clean

batches.py

@@ -7,6 +7,9 @@
 import math
 
 
+n_boxes = 8
+
+
 class BufferedWrapper(object):
     """Fetch next batch asynchronuously to avoid bottleneck during GPU
     training."""

main.py

@@ -3,20 +3,17 @@
 config.gpu_options.allow_growth = False
 session = tf.Session(config = config)
 
-import os, logging, shutil, datetime, time, math, pickle
+import os, logging, shutil, datetime
 import glob
 import argparse
 import numpy as np
 from tqdm import tqdm, trange
-import PIL
 
 import nn
 import models
-from batches import get_batches, plot_batch, postprocess
+from batches import get_batches, plot_batch, postprocess, n_boxes
 import deeploss
 
-N_BOXES = 8
-
 
 def init_logging(out_base_dir):
     # get unique output directory based on current time
@@ -45,7 +42,7 @@ def __init__(self, opt, out_dir, logger):
         self.batch_size = opt.batch_size
         self.img_shape = 2*[opt.spatial_size] + [3]
         redux = 2
-        self.imgn_shape = 2*[opt.spatial_size//(2**redux)] + [N_BOXES*3]
+        self.imgn_shape = 2*[opt.spatial_size//(2**redux)] + [n_boxes*3]
         self.init_batches = opt.init_batches
 
         self.initial_lr = opt.lr
@@ -156,7 +153,6 @@ def define_graph(self):
                 self.lr_decay_end // 2, 3 * self.lr_decay_end // 4,
                 1e-6, 1.0,
                 1e-6, 1.0)
-        #kl_weight = tf.to_float(0.1)
 
         # initialization
         self.x_init = tf.placeholder(
@@ -362,7 +358,6 @@ def log_result(self, result, **kwargs):
                 for i in range(bs):
                     x_infer = XN_batch[i,...]
                     c_infer = CN_batch[i,...]
-                    #imgs.append(x_infer)
                     imgs.append(X_batch[i,...])
 
                     x_infer_batch = x_infer[None,...].repeat(bs, axis = 0)
@@ -428,9 +423,9 @@ def transfer(self, x_encode, c_encode, c_decode):
     default_log_dir = os.path.join(os.getcwd(), "log")
 
     parser = argparse.ArgumentParser()
-    parser.add_argument("--data_index", required = True, help = "path to training or testing data index")
+    parser.add_argument("--data_index", required = True, help = "path to data index")
     parser.add_argument("--mode", default = "train",
-            choices=["train", "test", "mcmc", "add_reconstructions", "transfer"])
+            choices=["train", "test", "add_reconstructions", "transfer"])
     parser.add_argument("--log_dir", default = default_log_dir, help = "path to log into")
     parser.add_argument("--batch_size", default = 8, type = int, help = "batch size")
     parser.add_argument("--init_batches", default = 4, type = int, help = "number of batches for initialization")