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train_for_DAVIS.py
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train_for_DAVIS.py
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import os
import numpy as np
import cv2
import random
import torch
import torch.nn.functional as F
from dataloader.data_for_video import get_loader, get_testloader
from loss import IoU_loss
from model.model_for_video import DATA, weights_init
import config as config
from logger import *
os.environ["CUDA_VISIBLE_DEVICES"] = config.TRAIN['GPU']
folder = os.path.join(config.DATA['data_root'], config.DATA['DAVIS_val'])
valid_list = [name for name in os.listdir(folder) if os.path.isdir(os.path.join(folder, name))]
best_jaccard = 0
def load_my_state_dict(model, state_dict):
own_state = model.state_dict()
ckpt_name = []
cnt = 0
for name, param in state_dict.items():
name = name.replace("module.", "")
try:
if name not in list(own_state.keys()):
ckpt_name.append(name)
continue
own_state[name].copy_(param)
cnt += 1
print(name)
except:
pass
try:
new_name = name.replace("rgb_", "flow_")
if new_name not in list(own_state.keys()):
ckpt_name.append(new_name)
continue
own_state[new_name].copy_(param)
cnt += 1
print(new_name)
except:
pass
print('#reused param: {}'.format(cnt))
return model
def train(epoch, trainloader, optimizer, model, device, scheduler):
model.train()
avg_loss1 = 0
PTTM = pttm()
for idx, batch in enumerate(trainloader):
PTTM.print_status(epoch, idx, trainloader)
images, gts, flows, ref_images, ref_flows = batch
s = random.choice([384, 416, 448, 480, 512])
if images.size(0) == 1:
continue
images, gts, flows = images.to(device), gts.to(device), flows.to(device)
ref_images, ref_flows = ref_images.to(device), ref_flows.to(device)
images = F.interpolate(images, size=(s, s), mode='bicubic', align_corners=False)
flows = F.interpolate(flows, size=(s, s), mode='bicubic', align_corners=False)
gts = F.interpolate(gts, size=(s, s), mode='bicubic', align_corners=False)
ref_images = F.interpolate(ref_images, size=(s, s), mode='bicubic', align_corners=False)
ref_flows = F.interpolate(ref_flows, size=(s, s), mode='bicubic', align_corners=False)
optimizer.zero_grad()
pred = model(images, flows, ref_images, ref_flows, s)
loss1 = IoU_loss(pred, gts)
total_loss = loss1
avg_loss1 += loss1.item()
total_loss.backward()
optimizer.step()
scheduler.step()
print("")
avg_loss1 = avg_loss1 / (idx + 1)
print(
"Epoch: #{0} Batch: {1}\t"
"Lr: {lr:.7f}\t"
"LOSS pred: {loss1:.4f}\n"
.format(epoch, idx, lr=optimizer.param_groups[-1]['lr'], loss1=avg_loss1)
)
avg_loss1 = 0
def valid(epoch, model, device, work_dir):
global best_jaccard
print("Evaluating model...")
test_loader = get_testloader(config.DATA['DAVIS_val'])
J_buffer = {item:[] for item in valid_list}
model.eval()
with torch.no_grad():
PTTM = pttm()
for idx, batch in enumerate(test_loader):
PTTM.print_status(epoch, idx, test_loader)
image, gt, flow, info, _, ref_images, ref_flows = batch
B = image.shape[0]
ori_H = info[0][0]
ori_W = info[0][1]
image = image.to(device)
flow = flow.to(device)
gt = gt.to(device)
ref_images = ref_images.to(device)
ref_flows = ref_flows.to(device)
preds = model(image, flow, ref_images, ref_flows, 512)
res = preds[3]
for b in range(B):
res_slice = res[b, :, :, :].unsqueeze(0).float()
gt_slice = gt[b, :, :, :].unsqueeze(0).float()
info_slice = info[1][b]
res_slice = F.upsample(res_slice, size=(ori_H[b].item(), ori_W[b].item()), mode='bilinear', align_corners=False)
res_slice = (res_slice - res_slice.min()) / (res_slice.max() - res_slice.min() + 1e-8)
res_slice[res_slice > 0.5] = 1
res_slice[res_slice <= 0.5] = 0
gt_slice = F.upsample(gt_slice, size=(ori_H[b].item(), ori_W[b].item()), mode='bilinear', align_corners=False)
gt_slice /= (torch.max(gt_slice) + 1e-8)
J_buffer[info_slice].append((torch.sum(res_slice * gt_slice) / (torch.sum(res_slice) + torch.sum(gt_slice) - torch.sum(res_slice * gt_slice))).item())
print("")
total_jaccard_list = []
for name in valid_list:
total_jaccard_list.append(np.mean(np.array(J_buffer[name])))
total_jaccard = np.mean(np.array(total_jaccard_list))
print("total J: {:.4f}".format(total_jaccard), "best J: {:.4f}".format(best_jaccard))
if total_jaccard > best_jaccard:
best_jaccard = total_jaccard
save_model(work_dir, epoch, model, 'best')
print("Saved best model!")
return True
return False
def visual(device, work_dir):
model = DATA()
model = model.to(device)
model = torch.nn.DataParallel(model)
model_dir = os.path.join(work_dir, "model")
checkpoint = torch.load(model_dir + "/best_model.pth")
model.load_state_dict(checkpoint['model_state_dict'])
test_loader = get_testloader(config.DATA['DAVIS_val'])
model.eval()
with torch.no_grad():
for _, batch in enumerate(test_loader):
image, gt, flow, info, img_for_post, ref_images, ref_flows = batch
B = image.shape[0]
ori_H = info[0][0]
ori_W = info[0][1]
image = image.to(device)
flow = flow.to(device)
ref_images = ref_images.to(device)
ref_flows = ref_flows.to(device)
preds = model(image, flow, ref_images, ref_flows, 512)
flow = flow.permute(0, 2, 3, 1).cpu().detach()
res = preds[3]
for b in range(B):
res_slice = res[b, :, :, :].unsqueeze(0)
gt_slice = gt[b, :, :, :].squeeze(0)
flow_slice = flow[b, :, :, :].squeeze(0)
ori_image_slice = img_for_post[b, :, :, :].squeeze(0)
gt_slice = np.asarray(gt_slice, np.float32)
gt_slice /= (gt_slice.max() + 1e-8)
res_slice = F.upsample(res_slice, size=(ori_H[b].item(), ori_W[b].item()), mode='bilinear', align_corners=False)
res_slice = res_slice.permute(0, 2, 3, 1).cpu().detach().squeeze(0).squeeze(-1).numpy()
res_slice = (res_slice - res_slice.min()) / (res_slice.max() - res_slice.min() + 1e-8)
cat_res = cv2.cvtColor(np.array(res_slice * 255), cv2.COLOR_GRAY2BGR)
cat_res = cv2.resize(cat_res, dsize=(ori_H[b].item(), ori_W[b].item()), interpolation=cv2.INTER_AREA)
cat_res = cat_res.astype(np.uint8)
cat_gt = cv2.cvtColor(np.array(gt_slice * 255), cv2.COLOR_GRAY2BGR)
cat_gt = cv2.resize(cat_gt, dsize=(ori_H[b].item(), ori_W[b].item()), interpolation=cv2.INTER_AREA)
cat_gt = cat_gt.astype(np.uint8)
cat_flow = cv2.cvtColor(np.array(flow_slice * 255), cv2.COLOR_RGB2BGR)
cat_flow = cv2.resize(cat_flow, dsize=(ori_H[b].item(), ori_W[b].item()), interpolation=cv2.INTER_AREA)
cat_flow = cat_flow.astype(np.uint8)
cat_ori = cv2.cvtColor(np.array(ori_image_slice), cv2.COLOR_RGB2BGR)
cat_ori = cv2.resize(cat_ori, dsize=(ori_H[b].item(), ori_W[b].item()), interpolation=cv2.INTER_AREA)
cat_ori = cat_ori.astype(np.uint8)
result = cv2.hconcat([cat_ori, cat_flow, cat_res, cat_gt])
valid_name = info[1][b]
name = info[2][b]
total_dir = os.path.join(work_dir, "result", "total", valid_name)
if not os.path.exists(total_dir):
os.makedirs(total_dir)
pred_dir = os.path.join(work_dir, "result", "pred", valid_name)
if not os.path.exists(pred_dir):
os.makedirs(pred_dir)
gt_dir = os.path.join(work_dir, "result", "gt", valid_name)
if not os.path.exists(gt_dir):
os.makedirs(gt_dir)
cv2.imwrite(os.path.join(total_dir, name), result)
cv2.imwrite(os.path.join(pred_dir, name), cat_res)
cv2.imwrite(os.path.join(gt_dir, name), cat_gt)
def main():
work_dir = make_new_work_space()
save_config_file(work_dir)
print("Check device...")
device = torch.device("cuda")
print(device)
print("ok!")
print("Load model...")
model = DATA()
model.apply(weights_init)
model.rgb_encoder.vgg.load_state_dict(torch.load("./pretrain/vgg16_feat.pth"))
model.flow_encoder.vgg.load_state_dict(torch.load("./pretrain/vgg16_feat.pth"))
checkpoint = torch.load(config.DATA['best_pretrained_model'])
model = load_my_state_dict(model, checkpoint['model_state_dict'])
model = model.to(device)
model = torch.nn.DataParallel(model)
num_params = sum([np.prod(p.size()) for p in model.parameters()])
print("Total number of parameters: {}".format(num_params))
num_params_update = sum([np.prod(p.shape) for p in model.parameters() if p.requires_grad])
print("Total number of learning parameters: {}".format(num_params_update))
print("ok!")
print("Load optimizer...")
params = model.parameters()
optimizer = torch.optim.Adam(params, config.TRAIN['learning_rate'])
train_loader = get_loader()
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=len(train_loader)*config.TRAIN['epoch'], eta_min=config.TRAIN['learning_rate']/10)
print("ok!")
print("Training start!")
for epoch in range(config.TRAIN['epoch']):
print("Load dataset...")
train_loader = get_loader()
print("ok!")
train(epoch, train_loader, optimizer, model, device, scheduler)
valid(epoch, model, device, work_dir)
visual(device, work_dir)
print("Training finish!")
if __name__ == '__main__':
main()