Moved paths to kitti_settings

kitti_evaluate.py

@@ -3,8 +3,11 @@
 Calculates mean-squared error and plots predictions.
 '''
 
+import os
 import numpy as np
 from six.moves import cPickle
+import matplotlib
+matplotlib.use('Agg')
 import matplotlib.pyplot as plt
 import matplotlib.gridspec as gridspec
 
@@ -14,18 +17,17 @@
 
 from prednet import PredNet
 from process_kitti import SequenceGenerator
+import kitti_settings
 
-plot_save_dir = './kitti_plots/'
 n_plot = 20
-
-weights_file = 'prednet_weights.hdf5'
-config_file = 'prednet_config.pkl'
-test_file = './kitti_data/X_test.hkl'
-test_sources = './kitti_data/sources_test.hkl'
-
-batch_size = 5
+batch_size = 10
 nt = 10
 
+weights_file = os.path.join(weights_dir, 'prednet_kitti_weights.hdf5')
+config_file = os.path.join(weights_dir, 'prednet_kitti_config.pkl')
+test_file = os.path.join(data_dir, 'X_test.hkl')
+test_sources = os.path.join(data_dir, 'sources_test.hkl')
+
 # Load trained model
 config = cPickle.load(open(config_file))
 train_model = Model.from_config(config, custom_objects = {'PredNet': PredNet})
@@ -54,7 +56,7 @@
 plt.figure(figsize = (nt, 2*aspect_ratio))
 gs = gridspec.GridSpec(2, nt)
 gs.update(wspace=0.025, hspace=0.05)
-if not os.path.exists(plot_save_dir): os.mkdir(plot_save_dir)
+if not os.path.exists(eval_save_dir): os.mkdir(eval_save_dir)
 for i in range(n_plot):
     for t in range(nt):
         plt.subplot(gs[t])
@@ -67,5 +69,5 @@
         plt.tick_params(axis='both', which='both', bottom='off', top='off', left='off', right='off', labelbottom='off', labelleft='off')
         if t==0: plt.ylabel('Predicted')
 
-    plt.savefig(plot_save_dir + 'plot_' + str(i) + '.png')
+    plt.savefig(os.path.join(eval_save_dir, 'plot_' + str(i) + '.png'))
     plt.clf()

kitti_settings.py

@@ -0,0 +1,10 @@
+# Where KITTI data will be saved if you run process_kitti.py
+# If you directly download the processed data, change to the path of the data.
+data_dir = './kitti_data/'
+
+# Where model weights and config will be saved if you run kitti_train.py
+# If you directly download the trained weights, change to appropriate path.
+weights_dir = './'
+
+# Where evaluation results (prediction plots) will be saved.
+eval_save_dir = './'

kitti_train.py

@@ -2,6 +2,7 @@
 Train PredNet on KITTI sequences. (Geiger et al. 2013, http://www.cvlibs.net/datasets/kitti/)
 '''
 
+import os
 import numpy as np
 from six.moves import cPickle
 
@@ -11,25 +12,27 @@
 from keras.layers.recurrent import LSTM
 from keras.layers.wrappers import TimeDistributed
 from keras.callbacks import EarlyStopping, ModelCheckpoint
+from keras.optimizers import Adam
 
 from prednet import PredNet
 from process_kitti import SequenceGenerator
+import kitti_settings
+
+save_model = True  # if weights will be saved
+weights_file = os.path.join(weights_dir, 'prednet_kitti_weights.hdf5')  # where weights will be saved
+config_file = os.path.join(weights_dir, 'prednet_kitti_config.pkl')
+
+# Data files
+train_file = os.path.join(data_dir, 'X_train.hkl')
+train_sources = os.path.join(data_dir, 'sources_train.hkl')
+val_file = os.path.join(data_dir, 'X_val.hkl')
+val_sources = os.path.join(data_dir, 'sources_val.hkl')
 
 # Training parameters
-nb_epoch = 2
+nb_epoch = 100
 batch_size = 5
-samples_per_epoch = 100 #500
+samples_per_epoch = 500
 N_seq_val = 100  # number of sequences to use for validation
-use_early_stopping = True
-patience = 5
-save_model = True
-save_name = 'prednet_'
-
-# Data files
-train_file = './kitti_data/X_train.hkl'
-train_sources = './kitti_data/sources_train.hkl'
-val_file = './kitti_data/X_val.hkl'
-val_sources = './kitti_data/sources_val.hkl'
 
 # Model parameters
 nt = 10
@@ -55,23 +58,19 @@
 errors_by_time = Flatten()(errors_by_time)  # will be (batch_size, nt)
 final_errors = Dense(1, weights=[time_loss_weights, np.zeros(1)], trainable=False)(errors_by_time)  # weight errors by time
 model = Model(input=inputs, output=final_errors)
-model.compile(loss='mean_absolute_error', optimizer='adam')
+optimizer = Adam(lr=0.0005)
+model.compile(loss='mean_absolute_error', optimizer=optimizer)
 
 train_generator = SequenceGenerator(train_file, train_sources, nt, batch_size=batch_size)
 val_generator = SequenceGenerator(val_file, val_sources, nt, batch_size=batch_size, N_seq=N_seq_val)
 callbacks = []
-if use_early_stopping:
-    callbacks.append(EarlyStopping(monitor='val_loss', patience=patience))
 if save_model:
-    callbacks.append(ModelCheckpoint(filepath=save_name + 'weights.hdf5', monitor='val_loss', save_best_only=True))
+    if not os.path.exists(weights_dir): os.mkdir(weights_dir)
+    callbacks.append(ModelCheckpoint(filepath=weights_file, monitor='val_loss', save_best_only=True))
 
-history = model.fit_generator(train_generator, samples_per_epoch, nb_epoch, callbacks=callbacks,
-                              validation_data=val_generator, nb_val_samples=val_generator.N_sequences/batch_size)
+model.fit_generator(train_generator, samples_per_epoch, nb_epoch, callbacks=callbacks,
+                    validation_data=val_generator, nb_val_samples=N_seq_val)
 
 if save_model:
     config = model.get_config()
-    cPickle.dump(config, open(save_name + '_config.pkl', 'w'))
-
-#TODO:  remove
-from helpers import plot_training_error
-plot_training_error(train_err = history.history['loss'], val_err = history.history['val_loss'], run_name = 'kitti', out_file = 'error_plot.jpg')
+    cPickle.dump(config, open(config_file, 'w'))

process_kitti.py

@@ -12,22 +12,20 @@
 
 from keras import backend as K
 from keras.preprocessing.image import Iterator
+import kitti_settings
 
-np.random.seed(123)
 
-data_dir = './kitti_data/'
 desired_im_sz = (128, 160)
 n_val_by_cat = {'city': 1}  # number of recordings to use for validation out of each category
 n_test_by_cat = {'city': 1, 'residential': 1, 'road': 1}  # number of recordings for testing
-
 categories = ['city', 'residential', 'road']
 
-if data_dir[-1] != '/': data_dir += '/'
+np.random.seed(123)
 if not os.path.exists(data_dir): os.mkdir(data_dir)
 
 # Download raw zip files by scraping KITTI website
 def download_data():
-    base_dir = data_dir + 'raw/'
+    base_dir = os.path.join(data_dir, 'raw/')
     if not os.path.exists(base_dir): os.mkdir(base_dir)
     for c in categories:
         url = "http://www.cvlibs.net/datasets/kitti/raw_data.php?type=" + c
@@ -47,7 +45,7 @@ def download_data():
 # unzip images
 def extract_data():
     for c in categories:
-        c_dir = data_dir + 'raw/' + c + '/'
+        c_dir = os.path.join(data_dir, 'raw/', c + '/')
         _, _, zip_files = os.walk(c_dir).next()
         for f in zip_files:
             print 'unpacking: ' + f
@@ -61,7 +59,7 @@ def extract_data():
 def process_data():
     splits = {s: [] for s in ['train', 'test', 'val']}
     for c in categories:  # Randomly assign recordings to training and testing. Cross-validation done across entire recordings.
-        c_dir = data_dir + 'raw/' + c + '/'
+        c_dir = os.path.join(data_dir, 'raw', c + '/')
         _, folders, _ = os.walk(c_dir).next()
         folders = np.random.permutation(folders)
         n_val = 0 if c not in n_val_by_cat else n_val_by_cat[c]
@@ -74,7 +72,7 @@ def process_data():
         im_list = []
         source_list = []  # corresponds to recording that image came from
         for category, folder in splits[split]:
-            im_dir = data_dir + 'raw/' + category + '/' + folder + '/' + folder[:10] + '/' + folder + '/image_03/data/'
+            im_dir = os.path.join('raw/', category, folder, folder[:10], folder, '/image_03/data/')
             _, _, files = os.walk(im_dir).next()
             im_list += [im_dir + f for f in sorted(files)]
             source_list += [category + '-' + folder] * len(files)
@@ -85,8 +83,8 @@ def process_data():
             im = imread(im_file)
             X[i] = process_im(im, desired_im_sz)
 
-        hkl.dump(X, data_dir + 'X_' + split + '.hkl')
-        hkl.dump(source_list, data_dir + 'sources_' + split + '.hkl')
+        hkl.dump(X, os.path.join(data_dir, 'X_' + split + '.hkl'))
+        hkl.dump(source_list, os.path.join(data_dir, 'sources_' + split + '.hkl'))
 
 
 # resize and crop image
@@ -144,9 +142,9 @@ def next(self):
         for i, idx in enumerate(index_array):
             idx = self.possible_starts[idx]
             batch_x[i] = self.preprocess(self.X[idx:idx+self.nt])
-        if output_mode == 'error':  # model outputs errors, so y should be zeros
+        if self.output_mode == 'error':  # model outputs errors, so y should be zeros
             batch_y = np.zeros(current_batch_size, np.float32)
-        elif output_mode == 'prediction':  # output actual pixels
+        elif self.output_mode == 'prediction':  # output actual pixels
             batch_y = batch_x
         return batch_x, batch_y
 
@@ -161,9 +159,6 @@ def create_all(self):
 
 
 if __name__ == '__main__':
-    import time
-    t0 = time.time()
     download_data()
-    print 'time to download: ' + str( (time.time() - t0)/60 )
-    #extract_data()
-    #process_data()
+    extract_data()
+    process_data()