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pseudo_label.py
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pseudo_label.py
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import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import DataLoader, TensorDataset
import math, time, json, os
from models.model import get_network, get_optimizer
from torchmetrics import MeanMetric, Accuracy
from data_utils.DataLoaders import create_data_loader
from tqdm import tqdm
import pkbar
import torch
import pandas as pd
from torchsummary import summary
import torch
import random
import numpy as np
from data_utils import get_dataset
from data_utils.DataLoaders import CustomDataset
device = torch.device("cuda")
iterations = 500000
warmup = 200000
lr_decay_iter = 400000
lr_decay_factor = 0.2
validation = 1500
class PL(nn.Module):
def __init__(self, threshold=0.95, n_classes=10):
super().__init__()
self.th = threshold
self.n_classes = n_classes
def forward(self, x, y, model, mask):
y_probs = y.softmax(1)
onehot_label = self.__make_one_hot(y_probs.max(1)[1]).float()
gt_mask = (y_probs > self.th).float()
gt_mask = gt_mask.max(1)[0] # reduce_any
lt_mask = 1 - gt_mask # logical not
p_target = gt_mask[:, None] * 10 * onehot_label + lt_mask[:, None] * y_probs
model.update_batch_stats(False)
output = model(x)
loss = (-(p_target.detach() * F.log_softmax(output, 1)).sum(1)*mask).mean()
model.update_batch_stats(True)
return loss
def __make_one_hot(self, y ):
return torch.eye(self.n_classes)[y].to(y.device)
class transform:
def __init__(self, flip=True, r_crop=True, g_noise=False):
self.flip = flip
self.r_crop = r_crop
self.g_noise = g_noise
print("holizontal flip : {}, random crop : {}, gaussian noise : {}".format(
self.flip, self.r_crop, self.g_noise
))
def __call__(self, x):
if self.flip and random.random() > 0.5:
x = x.flip(-1)
if self.r_crop:
h, w = x.shape[-2:]
x = F.pad(x, [2,2,2,2], mode="reflect")
l, t = random.randint(0, 4), random.randint(0,4)
x = x[:,:,t:t+h,l:l+w]
if self.g_noise:
n = torch.randn_like(x) * 0.15
x = n + x
return x
def get_datasets(dso, dataloader="my", semi=True, dataset=""):
if dataloader == "my":
# dso, data_config = get_dataset.read_data_sets(dataset, one_hot=False, semi=semi, scale=True)
print("!! check label imag shape", dso.train.labeled_ds.images.shape, np.max(dso.test.images[10,0,0,0]))
l_train_dataset = TensorDataset(torch.Tensor(dso.train.labeled_ds.images),
torch.Tensor(dso.train.labeled_ds.labels))
u_train_dataset = TensorDataset(torch.Tensor(dso.train.unlabeled_ds.images),
torch.Tensor(np.zeros_like(dso.train.unlabeled_ds.labels) - 1))
test_dataset = TensorDataset(torch.Tensor(dso.test.images), torch.Tensor(dso.test.labels))
transform_fn = None
else: # dataloader == "custom":
dso, data_config = get_dataset.read_data_sets(dataset, one_hot=False, semi=semi, scale=False,
channel_first=False)
print("!! check label imag shape", dso.train.labeled_ds.images.shape)
test_dataset = CustomDataset(dso.test.images, dso.test.labels)
l_train_dataset = CustomDataset(dso.train.labeled_ds.images, dso.train.labeled_ds.labels) # create your datset
u_train_dataset = CustomDataset(dso.train.unlabeled_ds.images,
(np.zeros_like(dso.train.unlabeled_ds.labels) - 1))
transform_fn = None # dataset includes transforms
return l_train_dataset, u_train_dataset, test_dataset
class RandomSampler(torch.utils.data.Sampler):
""" sampling without replacement """
def __init__(self, num_data, num_sample):
iterations = num_sample // num_data + 1
self.indices = torch.cat([torch.randperm(num_data) for _ in range(iterations)]).tolist()[:num_sample]
def __iter__(self):
return iter(self.indices)
def __len__(self):
return len(self.indices)
def get_data_loaders(dso, dl="my", t_type="original", alg="PL", bs=128, steps=500000):
l_train_dataset, u_train_dataset, test_dataset = get_datasets(dso, dataloader=dl)
print("datasets size:: ",len(l_train_dataset),len(u_train_dataset), len(test_dataset))
if t_type == "original":
if alg != "supervised":
# batch size = 0.5 x batch size
l_loader = DataLoader(l_train_dataset, bs // 2, drop_last=True,
sampler=RandomSampler(len(l_train_dataset), steps * bs // 2))
else:
l_loader = DataLoader(l_train_dataset, bs, drop_last=True,
sampler=RandomSampler(len(l_train_dataset), steps * bs))
u_loader = DataLoader(u_train_dataset, bs // 2, drop_last=True,
sampler=RandomSampler(len(u_train_dataset), steps * bs // 2))
else:
l_loader = DataLoader(l_train_dataset, bs, drop_last=True)
u_loader = DataLoader(u_train_dataset, bs // 2, drop_last=True, )
test_loader = DataLoader(test_dataset, 128, shuffle=False, drop_last=False)
return l_loader, u_loader, test_loader
def set_model(arch, data_config, weights, loss_type="", opt="adam", lr=1e-3):
ssl_obj = PL(n_classes=data_config.nc)
model, _ = get_network(arch, data_config.size, data_config.channels,num_classes=data_config.nc)
optimizer = get_optimizer(opt, lr, model.parameters())
# model, optimizer, criterion = get_model(arch, data_config, weights, loss_type, opt, lr)
summary(model, input_size=(data_config.channels, data_config.size, data_config.size), device="cpu") # .to(device)
# model = ssdl.SSDL(3, 64, dataset_cfg["num_classes"], transform_fn=transform_fn)
# optimizer = optim.Adam(model.parameters(), lr=alg_cfg["lr"])
return model, ssl_obj, optimizer, F.cross_entropy
def train_orig(model, ssl_obj, optimizer, l_loader, u_loader, test_loader, alg="PL", ):
print()
consis_coef = 1 # for PL
iteration = 0
s = time.time()
for l_data, u_data in zip(l_loader, u_loader):
iteration += 1
l_input, target = l_data
l_input, target = l_input.to(device).float(), target.to(device).long()
if alg != "supervised": # for ssl algorithm
u_input, dummy_target = u_data
u_input, dummy_target = u_input.to(device).float(), dummy_target.to(device).long()
target = torch.cat([target, dummy_target], 0)
unlabeled_mask = (target == -1).float()
inputs = torch.cat([l_input, u_input], 0)
outputs = model(inputs)
# ramp up exp(-5(1 - t)^2)
coef = consis_coef * math.exp(-5 * (1 - min(iteration/warmup, 1))**2)
ssl_loss = ssl_obj(inputs, outputs.detach(), model, unlabeled_mask) * coef
else:
outputs = model(l_input)
coef = 0
ssl_loss = torch.zeros(1).to(device)
# supervised loss
cls_loss = F.cross_entropy(outputs, target, reduction="none", ignore_index=-1).mean()
loss = cls_loss + ssl_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
# display
if iteration == 1 or (iteration % 100) == 0:
wasted_time = time.time() - s
rest = (iterations - iteration)/100 * wasted_time / 60
print("iteration [{}/{}] cls loss : {:.4f}, SSL loss : {:.4f}, coef : {:.5e}, lr : {}".format(iteration,
iterations, cls_loss.item(), ssl_loss.item(), coef, optimizer.param_groups[0]["lr"]),
"\r", end="")
s = time.time()
# validation
if (iteration % validation) == 0 or iteration == iterations:
sum_acc = 0.
s = time.time()
for j, data in enumerate(test_loader):
input, target = data
input, target = input.to(device).float(), target.to(device).long()
output = model(input)
pred_label = output.max(1)[1]
sum_acc += (pred_label == target).float().sum()
if ((j+1) % 10) == 0:
d_p_s = 100/(time.time()-s)
s = time.time()
test_acc = sum_acc / float(len(test_loader.dataset))
print("test accuracy : {:.4f}".format(test_acc))
model.train()
s = time.time()
# lr decay
if iteration == lr_decay_iter:
optimizer.param_groups[0]["lr"] *= lr_decay_factor
return test_acc
def train_pbar(model, ssl_obj, optimizer, lab_loader, unlab_loader, epochs=10, bs=128, verbose=True, dev=device,
t_loader=None, print_freq=10, alg="PL"):
log = []
consis_coef = 1
step = 0
num_of_batches_per_epoch = np.ceil(len(unlab_loader.dataset) / bs)
train_per_epoch = num_of_batches_per_epoch
if alg == "supervised":
train_per_epoch /= 2
if verbose:
loop_range = range(epochs)
else:
loop_range = tqdm(range(epochs))
accuracy_metric = Accuracy().to(dev)
loss_metric = MeanMetric().to(dev)
loss_ssl_metric = MeanMetric().to(dev)
model.to(dev)
for epoch in loop_range:
accuracy_metric.reset()
loss_metric.reset()
loss_ssl_metric.reset()
model.train()
kbar = pkbar.Kbar(target=train_per_epoch, epoch=epoch, num_epochs=epochs, width=8, always_stateful=True)
for i, (l_data, u_data) in enumerate(zip(lab_loader, unlab_loader)):
step += 1
l_input, target = l_data
l_input, target = l_input.to(device).float(), target.to(device).long()
if alg != "supervised": # for ssl algorithm
u_input, dummy_target = u_data
u_input, dummy_target = u_input.to(device).float(), dummy_target.to(device).long()
target = torch.cat([target, dummy_target], 0)
unlabeled_mask = (target == -1).float()
inputs = torch.cat([l_input, u_input], 0)
outputs = model(inputs)
# ramp up exp(-5(1 - t)^2)
coef = consis_coef * math.exp(-5 * (1 - min(step / warmup, 1)) ** 2)
ssl_loss = ssl_obj(inputs, outputs.detach(), model, unlabeled_mask) * coef
else:
outputs = model(l_input)
coef = 0
ssl_loss = torch.zeros(1).to(device)
# supervised loss
cls_loss = F.cross_entropy(outputs, target, reduction="none", ignore_index=-1).mean()
loss = cls_loss + ssl_loss
loss_ssl_metric.update(ssl_loss)
loss_metric.update(ssl_loss)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if i == lr_decay_iter:
optimizer.param_groups[0]["lr"] *= lr_decay_factor
# display
if verbose:
kbar.update(i, values=[("sup-loss", loss_metric.compute()), ("ssl-loss", loss_ssl_metric.compute()),
("wt", coef)])
template = "iteration [{}/{}] cls loss : {:.4f}, SSL loss : {:.4f}, coef : {:.5e}, lr : {}"
# print(template.format(i, shared_cfg["iteration"], cls_loss.item(), ssl_loss.item(), coef,
# optimizer.param_groups[0]["lr"]), "\r", end="")
# validation
if (epoch+1) % print_freq == 0 and verbose:
if t_loader is not None:
val_loss, test_acc = evaluate(model, t_loader, dev=dev)
kbar.add(1, values=[("val_loss", val_loss), ("val_acc", test_acc)])
print("test accuracy : {:.4f}".format(test_acc))
return test_acc
def evaluate(net, imgs, lbls=None, loss_fn=None, verbose=False, dev=None, bs=128):
if isinstance(imgs, DataLoader):
dl = imgs
else:
dl = create_data_loader(imgs, lbls, bs=bs)
net.to(dev)
net.eval()
num_of_batches_per_epoch = np.ceil(len(dl.dataset) / dl.batch_size)
kbar = pkbar.Kbar(target=num_of_batches_per_epoch, epoch=None, num_epochs=None, width=8, always_stateful=False)
acc = Accuracy().to(dev)
loss = MeanMetric().to(dev)
test_loss = 0.
with torch.no_grad():
for i, (data, target) in enumerate(dl):
data, target = data.to(dev), target.to(dev)
target = target.type(torch.LongTensor).to(dev)
output = net(data)
_, preds = torch.max(output.data, 1)
# if loss_fn:
test_loss = F.cross_entropy(output, target, reduction="none", ignore_index=-1).mean() #(output, target)
loss.update(test_loss)
acc.update(preds, target)
if verbose:
kbar.update(i, values=[("val_loss", loss.compute()), ("val_acc", acc.compute())])
return loss.compute().cpu().numpy().squeeze(), acc.compute().cpu().numpy()
def start_training(model, dso, epochs=100, semi=True, bs=100, verb=True, train_type="original", alg="PL", dl="my"):
# images, labels = dso.train.labeled_ds.images, dso.train.labeled_ds.labels
l_loader, u_loader, test_loader = get_data_loaders(dso, dl=dl, t_type=train_type, alg=alg, bs=bs, steps=iterations)
model, ssl_obj, optimizer, criterion = model
if train_type == "original":
train_orig(model, ssl_obj, optimizer, l_loader, u_loader, test_loader, alg)
else:
train_pbar(model, ssl_obj, optimizer, l_loader, u_loader, epochs=epochs, verbose=verb, t_loader=test_loader,
alg=alg)