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datasets.py
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### python lib
import os, sys, math, random, glob, cv2, argparse, natsort
import numpy as np
### torch lib
import torch
import torch.utils.data as data
import torchvision.transforms as tv
from torch.utils.data.sampler import Sampler
from torch.utils.data import DataLoader
def read_img(filename, resize= False, scale_factor= 1.0):
## read image and convert to RGB in [0, 1]
img = cv2.imread(filename)
if resize:
img = cv2.resize(img, (0,0), fx= scale_factor, fy= scale_factor)
#print(img.shape)
if img is None:
raise Exception("Image %s does not exist" %filename)
#img = img[:, :, ::-1] ## BGR to RGB
img = np.float32(img) / 255.0
return img
crop_thingies = (0, 0, 0, 0)
class RandomCrop(object):
def __init__(self, image_size, crop_size):
self.ch, self.cw = crop_size
ih, iw = image_size
self.h1 = random.randint(0, ih - self.ch - 0)
self.w1 = random.randint(0, iw - self.cw - 0)
self.h2 = self.h1 + self.ch
self.w2 = self.w1 + self.cw
global crop_thingies
crop_thingies = (self.h1, self.h2, self.w1, self.w2)
def __call__(self, img):
#print("Came in to cropper...", crop_thingies)
#sys.exit()
if len(img.shape) == 3:
return img[self.h1 : self.h2, self.w1 : self.w2, :]
else:
return img[self.h1 : self.h2, self.w1 : self.w2]
class FixedCrop(object):
def __init__(self, crop_size, mh, mw):
self.ch, self.cw = crop_size
self.h1 = mh
self.w1 = mw
self.h2 = self.h1 + self.ch
self.w2 = self.w1 + self.cw
#global crop_thingies
#crop_thingies = (self.h1, self.h2, self.w1, self.w2)
def __call__(self, img):
#print("Came in to cropper...", crop_thingies)
#sys.exit()
if len(img.shape) == 3:
return img[self.h1 : self.h2, self.w1 : self.w2, :]
else:
return img[self.h1 : self.h2, self.w1 : self.w2]
class MultiFramesDataset(data.Dataset):
def __init__(self, mode= 'train', opts= None, get_lbls= False, prev_iter_counter= 0):
super(MultiFramesDataset, self).__init__()
self.mode = mode
self.ip_dir = opts.data_dir
self.dataset_task_list = []
self.num_frames = []
self.get_lbls = get_lbls
self.iter_counter = prev_iter_counter
if self.mode == "train":
self.sample_frames = opts.sample_frames
self.geometry_aug = opts.geometry_aug
self.scale_min = opts.scale_min
self.scale_max = opts.scale_max
self.crop_size = opts.crop_size
self.order_aug = opts.order_aug
self.size_multiplier = opts.size_multiplier
self.dataset_task_list = natsort.natsorted(glob.glob(os.path.join(self.ip_dir + "*/")))
self.dataset_lbls_list = natsort.natsorted(glob.glob(os.path.join(opts.train_lbl_dir + "*/")))
self.shuffled_inds = np.arange(len(self.dataset_task_list))
np.random.shuffle(self.shuffled_inds)
self.num_frames = [len(os.listdir(dir)) for dir in self.dataset_task_list]
#list_lbl = natsort.natsorted(glob.glob(os.path.join(self.lbl_dir + "*/")))
elif self.mode == "val":
self.dataset_task_list = natsort.natsorted(glob.glob(os.path.join(self.ip_dir + "*/")))
#self.dataset_task_list = [os.path.join(self.ip_dir+i+'/') for i in ['Crowd_7','Parallax_10','Regular_16','Running_16','Zooming_11']]
self.num_frames = [len(os.listdir(dir)) for dir in self.dataset_task_list]
# print(self.dataset_task_list)
else:
raise ValueError("Only train and val mode is implemented at the moment...")
print("+"*20, "Total task_list:", "+"*20)
video_names = []
for task in self.dataset_task_list:
video_names.append(task.split("/")[-2])
print(video_names)
print("+"*50)
self.num_tasks = len(self.dataset_task_list)
global crop_thingies
print("[%s] Total %d videos (%d frames)" %(self.__class__.__name__, len(self.dataset_task_list), sum(self.num_frames)))
def __len__(self):
return len(self.dataset_task_list)
def __getitem__(self, index):
meta_data = {}
meta_data['idx'] = index
## random select starting frame index t between [0, N - number_of_sample_frames] for "mode = "train" | "validate""
if self.mode == "train":
index_ = self.shuffled_inds[index]
N = self.num_frames[index_]
T = random.randint(0, N - self.sample_frames)
#T = 0 #### Change this again for later experiments to randomize the frames for training
meta_data['starting_frame'] = T
input_dir = self.dataset_task_list[index_]
if self.get_lbls:
lbl_dir = self.dataset_lbls_list[index_]
meta_data['unstable_video_path'] = input_dir
if self.get_lbls:
meta_data['stable_video_path'] = lbl_dir
## sample from T to T + #sample_frames - 1
frame_ip = []
if self.get_lbls:
frame_lbl = []
ip_frame_list = natsort.natsorted(glob.glob(os.path.join(input_dir, "*.*")))
if self.get_lbls:
lbl_frame_list = natsort.natsorted(glob.glob(os.path.join(lbl_dir, "*.*")))
for t in range(T, T + self.sample_frames):
if self.iter_counter < 200000:
frame_ip.append(read_img(ip_frame_list[t], True, 1.0))
if self.get_lbls:
frame_lbl.append(read_img(lbl_frame_list[t], True, 1.0))
else:
frame_ip.append(read_img(ip_frame_list[t], False, 1.0))
if self.get_lbls:
frame_lbl.append(read_img(lbl_frame_list[t], False, 1.0))
meta_data['ip_frame_paths'] = ip_frame_list[T:T + self.sample_frames]
if self.get_lbls:
meta_data['op_frame_paths'] = lbl_frame_list[T:T + self.sample_frames]
self.iter_counter += 1
## data augmentation
if self.geometry_aug:
## random scale
H_in = frame_ip[0].shape[0]
W_in = frame_ip[0].shape[1]
sc = np.random.uniform(self.scale_min, self.scale_max)
H_out = int(math.floor(H_in * sc))
W_out = int(math.floor(W_in * sc))
## scaled size should be equal to opts.crop_size
if H_out < W_out:
if H_out < self.crop_size:
H_out = self.crop_size
W_out = int(math.floor(W_in * float(H_out) / float(H_in)))
else: ## W_out < H_out
if W_out < self.crop_size:
W_out = self.crop_size
H_out = int(math.floor(H_in * float(W_out) / float(W_in)))
for t in range(self.sample_frames):
frame_ip[t] = cv2.resize(frame_ip[t], (W_out, H_out))
if self.get_lbls:
frame_lbl[t] = cv2.resize(frame_lbl[t], (W_out, H_out))
meta_data['scale_factor'] = sc
## random crop
cropper = RandomCrop(frame_ip[0].shape[:2], (self.crop_size, self.crop_size))
for t in range(self.sample_frames):
frame_ip[t] = cropper(frame_ip[t])
if self.get_lbls:
frame_lbl[t] = cropper(frame_lbl[t])
if frame_ip[t].shape[1] != self.crop_size and frame_ip[t].shape[0] != self.crop_size:
print("[MultiFrameDataset]: size mismatch occured... =>", frame_ip[t].shape)
if self.get_lbls:
if frame_lbl[t].shape[1] != self.crop_size and frame_lbl[t].shape[0] != self.crop_size:
print("[MultiFrameDataset]: size mismatch occured... =>", frame_lbl[t].shape)
meta_data['crop_coords'] = crop_thingies
if self.geometry_aug:
#meta_data['rotation'] = False
### random rotate
rotate = random.randint(0, 3)
if rotate != 0:
for t in range(self.sample_frames):
frame_ip[t] = np.rot90(frame_ip[t], rotate)
if self.get_lbls:
frame_lbl[t] = np.rot90(frame_lbl[t], rotate)
#meta_data['rotation'] = True
## horizontal flip
if np.random.random() >= 0.5:
for t in range(self.sample_frames):
frame_ip[t] = cv2.flip(frame_ip[t], flipCode=0)
if self.get_lbls:
frame_lbl[t] = cv2.flip(frame_lbl[t], flipCode=0)
#meta_data['hflip'] = True
if self.order_aug:
## reverse temporal order
#meta_data['order'] = "normal"
if np.random.random() >= 0.5:
#meta_data['order'] = "reversed"
frame_ip.reverse()
if self.get_lbls:
frame_lbl.reverse()
elif self.mode == "val":
input_dir = self.dataset_task_list[index]
#lbl_dir = self.dataset_task_list[index][1]
meta_data['unstable_video_path'] = input_dir
meta_data['video_name'] = input_dir
#meta_data['stable_video_path'] = lbl_dir
## sample from T to T + #sample_frames - 1
frame_ip = []
#frame_lbl = []
ip_frame_list = natsort.natsorted(glob.glob(os.path.join(input_dir, "*.*")))
#lbl_frame_list = natsort.natsorted(glob.glob(os.path.join(lbl_dir, "*.*")))
for t in range(0, self.num_frames[index]):
frame_ip.append(read_img(ip_frame_list[t]))
#frame_lbl.append(read_img(lbl_frame_list[t]))
meta_data['ip_frame_paths'] = ip_frame_list
### convert (H, W, C) array to (C, H, W) tensor
X = []
if self.mode == "train":
if self.get_lbls:
Y = []
for t in range(len(frame_ip)):
X.append(torch.from_numpy(frame_ip[t].transpose(2, 0, 1).astype(np.float32)))
if self.get_lbls:
Y.append(torch.from_numpy(frame_lbl[t].transpose(2, 0, 1).astype(np.float32)))
if self.get_lbls:
return {'X': X, 'Y': Y, 'meta_data': meta_data}
else:
return {'X': X, 'meta_data': meta_data}
else:
for t in range(len(frame_ip)):
X.append(torch.unsqueeze(torch.from_numpy(frame_ip[t].transpose(2, 0, 1).astype(np.float32)), 0))
return {'X': X, 'meta_data': meta_data}
class MultiFramesDatasetHybrid(data.Dataset):
def __init__(self, mode= 'train', opts= None, get_lbls= True):
super(MultiFramesDatasetHybrid, self).__init__()
self.ip_dir = opts.data_dir
self.ip_dir_synth = opts.data_dir_synth
self.lbl_dir = opts.train_lbl_dir
self.sample_frames = opts.sample_frames
self.geometry_aug = opts.geometry_aug
self.scale_min = opts.scale_min
self.scale_max = opts.scale_max
self.crop_size = opts.crop_size
self.order_aug = opts.order_aug
self.size_multiplier = opts.size_multiplier
self.mode = mode
self.dataset_task_list = []
self.num_frames = []
self.get_lbls = get_lbls
self.iter_counter = 0
if self.mode == "train":
self.dataset_task_list = natsort.natsorted(glob.glob(os.path.join(self.ip_dir + "*/")))
self.dataset_syn_ip_list = natsort.natsorted(glob.glob(os.path.join(self.ip_dir_synth + "*/")))
self.dataset_syn_lbls_list = natsort.natsorted(glob.glob(os.path.join(self.lbl_dir + "*/")))
self.shuffled_inds = np.arange(len(self.dataset_task_list))
np.random.shuffle(self.shuffled_inds)
#list_lbl = natsort.natsorted(glob.glob(os.path.join(self.lbl_dir + "*/")))
elif self.mode == "val":
self.dataset_task_list = natsort.natsorted(glob.glob(os.path.join(self.ip_dir + "*/")))
else:
raise ValueError("Only train and val mode is implemented at the moment...")
print("+"*20, "Total task_list:", "+"*20)
video_names = []
for task in self.dataset_task_list:
video_names.append(task.split("/")[-2])
print(video_names)
print("+"*50)
self.num_tasks = len(self.dataset_task_list)
for ip in self.dataset_task_list:
self.num_frames.append(len(natsort.natsorted(os.listdir(ip))))
for ip in self.synth_list:
self.num_frames_synth.append(len(natsort.natsorted(os.listdir(ip))))
global crop_thingies
print("[%s] Total %d videos (%d frames)" %(self.__class__.__name__, len(self.dataset_task_list), sum(self.num_frames)))
def __len__(self):
return len(self.dataset_task_list)
def __getitem__(self, index):
meta_data = {}
meta_data['idx'] = index
## random select starting frame index t between [0, N - number_of_sample_frames] for "mode = "train" | "validate""
if self.mode == "train":
index_ = self.shuffled_inds[index]
rand_num = random.randint(0, len(self.synth_list))
N = self.num_frames[index_]
Ns = self.num_frames_synth[rand_num]
T = random.randint(0, N - self.sample_frames)
Ts = random.randint(0, Ns - self.sample_frames)
#T = 0 #### Change this again for later experiments to randomize the frames for training
meta_data['starting_frame'] = T
input_dir = self.dataset_task_list[index_]
if self.get_lbls:
syn_ip_dir = self.dataset_syn_ip_list[rand_num]
lbl_dir = self.dataset_syn_lbls_list[rand_num]
meta_data['unstable_video_path'] = input_dir
if self.get_lbls:
meta_data['stable_video_path'] = lbl_dir
## sample from T to T + #sample_frames - 1
frame_ip = []
if self.get_lbls:
frame_syn = []
frame_lbl = []
ip_frame_list = natsort.natsorted(glob.glob(os.path.join(input_dir, "*.*")))
if self.get_lbls:
syn_ip_list = natsort.natsorted(glob.glob(os.path.join(syn_ip_dir, "*.*")))
lbl_frame_list = natsort.natsorted(glob.glob(os.path.join(lbl_dir, "*.*")))
for t in range(T, T + self.sample_frames):
if self.iter_counter < 1000:
frame_ip.append(read_img(ip_frame_list[t], True, 0.3))
if self.get_lbls:
frame_syn.append(read_img(syn_ip_list[t], True, 0.3))
frame_lbl.append(read_img(lbl_frame_list[t], True, 0.3))
elif self.iter_counter < 2000:
frame_ip.append(read_img(ip_frame_list[t], True, 0.4))
if self.get_lbls:
frame_syn.append(read_img(syn_ip_list[t], True, 0.4))
frame_lbl.append(read_img(lbl_frame_list[t], True, 0.4))
elif self.iter_counter < 3000:
frame_ip.append(read_img(ip_frame_list[t], True, 0.45))
if self.get_lbls:
frame_syn.append(read_img(syn_ip_list[t], True, 0.45))
frame_lbl.append(read_img(lbl_frame_list[t], True, 0.45))
elif self.iter_counter < 4000:
frame_ip.append(read_img(ip_frame_list[t], True, 0.5))
if self.get_lbls:
frame_syn.append(read_img(syn_ip_list[t], True, 0.5))
frame_lbl.append(read_img(lbl_frame_list[t], True, 0.5))
elif self.iter_counter < 5000:
frame_ip.append(read_img(ip_frame_list[t], True, 0.55))
if self.get_lbls:
frame_syn.append(read_img(syn_ip_list[t], True, 0.55))
frame_lbl.append(read_img(lbl_frame_list[t], True, 0.55))
elif self.iter_counter < 6000:
frame_ip.append(read_img(ip_frame_list[t], True, 0.6))
if self.get_lbls:
frame_syn.append(read_img(syn_ip_list[t], True, 0.6))
frame_lbl.append(read_img(lbl_frame_list[t], True, 0.6))
elif self.iter_counter < 7000:
frame_ip.append(read_img(ip_frame_list[t], True, 0.7))
if self.get_lbls:
frame_syn.append(read_img(syn_ip_list[t], True, 0.7))
frame_lbl.append(read_img(lbl_frame_list[t], True, 0.7))
elif self.iter_counter < 8000:
frame_ip.append(read_img(ip_frame_list[t], True, 0.8))
if self.get_lbls:
frame_syn.append(read_img(syn_ip_list[t], True, 0.8))
frame_lbl.append(read_img(lbl_frame_list[t], True, 0.8))
elif self.iter_counter < 9000:
frame_ip.append(read_img(ip_frame_list[t], True, 0.9))
if self.get_lbls:
frame_syn.append(read_img(syn_ip_list[t], True, 0.9))
frame_lbl.append(read_img(lbl_frame_list[t], True, 0.9))
else:
frame_ip.append(read_img(ip_frame_list[t], False, 1.0))
if self.get_lbls:
frame_syn.append(read_img(syn_ip_list[t], True, 1.0))
frame_lbl.append(read_img(lbl_frame_list[t], False, 1.0))
meta_data['ip_frame_paths'] = ip_frame_list[T:T + self.sample_frames]
if self.get_lbls:
meta_data['op_frame_paths'] = lbl_frame_list[T:T + self.sample_frames]
self.iter_counter += 1
## data augmentation
if self.geometry_aug:
## random scale
H_in = frame_ip[0].shape[0]
W_in = frame_ip[0].shape[1]
sc = np.random.uniform(self.scale_min, self.scale_max)
H_out = int(math.floor(H_in * sc))
W_out = int(math.floor(W_in * sc))
## scaled size should be equal to opts.crop_size
if H_out < W_out:
if H_out < self.crop_size:
H_out = self.crop_size
W_out = int(math.floor(W_in * float(H_out) / float(H_in)))
else: ## W_out < H_out
if W_out < self.crop_size:
W_out = self.crop_size
H_out = int(math.floor(H_in * float(W_out) / float(W_in)))
for t in range(self.sample_frames):
frame_ip[t] = cv2.resize(frame_ip[t], (W_out, H_out))
if self.get_lbls:
frame_syn[t] = cv2.resize(frame_syn[t], (W_out, H_out))
frame_lbl[t] = cv2.resize(frame_lbl[t], (W_out, H_out))
meta_data['scale_factor'] = sc
## random crop
cropper = RandomCrop(frame_ip[0].shape[:2], (self.crop_size, self.crop_size))
for t in range(self.sample_frames):
frame_ip[t] = cropper(frame_ip[t])
if self.get_lbls:
frame_lbl[t] = cropper(frame_lbl[t])
frame_syn[t] = cropper(frame_syn[t])
if frame_ip[t].shape[1] != self.crop_size and frame_ip[t].shape[0] != self.crop_size:
print("[MultiFrameDataset]: size mismatch occured... =>", frame_ip[t].shape)
if self.get_lbls:
if frame_lbl[t].shape[1] != self.crop_size and frame_lbl[t].shape[0] != self.crop_size:
print("[MultiFrameDataset]: size mismatch occured... =>", frame_lbl[t].shape)
if frame_syn[t].shape[1] != self.crop_size and frame_syn[t].shape[0] != self.crop_size:
print("[MultiFrameDataset]: size mismatch occured... =>", frame_syn[t].shape)
meta_data['crop_coords'] = crop_thingies
if self.geometry_aug:
#meta_data['rotation'] = False
### random rotate
rotate = random.randint(0, 3)
if rotate != 0:
for t in range(self.sample_frames):
frame_ip[t] = np.rot90(frame_ip[t], rotate)
if self.get_lbls:
frame_syn[t] = np.rot90(frame_syn[t], rotate)
frame_lbl[t] = np.rot90(frame_lbl[t], rotate)
#meta_data['rotation'] = True
## horizontal flip
if np.random.random() >= 0.5:
for t in range(self.sample_frames):
frame_ip[t] = cv2.flip(frame_ip[t], flipCode=0)
if self.get_lbls:
frame_syn[t] = cv2.flip(frame_syn[t], flipCode=0)
frame_lbl[t] = cv2.flip(frame_lbl[t], flipCode=0)
#meta_data['hflip'] = True
if self.order_aug:
## reverse temporal order
#meta_data['order'] = "normal"
if np.random.random() >= 0.5:
#meta_data['order'] = "reversed"
frame_ip.reverse()
if self.get_lbls:
frame_syn.reverse()
frame_lbl.reverse()
elif self.mode == "val":
input_dir = self.dataset_task_list[index]
#lbl_dir = self.dataset_task_list[index][1]
meta_data['unstable_video_path'] = input_dir
meta_data['video_name'] = input_dir
#meta_data['stable_video_path'] = lbl_dir
## sample from T to T + #sample_frames - 1
frame_ip = []
#frame_lbl = []
ip_frame_list = natsort.natsorted(glob.glob(os.path.join(input_dir, "*.*")))
#lbl_frame_list = natsort.natsorted(glob.glob(os.path.join(lbl_dir, "*.*")))
for t in range(0, self.sample_frames):
frame_ip.append(read_img(ip_frame_list[t]))
#frame_lbl.append(read_img(lbl_frame_list[t]))
meta_data['ip_frame_paths'] = ip_frame_list
### convert (H, W, C) array to (C, H, W) tensor
X = []
if self.mode == "train":
if self.get_lbls:
X2 = []
Y = []
for t in range(len(frame_ip)):
X.append(torch.from_numpy(frame_ip[t].transpose(2, 0, 1).astype(np.float32)))
if self.get_lbls:
X2.append(torch.from_numpy(frame_syn[t].transpose(2, 0, 1).astype(np.float32)))
Y.append(torch.from_numpy(frame_lbl[t].transpose(2, 0, 1).astype(np.float32)))
if self.get_lbls:
return {'X': X, 'X2': X2, 'Y': Y, 'meta_data': meta_data}
else:
return {'X': X, 'meta_data': meta_data}
else:
for t in range(len(frame_ip)):
X.append(torch.unsqueeze(torch.from_numpy(frame_ip[t].transpose(2, 0, 1).astype(np.float32)), 0))
return {'X': X, 'meta_data': meta_data}
class SubsetSequentialSampler(Sampler):
def __init__(self, indices):
self.indices = indices
def __iter__(self):
return (self.indices[i] for i in range(len(self.indices)))
def __len__(self):
return len(self.indices)
def create_data_loader(data_set, mode, train_epoch_size= 1000, batch_size= 4, threads= 8):
### generate random index
if mode == 'train':
total_samples = train_epoch_size * batch_size
else:
raise ValueError("Only train mode is implemented at the moment...")
num_epochs = int(math.ceil(float(total_samples) / len(data_set)))
indices = np.random.permutation(len(data_set))
indices = np.tile(indices, num_epochs)
indices = indices[:total_samples]
### generate data sampler and loader
sampler = SubsetSequentialSampler(indices)
if mode == "train":
data_loader = DataLoader(dataset= data_set, num_workers= threads, batch_size= batch_size, sampler= sampler, pin_memory= True)
return data_loader
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Meta Stabilization")
### model options
parser.add_argument('-model', type=str, default="ours", help='Model to use')
parser.add_argument('-ckp_name', type=str, default="best_stab_vf_v1.h5", help='TransformNet')
parser.add_argument('-model_name', type=str, default='meta_stab_w_homo', help='path to save model')
### dataset options
parser.add_argument('-train_data_dir', type=str, default='data/', help='path to train data folder')
parser.add_argument('-data_dir', type=str, default='data/', help='path to test data folder')
parser.add_argument('-checkpoint_dir', type=str, default='checkpoints', help='path to checkpoint folder')
parser.add_argument('-crop_size', type=int, default=192, help='patch size')
parser.add_argument('-geometry_aug', type=int, default=0, help='geometry augmentation (rotation, scaling, flipping)')
parser.add_argument('-order_aug', type=int, default=1, help='temporal ordering augmentation')
parser.add_argument('-scale_min', type=float, default=0.5, help='min scaling factor')
parser.add_argument('-scale_max', type=float, default=2.0, help='max scaling factor')
parser.add_argument('-sample_frames', type=int, default=70, help='#frames for training')
### training options
parser.add_argument('-solver', type=str, default="ADAM", choices=["SGD", "ADAM"], help="optimizer")
parser.add_argument('-momentum', type=float, default=0.9, help='momentum for SGD')
parser.add_argument('-beta1', type=float, default=0.9, help='beta1 for ADAM')
parser.add_argument('-beta2', type=float, default=0.999, help='beta2 for ADAM')
parser.add_argument('-weight_decay', type=float, default=0, help='weight decay')
parser.add_argument('-batch_size', type=int, default=2, help='training batch size')
parser.add_argument('-train_epoch_size',type=int, default=20, help='train epoch size')
parser.add_argument('-valid_epoch_size',type=int, default=100, help='valid epoch size')
parser.add_argument('-epoch_max', type=int, default=100, help='max #epochs')
### learning rate options
parser.add_argument('-lr_init', type=float, default=5e-5, help='initial learning Rate')
parser.add_argument('-lr_offset', type=int, default=20, help='epoch to start learning rate drop [-1 = no drop]')
parser.add_argument('-lr_step', type=int, default=20, help='step size (epoch) to drop learning rate')
parser.add_argument('-lr_drop', type=float, default=0.5, help='learning rate drop ratio')
parser.add_argument('-lr_min_m', type=float, default=0.1, help='minimal learning Rate multiplier (lr >= lr_init * lr_min)')
### other options
parser.add_argument('-loss', type=str, default="L1", help="Loss [Options: L1, L2]")
parser.add_argument('-seed', type=int, default=9487, help='random seed to use')
parser.add_argument('-threads', type=int, default=8, help='number of threads for data loader to use')
parser.add_argument('-suffix', type=str, default='', help='name suffix')
parser.add_argument('-gpu', type=int, default=1, help='gpu device id')
parser.add_argument('-cpu', action='store_true', help='use cpu?')
opts = parser.parse_args()
opts.size_multiplier = 2 ** 6 ## Inputs to FlowNet need to be divided by 64
train_dataset = MultiFramesDataset(mode= "train", opts= opts)
data_loader = create_data_loader(train_dataset, mode= "train", batch_size= 1, threads= 0)
sane_path = "./sanitycheck/"
for iteration, (data) in enumerate(data_loader, 1):
x = data['X']
if iteration > 3:
break
if iteration == 1:
if not os.path.exists(sane_path):
os.mkdir(sane_path)
i = 0
for xs in x:
img = xs[0]
img = np.transpose(img.numpy(), (1, 2, 0))
cv2.imwrite(sane_path + "X-" + str(iteration) + "-" + str(i) + ".png", img * 255)
i += 1
else:
print(x[0].shape)