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train.py
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train.py
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from unet_model import UNet
from dataset import ISBI_Loader
from torch import optim
import torch.nn as nn
import torch
import time
import collections
import sys
import numpy as np
class Progbar(object):
"""Displays a progress bar.
# Arguments
target: Total number of steps expected, None if unknown.
width: Progress bar width on screen.
verbose: Verbosity mode, 0 (silent), 1 (verbose), 2 (semi-verbose)
stateful_metrics: Iterable of string names of metrics that
should *not* be averaged over time. Metrics in this list
will be displayed as-is. All others will be averaged
by the progbar before display.
interval: Minimum visual progress update interval (in seconds).
"""
def __init__(self, target, width=30, verbose=1, interval=0.05,
stateful_metrics=None):
self.target = target
self.width = width
self.verbose = verbose
self.interval = interval
if stateful_metrics:
self.stateful_metrics = set(stateful_metrics)
else:
self.stateful_metrics = set()
self._dynamic_display = ((hasattr(sys.stdout, 'isatty') and
sys.stdout.isatty()) or
'ipykernel' in sys.modules)
self._total_width = 0
self._seen_so_far = 0
self._values = collections.OrderedDict()
self._start = time.time()
self._last_update = 0
def update(self, current, values=None):
"""Updates the progress bar.
# Arguments
current: Index of current step.
values: List of tuples:
`(name, value_for_last_step)`.
If `name` is in `stateful_metrics`,
`value_for_last_step` will be displayed as-is.
Else, an average of the metric over time will be displayed.
"""
values = values or []
for k, v in values:
if k not in self.stateful_metrics:
if k not in self._values:
self._values[k] = [v * (current - self._seen_so_far),
current - self._seen_so_far]
else:
self._values[k][0] += v * (current - self._seen_so_far)
self._values[k][1] += (current - self._seen_so_far)
else:
self._values[k] = v
self._seen_so_far = current
now = time.time()
info = ' - %.0fs' % (now - self._start)
if self.verbose == 1:
if (now - self._last_update < self.interval and
self.target is not None and current < self.target):
return
prev_total_width = self._total_width
if self._dynamic_display:
sys.stdout.write('\b' * prev_total_width)
sys.stdout.write('\r')
else:
sys.stdout.write('\n')
if self.target is not None:
numdigits = int(np.floor(np.log10(self.target))) + 1
barstr = '%%%dd/%d [' % (numdigits, self.target)
bar = barstr % current
prog = float(current) / self.target
prog_width = int(self.width * prog)
if prog_width > 0:
bar += ('=' * (prog_width - 1))
if current < self.target:
bar += '>'
else:
bar += '='
bar += ('.' * (self.width - prog_width))
bar += ']'
else:
bar = '%7d/Unknown' % current
self._total_width = len(bar)
sys.stdout.write(bar)
if current:
time_per_unit = (now - self._start) / current
else:
time_per_unit = 0
if self.target is not None and current < self.target:
eta = time_per_unit * (self.target - current)
if eta > 3600:
eta_format = '%d:%02d:%02d' % (eta // 3600, (eta % 3600) // 60, eta % 60)
elif eta > 60:
eta_format = '%d:%02d' % (eta // 60, eta % 60)
else:
eta_format = '%ds' % eta
info = ' - ETA: %s' % eta_format
else:
if time_per_unit >= 1:
info += ' %.0fs/step' % time_per_unit
elif time_per_unit >= 1e-3:
info += ' %.0fms/step' % (time_per_unit * 1e3)
else:
info += ' %.0fus/step' % (time_per_unit * 1e6)
for k in self._values:
info += ' - %s:' % k
if isinstance(self._values[k], list):
avg = np.mean(
self._values[k][0] / max(1, self._values[k][1]))
if abs(avg) > 1e-3:
info += ' %.4f' % avg
else:
info += ' %.4e' % avg
else:
info += ' %s' % self._values[k]
self._total_width += len(info)
if prev_total_width > self._total_width:
info += (' ' * (prev_total_width - self._total_width))
if self.target is not None and current >= self.target:
info += '\n'
sys.stdout.write(info)
sys.stdout.flush()
elif self.verbose == 2:
if self.target is None or current >= self.target:
for k in self._values:
info += ' - %s:' % k
avg = np.mean(
self._values[k][0] / max(1, self._values[k][1]))
if avg > 1e-3:
info += ' %.4f' % avg
else:
info += ' %.4e' % avg
info += '\n'
sys.stdout.write(info)
sys.stdout.flush()
self._last_update = now
def add(self, n, values=None):
self.update(self._seen_so_far + n, values)
def train_net(net, device, data_path, epochs=40, batch_size=1, lr=0.00001):
isbi_dataset = ISBI_Loader(data_path)
train_loader = torch.utils.data.DataLoader(dataset=isbi_dataset,
batch_size=batch_size,
shuffle=True)
optimizer = optim.RMSprop(net.parameters(), lr=lr, weight_decay=1e-8, momentum=0.9)
criterion = nn.BCEWithLogitsLoss()
best_loss = float("inf")
for epoch in range(epochs):
n = len(train_loader)
pbar = Progbar(target=n)
net.train()
for j, (image, label) in enumerate(train_loader):
optimizer.zero_grad()
image = image.to(device=device, dtype=torch.float32)
label = label.to(device=device, dtype=torch.float32)
pred = net(image)
loss = criterion(pred, label)
loss_val = round(float(loss.item()), 4)
pbar.update(j + 1, values=[('epoch is ', epoch + 1), ('loss val', loss_val)])
if loss < best_loss:
best_loss = loss
torch.save(net.state_dict(), './model/best_model.pth')
loss.backward()
optimizer.step()
if __name__ == "__main__":
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# 加载网络，图片单通道1，分类为1。
net = UNet(n_channels=1, n_classes=1)
net.to(device=device)
data_path = "data/train/"
train_net(net, device, data_path)