def load_model(file_prefix):
"""
loads model and associated config file.
:param file_prefix: file name prefix (without extension) of the model and config file name.
:return: The model and config files if they exist, None otherwise
"""
dir_path = os.path.dirname(os.path.realpath(__file__))
predicate = os.path.join(dir_path, '..\\data', file_prefix)
full_path_model = predicate + ".pth"
full_path_cfg = predicate + ".txt"
exists = os.path.isfile(full_path_model)
if exists:
with open(full_path_cfg) as f:
cfg = f.read()
# load config file
cfg = cfg.replace("'", '"')
cfg = json.loads(cfg)
num_covariates = cfg['num_covariates']
num_time_idx = cfg['num_time_idx']
num_targets = cfg['num_targets']
input_dim = num_time_idx + num_targets + num_covariates
# create model object
model_ = model.model(num_lstms=cfg['num_lstms'],
input_dim=input_dim,
output_dim=cfg['num_targets'],
hidden_dim=cfg['hidden_dim'])
f.close()
# load weights and populate state dict for the model
model_.load_state_dict(torch.load(full_path_model))
return model_, cfg
else:
return None
def execParallelGA(year, region, qntYears=5, times=1):
"""
Creates the GAModel with SC catalog with parallel and
distributed island model
"""
observations = list()
means = list()
for i in range(qntYears):
observation = model.loadModelDB(region + 'jmaData', year + i)
observation.bins = observation.bins.tolist()
observations.append(observation)
means.append(observation.bins)
mean = np.mean(means, axis=0)
for i in range(times):
model_ = model.model()
model_ = parallelGA.gaModel(
NGEN=100,
CXPB=0.9,
MUTPB=0.1,
modelOmega=observations,
year=year +
qntYears,
region=region,
mean=mean,
FREQ=10,
tournsize=2,
n_aval=50000)
model_.executionNumber = i
model_.year = year + qntYears
model_.modelName = region + 'parallelGA'
def main():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
TEXT = data.Field()
LABEL = data.Field(sequential=False, dtype=torch.long)
train, val, test = datasets.SST.splits(TEXT,
LABEL,
fine_grained=True,
train_subtrees=False)
LABEL.build_vocab(train)
train_iter, val_iter, test_iter = data.BucketIterator.splits(
(train, val, test), batch_size=8, device=device)
TEXT.build_vocab(train, vectors=Vectors(name="vector.txt", cache="./data"))
criterion = nn.CrossEntropyLoss()
criterion = criterion.to(device)
INPUT_DIM = len(TEXT.vocab)
EMBEDDING_DIM = 300
HIDDEN_DIM = 768
OUTPUT_DIM = 5
for file in os.listdir("./trained_models"):
print(file)
checkpoint = torch.load("./trained_models/" + file)
print(checkpoint)
for k in checkpoint:
print(k)
model = md.model(file[:-9])(INPUT_DIM, EMBEDDING_DIM, HIDDEN_DIM,
OUTPUT_DIM).load_state_dict(checkpoint)
model = model.to(device)
# Test
test_loss, test_acc = solver.evaluate(model, test_iter, criterion)
print(file[:-9] +
f" Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%")
def execParallelGA(year, region, qntYears=5, times=10):
"""
Creates the GAModel with SC catalog with parallel and distributed island model
"""
observations = list()
means = list()
for i in range(qntYears):
observation = model.loadModelDB(region+'jmaData', year+i)
aux = model.loadModelFromFile('../../Zona3/realSCwithP_AVR/'
+ region + 'real' + "_" + str(year + i) + '.txt')
aux.values4poisson = [x+1 for x in aux.values4poisson]
observation.values4poisson = aux.values4poisson
del aux
observation.bins = observation.bins.tolist()
observations.append(observation)
means.append(observation.bins)
mean = np.mean(means, axis=0)
for i in range(times):
model_=model.model()
model_ = parallelGAModelP_AVR.gaModel(
NGEN=10,
CXPB=0.9,
MUTPB=0.1,
modelOmega=observations,
year=year +
qntYears,
region=region,
mean=mean,
n_aval=50)
model_.executionNumber=i
model_.year=year+qntYears
model_.modelName = region+'parallelGA'
parallelGA_ = model.loadModelDB(region+'parallelGA', year)
def train(args):
# Pre-processing
imdbMat = scipy.io.loadmat('imdb_crop/imdb.mat')
imdbPlace = imdbMat['imdb'][0][0]
place = imdbPlace
# Todo remove redundant variable
# place = imdbMat['imdb'][0][0]
where = 'imdb_crop'
img_loc = []
corr_ages = []
for i in range(460723):
# print(place[0][0][i])
bYear = int(place[0][0][i] / 365) # birth year
# print(bYear)
taken = place[1][0][i] # photo taken
# print(taken)
path = place[2][0][i][0]
age = taken - bYear
img_loc.append(os.path.join(where, path))
corr_ages.append(age)
df = pd.DataFrame(img_loc, columns=['Image Location'])
df['Age'] = corr_ages
# Training
if args.pre_trained == 'facenet':
from models.Face_recognition import FR_model
FR = FR_model()
Model = model()
Model.compile(loss='mean_absolute_error', optimizer='adam')
# training loop
length = len(df)
print("length are {}".format(length))
assert length > 0
batch_size = args.batch_size
n_batches = length // batch_size
epochs = args.epochs
iters = (int)(epochs * n_batches)
assert iters > 0
print("iters are {}".format(iters))
for i in range(iters):
X, Y = get_images(batch_size, df['Image Location'], df['Age'], (args.img_size, args.img_size))
X = np.array(X)
Y = np.array(Y)
X = FR(X)
assert X.shape == (batch_size, 128), 'expected shape {} O/p shape {}'.format((batch_size, 128), X.shape)
history = Model.fit(X, Y, batch_size, 1, verbose=0)
if (i + 1) % args.log_step == 0:
print("Iters [{}/{}] Loss {} Batch size {} ".format(i + 1, iters, history.history['loss'],
args.batch_size))
Model.save(args.save_path)
num_covariates = cfg['num_covariates']
num_time_idx = cfg['num_time_idx']
num_targets = cfg['num_targets']
input_dim = num_time_idx + num_targets + num_covariates
# Total context window size used for training. The context window consists of 1. conditioning_context where input
# data points are available and network predictions are conditioned on actual input data, and 2. prediction_context,
# where the network predictions are conditioned on network output at the previous step. Covariates are assumed
# to be available for the entire context window
ctx_win_len = cfg['ctx_win_len']
cond_win_len = cfg['cond_win_len']
pred_win_len = ctx_win_len - cond_win_len - 1
batch_size = cfg['batch_size']
model = model.model(num_lstms=cfg['num_lstms'],
input_dim=input_dim,
output_dim=cfg['num_targets'],
hidden_dim=cfg['hidden_dim']).to(device)
criterion = torch.nn.MSELoss()
optimizer = optim.Adam(model.parameters(), cfg['lr'])
scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=cfg['lr_step_size'],
gamma=cfg['lr_gamma'])
train_sampler, test_sampler = dataset.get_train_test_samplers(
cfg['train_test_split'])
train_dataloader = DataLoader(dataset,
batch_size=batch_size,
sampler=train_sampler,
shuffle=False,
def wrapper(param, data_x, data_y, length_x, learning_rate, lr_gamma,
hidden_dim, layers):
device = param['device']
model_name = param['model']
brain = param['brain_region']
input_dim = param['region_n']
n_epochs = param['n_epochs']
minmax_y = param['minmax_y']
rate_tr = param['rate_train']
rate_va = param['rate_valid']
rate_te = param['rate_test']
now_time = param['present_time']
train_num = param['number_train']
valid_num = param['number_valid']
test_num = param['number_test']
layer_rate = learning_rate
total_num = len(data_y)
if train_num % rate_tr != 0:
print('Please reset rate_train')
if valid_num % rate_va != 0:
print('Please reset rate_valid')
if test_num % rate_te != 0:
print('Please reset rate_test')
cwd = os.getcwd()
out_fname = f'{now_time}_h_{hidden_dim}_l_{layers}_lg_{lr_gamma}' \
f'_n_{n_epochs}_lr{layer_rate}_model{model_name}'
out_path = os.path.join(cwd, out_fname)
safe_make_dir(out_path)
temp_path = os.path.join(out_path, 'temp')
safe_make_dir(temp_path)
start = time.time() # Start Learning
print("Start Learning " + out_fname)
output_dim = 1
loss_list = []
step_list = []
epoch_list = []
if brain == 'right' or brain == 'left':
input_dim = input_dim // 2
mynet = model(param, input_dim, hidden_dim, output_dim, layers, device)
criterion = nn.MSELoss().to(device)
optimizer = torch.optim.Adam(mynet.parameters(), lr=layer_rate)
lr_sche = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=100, gamma=lr_gamma)
train_xdata = data_x[0:train_num, :, :]
train_ydata = data_y[0:train_num]
train_length_x = length_x[0:train_num]
valid_xdata = data_x[train_num:train_num + valid_num, :, :]
valid_ydata = data_y[train_num:train_num + valid_num]
valid_length_x = length_x[train_num:train_num + valid_num]
total_loss_valid_min = np.Inf
for i in range(n_epochs):
# Train
mynet.train()
loss = 0
for tr in range(int(train_num / rate_tr)):
train_x_tensor, train_y_tensor, train_length_tensor = train(
device, tr*rate_tr, rate_tr, train_xdata, train_ydata,
train_length_x)
if model_name != 'FC':
train_x_tensor = pack_padded_sequence(
train_x_tensor, train_length_tensor,
batch_first=True, enforce_sorted=False)
optimizer.zero_grad()
outputs = mynet(train_x_tensor)
loss_train = criterion(outputs, train_y_tensor)
loss_train.backward()
optimizer.step()
loss += float(loss_train)
epoch_list.append(i)
loss_list.append(loss)
step_list.append('train')
# Validation
mynet.eval()
valid_loss = 0
for va in range(int(valid_num / rate_va)):
valid_x_tensor, valid_y_tensor, valid_length_tensor = \
valid(device, va*rate_va, rate_va, valid_xdata,
valid_ydata, valid_length_x)
if model_name != 'FC':
valid_x_tensor = pack_padded_sequence(
valid_x_tensor, valid_length_tensor,
batch_first=True, enforce_sorted=False)
valid_result = mynet(valid_x_tensor)
loss_valid = criterion(valid_result, valid_y_tensor)
valid_loss = valid_loss + loss_valid
epoch_list.append(i)
loss_list.append(valid_loss.item())
step_list.append('validation')
if valid_loss.item() <= total_loss_valid_min:
torch.save(mynet.state_dict(), os.path.join(temp_path, 'model.pt'))
total_loss_valid_min = valid_loss.item()
lr_sche.step()
epoch_arr = np.array(epoch_list)
loss_arr = np.array(loss_list)
step_arr = np.array(step_list)
# Write loss values in csv file
write_loss(epoch_arr, loss_arr, step_arr, out_path)
# Plot train and validation losses
plot_train_val_loss(out_path, out_fname, dpi=800,
yscale='log', ylim=[0.0001, 10])
end = time.time() # Learning Done
print(f"Learning Done in {end-start}s")
# Test
mynet.load_state_dict(torch.load(os.path.join(temp_path, 'model.pt')))
mynet.eval()
with torch.no_grad():
test_x_tensor, test_y_tensor, test_length_tensor = get_tensor(
device, data_x, data_y, length_x, train_num + valid_num, total_num)
if model_name != 'FC':
test_x_tensor = pack_padded_sequence(
test_x_tensor, test_length_tensor,
batch_first=True, enforce_sorted=False)
test_result = mynet(test_x_tensor)
test_loss = criterion(test_result, test_y_tensor)
print(f"Test Loss: {test_loss.item()}")
plot_result(test_y_tensor, test_result, minmax_y, out_path, out_fname)
real_arr = normalize_tensor(test_y_tensor, minmax_y)[:, -1]
result_arr = normalize_tensor(test_result, minmax_y)[:, -1]
df_result = pd.DataFrame({'test_age': real_arr, 'real_age': result_arr})
df_result.to_csv(os.path.join(out_path, 'test_vs_real.csv'))
def main(argv):
data_path = None
input_image = None
output = None
weight_path = None
mode=5
drop_rate=0
lab=s.lab
classification=False
temp=.4
try:
opts, args = getopt.getopt(argv,"w:p:b:m:ld:ct:i:o:",["weight-path=", "datapath=",'model=','lab','drop-rate=','input=','output='])
except getopt.GetoptError as error:
print(error)
print( 'demo.py -w <path to weights file> -p <path to folder of images> OR -i <path to single image> -l <no argument. use if lab should be used>\
-d <amount of dropout used in model> -c <no argument. Use if model is classifier> -t <temperature for annealed mean> -o <output path for images>')
sys.exit(2)
print("opts", opts)
for opt, arg in opts:
if opt in ("-w", "--weight-path"):
weight_path = arg
elif opt in ("--datapath", "-p"):
data_path = arg
elif opt=='-m':
if arg in ('custom','0'):
mode = 0
elif arg in ('u','1','unet'):
mode = 1
elif arg in ('ende','2'):
mode = 2
elif arg in ('richzhang','classende','3'):
mode = 3
elif arg in ('colorunet','cu','4'):
mode = 4
elif arg in ('mu','5','middle'):
mode = 5
elif opt in ('-l','--lab'):
lab=True
elif opt in ("-d", "--drop-rate"):
drop_rate = float(arg)
elif opt =='-c':
classification=True
lab=True
elif opt=='-t':
temp=float(arg)
elif opt in ('-i','--input'):
input_image = arg
elif opt in ('-o','--output'):
output = arg
if data_path is None and input_image is None:
print('Please select an image or folder')
sys.exit()
trafo=transforms.Compose([transforms.Grayscale(3 if lab else 1), transforms.Resize((96,96))])
device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if data_path is not None:
dataset = ImageDataset(data_path,lab=lab,pretrafo=trafo)
loader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, num_workers=0)
if input_image is not None:
img=trafo(Image.open(input_image))
if lab:
img=color.rgb2lab(np.asarray(img)/255)[...,:1]-np.array([50])[None,None,:]
loader = [(transforms.ToTensor()(img)[None,...].float(),input_image)]
classes=(150 if classification else 2) if lab else 3
#define model
UNet=None
zoom=False
if mode == 0:
UNet=model(col_channels=classes)
elif mode ==1:
UNet=unet(drop_rate=drop_rate,classes=classes)
elif mode ==2:
UNet=generator(drop_rate,classes)
elif mode == 3:
UNet=richzhang(drop_rate,classes)
zoom=True
elif mode == 4:
UNet=color_unet(True,drop_rate,classes)
elif mode == 5:
UNet = middle_unet(True,drop_rate,classes)
#load weights
try:
UNet.load_state_dict(torch.load(weight_path, map_location=device))
print("Loaded network weights from", weight_path)
except FileNotFoundError:
print("Did not find weight files.")
sys.exit()
outpath=None
UNet.to(device)
UNet.eval()
with torch.no_grad():
for i,(X,name) in enumerate(loader):
X=X.to(device)
unet_col=UNet(X)
col=show_colorization(unet_col,original=X,lab=lab,cl=classification,zoom=zoom,T=temp,return_img=output is not None)
if output:
try:
fp,f=os.path.split(name)
except TypeError:
fp,f=os.path.split(name[0])
n,e=f.split('.')
f='.'.join((n+'_color','png'))
outpath=output if os.path.isdir(output) or os.path.isdir(os.path.basename(output)) else fp
Image.fromarray(toInt(col[0])).save(os.path.join(outpath,f))
if output:
print('Finished colorization. Go to "%s" to see the colorized version(s) of the image(s)'%os.path.realpath(outpath))
def main():
start_epoch = 1
saver_dir = mk_save(save_dir, cfg_dir)
logging = init_log(saver_dir)
_print = logging.info
################
# read dataset #
################
trainset, testset, trainloader, testloader = dataloader(data_dir, 0)
################
# define model #
################
net = model.model()
##########
# resume #
##########
if resume:
ckpt = torch.load(resume)
net_dict = net.state_dict()
pre_dict = {
k: v
for k, v in ckpt['state_dict'].items() if k in net_dict
}
net_dict.update(pre_dict)
net.load_state_dict(net_dict)
start_epoch = ckpt['epoch'] + 1
print('resume', start_epoch)
start_epoch = START_EPOCH
criterion = nn.CrossEntropyLoss().cuda()
kd = nn.KLDivLoss().cuda()
#####################
# TODO:num of parameters #
#####################
params_count(net)
#############################
#TODO: OPTIMIZER FOR BN SLIMMING #
#############################
slim_params = params_extract(net)
net = DataParallel(net.cuda())
for epoch in range(start_epoch, 500):
"""
########################## train the model ###############################
"""
_print('--' * 50)
net.train()
for i, data in enumerate(trainloader):
# warm up Learning rate
lr = warm_lr(i, epoch, trainloader)
########################
# Define Optimizer #
########################
optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad,
net.parameters()),
lr=lr,
momentum=0.9,
weight_decay=WD,
nesterov=True)
img, label = data[0].cuda(), data[1].cuda()
batch_size = img.size(0)
optimizer.zero_grad()
L1_norm = 0.
logits = net(img)
#############
# L1 penaty #
#############
L1_norm = sum([L1_penalty(m).cuda() for m in slim_params])
loss = criterion(logits, label) + LAMBDA1 * L1_norm
loss.backward()
optimizer.step()
progress_bar(i,
len(trainloader),
loss.item(),
L1_norm,
lr,
msg='train')
########################## evaluate net and save model ###############################
if epoch % SAVE_FREQ == 0:
"""
# evaluate net on train set
"""
net.eval()
train_loss, train_correct, total = test(trainloader)
train_acc = float(train_correct) / total
train_loss = train_loss / total
total_loss = total_loss / total
_print(
'epoch:{} - train_loss: {:.4f}, train acc: {:.4f}, L1:{:.4f}, lr:{:.6f}, total sample: {}'
.format(epoch, train_loss, train_acc, L1_norm, lr, total))
"""
# evaluate net on test set
"""
test_loss, test_correct, total = test(testloader)
test_acc = float(test_correct) / total
test_loss = test_loss / total
_print(
'epoch:{} - test loss: {:.4f} and test acc: {:.4f} total sample: {}'
.format(epoch, test_loss, test_acc, total))
########################## save model ###############################
net_state_dict = net.module.state_dict()
if not os.path.exists(save_dir):
os.mkdir(save_dir)
torch.save(
{
'epoch': epoch,
'train_loss': train_loss,
'train_acc': train_acc,
'test_loss': test_loss,
'test_acc': test_acc,
'L1': L1_norm,
'state_dict': net_state_dict
}, os.path.join(save_dir, '%03d.ckpt' % epoch))
print('finishing training')
def main(argv):
# setting argument defaults
mbsize = s.batch_size
report_freq = s.report_freq
weight_path = s.weights_path
weights_name = s.weights_name
lr = s.learning_rate
save_freq = s.save_freq
mode = 3
epochs = s.epochs
beta1, beta2 = s.betas
infinite_loop = s.infinite_loop
data_path = s.data_path
drop_rate = 0
lab = s.lab
load_list = s.load_list
help = 'train_regression.py -b <batch size> -e <amount of epochs to train. standard: infinite> -r <report frequency> -w <path to weights folder> \
-n <name> -s <save freq.> -l <learning rate> -p <path to data set> -d <dropout rate> -m <mode: differnet models> --beta1 <beta1 for adam>\
--beta2 <beta2 for adam> --lab <No argument. If used lab colorspace is used> \
--lambda <hyperparameter for class weights>'
try:
opts, args = getopt.getopt(argv, "he:b:r:w:l:s:n:m:p:d:i:", [
'epochs=', "mbsize=", "report-freq=", 'weight-path=', 'lr=',
'save-freq=', 'weight-name=', 'mode=', 'data_path=', 'drop_rate='
'beta1=', 'beta2=', 'lab', 'image-loss-weight=', 'load-list'
])
except getopt.GetoptError:
print(help)
sys.exit(2)
print("opts", opts)
for opt, arg in opts:
if opt == '-h':
print(help)
sys.exit()
elif opt in ("-b", "--mbsize"):
mbsize = int(arg)
elif opt in ("-e", "--epochs"):
epochs = int(arg)
infinite_loop = False
elif opt in ('-r', '--report-freq'):
report_freq = int(arg)
elif opt in ("-w", "--weight-path"):
weight_path = arg
elif opt in ("-n", "--weight-name"):
weights_name = arg
elif opt in ("-s", "--save-freq"):
save_freq = int(arg)
elif opt in ("-l", "--lr"):
lr = float(arg)
elif opt == '-m':
if arg in ('custom', '0'):
mode = 0
elif arg in ('u', '1', 'unet'):
mode = 1
elif arg in ('ende', '2'):
mode = 2
elif arg in ('color', '3', 'cu'):
mode = 3
elif opt in ("-p", "--data_path"):
data_path = str(arg)
elif opt in ("-d", "--drop_rate"):
drop_rate = float(arg)
elif opt == '--beta1':
beta1 = float(arg)
elif opt == '--beta2':
beta2 = float(arg)
elif opt == '--lab':
lab = True
elif opt in ('--load-list'):
load_list = True
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
dataset = None
if 'cifar' in data_path:
in_size = 32
dataset = 0
elif 'places' in data_path:
in_size = 224
dataset = 1
elif 'stl' in data_path:
in_size = 96
dataset = 2
in_shape = (3, in_size, in_size)
#out_shape=(s.classes,32,32)
betas = (beta1, beta2)
weight_path_ending = os.path.join(weight_path, weights_name + '.pth')
loss_path_ending = os.path.join(weight_path,
weights_name + "_" + s.loss_name)
trainset = load_trainset(data_path, lab=lab, load_list=load_list)
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=mbsize,
shuffle=True,
num_workers=2)
print("NETWORK PATH:", weight_path_ending)
#define output channels of the model
classes = 2 if lab else 3
#define model
UNet = None
if mode == 0:
UNet = model(col_channels=classes)
elif mode == 1:
UNet = unet(drop_rate=drop_rate, classes=classes)
elif mode == 2:
UNet = generator(drop_rate, classes)
elif mode == 3:
UNet = color_unet(True, drop_rate, classes)
#load weights
try:
UNet.load_state_dict(
torch.load(weight_path_ending, map_location=device))
print("Loaded network weights from", weight_path)
except FileNotFoundError:
print("Initialize new weights for the generator.")
UNet.to(device)
#save the hyperparameters to a JSON-file for better oranization
model_description_path_ending = os.path.join(weight_path,
s.model_description_name)
# initialize model dict
try:
with open(model_description_path_ending, "r") as file:
model_dict = json.load(file)
except FileNotFoundError:
model_dict = {}
prev_epochs = 0
# save settings in dict if new weights are beeing initialized
if not weights_name in model_dict.keys():
model_dict[weights_name] = {
"loss_name": loss_path_ending,
"epochs": 0,
"batch_size": mbsize,
"lr": lr,
"lab": lab,
"betas": betas,
"model": ['custom', 'unet', 'encoder-decoder', 'color-unet'][mode]
}
else:
#load specified parameters from model_dict
params = model_dict[weights_name]
mbsize = params['batch_size']
betas = params['betas']
lr = params['lr']
lab = params['lab']
loss_path_ending = params['loss_name']
#memorize how many epochs already were trained if we continue training
prev_epochs = params['epochs'] + 1
#optimizer
optimizer_g = optim.Adam(UNet.parameters(), lr=lr, betas=betas)
# l1 loss
l1loss = nn.L1Loss().to(device)
loss_hist = []
UNet.train()
gray = torch.tensor([0.2989, 0.5870, 0.1140])[:, None, None].float()
# run over epochs
for e in (range(prev_epochs, prev_epochs +
epochs) if not infinite_loop else count(prev_epochs)):
g_running = 0
#load batches
for i, batch in enumerate(trainloader):
if dataset == 0: #cifar 10
(image, _) = batch
elif dataset in (1, 2): #places and stl 10
image = batch
X = None
#differentiate between the two available color spaces RGB and Lab
if lab:
if dataset == 0: #cifar 10
image = np.transpose(image, (0, 2, 3, 1))
image = np.transpose(color.rgb2lab(image), (0, 3, 1, 2))
image = torch.from_numpy(
(image +
np.array([-50, 0, 0])[None, :, None, None])).float()
X = torch.unsqueeze(image[:, 0, :, :], 1).to(
device) #set X to the Lightness of the image
image = image[:,
1:, :, :].to(device) #image is a and b channel
else:
#convert to grayscale image
#using the matlab formula: 0.2989 * R + 0.5870 * G + 0.1140 * B and load data to gpu
X = (image.clone() * gray).sum(1).to(device).view(
-1, 1, *in_shape[1:])
image = image.float().to(device)
#----------------------------------------------------------------------------------------
################################### Unet optimization ###################################
#----------------------------------------------------------------------------------------
#clear gradients
optimizer_g.zero_grad()
#generate colorized version with unet
unet_col = None
#print(X.shape,image.shape,classes)
if mode == 0:
unet_col = UNet(torch.stack((X, X, X), 1)[:, :, 0, :, :])
else:
unet_col = UNet(X)
#calculate how close the generated pictures are to the ground truth
loss_g = l1loss(unet_col, image)
#backpropagation
loss_g.backward()
optimizer_g.step()
g_running += loss_g.item()
loss_hist.append([e, i, loss_g.item()])
#report running loss
if (i + len(trainloader) * e) % report_freq == report_freq - 1:
print('Epoch %i, batch %i: \tunet loss=%.2e' %
(e + 1, i + 1, g_running / report_freq))
g_running = 0
if s.save_weights and (
i + len(trainloader) * e) % save_freq == save_freq - 1:
#save parameters
try:
torch.save(UNet.state_dict(), weight_path_ending)
except FileNotFoundError:
os.makedirs(weight_path)
torch.save(UNet.state_dict(), weight_path_ending)
print("Parameters saved")
if s.save_loss:
#save loss history to file
try:
f = open(loss_path_ending, 'a')
np.savetxt(f, loss_hist, '%e')
f.close()
except FileNotFoundError:
os.makedirs(s.loss_path)
np.savetxt(loss_path_ending, loss_hist, '%e')
loss_hist = []
#update epoch count in dict after each epoch
model_dict[weights_name]["epochs"] = e
#save it to file
try:
with open(model_description_path_ending, "w") as file:
json.dump(model_dict, file, sort_keys=True, indent=4)
except:
print('Could not save to model dictionary (JSON-file)')
def main(argv):
data_path = s.data_path
weight_path = s.weights_path
mode = 1
drop_rate = 0
lab = s.lab
classification = False
temp = .4
try:
opts, args = getopt.getopt(argv, "h:w:p:b:m:ld:ct:", [
"help", "weight-path=", "datapath=", 'model=', 'lab', 'drop-rate='
])
except getopt.GetoptError as error:
print(error)
print(
'test.py -w <path to weights file> -p <path to dataset> -l <no argument. use if lab should be used> -m <mode: different models>\
-d <amount of dropout used in model> -c <no argument. Use if model is classifier> -t <temperature for annealed mean> -o <output path for images>'
)
sys.exit(2)
print("opts", opts)
for opt, arg in opts:
if opt == '-h':
print('test.py -i <Boolean> -s <Boolean>')
sys.exit()
elif opt in ("-w", "--weight-path"):
weight_path = arg
elif opt in ("--datapath", "-p"):
data_path = arg
elif opt in ("--batchnorm", "-b"):
batch_norm = arg in ["True", "true", "1"]
elif opt == '-m':
if arg in ('custom', '0'):
mode = 0
elif arg in ('u', '1', 'unet'):
mode = 1
elif arg in ('ende', '2'):
mode = 2
elif arg in ('richzhang', 'classende', '3'):
mode = 3
elif arg in ('colorunet', 'cu', '4'):
mode = 4
elif arg in ('mu', '5', 'middle'):
mode = 5
elif opt in ('-l', '--lab'):
lab = True
elif opt in ("-d", "--drop-rate"):
drop_rate = float(arg)
elif opt == '-c':
classification = True
lab = True
elif opt == '-t':
temp = float(arg)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
dataset = None
if data_path == './cifar-10':
in_size = 32
dataset = 0
elif 'places' in data_path:
in_size = 224
dataset = 1
elif 'stl' in data_path:
in_size = 96
dataset = 2
in_shape = (3, in_size, in_size)
#out_shape=(s.classes,32,32)
trainset = load_trainset(data_path, train=False, lab=lab)
trainloader = torch.utils.data.DataLoader(
trainset,
batch_size=3,
shuffle=True,
num_workers=2 if dataset in (0, 1) else 0)
print("Loaded dataset from", data_path)
classes = (150 if classification else 2) if lab else 3
#define model
UNet = None
zoom = False
if mode == 0:
UNet = model(col_channels=classes)
elif mode == 1:
UNet = unet(drop_rate=drop_rate, classes=classes)
elif mode == 2:
UNet = generator(drop_rate, classes)
elif mode == 3:
UNet = richzhang(drop_rate, classes)
zoom = True
elif mode == 4:
UNet = color_unet(True, drop_rate, classes)
elif mode == 5:
UNet = middle_unet(True, drop_rate, classes)
#load weights
try:
UNet.load_state_dict(torch.load(weight_path, map_location=device))
print("Loaded network weights from", weight_path)
except FileNotFoundError:
print("Did not find weight files.")
#sys.exit(2)
UNet.to(device)
UNet.eval()
gray = torch.tensor([0.2989, 0.5870, 0.1140])[:, None, None].float()
with torch.no_grad():
for i, batch in enumerate(trainloader):
if dataset == 0: #cifar 10
(image, _) = batch
elif dataset in (1, 2): #places
image = batch
X = None
if lab:
if dataset == 0: #cifar 10
image = np.transpose(image, (0, 2, 3, 1))
image = np.transpose(color.rgb2lab(image), (0, 3, 1, 2))
image = torch.from_numpy(
(image -
np.array([50, 0, 0])[None, :, None, None])).float()
X = torch.unsqueeze(image[:, 0, :, :], 1).to(
device) #set X to the Lightness of the image
image = image[:, 1:, :, :] #image is a and b channel
else:
#convert to grayscale image
#using the matlab formula: 0.2989 * R + 0.5870 * G + 0.1140 * B and load data to gpu
X = (image.clone() * gray).sum(1).to(device).view(
-1, 1, *in_shape[1:])
image = image.float()
#print(X.min(),X.max())
#generate colorized version with unet
#for arr in (image[:,0,...],image[:,1,...],X):
# print(arr.min(),arr.max())
try:
unet_col = UNet(X)
except:
unet_col = UNet(torch.stack((X, X, X), 1)[:, :, 0, :, :])
#for arr in (unet_col[0,...],unet_col[1,...]):
# print(arr.min().item(),arr.max().item())
show_colorization(unet_col,
image,
X,
lab=lab,
cl=classification,
zoom=zoom,
T=temp)
logger.addHandler(rf_handler)
amBegin = dt.datetime.combine(dt.datetime.now().date(),
dt.time(hour=9, minute=30, second=30))
amEnd = dt.datetime.combine(dt.datetime.now().date(),
dt.time(hour=11, minute=30, second=0))
pmBegin = dt.datetime.combine(dt.datetime.now().date(),
dt.time(hour=13, minute=0, second=0))
pmEnd = dt.datetime.combine(dt.datetime.now().date(),
dt.time(hour=15, minute=0, second=0))
buyBegin = dt.datetime.combine(dt.datetime.now().date(),
dt.time(hour=9, minute=44, second=25))
oneMinuteOpenTime = 0
allStocksOpen = None
model = model.model(log=logger)
dfcf = dfcf_data.IndustryData()
cyb_b = leastsq.cyb_result()
sz50_k, sz50_b = leastsq.sz50_result()
past_config = configparser.ConfigParser() # 注意大小写
past_config.read("config\\past.info") # 配置文件的路径
keep_alive_time = 0
while True:
try:
now = dt.datetime.now()
judgd_factors = factors.judge_industry(dfcf)
mean = judgd_factors.get_six_industry()
a50_f = judgd_factors.sz50_factor()
cyb_f = judgd_factors.cyb_factor()
cyb_real = judgd_factors.cyb_real()
def main():
parser = argparse.ArgumentParser(description="SST")
parser.add_argument("--load", help="Load the saved model")
parser.add_argument("--save-path",
default="model.pkl",
help="Path to save model")
parser.add_argument("--model",
default="naive_lstm",
help="Choose the model")
parser.add_argument("--max-epoch",
type=int,
default=200,
help="Restrict max epochs")
parser.add_argument("--batch-size",
type=int,
default=8,
help="Training batch size")
parser.add_argument(
"--log-interval",
type=int,
default=10,
help="Log interval when training in batches",
)
parser.add_argument("--save-interval",
type=int,
default=1,
help="Model save interval when training")
parser.add_argument("--optim",
default="sgd",
help="Optimizer. Choose between adam and sgd")
parser.add_argument("--lr", type=float, default=0.1, help="Learning rate")
args = parser.parse_args()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
################################
# DataLoader
################################
# set up fields
TEXT = data.Field()
LABEL = data.Field(sequential=False, dtype=torch.long)
# make splits for data
# DO NOT MODIFY: fine_grained=True, train_subtrees=False
train, val, test = datasets.SST.splits(TEXT,
LABEL,
fine_grained=True,
train_subtrees=False)
# build the vocabulary
# you can use other pretrained vectors, refer to https://github.com/pytorch/text/blob/master/torchtext/vocab.py
TEXT.build_vocab(train, vectors=Vectors(name="vector.txt", cache="./data"))
LABEL.build_vocab(train)
# We can also see the vocabulary directly using either the stoi (string to int) or itos (int to string) method.
# make iterator for splits
train_iter, val_iter, test_iter = data.BucketIterator.splits(
(train, val, test), batch_size=args.batch_size, device=device)
# Copy the pre-trained word embeddings we loaded earlier into the embedding layer of our model.
pretrained_embeddings = TEXT.vocab.vectors
print("pretrained_embeddings.shape: ", pretrained_embeddings.shape)
INPUT_DIM = len(TEXT.vocab)
EMBEDDING_DIM = pretrained_embeddings.shape[1]
HIDDEN_DIM = 768
OUTPUT_DIM = 5
model = md.model(args.model)(INPUT_DIM, EMBEDDING_DIM, HIDDEN_DIM,
OUTPUT_DIM)
# you should maintain a nn.embedding layer in your network
model.embedding.weight.data.copy_(pretrained_embeddings)
print(
f"The model has {solver.count_parameters(model):,} trainable parameters"
)
# Training
opitmizer = optim.SGD(model.parameters(), 0.1)
criterion = nn.CrossEntropyLoss()
model = model.to(device)
criterion = criterion.to(device)
num_of_epochs = args.max_epoch
best_valid_loss = float("inf")
train_loss_list, train_acc_list = [], []
val_loss_list, val_acc_list = [], []
for epoch in range(num_of_epochs):
start_time = time.time()
train_loss, train_acc = solver.train_one_epoch(model, train_iter,
opitmizer, criterion)
valid_loss, valid_acc = solver.evaluate(model, val_iter, criterion)
train_loss_list.append(train_loss)
train_acc_list.append(train_acc)
val_loss_list.append(valid_loss)
val_acc_list.append(valid_acc)
end_time = time.time()
epoch_mins, epoch_secs = solver.epoch_time(start_time, end_time)
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
torch.save(model.state_dict(),
"./trained_models/" + args.model + "-model.pt")
print(f"Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s")
print(
f"\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%"
)
print(
f"\t Val. Loss: {valid_loss:.3f} | Val. Acc: {valid_acc*100:.2f}%"
)
# Test
test_loss, test_acc = solver.evaluate(model, test_iter, criterion)
print(f"Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%")
# Save Result
result_dict = {
args.model + " Training": [train_loss_list, train_acc_list],
args.model + " Validating": [val_loss_list, val_acc_list],
}
save_result(result_dict, args.model + "_result")
plot.plot_loss_and_acc_save(result_dict, "./plots/" + args.model)