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scribbleNet_main.py
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import os
import copy
import timeit
import torch
import torch.optim as optim
from torchvision import transforms
from torch.utils.data import DataLoader
from torch.autograd import Variable
import scipy.misc as sm
import numpy as np
from davisinteractive import utils as interactive_utils
from dataloaders import davis_2017 as db
from dataloaders import davis_custom as db_scribblenet
from mypath import Path
from dataloaders import custom_transforms as tr
from layers.osvos_layers import class_balanced_cross_entropy_loss
from networks.scribble_net import ScribbleNet
class ScribbleNetMain(object):
def __init__(self, parent_model, save_model_dir, gpu_id, time_budget, save_result_dir=None):
self.save_model_dir = save_model_dir
self.parent_model = parent_model
self.save_res_dir = save_result_dir
self.net = ScribbleNet()
if gpu_id >= 0:
torch.cuda.set_device(device=gpu_id)
self.net.cuda()
self.gpu_id = gpu_id
self.time_budget = time_budget
self.meanval = (104.00699, 116.66877, 122.67892)
self.train_batch = 1
self.test_batch = 1
self.prev_models = {}
# self.parent_model_state = torch.load(os.path.join(Path.models_dir(), self.parent_model),
# map_location=lambda storage, loc: storage)
def train(self, first_frame, n_interaction, obj_id, scribbles_data, scribble_iter, subset, use_previous_mask=False):
nAveGrad = 1
num_workers = 4
train_batch = min(n_interaction, self.train_batch)
frames_list = interactive_utils.scribbles.annotated_frames_object(scribbles_data, obj_id)
scribbles_list = scribbles_data['scribbles']
seq_name = scribbles_data['sequence']
if obj_id == 1 and n_interaction == 1:
self.prev_models = {}
# # Network definition
# if n_interaction == 1:
# print('Loading weights from: {}'.format(self.parent_model))
# self.net.load_state_dict(self.parent_model_state)
# self.prev_models[obj_id] = None
# else:
# print('Loading weights from previous network: objId-{}_interaction-{}_scribble-{}.pth'
# .format(obj_id, n_interaction-1, scribble_iter))
# self.net.load_state_dict(self.prev_models[obj_id])
lr = 1e-5
wd = 0.0002
# optimizer = optim.SGD([
# {'params': [pr[1] for pr in self.net.stages.named_parameters() if 'weight' in pr[0]], 'weight_decay': wd},
# {'params': [pr[1] for pr in self.net.stages.named_parameters() if 'bias' in pr[0]], 'lr': lr * 2},
# {'params': [pr[1] for pr in self.net.side_prep.named_parameters() if 'weight' in pr[0]], 'weight_decay': wd},
# {'params': [pr[1] for pr in self.net.side_prep.named_parameters() if 'bias' in pr[0]], 'lr': lr * 2},
# {'params': [pr[1] for pr in self.net.upscale.named_parameters() if 'weight' in pr[0]], 'lr': 0},
# {'params': [pr[1] for pr in self.net.upscale_.named_parameters() if 'weight' in pr[0]], 'lr': 0},
# {'params': self.net.fuse.weight, 'lr': lr / 100, 'weight_decay': wd},
# {'params': self.net.fuse.bias, 'lr': 2 * lr / 100},
# ], lr=lr, momentum=0.9)
optimizer = optim.Adam(filter(lambda p: p.requires_grad, self.net.parameters()), lr=lr, momentum=0.9)
prev_mask_path = os.path.join(self.save_res_dir, 'interaction-{}'.format(n_interaction-1),
'scribble-{}'.format(scribble_iter))
composed_transforms_tr = transforms.Compose([tr.CenterCrop((480,832)),tr.SubtractMeanImage(self.meanval),
tr.CustomScribbleInteractive(scribbles_list, first_frame,
use_previous_mask=use_previous_mask,
previous_mask_path=prev_mask_path),
tr.RandomHorizontalFlip(),
tr.ScaleNRotate(rots=(-30, 30), scales=(.75, 1.25)),
tr.ToTensor()])
# Training dataset and its iterator
# db_train = db.DAVIS2017(split=subset, transform=composed_transforms_tr,
# custom_frames=frames_list, seq_name=seq_name,
# obj_id=obj_id, no_gt=True, retname=True)
db_train = db_scribblenet.DAVIS2017(split=subset, transform=composed_transforms_tr,
custom_frames=frames_list, seq_name=seq_name,
obj_id=obj_id, no_gt=True, retname=True)
trainloader = DataLoader(db_train, batch_size=train_batch, shuffle=True, num_workers=num_workers)
num_img_tr = len(trainloader)
loss_tr = []
aveGrad = 0
# List of all previous masks and aggregated features
prev_masks = []
prev_aggs = []
start_time = timeit.default_timer()
# Main Training and Testing Loop
epoch = 0
while 1:
# One training epoch
running_loss_tr = 0
for ii, sample_batched in enumerate(trainloader):
optimizer.zero_grad()
# Parse from dataset loader
inputs = sample_batched['images'].cuda()
gts = sample_batched['scribble_gt'].cuda()
scribbles = sample_batched['scribble_raw'].cuda()
scribbles_idx = sample_batched['scribble_idx'].cuda()
# Forward-Backward of the mini-batch
# prev_masks = torch.tensor(prev_masks).unsqueeze(0)
# prev_aggs = torch.tensor(prev_aggs).unsqueeze(0)
masks, agg = self.net.forward(inputs, scribbles, scribble_idx, prev_masks, prev_agg)
# Compute the fuse loss
loss = class_balanced_cross_entropy_loss(masks, gts, scribble_idx)
running_loss_tr += loss.item()
# Print stuff
if epoch % 10 == 0:
running_loss_tr /= num_img_tr
loss_tr.append(running_loss_tr)
print('[Epoch: %d, numImages: %5d]' % (epoch + 1, ii + 1))
print('Loss: %f' % running_loss_tr)
# Backward the averaged gradient
loss.backward()
optimizer.step()
# Update the current round data
prev_masks.append(masks.detach())
prev_aggs.append(agg.detach())
epoch += train_batch
stop_time = timeit.default_timer()
if stop_time - start_time > self.time_budget:
break
# Save the model into dictionary
self.prev_models[obj_id] = copy.deepcopy(self.net.state_dict())
def test(self, sequence, n_interaction, obj_id, subset, scribble_iter=0):
if self.save_res_dir:
save_dir_res = os.path.join(self.save_res_dir, 'interaction-{}'.format(n_interaction),
'scribble-{}'.format(scribble_iter),
sequence, str(obj_id))
if not os.path.exists(save_dir_res):
os.makedirs(save_dir_res)
composed_transforms_ts = transforms.Compose([tr.CenterCrop((480,832)),
tr.SubtractMeanImage(self.meanval),
tr.ToTensor()])
# Testing dataset and its iterator
# db_test = db.DAVIS2017(split=subset, transform=composed_transforms_ts, seq_name=sequence, no_gt=True, retname=True)
db_test = db_scribblenet.DAVIS2017(split=subset, transform=composed_transforms_ts, seq_name=sequence, no_gt=True, retname=True)
testloader = DataLoader(db_test, batch_size=self.test_batch, shuffle=False, num_workers=2)
print('Testing Network for obj_id={}'.format(obj_id))
print('Loading weights from objId-{}_interaction-{}_scribble-{}.pth'
.format(obj_id, n_interaction, scribble_iter))
# Main Testing Loop
masks = []
for ii, sample_batched in enumerate(testloader):
img, gt, meta = sample_batched['image'], sample_batched['gt'], sample_batched['meta']
# Forward of the mini-batch
inputs, gts = Variable(img, volatile=True), Variable(gt, volatile=True)
if self.gpu_id >= 0:
inputs, gts = inputs.cuda(), gts.cuda()
outputs = self.net.forward(inputs)[-1].cpu().data.numpy()
for jj in range(int(inputs.size()[0])):
pred = np.transpose(outputs[jj, :, :, :], (1, 2, 0))
pred = 1 / (1 + np.exp(-pred))
pred = np.squeeze(pred)
if self.save_res_dir:
# Save the result, attention to the index jj
sm.imsave(os.path.join(save_dir_res, os.path.basename(meta['frame_id'][jj]) + '.png'), pred)
masks.append(pred)
return masks