def submit_video_2():
dic = {}
dic[0] = 7
dic[1] = 11
dic[2] = 6
dic[3] = 8
dic[4] = 10
model = TSN(5, 5, modality='RGB', partial_bn=True, is_shift=True)
pretrained_dict = torch.load(
'/data/xudw/temporal-shift-module-master/Fall_down_5_frame_5.13.pth.tar',
map_location='cpu')
try:
model_dict = model.module.state_dict()
except AttributeError:
model_dict = model.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
print("load pretrain model")
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
model = model.cuda()
model.eval()
path = '/data/xudw/labels_submit_video_camera2/' #come from the singe frame label
import os
import csv
import re
import cv2
import pandas as pd
import numpy as np
with torch.no_grad():
for Subject in os.listdir(path):
c1 = re.findall('(\d+)', Subject)
Subject_id = c1[0]
for Activity in os.listdir(path + Subject):
c2 = re.findall('(\d+)', Activity)
Activity_id = c2[0]
for csv_1 in os.listdir(path + Subject + '/' + Activity):
csv_data = pd.read_csv(path + Subject + '/' + Activity +
'/' + csv_1)
c3 = re.findall('(\d+)', csv_1)
Trial_id = c3[-1]
loc = np.array(csv_data["Timestamp"])
loc = list(loc)
label = np.array(csv_data['Tag'])
label = list(label)
print(path + Subject + '/' + Activity + '/' + csv_1)
# csv_data.close()
f = open(path + Subject + '/' + Activity + '/' + csv_1,
'w',
encoding='utf-8',
newline='')
# 2. 基于文件对象构建 csv写入对象
csv_writer = csv.writer(f)
# 3. 构建列表头
csv_writer.writerow(["Timestamp", "Tag"])
frames = sorted(
os.listdir('/data/xudw/Fall_Down_data/' + 'Subject' +
Subject_id + 'Activity' + Activity_id +
'Trial' + Trial_id + 'Camera2'))
for i in range(len(loc)):
if 0 <= i <= 3:
print(loc[i], label[i])
csv_writer.writerow([loc[i], label[i]])
else:
buffer = []
frame_1 = cv2.imread('/data/xudw/Fall_Down_data/' +
'Subject' + Subject_id +
'Activity' + Activity_id +
'Trial' + Trial_id +
'Camera2/' + frames[i - 4])
frame_1 = frame_1[73:459, 208:394, :]
# if a == '2':
# frame = cv2.imread(i)
# frame = frame[73:459, 208:394, :]
frame_1 = cv2.cvtColor(frame_1, cv2.COLOR_BGR2RGB)
buffer.append(frame_1)
frame_2 = cv2.imread('/data/xudw/Fall_Down_data/' +
'Subject' + Subject_id +
'Activity' + Activity_id +
'Trial' + Trial_id +
'Camera2/' + frames[i - 3])
frame_2 = frame_2[73:459, 208:394, :]
frame_2 = cv2.cvtColor(frame_2, cv2.COLOR_BGR2RGB)
buffer.append(frame_2)
frame_3 = cv2.imread('/data/xudw/Fall_Down_data/' +
'Subject' + Subject_id +
'Activity' + Activity_id +
'Trial' + Trial_id +
'Camera2/' + frames[i - 2])
frame_3 = frame_3[73:459, 208:394, :]
frame_3 = cv2.cvtColor(frame_3, cv2.COLOR_BGR2RGB)
buffer.append(frame_3)
frame_4 = cv2.imread('/data/xudw/Fall_Down_data/' +
'Subject' + Subject_id +
'Activity' + Activity_id +
'Trial' + Trial_id +
'Camera2/' + frames[i - 1])
frame_4 = frame_4[73:459, 208:394, :]
frame_4 = cv2.cvtColor(frame_4, cv2.COLOR_BGR2RGB)
buffer.append(frame_4)
frame_5 = cv2.imread('/data/xudw/Fall_Down_data/' +
'Subject' + Subject_id +
'Activity' + Activity_id +
'Trial' + Trial_id +
'Camera2/' + frames[i])
frame_5 = frame_5[73:459, 208:394, :]
frame_5 = cv2.cvtColor(frame_5, cv2.COLOR_BGR2RGB)
buffer.append(frame_5)
temp = []
for j in buffer:
temp.append(j)
buffer = np.concatenate(temp, axis=2)
labels = model(
test_transform(buffer).unsqueeze(dim=0).cuda())
# print(label)
_, predicted = torch.max(labels.data, 1)
print(loc[i])
print(label[i])
print("----" * 10)
print(loc[i], dic[int(predicted)])
csv_writer.writerow(
[loc[i], str(dic[int(predicted)])])
def main():
from dataset import VideoDataset_single,VideoDataset_3_frames
import warnings
warnings.filterwarnings("ignore")
from TSM import diff
cudnn.benchmark = False
cur_time = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime(time.time()))
# logdir = os.path.join(params['log'], cur_time)
# if not os.path.exists(logdir):
# os.makedirs(logdir)
print("Loading dataset")
train_dataloader = \
DataLoader(
VideoDataset_single(mode='train'),
batch_size=128, shuffle=True)
val_dataloader = \
DataLoader(
VideoDataset_single(mode='test'),
batch_size=64, shuffle=False)
# test_dataloader = \
# DataLoader(
# VideoDataset(1,mode='test', clip_len=params['clip_len'], frame_sample_rate=params['frame_sample_rate']),
# batch_size=32, shuffle=False, num_workers=params['num_workers'])
model = TSN(5,1,base_model = 'resnet34',modality = 'RGB',partial_bn=True,is_shift=False)
# print(model)
# (layer3): Sequential(
# (0): Bottleneck(
# (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
politics =model.get_optim_policies()
# #--gd 20 --lr 0.02 --wd 1e-4 --lr_steps 20 40 --epochs 50 \
#
# pretrained_dict = torch.load('/data/xudw/temporal-shift-module-master/Fall_down_5_frame.pth.tar', map_location='cpu')
# try:
# model_dict = model.module.state_dict()
# except AttributeError:
# model_dict = model.state_dict()
#
# pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
# print("load pretrain model")
#
# model_dict.update(pretrained_dict)
# model.load_state_dict(model_dict)
model = model.cuda()
model = nn.DataParallel(model) # multi-Gpu
criterion = nn.CrossEntropyLoss().cuda()
# optimizer = optim.SGD(model.parameters(), lr=1e-3, momentum=0.9, weight_decay=5e-4)
optimizer = optim.SGD(politics, lr=1e-2, momentum=0.9, weight_decay=5e-3)
scheduler = optim.lr_scheduler.StepLR(optimizer,step_size=2,gamma=0.1)
for epoch in range(1000):
train(model, train_dataloader, epoch, criterion, optimizer)
# if epoch%2==0:
validation(model, val_dataloader, epoch, criterion, optimizer)
# if epoch % 2== 0:
scheduler.step()
def submit_2():
dic = {}
dic[0] = 7
dic[1] = 11
dic[2] = 6
dic[3] = 8
dic[4] = 10
model = TSN(5,
1,
base_model='resnet34',
modality='RGB',
partial_bn=True,
is_shift=False)
pretrained_dict = torch.load(
'/data/xudw/temporal-shift-module-master/Fall_down_Single_test111_5.13.pth.tar',
map_location='cpu')
try:
model_dict = model.module.state_dict()
except AttributeError:
model_dict = model.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
print("load pretrain model")
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
model = model.cuda()
model.eval()
path = '/data/xudw/labels_submit_2/'
import os
import csv
import re
import cv2
import pandas as pd
import numpy as np
with torch.no_grad():
for Subject in os.listdir(path):
c1 = re.findall('(\d+)', Subject)
Subject_id = c1[0]
for Activity in os.listdir(path + Subject):
c2 = re.findall('(\d+)', Activity)
Activity_id = c2[0]
for csv_1 in os.listdir(path + Subject + '/' + Activity):
csv_data = pd.read_csv(path + Subject + '/' + Activity +
'/' + csv_1)
c3 = re.findall('(\d+)', csv_1)
Trial_id = c3[-1]
loc = np.array(csv_data["Timestamp"])
loc = list(loc)
label = np.array(csv_data['Tag'])
label = list(label)
# csv_data.close()
f = open(path + Subject + '/' + Activity + '/' + csv_1,
'w',
encoding='utf-8',
newline='')
# 2. 基于文件对象构建 csv写入对象
csv_writer = csv.writer(f)
# 3. 构建列表头
csv_writer.writerow(["Timestamp", "Tag"])
frames = sorted(
os.listdir('/data/xudw/Fall_Down_data/' + 'Subject' +
Subject_id + 'Activity' + Activity_id +
'Trial' + Trial_id + 'Camera2'))
print(path + Subject + '/' + Activity + '/' + csv_1)
for i in range(len(loc)):
frame = cv2.imread('/data/xudw/Fall_Down_data/' +
'Subject' + Subject_id +
'Activity' + Activity_id + 'Trial' +
Trial_id + 'Camera2/' + frames[i])
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
labels = model(
test_transform(frame).unsqueeze(dim=0).cuda())
# print(label)
_, predicted = torch.max(labels.data, 1)
print(label[i])
print(dic[int(predicted)])
csv_writer.writerow([loc[i], str(dic[int(predicted)])])