diff --git a/1.pdf b/1.pdf new file mode 100644 index 0000000..58e9f74 Binary files /dev/null and b/1.pdf differ diff --git a/README.md b/README.md index f831f11..95d7e75 100644 --- a/README.md +++ b/README.md @@ -1 +1,65 @@ -# First-Impression \ No newline at end of file +# First-Impression + +This is the solution to the poblem "First Impressions V2" given in CVPR'17, ECCV '16 & ICPR '16 and this piece of code is the implementation of the [paper](https://cs.nju.edu.cn/wujx/paper/eccvw16_APA.pdf) which is the winner of ECCV 2016 + +This problem is a challenge on “first impressions”, in which participants will develop solutions for recognizing personality traits of users in short video sequences. They have made available a large newly collected data set sponsored by Microsoft of at least 10,000 15-second videos collected from YouTube, annotated with personality traits by AMT workers. + +The traits to be recognized will correspond to the “big five” personality traits used in psychology and well known of hiring managers using standardized personality profiling: +* Extroversion +* Agreeableness +* Conscientiousness +* Neuroticism +* Openness to experience. + +As is known, the first impression made is highly important in many contexts, such as human resourcing or job interviews. This work could become very relevant to training young people to present themselves better by changing their behavior in simple ways. The participants who obtain the best results in the challenge will be invited to submit a paper to the workshop. + + +## Getting Started + +These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. + +### Prerequisites + +* [Python2](https://www.python.org/download/releases/2.7.2/) - Python version 2.7.2 +* [Numpy](http://www.numpy.org/) - Multidimensioanl Mathematical Computing +* [Tensorflow](https://www.tensorflow.org/) - Deep Learning python module +* [Pandas](https://pandas.pydata.org/) - +* [Cha-Learn Dataset](http://chalearnlap.cvc.uab.es/dataset/24/description/) - Dataset for this problem +* [Pretrained VGG16 Model](https://www.cs.toronto.edu/~frossard/vgg16/vgg16_weights.npz) Pretrained Vgg16 mode + +### Installing + +* Clone the repository + +``` +git clone https://github.com/zishansami102/CNN-from-Scratch +``` + +* Downlad the training dataset and copy that to this folder and extract it into a new /data directory with all 75 zip files as it is + +* [Download](https://www.cs.toronto.edu/~frossard/vgg16/vgg16_weights.npz) Pretrained Vgg16 model and move it to the root directory + + +* Run the vidToimg.py file to scrape the images from the videos and save it to a new ImageData directory + +``` +python VidToimg.py +``` + + +* If succesfully completed then run the savetfreco.py file to form a data pipeline by saving the all the images into train500.tfrecords file to load it later during training + +``` +python savetfreco.py +``` + +* Start the training by running the following command + +``` +python train.py + + +## Acknowledgments + +* [paper](https://cs.nju.edu.cn/wujx/paper/eccvw16_APA.pdf) - Implemented paper +* [TfRecord Data Pipeline](http://machinelearninguru.com/deep_learning/data_preparation/tfrecord/tfrecord.html#read) - Used to make data pipeline diff --git a/dan.py b/dan.py new file mode 100644 index 0000000..798d228 --- /dev/null +++ b/dan.py @@ -0,0 +1,251 @@ +######################################################################################## +# Davi Frossard, 2016 # +# VGG16 implementation in TensorFlow # +# Details: # +# http://www.cs.toronto.edu/~frossard/post/vgg16/ # +# # +# Model from https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md # +# Weights from Caffe converted using https://github.com/ethereon/caffe-tensorflow # +######################################################################################## + +import tensorflow as tf +import numpy as np +import warnings + + +warnings.filterwarnings("ignore") + + + + +class DAN: + def __init__(self, imgs, weights=None, sess=None): + self.imgs = imgs + self.convlayers() + self.dan_part() + if weights is not None and sess is not None: + self.load_weights(weights, sess) + self.output = tf.nn.sigmoid(self.reg_head) + + + + def convlayers(self): + self.parameters = [] + + # zero-mean input + with tf.name_scope('preprocess') as scope: + mean = tf.constant([123.68, 116.779, 103.939], dtype=tf.float32, shape=[1, 1, 1, 3], name='img_mean') + images = self.imgs-mean + + # conv1_1 + with tf.name_scope('conv1_1') as scope: + kernel = tf.Variable(tf.truncated_normal([3, 3, 3, 64], dtype=tf.float32, + stddev=1e-1), name='weights') + conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME') + biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32), + trainable=True, name='biases') + out = tf.nn.bias_add(conv, biases) + self.conv1_1 = tf.nn.relu(out, name=scope) + self.parameters += [kernel, biases] + + # conv1_2 + with tf.name_scope('conv1_2') as scope: + kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 64], dtype=tf.float32, + stddev=1e-1), name='weights') + conv = tf.nn.conv2d(self.conv1_1, kernel, [1, 1, 1, 1], padding='SAME') + biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32), + trainable=True, name='biases') + out = tf.nn.bias_add(conv, biases) + self.conv1_2 = tf.nn.relu(out, name=scope) + self.parameters += [kernel, biases] + + # pool1 + self.pool1 = tf.nn.max_pool(self.conv1_2, + ksize=[1, 2, 2, 1], + strides=[1, 2, 2, 1], + padding='SAME', + name='pool1') + + # conv2_1 + with tf.name_scope('conv2_1') as scope: + kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 128], dtype=tf.float32, + stddev=1e-1), name='weights') + conv = tf.nn.conv2d(self.pool1, kernel, [1, 1, 1, 1], padding='SAME') + biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32), + trainable=True, name='biases') + out = tf.nn.bias_add(conv, biases) + self.conv2_1 = tf.nn.relu(out, name=scope) + self.parameters += [kernel, biases] + + # conv2_2 + with tf.name_scope('conv2_2') as scope: + kernel = tf.Variable(tf.truncated_normal([3, 3, 128, 128], dtype=tf.float32, + stddev=1e-1), name='weights') + conv = tf.nn.conv2d(self.conv2_1, kernel, [1, 1, 1, 1], padding='SAME') + biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32), + trainable=True, name='biases') + out = tf.nn.bias_add(conv, biases) + self.conv2_2 = tf.nn.relu(out, name=scope) + self.parameters += [kernel, biases] + + # pool2 + self.pool2 = tf.nn.max_pool(self.conv2_2, + ksize=[1, 2, 2, 1], + strides=[1, 2, 2, 1], + padding='SAME', + name='pool2') + + # conv3_1 + with tf.name_scope('conv3_1') as scope: + kernel = tf.Variable(tf.truncated_normal([3, 3, 128, 256], dtype=tf.float32, + stddev=1e-1), name='weights') + conv = tf.nn.conv2d(self.pool2, kernel, [1, 1, 1, 1], padding='SAME') + biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32), + trainable=True, name='biases') + out = tf.nn.bias_add(conv, biases) + self.conv3_1 = tf.nn.relu(out, name=scope) + self.parameters += [kernel, biases] + + # conv3_2 + with tf.name_scope('conv3_2') as scope: + kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256], dtype=tf.float32, + stddev=1e-1), name='weights') + conv = tf.nn.conv2d(self.conv3_1, kernel, [1, 1, 1, 1], padding='SAME') + biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32), + trainable=True, name='biases') + out = tf.nn.bias_add(conv, biases) + self.conv3_2 = tf.nn.relu(out, name=scope) + self.parameters += [kernel, biases] + + # conv3_3 + with tf.name_scope('conv3_3') as scope: + kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256], dtype=tf.float32, + stddev=1e-1), name='weights') + conv = tf.nn.conv2d(self.conv3_2, kernel, [1, 1, 1, 1], padding='SAME') + biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32), + trainable=True, name='biases') + out = tf.nn.bias_add(conv, biases) + self.conv3_3 = tf.nn.relu(out, name=scope) + self.parameters += [kernel, biases] + + # pool3 + self.pool3 = tf.nn.max_pool(self.conv3_3, + ksize=[1, 2, 2, 1], + strides=[1, 2, 2, 1], + padding='SAME', + name='pool3') + + # conv4_1 + with tf.name_scope('conv4_1') as scope: + kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 512], dtype=tf.float32, + stddev=1e-1), name='weights') + conv = tf.nn.conv2d(self.pool3, kernel, [1, 1, 1, 1], padding='SAME') + biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32), + trainable=True, name='biases') + out = tf.nn.bias_add(conv, biases) + self.conv4_1 = tf.nn.relu(out, name=scope) + self.parameters += [kernel, biases] + + # conv4_2 + with tf.name_scope('conv4_2') as scope: + kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32, + stddev=1e-1), name='weights') + conv = tf.nn.conv2d(self.conv4_1, kernel, [1, 1, 1, 1], padding='SAME') + biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32), + trainable=True, name='biases') + out = tf.nn.bias_add(conv, biases) + self.conv4_2 = tf.nn.relu(out, name=scope) + self.parameters += [kernel, biases] + + # conv4_3 + with tf.name_scope('conv4_3') as scope: + kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32, + stddev=1e-1), name='weights') + conv = tf.nn.conv2d(self.conv4_2, kernel, [1, 1, 1, 1], padding='SAME') + biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32), + trainable=True, name='biases') + out = tf.nn.bias_add(conv, biases) + self.conv4_3 = tf.nn.relu(out, name=scope) + self.parameters += [kernel, biases] + + # pool4 + self.pool4 = tf.nn.max_pool(self.conv4_3, + ksize=[1, 2, 2, 1], + strides=[1, 2, 2, 1], + padding='SAME', + name='pool4') + + # conv5_1 + with tf.name_scope('conv5_1') as scope: + kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32, + stddev=1e-1), name='weights') + conv = tf.nn.conv2d(self.pool4, kernel, [1, 1, 1, 1], padding='SAME') + biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32), + trainable=True, name='biases') + out = tf.nn.bias_add(conv, biases) + self.conv5_1 = tf.nn.relu(out, name=scope) + self.parameters += [kernel, biases] + + # conv5_2 + with tf.name_scope('conv5_2') as scope: + kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32, + stddev=1e-1), name='weights') + conv = tf.nn.conv2d(self.conv5_1, kernel, [1, 1, 1, 1], padding='SAME') + biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32), + trainable=True, name='biases') + out = tf.nn.bias_add(conv, biases) + self.conv5_2 = tf.nn.relu(out, name=scope) + self.parameters += [kernel, biases] + + # conv5_3 + with tf.name_scope('conv5_3') as scope: + kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32, + stddev=1e-1), name='weights') + conv = tf.nn.conv2d(self.conv5_2, kernel, [1, 1, 1, 1], padding='SAME') + biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32), + trainable=True, name='biases') + out = tf.nn.bias_add(conv, biases) + self.conv5_3 = tf.nn.relu(out, name=scope) + self.parameters += [kernel, biases] + + # MaxPool5 + self.maxpool5 = tf.nn.max_pool(self.conv5_3, + ksize=[1, 2, 2, 1], + strides=[1, 2, 2, 1], + padding='SAME', + name='maxpool5') + + # AvgPool5 + self.avgpool5 = tf.nn.avg_pool(self.conv5_3, + ksize=[1, 2, 2, 1], + strides=[1, 2, 2, 1], + padding='SAME', + name='avgpool5') + + def dan_part(self): + + # fc1 + with tf.name_scope('reg_head') as scope: + shape = 2*int(np.prod(self.maxpool5.get_shape()[1:])) + # shape = int(np.prod(self.pool5.get_shape()[1:])) + fc1w = tf.Variable(tf.truncated_normal([shape, 5], + dtype=tf.float32, + stddev=1e-1), name='weights') + fc1b = tf.Variable(tf.constant(1.0, shape=[5], dtype=tf.float32), + trainable=True, name='biases') + + maxpool5_flat = tf.nn.l2_normalize(tf.reshape(self.maxpool5, [-1, shape/2]), 1) + avgpool5_flat = tf.nn.l2_normalize(tf.reshape(self.avgpool5, [-1, shape/2]), 1) + + self.concat = tf.concat([maxpool5_flat, avgpool5_flat], 1) + self.reg_head = tf.nn.bias_add(tf.matmul(self.concat, fc1w), fc1b) + self.parameters += [fc1w, fc1b] + + + def load_weights(self, weight_file, sess): + weights = np.load(weight_file) + keys = sorted(weights.keys()) + for i, k in enumerate(keys): + if i==len(self.parameters)-2: + break + sess.run(self.parameters[i].assign(weights[k])) \ No newline at end of file diff --git a/dan.pyc b/dan.pyc new file mode 100644 index 0000000..6587ad4 Binary files /dev/null and b/dan.pyc differ diff --git a/remtime.py b/remtime.py new file mode 100644 index 0000000..dbe9123 --- /dev/null +++ b/remtime.py @@ -0,0 +1,46 @@ +import tensorflow as tf + +def printTime(remtime): + hrs = int(remtime)/3600 + mins = int((remtime/60-hrs*60)) + secs = int(remtime-mins*60-hrs*3600) + print(str(hrs)+"Hrs "+str(mins)+"Mins "+str(secs)+"Secs remaining..") + +def dataBatch(data_path, BATCH_SIZE, N_EPOCHS=1): + reader = tf.TFRecordReader() + filename_queue = tf.train.string_input_producer([data_path], num_epochs=N_EPOCHS) + _, serialized_example = reader.read(filename_queue) + # Decode the record read by the reader + feature = {'train/image': tf.FixedLenFeature([], tf.string), 'train/label': tf.FixedLenFeature([], tf.string)} + features = tf.parse_single_example(serialized_example, features=feature) + # Convert the image data from string back to the numbers + image = tf.decode_raw(features['train/image'], tf.float32) + label = tf.decode_raw(features['train/label'], tf.float32) + # Reshape image data into the original shape + image = tf.reshape(image, [224, 224, 3]) + label = tf.reshape(label, [5]) + + images, labels = tf.train.shuffle_batch([image, label], batch_size=BATCH_SIZE, capacity=100, min_after_dequeue=BATCH_SIZE, allow_smaller_final_batch=True) + return images, labels + + + +# VID_BATCH = 100 + + +# df = pd.read_csv('small_train_sample.csv') +# NUM_VID = len(df) +# images=np.empty((NUM_VID*100, 224, 224, 3)) +# traits=np.zeros((NUM_VID*100, 5)) + +# for i in range(NUM_VID): +# filelist=glob.glob('ImageData/trainingData/'+(df['VideoName'].iloc[i]).split('.mp4')[0]+'/*.jpg') +# print(str(i)+" "+ str(len(filelist))+" "+df['VideoName'].iloc[i]) +# images[i*100:(i+1)*100] = np.array([np.array(Image.open(fname).resize((224,224), Image.ANTIALIAS)) for fname in filelist]) +# traits[i*100:(i+1)*100]=np.array(df.drop(['VideoName'], 1, inplace=False).iloc[i]) + + +# ind = [i for i in range(NUM_IMAGES)] + +# print(images.shape) +# print(traits.shape) \ No newline at end of file diff --git a/remtime.pyc b/remtime.pyc new file mode 100644 index 0000000..d1fc7e4 Binary files /dev/null and b/remtime.pyc differ diff --git a/savetfreco.py b/savetfreco.py new file mode 100644 index 0000000..8c538e8 --- /dev/null +++ b/savetfreco.py @@ -0,0 +1,73 @@ +from random import shuffle +import glob +import pandas as pd +import tensorflow as tf +import sys +import numpy as np +import Image +import cv2 + + + +df = pd.read_csv('small_train_sample.csv') +NUM_VID = len(df) +addrs = [] +labels = [] +for i in range(NUM_VID): + filelist=glob.glob('ImageData/trainingData/'+(df['VideoName'].iloc[i]).split('.mp4')[0]+'/*.jpg') + addrs+=filelist + labels+=[np.array(df.drop(['VideoName'], 1, inplace=False).iloc[i]).astype(np.float32)]*100 +# print labels[101] +# print len(labels[0]), len(addrs) + +c = list(zip(addrs, labels)) +shuffle(c) +addrs, labels = zip(*c) + +# Divide the hata into 60% train, 20% validation, and 20% test +train_addrs = addrs[0:int(0.8*len(addrs))] +train_labels = labels[0:int(0.8*len(labels))] + +val_addrs = addrs[int(0.8*len(addrs)):] +val_labels = labels[int(0.8*len(addrs)):] + +def load_image(addr): + # read an image and resize to (224, 224) + # cv2 load images as BGR, convert it to RGB + img = cv2.imread(addr) + img = cv2.resize(img, (224, 224), interpolation=cv2.INTER_CUBIC) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = img.astype(np.float32) + return img + +def _float_feature(value): + return tf.train.Feature(float_list=tf.train.FloatList(value=[value])) +def _bytes_feature(value): + return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) + + +train_filename = 'train500.tfrecords' # address to save the TFRecords file +# open the TFRecords file +writer = tf.python_io.TFRecordWriter(train_filename) +for i in range(len(train_addrs)): + # print how many images are saved every 1000 images + if not i % 1000: + print 'Train data: {}/{}'.format(i, len(train_addrs)) + sys.stdout.flush() + # Load the image + img = load_image(train_addrs[i]) + # print img.shape + label = train_labels[i].astype(np.float32) + # print label.shape + # print label + # Create a feature + feature = {'train/label': _bytes_feature(tf.compat.as_bytes(label.tostring())), + 'train/image': _bytes_feature(tf.compat.as_bytes(img.tostring()))} + # Create an example protocol buffer + example = tf.train.Example(features=tf.train.Features(feature=feature)) + + # Serialize to string and write on the file + writer.write(example.SerializeToString()) + +writer.close() +sys.stdout.flush() \ No newline at end of file diff --git a/small_train_sample.csv b/small_train_sample.csv new file mode 100644 index 0000000..6fd93df --- /dev/null +++ b/small_train_sample.csv @@ -0,0 +1,501 @@ +VideoName,ValueExtraversion,ValueAgreeableness,ValueConscientiousness,ValueNeurotisicm,ValueOpenness 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+l2YS_qsF7SQ.004.mp4,0.5046728972,0.5934065934,0.5048543689,0.6041666667,0.6222222222 +tEQEKN07KgQ.005.mp4,0.2803738318,0.4615384615,0.3495145631,0.3125,0.4111111111 diff --git a/train.py b/train.py new file mode 100644 index 0000000..d7af4d6 --- /dev/null +++ b/train.py @@ -0,0 +1,68 @@ +import tensorflow as tf +import pandas as pd +import numpy as np +from dan import DAN +import time +import numpy as np +from remtime import * +import random +import glob +import Image +import warnings + +warnings.filterwarnings("ignore") + + +LEARNING_RATE = 0.001 +BATCH_SIZE = 25 +N_EPOCHS = 5 +PER=5.0 +NUM_IMAGES = 8000 + + + +imgs = tf.placeholder('float', [None, 224, 224, 3]) +val = tf.placeholder('float', [None, 5]) + +config = tf.ConfigProto() +config.gpu_options.allow_growth = True +config.gpu_options.per_process_gpu_memory_fraction = 0.8 +with tf.Session(config=config) as sess: + + model = DAN(imgs) + + cost = tf.reduce_mean(tf.squared_difference(model.output, val)) + optimizer = tf.train.AdamOptimizer(learning_rate=LEARNING_RATE).minimize(cost) + images, labels = dataBatch('train500.tfrecords', BATCH_SIZE=BATCH_SIZE, N_EPOCHS=N_EPOCHS) + init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) + sess.run(init_op) + coord = tf.train.Coordinator() + threads = tf.train.start_queue_runners(coord=coord) + model.load_weights('vgg16_weights.npz', sess) + for epoch in range(N_EPOCHS): + sess.run(tf.local_variables_initializer()) + mean_acc = 0 + i=0 + stime = time.time() + while i