def main():
parser = argparse.ArgumentParser(description='Execute different functions of our system')
parser.add_argument('mode',
choices=[
'window_stats', 'evaluate_metaparams', 'evaluate_jw',
'evaluate_get_pos_windows', 'train_svm',
'extract_sift','extract_assignments','extract_codebook',
'evaluate_jw_grid', 'final_metaparams',
'assemble_dpm_dets','ctfdet','assemble_ctf_dets'
])
parser.add_argument('--test_dataset', choices=['val','test','train'],
default='test', help='dataset to use for testing. the training dataset \
is automatically inferred (val->train and test->trainval).')
parser.add_argument('--first_n', type=int,
help='only take the first N images in the datasets')
parser.add_argument('--bounds', type=str,
help='the start_time and deadline_time for the ImagePolicy and corresponding evaluation. ex: (1,5)')
parser.add_argument('--name', help='name for this run')
parser.add_argument('--priors', default='random', help= \
"list of choice for the policy for selecting the next action. choose from random, oracle,fixed_order, no_smooth, backoff. ex: --priors=random,oracle,no_smooth")
parser.add_argument('--compare_evals', action='store_true',
default=False, help='plot all the priors modes given on same plot'),
parser.add_argument('--detector', choices=['perfect','perfect_with_noise', 'dpm','ctf'],
default='perfect', help='detector type')
parser.add_argument('--force', action='store_true',
default=False, help='force overwrite')
parser.add_argument('--gist', action='store_true',
default=False, help='use GIST as one of the actions')
parser.add_argument('--clear_tmp', action='store_true',
default=False, help='clear the cached windows folder before running'),
parser.add_argument('--feature_type', choices=['sift','dsift'],
default='dsift', help='use this feature type'),
parser.add_argument('--kernel', choices=['chi2','rbf'],
default='chi2', help='kernel to train svm on'),
args = parser.parse_args()
if args.priors:
args.priors = args.priors.split(',')
if args.bounds:
args.bounds = [float(x) for x in re.findall(r'\d+', args.bounds)]
assert(len(args.bounds)==2)
print(args)
# Load the dataset
dataset = Dataset('full_pascal_'+args.test_dataset)
if args.first_n:
dataset.images = dataset.images[:args.first_n]
# Infer train_dataset
if args.test_dataset=='test':
train_dataset = Dataset('full_pascal_trainval')
elif args.test_dataset=='val':
train_dataset = Dataset('full_pascal_train')
else:
print("Impossible, setting train_dataset to dataset")
train_dataset = dataset
# Create window generator
sw = SlidingWindows(dataset,train_dataset)
if args.clear_tmp:
dirname = config.get_sliding_windows_cached_dir(train_dataset.get_name())
shutil.rmtree(dirname)
dirname = config.get_sliding_windows_cached_dir(dataset.get_name())
shutil.rmtree(dirname)
if args.mode=='assemble_dpm_dets':
policy = DatasetPolicy(dataset,train_dataset,sw)
dets = policy.load_ext_detections(dataset,suffix='dpm_may25')
if args.mode=='assemble_ctf_dets':
policy = DatasetPolicy(dataset,train_dataset,sw)
dets = policy.load_ext_detections(dataset,'ctf','ctf_default')
dets = policy.load_ext_detections(dataset,'ctf','ctf_nohal')
dets = policy.load_ext_detections(dataset,'ctf', 'ctf_halfsize')
if args.mode=='evaluate_get_pos_windows':
evaluate_get_pos_windows(train_dataset)
return
if args.mode=='window_stats':
"Compute and plot the statistics of ground truth window parameters."
results = SlidingWindows.get_dataset_window_stats(train_dataset,plot=True)
if args.mode=='ctfdet':
"""Run Pedersoli's detector on the dataset and assemble into one Table."""
run_pedersoli(dataset)
if args.mode=='evaluate_jw':
"""
Evaluate the jumping window approach by producing plots of recall vs.
#windows.
"""
# TODO hack: both sw and jw should subclass something like WindowGenerator
jw = JumpingWindowsDetector(use_scale=True)
sw.jw = jw
#classes = dataset.classes
classes = ['car']
# classes = ['bicycle' ,'car','horse', 'sofa',\
# 'bird', 'chair', 'motorbike', 'train',\
# 'boat', 'cow', 'person', 'tvmonitor',\
# 'bottle','diningtable', 'pottedplant',\
# 'bus','dog' ,'sheep']
for cls_idx in range(comm_rank, len(classes), comm_size):
#for cls in dataset.classes:
cls = classes[cls_idx]
dirname = config.get_jumping_windows_dir(dataset.get_name())
filename = os.path.join(dirname,'%s'%cls)
sw.evaluate_recall(cls, filename, metaparams=None, mode='jw', plot=True)
if args.mode=='evaluate_jw_grid':
"""
Evaluate the jumping window approach by producing plots of recall vs.
#windows.
"""
sw = SlidingWindows(dataset,train_dataset)
jw = JumpingWindowsDetectorGrid()
sw.jw = jw
for cls in dataset.classes:
dirname = config.get_jumping_windows_dir(dataset.get_name())
filename = os.path.join(dirname,'%s'%cls)
if os.path.isfile(config.data_dir + 'JumpingWindows/'+cls):
sw.evaluate_recall(cls, filename, metaparams=None, mode='jw', plot=True)
if args.mode=='train_svm':
randomize = not os.path.exists('/home/tobibaum')
d = Dataset('full_pascal_train')
dtest = Dataset('full_pascal_val')
e = Extractor()
classes = config.pascal_classes
num_words = 3000
iters = 5
feature_type = 'dsift'
codebook_samples = 15
num_pos = 'max'
testsize = 'max'
if args.first_n:
num_pos = args.first_n
testsize = 1.5*num_pos
kernel = args.kernel
if comm_rank == 0:
ut.makedirs(config.data_dir + 'features/' + feature_type + '/times/')
ut.makedirs(config.data_dir + 'features/' + feature_type + '/codebooks/times/')
ut.makedirs(config.data_dir + 'features/' + feature_type + '/svms/train_times/')
for cls_idx in range(comm_rank, len(classes), comm_size):
#for cls in classes:
cls = classes[cls_idx]
codebook = e.get_codebook(d, feature_type)
pos_arr = d.get_pos_windows(cls)
neg_arr = d.get_neg_windows(pos_arr.shape[0], cls, max_overlap=0)
if not num_pos == 'max':
if not randomize:
pos_arr = pos_arr[:num_pos]
neg_arr = pos_arr[:num_pos]
else:
rand = np.random.random_integers(0, pos_arr.shape[0] - 1, size=num_pos)
pos_arr = pos_arr[rand]
rand = np.random.random_integers(0, neg_arr.shape[0] - 1, size=num_pos)
neg_arr = neg_arr[rand]
pos_table = Table(pos_arr, ['x','y','w','h','img_ind'])
neg_table = Table(neg_arr, pos_table.cols)
train_with_hard_negatives(d, dtest, num_words,codebook_samples,codebook,\
cls, pos_table, neg_table,feature_type, \
iterations=iters, kernel=kernel, L=2, \
testsize=testsize,randomize=randomize)
if args.mode=='evaluate_metaparams':
"""
Grid search over metaparams values for get_windows_new, with the AUC of
recall vs. # windows evaluation.
"""
sw.grid_search_over_metaparams()
return
if args.mode=='final_metaparams':
dirname = config.get_sliding_windows_metaparams_dir(train_dataset.get_name())
# currently these are the best auc/complexity params
best_params_for_classes = [
(62,15,12,'importance',0), #aeroplane
(83,15,12,'importance',0), #bicycle
(62,15,12,'importance',0), #bird
(62,15,12,'importance',0), #boat
(125,12,12,'importance',0), #bottle
(83,12,9,'importance',0), #bus
(125,15,9,'importance',0), #car
(125,12,12,'linear',0), #cat
(125,15,9,'importance',0), #chair
(125,9,6,'importance',0), #cow
(125,15,6,'linear',0), #diningtable
(62,15,12,'importance',0), #dog
(83,15,6,'importance',0), #horse
(83,12,6,'importance',0), #motorbike
(83,15,12,'importance',0), #person
(83,15,6,'importance',0), #pottedplant
(83,15,12,'importance',0), #sheep
(83,9,6,'importance',0), #sofa
(62,12,6,'importance',0), #train
(62,12,12,'importance',0), #tvmonitor
(125,9,12,'importance',0) #all
]
# ACTUALLY THEY ARE ALL THE SAME!
cheap_params = (62, 9, 6, 'importance', 0)
for i in range(comm_rank,dataset.num_classes(),comm_size):
cls = dataset.classes[i]
best_params = best_params_for_classes[i]
#samples,num_scales,num_ratios,mode,priority,cls = cheap_params
metaparams = {
'samples_per_500px': samples,
'num_scales': num_scales,
'num_ratios': num_ratios,
'mode': mode,
'priority': 0 }
filename = '%s_%d_%d_%d_%s_%d'%(
cls,
metaparams['samples_per_500px'],
metaparams['num_scales'],
metaparams['num_ratios'],
metaparams['mode'],
metaparams['priority'])
filename = os.path.join(dirname,filename)
tables = sw.evaluate_recall(cls,filename,metaparams,'sw',plot=True,force=False)
metaparams = {
'samples_per_500px': samples,
'num_scales': num_scales,
'num_ratios': num_ratios,
'mode': mode,
'priority': 1 }
filename = '%s_%d_%d_%d_%s_%d'%(
cls,
metaparams['samples_per_500px'],
metaparams['num_scales'],
metaparams['num_ratios'],
metaparams['mode'],
metaparams['priority'])
filename = os.path.join(dirname,filename)
tables = sw.evaluate_recall(cls,filename,metaparams,'sw',plot=True,force=False)
return
if args.mode=='extract_sift':
e=Extractor()
e.extract_all(['sift'], ['full_pascal_trainval','full_pascal_test'], 0, 0)
if args.mode=='extract_assignments':
e=Extractor()
feature_type = 'sift'
for image_set in ['full_pascal_trainval','full_pascal_test']:
d = Dataset(image_set)
codebook = e.get_codebook(d, feature_type)
print 'codebook loaded'
for img_ind in range(comm_rank,len(d.images),comm_size):
img = d.images[img_ind]
#for img in d.images:
e.get_assignments(np.array([0,0,img.size[0],img.size[1]]), feature_type, \
codebook, img)
if args.mode=='extract_codebook':
d = Dataset('full_pascal_trainval')
e = Extractor()
codebook = e.get_codebook(d, args.feature_type)
def grid_search_over_metaparams(self):
"""
Evaluates different metaparams for the get_windows_new method with the
metric of AUC under the recall vs. # windows curve.
"""
# approximately strides of 4, 6, and 8 px
samples_per_500px_vals = [62, 83, 125]
num_scales_vals = [9, 12, 15]
num_ratios_vals = [6, 9, 12]
mode_vals = ['linear', 'importance']
priority_vals = [0]
classes = self.dataset.classes + ['all']
dirname = config.get_sliding_windows_metaparams_dir(self.train_name)
table_filename = os.path.join(dirname, 'table.csv')
if os.path.exists(table_filename):
table = Table.load_from_csv(table_filename)
else:
grid_vals = [
x for x in itertools.product(samples_per_500px_vals,
num_scales_vals, num_ratios_vals,
mode_vals, priority_vals, classes)
]
num_combinations = len(grid_vals)
print("Running over %d combinations of class and parameters." %
num_combinations)
cols = [
'cls_ind', 'samples_per_500px', 'num_scales', 'num_ratios',
'mode_ind', 'priority', 'complexity', 'auc', 'max_recall'
]
grid = np.zeros((num_combinations, len(cols)))
for i in range(comm_rank, num_combinations, comm_size):
grid_val = grid_vals[i]
print(grid_val)
samples, num_scales, num_ratios, mode, priority, cls = grid_val
cls_ind = self.dataset.get_ind(cls)
metaparams = {
'samples_per_500px': samples,
'num_scales': num_scales,
'num_ratios': num_ratios,
'mode': mode,
'priority': priority
}
mode_ind = mode_vals.index(metaparams['mode'])
filename = '%s_%d_%d_%d_%s_%d' % (
cls, metaparams['samples_per_500px'],
metaparams['num_scales'], metaparams['num_ratios'],
metaparams['mode'], metaparams['priority'])
cls_dirname = os.path.join(dirname, cls)
ut.makedirs(cls_dirname)
filename = os.path.join(cls_dirname, filename)
tables = self.evaluate_recall(cls,
filename,
metaparams,
'sw',
plot=False,
force=False)
# compute the final metrics
auc, max_rec = SlidingWindows.get_recall_vs_num_auc(
tables[1]) # the ov=0.5 table
complexity = samples * num_scales * num_ratios
grid[i, :] = np.array([
cls_ind, samples, num_scales, num_ratios, mode_ind,
priority, complexity, auc, max_rec
])
# Reduce the MPI jobs
if comm_rank == 0:
grid_all = np.zeros((num_combinations, len(cols)))
else:
grid_all = None
safebarrier(comm)
comm.Reduce(grid, grid_all)
table = Table(grid_all, cols)
table.save_csv(table_filename)
# print the winning parameters in the table
for cls in self.dataset.classes + ['all']:
st = table.filter_on_column('cls_ind', self.dataset.get_ind(cls))
aucs = st.subset_arr('auc')
max_recalls = st.subset_arr('max_recall')
best_auc_ind = aucs.argmax()
best_max_recall_ind = max_recalls.argmax()
print("%s: best AUC is %.3f with metaparams (%d, %d, %d, %s, %d)" %
(cls, aucs[best_auc_ind], st.arr[best_auc_ind,
1], st.arr[best_auc_ind, 2],
st.arr[best_auc_ind, 3], mode_vals[int(
st.arr[best_auc_ind, 4])], st.arr[best_auc_ind, 5]))
print(
"%s: best max recall is %.3f with metaparams (%d, %d, %d, %s, %d)"
% (cls, max_recalls[best_max_recall_ind],
st.arr[best_max_recall_ind, 1], st.arr[best_max_recall_ind,
2],
st.arr[best_max_recall_ind, 3], mode_vals[int(
st.arr[best_max_recall_ind,
4])], st.arr[best_max_recall_ind, 5]))
complexities = st.subset_arr('complexity')
complexities /= complexities.max()
d_max_recalls = max_recalls / complexities
d_aucs = aucs / complexities
best_auc_ind = d_aucs.argmax()
best_max_recall_ind = d_max_recalls.argmax()
print(
"%s: best AUC/complexity is %.3f with metaparams (%d, %d, %d, %s, %d)"
% (cls, aucs[best_auc_ind], st.arr[best_auc_ind,
1], st.arr[best_auc_ind, 2],
st.arr[best_auc_ind, 3], mode_vals[int(
st.arr[best_auc_ind, 4])], st.arr[best_auc_ind, 5]))
print(
"%s: best max recall/complexity is %.3f with metaparams (%d, %d, %d, %s, %d)"
% (cls, max_recalls[best_max_recall_ind],
st.arr[best_max_recall_ind, 1], st.arr[best_max_recall_ind,
2],
st.arr[best_max_recall_ind, 3], mode_vals[int(
st.arr[best_max_recall_ind,
4])], st.arr[best_max_recall_ind, 5]))
return table
def grid_search_over_metaparams(self):
"""
Evaluates different metaparams for the get_windows_new method with the
metric of AUC under the recall vs. # windows curve.
"""
# approximately strides of 4, 6, and 8 px
samples_per_500px_vals = [62, 83, 125]
num_scales_vals = [9,12,15]
num_ratios_vals = [6,9,12]
mode_vals = ['linear','importance']
priority_vals = [0]
classes = self.dataset.classes+['all']
dirname = config.get_sliding_windows_metaparams_dir(self.train_name)
table_filename = os.path.join(dirname,'table.csv')
if os.path.exists(table_filename):
table = Table.load_from_csv(table_filename)
else:
grid_vals = [x for x in itertools.product(
samples_per_500px_vals,num_scales_vals,num_ratios_vals,mode_vals,priority_vals,classes)]
num_combinations = len(grid_vals)
print("Running over %d combinations of class and parameters."%num_combinations)
cols = ['cls_ind','samples_per_500px','num_scales','num_ratios','mode_ind','priority','complexity','auc','max_recall']
grid = np.zeros((num_combinations,len(cols)))
for i in range(comm_rank, num_combinations, comm_size):
grid_val = grid_vals[i]
print(grid_val)
samples,num_scales,num_ratios,mode,priority,cls = grid_val
cls_ind = self.dataset.get_ind(cls)
metaparams = {
'samples_per_500px': samples,
'num_scales': num_scales,
'num_ratios': num_ratios,
'mode': mode,
'priority': priority}
mode_ind = mode_vals.index(metaparams['mode'])
filename = '%s_%d_%d_%d_%s_%d'%(
cls,
metaparams['samples_per_500px'],
metaparams['num_scales'],
metaparams['num_ratios'],
metaparams['mode'],
metaparams['priority'])
cls_dirname = os.path.join(dirname,cls)
ut.makedirs(cls_dirname)
filename = os.path.join(cls_dirname,filename)
tables = self.evaluate_recall(cls,filename,metaparams,'sw',plot=False,force=False)
# compute the final metrics
auc,max_rec = SlidingWindows.get_recall_vs_num_auc(tables[1]) # the ov=0.5 table
complexity = samples*num_scales*num_ratios
grid[i,:] = np.array([cls_ind, samples, num_scales, num_ratios, mode_ind, priority, complexity, auc, max_rec])
# Reduce the MPI jobs
if comm_rank == 0:
grid_all = np.zeros((num_combinations,len(cols)))
else:
grid_all = None
safebarrier(comm)
comm.Reduce(grid,grid_all)
table = Table(grid_all,cols)
table.save_csv(table_filename)
# print the winning parameters in the table
for cls in self.dataset.classes+['all']:
st = table.filter_on_column('cls_ind',self.dataset.get_ind(cls))
aucs = st.subset_arr('auc')
max_recalls = st.subset_arr('max_recall')
best_auc_ind = aucs.argmax()
best_max_recall_ind = max_recalls.argmax()
print("%s: best AUC is %.3f with metaparams (%d, %d, %d, %s, %d)"%(
cls, aucs[best_auc_ind],
st.arr[best_auc_ind,1], st.arr[best_auc_ind,2], st.arr[best_auc_ind,3],
mode_vals[int(st.arr[best_auc_ind,4])], st.arr[best_auc_ind,5]))
print("%s: best max recall is %.3f with metaparams (%d, %d, %d, %s, %d)"%(
cls, max_recalls[best_max_recall_ind],
st.arr[best_max_recall_ind,1], st.arr[best_max_recall_ind,2],
st.arr[best_max_recall_ind,3],
mode_vals[int(st.arr[best_max_recall_ind,4])], st.arr[best_max_recall_ind,5]))
complexities = st.subset_arr('complexity')
complexities /= complexities.max()
d_max_recalls = max_recalls/complexities
d_aucs = aucs/complexities
best_auc_ind = d_aucs.argmax()
best_max_recall_ind = d_max_recalls.argmax()
print("%s: best AUC/complexity is %.3f with metaparams (%d, %d, %d, %s, %d)"%(
cls, aucs[best_auc_ind],
st.arr[best_auc_ind,1], st.arr[best_auc_ind,2], st.arr[best_auc_ind,3],
mode_vals[int(st.arr[best_auc_ind,4])], st.arr[best_auc_ind,5]))
print("%s: best max recall/complexity is %.3f with metaparams (%d, %d, %d, %s, %d)"%(
cls, max_recalls[best_max_recall_ind],
st.arr[best_max_recall_ind,1], st.arr[best_max_recall_ind,2],
st.arr[best_max_recall_ind,3],
mode_vals[int(st.arr[best_max_recall_ind,4])], st.arr[best_max_recall_ind,5]))
return table
