Skorch RuntimeError: Input type (torch.cuda.ByteTensor) and weight type (torch.cuda.FloatTensor) should be the same
Skorch RuntimeError: Input type (torch.cuda.ByteTensor) and weight type (torch.cuda.FloatTensor) should be the same
我正在尝试开发图像分割模型。在下面的代码中，我不断遇到 RuntimeError：输入类型 (torch.cuda.ByteTensor) 和权重类型 (torch.cuda.FloatTensor) 应该相同。我不确定为什么，因为我尝试使用 .cuda() 将我的数据和 UNet 模型加载到 GPU（尽管不是 skorch 模型——不知道该怎么做）。我正在使用一个图书馆进行主动学习，modAL，它包装了 skorch。
from modAL.models import ActiveLearner
import numpy as np
import torch
from torch import nn
from torch import Tensor
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
from skorch.net import NeuralNet
from modAL.models import ActiveLearner
from modAL.uncertainty import classifier_uncertainty, classifier_margin
from modAL.utils.combination import make_linear_combination, make_product
from modAL.utils.selection import multi_argmax
from modAL.uncertainty import uncertainty_sampling
from model import UNet
from skorch.net import NeuralNet
from skorch.helper import predefined_split
from torch.optim import SGD
import cv2
# Map style dataset,
class ImagesDataset(Dataset):
"""Constructs dataset of satellite images + masks"""
def __init__(self, image_paths):
super().__init__()
self.image_paths = image_paths
def __len__(self):
return len(self.image_paths)
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
print("idx:", idx)
sample_dir = self.image_paths[idx]
img_path = sample_dir +"/images/"+ Path(sample_dir).name +'.png'
mask_path = sample_dir +'/mask.png'
img, mask = cv2.imread(img_path), cv2.imread(mask_path)
print("shape of img", img.shape)
return img, mask
# turn data into dataset
train_ds = ImagesDataset(train_dirs)
val_ds = ImagesDataset(valid_dirs)
train_loader = torch.utils.data.DataLoader(train_ds, batch_size=3, shuffle=True, pin_memory=True)
val_loader = torch.utils.data.DataLoader(val_ds, batch_size=1, shuffle=True, pin_memory=True)
# make sure data loaded in cuda for train, validation
for i, (tr, val) in enumerate(train_loader):
tr, val = tr.cuda(), val.cuda()
for i, (tr2, val2) in enumerate(val_loader):
tr2, val2 = tr2.cuda(), val2.cuda()
X, y = next(iter(train_loader))
X_train = np.array(X.reshape(3,3,1024,1024))
y_train = np.array(y.reshape(3,3,1024,1024))
X2, y2 = next(iter(val_loader))
X_test = np.array(X2.reshape(1,3,1024,1024))
y_test = np.array(y2.reshape(1,3,1024,1024))
module = UNet(pretrained=True)
if torch.cuda.is_available():
module = module.cuda()
# create the classifier
net = NeuralNet(
module,
criterion=torch.nn.NLLLoss,
batch_size=32,
max_epochs=20,
optimizer=SGD,
optimizer__momentum=0.9,
iterator_train__shuffle=True,
iterator_train__num_workers=4,
iterator_valid__shuffle=False,
iterator_valid__num_workers=4,
train_split=predefined_split(val_ds),
device='cuda',
)
# assemble initial data
n_initial = 1
initial_idx = np.random.choice(range(len(X_train)), size=n_initial, replace=False)
X_initial = X_train[initial_idx]
y_initial = y_train[initial_idx]
# generate the pool, remove the initial data from the training dataset
X_pool = np.delete(X_train, initial_idx, axis=0)
y_pool = np.delete(y_train, initial_idx, axis=0)
# train the activelearner
# shape of 4D matrix is 'batch', 'channel', 'width', 'height')
learner = ActiveLearner(
estimator= net,
X_training=X_initial, y_training=y_initial,
)
完整的错误跟踪是：
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-83-0af6007b6b72> in <module>
8 learner = ActiveLearner(
9 estimator= net,
---> 10 X_training=X_initial, y_training=y_initial,
11 # X_training=X_initial, y_training=y_initial,
12 )
~/.local/lib/python3.7/site-packages/modAL/models/learners.py in __init__(self, estimator, query_strategy, X_training, y_training, bootstrap_init, on_transformed, **fit_kwargs)
80 ) -> None:
81 super().__init__(estimator, query_strategy,
---> 82 X_training, y_training, bootstrap_init, on_transformed, **fit_kwargs)
83
84 def teach(self, X: modALinput, y: modALinput, bootstrap: bool = False, only_new: bool = False, **fit_kwargs) -> None:
~/.local/lib/python3.7/site-packages/modAL/models/base.py in __init__(self, estimator, query_strategy, X_training, y_training, bootstrap_init, on_transformed, force_all_finite, **fit_kwargs)
70 self.y_training = y_training
71 if X_training is not None:
---> 72 self._fit_to_known(bootstrap=bootstrap_init, **fit_kwargs)
73 self.Xt_training = self.transform_without_estimating(self.X_training) if self.on_transformed else None
74
~/.local/lib/python3.7/site-packages/modAL/models/base.py in _fit_to_known(self, bootstrap, **fit_kwargs)
160 """
161 if not bootstrap:
--> 162 self.estimator.fit(self.X_training, self.y_training, **fit_kwargs)
163 else:
164 n_instances = self.X_training.shape[0]
~/.local/lib/python3.7/site-packages/skorch/net.py in fit(self, X, y, **fit_params)
901 self.initialize()
902
--> 903 self.partial_fit(X, y, **fit_params)
904 return self
905
~/.local/lib/python3.7/site-packages/skorch/net.py in partial_fit(self, X, y, classes, **fit_params)
860 self.notify('on_train_begin', X=X, y=y)
861 try:
--> 862 self.fit_loop(X, y, **fit_params)
863 except KeyboardInterrupt:
864 pass
~/.local/lib/python3.7/site-packages/skorch/net.py in fit_loop(self, X, y, epochs, **fit_params)
774
775 self.run_single_epoch(dataset_train, training=True, prefix="train",
--> 776 step_fn=self.train_step, **fit_params)
777
778 if dataset_valid is not None:
~/.local/lib/python3.7/site-packages/skorch/net.py in run_single_epoch(self, dataset, training, prefix, step_fn, **fit_params)
810 yi_res = yi if not is_placeholder_y else None
811 self.notify("on_batch_begin", X=Xi, y=yi_res, training=training)
--> 812 step = step_fn(Xi, yi, **fit_params)
813 self.history.record_batch(prefix + "_loss", step["loss"].item())
814 self.history.record_batch(prefix + "_batch_size", get_len(Xi))
~/.local/lib/python3.7/site-packages/skorch/net.py in train_step(self, Xi, yi, **fit_params)
707 return step['loss']
708
--> 709 self.optimizer_.step(step_fn)
710 return step_accumulator.get_step()
711
~/.local/lib/python3.7/site-packages/torch/autograd/grad_mode.py in decorate_context(*args, **kwargs)
24 def decorate_context(*args, **kwargs):
25 with self.__class__():
---> 26 return func(*args, **kwargs)
27 return cast(F, decorate_context)
28
~/.local/lib/python3.7/site-packages/torch/optim/sgd.py in step(self, closure)
84 if closure is not None:
85 with torch.enable_grad():
---> 86 loss = closure()
87
88 for group in self.param_groups:
~/.local/lib/python3.7/site-packages/skorch/net.py in step_fn()
703 def step_fn():
704 self.optimizer_.zero_grad()
--> 705 step = self.train_step_single(Xi, yi, **fit_params)
706 step_accumulator.store_step(step)
707 return step['loss']
~/.local/lib/python3.7/site-packages/skorch/net.py in train_step_single(self, Xi, yi, **fit_params)
643 """
644 self.module_.train()
--> 645 y_pred = self.infer(Xi, **fit_params)
646 loss = self.get_loss(y_pred, yi, X=Xi, training=True)
647 loss.backward()
~/.local/lib/python3.7/site-packages/skorch/net.py in infer(self, x, **fit_params)
1046 x_dict = self._merge_x_and_fit_params(x, fit_params)
1047 return self.module_(**x_dict)
-> 1048 return self.module_(x, **fit_params)
1049
1050 def _get_predict_nonlinearity(self):
~/.local/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
~/al/model.py in forward(self, x)
51
52 def forward(self, x):
---> 53 conv1 = self.conv1(x)
54 conv2 = self.conv2(conv1)
55 conv3 = self.conv3(conv2)
~/.local/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
~/.local/lib/python3.7/site-packages/torch/nn/modules/container.py in forward(self, input)
115 def forward(self, input):
116 for module in self:
--> 117 input = module(input)
118 return input
119
~/.local/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
~/.local/lib/python3.7/site-packages/torch/nn/modules/conv.py in forward(self, input)
421
422 def forward(self, input: Tensor) -> Tensor:
--> 423 return self._conv_forward(input, self.weight)
424
425 class Conv3d(_ConvNd):
~/.local/lib/python3.7/site-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight)
418 _pair(0), self.dilation, self.groups)
419 return F.conv2d(input, weight, self.bias, self.stride,
--> 420 self.padding, self.dilation, self.groups)
421
422 def forward(self, input: Tensor) -> Tensor:
RuntimeError: Input type (torch.cuda.ByteTensor) and weight type (torch.cuda.FloatTensor) should be the same
如果有人能提供帮助，我们将不胜感激！尽管进行了全面搜索，但我真的被困住了——将我的 UNet 模型转换为浮点数没有帮助，我想我已经在我应该调用的地方调用了 .cuda()。
我尝试过的具体事情：
将我的 DataLoader 的条目加载到 CUDA - 正在将 pin_memory 添加到我的 DataLoader
- 按照此处所述将我的 skorch NeuralNetwork 加载到 CUDA
（这不起作用，因为它不是 skorch 中的函数）
- 将我的数据转换为浮点数（https://discuss.pytorch.org/t/input-type-torch-cuda-doubletensor-and-weight-type-torch-cuda-floattensor-should-be-the-same/22704）
cv2.imread 给你 np.uint8 数据类型，它将被转换为 PyTorch 的字节。 byte 类型不能与 float 类型一起使用（您的模型很可能使用它）。
需要通过修改数据集将byte类型转为float类型（以及Tensor）
import torchvision.transforms as transforms
class ImagesDataset(Dataset):
"""Constructs dataset of satellite images + masks"""
def __init__(self, image_paths):
super().__init__()
self.image_paths = image_paths
self.transform = transforms.Compose([transforms.ToTensor()])
def __len__(self):
return len(self.image_paths)
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
print("idx:", idx)
sample_dir = self.image_paths[idx]
img_path = sample_dir +"/images/"+ Path(sample_dir).name +'.png'
mask_path = sample_dir +'/mask.png'
img, mask = cv2.imread(img_path), cv2.imread(mask_path)
img = self.transform(img)
mask = self.transform(mask)
print("shape of img", img.shape)
return img, mask
我正在尝试开发图像分割模型。在下面的代码中，我不断遇到 RuntimeError：输入类型 (torch.cuda.ByteTensor) 和权重类型 (torch.cuda.FloatTensor) 应该相同。我不确定为什么，因为我尝试使用 .cuda() 将我的数据和 UNet 模型加载到 GPU（尽管不是 skorch 模型——不知道该怎么做）。我正在使用一个图书馆进行主动学习，modAL，它包装了 skorch。
from modAL.models import ActiveLearner
import numpy as np
import torch
from torch import nn
from torch import Tensor
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
from skorch.net import NeuralNet
from modAL.models import ActiveLearner
from modAL.uncertainty import classifier_uncertainty, classifier_margin
from modAL.utils.combination import make_linear_combination, make_product
from modAL.utils.selection import multi_argmax
from modAL.uncertainty import uncertainty_sampling
from model import UNet
from skorch.net import NeuralNet
from skorch.helper import predefined_split
from torch.optim import SGD
import cv2
# Map style dataset,
class ImagesDataset(Dataset):
"""Constructs dataset of satellite images + masks"""
def __init__(self, image_paths):
super().__init__()
self.image_paths = image_paths
def __len__(self):
return len(self.image_paths)
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
print("idx:", idx)
sample_dir = self.image_paths[idx]
img_path = sample_dir +"/images/"+ Path(sample_dir).name +'.png'
mask_path = sample_dir +'/mask.png'
img, mask = cv2.imread(img_path), cv2.imread(mask_path)
print("shape of img", img.shape)
return img, mask
# turn data into dataset
train_ds = ImagesDataset(train_dirs)
val_ds = ImagesDataset(valid_dirs)
train_loader = torch.utils.data.DataLoader(train_ds, batch_size=3, shuffle=True, pin_memory=True)
val_loader = torch.utils.data.DataLoader(val_ds, batch_size=1, shuffle=True, pin_memory=True)
# make sure data loaded in cuda for train, validation
for i, (tr, val) in enumerate(train_loader):
tr, val = tr.cuda(), val.cuda()
for i, (tr2, val2) in enumerate(val_loader):
tr2, val2 = tr2.cuda(), val2.cuda()
X, y = next(iter(train_loader))
X_train = np.array(X.reshape(3,3,1024,1024))
y_train = np.array(y.reshape(3,3,1024,1024))
X2, y2 = next(iter(val_loader))
X_test = np.array(X2.reshape(1,3,1024,1024))
y_test = np.array(y2.reshape(1,3,1024,1024))
module = UNet(pretrained=True)
if torch.cuda.is_available():
module = module.cuda()
# create the classifier
net = NeuralNet(
module,
criterion=torch.nn.NLLLoss,
batch_size=32,
max_epochs=20,
optimizer=SGD,
optimizer__momentum=0.9,
iterator_train__shuffle=True,
iterator_train__num_workers=4,
iterator_valid__shuffle=False,
iterator_valid__num_workers=4,
train_split=predefined_split(val_ds),
device='cuda',
)
# assemble initial data
n_initial = 1
initial_idx = np.random.choice(range(len(X_train)), size=n_initial, replace=False)
X_initial = X_train[initial_idx]
y_initial = y_train[initial_idx]
# generate the pool, remove the initial data from the training dataset
X_pool = np.delete(X_train, initial_idx, axis=0)
y_pool = np.delete(y_train, initial_idx, axis=0)
# train the activelearner
# shape of 4D matrix is 'batch', 'channel', 'width', 'height')
learner = ActiveLearner(
estimator= net,
X_training=X_initial, y_training=y_initial,
)
完整的错误跟踪是：
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-83-0af6007b6b72> in <module>
8 learner = ActiveLearner(
9 estimator= net,
---> 10 X_training=X_initial, y_training=y_initial,
11 # X_training=X_initial, y_training=y_initial,
12 )
~/.local/lib/python3.7/site-packages/modAL/models/learners.py in __init__(self, estimator, query_strategy, X_training, y_training, bootstrap_init, on_transformed, **fit_kwargs)
80 ) -> None:
81 super().__init__(estimator, query_strategy,
---> 82 X_training, y_training, bootstrap_init, on_transformed, **fit_kwargs)
83
84 def teach(self, X: modALinput, y: modALinput, bootstrap: bool = False, only_new: bool = False, **fit_kwargs) -> None:
~/.local/lib/python3.7/site-packages/modAL/models/base.py in __init__(self, estimator, query_strategy, X_training, y_training, bootstrap_init, on_transformed, force_all_finite, **fit_kwargs)
70 self.y_training = y_training
71 if X_training is not None:
---> 72 self._fit_to_known(bootstrap=bootstrap_init, **fit_kwargs)
73 self.Xt_training = self.transform_without_estimating(self.X_training) if self.on_transformed else None
74
~/.local/lib/python3.7/site-packages/modAL/models/base.py in _fit_to_known(self, bootstrap, **fit_kwargs)
160 """
161 if not bootstrap:
--> 162 self.estimator.fit(self.X_training, self.y_training, **fit_kwargs)
163 else:
164 n_instances = self.X_training.shape[0]
~/.local/lib/python3.7/site-packages/skorch/net.py in fit(self, X, y, **fit_params)
901 self.initialize()
902
--> 903 self.partial_fit(X, y, **fit_params)
904 return self
905
~/.local/lib/python3.7/site-packages/skorch/net.py in partial_fit(self, X, y, classes, **fit_params)
860 self.notify('on_train_begin', X=X, y=y)
861 try:
--> 862 self.fit_loop(X, y, **fit_params)
863 except KeyboardInterrupt:
864 pass
~/.local/lib/python3.7/site-packages/skorch/net.py in fit_loop(self, X, y, epochs, **fit_params)
774
775 self.run_single_epoch(dataset_train, training=True, prefix="train",
--> 776 step_fn=self.train_step, **fit_params)
777
778 if dataset_valid is not None:
~/.local/lib/python3.7/site-packages/skorch/net.py in run_single_epoch(self, dataset, training, prefix, step_fn, **fit_params)
810 yi_res = yi if not is_placeholder_y else None
811 self.notify("on_batch_begin", X=Xi, y=yi_res, training=training)
--> 812 step = step_fn(Xi, yi, **fit_params)
813 self.history.record_batch(prefix + "_loss", step["loss"].item())
814 self.history.record_batch(prefix + "_batch_size", get_len(Xi))
~/.local/lib/python3.7/site-packages/skorch/net.py in train_step(self, Xi, yi, **fit_params)
707 return step['loss']
708
--> 709 self.optimizer_.step(step_fn)
710 return step_accumulator.get_step()
711
~/.local/lib/python3.7/site-packages/torch/autograd/grad_mode.py in decorate_context(*args, **kwargs)
24 def decorate_context(*args, **kwargs):
25 with self.__class__():
---> 26 return func(*args, **kwargs)
27 return cast(F, decorate_context)
28
~/.local/lib/python3.7/site-packages/torch/optim/sgd.py in step(self, closure)
84 if closure is not None:
85 with torch.enable_grad():
---> 86 loss = closure()
87
88 for group in self.param_groups:
~/.local/lib/python3.7/site-packages/skorch/net.py in step_fn()
703 def step_fn():
704 self.optimizer_.zero_grad()
--> 705 step = self.train_step_single(Xi, yi, **fit_params)
706 step_accumulator.store_step(step)
707 return step['loss']
~/.local/lib/python3.7/site-packages/skorch/net.py in train_step_single(self, Xi, yi, **fit_params)
643 """
644 self.module_.train()
--> 645 y_pred = self.infer(Xi, **fit_params)
646 loss = self.get_loss(y_pred, yi, X=Xi, training=True)
647 loss.backward()
~/.local/lib/python3.7/site-packages/skorch/net.py in infer(self, x, **fit_params)
1046 x_dict = self._merge_x_and_fit_params(x, fit_params)
1047 return self.module_(**x_dict)
-> 1048 return self.module_(x, **fit_params)
1049
1050 def _get_predict_nonlinearity(self):
~/.local/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
~/al/model.py in forward(self, x)
51
52 def forward(self, x):
---> 53 conv1 = self.conv1(x)
54 conv2 = self.conv2(conv1)
55 conv3 = self.conv3(conv2)
~/.local/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
~/.local/lib/python3.7/site-packages/torch/nn/modules/container.py in forward(self, input)
115 def forward(self, input):
116 for module in self:
--> 117 input = module(input)
118 return input
119
~/.local/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
~/.local/lib/python3.7/site-packages/torch/nn/modules/conv.py in forward(self, input)
421
422 def forward(self, input: Tensor) -> Tensor:
--> 423 return self._conv_forward(input, self.weight)
424
425 class Conv3d(_ConvNd):
~/.local/lib/python3.7/site-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight)
418 _pair(0), self.dilation, self.groups)
419 return F.conv2d(input, weight, self.bias, self.stride,
--> 420 self.padding, self.dilation, self.groups)
421
422 def forward(self, input: Tensor) -> Tensor:
RuntimeError: Input type (torch.cuda.ByteTensor) and weight type (torch.cuda.FloatTensor) should be the same
如果有人能提供帮助，我们将不胜感激！尽管进行了全面搜索，但我真的被困住了——将我的 UNet 模型转换为浮点数没有帮助，我想我已经在我应该调用的地方调用了 .cuda()。
我尝试过的具体事情：
- 正在将 pin_memory 添加到我的 DataLoader
- 按照此处所述将我的 skorch NeuralNetwork 加载到 CUDA
- 将我的数据转换为浮点数（https://discuss.pytorch.org/t/input-type-torch-cuda-doubletensor-and-weight-type-torch-cuda-floattensor-should-be-the-same/22704）
cv2.imread 给你 np.uint8 数据类型，它将被转换为 PyTorch 的字节。 byte 类型不能与 float 类型一起使用（您的模型很可能使用它）。
需要通过修改数据集将byte类型转为float类型（以及Tensor）
import torchvision.transforms as transforms
class ImagesDataset(Dataset):
"""Constructs dataset of satellite images + masks"""
def __init__(self, image_paths):
super().__init__()
self.image_paths = image_paths
self.transform = transforms.Compose([transforms.ToTensor()])
def __len__(self):
return len(self.image_paths)
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
print("idx:", idx)
sample_dir = self.image_paths[idx]
img_path = sample_dir +"/images/"+ Path(sample_dir).name +'.png'
mask_path = sample_dir +'/mask.png'
img, mask = cv2.imread(img_path), cv2.imread(mask_path)
img = self.transform(img)
mask = self.transform(mask)
print("shape of img", img.shape)
return img, mask