Changed the preprocessing to random cropping in image_reader.py

.gitignore

@@ -0,0 +1,2 @@
+*.pyc
+*~

deeplab_resnet/__init__.py

@@ -1,3 +1,3 @@
 from .model import DeepLabResNetModel
 from .image_reader import ImageReader
-from .utils import decode_labels, inv_preprocess, prepare_label
+from .utils import decode_labels, inv_preprocess, prepare_label, decode_labels_old

deeplab_resnet/image_reader.py

@@ -89,6 +89,7 @@ def read_labeled_image_list(data_dir, data_list):
             image = mask = line.strip("\n")
         images.append(data_dir + image)
         masks.append(data_dir + mask)
+        print(data_dir + image + " " + data_dir + mask)
     return images, masks
 
 def read_images_from_disk(input_queue, input_size, random_scale, random_mirror, ignore_label, img_mean): # optional pre-processing arguments

deeplab_resnet/utils.py

@@ -38,6 +38,23 @@ def decode_labels(mask, num_images=1, num_classes=21):
       outputs[i] = np.array(img)
     return outputs
 
+def decode_labels_old(mask, num_classes):
+    """Decode batch of segmentation masks.
+
+    Args:
+      label_batch: result of inference after taking argmax.
+
+    Returns:
+      An batch of RGB images of the same size
+    """
+    img = Image.new('RGB', (len(mask[0]), len(mask)))
+    pixels = img.load()
+    for j_, j in enumerate(mask):
+        for k_, k in enumerate(j):
+            if k < num_classes:
+                pixels[k_,j_] = label_colours[k]
+    return np.array(img)
+
 def prepare_label(input_batch, new_size, num_classes, one_hot=True):
     """Resize masks and perform one-hot encoding.
 

evaluate_msc.py

@@ -11,11 +11,14 @@
 import os
 import sys
 import time
-
+import pandas as pd
 import tensorflow as tf
+import cv2
+from PIL import Image
 import numpy as np
+import scipy.io as sio
 
-from deeplab_resnet import DeepLabResNetModel, ImageReader, prepare_label
+from deeplab_resnet import DeepLabResNetModel, ImageReader, prepare_label, decode_labels_old
 
 IMG_MEAN = np.array((104.00698793,116.66876762,122.67891434), dtype=np.float32)
 
@@ -25,6 +28,8 @@
 NUM_CLASSES = 21
 NUM_STEPS = 1449 # Number of images in the validation set.
 RESTORE_FROM = './deeplab_resnet.ckpt'
+OUTPUT_IMGS=True
+
 
 def get_arguments():
     """Parse all the arguments provided from the CLI.
@@ -45,6 +50,10 @@ def get_arguments():
                         help="Number of images in the validation set.")
     parser.add_argument("--restore-from", type=str, default=RESTORE_FROM,
                         help="Where restore model parameters from.")
+    parser.add_argument("--save-dir", type=str, default=None,
+                        help="Save output directory")
+    parser.add_argument("--save-dir-ind", type=str, default=None,
+                        help="Save output directory")    
     return parser.parse_args()
 
 def load(saver, sess, ckpt_path):
@@ -105,10 +114,10 @@ def main():
     pred = tf.expand_dims(raw_output, dim=3) # Create 4-d tensor.
 
     # mIoU
-    pred = tf.reshape(pred, [-1,])
+    pred_lin = tf.reshape(pred, [-1, ])
     gt = tf.reshape(label_batch, [-1,])
     weights = tf.cast(tf.less_equal(gt, args.num_classes - 1), tf.int32) # Ignoring all labels greater than or equal to n_classes.
-    mIoU, update_op = tf.contrib.metrics.streaming_mean_iou(pred, gt, num_classes=args.num_classes, weights=weights)
+    mIoU, update_op = tf.contrib.metrics.streaming_mean_iou(pred_lin, gt, num_classes=args.num_classes, weights=weights)
 
     # Set up tf session and initialize variables. 
     config = tf.ConfigProto()
@@ -124,14 +133,39 @@ def main():
     if args.restore_from is not None:
         load(loader, sess, args.restore_from)
 
+    # Output dirs
+    if not os.path.exists(args.save_dir):
+        os.makedirs(args.save_dir) 
+    if not os.path.exists(args.save_dir_ind):
+        os.makedirs(args.save_dir_ind)
+    # Iterate over test files
+    data_info = pd.read_csv(args.data_list, header=None)
+    num_test_files = data_info.shape[0]  # For computing Iter
+    imgList = np.asarray(data_info.iloc[:, 0])
+    NUM_STEPS = len(imgList)
+    print("Num test files = " + str(NUM_STEPS))
+
     # Start queue threads.
     threads = tf.train.start_queue_runners(coord=coord, sess=sess)
 
     # Iterate over training steps.
-    for step in range(args.num_steps):
-        preds, _ = sess.run([pred, update_op])
+    # for step in range(args.num_steps):
+    for step in range(NUM_STEPS):
+        preds, preds_lin, _ = sess.run([pred, pred_lin, update_op])
         if step % 100 == 0:
             print('step {:d}'.format(step))
+        if OUTPUT_IMGS:
+            # print(np.array(preds).shape)
+            msk = decode_labels_old(np.array(preds)[0, :, :, 0], args.num_classes)
+            im = Image.fromarray(msk)
+            im_name = imgList[step].split('/')[2]
+            im.save(args.save_dir + '/' + im_name)
+
+            mask_ind = np.array(preds)[0, :, :, 0]
+            cv2.imwrite(args.save_dir_ind + '/' + im_name, mask_ind)
+
+
+
     print('Mean IoU: {:.3f}'.format(mIoU.eval(session=sess)))
     coord.request_stop()
     coord.join(threads)

scripts.sh

@@ -0,0 +1,30 @@
+#!/bin/bash
+
+# PATH=/home/garbade/anaconda2/bin:$PATH
+
+python train_msc.py --batch-size 2 \
+	--data-dir /home/garbade/datasets/nyu_depth_v2/ \
+	--data-list /home/garbade/datasets/nyu_depth_v2/filelists/train_drsleep.txt \
+	--snapshot-dir /home/garbade/models_tf/08_nyu_depth_v2/01_02_firstTry_nyu11 \
+	--grad-update-every 10 \
+	--ignore-label 0 \
+	--not-restore-last \
+	--num-classes 11 \
+	--num-steps 20001 \
+	--random-mirror \
+	--random-scale \
+	--restore-from /home/garbade/models_tf/dr_sleep_models/tf_v0.12/deeplab_resnet.ckpt \
+	--save-num-images 1 \
+	--learning-rate 1.0e-4
+
+
+
+
+python evaluate_msc.py --data-dir /home/garbade/datasets/nyu_depth_v2/ \
+	--data-list /home/garbade/datasets/nyu_depth_v2/filelists/test_drsleep.txt \
+	--ignore-label 0 \
+	--num-classes 11 \
+	--num-steps 1 \
+	--restore-from /home/garbade/models_tf/08_nyu_depth_v2/01_02_firstTry_nyu11/model.ckpt-10000 \
+	--save-dir /home/garbade/models_tf/08_nyu_depth_v2/01_02_firstTry_nyu11/res \
+	--save-dir-ind /home/garbade/models_tf/08_nyu_depth_v2/01_02_firstTry_nyu11/res_ind

train_msc.py

@@ -184,6 +184,11 @@ def main():
     raw_prediction075 = tf.reshape(raw_output075, [-1, args.num_classes])
     raw_prediction05 = tf.reshape(raw_output05, [-1, args.num_classes])
 
+    # Cross Entropy Loss --> Be careful about 'gt' input format -->has to be 0-based: [0:n_classes -1]
+    # if args.ignore_label == 0:
+    #     label_batch = tf.to_int32(label_batch)
+    #     label_batch = tf.subtract(label_batch , 1)
+
     label_proc = prepare_label(label_batch, tf.stack(raw_output.get_shape()[1:3]), num_classes=args.num_classes, one_hot=False) # [batch_size, h, w]
     label_proc075 = prepare_label(label_batch, tf.stack(raw_output075.get_shape()[1:3]), num_classes=args.num_classes, one_hot=False)
     label_proc05 = prepare_label(label_batch, tf.stack(raw_output05.get_shape()[1:3]), num_classes=args.num_classes, one_hot=False)
@@ -192,9 +197,9 @@ def main():
     raw_gt075 = tf.reshape(label_proc075, [-1,])
     raw_gt05 = tf.reshape(label_proc05, [-1,])
 
-    indices = tf.squeeze(tf.where(tf.less_equal(raw_gt, args.num_classes - 1)), 1)
-    indices075 = tf.squeeze(tf.where(tf.less_equal(raw_gt075, args.num_classes - 1)), 1)
-    indices05 = tf.squeeze(tf.where(tf.less_equal(raw_gt05, args.num_classes - 1)), 1)
+    indices = tf.squeeze(tf.where(tf.not_equal(raw_gt, args.ignore_label)), 1)
+    indices075 = tf.squeeze(tf.where(tf.not_equal(raw_gt075, args.ignore_label)), 1)
+    indices05 = tf.squeeze(tf.where(tf.not_equal(raw_gt05, args.ignore_label)), 1)
 
     gt = tf.cast(tf.gather(raw_gt, indices), tf.int32)
     gt075 = tf.cast(tf.gather(raw_gt075, indices075), tf.int32)
@@ -220,6 +225,9 @@ def main():
     pred = tf.expand_dims(raw_output_up, dim=3)
 
     # Image summary.
+    # if args.ignore_label == 0:
+    #     label_batch = tf.add(label_batch , 1)
+
     images_summary = tf.py_func(inv_preprocess, [image_batch, args.save_num_images, IMG_MEAN], tf.uint8)
     labels_summary = tf.py_func(decode_labels, [label_batch, args.save_num_images, args.num_classes], tf.uint8)
     preds_summary = tf.py_func(decode_labels, [pred, args.save_num_images, args.num_classes], tf.uint8)
@@ -234,7 +242,7 @@ def main():
     base_lr = tf.constant(args.learning_rate)
     step_ph = tf.placeholder(dtype=tf.float32, shape=())
     learning_rate = tf.scalar_mul(base_lr, tf.pow((1 - step_ph / args.num_steps), args.power))
-
+    tf.summary.scalar('learning_rate', learning_rate)
     opt_conv = tf.train.MomentumOptimizer(learning_rate, args.momentum)
     opt_fc_w = tf.train.MomentumOptimizer(learning_rate * 10.0, args.momentum)
     opt_fc_b = tf.train.MomentumOptimizer(learning_rate * 20.0, args.momentum)
@@ -264,6 +272,12 @@ def main():
 
     train_op = tf.group(train_op_conv, train_op_fc_w, train_op_fc_b)
 
+    # Log variables
+
+    tf.summary.scalar("reduced_loss", reduced_loss)
+    for v in conv_trainable + fc_w_trainable + fc_b_trainable:
+        tf.summary.histogram(v.name.replace(":", "_"), v)
+    merged_summary_op = tf.summary.merge_all()
 
     # Set up tf session and initialize variables. 
     config = tf.ConfigProto()
@@ -274,7 +288,7 @@ def main():
     sess.run(init)
 
     # Saver for storing checkpoints of the model.
-    saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=10)
+    saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=4, keep_checkpoint_every_n_hours=4)
 
     # Load variables if the checkpoint is provided.
     if args.restore_from is not None:
@@ -303,7 +317,8 @@ def main():
 
         # Apply gradients.
         if step % args.save_pred_every == 0:
-            images, labels, summary, _ = sess.run([image_batch, label_batch, total_summary, train_op], feed_dict=feed_dict)
+        # if True:
+            images, labels, summary, _ = sess.run([image_batch, label_batch, merged_summary_op, train_op], feed_dict=feed_dict)
             summary_writer.add_summary(summary, step)
             save(saver, sess, args.snapshot_dir, step)
         else: