class PNNM(OWWidget):
name = "Pytorch CNN"
description = ""
# icon = "icons/robot.svg"
want_main_area = True
class Inputs:
data = Input('Data', ImageDataBunch, default=True)
def __init__(self):
super().__init__()
self.learn = None
# train_button = gui.button(self.controlArea, self, "开始训练", callback=self.train)
self.label = gui.label(self.mainArea, self, "模型结构")
#: The current evaluating task (if any)
self._task = None # type: Optional[Task]
#: An executor we use to submit learner evaluations into a thread pool
self._executor = ThreadExecutor()
# Device configuration
self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# Hyper parameters
num_epochs = 5
num_classes = 10
batch_size = 100
learning_rate = 0.001
dir_path = Path(__file__).resolve()
data_path = f'{dir_path.parent.parent.parent}/datasets/'
# MNIST dataset
self.train_dataset = torchvision.datasets.MNIST(root=data_path,
train=True,
transform=transforms.ToTensor(),
download=False)
self.test_dataset = torchvision.datasets.MNIST(root=data_path,
train=False,
transform=transforms.ToTensor())
# Data loader
self.train_loader = torch.utils.data.DataLoader(dataset=self.train_dataset,
batch_size=batch_size,
shuffle=False)
self.test_loader = torch.utils.data.DataLoader(dataset=self.test_dataset,
batch_size=batch_size,
shuffle=False)
# self.model = ConvNet(num_classes).to(self.device)
self.model = nn.Sequential(
self.conv(1, 8), # 14
nn.BatchNorm2d(8),
nn.ReLU(),
self.conv(8, 16), # 7
nn.BatchNorm2d(16),
nn.ReLU(),
self.conv(16, 32), # 4
nn.BatchNorm2d(32),
nn.ReLU(),
self.conv(32, 16), # 2
nn.BatchNorm2d(16),
nn.ReLU(),
self.conv(16, 10), # 1
nn.BatchNorm2d(10),
Flatten() # remove (1,1) grid
).to(self.device)
# Loss and optimizer
self.criterion = nn.CrossEntropyLoss()
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=learning_rate)
def handleNewSignals(self):
self._update()
def _update(self):
if self._task is not None:
# First make sure any pending tasks are cancelled.
self.cancel()
assert self._task is None
if self.data is None:
return
# collect all learners for which results have not yet been computed
if not self.learn:
return
# setup the task state
self._task = task = Task()
# The learning_curve[_with_test_data] also takes a callback function
# to report the progress. We instrument this callback to both invoke
# the appropriate slots on this widget for reporting the progress
# (in a thread safe manner) and to implement cooperative cancellation.
set_progress = methodinvoke(self, "setProgressValue", (float,))
def callback(finished):
# check if the task has been cancelled and raise an exception
# from within. This 'strategy' can only be used with code that
# properly cleans up after itself in the case of an exception
# (does not leave any global locks, opened file descriptors, ...)
if task.cancelled:
raise KeyboardInterrupt()
set_progress(finished * 100)
self.progressBarInit()
# Submit the evaluation function to the executor and fill in the
# task with the resultant Future.
# task.future = self._executor.submit(self.learn.fit_one_cycle(1))
fit_model = partial(train_model, self.model, 5, self.train_loader, self.test_loader, self.device,
self.criterion, self.optimizer, callback=callback)
task.future = self._executor.submit(fit_model)
# Setup the FutureWatcher to notify us of completion
task.watcher = FutureWatcher(task.future)
# by using FutureWatcher we ensure `_task_finished` slot will be
# called from the main GUI thread by the Qt's event loop
task.watcher.done.connect(self._task_finished)
@pyqtSlot(float)
def setProgressValue(self, value):
assert self.thread() is QThread.currentThread()
self.progressBarSet(value)
@pyqtSlot(concurrent.futures.Future)
def _task_finished(self, f):
"""
Parameters
----------
f : Future
The future instance holding the result of learner evaluation.
"""
assert self.thread() is QThread.currentThread()
assert self._task is not None
assert self._task.future is f
assert f.done()
self._task = None
self.progressBarFinished()
self.model.eval() # eval mode (batchnorm uses moving mean/variance instead of mini-batch mean/variance)
with torch.no_grad():
correct = 0
total = 0
for images, labels in self.test_loader:
images = images.to(self.device)
labels = labels.to(self.device)
outputs = self.model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))
# try:
# result = f.result() # type: List[Results]
# except Exception as ex:
# # Log the exception with a traceback
# log = logging.getLogger()
# log.exception(__name__, exc_info=True)
# self.error("Exception occurred during evaluation: {!r}".format(ex))
# # clear all results
# self.result= None
# else:
print(self.learn.validate())
# ... and update self.results
def cancel(self):
"""
Cancel the current task (if any).
"""
if self._task is not None:
self._task.cancel()
assert self._task.future.done()
# disconnect the `_task_finished` slot
self._task.watcher.done.disconnect(self._task_finished)
self._task = None
def onDeleteWidget(self):
self.cancel()
super().onDeleteWidget()
def conv(self, ni, nf):
return nn.Conv2d(ni, nf, kernel_size=3, stride=2, padding=1)
def train(self):
if self.learn is None:
return
self.learn.fit_one_cycle(3)
@Inputs.data
def set_data(self, data):
if data is not None:
self.data = data
self.learn = Learner(self.data, self.model, loss_func=nn.CrossEntropyLoss(), metrics=accuracy,
add_time=False, bn_wd=False, silent=True)
self.label.setText(self.learn.summary())
else:
self.data = None
class CNNM(OWWidget):
name = "M CNN"
description = ""
# icon = "icons/robot.svg"
want_main_area = True
class Inputs:
data = Input('Data', ImageDataBunch, default=True)
def __init__(self):
super().__init__()
self.learn = None
# train_button = gui.button(self.controlArea, self, "开始训练", callback=self.train)
self.label = gui.label(self.mainArea, self, "模型结构")
#: The current evaluating task (if any)
self._task = None # type: Optional[Task]
#: An executor we use to submit learner evaluations into a thread pool
self._executor = ThreadExecutor()
self.model = nn.Sequential(
self.conv(1, 8), # 14
nn.BatchNorm2d(8),
nn.ReLU(),
self.conv(8, 16), # 7
nn.BatchNorm2d(16),
nn.ReLU(),
self.conv(16, 32), # 4
nn.BatchNorm2d(32),
nn.ReLU(),
self.conv(32, 16), # 2
nn.BatchNorm2d(16),
nn.ReLU(),
self.conv(16, 10), # 1
nn.BatchNorm2d(10),
Flatten() # remove (1,1) grid
)
def handleNewSignals(self):
self._update()
def _update(self):
if self._task is not None:
# First make sure any pending tasks are cancelled.
self.cancel()
assert self._task is None
if self.data is None:
return
# collect all learners for which results have not yet been computed
if not self.learn:
return
# setup the task state
self._task = task = Task()
# The learning_curve[_with_test_data] also takes a callback function
# to report the progress. We instrument this callback to both invoke
# the appropriate slots on this widget for reporting the progress
# (in a thread safe manner) and to implement cooperative cancellation.
set_progress = methodinvoke(self, "setProgressValue", (float,))
def callback(finished):
# check if the task has been cancelled and raise an exception
# from within. This 'strategy' can only be used with code that
# properly cleans up after itself in the case of an exception
# (does not leave any global locks, opened file descriptors, ...)
if task.cancelled:
raise KeyboardInterrupt()
set_progress(finished * 100)
self.progressBarInit()
# Submit the evaluation function to the executor and fill in the
# task with the resultant Future.
# task.future = self._executor.submit(self.learn.fit_one_cycle(1))
with progress_disabled_ctx(self.learn) as learn:
fit_model = partial(my_fit, learn, 1, callback=callback)
task.future = self._executor.submit(fit_model)
# Setup the FutureWatcher to notify us of completion
task.watcher = FutureWatcher(task.future)
# by using FutureWatcher we ensure `_task_finished` slot will be
# called from the main GUI thread by the Qt's event loop
task.watcher.done.connect(self._task_finished)
@pyqtSlot(float)
def setProgressValue(self, value):
assert self.thread() is QThread.currentThread()
self.progressBarSet(value)
@pyqtSlot(concurrent.futures.Future)
def _task_finished(self, f):
"""
Parameters
----------
f : Future
The future instance holding the result of learner evaluation.
"""
assert self.thread() is QThread.currentThread()
assert self._task is not None
assert self._task.future is f
assert f.done()
self._task = None
self.progressBarFinished()
# try:
# result = f.result() # type: List[Results]
# except Exception as ex:
# # Log the exception with a traceback
# log = logging.getLogger()
# log.exception(__name__, exc_info=True)
# self.error("Exception occurred during evaluation: {!r}".format(ex))
# # clear all results
# self.result= None
# else:
print(self.learn.validate())
# ... and update self.results
def cancel(self):
"""
Cancel the current task (if any).
"""
if self._task is not None:
self._task.cancel()
assert self._task.future.done()
# disconnect the `_task_finished` slot
self._task.watcher.done.disconnect(self._task_finished)
self._task = None
def onDeleteWidget(self):
self.cancel()
super().onDeleteWidget()
def conv(self, ni, nf):
return nn.Conv2d(ni, nf, kernel_size=3, stride=2, padding=1)
def train(self):
if self.learn is None:
return
self.learn.fit_one_cycle(3)
@Inputs.data
def set_data(self, data):
if data is not None:
self.data = data
self.learn = Learner(self.data, self.model, loss_func=nn.CrossEntropyLoss(), metrics=accuracy,
add_time=False, bn_wd=False, silent=True)
self.label.setText(self.learn.summary())
else:
self.data = None