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main.py
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main.py
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from include import *
import argparse
from collections import defaultdict
import pandas as pd
from process.data import *
from loss.loss import softmax_loss
from utils import *
from torch.utils.data import DataLoader
from net.archead import *
from loss.cyclic_lr import *
from tqdm import tqdm
from ensemble import *
def softmax_mse_loss(input_logits, target_logits):
"""Takes softmax on both sides and returns MSE loss
Note:
- Returns the sum over all examples. Divide by the batch size afterwards
if you want the mean.
- Sends gradients to inputs but not the targets.
"""
assert input_logits.size() == target_logits.size()
input_softmax = F.softmax(input_logits, dim=1)
target_softmax = F.softmax(target_logits, dim=1)
num_classes = input_logits.size()[1]
return F.mse_loss(input_softmax, target_softmax, size_average=False) / num_classes
def get_current_consistency_weight(epoch, consistency, consistency_rampup):
# Consistency ramp-up from https://arxiv.org/abs/1610.02242
return consistency * sigmoid_rampup(epoch, consistency_rampup)
def update_ema_variables(model, ema_model, alpha, global_step):
# Use the true average until the exponential average is more correct
alpha = min(1 - 1 / (global_step + 1), alpha)
for ema_param, param in zip(ema_model.parameters(), model.parameters()):
ema_param.data.mul_(alpha).add_(1 - alpha, param.data)
def get_model(model, CLASSNUM=1139):
if model == 'xception_6channel':
from net.model_xception_6channel import Net
elif model == 'xception_large_6channel':
from net.model_xception_large_6channel import Net
net = Net(num_class=CLASSNUM,
is_arc=config.is_arc,
arc_s=config.arc_s,
arc_m=config.arc_m)
return net
def do_valid(net, valid_loader, predict_num = [0,1108]):
valid_num = 0
truths = []
losses = []
probs = []
labels = []
ids = []
with torch.no_grad():
for input, truth_, id in valid_loader:
ids += id
input = input.cuda()
truth_ = truth_.cuda()
input = to_var(input)
truth_ = to_var(truth_)
logit = net.forward(input)
logit = logit[:, predict_num[0]: predict_num[1]]
truth_ = truth_ - predict_num[0]
loss = softmax_loss(logit, truth_)
probs.append(logit)
labels.append(truth_)
valid_num += len(input)
loss_tmp = loss.data.cpu().numpy().reshape([1])
losses.append(loss_tmp)
truths.append(truth_.data.cpu().numpy())
assert (valid_num == len(valid_loader.sampler))
# ------------------------------------------------------
loss = np.concatenate(losses,axis=0)
loss = loss.mean()
prob = torch.cat(probs)
label = torch.cat(labels)
_, precision = metric(prob, label)
print('calculate balance acc')
logits = prob.cpu().numpy().reshape([valid_num, 1108])
prob_balance, _ = balance_plate_probability_training(logits, ids, plate_dict, a_dict, iters=0, is_show = False)
label_np = label.cpu().numpy().reshape([valid_num])
tmp = np.argmax(prob_balance, 1)
balance_acc = np.mean(label_np == tmp)
valid_loss = np.array([loss, precision[0], balance_acc, 0.0, prob, label])
return valid_loss
def run_pretrain(config):
if config.rgb:
model = config.model + '_rgb'
else:
model = config.model + '_6channel'
model_name = model+'_'+str(config.image_size)+'_fold'+str(config.train_fold_index)+'_'+config.tag
base_lr = 3e-3
config.train_epoch = 160
def adjust_lr_and_hard_ratio(optimizer, ep):
if ep < 100:
lr = 3e-4
hard_ratio = 1 * 1e-2
elif ep< 140:
lr = 1e-4
hard_ratio = 4 * 1e-3
else:
lr = 1e-5
hard_ratio = 4 * 1e-3
for p in optimizer.param_groups:
p['lr'] = lr
return lr, hard_ratio
batch_size = config.batch_size
## setup -----------------------------------------------------------------------------
out_dir = os.path.join(model_save_path, model_name)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
if not os.path.exists(os.path.join(out_dir,'checkpoint')):
os.makedirs(os.path.join(out_dir,'checkpoint'))
if not os.path.exists(os.path.join(out_dir,'train')):
os.makedirs(os.path.join(out_dir,'train'))
if not os.path.exists(os.path.join(out_dir,'backup')):
os.makedirs(os.path.join(out_dir,'backup'))
if config.pretrained_model is not None:
initial_checkpoint = os.path.join(out_dir, 'checkpoint', config.pretrained_model)
else:
initial_checkpoint = None
train_dataset = Dataset_(path_data + '/train_fold_'+str(config.train_fold_index)+'.csv',
data_dir,
mode='train',
image_size=config.image_size,
rgb=config.rgb,
augment=[0,0,0])
train_loader = DataLoaderX(train_dataset,
shuffle = True,
batch_size = batch_size,
drop_last = True,
num_workers = 8,
pin_memory = True)
valid_dataset = Dataset_(path_data + '/valid_fold_'+str(config.train_fold_index)+'.csv',
data_dir,
mode='valid',
image_size=config.image_size,
rgb=config.rgb,
augment=[0,0,0])
valid_loader = DataLoaderX(valid_dataset,
shuffle = False,
batch_size = batch_size,
drop_last = False,
num_workers = 8,
pin_memory = True)
net = get_model(model)
## optimiser ----------------------------------
net = torch.nn.DataParallel(net)
print(net)
net = net.cuda()
log = open(out_dir+'/log.pretrain.txt', mode='a')
log.write('\t__file__ = %s\n' % __file__)
log.write('\tout_dir = %s\n' % out_dir)
log.write('\n')
log.write('\t<additional comments>\n')
log.write('\t ... xxx baseline ... \n')
log.write('\n')
## dataset ----------------------------------------
log.write('** dataset setting **\n')
assert(len(train_dataset)>=batch_size)
log.write('batch_size = %d\n'%(batch_size))
log.write('train_dataset : \n%s\n'%(train_dataset))
log.write('valid_dataset : \n%s\n'%(valid_dataset))
log.write('\n')
## net ----------------------------------------
log.write('** net setting **\n')
if initial_checkpoint is not None:
log.write('\tinitial_checkpoint = %s\n' % initial_checkpoint)
net.load_state_dict(torch.load(initial_checkpoint, map_location=lambda storage, loc: storage))
print('\tinitial_checkpoint = %s\n' % initial_checkpoint)
log.write('%s\n'%(type(net)))
log.write('\n')
if config.bias_no_decay:
print('bias no decay !!!!!!!!!!!!!!!!!!')
train_params = split_weights(net)
else:
train_params = filter(lambda p: p.requires_grad, net.parameters())
optimizer = torch.optim.Adam(train_params, lr=base_lr, betas=(0.9, 0.999), eps=1e-08, weight_decay=0.0002)
iter_smooth = 20
start_iter = 0
log.write('\n')
## start training here! ##############################################
log.write('** top_n step 100,60,60,60 **\n')
log.write('** start training here! **\n')
log.write(' |------------ VALID -------------|-------- TRAIN/BATCH ----------| \n')
log.write('rate iter epoch | loss acc-1 acc-5 lb | loss acc-1 acc-5 lb | time \n')
log.write('----------------------------------------------------------------------------------------------------\n')
print('** start training here! **\n')
print(' |------------ VALID -------------|-------- TRAIN/BATCH ----------| \n')
print('rate iter epoch | loss acc-1 acc-5 lb | loss acc-1 acc-5 lb | time \n')
print('----------------------------------------------------------------------------------------------------\n')
valid_loss = np.zeros(6,np.float32)
batch_loss = np.zeros(6,np.float32)
i = 0
start = timer()
max_valid = 0
for epoch in range(config.train_epoch):
sum_train_loss = np.zeros(6,np.float32)
sum = 0
optimizer.zero_grad()
rate, hard_ratio = adjust_lr_and_hard_ratio(optimizer, epoch + 1)
print('change lr: '+str(rate))
for input, truth_, _ in train_loader:
iter = i + start_iter
# one iteration update -------------
net.train()
input = input.cuda()
truth_ = truth_.cuda()
input = to_var(input)
truth_ = to_var(truth_)
if config.is_arc:
logit = net(input, truth_)
else:
logit = net(input)
loss = softmax_loss(logit, truth_) * config.softmax_w
_, precision = metric(logit, truth_)
loss.backward()
optimizer.step()
optimizer.zero_grad()
batch_loss[:4] = np.array((loss.data.cpu().numpy(),
precision[0].data.cpu().numpy(),
precision[2].data.cpu().numpy(),
loss.data.cpu().numpy())).reshape([4])
sum_train_loss += batch_loss
sum += 1
if iter%iter_smooth == 0:
sum_train_loss = np.zeros(6,np.float32)
sum = 0
if i % 10 == 0:
print(model_name + ' %0.7f %5.1f %6.1f | %0.3f %0.3f %0.3f (%0.3f)%s | %0.3f %0.3f %0.3f (%0.3f) | %s' % (\
rate, iter, epoch,
valid_loss[0], valid_loss[1], valid_loss[2], valid_loss[3],' ',
batch_loss[0], batch_loss[1], batch_loss[2], batch_loss[3],
time_to_str((timer() - start),'min')))
if i % 100 == 0:
log.write('%0.7f %5.1f %6.1f | %0.3f %0.3f %0.3f (%0.3f)%s | %0.3f %0.3f %0.3f (%0.3f) | %s' % (\
rate, iter, epoch,
valid_loss[0], valid_loss[1], valid_loss[2], valid_loss[3],' ',
batch_loss[0], batch_loss[1], batch_loss[2], batch_loss[3],
time_to_str((timer() - start),'min')))
log.write('\n')
i=i+1
if i%1000==0:
net.eval()
valid_loss = do_valid(net, valid_loader)
net.train()
if max_valid < valid_loss[2]:
max_valid = valid_loss[2]
print('save max valid!!!!!! : ' + str(max_valid))
log.write('save max valid!!!!!! : ' + str(max_valid))
log.write('\n')
torch.save(net.state_dict(), out_dir + '/checkpoint/max_valid_model.pth')
if (epoch+1) % config.iter_save_interval ==0 and epoch>0:
torch.save(net.state_dict(), out_dir + '/checkpoint/%08d_model.pth' % (epoch))
net.eval()
valid_loss = do_valid(net, valid_loader)
net.train()
if max_valid < valid_loss[2]:
max_valid = valid_loss[2]
print('save max valid!!!!!! : ' + str(max_valid))
log.write('save max valid!!!!!! : ' + str(max_valid))
log.write('\n')
torch.save(net.state_dict(), out_dir + '/checkpoint/max_valid_model.pth')
def run_finetune_CELL_cyclic_semi_final(config,
cell,
tag = None,
fold_index = 0,
epoch_ratio = 1.0,
online_pseudo = True):
if config.rgb:
model = config.model + '_rgb'
else:
model = config.model + '_6channel'
model_name = model + '_' + str(config.image_size)+'_fold'+str(config.train_fold_index)+'_'+config.tag
batch_size = config.batch_size
## setup -----------------------------------------------------------------------------
out_dir = os.path.join(model_save_path, model_name)
print(out_dir)
if config.pretrained_model is not None:
initial_checkpoint = os.path.join(out_dir, 'checkpoint', config.pretrained_model)
else:
initial_checkpoint = None
train_dataset = Dataset_(path_data + r'/train_fold_'+str(fold_index)+'.csv',
img_dir=data_dir,
mode='train',
image_size=config.image_size,
rgb=config.rgb,
cell = cell,
augment=[0,0,0],
is_TransTwice=True)
semi_valid_dataset = Dataset_(path_data + r'/valid_fold_'+str(fold_index)+'.csv',
img_dir=data_dir,
mode='semi_valid',
image_size=config.image_size,
rgb=config.rgb,
cell=cell,
is_TransTwice=True)
infer_dataset = Dataset_(data_dir + '/test.csv',
data_dir,
image_size=config.image_size,
mode='semi_test',
rgb=config.rgb,
cell=cell,
is_TransTwice=True)
train_loader = DataLoader(train_dataset + semi_valid_dataset + infer_dataset,
shuffle = True,
batch_size = batch_size,
drop_last = True,
num_workers = 8,
pin_memory = True)
pseudo_loader = DataLoader(semi_valid_dataset + infer_dataset,
shuffle = False,
batch_size = batch_size,
drop_last = False,
num_workers = 8,
pin_memory = True)
valid_dataset = Dataset_(path_data + r'/valid_fold_'+str(fold_index)+'.csv',
img_dir=data_dir,
mode='valid',
image_size=config.image_size,
rgb=config.rgb,
cell=cell,
augment=[0,0,0])
valid_loader = DataLoader(valid_dataset,
shuffle = False,
batch_size = batch_size,
drop_last = False,
num_workers = 8,
pin_memory = True)
out_dir = os.path.join(out_dir, 'checkpoint')
if not os.path.exists(out_dir):
os.makedirs(out_dir)
if tag is not None:
cell += '_' + tag
log = open(out_dir+'/log.'+cell+'_finetune_train.txt', mode='a')
log.write('\t__file__ = %s\n' % __file__)
log.write('\tout_dir = %s\n' % out_dir)
log.write('\n')
log.write('\t<additional comments>\n')
log.write('\t ... xxx baseline ... \n')
log.write('\n')
## dataset ----------------------------------------
log.write('** dataset setting **\n')
assert(len(train_dataset)>=batch_size)
log.write('batch_size = %d\n'%(batch_size))
log.write('train_dataset : \n%s\n'%(train_dataset))
log.write('valid_dataset : \n%s\n'%(valid_dataset))
log.write('\n')
net = get_model(model)
ema_net = get_model(model)
for param in ema_net.parameters():
param.detach_()
## net ----------------------------------------
log.write('** net setting **\n')
if initial_checkpoint is not None:
log.write('\tinitial_checkpoint = %s\n' % initial_checkpoint)
net.load_pretrain(initial_checkpoint, is_skip_fc=True)
ema_net.load_pretrain(initial_checkpoint, is_skip_fc=False)
print('\tinitial_checkpoint = %s\n' % initial_checkpoint)
## optimiser ----------------------------------
net = torch.nn.DataParallel(net)
net = net.cuda()
ema_net = torch.nn.DataParallel(ema_net)
ema_net = ema_net.cuda()
log.write('%s\n'%(type(net)))
log.write('\n')
if config.bias_no_decay:
print('bias no decay')
train_params = split_weights(net)
else:
train_params = filter(lambda p: p.requires_grad, net.parameters())
iter_smooth = 20
start_iter = 0
log.write('\n')
## start training here! ##############################################
log.write('** top_n step 100,60,60,60 **\n')
log.write('** start training here! **\n')
log.write(' |------------ VALID -------------|-------- TRAIN/BATCH ----------| \n')
log.write('rate iter epoch | loss acc-1 acc-5 lb | loss acc-1 acc-5 lb | time \n')
log.write('----------------------------------------------------------------------------------------------------\n')
print('** start training here! **\n')
print(' |------------ VALID -------------|-------- TRAIN/BATCH ----------| \n')
print('rate iter epoch | loss acc-1 acc-5 lb | loss acc-1 acc-5 lb | time \n')
print('----------------------------------------------------------------------------------------------------\n')
##### pay attention to this
all_semi_ids = list(semi_valid_dataset.records['id_code']) + list(infer_dataset.records['id_code'])
pseudo_logit = np.zeros([len(infer_dataset) + len(semi_valid_dataset), 1108])
pseudo_labels = np.zeros([len(infer_dataset) + len(semi_valid_dataset), 1])
def get_pseudo_labels(pseudo_labels, ids, all_semi_ids):
indexs = [all_semi_ids.index(tmp) for tmp in ids]
labels = np.argmax(pseudo_labels, axis=1)
batch_labels = torch.LongTensor(np.asarray(labels[indexs]))
return batch_labels
def update_pseudo_labels(pseudo_logit, ids, ratio = 0.9, tta_num = 1, iters = 3000):
probs_all = np.zeros([len(infer_dataset) + len(valid_dataset), 1108])
for i in range(tta_num):
pseudo_num = 0
probs = []
with torch.no_grad():
for _, input, _, _, _ in pseudo_loader:
input = input.cuda()
input = to_var(input)
logit = ema_net.forward(input)
logit = logit[:, 0: 1108]
probs.append(logit)
pseudo_num += len(input)
assert (pseudo_num == len(pseudo_loader.sampler))
probs_ = torch.cat(probs).cpu().numpy().reshape([pseudo_num, 1108])
probs_all += probs_ / tta_num
pseudo_logit = pseudo_logit * (1-ratio) + probs_all * ratio
pseudo_labels, ids = balance_plate_probability_training(pseudo_logit, ids, plate_dict, a_dict, iters)
return pseudo_logit, pseudo_labels
i = 0
start = timer()
base_lr = 1e-1
optimizer = torch.optim.SGD(train_params, lr=base_lr, weight_decay=0.0002)
cycle_inter = int(40 * epoch_ratio)
cycle_num = 4
sgdr = CosineAnnealingLR_with_Restart(optimizer,
T_max=cycle_inter,
T_mult=1,
model=net,
out_dir='../input/',
take_snapshot=False,
eta_min=1e-3)
last_lr = 1e-4
last_epoch = 5
epoch_all = 0
epoch_stop = cycle_inter * cycle_num + last_epoch
max_valid_all = 0
max_valid_ema = 0
valid_loss_ema = np.zeros(6, np.float32)
for cycle_index in range(cycle_num+1):
print('cycle index: ' + str(cycle_index))
max_valid = 0
valid_loss = np.zeros(6, np.float32)
batch_loss = np.zeros(6, np.float32)
for epoch in range(cycle_inter):
epoch_all += 1
if epoch_all > epoch_stop:
return
elif epoch_all > cycle_inter * cycle_num:
for p in optimizer.param_groups:
p['lr'] = last_lr
rate = last_lr
else:
sgdr.step()
rate = optimizer.param_groups[0]['lr']
print('change lr: ' + str(rate))
net.eval()
valid_loss = do_valid(net, valid_loader)
net.train()
ema_net.eval()
valid_loss_ema = do_valid(ema_net, valid_loader)
ema_net.train()
if max_valid < valid_loss[2] and epoch > 0 :
max_valid = valid_loss[2]
print('save max valid!!!!!! : ' + str(max_valid))
log.write('save max valid!!!!!! : ' + str(max_valid))
log.write('\n')
if max_valid_all < valid_loss[2] and epoch > 0 :
max_valid_all = valid_loss[2]
print('save max valid all!!!!!! : ' + str(max_valid_all))
log.write('save max valid all!!!!!! : ' + str(max_valid_all))
log.write('\n')
torch.save(net.state_dict(), out_dir + '/max_valid_model_' + cell +
'_snapshot_all.pth')
if epoch_all == (10 * 2 * epoch_ratio - 1):
iters_balance = 500
pseudo_logit, pseudo_labels = update_pseudo_labels(pseudo_logit,
all_semi_ids,
ratio=1.0,
tta_num=4,
iters=iters_balance)
if max_valid_ema < valid_loss_ema[2] and epoch > 0 :
max_valid_ema = valid_loss_ema[2]
print('save max valid ema!!!!!! : ' + str(max_valid_ema))
log.write('save max valid ema!!!!!! : ' + str(max_valid_ema))
log.write('\n')
torch.save(net.state_dict(), out_dir + '/max_valid_model_' + cell +
'_snapshot_ema.pth')
if online_pseudo:
print('update_pseudo_labels')
if epoch_all > (10*2*epoch_ratio-1):
iters_balance = 500
pseudo_logit, pseudo_labels = update_pseudo_labels(pseudo_logit,
all_semi_ids,
ratio=0.9,
tta_num=4,
iters=iters_balance)
elif epoch_all > (20*2*epoch_ratio-1):
iters_balance = 1000
pseudo_logit, pseudo_labels = update_pseudo_labels(pseudo_logit,
all_semi_ids,
ratio=0.9,
tta_num=4,
iters=iters_balance)
elif epoch_all > (40*2*epoch_ratio-1):
iters_balance = 3000
pseudo_logit, pseudo_labels = update_pseudo_labels(pseudo_logit,
all_semi_ids,
ratio=0.9,
tta_num=8,
iters=iters_balance)
sum_train_loss = np.zeros(6,np.float32)
sum = 0
optimizer.zero_grad()
for input, input_easy, input_hard, truth_, id in train_loader:
iter = i + start_iter
# one iteration update -------------
net.train()
input = input.cuda()
input_easy = input_easy.cuda()
input_hard = input_hard.cuda()
truth_ = truth_.cuda()
input = to_var(input)
input_easy = to_var(input_easy)
input_hard = to_var(input_hard)
truth_ = to_var(truth_)
indexs_supervised = (truth_ != -1).nonzero().view(-1)
indexs_semi = (truth_ == -1).nonzero().view(-1)
if len(indexs_semi) == 0 or len(indexs_supervised) == 0 :
continue
if config.is_arc:
logit_arc = net(input[indexs_supervised], truth_[indexs_supervised])
logit = net(input_easy)
ema_logit = ema_net(input_hard)
##################### consistency loss ########################################################################
consistency = 100.0
consistency_rp = 5
if consistency:
consistency_weight = get_current_consistency_weight(epoch_all, consistency, consistency_rp)
ema_logit = Variable(ema_logit.detach().data, requires_grad=False)
consistency_loss = consistency_weight * softmax_mse_loss(logit, ema_logit) / batch_size
else:
consistency_loss = 0
##################### online_pseudo label #####################################################################
if online_pseudo:
if epoch_all < (10*2*epoch_ratio):
weight = 0.0
else:
weight = 0.05
id_smi = [id[index] for index in list(indexs_semi.cpu().numpy().reshape([-1]))]
id_smi_pseudo = get_pseudo_labels(pseudo_labels, id_smi, all_semi_ids).cuda()
id_smi_pseudo = to_var(id_smi_pseudo)
pseudo_loss = softmax_loss(logit[indexs_semi], id_smi_pseudo) * weight
pseudo_loss_log = pseudo_loss.data.cpu().numpy()
supervised_loss = softmax_loss(logit_arc, truth_[indexs_supervised]) * (1.0 - weight)
else:
pseudo_loss = 0.0
pseudo_loss_log = 0.0
supervised_loss = softmax_loss(logit_arc, truth_[indexs_supervised])
##################### loss ####################################################################################
loss = supervised_loss + consistency_loss + pseudo_loss
_, precision = metric(logit_arc, truth_[indexs_supervised])
loss.backward()
optimizer.step()
optimizer.zero_grad()
batch_loss[:4] = np.array((precision[0].data.cpu().numpy(),
supervised_loss.data.cpu().numpy(),
pseudo_loss_log,
consistency_loss.data.cpu().numpy())).reshape([4])
sum_train_loss += batch_loss
sum += 1
if epoch_all > 20:
alpha_ema = 0.999
elif epoch_all > 10:
alpha_ema = 0.99
elif epoch_all > 1:
alpha_ema = 0.9
else:
alpha_ema = 0.5
update_ema_variables(net, ema_net, alpha_ema, i)
if iter%iter_smooth == 0:
sum_train_loss = np.zeros(6,np.float32)
sum = 0
if i % 10 == 0:
print(model_name +' finetune '+ cell +'%6.1f %0.7f %5.1f %6.1f | %0.3f %0.3f %0.3f (%0.3f)%s | %0.3f %0.3f %0.3f (%0.3f)%s | %0.3f %0.3f %0.3f (%0.3f) | %s' % (\
cycle_index, rate, iter, epoch,
valid_loss[0], valid_loss[1], valid_loss[2], valid_loss[3],' ',
valid_loss_ema[0], valid_loss_ema[1], valid_loss_ema[2], valid_loss_ema[3], ' ',
batch_loss[0], batch_loss[1], batch_loss[2], batch_loss[3],
time_to_str((timer() - start),'min')))
if i % 100 == 0:
log.write('%6.1f %0.7f %5.1f %6.1f | %0.3f %0.3f %0.3f (%0.3f)%s |%0.3f %0.3f %0.3f (%0.3f)%s | %0.3f %0.3f %0.3f (%0.3f) | %s' % (\
cycle_index, rate, iter, epoch,
valid_loss[0], valid_loss[1], valid_loss[2], valid_loss[3],' ',
valid_loss_ema[0], valid_loss_ema[1], valid_loss_ema[2], valid_loss_ema[3], ' ',
batch_loss[0], batch_loss[1], batch_loss[2], batch_loss[3],
time_to_str((timer() - start),'min')))
log.write('\n')
i=i+1
def run_infer_oof_finetuned(config, cell, initial_checkpoint):
if config.rgb:
model = config.model + '_rgb'
else:
model = config.model + '_6channel'
batch_size = config.batch_size
## setup -----------------------------------------------------------------------------
net = get_model(model)
net = torch.nn.DataParallel(net)
if initial_checkpoint is not None:
print(initial_checkpoint)
net.load_state_dict(torch.load(initial_checkpoint, map_location=lambda storage, loc: storage))
net = net.cuda()
net.eval()
augments = []
# 8TTA
augments.append([0, 0, 0])
augments.append([0, 0, 1])
augments.append([0, 1, 0])
augments.append([0, 1, 1])
augments.append([1, 0, 0])
augments.append([1, 0, 1])
augments.append([1, 1, 0])
augments.append([1, 1, 1])
predict_num = 1108
def get_valid_label(fold=0):
df = pd.read_csv(path_data + '/valid_fold_' + str(fold) + '.csv')
if cell is not None:
df_all = []
for i in range(100):
df_ = pd.DataFrame(df.loc[df['experiment'] == (cell + '-' + str(100 + i)[-2:])])
df_all.append(df_)
df = pd.concat(df_all)
val_label = np.asarray(list(df[r'sirna'])).reshape([-1])
return val_label, list(df[r'id_code'])
val_label, ids = get_valid_label(fold=config.train_fold_index)
#####################################infer valid#############################################
probs_all = []
for index in range(len(augments)):
for site in range(2):
valid_dataset = Dataset_(path_data + '/valid_fold_' + str(config.train_fold_index) + '.csv',
data_dir,
mode='valid',
image_size=config.image_size,
site=site+1,
rgb=config.rgb,
cell=cell,
augment=augments[index])
valid_loader = DataLoaderX(valid_dataset,
shuffle=False,
batch_size=batch_size,
drop_last=False,
num_workers=4,
pin_memory=True)
# infer test
test_ids = []
probs = []
from tqdm import tqdm
for i,(input, label, id) in enumerate(tqdm(valid_loader)):
test_ids += id
input = input.cuda()
input = to_var(input)
logit = net.forward(input)
logit = logit[:, 0:predict_num]
prob = logit
probs += prob.data.cpu().numpy().tolist()
probs = np.asarray(probs)
val_ = np.argmax(probs, axis=1).reshape([-1])
print(np.mean(val_ == val_label))
probs_all.append(probs)
probs_all = np.mean(probs_all,axis=0)
print(probs_all.shape)
val_ = np.argmax(probs_all, axis=1).reshape([-1])
print(np.mean(val_ == val_label))
save_path = initial_checkpoint.replace('.pth', '_npy')
if not os.path.exists(save_path):
os.makedirs(save_path)
save_path = os.path.join(save_path, cell+'_val.npy')
print(save_path)
np.save(save_path, probs_all)
# return
#####################################infer test#############################################
probs_all = []
for index in range(len(augments)):
for site in range(2):
valid_dataset = Dataset_(data_dir+'/test.csv',
data_dir,
mode='test',
image_size=config.image_size,
site=site+1,
rgb=config.rgb,
cell=cell,
augment=augments[index])
valid_loader = DataLoaderX(valid_dataset,
shuffle=False,
batch_size=batch_size,
drop_last=False,
num_workers=4,
pin_memory=True)
# infer test
test_ids = []
probs = []
from tqdm import tqdm
for i,(id, input) in enumerate(tqdm(valid_loader)):
test_ids += id
input = input.cuda()
input = to_var(input)
logit = net.forward(input)
logit = logit[:, 0:predict_num]
prob = logit
probs += prob.data.cpu().numpy().tolist()
probs = np.asarray(probs)
probs_all.append(probs)
probs_all = np.mean(probs_all,axis=0)
print(probs_all.shape)
save_path = initial_checkpoint.replace('.pth', '_npy')
if not os.path.exists(save_path):
os.makedirs(save_path)
save_path = os.path.join(save_path, cell + '_test.npy')
print(save_path)
np.save(save_path, probs_all)
def main(config):
if config.mode == 'pretrain':
# pretrain on all cell types
run_pretrain(config)
elif config.mode == 'semi_finetune':
# semi-supervised learning finetune on each cell type
cell_types = ['U2OS','RPE','HEPG2','HUVEC']
epoch_ratios = [ 1.0, 0.5, 0.5, 0.25]
for cell, epoch_ratio in zip(cell_types, epoch_ratios):
config.pretrained_model = r'max_valid_model.pth'
config.finetune_tag = r'semi'
run_finetune_CELL_cyclic_semi_final(config,
cell = cell,
fold_index = config.train_fold_index,
tag = config.finetune_tag,
epoch_ratio = epoch_ratio,
online_pseudo = True)
elif config.mode == 'infer':
if config.rgb:
model = config.model + '_rgb'
else:
model = config.model + '_6channel'
cell_types = ['U2OS','RPE','HEPG2','HUVEC']
for cell in cell_types:
model_name = model + '_' + str(config.image_size) + '_fold' + str(config.train_fold_index) + '_' + config.tag
out_dir = os.path.join(model_save_path, model_name)
initial_checkpoint = os.path.join(out_dir, 'checkpoint', 'max_valid_model_' + cell + '_semi_snapshot_all.pth')
with torch.no_grad():
run_infer_oof_finetuned(config, cell=cell, initial_checkpoint=initial_checkpoint)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--tag', type=str, default='final')
parser.add_argument('--train_fold_index', type=int, default = 0)
parser.add_argument('--rgb', type=bool, default=False)
parser.add_argument('--is_arc', type=bool, default=True)
parser.add_argument('--arc_s', type=float, default=30.0)
parser.add_argument('--arc_m', type=float, default=0.1)
parser.add_argument('--bias_no_decay', type=bool, default=False)
parser.add_argument('--model', type=str, default='xception_large')
parser.add_argument('--batch_size', type=int, default=1)
parser.add_argument('--image_size', type=int, default=512)
parser.add_argument('--softmax_w', type=float, default=1.0)
parser.add_argument('--mode', type=str, default='pretrain', choices=['pretrain', 'semi_finetune', 'infer'])
parser.add_argument('--pretrained_model', type=str, default=None)
parser.add_argument('--iter_save_interval', type=int, default=10)
parser.add_argument('--train_epoch', type=int, default=80)
config = parser.parse_args()
print(config)
main(config)