Create Experiment_CelebFaces.py

Experiment_CelebFaces.py

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+__author__ = "Yinchong Yang"
+__copyright__ = "Siemens AG, 2017"
+__licencse__ = "MIT"
+__version__ = "0.1"
+
+"""
+MIT License
+Copyright (c) 2017 Siemens AG
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+"""
+
+
+import sys
+import datetime
+import time
+
+import os
+import numpy as np
+import pickle
+
+from keras.layers import Input, GRU, LSTM, Dense, Dropout, Masking, BatchNormalization
+from keras.models import Model
+from keras.optimizers import *
+from keras.regularizers import l2
+from keras.preprocessing.sequence import pad_sequences
+from keras.utils.np_utils import to_categorical
+
+from sklearn.metrics import average_precision_score, roc_auc_score, accuracy_score, precision_score, recall_score
+
+# Custom Functions -----------------------------------------------------------------------------------------------------
+
+from TTRNN import TT_GRU, TT_LSTM
+
+
+def get_clips(class_name):
+    files = os.listdir(data_path + class_name)
+    files.sort()
+    clip_list = []
+    for this_file in files:
+        clip_list.append( data_path + class_name + '/' + this_file )
+    return clip_list
+
+
+def load_data(inds, mode = 'train', maxlen = 85):
+    N = len(inds)
+    X = np.zeros((N, maxlen, 120*160*3), dtype='int8')
+    if mode == 'train':
+        set = train_set
+    else:
+        set = test_set
+    for i in range(N):
+        read_in = open(set[0][inds[i]])
+        this_clip = pickle.load(read_in)[0]
+        read_in.close()
+        this_clip = this_clip.reshape(this_clip.shape[0], -1)
+        this_clip = (this_clip - 128).astype('int8')
+        X[i] = pad_sequences([this_clip], maxlen=maxlen, truncating='post', dtype='int8')[0]
+    Y = set[1][inds]
+
+    return [X, Y]
+
+
+# Load the data --------------------------------------------------------------------------------------------------------
+np.random.seed(11111986)
+
+# Settings:
+# CV_setting = int(sys.argv[1])
+# model_type= int(sys.argv[2])  # [0, 1] for GRU, LSTM
+# use_TT = int(sys.argv[3])  # [0, 1] for False, True
+
+CV_setting = 0
+model_type = 1
+use_TT = 0
+
+# Had to remove due to anonymity
+data_path = ''
+write_out_path = ''
+
+files = os.listdir(data_path)
+files.sort()
+
+N = len(files)
+targets = ['']*N
+for l in range(N):
+    # l = 0
+    this_file = files[l]
+    this_file_split = this_file.split('_')
+    this_file_split[-1] = this_file_split[-1].split('.')[0]
+    targets[l] = this_file_split[-2] + '_' + this_file_split[-1]
+
+targets = np.array(targets)
+
+classes = np.unique(targets)
+
+Y = np.zeros(N, dtype='int8')
+for l in range(N):
+    Y[l] = np.where(classes == targets[l])[0]
+Y = to_categorical(Y).astype('int8')
+
+
+GLOBAL_MAX_LEN=85
+
+clips = np.array([data_path + this_file for this_file in files])
+shuffle_ind = np.random.choice(range(N), N, False)
+clips = clips[shuffle_ind]
+Y = Y[shuffle_ind]
+
+CV_splits = np.array_split(np.arange(N), 5)
+
+test_inds = CV_splits[CV_setting]
+train_inds = np.setdiff1d(np.arange(N), test_inds)
+
+train_set = [clips[train_inds], Y[train_inds]]
+test_set = [clips[test_inds], Y[test_inds]]
+
+
+n_tr = len(train_set[0])
+n_te = len(test_set[0])
+
+# X_train, Y_train = load_data(np.arange(0, 512), mode='train')
+X_train, Y_train = load_data(np.arange(0, n_tr), mode='train')
+# X_test, Y_test = load_data(np.arange(0, 256), mode='test')
+X_test, Y_test = load_data(np.arange(0, n_te), mode='test')
+
+
+# Define the model -----------------------------------------------------------------------------------------------------
+dropoutRate = 0.25
+alpha = 1e-2
+
+tt_input_shape = [4, 20, 20, 36]
+tt_output_shape = [4, 4, 4, 4]
+tt_ranks = [1, 4, 4, 4, 1]
+
+input = Input(shape=(GLOBAL_MAX_LEN, 120*160*3))
+if model_type == 0:
+    if use_TT == 0:
+        rnn_layer = GRU(output_dim=225,
+                        return_sequences=False,
+                        dropout=0.25, recurrent_dropout=0.25, activation='tanh')
+    elif use_TT == 1:
+        rnn_layer = TT_GRU(tt_input_shape=tt_input_shape, tt_output_shape=tt_output_shape,
+                           tt_ranks=tt_ranks,
+                           return_sequences=False,
+                           dropout=0.25, recurrent_dropout=0.25, activation='tanh')
+else:
+    if use_TT == 0:
+        rnn_layer = LSTM(output_dim=225,
+                         return_sequences=False,
+                         dropout=0.25, recurrent_dropout=0.25, activation='tanh')
+    elif use_TT == 1:
+        rnn_layer = TT_LSTM(tt_input_shape=tt_input_shape, tt_output_shape=tt_output_shape,
+                            tt_ranks=tt_ranks,
+                            return_sequences=False,
+                            dropout=0.25, recurrent_dropout=0.25, activation='tanh')
+h = rnn_layer(input)
+output = Dense(output_dim=47, activation='softmax', kernel_regularizer=l2(alpha))(h)
+model = Model(input, output)
+model.compile(optimizer=Adam(1e-3), loss='categorical_crossentropy', metrics=['accuracy'])
+
+
+if use_TT == 1:
+    compress_factor = rnn_layer.compress_factor
+else:
+    compress_factor = 1
+
+
+print n_tr
+print n_te
+
+
+# Start training -------------------------------------------------------------------------------------------------------
+if True:
+
+    file_name = str(CV_setting) + '_' + str(model_type) + '_' + str(use_TT)
+    start = datetime.datetime.now()
+
+    for l in range(101):
+        print 'iter ' + str(l)
+        model.fit(X_train, Y_train, nb_epoch=1, batch_size=16, verbose=1, validation_data=[X_test, Y_test])
+
+    stop = datetime.datetime.now()
+    print stop-start
+
+    res = model.evaluate(X_test, Y_test)
+    save_name = str(CV_setting) +'_'+ str(model_type) + '_' + str(use_TT)
+    write_out = open(write_out_path + save_name + '.pkl', 'wb')
+    pickle.dump([model.get_weights(), res, stop-start], write_out)
+    write_out.close()