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train.py
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train.py
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import numpy as np
import os
from torch.utils.data import DataLoader
from vad_datasets import unified_dataset_interface, cube_to_train_dataset
from vad_datasets import bbox_collate, img_tensor2numpy, img_batch_tensor2numpy, frame_size
from helper.misc import AverageMeter
import torch
from state_model import ConvTransformer_recon_correct
import torch.optim as optim
import torch.nn as nn
import argparse
import os
import sys
pyfile_name = os.path.basename(sys.argv[0]).split(".")[0]
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('boolean value expected')
parser = argparse.ArgumentParser()
parser.add_argument('-d', '--dataset', default='avenue', type=str)
parser.add_argument('-n_l', '--num_layers', default=3, type=int)
parser.add_argument('-n_h', '--num_heads', default=4, type=int)
parser.add_argument('-pe', '--positional_encoding', default='learned', type=str)
parser.add_argument('-e', '--epochs', default=20, type=int)
parser.add_argument('-b', '--batch_size', default=128, type=int)
parser.add_argument('-l', '--temporal_length', default=3, type=int)
parser.add_argument('-lam_r', '--lambda_raw', default=1, type=float)
parser.add_argument('-lam_o', '--lambda_of', default=1, type=float)
parser.add_argument('-train_b', '--train_bbox_saved', default=True, type=str2bool)
parser.add_argument('-train_f', '--train_foreground_saved', default=True, type=str2bool)
parser.add_argument('-f', '--use_flow', default=True, type=str2bool)
parser.add_argument('-bd', '--border_mode', default='elastic', type=str)
args = parser.parse_args()
def calc_block_idx(x_min, x_max, y_min, y_max, h_step, w_step, mode):
all_blocks = list()
center = np.array([(y_min + y_max) / 2, (x_min + x_max) / 2])
all_blocks.append(center + center)
if mode > 1:
all_blocks.append(np.array([y_min, center[1]]) + center)
all_blocks.append(np.array([y_max, center[1]]) + center)
all_blocks.append(np.array([center[0], x_min]) + center)
all_blocks.append(np.array([center[0], x_max]) + center)
if mode >= 9:
all_blocks.append(np.array([y_min, x_min]) + center)
all_blocks.append(np.array([y_max, x_max]) + center)
all_blocks.append(np.array([y_max, x_min]) + center)
all_blocks.append(np.array([y_min, x_max]) + center)
all_blocks = np.array(all_blocks) / 2
h_block_idxes = all_blocks[:, 0] / h_step
w_block_idxes = all_blocks[:, 1] / w_step
h_block_idxes, w_block_idxes = list(h_block_idxes.astype(np.int)), list(w_block_idxes.astype(np.int))
# delete repeated elements
all_blocks = set([x for x in zip(h_block_idxes, w_block_idxes)])
all_blocks = [x for x in all_blocks]
return all_blocks
# /*------------------------------------overall parameter setting------------------------------------------*/
dataset_name = args.dataset
raw_dataset_dir = 'raw_datasets'
foreground_extraction_mode = 'obj_det_with_motion'
data_root_dir = 'data'
modality = 'raw2flow'
mode ='train'
method = 'SelfComplete'
num_layers = args.num_layers
num_heads = args.num_heads
pe = args.positional_encoding
context_frame_num = args.temporal_length
context_of_num = args.temporal_length
patch_size = 32
h_block = 1
w_block = 1
train_block_mode = 1
bbox_saved = args.train_bbox_saved
foreground_saved = args.train_foreground_saved
motionThr = 0
# /*------------------------------------------foreground extraction----------------------------------------------*/
config_file = './obj_det_config/cascade_rcnn_r101_fpn_1x.py'
checkpoint_file = './obj_det_checkpoints/cascade_rcnn_r101_fpn_1x_20181129-d64ebac7.pth'
# set dataset for foreground extraction
dataset = unified_dataset_interface(dataset_name=dataset_name, dir=os.path.join(raw_dataset_dir, dataset_name),
context_frame_num=1, mode=mode, border_mode='hard')
if not bbox_saved:
# build the model from a config file and a checkpoint file
from fore_det.inference import init_detector
from fore_det.obj_det_with_motion import imshow_bboxes, getObBboxes, getFgBboxes, delCoverBboxes
from fore_det.simple_patch import get_patch_loc
model = init_detector(config_file, checkpoint_file, device='cuda:0')
collate_func = bbox_collate('train')
dataset_loader = DataLoader(dataset=dataset, batch_size=1, shuffle=False, num_workers=1,
collate_fn=collate_func.collate)
all_bboxes = list()
for idx in range(dataset.__len__()):
batch, _ = dataset.__getitem__(idx)
print('Extracting bboxes of {}-th frame'.format(idx + 1))
cur_img = img_tensor2numpy(batch[1])
if foreground_extraction_mode == 'obj_det_with_motion':
# A coarse detection of bboxes by pretrained object detector
ob_bboxes = getObBboxes(cur_img, model, dataset_name)
ob_bboxes = delCoverBboxes(ob_bboxes, dataset_name)
# further foreground detection by motion
fg_bboxes = getFgBboxes(cur_img, img_batch_tensor2numpy(batch), ob_bboxes, dataset_name, verbose=False)
if fg_bboxes.shape[0] > 0:
cur_bboxes = np.concatenate((ob_bboxes, fg_bboxes), axis=0)
else:
cur_bboxes = ob_bboxes
elif foreground_extraction_mode == 'obj_det':
# A coarse detection of bboxes by pretrained object detector
ob_bboxes = getObBboxes(cur_img, model, dataset_name)
cur_bboxes = delCoverBboxes(ob_bboxes, dataset_name)
elif foreground_extraction_mode == 'simple_patch':
patch_num_list = [(3, 4), (6, 8)]
cur_bboxes = list()
for h_num, w_num in patch_num_list:
cur_bboxes.append(get_patch_loc(frame_size[dataset_name][0], frame_size[dataset_name][1], h_num, w_num))
cur_bboxes = np.concatenate(cur_bboxes, axis=0)
else:
raise NotImplementedError
# imshow_bboxes(cur_img, cur_bboxes)
all_bboxes.append(cur_bboxes)
np.save(os.path.join(dataset.dir, 'bboxes_train_{}.npy'.format(foreground_extraction_mode)), all_bboxes)
print('bboxes for training data saved!')
else:
all_bboxes = np.load(os.path.join(dataset.dir, 'bboxes_train_{}.npy'.format(foreground_extraction_mode)),
allow_pickle=True)
print('bboxes for training data loaded!')
# /------------------------- extract foreground using extracted bboxes---------------------------------------/
border_mode = args.border_mode
if not foreground_saved:
if modality == 'raw_datasets':
file_format = frame_size[dataset_name][2]
elif modality == 'raw2flow':
file_format1 = frame_size[dataset_name][2]
file_format2 = '.npy'
else:
file_format = '.npy'
if modality == 'raw2flow':
dataset = unified_dataset_interface(dataset_name=dataset_name, dir=os.path.join('raw_datasets', dataset_name),
context_frame_num=context_frame_num, mode=mode, border_mode=border_mode,
all_bboxes=all_bboxes, patch_size=patch_size, file_format=file_format1)
dataset2 = unified_dataset_interface(dataset_name=dataset_name, dir=os.path.join('optical_flow', dataset_name),
context_frame_num=context_of_num, mode=mode, border_mode=border_mode,
all_bboxes=all_bboxes, patch_size=patch_size, file_format=file_format2)
else:
dataset = unified_dataset_interface(dataset_name=dataset_name, dir=os.path.join(modality, dataset_name),
context_frame_num=context_frame_num, mode=mode, border_mode=border_mode,
all_bboxes=all_bboxes, patch_size=patch_size, file_format=file_format)
if dataset_name == 'ShanghaiTech':
foreground_set = [[[[] for ww in range(w_block)] for hh in range(h_block)] for ss in range(dataset.scene_num)]
if modality == 'raw2flow':
foreground_set2 = [[[[] for ww in range(w_block)] for hh in range(h_block)] for ss in
range(dataset.scene_num)]
else:
foreground_set = [[[] for ww in range(w_block)] for hh in range(h_block)]
if modality == 'raw2flow':
foreground_set2 = [[[] for ww in range(w_block)] for hh in range(h_block)]
h_step, w_step = frame_size[dataset_name][0] / h_block, frame_size[dataset_name][1] / w_block
dataset_loader = DataLoader(dataset=dataset, batch_size=1, shuffle=False, num_workers=1,
collate_fn=bbox_collate(mode=mode).collate)
if dataset_name == 'ShanghaiTech' and modality == 'raw2flow':
randIdx = np.random.permutation(dataset.__len__())
cout = 0
segIdx = 0
saveSegNum = 40000
for iidx in range(dataset.__len__()):
if dataset_name == 'ShanghaiTech' and modality == 'raw2flow':
idx = randIdx[iidx]
cout += 1
else:
idx = iidx
batch, _ = dataset.__getitem__(idx)
if modality == 'raw2flow':
batch2, _ = dataset2.__getitem__(idx)
if dataset_name == 'ShanghaiTech':
print(
'Extracting foreground in {}-th batch, {} in total, scene: {}'.format(iidx + 1, dataset.__len__() // 1,
dataset.scene_idx[idx]))
else:
print('Extracting foreground in {}-th batch, {} in total'.format(iidx + 1, dataset.__len__() // 1))
cur_bboxes = all_bboxes[idx]
if len(cur_bboxes) > 0:
batch = img_batch_tensor2numpy(batch)
if modality == 'raw2flow':
batch2 = img_batch_tensor2numpy(batch2)
if modality == 'optical_flow':
if len(batch.shape) == 4:
mag = np.sum(np.sum(np.sum(batch ** 2, axis=3), axis=2), axis=1)
else:
mag = np.mean(np.sum(np.sum(np.sum(batch ** 2, axis=4), axis=3), axis=2), axis=1)
elif modality == 'raw2flow':
if len(batch2.shape) == 4:
mag = np.sum(np.sum(np.sum(batch2 ** 2, axis=3), axis=2), axis=1)
else:
mag = np.mean(np.sum(np.sum(np.sum(batch2 ** 2, axis=4), axis=3), axis=2), axis=1)
else:
mag = np.ones(batch.shape[0]) * 10000
for idx_bbox in range(cur_bboxes.shape[0]):
if mag[idx_bbox] > motionThr:
all_blocks = calc_block_idx(cur_bboxes[idx_bbox, 0], cur_bboxes[idx_bbox, 2],
cur_bboxes[idx_bbox, 1], cur_bboxes[idx_bbox, 3], h_step, w_step,
mode=train_block_mode)
for (h_block_idx, w_block_idx) in all_blocks:
if dataset_name == 'ShanghaiTech':
foreground_set[dataset.scene_idx[idx] - 1][h_block_idx][w_block_idx].append(batch[idx_bbox])
if modality == 'raw2flow':
foreground_set2[dataset.scene_idx[idx] - 1][h_block_idx][w_block_idx].append(
batch2[idx_bbox])
else:
foreground_set[h_block_idx][w_block_idx].append(batch[idx_bbox])
if modality == 'raw2flow':
foreground_set2[h_block_idx][w_block_idx].append(batch2[idx_bbox])
if dataset_name == 'ShanghaiTech' and modality == 'raw2flow':
if cout == saveSegNum:
foreground_set = [
[[np.array(foreground_set[ss][hh][ww]) for ww in range(w_block)] for hh in range(h_block)] for ss in
range(dataset.scene_num)]
foreground_set2 = [
[[np.array(foreground_set2[ss][hh][ww]) for ww in range(w_block)] for hh in range(h_block)] for ss
in range(dataset.scene_num)]
np.save(os.path.join(data_root_dir, modality,
dataset_name + '_' + 'foreground_train_{}_seg_{}_{}_border_{}-raw.npy'.format(
foreground_extraction_mode, segIdx, context_frame_num, border_mode)), foreground_set)
np.save(os.path.join(data_root_dir, modality,
dataset_name + '_' + 'foreground_train_{}_seg_{}_{}_border_{}-flow.npy'.format(
foreground_extraction_mode, segIdx, context_frame_num, border_mode)), foreground_set2)
del foreground_set, foreground_set2
cout = 0
segIdx += 1
foreground_set = [[[[] for ww in range(w_block)] for hh in range(h_block)] for ss in
range(dataset.scene_num)]
foreground_set2 = [[[[] for ww in range(w_block)] for hh in range(h_block)] for ss in
range(dataset.scene_num)]
if dataset_name == 'ShanghaiTech':
if modality != 'raw2flow':
foreground_set = [[[np.array(foreground_set[ss][hh][ww]) for ww in range(w_block)] for hh in range(h_block)]
for ss in range(dataset.scene_num)]
np.save(os.path.join(data_root_dir, modality,
dataset_name + '_' + 'foreground_train_{}.npy'.format(foreground_extraction_mode)),
foreground_set)
else:
if dataset.__len__() % saveSegNum != 0:
foreground_set = [
[[np.array(foreground_set[ss][hh][ww]) for ww in range(w_block)] for hh in range(h_block)]
for ss in range(dataset.scene_num)]
foreground_set2 = [
[[np.array(foreground_set2[ss][hh][ww]) for ww in range(w_block)] for hh in range(h_block)] for ss
in
range(dataset.scene_num)]
np.save(os.path.join(data_root_dir, modality,
dataset_name + '_' + 'foreground_train_{}_seg_{}_{}_border_{}-raw.npy'.format(
foreground_extraction_mode, segIdx, context_frame_num, border_mode)), foreground_set)
np.save(os.path.join(data_root_dir, modality,
dataset_name + '_' + 'foreground_train_{}_seg_{}_{}_border_{}-flow.npy'.format(
foreground_extraction_mode, segIdx, context_frame_num, border_mode)), foreground_set2)
else:
if modality == 'raw2flow':
foreground_set = [[np.array(foreground_set[hh][ww]) for ww in range(w_block)] for hh in range(h_block)]
np.save(os.path.join(data_root_dir, modality,
dataset_name + '_' + 'foreground_train_{}_{}_border_{}-raw.npy'.format(foreground_extraction_mode, context_frame_num, border_mode)),
foreground_set)
foreground_set2 = [[np.array(foreground_set2[hh][ww]) for ww in range(w_block)] for hh in range(h_block)]
np.save(os.path.join(data_root_dir, modality, dataset_name + '_' + 'foreground_train_{}_{}_border_{}-flow.npy'.format(
foreground_extraction_mode, context_frame_num, border_mode)), foreground_set2)
else:
foreground_set = [[np.array(foreground_set[hh][ww]) for ww in range(w_block)] for hh in range(h_block)]
np.save(os.path.join(data_root_dir, modality,
dataset_name + '_' + 'foreground_train_{}_{}_border_{}.npy'.format(foreground_extraction_mode, context_frame_num, border_mode)),
foreground_set)
print('foreground for training data saved!')
else:
if dataset_name != 'ShanghaiTech':
if modality == 'raw2flow':
foreground_set = np.load(os.path.join(data_root_dir, modality,
dataset_name + '_' + 'foreground_train_{}_{}_border_{}-raw.npy'.format(
foreground_extraction_mode, context_frame_num, border_mode)), allow_pickle=True)
foreground_set2 = np.load(os.path.join(data_root_dir, modality,
dataset_name + '_' + 'foreground_train_{}_{}_border_{}-flow.npy'.format(
foreground_extraction_mode, context_frame_num, border_mode)), allow_pickle=True)
else:
foreground_set = np.load(os.path.join(data_root_dir, modality,
dataset_name + '_' + 'foreground_train_{}_{}_border_{}.npy'.format(
foreground_extraction_mode, context_frame_num, border_mode)), allow_pickle=True)
print('foreground for training data loaded!')
else:
if modality != 'raw2flow':
foreground_set = np.load(os.path.join(data_root_dir, modality,
dataset_name + '_' + 'foreground_train_{}_{}_border_{}.npy'.format(
foreground_extraction_mode, context_frame_num, border_mode)), allow_pickle=True)
# /*------------------------------------------Normal event modeling----------------------------------------------*/
if method == 'SelfComplete':
loss_func = nn.MSELoss()
epochs = args.epochs
batch_size = args.batch_size
useFlow = args.use_flow
if border_mode == 'predict':
tot_frame_num = context_frame_num + 1
tot_of_num = context_of_num + 1
else:
tot_frame_num = 2 * context_frame_num + 1
tot_of_num = 2 * context_of_num + 1
rawRange = 10
if rawRange >= tot_frame_num: # if rawRange is out of the range, use all frames
rawRange = None
padding = False
lambda_raw = args.lambda_raw
lambda_of = args.lambda_of
assert modality == 'raw2flow'
if dataset_name == 'ShanghaiTech':
model_set = [[[[] for ww in range(w_block)] for hh in range(h_block)] for ss in
range(frame_size[dataset_name][-1])]
raw_training_scores_set = [[[[] for ww in range(w_block)] for hh in range(h_block)] for ss in
range(frame_size[dataset_name][-1])]
of_training_scores_set = [[[[] for ww in range(w_block)] for hh in range(h_block)] for ss in
range(frame_size[dataset_name][-1])]
else:
model_set = [[[] for ww in range(len(foreground_set[hh]))] for hh in range(len(foreground_set))]
raw_training_scores_set = [[[] for ww in range(len(foreground_set[hh]))] for hh in range(len(foreground_set))]
of_training_scores_set = [[[] for ww in range(len(foreground_set[hh]))] for hh in range(len(foreground_set))]
# Prepare training data in current block
if dataset_name == 'ShanghaiTech':
saveSegNum = 40000
totSegNum = np.int(np.ceil(dataset.__len__() / saveSegNum))
for s_idx in range(len(model_set)):
for h_idx in range(len(model_set[s_idx])):
for w_idx in range(len(model_set[s_idx][h_idx])):
raw_losses = AverageMeter()
of_losses = AverageMeter()
cur_model = torch.nn.DataParallel(ConvTransformer_recon_correct(
tot_raw_num = tot_frame_num, nums_hidden = [32, 64, 128], num_layers=num_layers,
num_dec_frames=1, num_heads=num_heads, with_residual=True,
with_pos=True, pos_kind=pe, mode=0, use_flow=useFlow)).cuda()
optimizer = optim.Adam(cur_model.parameters(), eps=1e-7, weight_decay=0.000)
cur_model.train()
for epoch in range(epochs):
for segIdx in range(totSegNum):
foreground_set = np.load(os.path.join(data_root_dir, modality,
dataset_name + '_' + 'foreground_train_{}_seg_{}_{}_border_{}-raw.npy'.format(
foreground_extraction_mode, segIdx, context_frame_num, border_mode)))
foreground_set2 = np.load(os.path.join(data_root_dir, modality,
dataset_name + '_' + 'foreground_train_{}_seg_{}_{}_border_{}-flow.npy'.format(
foreground_extraction_mode, segIdx, context_frame_num, border_mode)))
cur_training_data = foreground_set[s_idx][h_idx][w_idx]
cur_training_data2 = foreground_set2[s_idx][h_idx][w_idx]
cur_dataset = cube_to_train_dataset(cur_training_data, target=cur_training_data2)
cur_dataloader = DataLoader(dataset=cur_dataset, batch_size=batch_size, shuffle=True)
for idx, (inputs, of_targets_all, _) in enumerate(cur_dataloader):
inputs = inputs.cuda().type(torch.cuda.FloatTensor)
of_targets_all = of_targets_all.cuda().type(torch.cuda.FloatTensor)
of_outputs, raw_outputs, of_targets, raw_targets = cur_model(inputs, of_targets_all)
loss_raw = loss_func(raw_targets.detach(), raw_outputs)
if useFlow:
loss_of = loss_func(of_targets.detach(), of_outputs)
if useFlow:
loss = lambda_raw * loss_raw + lambda_of * loss_of
else:
loss = loss_raw
raw_losses.update(loss_raw.item(), inputs.size(0))
if useFlow:
of_losses.update(loss_of.item(), inputs.size(0))
else:
of_losses.update(0., inputs.size(0))
optimizer.zero_grad()
loss.backward()
optimizer.step()
if idx % 5 == 0:
print(
'Block: ({}, {}), epoch {}, seg {}, batch {} of {}, raw loss: {}, of loss: {}'.format(
h_idx, w_idx, epoch, segIdx, idx, cur_dataset.__len__() // batch_size,
raw_losses.avg,
of_losses.avg))
# break
# break
# break
model_set[s_idx][h_idx][w_idx].append(cur_model.state_dict())
# /*-- A forward pass to store the training scores of optical flow and raw datasets respectively*/
for segIdx in range(totSegNum):
foreground_set = np.load(os.path.join(data_root_dir, modality,
dataset_name + '_' + 'foreground_train_{}_seg_{}_{}_border_{}-raw.npy'.format(
foreground_extraction_mode, segIdx, context_frame_num, border_mode)))
foreground_set2 = np.load(os.path.join(data_root_dir, modality,
dataset_name + '_' + 'foreground_train_{}_seg_{}_{}_border_{}-flow.npy'.format(
foreground_extraction_mode, segIdx, context_frame_num, border_mode)))
cur_training_data = foreground_set[s_idx][h_idx][w_idx]
cur_training_data2 = foreground_set2[s_idx][h_idx][w_idx]
cur_dataset = cube_to_train_dataset(cur_training_data, target=cur_training_data2)
forward_dataloader = DataLoader(dataset=cur_dataset, batch_size=batch_size//4, shuffle=False)
score_func = nn.MSELoss(reduce=False)
cur_model.eval()
for idx, (inputs, of_targets_all, _) in enumerate(forward_dataloader):
inputs = inputs.cuda().type(torch.cuda.FloatTensor)
of_targets_all = of_targets_all.cuda().type(torch.cuda.FloatTensor)
of_outputs, raw_outputs, of_targets, raw_targets = cur_model(inputs, of_targets_all)
raw_scores = score_func(raw_targets, raw_outputs).cpu().data.numpy()
raw_scores = np.sum(np.sum(np.sum(np.sum(raw_scores, axis=4), axis=3), axis=2), axis=1) # mse
raw_training_scores_set[s_idx][h_idx][w_idx].append(raw_scores)
if useFlow:
of_scores = score_func(of_targets, of_outputs).cpu().data.numpy()
of_scores = np.sum(np.sum(np.sum(np.sum(of_scores, axis=4), axis=3), axis=2), axis=1) # mse
of_training_scores_set[s_idx][h_idx][w_idx].append(of_scores)
raw_training_scores_set[s_idx][h_idx][w_idx] = np.concatenate(
raw_training_scores_set[s_idx][h_idx][w_idx], axis=0)
if useFlow:
of_training_scores_set[s_idx][h_idx][w_idx] = np.concatenate(
of_training_scores_set[s_idx][h_idx][w_idx], axis=0)
del cur_model, raw_losses, of_losses
torch.save(raw_training_scores_set, os.path.join(data_root_dir, modality,
dataset_name + '_' + 'raw_training_scores_border_{}_head_{}_layer_{}_length_{}_pe_{}_epoch_{}_lambda_{}_{}'.format(
border_mode, num_heads, num_layers, context_frame_num, pe,
epochs, lambda_raw, lambda_of) + '_' + 'pyname_{}.npy'.format(pyfile_name)))
torch.save(of_training_scores_set, os.path.join(data_root_dir, modality,
dataset_name + '_' + 'of_training_scores_border_{}_head_{}_layer_{}_length_{}_pe_{}_epoch_{}_lambda_{}_{}'.format(
border_mode, num_heads, num_layers, context_frame_num, pe,
epochs, lambda_raw, lambda_of) + '_' + 'pyname_{}.npy'.format(pyfile_name)))
else:
raw_losses = AverageMeter()
of_losses = AverageMeter()
torch.autograd.set_detect_anomaly(True)
for h_idx in range(len(foreground_set)):
for w_idx in range(len(foreground_set[h_idx])):
cur_training_data = foreground_set[h_idx][w_idx]
if len(cur_training_data) > 1: # num > 1 for data parallel
cur_training_data2 = foreground_set2[h_idx][w_idx]
cur_dataset = cube_to_train_dataset(cur_training_data, target=cur_training_data2)
cur_dataloader = DataLoader(dataset=cur_dataset, batch_size=batch_size, shuffle=True)
cur_model = torch.nn.DataParallel(ConvTransformer_recon_correct(
tot_raw_num = tot_frame_num, nums_hidden = [32, 64, 128], num_layers=num_layers,
num_dec_frames=1, num_heads=num_heads, with_residual=True,
with_pos=True, pos_kind=pe, mode=0, use_flow=useFlow)).cuda()
if dataset_name == 'UCSDped2':
optimizer = optim.Adam(cur_model.parameters(), eps=1e-7, weight_decay=0.0)
else:
optimizer = optim.Adam(cur_model.parameters(), eps=1e-7, weight_decay=0.0)
cur_model.train()
for epoch in range(epochs):
for idx, (inputs, of_targets_all, _) in enumerate(cur_dataloader):
inputs = inputs.cuda().type(torch.cuda.FloatTensor)
# print(torch.max(inputs))
of_targets_all = of_targets_all.cuda().type(torch.cuda.FloatTensor)
of_outputs, raw_outputs, of_targets, raw_targets = cur_model(inputs, of_targets_all)
loss_raw = loss_func(raw_targets.detach(), raw_outputs)
if useFlow:
loss_of = loss_func(of_targets.detach(), of_outputs)
if useFlow:
loss = lambda_raw * loss_raw + lambda_of * loss_of
else:
loss = loss_raw
raw_losses.update(loss_raw.item(), inputs.size(0))
if useFlow:
of_losses.update(loss_of.item(), inputs.size(0))
else:
of_losses.update(0., inputs.size(0))
optimizer.zero_grad()
loss.backward()
optimizer.step()
if idx % 5 == 0:
max_num = 20
print(
'Block: ({}, {}), epoch {}, batch {} of {}, raw loss: {}, of loss: {}'.format(h_idx,
w_idx,
epoch,
idx,
cur_dataset.__len__() // batch_size,
raw_losses.avg,
of_losses.avg))
model_set[h_idx][w_idx].append(cur_model.state_dict())
# /*-- A forward pass to store the training scores of optical flow and raw datasets respectively*/
forward_dataloader = DataLoader(dataset=cur_dataset, batch_size=batch_size//4, shuffle=False)
# raw_score_func = nn.MSELoss(reduce=False)
# of_score_func = nn.L1Loss(reduce=False)
score_func = nn.MSELoss(reduce=False)
cur_model.eval()
for idx, (inputs, of_targets_all, _) in enumerate(forward_dataloader):
inputs = inputs.cuda().type(torch.cuda.FloatTensor)
of_targets_all = of_targets_all.cuda().type(torch.cuda.FloatTensor)
of_outputs, raw_outputs, of_targets, raw_targets = cur_model(inputs, of_targets_all)
raw_scores = score_func(raw_targets, raw_outputs).cpu().data.numpy()
raw_scores = np.sum(np.sum(np.sum(np.sum(raw_scores, axis=4), axis=3), axis=2), axis=1) # mse
raw_training_scores_set[h_idx][w_idx].append(raw_scores)
if useFlow:
of_scores = score_func(of_targets, of_outputs).cpu().data.numpy()
of_scores = np.sum(np.sum(np.sum(np.sum(of_scores, axis=4), axis=3), axis=2), axis=1) # mse
of_training_scores_set[h_idx][w_idx].append(of_scores)
raw_training_scores_set[h_idx][w_idx] = np.concatenate(raw_training_scores_set[h_idx][w_idx], axis=0)
if useFlow:
of_training_scores_set[h_idx][w_idx] = np.concatenate(of_training_scores_set[h_idx][w_idx],
axis=0)
torch.save(raw_training_scores_set, os.path.join(data_root_dir, modality,
dataset_name + '_' + 'raw_training_scores_border_{}_head_{}_layer_{}_length_{}_pe_{}_epoch_{}_lambda_{}_{}'.format(
border_mode, num_heads, num_layers, context_frame_num, pe, epochs, lambda_raw, lambda_of) + '_' + 'pyname_{}.npy'.format(pyfile_name)))
torch.save(of_training_scores_set, os.path.join(data_root_dir, modality,
dataset_name + '_' + 'of_training_scores_border_{}_head_{}_layer_{}_length_{}_pe_{}_epoch_{}_lambda_{}_{}'.format(
border_mode, num_heads, num_layers, context_frame_num, pe, epochs, lambda_raw, lambda_of) + '_' + 'pyname_{}.npy'.format(pyfile_name)))
print('training scores saved')
torch.save(model_set, os.path.join(data_root_dir, modality,
dataset_name + '_' + 'model_{}_head_{}_layer_{}_length_{}_pe_{}_epoch_{}_lambda_{}_{}'.format(
border_mode, num_heads, num_layers, context_frame_num, pe, epochs, lambda_raw, lambda_of) + '_' + 'pyname_{}.npy'.format(pyfile_name)))
print('Training of {} for dataset: {} has completed!'.format(method, dataset_name))
else:
raise NotImplementedError