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PaddleGAN

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提交 e8f33934 编写于 作者: L LielinJiang
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applications/DAIN/my_args.py 已删除 100644 → 0
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import os
import datetime
import argparse
import numpy
import networks
modelnames = networks.__all__
# import datasets
datasetNames = ('Vimeo_90K_interp') #datasets.__all__
parser = argparse.ArgumentParser(description='DAIN')
parser.add_argument('--debug', action='store_true', help='Enable debug mode')
parser.add_argument('--netName',
type=str,
default='DAIN',
choices=modelnames,
help='model architecture: ' + ' | '.join(modelnames) +
' (default: DAIN)')
parser.add_argument('--datasetName',
default='Vimeo_90K_interp',
choices=datasetNames,
nargs='+',
help='dataset type : ' + ' | '.join(datasetNames) +
' (default: Vimeo_90K_interp)')
parser.add_argument('--video_path',
default='',
help='the path of selected videos')
parser.add_argument('--output_path', default='', help='the output root path')
parser.add_argument('--seed',
type=int,
default=1,
help='random seed (default: 1)')
parser.add_argument('--batch_size',
'-b',
type=int,
default=1,
help='batch size (default:1)')
parser.add_argument('--channels',
'-c',
type=int,
default=3,
choices=[1, 3],
help='channels of images (default:3)')
parser.add_argument('--filter_size',
'-f',
type=int,
default=4,
help='the size of filters used (default: 4)',
choices=[2, 4, 6, 5, 51])
parser.add_argument('--time_step',
type=float,
default=0.5,
help='choose the time steps')
parser.add_argument(
'--alpha',
type=float,
nargs='+',
default=[0.0, 1.0],
help=
'the ration of loss for interpolated and rectified result (default: [0.0, 1.0])'
)
parser.add_argument('--frame_rate',
type=int,
default=None,
help='frame rate of the input video')
parser.add_argument('--patience',
type=int,
default=5,
help='the patience of reduce on plateou')
parser.add_argument('--factor',
type=float,
default=0.2,
help='the factor of reduce on plateou')
parser.add_argument('--saved_model',
type=str,
default='',
help='path to the model weights')
parser.add_argument('--no-date',
action='store_true',
help='don\'t append date timestamp to folder')
parser.add_argument('--use_cuda',
default=True,
type=bool,
help='use cuda or not')
parser.add_argument('--use_cudnn', default=1, type=int, help='use cudnn or not')
applications/DAIN/predict.py 已删除 100644 → 0
浏览文件 @ 1744b3a6
import os
import sys
cur_path = os.path.abspath(os.path.dirname(__file__))
sys.path.append(cur_path)
import time
import glob
import numpy as np
from imageio import imread, imsave
from tqdm import tqdm
import cv2
import paddle.fluid as fluid
from paddle.utils.download import get_path_from_url
from ppgan.utils.video import video2frames, frames2video
from util import *
from my_args import parser
DAIN_WEIGHT_URL = 'https://paddlegan.bj.bcebos.com/applications/DAIN_weight.tar'
def infer_engine(model_dir,
run_mode='fluid',
batch_size=1,
use_gpu=False,
min_subgraph_size=3):
if not use_gpu and not run_mode == 'fluid':
raise ValueError(
"Predict by TensorRT mode: {}, expect use_gpu==True, but use_gpu == {}"
.format(run_mode, use_gpu))
precision_map = {
'trt_fp32': fluid.core.AnalysisConfig.Precision.Float32,
'trt_fp16': fluid.core.AnalysisConfig.Precision.Half
}
config = fluid.core.AnalysisConfig(os.path.join(model_dir, 'model'),
os.path.join(model_dir, 'params'))
if use_gpu:
# initial GPU memory(M), device ID
config.enable_use_gpu(100, 0)
# optimize graph and fuse op
config.switch_ir_optim(True)
else:
config.disable_gpu()
if run_mode in precision_map.keys():
config.enable_tensorrt_engine(workspace_size=1 << 10,
max_batch_size=batch_size,
min_subgraph_size=min_subgraph_size,
precision_mode=precision_map[run_mode],
use_static=False,
use_calib_mode=False)
# disable print log when predict
config.disable_glog_info()
# enable shared memory
config.enable_memory_optim()
# disable feed, fetch OP, needed by zero_copy_run
config.switch_use_feed_fetch_ops(False)
predictor = fluid.core.create_paddle_predictor(config)
return predictor
def executor(model_dir, use_gpu=False):
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
program, feed_names, fetch_targets = fluid.io.load_inference_model(
dirname=model_dir,
executor=exe,
model_filename='model',
params_filename='params')
return exe, program, fetch_targets
class VideoFrameInterp(object):
def __init__(self,
time_step,
model_path,
video_path,
use_gpu=True,
key_frame_thread=0.,
output_path='output'):
self.video_path = video_path
self.output_path = os.path.join(output_path, 'DAIN')
if model_path is None:
model_path = get_path_from_url(DAIN_WEIGHT_URL, cur_path)
self.model_path = model_path
self.time_step = time_step
self.key_frame_thread = key_frame_thread
self.exe, self.program, self.fetch_targets = executor(model_path,
use_gpu=use_gpu)
def run(self):
frame_path_input = os.path.join(self.output_path, 'frames-input')
frame_path_interpolated = os.path.join(self.output_path,
'frames-interpolated')
frame_path_combined = os.path.join(self.output_path, 'frames-combined')
video_path_output = os.path.join(self.output_path, 'videos-output')
if not os.path.exists(self.output_path):
os.makedirs(self.output_path)
if not os.path.exists(frame_path_input):
os.makedirs(frame_path_input)
if not os.path.exists(frame_path_interpolated):
os.makedirs(frame_path_interpolated)
if not os.path.exists(frame_path_combined):
os.makedirs(frame_path_combined)
if not os.path.exists(video_path_output):
os.makedirs(video_path_output)
timestep = self.time_step
num_frames = int(1.0 / timestep) - 1
if self.video_path.endswith('.mp4'):
videos = [self.video_path]
else:
videos = sorted(glob.glob(os.path.join(self.video_path, '*.mp4')))
for cnt, vid in enumerate(videos):
print("Interpolating video:", vid)
cap = cv2.VideoCapture(vid)
fps = cap.get(cv2.CAP_PROP_FPS)
print("Old fps (frame rate): ", fps)
times_interp = int(1.0 / timestep)
r2 = str(int(fps) * times_interp)
print("New fps (frame rate): ", r2)
out_path = video2frames(vid, frame_path_input)
vidname = vid.split('/')[-1].split('.')[0]
tot_timer = AverageMeter()
proc_timer = AverageMeter()
end = time.time()
frames = sorted(glob.glob(os.path.join(out_path, '*.png')))
img = imread(frames[0])
int_width = img.shape[1]
int_height = img.shape[0]
channel = img.shape[2]
if not channel == 3:
continue
if int_width != ((int_width >> 7) << 7):
int_width_pad = (
((int_width >> 7) + 1) << 7) # more than necessary
padding_left = int((int_width_pad - int_width) / 2)
padding_right = int_width_pad - int_width - padding_left
else:
int_width_pad = int_width
padding_left = 32
padding_right = 32
if int_height != ((int_height >> 7) << 7):
int_height_pad = (
((int_height >> 7) + 1) << 7) # more than necessary
padding_top = int((int_height_pad - int_height) / 2)
padding_bottom = int_height_pad - int_height - padding_top
else:
int_height_pad = int_height
padding_top = 32
padding_bottom = 32
frame_num = len(frames)
print('processing {} frames, from video: {}'.format(frame_num, vid))
if not os.path.exists(os.path.join(frame_path_interpolated,
vidname)):
os.makedirs(os.path.join(frame_path_interpolated, vidname))
if not os.path.exists(os.path.join(frame_path_combined, vidname)):
os.makedirs(os.path.join(frame_path_combined, vidname))
for i in tqdm(range(frame_num - 1)):
first = frames[i]
second = frames[i + 1]
img_first = imread(first)
img_second = imread(second)
'''--------------Frame change test------------------------'''
img_first_gray = np.dot(img_first[..., :3],
[0.299, 0.587, 0.114])
img_second_gray = np.dot(img_second[..., :3],
[0.299, 0.587, 0.114])
img_first_gray = img_first_gray.flatten(order='C')
img_second_gray = img_second_gray.flatten(order='C')
corr = np.corrcoef(img_first_gray, img_second_gray)[0, 1]
key_frame = False
if corr < self.key_frame_thread:
key_frame = True
'''-------------------------------------------------------'''
X0 = img_first.astype('float32').transpose((2, 0, 1)) / 255
X1 = img_second.astype('float32').transpose((2, 0, 1)) / 255
if key_frame:
y_ = [
np.transpose(255.0 * X0.clip(0, 1.0), (1, 2, 0))
for i in range(num_frames)
]
else:
assert (X0.shape[1] == X1.shape[1])
assert (X0.shape[2] == X1.shape[2])
X0 = np.pad(X0, ((0,0), (padding_top, padding_bottom), \
(padding_left, padding_right)), mode='edge')
X1 = np.pad(X1, ((0,0), (padding_top, padding_bottom), \
(padding_left, padding_right)), mode='edge')
X0 = np.expand_dims(X0, axis=0)
X1 = np.expand_dims(X1, axis=0)
X0 = np.expand_dims(X0, axis=0)
X1 = np.expand_dims(X1, axis=0)
X = np.concatenate((X0, X1), axis=0)
proc_end = time.time()
o = self.exe.run(self.program,
fetch_list=self.fetch_targets,
feed={"image": X})
y_ = o[0]
proc_timer.update(time.time() - proc_end)
tot_timer.update(time.time() - end)
end = time.time()
y_ = [
np.transpose(
255.0 * item.clip(
0, 1.0)[0, :,
padding_top:padding_top + int_height,
padding_left:padding_left + int_width],
(1, 2, 0)) for item in y_
]
time_offsets = [
kk * timestep for kk in range(1, 1 + num_frames, 1)
]
count = 1
for item, time_offset in zip(y_, time_offsets):
out_dir = os.path.join(
frame_path_interpolated, vidname,
"{:0>6d}_{:0>4d}.png".format(i, count))
count = count + 1
imsave(out_dir, np.round(item).astype(np.uint8))
num_frames = int(1.0 / timestep) - 1
input_dir = os.path.join(frame_path_input, vidname)
interpolated_dir = os.path.join(frame_path_interpolated, vidname)
combined_dir = os.path.join(frame_path_combined, vidname)
combine_frames(input_dir, interpolated_dir, combined_dir,
num_frames)
frame_pattern_combined = os.path.join(frame_path_combined, vidname,
'%08d.png')
video_pattern_output = os.path.join(video_path_output,
vidname + '.mp4')
if os.path.exists(video_pattern_output):
os.remove(video_pattern_output)
frames2video(frame_pattern_combined, video_pattern_output, r2)
return frame_pattern_combined, video_pattern_output
if __name__ == '__main__':
args = parser.parse_args()
predictor = VideoFrameInterp(args.time_step, args.saved_model,
args.video_path, args.output_path)
predictor.run()
applications/DAIN/util.py 已删除 100644 → 0
浏览文件 @ 1744b3a6
import os, sys
import glob
import shutil
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def combine_frames(input, interpolated, combined, num_frames):
frames1 = sorted(glob.glob(os.path.join(input, '*.png')))
frames2 = sorted(glob.glob(os.path.join(interpolated, '*.png')))
num1 = len(frames1)
num2 = len(frames2)
# assert (num1 - 1) * num_frames == num2
for i in range(num1):
src = frames1[i]
imgname = int(src.split('/')[-1].split('.')[-2])
assert i == imgname
dst = os.path.join(combined, '{:08d}.png'.format(i * (num_frames + 1)))
shutil.copy2(src, dst)
if i < num1 - 1:
try:
for k in range(num_frames):
src = frames2[i * num_frames + k]
dst = os.path.join(
combined,
'{:08d}.png'.format(i * (num_frames + 1) + k + 1))
shutil.copy2(src, dst)
except Exception as e:
print(e)
print(len(frames2), num_frames, i, k, i * num_frames + k)
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