:
             running, start = None, time.time()
@@ -460,8 +468,8 @@ class NeuralEnhancer(object):
                 if epoch >= args.discriminator_start: self.model.disc_lr.set_value(l_r)
 
                 for _ in range(args.epoch_size):
-                    self.thread.copy(images)
+                    self.thread.copy(images, seeds)
-                    output = self.model.fit(images)
+                    output = self.model.fit(images, seeds)
                     losses = np.array(output[:3], dtype=np.float32)
                     stats = (stats + output[3]) if stats is not None else output[3]
                     total = total + losses if total is not None else losses
@@ -470,7 +478,7 @@ class NeuralEnhancer(object):
                     running = l if running is None else running * 0.95 + 0.05 * l
                     print('↑' if l > running else '↓', end='', flush=True)
 
-                orign, scald, repro = self.model.predict(images)
+                orign, scald, repro = self.model.predict(images, seeds)
                 self.show_progress(orign, scald, repro)
                 total /= args.epoch_size
                 stats /= args.epoch_size
@@ -498,7 +506,7 @@ class NeuralEnhancer(object):
 
     def process(self, image):
         img = np.transpose(image / 255.0 - 0.5, (2, 0, 1))[np.newaxis].astype(np.float32)
-        *_, repro = self.model.predict(img)
+        *_, repro = self.model.predict(img, img)
         repro = np.transpose(repro[0] + 0.5, (1, 2, 0)).clip(0.0, 1.0)
         return scipy.misc.toimage(repro * 255.0, cmin=0, cmax=255)