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action_predict.py
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action_predict.py
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import time
import yaml
import wget
import cv2
from utils import *
from base_models import AlexNet, C3DNet, convert_to_fcn
from base_models import I3DNet
from tensorflow.keras.layers import Input, Concatenate, Dense
from tensorflow.keras.layers import GRU, LSTM, GRUCell
from tensorflow.keras.layers import Dropout, LSTMCell, RNN
from tensorflow.keras.utils import plot_model
from tensorflow.keras.layers import Flatten, Average, Add
from tensorflow.keras.layers import ConvLSTM2D, Conv2D
from tensorflow.keras.models import Model, load_model
from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint
from tensorflow.keras.applications import vgg16, resnet50
from tensorflow.keras.layers import GlobalAveragePooling2D, GlobalMaxPooling2D, Lambda, dot, concatenate, Activation
from tensorflow.keras.optimizers import Adam, SGD, RMSprop
from tensorflow.keras import regularizers
from tensorflow.keras import backend as K
from tensorflow.keras.utils import Sequence
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.metrics import roc_auc_score, roc_curve, precision_recall_curve
from sklearn.svm import LinearSVC
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
# TODO: Make all global class parameters to minimum , e.g. no model generation
class ActionPredict(object):
"""
A base interface class for creating prediction models
"""
def __init__(self,
global_pooling='avg',
regularizer_val=0.0001,
backbone='vgg16',
**kwargs):
"""
Class init function
Args:
global_pooling: Pooling method for generating convolutional features
regularizer_val: Regularization value for training
backbone: Backbone for generating convolutional features
"""
# Network parameters
self._regularizer_value = regularizer_val
self._regularizer = regularizers.l2(regularizer_val)
self._global_pooling = global_pooling
self._backbone = backbone
self._generator = None # use data generator for train/test
# Processing images anf generate features
def load_images_crop_and_process(self, img_sequences, bbox_sequences,
ped_ids, save_path,
data_type='train',
crop_type='none',
crop_mode='warp',
crop_resize_ratio=2,
target_dim=(224, 224),
process=True,
regen_data=False):
"""
Generate visual feature sequences by reading and processing images
Args:
img_sequences: Sequences of image na,es
bbox_sequences: Sequences of bounding boxes
ped_ids: Sequences of pedestrian ids
save_path: Path to the root folder to save features
data_type: The type of features, train/test/val
crop_type: The method to crop the images.
Options are 'none' (no cropping)
'bbox' (crop using bounding box coordinates),
'context' (A region containing pedestrian and their local surround)
'surround' (only the region around the pedestrian. Pedestrian appearance
is suppressed)
crop_mode: How to resize ond/or pad the corpped images (see utils.img_pad)
crop_resize_ratio: The ratio by which the image is enlarged to capture the context
Used by crop types 'context' and 'surround'.
target_dim: Dimension of final visual features
process: Whether process the raw images using a neural network
regen_data: Whether regenerate visual features. This will overwrite the cached features
Returns:
Numpy array of visual features
Tuple containing the size of features
"""
# load the feature files if exists
print("Generating {} features crop_type={} crop_mode={}\
\nsave_path={}, ".format(data_type, crop_type, crop_mode,
save_path))
preprocess_dict = {'vgg16': vgg16.preprocess_input, 'resnet50': resnet50.preprocess_input}
backbone_dict = {'vgg16': vgg16.VGG16, 'resnet50': resnet50.ResNet50}
preprocess_input = preprocess_dict.get(self._backbone, None)
if process:
assert (self._backbone in ['vgg16', 'resnet50']), "{} is not supported".format(self._backbone)
convnet = backbone_dict[self._backbone](input_shape=(224, 224, 3),
include_top=False, weights='imagenet') if process else None
sequences = []
bbox_seq = bbox_sequences.copy()
i = -1
for seq, pid in zip(img_sequences, ped_ids):
i += 1
update_progress(i / len(img_sequences))
img_seq = []
for imp, b, p in zip(seq, bbox_seq[i], pid):
flip_image = False
set_id = imp.split('/')[-3]
vid_id = imp.split('/')[-2]
img_name = imp.split('/')[-1].split('.')[0]
img_save_folder = os.path.join(save_path, set_id, vid_id)
# Modify the path depending on crop mode
if crop_type == 'none':
img_save_path = os.path.join(img_save_folder, img_name + '.pkl')
else:
img_save_path = os.path.join(img_save_folder, img_name + '_' + p[0] + '.pkl')
# Check whether the file exists
if os.path.exists(img_save_path) and not regen_data:
if not self._generator:
with open(img_save_path, 'rb') as fid:
try:
img_features = pickle.load(fid)
except:
img_features = pickle.load(fid, encoding='bytes')
else:
if 'flip' in imp:
imp = imp.replace('_flip', '')
flip_image = True
if crop_type == 'none':
img_data = cv2.imread(imp)
img_features = cv2.resize(img_data, target_dim)
if flip_image:
img_features = cv2.flip(img_features, 1)
else:
img_data = cv2.imread(imp)
if flip_image:
img_data = cv2.flip(img_data, 1)
if crop_type == 'bbox':
b = list(map(int, b[0:4]))
cropped_image = img_data[b[1]:b[3], b[0]:b[2], :]
img_features = img_pad(cropped_image, mode=crop_mode, size=target_dim[0])
elif 'context' in crop_type:
bbox = jitter_bbox(imp, [b], 'enlarge', crop_resize_ratio)[0]
bbox = squarify(bbox, 1, img_data.shape[1])
bbox = list(map(int, bbox[0:4]))
cropped_image = img_data[bbox[1]:bbox[3], bbox[0]:bbox[2], :]
img_features = img_pad(cropped_image, mode='pad_resize', size=target_dim[0])
elif 'surround' in crop_type:
b_org = list(map(int, b[0:4])).copy()
bbox = jitter_bbox(imp, [b], 'enlarge', crop_resize_ratio)[0]
bbox = squarify(bbox, 1, img_data.shape[1])
bbox = list(map(int, bbox[0:4]))
img_data[b_org[1]:b_org[3], b_org[0]:b_org[2], :] = 128
cropped_image = img_data[bbox[1]:bbox[3], bbox[0]:bbox[2], :]
img_features = img_pad(cropped_image, mode='pad_resize', size=target_dim[0])
else:
raise ValueError('ERROR: Undefined value for crop_type {}!'.format(crop_type))
if preprocess_input is not None:
img_features = preprocess_input(img_features)
if process:
expanded_img = np.expand_dims(img_features, axis=0)
img_features = convnet.predict(expanded_img)
# Save the file
if not os.path.exists(img_save_folder):
os.makedirs(img_save_folder)
with open(img_save_path, 'wb') as fid:
pickle.dump(img_features, fid, pickle.HIGHEST_PROTOCOL)
# if using the generator save the cached features path and size of the features
if process and not self._generator:
if self._global_pooling == 'max':
img_features = np.squeeze(img_features)
img_features = np.amax(img_features, axis=0)
img_features = np.amax(img_features, axis=0)
elif self._global_pooling == 'avg':
img_features = np.squeeze(img_features)
img_features = np.average(img_features, axis=0)
img_features = np.average(img_features, axis=0)
else:
img_features = img_features.ravel()
if self._generator:
img_seq.append(img_save_path)
else:
img_seq.append(img_features)
sequences.append(img_seq)
sequences = np.array(sequences)
# compute size of the features after the processing
if self._generator:
with open(sequences[0][0], 'rb') as fid:
feat_shape = pickle.load(fid).shape
if process:
if self._global_pooling in ['max', 'avg']:
feat_shape = feat_shape[-1]
else:
feat_shape = np.prod(feat_shape)
if not isinstance(feat_shape, tuple):
feat_shape = (feat_shape,)
feat_shape = (np.array(bbox_sequences).shape[1],) + feat_shape
else:
feat_shape = sequences.shape[1:]
return sequences, feat_shape
# Processing images anf generate features
def get_optical_flow(self, img_sequences, bbox_sequences,
ped_ids, save_path,
data_type='train',
crop_type='none',
crop_mode='warp',
crop_resize_ratio=2,
target_dim=(224, 224),
regen_data=False):
"""
Generate visual feature sequences by reading and processing images
Args:
img_sequences: Sequences of image na,es
bbox_sequences: Sequences of bounding boxes
ped_ids: Sequences of pedestrian ids
save_path: Path to the root folder to save features
data_type: The type of features, train/test/val
crop_type: The method to crop the images.
Options are 'none' (no cropping)
'bbox' (crop using bounding box coordinates),
'context' (A region containing pedestrian and their local surround)
'surround' (only the region around the pedestrian. Pedestrian appearance
is suppressed)
crop_mode: How to resize ond/or pad the corpped images (see utils.img_pad)
crop_resize_ratio: The ratio by which the image is enlarged to capture the context
Used by crop types 'context' and 'surround'.
target_dim: Dimension of final visual features
regen_data: Whether regenerate visual features. This will overwrite the cached features
Returns:
Numpy array of visual features
Tuple containing the size of features
"""
# load the feature files if exists
print("Generating {} features crop_type={} crop_mode={}\
\nsave_path={}, ".format(data_type, crop_type, crop_mode, save_path))
sequences = []
bbox_seq = bbox_sequences.copy()
i = -1
# flow size (h,w)
flow_size = read_flow_file(img_sequences[0][0].replace('images', 'optical_flow').replace('png', 'flo')).shape
img_size = cv2.imread(img_sequences[0][0]).shape
# A ratio to adjust the dimension of bounding boxes (w,h)
box_resize_coef = (flow_size[1]/img_size[1], flow_size[0]/img_size[0])
for seq, pid in zip(img_sequences, ped_ids):
i += 1
update_progress(i / len(img_sequences))
flow_seq = []
for imp, b, p in zip(seq, bbox_seq[i], pid):
flip_image = False
set_id = imp.split('/')[-3]
vid_id = imp.split('/')[-2]
img_name = imp.split('/')[-1].split('.')[0]
optflow_save_folder = os.path.join(save_path, set_id, vid_id)
ofp = imp.replace('images', 'optical_flow').replace('png', 'flo')
# Modify the path depending on crop mode
if crop_type == 'none':
optflow_save_path = os.path.join(optflow_save_folder, img_name + '.flo')
else:
optflow_save_path = os.path.join(optflow_save_folder, img_name + '_' + p[0] + '.flo')
# Check whether the file exists
if os.path.exists(optflow_save_path) and not regen_data:
if not self._generator:
ofp_data = read_flow_file(optflow_save_path)
else:
if 'flip' in imp:
ofp = ofp.replace('_flip', '')
flip_image = True
if crop_type == 'none':
ofp_image = read_flow_file(ofp)
ofp_data = cv2.resize(ofp_image, target_dim)
if flip_image:
ofp_data = cv2.flip(ofp_data, 1)
else:
ofp_image = read_flow_file(ofp)
# Adjust the size of bbox according to the dimensions of flow map
b = list(map(int, [b[0] * box_resize_coef[0], b[1] * box_resize_coef[1],
b[2] * box_resize_coef[0], b[3] * box_resize_coef[1]]))
if flip_image:
ofp_image = cv2.flip(ofp_image, 1)
if crop_type == 'bbox':
cropped_image = ofp_image[b[1]:b[3], b[0]:b[2], :]
ofp_data = img_pad(cropped_image, mode=crop_mode, size=target_dim[0])
elif 'context' in crop_type:
bbox = jitter_bbox(imp, [b], 'enlarge', crop_resize_ratio)[0]
bbox = squarify(bbox, 1, ofp_image.shape[1])
bbox = list(map(int, bbox[0:4]))
cropped_image = ofp_image[bbox[1]:bbox[3], bbox[0]:bbox[2], :]
ofp_data = img_pad(cropped_image, mode='pad_resize', size=target_dim[0])
elif 'surround' in crop_type:
b_org = b.copy()
bbox = jitter_bbox(imp, [b], 'enlarge', crop_resize_ratio)[0]
bbox = squarify(bbox, 1, ofp_image.shape[1])
bbox = list(map(int, bbox[0:4]))
ofp_image[b_org[1]:b_org[3], b_org[0]: b_org[2], :] = 0
cropped_image = ofp_image[bbox[1]:bbox[3], bbox[0]:bbox[2], :]
ofp_data = img_pad(cropped_image, mode='pad_resize', size=target_dim[0])
else:
raise ValueError('ERROR: Undefined value for crop_type {}!'.format(crop_type))
# Save the file
if not os.path.exists(optflow_save_folder):
os.makedirs(optflow_save_folder)
write_flow(ofp_data, optflow_save_path)
# if using the generator save the cached features path and size of the features
if self._generator:
flow_seq.append(optflow_save_path)
else:
flow_seq.append(ofp_data)
sequences.append(flow_seq)
sequences = np.array(sequences)
# compute size of the features after the processing
if self._generator:
feat_shape = read_flow_file(sequences[0][0]).shape
if not isinstance(feat_shape, tuple):
feat_shape = (feat_shape,)
feat_shape = (np.array(bbox_sequences).shape[1],) + feat_shape
else:
feat_shape = sequences.shape[1:]
return sequences, feat_shape
def get_data_sequence(self, data_type, data_raw, opts):
"""
Generates raw sequences from a given dataset
Args:
data_type: Split type of data, whether it is train, test or val
data_raw: Raw tracks from the dataset
opts: Options for generating data samples
Returns:
A list of data samples extracted from raw data
Positive and negative data counts
"""
print('\n#####################################')
print('Generating raw data')
print('#####################################')
d = {'center': data_raw['center'].copy(),
'box': data_raw['bbox'].copy(),
'ped_id': data_raw['pid'].copy(),
'crossing': data_raw['activities'].copy(),
'image': data_raw['image'].copy()}
balance = opts['balance_data'] if data_type == 'train' else False
obs_length = opts['obs_length']
time_to_event = opts['time_to_event']
normalize = opts['normalize_boxes']
try:
d['speed'] = data_raw['obd_speed'].copy()
except KeyError:
d['speed'] = data_raw['vehicle_act'].copy()
print('Jaad dataset does not have speed information')
print('Vehicle actions are used instead')
if balance:
self.balance_data_samples(d, data_raw['image_dimension'][0])
d['box_org'] = d['box'].copy()
d['tte'] = []
if isinstance(time_to_event, int):
for k in d.keys():
for i in range(len(d[k])):
d[k][i] = d[k][i][- obs_length - time_to_event:-time_to_event]
d['tte'] = [[time_to_event]]*len(data_raw['bbox'])
else:
overlap = opts['overlap'] # if data_type == 'train' else 0.0
olap_res = obs_length if overlap == 0 else int((1 - overlap) * obs_length)
olap_res = 1 if olap_res < 1 else olap_res
for k in d.keys():
seqs = []
for seq in d[k]:
start_idx = len(seq) - obs_length - time_to_event[1]
end_idx = len(seq) - obs_length - time_to_event[0]
seqs.extend([seq[i:i + obs_length] for i in
range(start_idx, end_idx + 1, olap_res)])
d[k] = seqs
for seq in data_raw['bbox']:
start_idx = len(seq) - obs_length - time_to_event[1]
end_idx = len(seq) - obs_length - time_to_event[0]
d['tte'].extend([[len(seq) - (i + obs_length)] for i in
range(start_idx, end_idx + 1, olap_res)])
if normalize:
for k in d.keys():
if k != 'tte':
if k != 'box' and k != 'center':
for i in range(len(d[k])):
d[k][i] = d[k][i][1:]
else:
for i in range(len(d[k])):
d[k][i] = np.subtract(d[k][i][1:], d[k][i][0]).tolist()
d[k] = np.array(d[k])
else:
for k in d.keys():
d[k] = np.array(d[k])
d['crossing'] = np.array(d['crossing'])[:, 0, :]
pos_count = np.count_nonzero(d['crossing'])
neg_count = len(d['crossing']) - pos_count
print("Negative {} and positive {} sample counts".format(neg_count, pos_count))
return d, neg_count, pos_count
def balance_data_samples(self, d, img_width, balance_tag='crossing'):
"""
Balances the ratio of positive and negative data samples. The less represented
data type is augmented by flipping the sequences
Args:
d: Sequence of data samples
img_width: Width of the images
balance_tag: The tag to balance the data based on
"""
print("Balancing with respect to {} tag".format(balance_tag))
gt_labels = [gt[0] for gt in d[balance_tag]]
num_pos_samples = np.count_nonzero(np.array(gt_labels))
num_neg_samples = len(gt_labels) - num_pos_samples
# finds the indices of the samples with larger quantity
if num_neg_samples == num_pos_samples:
print('Positive and negative samples are already balanced')
else:
print('Unbalanced: \t Positive: {} \t Negative: {}'.format(num_pos_samples, num_neg_samples))
if num_neg_samples > num_pos_samples:
gt_augment = 1
else:
gt_augment = 0
num_samples = len(d[balance_tag])
for i in range(num_samples):
if d[balance_tag][i][0][0] == gt_augment:
for k in d:
if k == 'center':
flipped = d[k][i].copy()
flipped = [[img_width - c[0], c[1]]
for c in flipped]
d[k].append(flipped)
if k == 'box':
flipped = d[k][i].copy()
flipped = [np.array([img_width - b[2], b[1], img_width - b[0], b[3]])
for b in flipped]
d[k].append(flipped)
if k == 'image':
flipped = d[k][i].copy()
flipped = [im.replace('.png', '_flip.png') for im in flipped]
d[k].append(flipped)
if k in ['speed', 'ped_id', 'crossing', 'walking', 'looking']:
d[k].append(d[k][i].copy())
gt_labels = [gt[0] for gt in d[balance_tag]]
num_pos_samples = np.count_nonzero(np.array(gt_labels))
num_neg_samples = len(gt_labels) - num_pos_samples
if num_neg_samples > num_pos_samples:
rm_index = np.where(np.array(gt_labels) == 0)[0]
else:
rm_index = np.where(np.array(gt_labels) == 1)[0]
# Calculate the difference of sample counts
dif_samples = abs(num_neg_samples - num_pos_samples)
# shuffle the indices
np.random.seed(42)
np.random.shuffle(rm_index)
# reduce the number of indices to the difference
rm_index = rm_index[0:dif_samples]
# update the data
for k in d:
seq_data_k = d[k]
d[k] = [seq_data_k[i] for i in range(0, len(seq_data_k)) if i not in rm_index]
new_gt_labels = [gt[0] for gt in d[balance_tag]]
num_pos_samples = np.count_nonzero(np.array(new_gt_labels))
print('Balanced:\t Positive: %d \t Negative: %d\n'
% (num_pos_samples, len(d[balance_tag]) - num_pos_samples))
def get_context_data(self, model_opts, data, data_type, feature_type):
print('\n#####################################')
print('Generating {} {}'.format(feature_type, data_type))
print('#####################################')
process = model_opts.get('process', True)
aux_name = [self._backbone]
if not process:
aux_name.append('raw')
aux_name = '_'.join(aux_name).strip('_')
eratio = model_opts['enlarge_ratio']
dataset = model_opts['dataset']
data_gen_params = {'data_type': data_type, 'crop_type': 'none',
'target_dim': model_opts.get('target_dim', (224, 224))}
if 'local_box' in feature_type:
data_gen_params['crop_type'] = 'bbox'
data_gen_params['crop_mode'] = 'pad_resize'
elif 'local_context' in feature_type:
data_gen_params['crop_type'] = 'context'
data_gen_params['crop_resize_ratio'] = eratio
elif 'surround' in feature_type:
data_gen_params['crop_type'] = 'surround'
data_gen_params['crop_resize_ratio'] = eratio
elif 'scene_context' in feature_type:
data_gen_params['crop_type'] = 'none'
save_folder_name = feature_type
if 'flow' not in feature_type:
save_folder_name = '_'.join([feature_type, aux_name])
if 'local_context' in feature_type or 'surround' in feature_type:
save_folder_name = '_'.join([save_folder_name, str(eratio)])
data_gen_params['save_path'], _ = get_path(save_folder=save_folder_name,
dataset=dataset, save_root_folder='data/features')
if 'flow' in feature_type:
return self.get_optical_flow(data['image'],
data['box_org'],
data['ped_id'],
**data_gen_params)
else:
return self.load_images_crop_and_process(data['image'],
data['box_org'],
data['ped_id'],
process=process,
**data_gen_params)
def get_data(self, data_type, data_raw, model_opts):
"""
Generates data train/test/val data
Args:
data_type: Split type of data, whether it is train, test or val
data_raw: Raw tracks from the dataset
model_opts: Model options for generating data
Returns:
A dictionary containing, data, data parameters used for model generation,
effective dimension of data (the number of rgb images to be used calculated accorfing
to the length of optical flow window) and negative and positive sample counts
"""
self._generator = model_opts.get('generator', False)
data_type_sizes_dict = {}
process = model_opts.get('process', True)
dataset = model_opts['dataset']
data, neg_count, pos_count = self.get_data_sequence(data_type, data_raw, model_opts)
data_type_sizes_dict['box'] = data['box'].shape[1:]
if 'speed' in data.keys():
data_type_sizes_dict['speed'] = data['speed'].shape[1:]
# Store the type and size of each image
_data = []
data_sizes = []
data_types = []
for d_type in model_opts['obs_input_type']:
if 'local' in d_type or 'context' in d_type:
features, feat_shape = self.get_context_data(model_opts, data, data_type, d_type)
elif 'pose' in d_type:
path_to_pose, _ = get_path(save_folder='poses',
dataset=dataset,
save_root_folder='data/features')
features = get_pose(data['image'],
data['ped_id'],
data_type=data_type,
file_path=path_to_pose,
dataset=model_opts['dataset'])
feat_shape = features.shape[1:]
else:
features = data[d_type]
feat_shape = features.shape[1:]
_data.append(features)
data_sizes.append(feat_shape)
data_types.append(d_type)
# create the final data file to be returned
if self._generator:
_data = (DataGenerator(data=_data,
labels=data['crossing'],
data_sizes=data_sizes,
process=process,
global_pooling=self._global_pooling,
input_type_list=model_opts['obs_input_type'],
batch_size=model_opts['batch_size'],
shuffle=data_type != 'test',
to_fit=data_type != 'test'), data['crossing']) # set y to None
else:
_data = (_data, data['crossing'])
return {'data': _data,
'ped_id': data['ped_id'],
'image': data['image'],
'tte': data['tte'],
'data_params': {'data_types': data_types, 'data_sizes': data_sizes},
'count': {'neg_count': neg_count, 'pos_count': pos_count}}
def log_configs(self, config_path, batch_size, epochs,
lr, model_opts):
# TODO: Update config by adding network attributes
"""
Logs the parameters of the model and training
Args:
config_path: The path to save the file
batch_size: Batch size of training
epochs: Number of epochs for training
lr: Learning rate of training
model_opts: Data generation parameters (see get_data)
"""
# Save config and training param files
with open(config_path, 'wt') as fid:
yaml.dump({'model_opts': model_opts,
'train_opts': {'batch_size':batch_size, 'epochs': epochs, 'lr': lr}},
fid, default_flow_style=False)
# with open(config_path, 'wt') as fid:
# fid.write("####### Model options #######\n")
# for k in opts:
# fid.write("%s: %s\n" % (k, str(opts[k])))
# fid.write("\n####### Network config #######\n")
# # fid.write("%s: %s\n" % ('hidden_units', str(self._num_hidden_units)))
# # fid.write("%s: %s\n" % ('reg_value ', str(self._regularizer_value)))
# fid.write("\n####### Training config #######\n")
# fid.write("%s: %s\n" % ('batch_size', str(batch_size)))
# fid.write("%s: %s\n" % ('epochs', str(epochs)))
# fid.write("%s: %s\n" % ('lr', str(lr)))
print('Wrote configs to {}'.format(config_path))
def class_weights(self, apply_weights, sample_count):
"""
Computes class weights for imbalanced data used during training
Args:
apply_weights: Whether to apply weights
sample_count: Positive and negative sample counts
Returns:
A dictionary of class weights or None if no weights to be calculated
"""
if not apply_weights:
return None
total = sample_count['neg_count'] + sample_count['pos_count']
# formula from sklearn
#neg_weight = (1 / sample_count['neg_count']) * (total) / 2.0
#pos_weight = (1 / sample_count['pos_count']) * (total) / 2.0
# use simple ratio
neg_weight = sample_count['pos_count']/total
pos_weight = sample_count['neg_count']/total
print("### Class weights: negative {:.3f} and positive {:.3f} ###".format(neg_weight, pos_weight))
return {0: neg_weight, 1: pos_weight}
def get_callbacks(self, learning_scheduler, model_path):
"""
Creates a list of callabcks for training
Args:
learning_scheduler: Whether to use callbacks
Returns:
A list of call backs or None if learning_scheduler is false
"""
callbacks = None
# Set up learning schedulers
if learning_scheduler:
callbacks = []
if 'early_stop' in learning_scheduler:
default_params = {'monitor': 'val_loss',
'min_delta': 1.0, 'patience': 5,
'verbose': 1}
default_params.update(learning_scheduler['early_stop'])
callbacks.append(EarlyStopping(**default_params))
if 'plateau' in learning_scheduler:
default_params = {'monitor': 'val_loss',
'factor': 0.2, 'patience': 5,
'min_lr': 1e-08, 'verbose': 1}
default_params.update(learning_scheduler['plateau'])
callbacks.append(ReduceLROnPlateau(**default_params))
if 'checkpoint' in learning_scheduler:
default_params = {'filepath': model_path, 'monitor': 'val_loss',
'save_best_only': True, 'save_weights_only': False,
'save_freq': 'epoch', 'verbose': 2}
default_params.update(learning_scheduler['checkpoint'])
callbacks.append(ModelCheckpoint(**default_params))
return callbacks
def get_optimizer(self, optimizer):
"""
Return an optimizer object
Args:
optimizer: The type of optimizer. Supports 'adam', 'sgd', 'rmsprop'
Returns:
An optimizer object
"""
assert optimizer.lower() in ['adam', 'sgd', 'rmsprop'], \
"{} optimizer is not implemented".format(optimizer)
if optimizer.lower() == 'adam':
return Adam
elif optimizer.lower() == 'sgd':
return SGD
elif optimizer.lower() == 'rmsprop':
return RMSprop
def train(self, data_train,
data_val=None,
batch_size=32,
epochs=60,
lr=0.000005,
optimizer='adam',
learning_scheduler=None,
model_opts=None):
"""
Trains the models
Args:
data_train: Training data
data_val: Validation data
batch_size: Batch size for training
epochs: Number of epochs to train
lr: Learning rate
optimizer: Optimizer for training
learning_scheduler: Whether to use learning schedulers
model_opts: Model options
Returns:
The path to the root folder of models
"""
learning_scheduler = learning_scheduler or {}
# Set the path for saving models
model_folder_name = time.strftime("%d%b%Y-%Hh%Mm%Ss")
path_params = {'save_folder': os.path.join(self.__class__.__name__, model_folder_name),
'save_root_folder': 'data/models/',
'dataset': model_opts['dataset']}
model_path, _ = get_path(**path_params, file_name='model.h5')
# Read train data
data_train = self.get_data('train', data_train, {**model_opts, 'batch_size': batch_size})
if data_val is not None:
data_val = self.get_data('val', data_val, {**model_opts, 'batch_size': batch_size})['data']
if self._generator:
data_val = data_val[0]
# Create model
train_model = self.get_model(data_train['data_params'])
# Train the model
class_w = self.class_weights(model_opts['apply_class_weights'], data_train['count'])
optimizer = self.get_optimizer(optimizer)(lr=lr)
train_model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
callbacks = self.get_callbacks(learning_scheduler, model_path)
history = train_model.fit(x=data_train['data'][0],
y=None if self._generator else data_train['data'][1],
batch_size=batch_size,
epochs=epochs,
validation_data=data_val,
class_weight=class_w,
verbose=1,
callbacks=callbacks)
if 'checkpoint' not in learning_scheduler:
print('Train model is saved to {}'.format(model_path))
train_model.save(model_path)
# Save data options and configurations
model_opts_path, _ = get_path(**path_params, file_name='model_opts.pkl')
with open(model_opts_path, 'wb') as fid:
pickle.dump(model_opts, fid, pickle.HIGHEST_PROTOCOL)
config_path, _ = get_path(**path_params, file_name='configs.yaml')
self.log_configs(config_path, batch_size, epochs,
lr, model_opts)
# Save training history
history_path, saved_files_path = get_path(**path_params, file_name='history.pkl')
with open(history_path, 'wb') as fid:
pickle.dump(history.history, fid, pickle.HIGHEST_PROTOCOL)
return saved_files_path
# Test Functions
def test(self, data_test, model_path=''):
"""
Evaluates a given model
Args:
data_test: Test data
model_path: Path to folder containing the model and options
save_results: Save output of the model for visualization and analysis
Returns:
Evaluation metrics
"""
with open(os.path.join(model_path, 'configs.yaml'), 'r') as fid:
opts = yaml.safe_load(fid)
# try:
# model_opts = pickle.load(fid)
# except:
# model_opts = pickle.load(fid, encoding='bytes')
test_model = load_model(os.path.join(model_path, 'model.h5'))
test_model.summary()
test_data = self.get_data('test', data_test, {**opts['model_opts'], 'batch_size': 1})
test_results = test_model.predict(test_data['data'][0],
batch_size=1, verbose=1)
acc = accuracy_score(test_data['data'][1], np.round(test_results))
f1 = f1_score(test_data['data'][1], np.round(test_results))
auc = roc_auc_score(test_data['data'][1], np.round(test_results))
roc = roc_curve(test_data['data'][1], test_results)
precision = precision_score(test_data['data'][1], np.round(test_results))
recall = recall_score(test_data['data'][1], np.round(test_results))
pre_recall = precision_recall_curve(test_data['data'][1], test_results)
# THIS IS TEMPORARY, REMOVE BEFORE RELEASE
with open(os.path.join(model_path, 'test_output.pkl'), 'wb') as picklefile:
pickle.dump({'tte': test_data['tte'],
'pid': test_data['ped_id'],
'gt':test_data['data'][1],
'y': test_results,
'image': test_data['image']}, picklefile)
print('acc:{:.2f} auc:{:.2f} f1:{:.2f} precision:{:.2f} recall:{:.2f}'.format(acc, auc, f1, precision, recall))
save_results_path = os.path.join(model_path, '{:.2f}'.format(acc) + '.yaml')
if not os.path.exists(save_results_path):
results = {'acc': acc,
'auc': auc,
'f1': f1,
'roc': roc,
'precision': precision,
'recall': recall,
'pre_recall_curve': pre_recall}
with open(save_results_path, 'w') as fid:
yaml.dump(results, fid)
return acc, auc, f1, precision, recall
def get_model(self, data_params):
"""
Generates a model
Args:
data_params: Data parameters to use for model generation
Returns:
A model
"""
raise NotImplementedError("get_model should be implemented")
# Auxiliary function
def _gru(self, name='gru', r_state=False, r_sequence=False):
"""
A helper function to create a single GRU unit
Args:
name: Name of the layer
r_state: Whether to return the states of the GRU
r_sequence: Whether to return a sequence
Return:
A GRU unit
"""
return GRU(units=self._num_hidden_units,
return_state=r_state,
return_sequences=r_sequence,
stateful=False,
kernel_regularizer=self._regularizer,
recurrent_regularizer=self._regularizer,
bias_regularizer=self._regularizer,
name=name)
def _lstm(self, name='lstm', r_state=False, r_sequence=False):
"""
A helper function to create a single LSTM unit
Args:
name: Name of the layer
r_state: Whether to return the states of the LSTM
r_sequence: Whether to return a sequence
Return:
A LSTM unit
"""
return LSTM(units=self._num_hidden_units,
return_state=r_state,
return_sequences=r_sequence,
stateful=False,
kernel_regularizer=self._regularizer,
recurrent_regularizer=self._regularizer,
bias_regularizer=self._regularizer,
name=name)
def create_stack_rnn(self, size, r_state=False, r_sequence=False):
"""
Creates a stack of recurrent cells
Args:
size: The size of stack
r_state: Whether to return the states of the GRU
r_sequence: Whether the last stack layer to return a sequence
Returns:
A stacked recurrent model
"""
cells = []
for i in range(size):
cells.append(self._rnn_cell(units=self._num_hidden_units,
kernel_regularizer=self._regularizer,
recurrent_regularizer=self._regularizer,
bias_regularizer=self._regularizer, ))
return RNN(cells, return_sequences=r_sequence, return_state=r_state)
class SingleRNN(ActionPredict):
""" A simple recurrent network """
def __init__(self,
num_hidden_units=256,
cell_type='gru', **kwargs):
"""
Class init function
Args:
num_hidden_units: Number of recurrent hidden layers
cell_type: Type of RNN cell
"""
super().__init__(**kwargs)
# Network parameters
self._num_hidden_units = num_hidden_units
self._rnn_type = cell_type
def get_model(self, data_params):
network_inputs = []
data_sizes = data_params['data_sizes']
data_types = data_params['data_types']
core_size = len(data_sizes)
_rnn = self._gru if self._rnn_type == 'gru' else self._lstm
for i in range(core_size):
network_inputs.append(Input(shape=data_sizes[i],
name='input_' + data_types[i]))
if len(network_inputs) > 1:
inputs = Concatenate(axis=2)(network_inputs)
else:
inputs = network_inputs[0]
encoder_output = _rnn(name='encoder')(inputs)
encoder_output = Dense(1, activation='sigmoid',
name='output_dense')(encoder_output)
net_model = Model(inputs=network_inputs,
outputs=encoder_output)
return net_model
class StackedRNN(ActionPredict):
""" A stacked recurrent prediction model based on
Yue-Hei et al. "Beyond short snippets: Deep networks for video classification."
CVPR, 2015." """
def __init__(self,
num_hidden_units=256,
cell_type='gru', **kwargs):
"""
Class init function
Args:
num_hidden_units: Number of recurrent hidden layers
cell_type: Type of RNN cell
"""
super().__init__(**kwargs)
# Network parameters
self._num_hidden_units = num_hidden_units
self._rnn = self._gru if cell_type == 'gru' else self._lstm
self._rnn_cell = GRUCell if cell_type == 'gru' else LSTMCell
def get_model(self, data_params):
network_inputs = []
data_sizes = data_params['data_sizes']
data_types = data_params['data_types']
core_size = len(data_sizes)
for i in range(core_size):
network_inputs.append(Input(shape=data_sizes[i], name='input_' + data_types[i]))
if len(network_inputs) > 1:
inputs = Concatenate(axis=2)(network_inputs)
else:
inputs = network_inputs[0]
encoder_output = self.create_stack_rnn(core_size)(inputs)
encoder_output = Dense(1, activation='sigmoid',
name='output_dense')(encoder_output)
net_model = Model(inputs=network_inputs,
outputs=encoder_output)
return net_model
class MultiRNN(ActionPredict):
"""
A multi-stream recurrent prediction model inspired by
Bhattacharyya et al. "Long-term on-board prediction of people in traffic
scenes under uncertainty." CVPR, 2018.
"""
def __init__(self,