Merge pull request #2 from igv/master

Pull recent igv commits (2019-10-04)

Author: HelpSeeker

FSRCNN.py

@@ -72,7 +72,8 @@ def model(self):
     deconv_biases = tf.get_variable('deconv_b', initializer=tf.zeros([self.scale**2]))
     deconv = tf.nn.conv2d(conv, deconv_weights, strides=[1,1,1,1], padding='SAME', data_format='NHWC')
     deconv = tf.nn.bias_add(deconv, deconv_biases, data_format='NHWC')
-    deconv = tf.depth_to_space(deconv, self.scale, name='pixel_shuffle', data_format='NHWC')
+    if self.scale > 1:
+        deconv = tf.depth_to_space(deconv, self.scale, name='pixel_shuffle', data_format='NHWC')
 
     return deconv
 

gen.py

@@ -28,7 +28,8 @@ def format_weights(weights, n, length=4):
 
 def base_header(file):
     file.write('//!HOOK LUMA\n')
-    file.write('//!WHEN OUTPUT.w LUMA.w / {0}.400 > OUTPUT.h LUMA.h / {0}.400 > *\n'.format(scale - 1))
+    if scale > 1:
+        file.write('//!WHEN OUTPUT.w LUMA.w / {0}.400 > OUTPUT.h LUMA.h / {0}.400 > *\n'.format(scale - 1))
 
 def header1(file, n, d):
     base_header(file)
@@ -75,8 +76,9 @@ def header5(file, n, d, inp):
     file.write('//!DESC sub-pixel convolution {}\n'.format((n//comps) + 1))
     for i in range(d//4):
         file.write('//!BIND {}{}\n'.format(inp, i + 1))
-    file.write('//!SAVE SUBCONV{}\n'.format((n//comps) + 1))
-    file.write('//!COMPONENTS {}\n'.format(comps))
+    if scale > 1:
+        file.write('//!SAVE SUBCONV{}\n'.format((n//comps) + 1))
+        file.write('//!COMPONENTS {}\n'.format(comps))
 
 def header6(file):
     base_header(file)
@@ -219,45 +221,47 @@ def main():
         ln = get_line_number("deconv_b", fname)
         biases = read_weights(fname, ln)
         inp = "EXPANDED" if shrinking else "RES"
-        comps = 3 if scale == 3 else 4
+        comps = scale if scale % 2 == 1 else 4
         for n in range(0, scale**2, comps):
             header5(file, n, d, inp)
             file.write('vec4 hook()\n')
             file.write('{\n')
-            file.write('vec{0} res = vec{0}({1});\n'.format(comps, format_weights(biases[0], n, length=comps)))
+            if scale == 1:
+                file.write('float res = {};\n'.format(format_weights(biases[0], n, length=comps)))
+            else:
+                file.write('vec{0} res = vec{0}({1});\n'.format(comps, format_weights(biases[0], n, length=comps)))
             p = 0
             for l in range(0, len(weights), 4):
                 if l % d == 0:
                     y, x = p%(radius*2+1)-radius, p//(radius*2+1)-radius
                     p += 1
                 idx = (l//4)%(d//4)
-                file.write('res += mat4x{}({},{},{},{}) * {}{}_texOff(vec2({},{}));\n'.format(
-                           comps, format_weights(weights[l], n, length=comps), format_weights(weights[l+1], n, length=comps),
+                file.write('res += {}{}({},{},{},{}){} {}{}_texOff(vec2({},{})){};\n'.format(
+                           "mat4x" if scale > 1 else "dot(", comps if scale > 1 else "vec4",
+                           format_weights(weights[l], n, length=comps), format_weights(weights[l+1], n, length=comps),
                            format_weights(weights[l+2], n, length=comps), format_weights(weights[l+3], n, length=comps),
-                           inp, idx + 1, x, y))
-            if comps == 4:
-                file.write('return res;\n')
-            else:
-                file.write('return vec4(res, 0);\n')
+                           " *" if scale > 1 else ",", inp, idx + 1, x, y, "" if scale > 1 else ")"))
+            file.write('return vec4(res{});\n'.format(", 0" * (4 - comps)))
             file.write('}\n\n')
 
-        # Aggregation
-        header6(file)
-        file.write('vec4 hook()\n')
-        file.write('{\n')
-        file.write('vec2 fcoord = fract(SUBCONV1_pos * SUBCONV1_size);\n')
-        file.write('vec2 base = SUBCONV1_pos + (vec2(0.5) - fcoord) * SUBCONV1_pt;\n')
-        file.write('ivec2 index = ivec2(fcoord * vec2({}));\n'.format(scale))
-        if scale > 2:
-            file.write('mat{0} res = mat{0}(SUBCONV1_tex(base).{1}'.format(scale, "rgba"[:comps]))
-            for i in range(scale-1):
-                file.write(',SUBCONV{}_tex(base).{}'.format(i + 2, "rgba"[:comps]))
-            file.write(');\n')
-            file.write('return vec4(res[index.x][index.y], 0, 0, 1);\n')
-        else:
-            file.write('vec4 res = SUBCONV1_tex(base);\n')
-            file.write('return vec4(res[index.x * {} + index.y], 0, 0, 1);\n'.format(scale))
-        file.write('}\n')
+        if scale > 1:
+            # Aggregation
+            header6(file)
+            file.write('vec4 hook()\n')
+            file.write('{\n')
+            file.write('vec2 fcoord = fract(SUBCONV1_pos * SUBCONV1_size);\n')
+            file.write('vec2 base = SUBCONV1_pos + (vec2(0.5) - fcoord) * SUBCONV1_pt;\n')
+            file.write('ivec2 index = ivec2(fcoord * vec2({}));\n'.format(scale))
+            if scale > 2:
+                file.write('mat{0} res = mat{0}(SUBCONV1_tex(base).{1}'.format(scale, "rgba"[:comps]))
+                for i in range(scale-1):
+                    file.write(',SUBCONV{}_tex(base).{}'.format(i + 2, "rgba"[:comps]))
+                file.write(');\n')
+                file.write('return vec4(res[index.x][index.y], 0, 0, 1);\n')
+            else:
+                file.write('vec4 res = SUBCONV1_tex(base);\n')
+                file.write('return vec4(res[index.x * {} + index.y], 0, 0, 1);\n'.format(scale))
+            file.write('}\n')
 
   else:
     print("Missing argument: You must specify a file name")

main.py

@@ -23,7 +23,6 @@
 flags.DEFINE_string("output_dir", "result", "Name of test output directory [result]")
 flags.DEFINE_string("data_dir", "Train", "Name of data directory to train on [FastTrain]")
 flags.DEFINE_boolean("train", True, "True for training, false for testing [True]")
-flags.DEFINE_integer("threads", 1, "Number of processes to pre-process data with [1]")
 flags.DEFINE_boolean("distort", False, "Distort some images with JPEG compression artifacts after downscaling [False]")
 flags.DEFINE_boolean("params", False, "Save weight and bias parameters [False]")
 

model.py

@@ -1,6 +1,5 @@
 from utils import (
-  thread_train_setup,
-  train_input_setup,
+  multiprocess_train_setup,
   test_input_setup,
   save_params,
   merge,
@@ -31,14 +30,13 @@ def __init__(self, sess, config):
     self.radius = config.radius
     self.batch_size = config.batch_size
     self.learning_rate = config.learning_rate
-    self.threads = config.threads
     self.distort = config.distort
     self.params = config.params
 
     self.padding = 4
     # Different image/label sub-sizes for different scaling factors x2, x3, x4
-    scale_factors = [[20 + self.padding, 40], [14 + self.padding, 42], [12 + self.padding, 48]]
-    self.image_size, self.label_size = scale_factors[self.scale - 2]
+    scale_factors = [[40 + self.padding, 40], [20 + self.padding, 40], [14 + self.padding, 42], [12 + self.padding, 48]]
+    self.image_size, self.label_size = scale_factors[self.scale - 1]
 
     self.stride = self.image_size - self.padding
 
@@ -94,11 +92,8 @@ def run(self):
   def run_train(self):
     start_time = time.time()
     print("Beginning training setup...")
-    if self.threads == 1:
-      train_data, train_label = train_input_setup(self)
-    else:
-      train_data, train_label = thread_train_setup(self)
-    print("Training setup took {} seconds with {} threads".format(time.time() - start_time, self.threads))
+    train_data, train_label = multiprocess_train_setup(self)
+    print("Training setup took {} seconds".format(time.time() - start_time))
 
     print("Training...")
     start_time = time.time()

utils.py

@@ -12,7 +12,7 @@
 import tensorflow as tf
 from PIL import Image
 import numpy as np
-from multiprocessing import Pool, Lock, active_children
+import multiprocessing
 
 FLAGS = tf.app.flags.FLAGS
 
@@ -145,46 +145,19 @@ def train_input_worker(args):
 
   return [single_input_sequence, single_label_sequence]
 
-
-def thread_train_setup(config):
+def multiprocess_train_setup(config):
   """
-  Spawns |config.threads| worker processes to pre-process the data
-
-  This has not been extensively tested so use at your own risk.
-  Also this is technically multiprocessing not threading, I just say thread
-  because it's shorter to type.
+  Spawns several processes to pre-process the data
   """
   if downsample == False:
     import sys
     sys.exit()
 
-  sess = config.sess
-
-  # Load data path
-  data = prepare_data(sess, dataset=config.data_dir)
-
-  # Initialize multiprocessing pool with # of processes = config.threads
-  pool = Pool(config.threads)
-
-  # Distribute |images_per_thread| images across each worker process
-  config_values = [config.image_size, config.label_size, config.stride, config.scale, config.padding // 2, config.distort]
-  images_per_thread = len(data) // config.threads
-  workers = []
-  for thread in range(config.threads):
-    args_list = [(data[i], config_values) for i in range(thread * images_per_thread, (thread + 1) * images_per_thread)]
-    worker = pool.map_async(train_input_worker, args_list)
-    workers.append(worker)
-  print("{} worker processes created".format(config.threads))
-
-  pool.close()
+  data = prepare_data(config.sess, dataset=config.data_dir)
 
-  results = []
-  for i in range(len(workers)):
-    print("Waiting for worker process {}".format(i))
-    results.extend(workers[i].get(timeout=240))
-    print("Worker process {} done".format(i))
-
-  print("All worker processes done!")
+  with multiprocessing.Pool(max(multiprocessing.cpu_count() - 1, 1)) as pool:
+    config_values = [config.image_size, config.label_size, config.stride, config.scale, config.padding // 2, config.distort]
+    results = pool.map(train_input_worker, [(data[i], config_values) for i in range(len(data))])
 
   sub_input_sequence, sub_label_sequence = [], []
 
@@ -198,47 +171,6 @@ def thread_train_setup(config):
 
   return (arrdata, arrlabel)
 
-def train_input_setup(config):
-  """
-  Read image files, make their sub-images, and save them as a h5 file format.
-  """
-  if downsample == False:
-    import sys
-    sys.exit()
-
-  sess = config.sess
-  image_size, label_size, stride, scale, padding = config.image_size, config.label_size, config.stride, config.scale, config.padding // 2
-
-  # Load data path
-  data = prepare_data(sess, dataset=config.data_dir)
-
-  sub_input_sequence, sub_label_sequence = [], []
-
-  for i in range(len(data)):
-    input_, label_ = preprocess(data[i], scale, distort=config.distort)
-
-    if len(input_.shape) == 3:
-      h, w, _ = input_.shape
-    else:
-      h, w = input_.shape
-
-    for x in range(0, h - image_size + 1, stride):
-      for y in range(0, w - image_size + 1, stride):
-        sub_input = input_[x : x + image_size, y : y + image_size]
-        x_loc, y_loc = x + padding, y + padding
-        sub_label = label_[x_loc * scale : x_loc * scale + label_size, y_loc * scale : y_loc * scale + label_size]
-
-        sub_input = sub_input.reshape([image_size, image_size, 1])
-        sub_label = sub_label.reshape([label_size, label_size, 1])
-        
-        sub_input_sequence.append(sub_input)
-        sub_label_sequence.append(sub_label)
-
-  arrdata = np.asarray(sub_input_sequence)
-  arrlabel = np.asarray(sub_label_sequence)
-
-  return (arrdata, arrlabel)
-
 def test_input_setup(config):
   sess = config.sess