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import os
import shutil
import sys
import tempfile
import unittest
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import model_helper, workspace
try:
import lmdb
except ImportError:
raise unittest.SkipTest("python-lmdb is not installed")
class VideoInputOpTest(unittest.TestCase):
def create_a_list(self, output_file, line, n):
# create a list that repeat a line n times
# used for creating a list file for simple test input
with open(output_file, "w") as file:
for _i in range(n):
file.write(line)
def create_video_db(self, list_file, output_file, use_list=False):
# Write to lmdb database...
LMDB_MAP_SIZE = 1 << 40 # MODIFY
env = lmdb.open(output_file, map_size=LMDB_MAP_SIZE)
total_size = 0
file_name = []
start_frame = []
label = []
index = 0
with env.begin(write=True) as txn:
with open(list_file, "r") as data:
for line in data:
p = line.split()
file_name = p[0]
start_frame = int(p[1])
label = int(p[2])
if not use_list:
with open(file_name, mode="rb") as file:
video_data = file.read()
else:
video_data = file_name
tensor_protos = caffe2_pb2.TensorProtos()
video_tensor = tensor_protos.protos.add()
video_tensor.data_type = 4 # string data
video_tensor.string_data.append(video_data)
label_tensor = tensor_protos.protos.add()
label_tensor.data_type = 2
label_tensor.int32_data.append(label)
start_frame_tensor = tensor_protos.protos.add()
start_frame_tensor.data_type = 2
start_frame_tensor.int32_data.append(start_frame)
txn.put(
"{}".format(index).encode("ascii"),
tensor_protos.SerializeToString(),
)
index = index + 1
total_size = total_size + len(video_data) + sys.getsizeof(int)
return total_size
# sample one clip randomly from the video
def test_rgb_with_temporal_jittering(self):
random_label = np.random.randint(0, 100)
VIDEO = "/mnt/vol/gfsdataswarm-oregon/users/trandu/sample.avi"
if not os.path.exists(VIDEO):
raise unittest.SkipTest("Missing data")
temp_list = tempfile.NamedTemporaryFile(delete=False).name
line_str = "{} 0 {}\n".format(VIDEO, random_label)
self.create_a_list(temp_list, line_str, 16)
video_db_dir = tempfile.mkdtemp()
self.create_video_db(temp_list, video_db_dir)
model = model_helper.ModelHelper(name="Video Loader from LMDB")
reader = model.CreateDB("sample", db=video_db_dir, db_type="lmdb")
# build the model
model.net.VideoInput(
reader,
["data", "label"],
name="data",
batch_size=16,
clip_per_video=1,
crop_size=112,
scale_w=171,
scale_h=128,
length_rgb=8,
sampling_rate_rgb=1,
decode_type=0,
video_res_type=0, # scale by scale_h and scale_w
)
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
data = workspace.FetchBlob("data")
label = workspace.FetchBlob("label")
np.testing.assert_equal(label, random_label)
np.testing.assert_equal(data.shape, [16, 3, 8, 112, 112])
os.remove(temp_list)
shutil.rmtree(video_db_dir)
# sample multiple clips uniformly from the video
def test_rgb_with_uniform_sampling(self):
random_label = np.random.randint(0, 100)
clip_per_video = np.random.randint(2, 11)
VIDEO = "/mnt/vol/gfsdataswarm-oregon/users/trandu/sample.avi"
if not os.path.exists(VIDEO):
raise unittest.SkipTest("Missing data")
temp_list = tempfile.NamedTemporaryFile(delete=False).name
line_str = "{} 0 {}\n".format(VIDEO, random_label)
self.create_a_list(temp_list, line_str, 16)
video_db_dir = tempfile.mkdtemp()
self.create_video_db(temp_list, video_db_dir)
model = model_helper.ModelHelper(name="Video Loader from LMDB")
reader = model.CreateDB("sample", db=video_db_dir, db_type="lmdb")
# build the model
model.net.VideoInput(
reader,
["data", "label"],
name="data",
batch_size=3,
clip_per_video=clip_per_video,
crop_size=112,
scale_w=171,
scale_h=128,
length_rgb=8,
sampling_rate_rgb=1,
decode_type=1,
video_res_type=0,
)
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
data = workspace.FetchBlob("data")
label = workspace.FetchBlob("label")
np.testing.assert_equal(label, random_label)
np.testing.assert_equal(data.shape, [3 * clip_per_video, 3, 8, 112, 112])
os.remove(temp_list)
shutil.rmtree(video_db_dir)
# test optical flow
def test_optical_flow_with_temporal_jittering(self):
random_label = np.random.randint(0, 100)
VIDEO = "/mnt/vol/gfsdataswarm-oregon/users/trandu/sample.avi"
if not os.path.exists(VIDEO):
raise unittest.SkipTest("Missing data")
temp_list = tempfile.NamedTemporaryFile(delete=False).name
line_str = "{} 0 {}\n".format(VIDEO, random_label)
self.create_a_list(temp_list, line_str, 16)
video_db_dir = tempfile.mkdtemp()
self.create_video_db(temp_list, video_db_dir)
model = model_helper.ModelHelper(name="Video Loader from LMDB")
reader = model.CreateDB("sample", db=video_db_dir, db_type="lmdb")
model.net.VideoInput(
reader,
["data", "label"],
name="data",
batch_size=16,
clip_per_video=1,
crop_size=112,
scale_w=171,
scale_h=128,
length_of=8,
sampling_rate_of=1,
frame_gap_of=1,
decode_type=0,
video_res_type=0,
get_rgb=False,
get_optical_flow=True,
)
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
data = workspace.FetchBlob("data")
label = workspace.FetchBlob("label")
np.testing.assert_equal(label, random_label)
np.testing.assert_equal(data.shape, [16, 2, 8, 112, 112])
os.remove(temp_list)
shutil.rmtree(video_db_dir)
# test rgb output VideoResType is
# USE_SHORTER_EDGE
def test_rgb_use_shorter_edge(self):
batch_size = 16
random_label = np.random.randint(0, 100)
VIDEO = "/mnt/vol/gfsdataswarm-oregon/users/trandu/sample.avi"
if not os.path.exists(VIDEO):
raise unittest.SkipTest("Missing data")
temp_list = tempfile.NamedTemporaryFile(delete=False).name
line_str = "{} 0 {}\n".format(VIDEO, random_label)
self.create_a_list(temp_list, line_str, batch_size)
video_db_dir = tempfile.mkdtemp()
self.create_video_db(temp_list, video_db_dir)
model = model_helper.ModelHelper(name="Video Loader from LMDB")
reader = model.CreateDB("sample", db=video_db_dir, db_type="lmdb")
model.net.VideoInput(
reader,
["data", "label"],
name="data",
batch_size=batch_size,
clip_per_video=1,
crop_size=112,
scale_w=171,
scale_h=128,
length_of=8,
frame_gap_of=1,
decode_type=0,
video_res_type=1, # use shorter edge
get_rgb=True,
length_rgb=8,
short_edge=112,
)
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
data = workspace.FetchBlob("data")
label = workspace.FetchBlob("label")
np.testing.assert_equal(label.shape, [batch_size])
for i in range(batch_size):
np.testing.assert_equal(label[i], random_label)
np.testing.assert_equal(data.shape, [batch_size, 3, 8, 112, 112])
os.remove(temp_list)
shutil.rmtree(video_db_dir)
# test optical flow output VideoResType is
# USE_SHORTER_EDGE
def test_optical_flow_use_shorter_edge(self):
batch_size = 16
random_label = np.random.randint(0, 100)
VIDEO = "/mnt/vol/gfsdataswarm-oregon/users/trandu/sample.avi"
if not os.path.exists(VIDEO):
raise unittest.SkipTest("Missing data")
temp_list = tempfile.NamedTemporaryFile(delete=False).name
line_str = "{} 0 {}\n".format(VIDEO, random_label)
self.create_a_list(temp_list, line_str, batch_size)
video_db_dir = tempfile.mkdtemp()
self.create_video_db(temp_list, video_db_dir)
model = model_helper.ModelHelper(name="Video Loader from LMDB")
reader = model.CreateDB("sample", db=video_db_dir, db_type="lmdb")
model.net.VideoInput(
reader,
["data", "label"],
name="data",
batch_size=batch_size,
clip_per_video=1,
crop_size=112,
scale_w=171,
scale_h=128,
length_of=8,
sampling_rate_of=1,
frame_gap_of=1,
decode_type=0,
video_res_type=1, # use shorter edge
get_rgb=False,
get_optical_flow=True,
short_edge=112,
)
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
data = workspace.FetchBlob("data")
label = workspace.FetchBlob("label")
np.testing.assert_equal(label.shape, [batch_size])
for i in range(batch_size):
np.testing.assert_equal(label[i], random_label)
np.testing.assert_equal(data.shape, [batch_size, 2, 8, 112, 112])
os.remove(temp_list)
shutil.rmtree(video_db_dir)
if __name__ == "__main__":
unittest.main()
|
