def plot_img_reconstructions(
root_path: str,
name_of_best_exp: str,
path_to_plot: str,
baseline: bool = False,
epoch: int = 49999,
):
dataset = MNISTDataset()
ae = AutoEncoder(30, False, False, 0.001, "test")
ae.load_state_dict(
torch.load(
os.path.join(
root_path,
name_of_best_exp,
f"params/step_{epoch}/rank_0/{'A' if not baseline else 'baseline'}.pt",
),
map_location=torch.device("cpu"),
)
)
digits: torch.Tensor = dataset.sample(50)
_, axes = plt.subplots(
nrows=10,
ncols=10,
figsize=(10, 8),
gridspec_kw=dict(
wspace=0.0, hspace=0.0, top=0.95, bottom=0.05, left=0.17, right=0.845
),
)
axes = axes.reshape(50, 2)
for digit, ax_column in zip(digits, axes):
ax_column[0].imshow(digit.squeeze().detach())
ax_column[0].set_axis_off()
rec = ae(digit.reshape(1, 1, 28, 28))
ax_column[1].imshow(rec.squeeze().detach())
ax_column[1].set_axis_off()
plt.show()
exit(1)
plt_path = f"plots/{path_to_plot}/reconstructions_baseline_{baseline}"
plt.savefig(plt_path + ".pdf")
plt.savefig(plt_path + ".svg")
plt.close()
def main(args):
model = AutoEncoder()
use_gpu = torch.cuda.is_available()
if use_gpu:
print('cuda is available!')
model.cuda()
weight_file = args.path_weight_file
model = AutoEncoder()
model.load_state_dict(
torch.load(weight_file, map_location=lambda storage, loc: storage))
test_dataset = dataloader('dogs_cats', 'test')
test_loader = torch.utils.data.DataLoader(
test_dataset, batch_size=args.batch_size, shuffle=False)
images, _ = iter(test_loader).next()
images = Variable(images, volatile=True)
imshow(torchvision.utils.make_grid(images.data[:25], nrow=5))
outputs = model(images)
imshow(torchvision.utils.make_grid(outputs.data[:25], nrow=5))
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--dataset_ratio",
type=float,
default=1,
help="the purcentage of data used in the dataset (default: 1)")
parser.add_argument("--image_size",
type=int,
default=512,
help="size of the input image (default: 512)")
parser.add_argument("--depth",
type=int,
default=6,
help="depth of the autoencoder (default: 6)")
parser.add_argument(
"--num_block",
type=int,
default=3,
help="number of blocks of the autoencoder (default: 3)")
parser.add_argument("--epoch",
type=int,
default=1,
help="number of epoch (default: 1)")
parser.add_argument("--batch",
type=int,
default=100,
help="number of batch (default: 100)")
parser.add_argument("--valpct",
type=float,
default=0.2,
help="proportion of test data (default: 0.2)")
parser.add_argument("--num_threads",
type=int,
default=1,
help="number of thread used (default: 1)")
parser.add_argument("--create_csv",
type=bool,
default=False,
help="create or not csv file (default: False)")
parser.add_argument("--log",
default=False,
action='store_true',
help="Write log or not (default: False)")
parser.add_argument("--l2_reg",
type=int,
default=0.001,
help="L2 regularisation (default: 0.001)")
args = parser.parse_args()
# if args.create_csv:
# g_csv.generate_csv(DATA_PATH + CSV_NAME, args.num_var,
# args.num_const, args.num_prob)
valid_ratio = args.valpct # Going to use 80%/20% split for train/valid
data_transforms = transforms.Compose([ToTensor(), Normalize()])
full_dataset = ImageLoader(csv_file_path=LABEL_FILE_PATH,
image_directory=IMAGE_FOLDER_PATH,
mask_directory=MASK_FOLDER_PATH,
dataset_size=int(args.dataset_ratio *
DATASET_SIZE),
image_size=args.image_size,
transform=data_transforms)
nb_train = int((1.0 - valid_ratio) * len(full_dataset))
nb_test = len(full_dataset) - nb_train
print("Size of full data set: ", len(full_dataset))
print("Size of training data: ", nb_train)
print("Size of testing data: ", nb_test)
train_dataset, test_dataset = torch.utils.data.dataset.random_split(
full_dataset, [nb_train, nb_test])
train_loader = DataLoader(dataset=train_dataset,
batch_size=args.batch,
shuffle=True,
num_workers=args.num_threads)
test_loader = DataLoader(dataset=test_dataset,
batch_size=args.batch,
shuffle=True,
num_workers=args.num_threads)
# TODO params
# num_param = args.num_var + args.num_const + (args.num_var*args.num_const)
model = AutoEncoder(num_block=args.num_block, depth=args.depth)
# print("Network architechture:\n", model)
use_gpu = torch.cuda.is_available()
if use_gpu:
device = torch.device('cuda')
else:
device = torch.device('cpu')
model.to(device)
# f_loss = nn.BCEWithLogitsLoss()
f_loss = loss.Custom_loss()
# define optimizer
optimizer = torch.optim.Adam(model.parameters(), weight_decay=args.l2_reg)
# Make run directory
run_name = "run-"
LogManager = lw.LogManager(LOG_DIR, run_name)
run_dir_path, num_run = LogManager.generate_unique_dir()
# setup model checkpoint
path_model_check_point = run_dir_path + MODEL_DIR
if not os.path.exists(path_model_check_point):
os.mkdir(path_model_check_point)
model_checkpoint = ModelCheckpoint(path_model_check_point + BEST_MODELE,
model)
# top_logdir = LOG_DIR + FC1
# if not os.path.exists(top_logdir):
# os.mkdir(top_logdir)
# model_checkpoint = ModelCheckpoint(top_logdir + BEST_MODELE, model)
if args.log:
print("Writing log")
# generate unique folder for new run
tensorboard_writer = SummaryWriter(log_dir=run_dir_path,
filename_suffix=".log")
LogManager.set_tensorboard_writer(tensorboard_writer)
LogManager.summary_writer(model, optimizer)
# write short description of the run
run_desc = "Epoch{}".format(args.epoch)
log_file_path = LOG_DIR + run_desc + "Run{}".format(num_run) + ".log"
# LogManager.summary_writer(model, optimizer)
# write short description of the run
# run_desc = "Epoch{}Reg{}Var{}Const{}CLoss{}Dlayer{}Alpha{}".format(
# args.num_epoch,
# args.l2_reg,
# args.num_var,
# args.num_const,
# args.custom_loss,
# args.num_deep_layer,
# args.alpha)
# log_file_path = LOG_DIR + "Run{}".format(num_run) + run_desc + ".log"
# log_file_path = lw.generate_unique_logpath(LOG_DIR, "Linear")
with tqdm(total=args.epoch) as pbar:
for t in range(args.epoch):
pbar.update(1)
pbar.set_description("Epoch {}".format(t))
# print(DIEZ + "Epoch Number: {}".format(t) + DIEZ)
train_loss, train_acc = train(model, train_loader, f_loss,
optimizer, device, LogManager)
progress(train_loss, train_acc)
# time.sleep(0.5)
val_loss, val_acc = test(model,
test_loader,
f_loss,
device,
log_manager=LogManager)
print(" Validation : Loss : {:.4f}, Acc : {:.4f}".format(
val_loss, val_acc))
model_checkpoint.update(val_loss)
# lw.write_log(log_file_path, val_acc, val_loss, train_acc, train_loss)
if args.log:
tensorboard_writer.add_scalars("Loss/", {
'train_loss': train_loss,
'val_loss': val_loss
}, t)
# tensorboard_writer.add_scalar(METRICS + 'train_loss', train_loss, t)
# tensorboard_writer.add_scalar(METRICS + 'train_acc', train_acc, t)
# tensorboard_writer.add_scalar(METRICS + 'val_loss', val_loss, t)
# tensorboard_writer.add_scalar(METRICS + 'val_acc', val_acc, t)
LogManager.write_log(log_file_path, val_acc, val_loss, train_acc,
train_loss)
model.load_state_dict(torch.load(path_model_check_point + BEST_MODELE))
print(DIEZ + " Final Test " + DIEZ)
_, _ = test(model,
train_loader,
f_loss,
device,
final_test=True,
log_manager=LogManager,
txt="training")
test_loss, test_acc = test(model,
test_loader,
f_loss,
device,
log_manager=LogManager,
final_test=True)
print(" Test : Loss : {:.4f}, Acc : {:.4f}".format(
test_loss, test_acc))
try:
pre_train_path = args.load_weight_dir
pretrained_dict = torch.load(pre_train_path) #load pre train model
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
} #load the layer only same with the target model
model_dict.update(pretrained_dict)
print('===================================')
print('load pre_train weight successfully')
print('===================================')
except:
print('===================================')
print(' random init the weight ')
print('===================================')
autoencoder.load_state_dict(model_dict)
autoencoder.cuda()
autoencoder.train()
'''opt setting'''
optimizer = torch.optim.Adam(
autoencoder.parameters(),
lr=args.learning_rate) # optimize all cnn parameters
loss_func = nn.MSELoss()
'''folder for saving weight and loss history'''
save_path = args.save_weight_dir
'''training code'''
for epoch in range(EPOCH):
loss_iter = 0
for step, (x, b_label) in enumerate(train_data):
x_in = torch.tensor(x).cuda()
def evaluate_experiment(path_dti: str, path_mtm: str, data_x: Tensor,
data_y: Tensor) -> pd.DataFrame:
result_df_container = []
for path_dti in glob.glob(f"{path_dti}/*"):
for i in range(2):
ae = AutoEncoder(30,
False,
False,
0.001,
"bruh",
pre_latent_dim=49)
repres = ae.encode(data_x).detach()
results = evaluate_representations(repres, data_y, 10, (30, ),
args, "Random features")
results["Agent"] = i
result_df_container.append(results)
for i in range(2):
ae = AutoEncoder(30,
False,
False,
0.001,
"bruh",
pre_latent_dim=49)
ae.load_state_dict(
torch.load(path_dti +
("/baseline.pt" if i == 0 else "/baseline_2.pt")))
repres = ae.encode(data_x).detach()
results = evaluate_representations(repres, data_y, 10, (30, ),
args, "AE")
results["Agent"] = i
result_df_container.append(results)
for i in range(3):
ae = AutoEncoder(30,
False,
False,
0.001,
"bruh",
pre_latent_dim=49)
ae.load_state_dict(
torch.load(path_dti + f"/{string.ascii_uppercase[i]}.pt"))
repres = ae.encode(data_x).detach()
results = evaluate_representations(repres, data_y, 10, (30, ),
args, "DTI")
results["Agent"] = i
result_df_container.append(results)
for path_mtm in glob.glob(f"{path_mtm}/*"):
for i in range(3):
ae = AutoEncoder(30,
False,
False,
0.001,
"bruh",
pre_latent_dim=49)
ae.load_state_dict(
torch.load(path_mtm + f"/{string.ascii_uppercase[i]}.pt"))
repres = ae.encode(data_x).detach()
results = evaluate_representations(repres, data_y, 10, (30, ),
args, "AE+MTM")
results["Agent"] = i
result_df_container.append(results)
results = pd.concat(result_df_container)
return results
def train(block=200,
data_name="bookcorpus",
downsample=-1,
dropout_rate=0.2,
history=None,
device="cuda:0",
params=None):
# version 1 - tfidf as feature
if data_name == "bookcorpus":
if history is None:
x_train, y_train = load_tfidf("train",
block,
verbose=True,
redo=False)
x_test, y_test = load_tfidf("test",
block,
verbose=True,
redo=False)
x_valid, y_valid = load_tfidf("valid",
block,
verbose=True,
redo=False)
else:
x_train, y_train = load_tfidf_long("train",
block,
verbose=True,
redo=False,
history=history)
x_test, y_test = load_tfidf_long("test",
block,
verbose=True,
redo=False,
history=history)
x_valid, y_valid = load_tfidf_long("valid",
block,
verbose=True,
redo=False,
history=history)
elif data_name == "coda19":
x_train, y_train = coda_load_tfidf("train",
block,
verbose=True,
redo=False)
x_test, y_test = coda_load_tfidf("test",
block,
verbose=True,
redo=False)
x_valid, y_valid = coda_load_tfidf("valid",
block,
verbose=True,
redo=False)
else:
print("Not supported yet")
quit()
if downsample != -1:
random_index = np.random.RandomState(5516).permutation(
x_train.shape[0])[:downsample]
x_train, y_train = x_train[random_index], y_train[random_index]
# parameter setting
vocab_size = x_train.shape[1]
output_size = y_train.shape[1]
hidden_size = 512 if params is None else params.hidden_size
epoch_num = 2000 if params is None else params.epoch_num
batch_size = 512 if params is None else params.batch_size
layer_num = 5 if params is None else params.layer_num
learning_rate = 1e-4 if params is None else params.learning_rate
early_stop_epoch = 20 if params is None else params.early_stop
device = device
if downsample == -1:
note = f"cosine - auto2 - {dropout_rate}"
else:
note = f"cosine - auto2 - {dropout_rate} - downsample"
# build dataset
training_dataset = TFIDF_Dataset(x_train, y_train)
training = data.DataLoader(training_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=2)
testing_dataset = TFIDF_Dataset(x_test, y_test)
testing = data.DataLoader(testing_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=2)
valid_dataset = TFIDF_Dataset(x_valid, y_valid)
valid = data.DataLoader(valid_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=2)
# build model
model = AutoEncoder(
vocab_size=vocab_size,
hidden_size=hidden_size,
output_size=output_size,
dropout_rate=dropout_rate,
device=device,
layer_num=layer_num,
).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
loss_function = lambda y_pred, y_batch: 1 - F.cosine_similarity(
y_pred, y_batch).mean()
# first evaluation
evaluate(model, valid, loss_function=loss_function)
best_epoch = 0
best_cosine = 0
best_model = copy.deepcopy(model.state_dict())
stopper = EarlyStop(mode="max", history=early_stop_epoch)
# train model
for epoch in range(1, epoch_num + 1):
# train
model.train()
total_loss = 0
total_count = np.ceil(x_train.shape[0] / batch_size)
total_cosine = 0
for count, (x_batch, y_batch) in enumerate(training, 1):
x_batch = x_batch.squeeze()
y_batch = y_batch.squeeze()
x_batch = x_batch.to(device)
y_batch = y_batch.to(device)
optimizer.zero_grad()
y_pred = model(x_batch)
loss = loss_function(y_pred, y_batch)
loss.backward()
optimizer.step()
total_loss += loss.item()
cosine = F.cosine_similarity(y_batch, y_pred)
total_cosine += cosine.mean().item()
print(
"\x1b[2K\rEpoch: {} / {} [{:.2f}%] Loss: {:.4f} Cosine: {:.4f}"
.format(epoch, epoch_num, 100.0 * count / total_count,
total_loss / count, total_cosine / count),
end="")
print()
# valid
if epoch % 1 == 0 or epoch == epoch_num:
cosine, _ = evaluate(model, valid, loss_function=loss_function)
if cosine > best_cosine:
best_model = copy.deepcopy(model.state_dict())
best_epoch = epoch
best_cosine = cosine
# check early stopping
if stopper.check(cosine):
print("Early Stopping at Epoch = ", epoch)
break
# load best model & test & save
print("loading model from epoch {}".format(best_epoch))
torch.save(
best_model,
os.path.join(model_dir, data_name,
"{}_autoencoder_{}.pt".format(note, block)))
model.load_state_dict(best_model)
cosine, y_pred = evaluate(model,
testing,
device=device,
loss_function=loss_function)
print("testing cosine:", cosine)
# config filename
if history is None:
filename = os.path.join(result_dir, f"{data_name}_dl_baseline.json")
prediction_filename = os.path.join(
predict_dir, "bookcorpus",
f"block{block}_autoencoder_{note.replace(' ', '')}.h5")
else:
filename = os.path.join(result_dir,
f"history_exp_{data_name}_dl_baseline.json")
prediction_filename = os.path.join(
predict_dir, "bookcorpus",
f"history_block{block}_autoencoder_{note.replace(' ', '')}.h5")
print_tfidf_metric(
{
"cosine": float(cosine),
"block": block,
"model": "autoencoder",
"note": "clean - autoencoder - tfidf - deep - {}".format(note)
},
filename=filename)
save_prediction(prediction_filename, y_pred)
type = int,
help = 'GPU device ids (CUDA_VISIBLE_DEVICES)')
global args
args = parser.parse_args()
'''set the training gpu'''
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpuid)
'''load_data'''
Load_data = load_data()
test_data,test_gt = Load_data.test()
'''init model'''
autoencoder = AutoEncoder()
autoencoder.load_state_dict(torch.load(args.load_weight_dir)) #load pre-train
autoencoder.cuda()
autoencoder.eval()
loss_func = nn.L1Loss()
loss = 0
with torch.no_grad(): #it can save the memory,prevent it allocated,we dont need to keep the grad during the evualation
for index in range(0,test_data.size()[0],50):
x_in = torch.tensor(test_data[index:index+49,:,:,:], dtype=torch.float32).cuda()
decoded = autoencoder(x_in)
loss = loss+loss_func(decoded, x_in) # L1 loss
'''pick some sample to check vision performance'''
plt.title('autoencoder input')
plt.imshow(x_in[0,0,:,:].data.cpu().numpy(), cmap='gray')
plt.show()
def to_img(img):
'''
Re-normalise the image
'''
mean = [114, 108, 100]
std = [46, 52, 51]
img = img * std + mean
return img
CUDA = torch.cuda.is_available()
model = AutoEncoder()
if args.load_model == 1:
model.load_state_dict(torch.load('conv_autoencoder_weight.pt'))
optimizer = torch.optim.Adam(model.parameters(),
lr=args.learning_rate,
weight_decay=1e-5)
optimizer.load_state_dict(torch.load('conv_autoencoder_optimizer.pt'))
if CUDA:
model = AutoEncoder().cuda()
criterion = nn.MSELoss()
if args.load_model == 0:
optimizer = torch.optim.Adam(model.parameters(),
lr=learning_rate,
weight_decay=1e-5)
print('Running')
done = False
for epoch in (range(num_epochs)):
if data_name == "eeg":
X_raw, y_raw = load_eeg_raw("{}data/raw/".format(base_path))
elif data_name == "syn":
X_raw, y_raw = load_one_syn_raw("{}data/raw/".format(base_path), data_idx)
elif data_name == "har":
X_raw, y_raw = load_har_raw("{}data/raw/".format(base_path))
else:
assert(False)
print("y_raw: {}".format(y_raw.shape))
X_sliding = sliding_window(X_raw, args["window_size"])
X_variable = Variable(torch.Tensor(X_sliding), requires_grad=False).to(device)
auto_encoder = AutoEncoder(input_dim=X_sliding.shape[1],
hidden_sizes=args["hidden_sizes"],
latent_dim=args["latent_dim"],
).to(device)
auto_encoder.load_state_dict(torch.load(checkpoint_path, map_location=device))
z = auto_encoder.encode(X_variable).detach().numpy()
print(z.shape)
def find_peaks(z):
dists = np.sqrt(np.sum(np.diff(z, axis=0) ** 2, axis=1))
print(dists.shape)
def mean(xs):
return sum(xs) * 1. / len(xs)
# inspect width, i.e. for t we inspect [t-d, t+d]
d = 50
indices = [
i
data = []
print('Reading Images')
for i in tqdm(range(len(os.listdir(image_filepath)[0:3]))):
img_name = os.listdir(image_filepath)[i]
img_name = image_filepath + img_name
img = plt.imread(img_name)
data.append(img)
CUDA = torch.cuda.is_available()
model = AutoEncoder()
model.eval()
model.load_state_dict(
torch.load('conv_autoencoder_weight.pt', map_location='cpu'))
# optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate, weight_decay =1e-5)
# optimizer.load_state_dict(torch.load('conv_autoencoder_optimizer.pt'))
if CUDA:
model = AutoEncoder().cuda()
for epoch in (range(num_epochs)):
dataset = random.sample(data, batch_size)
for image in dataset:
image, orig_im, dim, w, h = prep_image(image, 256)
image = Variable(torch.tensor(image))
img = image.view(1, image.shape[2], image.shape[0], image.shape[1])
if CUDA:
img = img.cuda()
# ===================forward=====================