In [3]:
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy
plt.ion()
충분한 크기의 데이터 세트를 갖긴 힘들기 때문에 잘 학습된 네트워크를 Pretrain한 후 사용하는 경우가 많음
convnet finetuning : 무작위 초기화 대신 imagenet 1000 데이터셋에서 학습한 네트워크를 사용해 네트워크 초기화하고 훈련은 동일하게 진행
In [6]:
data_transforms = {
'train': transforms.Compose([
transforms.RandomSizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'val': transforms.Compose([
transforms.Scale(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
/usr/local/lib/python3.6/site-packages/torchvision/transforms/transforms.py:397: UserWarning: The use of the transforms.RandomSizedCrop transform is deprecated, please use transforms.RandomResizedCrop instead.
"please use transforms.RandomResizedCrop instead.")
/usr/local/lib/python3.6/site-packages/torchvision/transforms/transforms.py:156: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.
"please use transforms.Resize instead.")
In [7]:
data_dir = 'hymenoptera_data'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
data_transforms[x])
for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
shuffle=True, num_workers=4)
for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes
use_gpu = torch.cuda.is_available()
In [8]:
def imshow(inp, title=None):
"""Imshow for Tensor."""
inp = inp.numpy().transpose((1, 2, 0)) # 데이터를 numpy 객체로 바꾼 후 Transpose
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
inp = std * inp + mean
inp = np.clip(inp, 0, 1)
plt.imshow(inp)
if title is not None:
plt.title(title)
plt.pause(0.001) # pause a bit so that plots are updated
In [9]:
# Get a batch of training data
# dataloaders의 객체는 iterable함
inputs, classes = next(iter(dataloaders['train']))
In [12]:
# Make a grid from batch
'''
torchvision.utils.make_grid(tensor, nrow=8, padding=2, normalize=False, range=None, scale_each=False, pad_value=0)
이미지의 그리드를 만드는 함수
Args:
tensor (Tensor or list): 4D mini-batch Tensor of shape (B x C x H x W)
or a list of images all of the same size.
nrow (int, optional): Number of images displayed in each row of the grid.
The Final grid size is (B / nrow, nrow). Default is 8.
padding (int, optional): amount of padding. Default is 2.
normalize (bool, optional): If True, shift the image to the range (0, 1),
by subtracting the minimum and dividing by the maximum pixel value.
range (tuple, optional): tuple (min, max) where min and max are numbers,
then these numbers are used to normalize the image. By default, min and max
are computed from the tensor.
scale_each (bool, optional): If True, scale each image in the batch of
images separately rather than the (min, max) over all images.
pad_value (float, optional): Value for the padded pixels.
'''
out = torchvision.utils.make_grid(inputs)
imshow(out, title=[class_names[x] for x in classes])
In [13]:
class_names
Out[13]:
['ants', 'bees']
In [14]:
classes
Out[14]:
1
0
0
1
[torch.LongTensor of size 4]
In [34]:
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
since = time.time()
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs-1))
print('-'*10)
for phase in ['train', 'val']:
if phase == 'train':
scheduler.step()
model.train(True) # train
else:
model.eval()
# model.train(False) # eval
running_loss = 0.0
running_corrects = 0
for data in dataloaders[phase]:
inputs, labels = data
inputs, labels = Variable(inputs), Variable(labels)
optimizer.zero_grad()
outputs = model(inputs)
_, preds = torch.max(outputs.data, 1) #argmax
loss = criterion(outputs, labels)
if phase == 'train':
loss.backward()
optimizer.step()
running_loss += loss.data[0] * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects / dataset_sizes[phase]
print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
# load best model weights
model.load_state_dict(best_model_wts)
return model
In [18]:
def visualize_model(model, num_images=6):
images_so_far = 0
fig = plt.figure()
for i, data in enmerate(dataloaders['val']):
inputs, labels = data
inputs, labels = Variable(inputs), Variable(labels)
outputs = model(inputs)
_, preds = torch.max(outputs.data, 1)
for j in range(inputs.size()[0]):
image_so_far += 1
ax = plt.subplot(num_images//2, 2, images_so_far)
ax.axis('off')
ax.set_title('predicted: {}'.format(class_names[preds[j]]))
imshow(inputs.cpu().data[j])
if images_so_far == num_images:
return
In [19]:
model_ft = models.resnet18(pretrained=True)
Downloading: "https://download.pytorch.org/models/resnet18-5c106cde.pth" to /Users/kyle/.torch/models/resnet18-5c106cde.pth
100%|██████████| 46827520/46827520 [00:04<00:00, 10896080.35it/s]
In [20]:
model_ft
Out[20]:
ResNet(
(conv1): Conv2d (3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
(relu): ReLU(inplace)
(maxpool): MaxPool2d(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1))
(layer1): Sequential(
(0): BasicBlock(
(conv1): Conv2d (64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
(relu): ReLU(inplace)
(conv2): Conv2d (64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
)
(1): BasicBlock(
(conv1): Conv2d (64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
(relu): ReLU(inplace)
(conv2): Conv2d (64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
)
)
(layer2): Sequential(
(0): BasicBlock(
(conv1): Conv2d (64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
(relu): ReLU(inplace)
(conv2): Conv2d (128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
(downsample): Sequential(
(0): Conv2d (64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
)
)
(1): BasicBlock(
(conv1): Conv2d (128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
(relu): ReLU(inplace)
(conv2): Conv2d (128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
)
)
(layer3): Sequential(
(0): BasicBlock(
(conv1): Conv2d (128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
(relu): ReLU(inplace)
(conv2): Conv2d (256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
(downsample): Sequential(
(0): Conv2d (128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
)
)
(1): BasicBlock(
(conv1): Conv2d (256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
(relu): ReLU(inplace)
(conv2): Conv2d (256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
)
)
(layer4): Sequential(
(0): BasicBlock(
(conv1): Conv2d (256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
(relu): ReLU(inplace)
(conv2): Conv2d (512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
(downsample): Sequential(
(0): Conv2d (256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
)
)
(1): BasicBlock(
(conv1): Conv2d (512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
(relu): ReLU(inplace)
(conv2): Conv2d (512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
)
)
(avgpool): AvgPool2d(kernel_size=7, stride=1, padding=0, ceil_mode=False, count_include_pad=True)
(fc): Linear(in_features=512, out_features=1000)
)
In [22]:
type(model_ft)
Out[22]:
torchvision.models.resnet.ResNet
In [25]:
dir(model_ft)
Out[25]:
['__call__',
'__class__',
'__delattr__',
'__dict__',
'__dir__',
'__doc__',
'__eq__',
'__format__',
'__ge__',
'__getattr__',
'__getattribute__',
'__gt__',
'__hash__',
'__init__',
'__init_subclass__',
'__le__',
'__lt__',
'__module__',
'__ne__',
'__new__',
'__reduce__',
'__reduce_ex__',
'__repr__',
'__setattr__',
'__setstate__',
'__sizeof__',
'__str__',
'__subclasshook__',
'__weakref__',
'_all_buffers',
'_apply',
'_backend',
'_backward_hooks',
'_buffers',
'_forward_hooks',
'_forward_pre_hooks',
'_make_layer',
'_modules',
'_parameters',
'add_module',
'apply',
'avgpool',
'bn1',
'children',
'conv1',
'cpu',
'cuda',
'double',
'dump_patches',
'eval',
'fc',
'float',
'forward',
'half',
'inplanes',
'layer1',
'layer2',
'layer3',
'layer4',
'load_state_dict',
'maxpool',
'modules',
'named_children',
'named_modules',
'named_parameters',
'parameters',
'register_backward_hook',
'register_buffer',
'register_forward_hook',
'register_forward_pre_hook',
'register_parameter',
'relu',
'share_memory',
'state_dict',
'train',
'training',
'type',
'zero_grad']
In [24]:
num_ftrs = model_ft.fc.in_features
In [26]:
num_ftrs
Out[26]:
512
In [27]:
model_ft.fc = nn.Linear(num_ftrs, 2)
In [28]:
if use_gpu:
model_ft = model_ft.cuda()
In [29]:
model_ft.parameters()
Out[29]:
<generator object Module.parameters at 0x1060bea98>
In [30]:
[i for i in model_ft.parameters()]
Out[30]:
[Parameter containing:
(0 ,0 ,.,.) =
-1.0419e-02 -6.1356e-03 -1.8098e-03 ... 5.6615e-02 1.7083e-02 -1.2694e-02
1.1083e-02 9.5276e-03 -1.0993e-01 ... -2.7124e-01 -1.2907e-01 3.7424e-03
-6.9434e-03 5.9089e-02 2.9548e-01 ... 5.1972e-01 2.5632e-01 6.3573e-02
... ⋱ ...
-2.7535e-02 1.6045e-02 7.2595e-02 ... -3.3285e-01 -4.2058e-01 -2.5781e-01
3.0613e-02 4.0960e-02 6.2850e-02 ... 4.1384e-01 3.9359e-01 1.6606e-01
-1.3736e-02 -3.6746e-03 -2.4084e-02 ... -1.5070e-01 -8.2230e-02 -5.7828e-03
(0 ,1 ,.,.) =
-1.1397e-02 -2.6619e-02 -3.4641e-02 ... 3.2521e-02 6.6221e-04 -2.5743e-02
4.5687e-02 3.3603e-02 -1.0453e-01 ... -3.1253e-01 -1.6051e-01 -1.2826e-03
-8.3730e-04 9.8420e-02 4.0210e-01 ... 7.0789e-01 3.6887e-01 1.2455e-01
... ⋱ ...
-5.5926e-02 -5.2239e-03 2.7081e-02 ... -4.6178e-01 -5.7080e-01 -3.6552e-01
3.2860e-02 5.5574e-02 9.9670e-02 ... 5.4636e-01 4.8276e-01 1.9867e-01
5.3051e-03 6.6938e-03 -1.7254e-02 ... -1.4822e-01 -7.7248e-02 7.2183e-04
(0 ,2 ,.,.) =
-2.0315e-03 -9.1617e-03 2.1209e-02 ... 8.9177e-02 3.3655e-02 -2.0102e-02
1.5398e-02 -1.8648e-02 -1.2591e-01 ... -2.5342e-01 -1.2980e-01 -2.7975e-02
9.8454e-03 4.9047e-02 2.1699e-01 ... 3.4872e-01 1.0433e-01 1.8413e-02
... ⋱ ...
-2.8356e-02 1.8404e-02 9.8647e-02 ... -1.1740e-01 -2.5760e-01 -1.5451e-01
2.0766e-02 -2.6286e-03 -3.7825e-02 ... 2.4141e-01 2.4345e-01 1.1796e-01
7.4684e-04 7.7677e-04 -1.0050e-02 ... -1.4865e-01 -1.1754e-01 -3.8350e-02
⋮
(1 ,0 ,.,.) =
-4.4154e-03 -4.0645e-03 3.1589e-03 ... -3.7026e-02 -2.5158e-02 -4.7945e-02
5.1310e-02 5.3402e-02 8.0436e-02 ... 1.4480e-01 1.4287e-01 1.2312e-01
-7.3337e-03 2.1755e-03 3.7580e-02 ... 6.1517e-02 8.0324e-02 1.1715e-01
... ⋱ ...
-2.6754e-02 -1.2297e-01 -1.3653e-01 ... -1.4068e-01 -1.1155e-01 -4.9556e-02
2.3524e-02 -1.7288e-02 -1.1122e-02 ... -1.8826e-02 -2.3320e-02 -2.9474e-02
2.8689e-02 2.1659e-02 4.7888e-02 ... 2.5498e-02 3.5346e-02 1.1280e-02
(1 ,1 ,.,.) =
4.6919e-04 1.2153e-02 4.2035e-02 ... 4.6403e-02 4.0423e-02 -1.4439e-02
4.3463e-02 6.8779e-02 1.3268e-01 ... 2.8606e-01 2.6905e-01 2.0935e-01
-5.7621e-02 -2.2642e-02 3.0547e-02 ... 1.3763e-01 1.6538e-01 1.7946e-01
... ⋱ ...
-1.0816e-01 -2.5227e-01 -2.9742e-01 ... -2.8503e-01 -2.1493e-01 -1.0320e-01
4.0709e-02 -3.2771e-02 -6.3450e-02 ... -9.2360e-02 -6.9876e-02 -4.9841e-02
8.2942e-02 8.7580e-02 1.0111e-01 ... 5.2714e-02 6.0968e-02 4.1198e-02
(1 ,2 ,.,.) =
-1.6391e-02 -1.3870e-02 5.2810e-03 ... 4.3698e-02 2.2707e-02 -4.5983e-02
3.3202e-02 4.2014e-02 9.3500e-02 ... 2.6162e-01 2.2970e-01 1.6694e-01
-4.5987e-02 -1.6365e-02 2.6811e-02 ... 1.4951e-01 1.3216e-01 1.3579e-01
... ⋱ ...
-7.2129e-02 -1.8902e-01 -2.3389e-01 ... -1.9038e-01 -1.5609e-01 -7.5974e-02
5.1161e-02 -2.5815e-02 -6.9357e-02 ... -5.8999e-02 -6.1550e-02 -4.4555e-02
1.1174e-01 7.8979e-02 6.5849e-02 ... 3.1617e-02 2.5221e-02 7.4257e-03
⋮
(2 ,0 ,.,.) =
-7.0826e-08 -6.4306e-08 -7.3806e-08 ... -9.8000e-08 -1.0905e-07 -8.3421e-08
-6.1125e-09 2.0613e-09 -8.0922e-09 ... -4.9840e-08 -4.3836e-08 -3.0538e-09
7.1953e-08 7.5616e-08 5.9282e-08 ... -9.7509e-09 -1.0951e-09 4.2442e-08
... ⋱ ...
9.5889e-08 1.0039e-07 7.9817e-08 ... -1.7491e-08 -4.7666e-08 -1.3265e-08
1.2904e-07 1.4762e-07 1.7477e-07 ... 1.3233e-07 1.0628e-07 9.3316e-08
1.2558e-07 1.3644e-07 1.8431e-07 ... 2.1399e-07 1.7710e-07 1.7166e-07
(2 ,1 ,.,.) =
-1.2690e-07 -9.6139e-08 -1.0372e-07 ... -1.1808e-07 -1.3309e-07 -1.0820e-07
-5.7412e-08 -2.5055e-08 -3.0115e-08 ... -7.2922e-08 -6.7022e-08 -2.2574e-08
2.1813e-08 4.8608e-08 3.1222e-08 ... -1.8694e-08 -7.9591e-09 3.9750e-08
... ⋱ ...
5.6013e-08 7.5526e-08 4.4496e-08 ... -4.4128e-08 -5.9930e-08 -1.8247e-08
7.7614e-08 9.8348e-08 1.0455e-07 ... 6.3272e-08 4.1781e-08 4.5901e-08
5.9834e-08 7.1006e-08 9.0437e-08 ... 1.1654e-07 8.7550e-08 9.8837e-08
(2 ,2 ,.,.) =
-4.3810e-08 1.3270e-08 7.8275e-09 ... -5.8804e-09 -2.6217e-08 -1.5649e-08
4.1700e-08 1.0778e-07 1.0946e-07 ... 7.6403e-08 7.1450e-08 9.7615e-08
1.0436e-07 1.6586e-07 1.5933e-07 ... 1.3517e-07 1.3487e-07 1.6449e-07
... ⋱ ...
9.8763e-08 1.5072e-07 1.2547e-07 ... 6.8316e-08 6.8382e-08 1.1367e-07
9.1435e-08 1.3576e-07 1.3793e-07 ... 1.1678e-07 1.1723e-07 1.4394e-07
6.2183e-08 8.8184e-08 1.0456e-07 ... 1.3941e-07 1.3333e-07 1.5844e-07
...
⋮
(61,0 ,.,.) =
-6.1896e-02 -3.0206e-02 1.9225e-02 ... 4.3665e-02 -2.2114e-02 -4.2214e-02
-3.8061e-02 6.0774e-03 4.5797e-02 ... 9.6029e-02 5.9254e-02 2.9958e-02
-2.9672e-02 2.7766e-03 2.0457e-02 ... 5.9828e-02 4.1422e-02 2.3134e-02
... ⋱ ...
1.1916e-02 4.5701e-02 4.4892e-02 ... 4.7419e-02 2.2274e-02 -5.4993e-03
-3.2468e-02 -1.2210e-02 2.2023e-02 ... 5.8061e-02 -7.5033e-03 -5.9736e-02
-4.3314e-02 -2.8162e-02 -5.9126e-03 ... 8.8460e-02 8.4406e-03 -5.0019e-02
(61,1 ,.,.) =
-6.1292e-02 -1.4004e-02 1.7229e-02 ... 1.8349e-02 -3.2708e-02 -4.1060e-02
-3.1506e-02 2.4460e-02 4.5516e-02 ... 6.6806e-02 4.6687e-02 3.3248e-02
-3.2216e-02 2.0718e-02 2.3343e-02 ... 3.5265e-02 3.6478e-02 3.1291e-02
... ⋱ ...
1.7739e-02 6.1040e-02 4.8247e-02 ... 3.7785e-02 2.8894e-02 1.3984e-02
-1.0890e-02 2.2079e-02 4.2737e-02 ... 6.0247e-02 1.6197e-02 -1.2493e-02
-2.2284e-02 1.3220e-02 3.0897e-02 ... 1.0403e-01 4.0119e-02 -5.3310e-03
(61,2 ,.,.) =
-8.5322e-02 -4.2603e-02 6.8145e-03 ... 3.0751e-02 -3.4818e-02 -4.9945e-02
-2.9215e-02 1.8165e-02 5.1092e-02 ... 9.0200e-02 5.3438e-02 4.0169e-02
-3.9932e-02 -1.1100e-03 9.6176e-03 ... 2.4114e-02 2.6298e-02 2.5489e-02
... ⋱ ...
-3.1890e-03 3.0454e-02 1.6316e-02 ... 5.5054e-03 -6.2689e-03 -8.4638e-03
-2.2995e-02 -2.8211e-03 2.3203e-02 ... 3.5888e-02 -1.4296e-02 -3.2419e-02
-9.8894e-03 7.0542e-03 1.0659e-02 ... 7.0495e-02 1.2996e-02 -8.3417e-03
⋮
(62,0 ,.,.) =
-7.8699e-03 1.9911e-02 3.4208e-02 ... 2.8694e-02 1.2820e-02 1.8142e-02
8.7942e-03 -3.2875e-02 -3.5713e-02 ... 7.2533e-02 4.5889e-02 5.2383e-02
-3.6122e-02 -1.1878e-01 -1.3767e-01 ... 3.3811e-02 3.7806e-02 2.6944e-02
... ⋱ ...
1.7322e-02 3.9589e-03 -8.2269e-03 ... 2.7543e-03 1.8313e-02 1.6057e-02
-9.5007e-04 1.6428e-02 1.7156e-02 ... 3.3672e-03 2.2857e-02 6.5783e-04
6.1727e-03 2.7145e-02 1.4340e-02 ... 7.5867e-03 1.8770e-02 1.5624e-02
(62,1 ,.,.) =
-1.3423e-02 -5.0696e-04 8.0959e-03 ... -6.0963e-03 9.2341e-03 1.5751e-02
-1.8343e-02 -6.7982e-02 -7.0685e-02 ... 2.9855e-02 2.6264e-02 2.3773e-02
-5.4359e-02 -1.4663e-01 -1.6211e-01 ... 1.1781e-02 3.2477e-02 1.1980e-02
... ⋱ ...
8.3686e-04 -1.7564e-02 -1.9535e-02 ... -4.1382e-03 2.4658e-02 1.2893e-02
-6.3183e-04 1.1788e-02 2.4810e-02 ... 6.1105e-03 3.9210e-02 9.6696e-03
-7.1831e-03 6.6918e-03 5.2723e-03 ... -7.6077e-03 2.7253e-02 1.7735e-02
(62,2 ,.,.) =
-2.3753e-04 -4.9343e-03 2.2991e-03 ... -4.7958e-02 -2.6154e-02 -2.3525e-02
-3.3053e-04 -5.1502e-02 -5.9977e-02 ... -1.7369e-02 -2.3337e-02 -3.7312e-02
-2.2674e-02 -9.9412e-02 -1.1176e-01 ... -1.1725e-02 -8.3744e-03 -4.0615e-02
... ⋱ ...
1.1437e-02 -8.0313e-03 -1.4955e-03 ... -3.4133e-02 -8.7267e-03 -2.3526e-02
2.9522e-03 6.7770e-04 1.9933e-02 ... -2.2002e-02 1.4814e-02 -1.4487e-02
-1.9085e-02 -2.9430e-02 -2.3284e-02 ... -4.8587e-02 -1.3049e-02 -2.4368e-02
⋮
(63,0 ,.,.) =
-3.6296e-02 7.1996e-03 1.9100e-02 ... 1.9602e-02 1.4870e-02 -1.7298e-02
-1.1061e-02 8.5665e-02 1.2667e-01 ... 1.3744e-02 -5.5036e-05 -3.0162e-02
1.1322e-01 1.8634e-01 5.0658e-02 ... -1.7333e-01 -7.2041e-02 -6.2474e-02
... ⋱ ...
-5.3062e-02 -2.5781e-01 -2.6747e-01 ... 2.6781e-01 1.4344e-01 5.5145e-02
-2.1009e-02 -2.9969e-02 1.0245e-01 ... 2.0843e-01 -4.1518e-03 -3.8118e-02
-2.2155e-02 1.2380e-02 8.4302e-02 ... -4.4992e-02 -1.4687e-01 -9.0890e-02
(63,1 ,.,.) =
-5.3969e-03 3.2799e-02 1.5486e-02 ... -7.7451e-03 3.0229e-03 1.1216e-03
6.1723e-02 1.4899e-01 1.4645e-01 ... -2.8897e-02 -2.0227e-02 -9.1878e-03
1.6146e-01 2.0886e-01 -2.5589e-02 ... -2.7278e-01 -1.0735e-01 -6.2971e-02
... ⋱ ...
-1.3723e-01 -4.0863e-01 -3.8551e-01 ... 4.0846e-01 2.6202e-01 1.3491e-01
-5.9388e-02 -6.1187e-02 1.4197e-01 ... 3.5780e-01 9.0893e-02 -1.7392e-03
7.8613e-03 5.8403e-02 1.5339e-01 ... 4.7045e-02 -1.0095e-01 -9.7920e-02
(63,2 ,.,.) =
-5.6799e-03 1.3425e-02 -2.6461e-02 ... 4.4881e-03 2.0666e-03 1.3902e-02
6.5943e-03 4.5181e-02 6.0260e-02 ... 1.4368e-02 -5.0725e-03 4.0505e-03
5.5257e-02 1.2397e-01 4.3193e-02 ... -1.4486e-01 -7.4489e-02 -5.7533e-02
... ⋱ ...
-3.1513e-02 -1.6334e-01 -1.5795e-01 ... 2.2904e-01 1.2017e-01 7.1998e-02
-1.0456e-02 -1.1248e-03 8.4582e-02 ... 1.5748e-01 2.2142e-02 -1.0083e-02
-4.8639e-03 -5.0065e-03 3.6341e-02 ... -2.4361e-02 -7.1195e-02 -6.6788e-02
[torch.FloatTensor of size 64x3x7x7], Parameter containing:
2.3487e-01
2.6626e-01
-5.1096e-08
5.1870e-01
3.4404e-09
2.2239e-01
4.2289e-01
1.3153e-07
2.5093e-01
1.5152e-06
3.1687e-01
2.5049e-01
3.7893e-01
1.0862e-05
2.7526e-01
2.3674e-01
2.4202e-01
3.9531e-01
4.6935e-01
2.9090e-01
2.7268e-01
2.7803e-01
2.9069e-01
2.0693e-01
2.5899e-01
2.7871e-01
2.9115e-01
3.1601e-01
3.8889e-01
3.0411e-01
2.6776e-01
2.1093e-01
2.8708e-01
3.3243e-01
4.2673e-01
3.7326e-01
7.4804e-08
1.9068e-01
1.4740e-08
2.2303e-01
1.7908e-01
2.4860e-01
2.7400e-01
2.5923e-01
2.9420e-01
2.9924e-01
2.2369e-01
2.6280e-01
2.2001e-08
2.6610e-01
2.2089e-01
2.8429e-01
3.3072e-01
2.2681e-01
3.6538e-01
2.1230e-01
2.3965e-01
2.4950e-01
5.2583e-01
2.4825e-01
2.9565e-01
2.5878e-01
4.8326e-01
2.6670e-01
[torch.FloatTensor of size 64], Parameter containing:
2.3072e-01
2.5382e-01
-1.0543e-06
-6.6439e-01
-1.6571e-08
1.6152e-01
4.5450e-01
-4.3020e-07
3.0051e-01
-8.0052e-06
3.4942e-01
3.1148e-01
-2.4953e-01
-3.4749e-05
1.0773e-01
2.1897e-01
3.8141e-01
-5.2988e-01
-6.2864e-01
5.7140e-01
2.9985e-01
5.8430e-01
4.8202e-01
3.2853e-01
1.9672e-01
1.9496e-01
1.5215e-01
8.5522e-02
5.1314e-01
1.5237e-02
1.6644e-01
3.3239e-01
2.4921e-01
4.4337e-01
-2.8017e-01
-2.0385e-02
-2.4507e-07
3.2134e-01
-4.9152e-08
2.3777e-01
2.3291e-01
3.1527e-01
4.2776e-01
2.9313e-01
2.6379e-01
6.7598e-01
4.2910e-01
3.4566e-01
-8.6909e-08
2.4729e-01
3.0316e-01
6.1577e-01
3.9835e-01
3.3207e-01
-4.1219e-01
3.7807e-01
1.7895e-01
2.5748e-01
-4.4908e-01
2.1306e-01
5.6934e-01
5.7274e-01
-4.0238e-01
2.3406e-01
[torch.FloatTensor of size 64], Parameter containing:
(0 ,0 ,.,.) =
5.7593e-02 -9.5114e-02 -2.0272e-02
-7.4556e-02 -7.9931e-01 -2.1284e-01
6.5571e-02 -9.6534e-02 -1.2111e-02
(0 ,1 ,.,.) =
-6.9944e-03 1.4266e-02 5.5824e-04
4.1238e-02 -1.6125e-01 -2.3208e-02
3.2887e-03 7.1779e-03 7.1686e-02
(0 ,2 ,.,.) =
-2.3627e-09 -3.9270e-08 -3.2971e-08
2.1737e-08 8.3299e-09 1.2543e-08
1.1382e-08 8.8096e-09 1.5506e-08
...
(0 ,61,.,.) =
-3.6921e-02 1.8294e-02 -2.9358e-02
-9.8615e-02 -4.3645e-02 -5.2717e-02
-7.9635e-02 2.9396e-02 4.1479e-03
(0 ,62,.,.) =
1.6948e-02 1.3978e-02 9.6727e-03
1.4297e-02 -6.6985e-04 -2.2077e-02
1.2398e-02 3.5454e-02 -2.2320e-02
(0 ,63,.,.) =
-2.2600e-02 -2.5331e-02 -2.3548e-02
6.0860e-02 -9.6779e-02 2.4057e-02
-1.2750e-02 9.2237e-02 4.0152e-03
⋮
(1 ,0 ,.,.) =
2.2160e-02 4.2177e-02 -1.6428e-02
-2.9667e-02 5.6865e-02 2.5486e-02
4.3847e-03 5.1188e-02 1.0436e-02
(1 ,1 ,.,.) =
2.5342e-02 5.4374e-02 5.3888e-02
-2.8334e-02 -2.0139e-01 -5.6358e-02
5.6774e-02 7.4188e-02 2.1585e-02
(1 ,2 ,.,.) =
-3.1458e-08 3.5335e-08 5.3791e-08
-2.6896e-08 5.1530e-08 5.4480e-08
-3.8487e-08 -1.1234e-08 -7.5787e-09
...
(1 ,61,.,.) =
-1.2754e-01 4.3552e-02 -6.5607e-02
-6.0462e-02 1.5989e-01 -7.7070e-03
-9.4202e-02 5.0750e-02 -7.8154e-02
(1 ,62,.,.) =
-3.3309e-02 1.6631e-03 -8.8497e-03
1.5553e-02 -5.8277e-02 -2.7437e-02
1.3126e-02 -3.0268e-02 -2.1661e-03
(1 ,63,.,.) =
-4.2313e-03 3.4517e-02 3.8193e-03
5.4317e-02 -1.2457e-02 3.2900e-02
2.2000e-04 1.6040e-02 1.2764e-01
⋮
(2 ,0 ,.,.) =
-3.5247e-02 8.0748e-03 2.0353e-02
1.7344e-02 -2.4320e-02 -1.5511e-04
-2.7634e-04 2.8024e-02 -2.3777e-03
(2 ,1 ,.,.) =
-2.3741e-02 -3.2057e-03 -5.7059e-03
-1.1582e-02 1.7200e-03 2.1067e-02
4.3606e-03 -4.6459e-02 -7.2954e-02
(2 ,2 ,.,.) =
3.1002e-08 5.3568e-08 3.1873e-08
-1.6063e-08 -1.8072e-08 -1.9508e-09
-5.8339e-08 -4.5366e-08 -1.2395e-08
...
(2 ,61,.,.) =
-1.9689e-03 -2.6809e-02 -4.3760e-02
2.4518e-02 -2.8396e-02 -3.5896e-02
-1.7883e-04 -2.4661e-02 -2.0085e-02
(2 ,62,.,.) =
2.1551e-02 2.2789e-03 -2.5823e-02
2.3272e-02 -7.9333e-03 -2.0814e-03
-5.7062e-03 -2.6934e-02 -1.4421e-02
(2 ,63,.,.) =
-1.9674e-02 2.7914e-02 -2.0025e-02
6.3222e-02 -3.9077e-02 -3.3220e-03
-2.7434e-02 1.1390e-02 -3.1608e-03
...
⋮
(61,0 ,.,.) =
4.3440e-03 -7.6970e-03 -6.4950e-02
1.3846e-02 -2.2803e-02 -4.6478e-02
2.7776e-02 1.6080e-02 -1.3363e-02
(61,1 ,.,.) =
4.7379e-02 -2.4982e-02 -2.7605e-02
7.0091e-02 4.2084e-03 -1.0805e-01
1.7526e-02 4.5647e-02 7.8810e-03
(61,2 ,.,.) =
2.6680e-09 2.7671e-08 2.4702e-08
6.3905e-09 4.1020e-08 3.3631e-08
5.8335e-09 1.3334e-08 9.6604e-09
...
(61,61,.,.) =
4.5900e-03 4.7084e-02 -8.6949e-03
-6.3011e-03 5.9585e-02 5.8667e-03
-2.0255e-02 4.3285e-02 4.5094e-03
(61,62,.,.) =
1.1253e-03 -5.7461e-03 -6.8411e-03
6.0616e-03 7.3295e-03 -1.1784e-02
-1.1455e-03 5.1868e-03 -1.9867e-02
(61,63,.,.) =
1.7529e-02 4.4606e-02 -2.6595e-02
2.2102e-02 4.5857e-02 2.3347e-02
1.8052e-02 5.9689e-02 1.7129e-02
⋮
(62,0 ,.,.) =
-2.9112e-02 3.4242e-03 -1.7523e-02
-2.3682e-02 2.2716e-02 -3.8301e-02
-1.0308e-02 -4.3802e-03 -2.3582e-02
(62,1 ,.,.) =
-4.9607e-02 -3.2724e-03 -1.5345e-02
-1.3524e-02 5.4842e-02 1.1187e-02
-2.3549e-02 -2.8495e-02 -6.6371e-02
(62,2 ,.,.) =
-4.9804e-08 -2.8211e-08 -2.0583e-08
-5.2389e-08 -2.8522e-08 -3.5099e-08
-3.2171e-08 -3.4110e-08 -4.3153e-08
...
(62,61,.,.) =
3.4487e-03 2.6532e-02 -1.1202e-02
7.0925e-03 3.7903e-02 -3.2481e-02
4.1381e-02 3.2329e-02 2.8309e-03
(62,62,.,.) =
-6.5955e-03 1.6476e-02 2.1810e-02
-1.2293e-02 2.2310e-02 1.2645e-02
-8.9897e-03 1.1948e-03 -5.2390e-03
(62,63,.,.) =
-2.5295e-03 7.2689e-02 -7.8046e-03
-4.2221e-02 7.9756e-02 -2.7738e-02
4.6716e-03 -5.6596e-02 -8.2261e-02
⋮
(63,0 ,.,.) =
5.2235e-02 3.5231e-03 -3.3131e-02
3.1048e-02 1.6193e-02 1.7283e-02
1.4446e-02 2.4302e-02 -1.9689e-03
(63,1 ,.,.) =
-2.4717e-02 8.3009e-03 -6.1336e-02
-1.6134e-02 5.5323e-02 -6.5029e-02
-2.4715e-02 1.0030e-03 3.2437e-02
(63,2 ,.,.) =
1.8496e-08 5.2798e-09 4.1820e-08
3.7489e-08 2.5450e-08 3.0419e-08
1.1246e-08 -5.6956e-09 -2.0008e-08
...
(63,61,.,.) =
7.1194e-03 -4.1052e-02 -1.0002e-02
2.5924e-02 -6.3819e-02 1.3366e-02
2.9751e-02 -7.9476e-03 1.4007e-02
(63,62,.,.) =
-2.5166e-03 2.2051e-02 -1.9967e-02
-5.9436e-02 4.3872e-02 2.6832e-02
-1.7509e-02 2.4625e-02 2.4822e-02
(63,63,.,.) =
3.5832e-02 -7.0357e-02 3.9452e-03
-2.9835e-02 9.2727e-02 1.9336e-02
-2.9145e-02 -9.7087e-03 -7.3388e-02
[torch.FloatTensor of size 64x64x3x3], Parameter containing:
0.3090
0.2147
0.2366
0.4259
0.5137
0.2181
0.2204
0.2300
0.2640
0.2695
0.2138
0.4602
0.2661
0.2319
0.3900
0.2389
0.2660
0.3634
0.3474
0.2477
0.3285
0.5349
0.6440
0.2275
0.4482
0.3078
0.2604
0.4651
0.2179
0.2858
0.3426
0.4420
0.4450
0.4500
0.5516
0.5092
0.2564
0.2634
0.5664
0.6410
0.2228
0.1986
0.2460
0.2242
0.2143
0.1982
0.6368
0.3106
0.5049
0.2403
0.3065
0.3760
0.3794
0.4281
0.2991
0.3326
0.2596
0.3345
0.2006
0.4351
0.1683
0.5149
0.2629
0.3254
[torch.FloatTensor of size 64], Parameter containing:
0.1657
0.2420
0.1780
-0.0431
-0.2053
0.1598
0.2929
0.0912
0.1116
0.0884
0.1104
-0.2035
0.1539
0.0857
-0.1094
0.0654
0.0766
-0.2067
-0.0212
0.1396
0.0401
-0.2827
-0.3257
-0.0035
-0.4373
-0.1248
0.1282
-0.0874
0.1199
-0.0829
-0.5315
-0.0780
-0.3876
-0.0547
-0.1816
-0.1888
0.1320
0.0031
-0.2697
-0.2984
0.1394
0.2597
0.1372
0.0053
0.0132
0.3295
-0.2715
-0.0187
-0.2467
0.1579
0.0165
-0.0890
-0.1903
-0.0787
0.1700
-0.4832
0.0619
-0.0677
0.3125
-0.5064
0.3138
-0.2617
-0.1545
0.0063
[torch.FloatTensor of size 64], Parameter containing:
(0 ,0 ,.,.) =
2.5947e-02 -1.0458e-01 -4.7712e-03
-8.6223e-02 -3.3021e-01 -1.0275e-01
-5.7426e-02 -1.9074e-01 -5.4646e-02
(0 ,1 ,.,.) =
-1.6951e-02 2.1384e-02 -2.1074e-03
-3.2983e-03 4.5014e-02 -1.1510e-02
-5.9602e-02 6.4942e-03 2.9080e-03
(0 ,2 ,.,.) =
-4.4903e-03 1.9637e-02 1.3167e-02
1.3050e-02 -7.7471e-03 1.1931e-02
1.3454e-02 1.1103e-02 5.5145e-03
...
(0 ,61,.,.) =
1.2706e-03 -7.7438e-03 2.0753e-02
-4.0024e-02 -4.0383e-02 -3.4821e-02
-2.0251e-02 -9.5164e-03 1.3954e-02
(0 ,62,.,.) =
-2.3430e-03 3.2303e-02 -4.3342e-03
8.6194e-03 1.0553e-02 1.8074e-03
-1.2760e-02 -1.0232e-02 4.5711e-03
(0 ,63,.,.) =
1.5302e-02 2.1361e-02 -7.0908e-03
-1.4221e-02 4.5979e-02 2.1369e-02
3.1312e-02 6.6428e-02 2.1465e-02
⋮
(1 ,0 ,.,.) =
5.3422e-02 4.0515e-02 9.6680e-03
3.2884e-02 -2.3474e-02 3.4642e-02
-1.2861e-02 5.0066e-02 5.4579e-02
(1 ,1 ,.,.) =
2.8764e-02 4.3431e-02 2.8258e-02
2.8734e-02 -3.5459e-02 -5.2788e-02
-5.5119e-02 -7.1813e-02 -8.2970e-02
(1 ,2 ,.,.) =
9.5293e-02 1.2549e-01 -6.4001e-02
-4.1166e-02 -9.0480e-04 5.1387e-02
-1.1311e-01 -7.9823e-02 1.4373e-01
...
(1 ,61,.,.) =
-7.6924e-03 2.0647e-02 1.9521e-02
-6.7352e-03 1.2601e-04 4.8309e-03
-6.2405e-03 -9.2119e-03 -2.5806e-04
(1 ,62,.,.) =
-2.6153e-02 -2.4641e-02 4.0970e-02
-1.9164e-02 -1.0160e-02 3.3163e-02
5.4200e-03 9.0485e-04 6.7799e-04
(1 ,63,.,.) =
7.7762e-03 2.6447e-02 6.3650e-02
-3.0608e-02 2.4959e-02 1.2951e-02
-2.0938e-02 -7.7342e-03 -3.8790e-02
⋮
(2 ,0 ,.,.) =
1.0893e-02 -1.4409e-02 1.5730e-02
1.6655e-02 4.4535e-02 6.3212e-02
3.4121e-02 7.3135e-02 5.9203e-02
(2 ,1 ,.,.) =
2.3195e-03 7.7598e-03 2.0308e-02
2.0457e-02 4.0029e-02 3.4744e-02
-4.7356e-02 -3.7286e-02 1.4542e-02
(2 ,2 ,.,.) =
-2.2742e-02 -1.9000e-02 -8.4317e-03
-9.8759e-04 2.1510e-02 6.3959e-03
-9.4558e-03 2.6833e-03 -3.1136e-02
...
(2 ,61,.,.) =
-7.5787e-03 -1.6056e-02 -6.4204e-04
-5.5104e-03 1.4252e-02 4.5000e-02
-9.2800e-03 2.2351e-02 4.1728e-02
(2 ,62,.,.) =
2.5705e-02 4.8207e-02 7.9145e-02
-4.4350e-03 3.8872e-03 4.1694e-02
8.0536e-04 -1.0601e-02 9.2706e-03
(2 ,63,.,.) =
-3.3892e-02 9.3543e-03 4.1746e-02
-1.6470e-02 3.9542e-03 6.2438e-02
-3.1055e-02 -3.6302e-03 7.0817e-02
...
⋮
(61,0 ,.,.) =
-7.1044e-05 -9.0020e-03 -2.6998e-03
3.0072e-03 1.1579e-02 1.5214e-02
3.4832e-03 1.1353e-05 1.6320e-02
(61,1 ,.,.) =
-2.6334e-02 2.1967e-02 -6.0039e-02
4.4519e-02 1.3203e-01 -9.1163e-03
5.4242e-02 1.3726e-01 2.7454e-02
(61,2 ,.,.) =
1.7122e-02 3.7646e-03 1.4872e-02
1.2092e-02 1.1319e-02 3.4667e-02
8.1790e-03 -2.0805e-02 2.7143e-02
...
(61,61,.,.) =
-1.0111e-02 -1.0526e-02 2.8394e-02
-2.5112e-02 -2.2196e-02 3.7229e-02
-3.8220e-02 -4.6644e-02 1.5660e-02
(61,62,.,.) =
-2.5913e-03 -2.4307e-02 1.0611e-02
-2.1730e-02 -4.3938e-02 -7.1536e-03
-2.5171e-02 -5.9467e-02 -2.5577e-02
(61,63,.,.) =
2.8652e-02 2.5850e-04 1.1416e-03
3.7812e-02 -1.1271e-03 9.6027e-03
3.9350e-02 1.0134e-02 1.0449e-02
⋮
(62,0 ,.,.) =
-7.9305e-03 7.0872e-03 2.1412e-02
-6.0065e-02 1.4147e-03 9.7281e-02
-6.0130e-02 -2.1725e-02 3.6863e-02
(62,1 ,.,.) =
2.8024e-02 2.6183e-02 -2.3027e-02
5.1900e-02 -2.0588e-03 -1.0940e-01
-3.2729e-02 -6.2752e-03 8.0630e-03
(62,2 ,.,.) =
-1.8062e-02 -1.9510e-02 4.3163e-02
4.6080e-02 2.9494e-02 4.0844e-02
5.9607e-03 -6.5891e-03 -6.4623e-03
...
(62,61,.,.) =
2.2193e-02 8.4653e-03 3.6764e-03
1.7549e-02 2.1971e-02 -4.5108e-03
2.1124e-02 3.4591e-02 -1.6310e-02
(62,62,.,.) =
3.8144e-02 4.8395e-02 -9.5556e-02
1.8923e-02 1.1341e-02 -7.6311e-02
4.7358e-03 3.2138e-02 -7.4777e-02
(62,63,.,.) =
-1.9031e-02 -3.2568e-02 -3.8251e-02
1.0705e-02 2.3121e-03 -7.5078e-02
3.3316e-02 3.5515e-02 -2.1023e-03
⋮
(63,0 ,.,.) =
-1.3330e-01 7.4683e-02 -3.8624e-03
9.1377e-02 8.2415e-02 3.9469e-02
-1.8265e-02 -5.9943e-02 8.9354e-02
(63,1 ,.,.) =
1.5566e-02 -4.1716e-02 1.0633e-02
7.2644e-03 3.1934e-02 1.2732e-03
-2.0851e-02 -3.7593e-03 -7.0170e-02
(63,2 ,.,.) =
-6.6139e-02 1.0627e-01 1.9590e-02
5.4987e-02 -1.5552e-01 -1.8819e-02
-4.2554e-03 4.4964e-02 -2.4632e-02
...
(63,61,.,.) =
-6.1691e-02 -4.5531e-02 -9.1721e-03
4.3995e-02 4.5703e-02 -7.0108e-02
1.1388e-02 4.4678e-02 -4.5953e-02
(63,62,.,.) =
4.3432e-03 2.3194e-02 -2.1895e-02
-8.0216e-02 -5.7606e-02 -9.8455e-03
-3.3285e-02 -1.1468e-01 -2.3779e-02
(63,63,.,.) =
-6.3785e-02 -2.4485e-02 -4.9061e-02
-6.1594e-02 1.0328e-01 5.9685e-03
8.1863e-02 -3.0314e-02 -4.6373e-03
[torch.FloatTensor of size 64x64x3x3], Parameter containing:
0.2496
0.2198
0.2756
0.6073
0.2654
0.2942
0.1136
0.4425
0.2868
0.2974
0.2506
0.4103
0.4855
0.3383
0.4670
0.1772
0.2171
0.5025
0.2263
0.3667
0.4867
0.4586
0.4652
0.2200
0.1510
0.2761
0.3813
0.2803
0.2382
0.3953
0.3032
0.3163
0.2025
0.2323
0.2003
0.1661
0.4690
0.3476
0.3414
0.2274
0.2485
0.2356
0.2726
0.4657
0.3429
0.2465
0.4674
0.2812
0.6241
0.4152
0.3403
0.4218
0.1152
0.2985
0.5802
0.2795
0.4706
0.4517
0.4303
0.2749
0.3427
0.1137
0.5069
0.4370
[torch.FloatTensor of size 64], Parameter containing:
0.2275
0.0087
-0.0673
-0.0688
0.3598
-0.2017
-0.0000
0.0237
0.3955
0.0371
0.0069
0.2758
-0.0703
-0.2397
-0.0818
-0.0941
-0.1454
0.0373
-0.3617
-0.3956
-0.4079
0.0036
-0.2788
-0.0353
-0.0703
0.2101
-0.0046
-0.1966
-0.2807
-0.0165
0.2645
-0.0894
-0.2105
-0.1303
0.1721
0.0534
-0.2230
-0.0480
0.2457
0.2095
0.1622
0.1137
0.1146
-0.1487
-0.0322
-0.3055
0.4912
0.1087
0.0128
0.1004
0.4155
-0.0147
0.0239
0.0998
-0.1727
0.1008
-0.1456
-0.2274
0.1364
0.2013
-0.0574
0.2353
-0.1130
0.3093
[torch.FloatTensor of size 64], Parameter containing:
(0 ,0 ,.,.) =
1.9712e-02 -5.2562e-03 -3.7619e-03
-1.9635e-02 -1.2336e-02 -3.5196e-02
5.0761e-02 7.5668e-02 4.3344e-02
(0 ,1 ,.,.) =
1.4160e-02 -8.6094e-03 -1.0541e-02
-4.2586e-02 -2.3814e-02 -5.4694e-02
-1.4018e-03 4.6720e-02 5.0898e-02
(0 ,2 ,.,.) =
2.1559e-02 4.1633e-03 -9.7118e-03
-9.3201e-03 -2.5432e-02 -2.8274e-02
-3.0107e-02 -4.8230e-02 -2.6001e-02
...
(0 ,61,.,.) =
5.4300e-03 9.1875e-02 3.1938e-03
-1.7945e-02 5.7266e-02 -8.4098e-03
-3.4961e-02 -2.3296e-02 -3.5089e-02
(0 ,62,.,.) =
2.5603e-02 -3.1689e-02 -5.4160e-02
6.9736e-02 -1.0716e-02 -6.8034e-02
3.5578e-02 3.4749e-02 -1.9334e-02
(0 ,63,.,.) =
-6.5420e-02 -4.6427e-03 -2.3362e-02
7.5833e-02 9.1174e-03 -4.9701e-02
6.2944e-02 -9.8735e-02 3.3158e-02
⋮
(1 ,0 ,.,.) =
-9.0557e-03 -3.0753e-02 1.1953e-02
-3.2539e-02 -6.2846e-03 -2.0235e-02
4.7996e-03 -2.1462e-02 -4.1557e-03
(1 ,1 ,.,.) =
1.7163e-02 -2.3303e-03 7.3972e-02
-3.2105e-02 -7.7536e-02 -1.2648e-02
3.8985e-02 -4.3170e-02 1.0904e-02
(1 ,2 ,.,.) =
-2.9643e-02 -5.8534e-02 -5.9736e-02
-2.9437e-02 -3.6441e-02 -1.2380e-02
-2.2775e-02 -2.4485e-03 -1.6124e-03
...
(1 ,61,.,.) =
2.6830e-02 1.4267e-02 6.2658e-02
3.0585e-04 -5.3241e-03 3.2786e-03
2.1097e-02 -2.3189e-02 1.2102e-02
(1 ,62,.,.) =
-6.1182e-02 -2.9227e-02 2.0036e-02
-7.6089e-02 -7.7057e-02 8.6544e-02
-3.9228e-02 -3.2361e-02 -8.8970e-02
(1 ,63,.,.) =
-1.3372e-01 8.8362e-02 8.3836e-02
-1.1688e-02 4.3156e-01 -3.3629e-03
-2.3925e-02 -1.0092e-01 -1.0184e-01
⋮
(2 ,0 ,.,.) =
8.0165e-02 4.3042e-02 2.7325e-03
3.5269e-02 -1.5504e-02 -3.5011e-02
-1.7164e-02 -2.6827e-02 -3.3946e-02
(2 ,1 ,.,.) =
4.5439e-02 5.1585e-02 1.8321e-02
-3.9647e-02 2.3956e-02 -2.6609e-02
-3.0358e-02 -6.4729e-02 2.5834e-02
(2 ,2 ,.,.) =
3.8105e-02 4.0986e-02 4.1005e-02
1.7584e-02 -1.6494e-02 -3.2716e-02
5.5886e-03 -1.7068e-02 -3.0605e-02
...
(2 ,61,.,.) =
-1.3694e-01 -1.4074e-01 5.1423e-02
-1.2521e-01 -1.3128e-01 7.5733e-02
-4.5032e-02 -1.7081e-02 7.1252e-02
(2 ,62,.,.) =
6.3381e-02 1.5874e-02 -2.7322e-02
8.0356e-02 3.6104e-02 -2.8506e-02
2.6638e-02 2.2021e-02 3.2345e-02
(2 ,63,.,.) =
-1.2068e-03 -4.6179e-02 -1.5351e-02
-1.1276e-02 1.9200e-02 3.4336e-02
1.6540e-02 -7.8592e-03 -2.5392e-02
...
⋮
(61,0 ,.,.) =
3.3384e-02 6.9963e-02 1.0745e-02
-1.7518e-02 -5.3524e-02 -6.4960e-02
3.4248e-04 -4.5557e-02 -4.7336e-02
(61,1 ,.,.) =
-5.1031e-03 7.9784e-03 -8.6553e-04
-1.6557e-03 1.4661e-02 5.3365e-03
-3.1784e-02 -6.6940e-02 -4.6889e-02
(61,2 ,.,.) =
-1.1775e-02 7.2759e-03 7.6622e-03
-6.1288e-02 -5.2078e-02 -4.5152e-02
-8.6584e-02 -9.7381e-02 -1.0405e-01
...
(61,61,.,.) =
2.1243e-02 6.2456e-02 2.5188e-02
-2.2911e-02 -2.1100e-03 -2.7573e-02
4.6557e-02 6.4980e-02 3.1879e-02
(61,62,.,.) =
6.2867e-03 2.4255e-02 8.9674e-02
-7.7718e-03 -5.4311e-02 -4.6843e-02
-6.7499e-03 -6.6857e-02 -4.9842e-02
(61,63,.,.) =
4.7326e-03 -3.9533e-02 1.1500e-03
-2.7957e-02 -1.3466e-01 -6.0753e-02
-3.2010e-03 7.2213e-02 1.1009e-01
⋮
(62,0 ,.,.) =
2.3763e-02 -1.7876e-02 -7.4843e-03
1.6239e-02 5.4479e-04 -3.3735e-02
-2.2854e-02 -1.4316e-03 1.1010e-02
(62,1 ,.,.) =
5.2277e-03 -2.5941e-03 5.9594e-03
-2.9058e-03 -7.3409e-03 3.0652e-02
7.5540e-02 6.6445e-03 2.5518e-03
(62,2 ,.,.) =
-6.5970e-02 -4.1286e-02 -3.0278e-02
-3.5108e-02 -3.9099e-02 -1.6818e-02
-1.0224e-02 -8.6995e-03 -5.9939e-04
...
(62,61,.,.) =
2.1233e-02 -2.4559e-02 -7.4436e-03
-4.3734e-03 -3.2864e-02 -3.3453e-02
8.9269e-03 -1.7646e-02 3.8375e-04
(62,62,.,.) =
-7.8930e-02 -7.2940e-02 -6.7911e-02
-8.4146e-02 -8.3657e-02 5.3666e-02
-3.5577e-02 -3.6835e-02 5.8987e-03
(62,63,.,.) =
8.3767e-02 8.0476e-05 7.2164e-02
-6.4219e-02 -1.2661e-01 4.6026e-02
9.3033e-02 -4.7521e-02 3.6777e-02
⋮
(63,0 ,.,.) =
4.1012e-02 1.3361e-03 -5.8616e-02
4.2461e-02 2.9437e-03 -2.0445e-02
7.6097e-02 5.2504e-02 -5.5636e-03
(63,1 ,.,.) =
2.2046e-02 4.0888e-03 1.4645e-02
-7.7532e-02 -1.1912e-01 -7.0892e-02
-1.0618e-02 -3.2121e-02 -2.3969e-02
(63,2 ,.,.) =
-2.1612e-02 -2.6110e-03 -3.1664e-02
-3.2892e-02 -3.9771e-02 -5.1463e-02
-2.6150e-02 -3.6554e-02 -2.3315e-02
...
(63,61,.,.) =
4.4600e-03 8.4181e-02 2.3199e-02
5.7595e-02 1.3036e-01 3.2172e-02
-2.2774e-03 4.2065e-02 -4.8619e-02
(63,62,.,.) =
3.1533e-02 -4.3655e-02 2.0361e-02
3.9973e-03 -5.1430e-02 -6.3839e-02
6.4002e-03 4.5347e-02 4.7346e-02
(63,63,.,.) =
-9.1818e-02 1.0264e-02 9.6565e-02
-2.1635e-03 -2.3452e-02 -5.9038e-02
1.9402e-02 2.8854e-02 -9.6113e-02
[torch.FloatTensor of size 64x64x3x3], Parameter containing:
0.3910
0.4375
0.3746
0.3990
0.3404
0.3503
0.2618
0.2707
0.2865
0.4308
0.1895
0.3041
0.3837
0.2944
0.2105
0.3304
0.2943
0.2887
0.2060
0.4627
0.2335
0.1831
0.4489
0.2830
0.3389
0.2997
0.3503
0.2735
0.3908
0.2817
0.2636
0.4462
0.3282
0.3776
0.4471
0.3878
0.2516
0.3172
0.3661
0.3166
0.3818
0.3128
0.2274
0.3627
0.2902
0.2381
0.2988
0.2469
0.3840
0.2886
0.3197
0.2879
0.3218
0.4559
0.3500
0.2420
0.3396
0.3519
0.3839
0.3806
0.4039
0.2826
0.4594
0.3342
[torch.FloatTensor of size 64], Parameter containing:
-0.0997
-0.4755
-0.0474
-0.2698
-0.0834
-0.0072
0.0474
0.1022
-0.0170
-0.1471
0.2307
0.1447
-0.1775
0.0273
0.1559
-0.1836
0.1238
-0.1522
0.0554
-0.2881
-0.2606
0.2316
-0.3242
-0.0219
-0.2645
0.0576
-0.2465
0.0481
-0.3530
0.0950
-0.1862
-0.1707
-0.0161
-0.2604
-0.3145
-0.1083
0.0659
-0.1427
-0.0570
-0.0076
-0.3006
-0.0744
-0.0683
-0.1104
0.0253
0.0489
-0.2515
0.1150
-0.3783
0.0846
-0.0368
0.1439
-0.0468
-0.3087
-0.0240
0.1397
-0.0908
-0.1795
-0.1129
-0.0793
-0.1491
0.0594
-0.4433
-0.0138
[torch.FloatTensor of size 64], Parameter containing:
(0 ,0 ,.,.) =
-2.1574e-02 -4.5688e-03 4.5483e-03
-8.1870e-03 4.1740e-02 2.3010e-02
-8.9283e-03 5.7352e-02 2.9818e-02
(0 ,1 ,.,.) =
5.8627e-02 4.2864e-02 4.4912e-02
2.2281e-02 -1.2969e-02 7.6099e-03
4.5373e-02 3.0712e-02 3.7700e-02
(0 ,2 ,.,.) =
-1.5456e-02 -3.8692e-02 -4.6010e-02
-2.3123e-02 2.8293e-02 4.7790e-03
-2.0328e-02 1.3756e-02 2.5883e-02
...
(0 ,61,.,.) =
5.1302e-02 4.2291e-02 5.7833e-02
4.5210e-02 5.5850e-02 1.4318e-02
1.4241e-02 1.7968e-02 1.4344e-02
(0 ,62,.,.) =
4.6012e-03 1.2566e-02 4.8931e-02
-6.5754e-03 -2.6431e-02 1.5855e-02
1.3192e-02 1.9011e-02 1.3842e-02
(0 ,63,.,.) =
6.1983e-02 6.9919e-02 6.1035e-02
6.1253e-02 9.9557e-02 5.9060e-02
5.8298e-02 8.1652e-02 8.1499e-02
⋮
(1 ,0 ,.,.) =
-1.0088e-02 -1.2959e-02 9.7798e-03
5.5408e-02 4.3501e-02 5.6983e-02
5.3427e-02 3.5118e-02 3.6782e-02
(1 ,1 ,.,.) =
2.4442e-03 -3.0207e-02 -1.0377e-02
-4.5297e-02 -4.5318e-02 5.4623e-03
-4.4762e-02 -1.5508e-02 6.9745e-03
(1 ,2 ,.,.) =
3.9658e-02 3.6838e-02 5.8796e-03
2.3207e-02 3.9240e-03 -2.0887e-02
-1.4829e-02 5.3606e-03 1.7404e-03
...
(1 ,61,.,.) =
3.2160e-02 5.9042e-02 4.8433e-02
-2.6464e-02 -8.0667e-03 -1.0359e-02
-2.6699e-02 -9.5411e-03 -2.8902e-02
(1 ,62,.,.) =
-2.9235e-02 -3.9078e-02 -4.4955e-02
-2.0346e-02 -4.4891e-02 -3.7477e-02
1.9653e-02 -1.5562e-03 -5.8245e-03
(1 ,63,.,.) =
-5.0696e-02 -4.8902e-02 9.1631e-03
5.1668e-03 2.0509e-02 6.6874e-02
2.8934e-02 4.6717e-02 2.1371e-02
⋮
(2 ,0 ,.,.) =
2.1744e-02 -2.8354e-02 -3.2557e-02
3.0519e-02 1.8536e-02 1.5244e-02
1.3832e-03 1.7051e-02 3.2020e-02
(2 ,1 ,.,.) =
-3.6293e-02 1.0914e-02 4.5371e-02
1.3399e-02 6.4272e-02 8.8210e-02
4.6697e-02 9.9653e-02 8.7606e-02
(2 ,2 ,.,.) =
-2.4336e-02 -2.9627e-02 1.9537e-02
-3.3412e-02 -2.2290e-02 -2.8879e-02
1.4765e-02 1.7234e-02 -1.8185e-02
...
(2 ,61,.,.) =
-3.9859e-02 -7.1075e-02 -5.8546e-02
2.2902e-02 1.1184e-02 -2.3654e-02
8.1897e-02 1.1996e-01 9.3242e-02
(2 ,62,.,.) =
3.1984e-02 7.4931e-02 6.6020e-02
2.8490e-02 1.1931e-01 1.2100e-01
7.9259e-04 4.3812e-02 4.4648e-02
(2 ,63,.,.) =
3.2748e-02 4.1444e-02 -8.1932e-03
4.5541e-02 2.9426e-02 -8.5440e-03
1.1634e-04 1.8045e-03 1.4826e-02
...
⋮
(61,0 ,.,.) =
-4.4144e-02 -8.3106e-02 -5.3073e-02
3.2124e-02 1.0286e-02 2.4409e-02
6.1606e-03 -1.9455e-02 4.0534e-02
(61,1 ,.,.) =
5.6026e-04 9.6961e-03 2.5010e-03
7.1679e-03 -1.7535e-02 -2.3857e-02
-9.8745e-03 -1.8550e-02 1.7301e-03
(61,2 ,.,.) =
4.3882e-03 4.2049e-02 7.5950e-02
-6.5610e-02 -3.6130e-02 -1.9404e-02
-3.8091e-02 -2.6749e-02 -1.3865e-02
...
(61,61,.,.) =
-4.5593e-02 -4.6050e-02 -2.2809e-02
-9.7648e-03 2.4910e-03 2.4503e-02
2.0381e-02 5.2393e-02 6.9019e-02
(61,62,.,.) =
9.3306e-04 1.2483e-02 -1.1817e-02
-1.2627e-02 -1.8756e-02 -1.4144e-03
-5.2490e-03 -4.6126e-03 -1.3224e-02
(61,63,.,.) =
7.4689e-04 -1.0135e-02 -7.8264e-03
1.2491e-02 -2.5865e-02 4.0514e-02
5.8855e-03 4.5990e-02 1.0651e-01
⋮
(62,0 ,.,.) =
1.2262e-02 -1.5378e-02 1.3862e-03
4.1166e-02 -2.4944e-02 -2.6686e-02
-1.7423e-02 5.2690e-03 -2.1861e-02
(62,1 ,.,.) =
-3.1207e-02 -3.3025e-02 2.2114e-02
-2.4009e-02 1.2988e-02 2.2430e-02
1.0332e-02 4.3601e-03 4.7321e-03
(62,2 ,.,.) =
2.0182e-02 6.1569e-02 -2.8771e-02
5.8231e-02 4.6767e-02 -2.8417e-05
3.7545e-02 -4.5886e-02 1.5849e-02
...
(62,61,.,.) =
7.0431e-03 -3.6082e-03 7.1986e-03
2.4895e-02 6.1671e-03 -3.2427e-02
7.2338e-03 2.2406e-03 -5.3330e-02
(62,62,.,.) =
2.8072e-02 -1.0571e-02 -1.3854e-02
-1.0879e-02 6.1929e-03 -5.6713e-03
-2.6083e-02 8.1861e-03 -3.2873e-02
(62,63,.,.) =
-3.1032e-02 -6.0485e-02 -2.5583e-02
-4.6239e-02 -2.2805e-02 -7.7678e-03
-9.4698e-03 4.0247e-03 -4.8637e-03
⋮
(63,0 ,.,.) =
2.3128e-02 -5.6038e-02 -3.4572e-02
1.0638e-03 5.7929e-02 -7.6970e-03
-3.0103e-02 3.5573e-02 -1.8143e-02
(63,1 ,.,.) =
9.6840e-02 -1.1186e-01 -7.8766e-02
-1.0444e-01 -1.0851e-01 -1.9553e-01
-1.1986e-01 -7.1474e-02 3.6750e-02
(63,2 ,.,.) =
-2.2194e-02 6.0298e-03 5.6914e-02
-4.8342e-02 7.8893e-02 -5.1026e-02
-5.1294e-02 -5.7434e-02 -1.9178e-02
...
(63,61,.,.) =
-4.4896e-02 -8.1267e-02 5.1794e-02
-8.3985e-02 -5.7778e-02 6.7891e-02
2.3837e-02 3.8954e-02 4.1141e-02
(63,62,.,.) =
4.6446e-03 2.7367e-02 -2.3154e-02
2.0675e-02 2.3429e-02 6.4380e-04
-5.2222e-02 -1.4854e-02 -2.5150e-02
(63,63,.,.) =
2.1291e-02 1.2736e-02 8.4553e-03
-8.2932e-02 7.2067e-02 1.3107e-01
8.5491e-03 1.3677e-01 3.9867e-02
[torch.FloatTensor of size 64x64x3x3], Parameter containing:
0.2560
0.5690
0.4042
0.5130
0.2178
0.4940
0.3315
0.5510
0.4354
0.5291
0.2081
0.4735
0.5945
0.5645
0.2761
0.2571
0.4853
0.6240
0.4370
0.2308
0.4970
0.3157
0.5706
0.2162
0.1932
0.1448
0.2218
0.2389
0.5871
0.3501
0.4109
0.3199
0.5808
0.3281
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0.1971
0.6139
0.4075
0.6304
0.3874
0.7605
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0.3071
0.4603
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0.1914
0.4431
0.2537
0.5745
0.6459
0.3914
0.3090
0.6782
0.1937
0.5814
0.2570
0.3514
0.2124
0.5794
0.3415
0.2051
0.0715
0.4090
0.4416
[torch.FloatTensor of size 64], Parameter containing:
-0.1778
-0.1287
0.0349
-0.1452
0.1864
-0.1413
-0.4201
-0.1334
0.2183
-0.1912
0.0311
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0.0047
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0.2092
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0.0286
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0.2862
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0.3080
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0.1136
-0.3157
0.0423
0.0567
0.2369
-0.0727
0.0465
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0.1309
0.0282
-0.1371
0.1464
-0.0717
-0.3237
-0.1583
-0.0424
-0.1278
-0.1703
0.0413
0.0891
0.0770
-0.0730
0.0683
-0.0391
0.0476
[torch.FloatTensor of size 64], Parameter containing:
( 0 , 0 ,.,.) =
-7.1555e-02 -1.1031e-01 -1.3711e-01
7.0593e-02 -1.4782e-02 -1.0053e-01
1.1938e-01 8.7330e-02 -8.2206e-03
( 0 , 1 ,.,.) =
-2.3999e-02 -6.3682e-03 2.4303e-03
6.1831e-03 1.8781e-02 2.5324e-02
2.3656e-03 -4.0037e-03 -1.1949e-02
( 0 , 2 ,.,.) =
6.0344e-03 6.3784e-03 -1.2247e-02
7.8854e-03 -1.3464e-02 -4.2702e-02
1.7380e-02 -1.3862e-02 -4.7145e-02
...
( 0 ,61 ,.,.) =
3.4324e-02 3.2257e-02 2.5819e-02
8.4676e-03 -4.5413e-04 -1.0832e-02
-6.7166e-03 -1.5052e-02 -2.6939e-02
( 0 ,62 ,.,.) =
-1.2089e-02 -2.3588e-02 -2.2689e-02
1.0135e-02 1.8285e-02 -1.5695e-02
2.1352e-02 5.8568e-02 4.2873e-02
( 0 ,63 ,.,.) =
1.4421e-02 -2.8298e-02 -7.0770e-03
3.0260e-02 -6.6294e-03 -1.6901e-02
3.9085e-02 1.4222e-02 2.2294e-02
⋮
( 1 , 0 ,.,.) =
-7.7911e-02 -7.3929e-02 -3.6671e-02
-3.4903e-02 -6.2355e-02 -3.7793e-02
-2.8379e-02 -5.4291e-02 -4.9411e-02
( 1 , 1 ,.,.) =
-1.2970e-02 -2.1825e-02 -2.8767e-04
7.6444e-03 1.7653e-02 1.6660e-02
3.8337e-02 2.3006e-02 -1.6620e-03
( 1 , 2 ,.,.) =
-8.7592e-02 -8.4735e-02 -5.5818e-02
-7.7731e-02 -8.0311e-02 -3.2554e-02
-5.6313e-02 -4.2047e-02 1.5247e-02
...
( 1 ,61 ,.,.) =
-3.2377e-02 -4.0018e-02 -2.9523e-02
-1.5294e-02 -1.4165e-02 2.7086e-03
1.1652e-02 2.3886e-02 2.4413e-02
( 1 ,62 ,.,.) =
2.0891e-03 -3.0475e-02 -3.3818e-02
6.7829e-03 3.8681e-04 -1.4540e-02
-3.1306e-03 6.7689e-03 8.4524e-03
( 1 ,63 ,.,.) =
3.0586e-02 4.6281e-02 3.8359e-04
5.3079e-02 6.7488e-02 3.0547e-02
2.3374e-02 4.3993e-02 -3.8713e-03
⋮
( 2 , 0 ,.,.) =
1.3878e-02 3.2724e-02 4.6584e-02
-8.0647e-03 1.6209e-03 1.5153e-02
-7.0342e-02 -5.3299e-02 -4.5920e-02
( 2 , 1 ,.,.) =
4.6035e-02 3.5400e-02 3.4941e-02
5.8351e-02 5.4640e-02 2.7162e-02
2.6799e-02 4.5056e-02 6.6886e-03
( 2 , 2 ,.,.) =
-3.3766e-02 -3.8605e-02 -2.4172e-02
-1.8285e-03 1.0888e-02 1.1425e-02
2.2282e-02 1.4024e-02 3.6332e-03
...
( 2 ,61 ,.,.) =
-1.6330e-02 -6.9552e-02 -8.9737e-02
3.9766e-02 1.5501e-02 -2.2695e-02
1.0290e-01 1.2294e-01 6.3867e-02
( 2 ,62 ,.,.) =
-4.2318e-03 4.9511e-02 -7.6289e-03
-2.7720e-02 7.0398e-03 -9.4052e-03
-6.7008e-02 -6.0542e-02 -2.5967e-02
( 2 ,63 ,.,.) =
-5.8560e-03 -1.7573e-02 -3.8016e-02
2.8579e-03 -4.1603e-03 1.0113e-02
2.6243e-02 3.5200e-02 3.1143e-02
...
⋮
(125, 0 ,.,.) =
-4.4193e-02 -6.5322e-02 -1.7594e-02
-9.3970e-02 -5.8291e-02 1.2093e-02
-2.2998e-02 3.2463e-02 7.1731e-02
(125, 1 ,.,.) =
-4.7220e-03 -3.0125e-03 -1.8075e-02
1.2667e-02 -8.0509e-03 -1.4605e-02
7.8220e-03 -1.0720e-02 -2.6515e-02
(125, 2 ,.,.) =
-2.5299e-02 -4.9383e-02 -1.2720e-02
-5.2206e-02 -4.7233e-02 -4.2470e-03
-4.8697e-02 -2.5320e-02 8.6178e-03
...
(125,61 ,.,.) =
-3.7617e-03 7.8398e-03 -5.9525e-03
4.0277e-03 7.3575e-03 -1.1667e-02
-3.9997e-02 -3.8038e-02 -5.0469e-02
(125,62 ,.,.) =
-3.8949e-03 -6.8965e-03 3.4102e-02
-6.9814e-03 -4.9762e-02 5.8711e-02
1.8361e-02 2.5874e-02 8.0028e-02
(125,63 ,.,.) =
-3.3014e-02 -2.1510e-02 -2.1509e-03
-4.3894e-02 -3.2009e-02 -1.6265e-02
-1.1037e-02 2.8872e-04 3.0937e-02
⋮
(126, 0 ,.,.) =
-4.9907e-02 -5.0222e-02 -5.0985e-02
2.2644e-02 -1.4098e-02 -2.4426e-02
1.9960e-02 9.6426e-02 1.0580e-01
(126, 1 ,.,.) =
-3.6873e-02 2.1413e-03 8.3469e-03
-4.0796e-02 -3.3767e-02 -3.4955e-02
3.9466e-02 7.0508e-02 8.6065e-02
(126, 2 ,.,.) =
1.4842e-02 6.6914e-03 1.4324e-02
-3.2621e-02 -4.4027e-02 -2.2269e-02
7.1982e-03 -1.9187e-02 -4.9348e-03
...
(126,61 ,.,.) =
-4.9938e-03 1.6018e-02 1.1242e-02
-4.7668e-03 2.1921e-02 2.2660e-02
-2.6753e-02 2.6917e-04 -5.6827e-03
(126,62 ,.,.) =
-8.7725e-03 1.0761e-02 7.3603e-03
-1.8010e-05 -1.7926e-02 4.8229e-03
4.2431e-02 -1.5764e-02 2.3554e-02
(126,63 ,.,.) =
-1.3830e-02 -3.0793e-03 -4.0854e-03
3.3363e-02 4.2952e-02 3.5867e-02
-3.9653e-02 -3.0855e-02 -4.3189e-02
⋮
(127, 0 ,.,.) =
-3.8617e-02 -3.1549e-03 2.5739e-03
-1.1592e-02 9.8761e-03 7.5235e-03
-1.9339e-02 -9.8779e-03 2.1755e-03
(127, 1 ,.,.) =
1.6889e-04 1.8302e-03 -8.9537e-03
5.8343e-03 1.7360e-02 -1.9029e-02
5.8642e-03 -7.4307e-04 1.4667e-03
(127, 2 ,.,.) =
-1.6506e-02 -2.8401e-02 1.3986e-02
-2.2922e-02 -4.3484e-02 1.0471e-02
-2.5801e-03 -4.5258e-02 7.9791e-03
...
(127,61 ,.,.) =
-1.5260e-03 -7.6469e-03 1.3597e-02
5.5301e-04 -2.9176e-03 2.2147e-02
3.2763e-03 -1.0775e-05 1.3163e-02
(127,62 ,.,.) =
5.1756e-03 1.8495e-02 -8.0268e-03
-3.5030e-02 2.6403e-02 -7.1220e-03
-5.2325e-02 -1.1185e-02 1.9146e-02
(127,63 ,.,.) =
-6.8805e-02 5.1618e-02 1.9787e-02
2.5533e-02 -6.1926e-02 4.9924e-02
1.0532e-01 -4.4136e-02 4.9907e-02
[torch.FloatTensor of size 128x64x3x3], Parameter containing:
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0.3613
0.2960
0.2913
0.3407
0.3435
0.3049
0.3308
0.3447
0.3860
0.3196
0.2622
0.2994
0.2189
0.2397
0.3744
0.3555
0.1948
0.3349
0.2159
0.3349
0.3454
0.3094
0.3769
0.3546
0.3267
0.3178
0.3272
0.3832
0.2585
0.2973
0.3481
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0.2995
0.3451
0.3471
0.3440
0.3344
0.3211
0.3180
0.2940
0.3353
0.3253
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0.3198
0.2987
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0.3262
0.3271
0.3410
0.3693
0.3320
0.3357
0.2951
0.3115
0.3185
0.3139
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0.3333
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0.2959
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0.2982
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0.3081
0.3171
0.2862
0.2952
0.3070
0.3583
0.3232
0.3345
0.3453
0.3043
0.3327
0.3337
[torch.FloatTensor of size 128], Parameter containing:
-0.0589
-0.1686
-0.0206
0.0027
-0.0955
-0.1048
0.0349
-0.0885
-0.2053
-0.1764
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0.2088
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0.2989
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-0.0720
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0.4979
-0.0811
-0.1021
-0.0788
-0.1802
-0.1011
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0.0023
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0.0009
-0.1525
-0.0963
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-0.0473
0.0884
-0.1028
-0.0907
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-0.0379
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-0.1609
-0.0903
-0.0898
-0.1282
-0.0830
-0.0186
-0.0232
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-0.1209
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-0.1181
0.2298
-0.2085
-0.1335
0.1418
-0.0891
-0.1273
0.0107
-0.1029
-0.1025
0.1562
-0.0937
-0.0657
-0.1245
-0.0451
-0.0707
-0.0447
0.0715
-0.0484
-0.0312
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-0.0927
-0.1465
-0.1151
-0.0183
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0.2491
0.0300
-0.1310
-0.0468
-0.0851
-0.0421
-0.0413
-0.0457
-0.1433
-0.0981
-0.1046
-0.1315
-0.1249
-0.0982
-0.0961
[torch.FloatTensor of size 128], Parameter containing:
( 0 , 0 ,.,.) =
-7.4379e-03 -9.8091e-03 2.7976e-03
-1.0780e-02 2.5794e-02 4.5517e-02
-2.7241e-02 5.3206e-03 1.3177e-02
( 0 , 1 ,.,.) =
3.5440e-02 2.5101e-02 7.8204e-03
4.0312e-03 1.9894e-02 2.7449e-02
3.5329e-02 3.5456e-02 1.3315e-02
( 0 , 2 ,.,.) =
1.9270e-02 -2.1333e-02 -3.6199e-02
-1.9590e-02 -1.8873e-02 -5.9538e-02
-2.1838e-02 -7.6875e-03 3.9487e-03
...
( 0 ,125,.,.) =
-6.8038e-03 1.0841e-02 -3.7045e-03
1.3479e-02 1.1362e-02 -1.3431e-03
8.1422e-03 1.9292e-04 5.5109e-04
( 0 ,126,.,.) =
7.6939e-03 7.7306e-03 4.3960e-03
-1.0202e-02 -1.1698e-02 -9.6343e-03
-3.9049e-03 1.8147e-02 1.3297e-02
( 0 ,127,.,.) =
1.2434e-02 -2.6889e-02 -1.1974e-02
2.6846e-02 2.6409e-02 -2.1473e-02
1.2892e-02 2.7632e-03 -5.4267e-03
⋮
( 1 , 0 ,.,.) =
1.2840e-02 1.8529e-02 -2.6782e-03
-1.6777e-02 -1.2281e-02 3.5471e-02
-8.6486e-04 2.4498e-02 1.8152e-02
( 1 , 1 ,.,.) =
-6.6870e-03 -2.0710e-02 -1.4421e-02
-7.3135e-03 4.2568e-02 7.4339e-03
2.7640e-02 1.5997e-02 1.5939e-02
( 1 , 2 ,.,.) =
-2.2903e-02 -2.0577e-02 2.3593e-02
-2.7524e-02 -5.6073e-02 -6.9899e-02
2.0502e-02 5.1301e-02 2.1989e-02
...
( 1 ,125,.,.) =
-2.7188e-02 -3.8969e-02 -3.9503e-02
-6.2117e-02 -7.4923e-02 -9.5650e-02
-6.0467e-02 -7.7697e-02 -7.4620e-02
( 1 ,126,.,.) =
2.8663e-02 2.9341e-02 2.8688e-02
7.9438e-03 4.7108e-02 1.4586e-02
-1.8200e-03 2.2035e-02 7.3670e-03
( 1 ,127,.,.) =
1.5625e-03 -1.6815e-02 -4.6104e-03
-8.1347e-03 -2.5480e-02 -5.2408e-02
-9.2823e-03 -9.6452e-04 -3.7804e-02
⋮
( 2 , 0 ,.,.) =
4.7439e-03 6.0541e-03 -7.1074e-03
2.3583e-02 -9.3094e-02 -7.9317e-02
-7.8970e-03 -5.0526e-02 -1.0469e-02
( 2 , 1 ,.,.) =
1.4810e-02 1.6199e-02 -5.1457e-02
8.5937e-03 8.1354e-03 -4.2865e-02
9.0780e-02 6.5382e-02 4.3530e-02
( 2 , 2 ,.,.) =
-1.3827e-02 -6.3971e-03 8.4965e-03
1.3832e-02 -1.2413e-02 5.3880e-03
2.0189e-02 -3.5255e-03 7.9905e-03
...
( 2 ,125,.,.) =
-9.2351e-04 1.8478e-02 -3.0603e-02
-1.7034e-02 5.6756e-03 -4.9061e-02
-3.2771e-02 -3.7422e-02 -4.5931e-02
( 2 ,126,.,.) =
-4.6355e-03 6.9231e-03 -1.0628e-03
-7.9292e-03 -4.9909e-02 4.2104e-02
-7.5158e-02 -4.7826e-03 -5.8031e-03
( 2 ,127,.,.) =
1.1503e-02 -1.4634e-02 3.7884e-02
1.4056e-02 4.8553e-02 2.3157e-02
2.1494e-02 -1.0090e-02 3.3782e-02
...
⋮
(125, 0 ,.,.) =
2.6448e-02 4.0213e-03 7.5348e-03
6.3626e-02 -3.1986e-02 -1.8433e-03
2.6220e-02 7.5575e-03 4.9462e-02
(125, 1 ,.,.) =
-2.8731e-02 -2.2669e-02 -5.1264e-02
-2.6000e-02 -4.8740e-02 -1.4003e-02
-1.7263e-02 -4.1574e-02 -1.1665e-02
(125, 2 ,.,.) =
-3.4972e-02 3.5634e-02 3.4700e-02
1.8265e-02 4.3594e-02 -2.6302e-02
1.7826e-02 3.5585e-02 1.1340e-02
...
(125,125,.,.) =
9.7429e-03 -1.7253e-02 -1.6983e-04
-1.9886e-02 8.1994e-02 1.2903e-02
-2.3786e-02 -4.7812e-03 4.8584e-02
(125,126,.,.) =
-2.4373e-02 -2.5836e-02 -3.5317e-02
-2.9582e-02 -9.6624e-02 -5.3546e-02
-1.5009e-02 5.9241e-03 -1.9719e-02
(125,127,.,.) =
6.8366e-03 -3.6779e-02 -2.5541e-02
-1.1634e-02 -2.3650e-02 -7.8005e-03
8.6452e-03 7.8958e-03 -1.8926e-02
⋮
(126, 0 ,.,.) =
3.2894e-02 2.9690e-02 1.1071e-02
3.8989e-02 -8.9897e-03 2.2632e-02
7.8374e-03 -2.7959e-02 -2.3005e-02
(126, 1 ,.,.) =
1.3667e-02 2.2886e-02 -1.8989e-02
2.7104e-03 1.1235e-02 7.4223e-03
2.1089e-02 4.3557e-02 1.0752e-02
(126, 2 ,.,.) =
-2.3662e-02 2.2110e-02 4.3471e-04
-3.0925e-02 6.0868e-02 1.6691e-02
-8.8467e-02 -8.3442e-02 -3.4247e-02
...
(126,125,.,.) =
-7.3418e-03 -1.9690e-02 1.7969e-03
7.2727e-03 -3.4322e-02 -2.4270e-02
-1.1512e-02 -6.0470e-02 -5.5070e-02
(126,126,.,.) =
3.0219e-03 2.6285e-03 1.7110e-02
-1.3418e-02 -8.5859e-03 9.0284e-03
1.9504e-02 9.4355e-03 4.5180e-03
(126,127,.,.) =
1.3674e-03 7.6213e-04 1.1925e-02
-2.3910e-03 -1.0733e-02 1.2625e-02
-5.0613e-03 -5.7724e-03 -1.4643e-02
⋮
(127, 0 ,.,.) =
-7.4213e-03 1.1754e-02 -4.2728e-02
1.6309e-03 -4.5691e-02 -1.3976e-01
-6.5419e-03 -2.0547e-03 -4.8392e-02
(127, 1 ,.,.) =
7.5053e-03 5.2659e-02 3.8849e-02
-1.2484e-02 8.4685e-02 6.2233e-03
1.3136e-03 -1.9656e-02 -8.2167e-02
(127, 2 ,.,.) =
-2.4916e-02 1.6551e-02 1.6914e-02
8.6507e-03 2.1444e-02 1.1694e-02
-9.0502e-04 3.0596e-02 1.3600e-02
...
(127,125,.,.) =
-7.8114e-03 2.2029e-02 -1.7545e-02
-7.5889e-03 -2.1149e-02 -3.6984e-03
1.2622e-02 -2.0709e-02 -5.3862e-03
(127,126,.,.) =
3.0152e-02 -8.2268e-03 -6.4910e-02
-2.3752e-02 -9.5375e-02 -5.3019e-02
-1.6835e-02 -1.1071e-02 9.9055e-04
(127,127,.,.) =
-2.4533e-02 -8.4685e-02 2.5065e-02
1.0639e-02 3.8693e-02 1.4004e-01
1.5497e-02 -9.5081e-03 4.0948e-03
[torch.FloatTensor of size 128x128x3x3], Parameter containing:
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0.2538
0.4086
0.3937
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0.3108
0.3406
0.2168
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0.3857
0.1971
0.2692
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0.2454
0.3232
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0.3711
0.2388
0.5347
0.1509
0.3213
0.4394
0.3229
0.4329
0.1489
0.3702
[torch.FloatTensor of size 128], Parameter containing:
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0.0593
0.1347
-0.1089
-0.0470
-0.1359
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0.0509
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0.0916
0.0031
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0.0099
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0.0467
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0.0585
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0.0509
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0.0115
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0.0115
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0.1189
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0.0009
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0.1403
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0.0661
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0.0422
-0.2786
-0.1006
-0.0696
-0.0761
0.0371
-0.0247
0.0916
-0.0200
-0.0176
0.0298
-0.0373
0.0466
-0.1371
[torch.FloatTensor of size 128], Parameter containing:
( 0 , 0 ,.,.) =
1.5916e-02
( 0 , 1 ,.,.) =
-3.1090e-01
( 0 , 2 ,.,.) =
1.2615e-02
...
( 0 ,61 ,.,.) =
-1.6723e-01
( 0 ,62 ,.,.) =
1.2692e-02
( 0 ,63 ,.,.) =
1.3152e-02
⋮
( 1 , 0 ,.,.) =
3.5526e-03
( 1 , 1 ,.,.) =
-1.0868e-03
( 1 , 2 ,.,.) =
-8.2883e-03
...
( 1 ,61 ,.,.) =
-2.3444e-02
( 1 ,62 ,.,.) =
-7.5592e-02
( 1 ,63 ,.,.) =
-1.2622e-02
⋮
( 2 , 0 ,.,.) =
-4.1898e-02
( 2 , 1 ,.,.) =
7.9478e-03
( 2 , 2 ,.,.) =
-1.6623e-01
...
( 2 ,61 ,.,.) =
3.1887e-02
( 2 ,62 ,.,.) =
-1.8766e-02
( 2 ,63 ,.,.) =
6.4507e-02
...
⋮
(125, 0 ,.,.) =
-2.8725e-02
(125, 1 ,.,.) =
4.7026e-02
(125, 2 ,.,.) =
-5.2251e-02
...
(125,61 ,.,.) =
-4.7365e-02
(125,62 ,.,.) =
5.8639e-02
(125,63 ,.,.) =
5.8808e-02
⋮
(126, 0 ,.,.) =
-7.7884e-03
(126, 1 ,.,.) =
-2.0288e-02
(126, 2 ,.,.) =
5.6392e-02
...
(126,61 ,.,.) =
7.8023e-01
(126,62 ,.,.) =
-2.2917e-03
(126,63 ,.,.) =
-2.5941e-02
⋮
(127, 0 ,.,.) =
-2.8316e-02
(127, 1 ,.,.) =
-1.3194e-02
(127, 2 ,.,.) =
-5.1356e-02
...
(127,61 ,.,.) =
2.3552e-02
(127,62 ,.,.) =
-6.7667e-02
(127,63 ,.,.) =
2.6754e-02
[torch.FloatTensor of size 128x64x1x1], Parameter containing:
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0.0581
0.0715
0.3442
0.1756
0.1509
0.1568
0.3100
0.1927
0.1516
0.3044
0.2238
0.3706
0.1739
0.3051
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0.1575
0.2015
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0.0495
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0.1818
0.0688
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0.4661
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0.1035
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0.1831
0.2894
0.2590
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0.3451
0.2017
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0.1127
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0.1111
0.3055
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0.1633
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0.1534
0.0742
0.2088
0.0394
0.2594
0.1385
-0.0051
0.1905
0.1275
0.3071
0.1682
[torch.FloatTensor of size 128], Parameter containing:
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0.0593
0.1347
-0.1089
-0.0470
-0.1359
-0.0550
0.0509
-0.0613
0.0916
0.0031
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0.0177
0.0432
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0.0548
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0.0486
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0.0355
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0.0447
0.1063
0.0528
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0.1055
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0.0100
0.1197
0.0170
0.0929
-0.0675
0.0987
0.1034
0.0501
0.0297
0.0281
-0.0075
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0.1255
0.0817
0.0635
0.0936
0.0213
0.0486
-0.1174
0.0237
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0.0099
-0.1883
0.0467
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0.0585
-0.0306
0.0509
0.0541
-0.1671
0.0115
-0.0302
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0.0115
0.0428
0.1189
-0.1289
0.0479
0.0474
-0.0625
0.0009
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0.0909
0.1342
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0.0560
0.0848
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0.0228
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0.1360
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0.0088
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0.0383
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0.0907
0.1612
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0.1011
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0.0838
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0.0533
0.1277
0.1403
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0.0661
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0.0422
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0.0371
-0.0247
0.0916
-0.0200
-0.0176
0.0298
-0.0373
0.0466
-0.1371
[torch.FloatTensor of size 128], Parameter containing:
( 0 , 0 ,.,.) =
-9.9023e-04 -7.7429e-03 -7.9740e-03
2.4844e-02 1.8642e-03 5.8352e-03
9.5089e-03 -1.6476e-02 3.9157e-03
( 0 , 1 ,.,.) =
-2.1488e-02 -1.2330e-03 -1.4281e-02
-1.7044e-02 9.5922e-03 7.0445e-03
1.0790e-02 -7.2350e-03 -1.1357e-02
( 0 , 2 ,.,.) =
-1.1126e-03 3.0388e-02 2.2247e-02
-6.1184e-02 -2.3797e-02 2.3747e-03
4.0678e-02 -1.0356e-01 -6.0011e-02
...
( 0 ,125,.,.) =
-8.5833e-03 1.1438e-02 2.0800e-02
-1.6565e-02 -3.9587e-02 1.2594e-02
-1.4314e-03 -5.4257e-03 3.6794e-02
( 0 ,126,.,.) =
-1.3687e-02 -2.9514e-02 -1.4745e-02
2.8299e-02 2.2096e-02 3.4839e-03
-4.3521e-03 -2.6706e-03 1.2258e-04
( 0 ,127,.,.) =
7.6403e-03 2.0666e-02 3.7429e-02
6.9478e-03 4.3983e-02 1.7538e-02
-9.7797e-03 -2.4789e-02 -1.1349e-03
⋮
( 1 , 0 ,.,.) =
8.4439e-02 8.4827e-02 -5.1478e-02
3.5253e-02 -1.1375e-03 -1.0331e-01
-6.4078e-02 -1.2660e-01 -1.2952e-01
( 1 , 1 ,.,.) =
1.0628e-03 -1.4083e-02 4.7109e-03
-2.1059e-02 -2.8778e-02 9.9708e-03
1.4074e-02 1.8691e-02 5.8192e-02
( 1 , 2 ,.,.) =
2.2139e-02 8.9027e-03 1.4790e-02
-1.7497e-02 -5.3924e-03 2.7834e-02
-1.3855e-02 -1.3346e-02 1.7668e-02
...
( 1 ,125,.,.) =
-3.8032e-02 -2.3097e-02 -7.1775e-03
-3.5089e-02 1.0861e-02 1.3640e-02
6.3449e-04 9.7476e-03 7.3670e-03
( 1 ,126,.,.) =
-4.4184e-02 -1.6190e-02 1.2243e-02
-4.0349e-02 -1.7894e-02 2.8911e-02
-6.5176e-03 -1.0490e-02 9.1658e-03
( 1 ,127,.,.) =
4.3621e-03 1.3119e-02 1.8442e-03
1.1555e-02 -1.3031e-02 -9.5657e-03
-2.3314e-02 1.1609e-03 2.6771e-03
⋮
( 2 , 0 ,.,.) =
-2.1180e-02 -6.2213e-03 1.7609e-03
-4.7424e-03 1.1101e-02 1.1296e-02
-1.4529e-02 2.9843e-02 2.4383e-03
( 2 , 1 ,.,.) =
6.9183e-03 9.2937e-03 3.0078e-02
-4.2612e-03 4.9560e-03 -4.7338e-03
3.1360e-02 1.9035e-03 -4.7242e-03
( 2 , 2 ,.,.) =
-3.6726e-02 5.7285e-03 1.3919e-01
-4.2992e-02 9.4023e-04 7.7141e-02
-5.0050e-02 -4.9479e-03 2.4693e-02
...
( 2 ,125,.,.) =
3.7203e-02 7.4712e-03 -4.2659e-02
-8.1729e-03 -9.2536e-02 -5.4934e-03
-2.5927e-02 8.3993e-04 7.4632e-02
( 2 ,126,.,.) =
1.8076e-02 4.5272e-03 -1.3757e-02
-1.8939e-02 -3.2739e-02 -2.9666e-02
-2.0608e-02 -4.6167e-03 1.3080e-03
( 2 ,127,.,.) =
-1.2078e-02 -2.0285e-03 -1.6998e-02
-3.4805e-02 -4.9195e-02 -3.1973e-02
-2.1021e-02 -5.1164e-03 -4.8522e-03
...
⋮
(125, 0 ,.,.) =
3.1791e-02 2.2948e-02 1.0390e-02
-1.2628e-02 -2.9320e-03 4.2645e-03
-2.1707e-02 -1.0856e-02 1.6094e-02
(125, 1 ,.,.) =
-1.4525e-03 -1.0131e-02 -4.6862e-04
2.2130e-02 2.2736e-02 5.0183e-03
-6.0125e-02 -4.3150e-02 -4.4480e-02
(125, 2 ,.,.) =
3.0761e-03 3.4396e-03 6.0877e-03
-1.3683e-02 4.0576e-03 -2.6544e-02
6.8231e-02 6.3474e-02 -9.3660e-03
...
(125,125,.,.) =
1.8752e-02 1.9400e-02 4.1691e-02
8.7770e-03 8.2394e-04 1.8619e-02
1.8796e-02 6.2238e-02 -2.3801e-02
(125,126,.,.) =
-2.9788e-02 -3.4598e-02 -2.5225e-02
8.4234e-03 -2.3222e-02 -9.4612e-03
6.9035e-03 6.9737e-02 -1.3359e-02
(125,127,.,.) =
2.6981e-03 -4.3182e-02 -1.6731e-02
2.5812e-02 -7.2025e-02 -6.5399e-02
4.6257e-02 2.9469e-02 -1.5811e-02
⋮
(126, 0 ,.,.) =
-2.1079e-02 3.8220e-02 8.3305e-03
-5.9912e-03 3.5584e-02 -1.7534e-03
1.8735e-02 7.0859e-03 -3.5151e-03
(126, 1 ,.,.) =
-4.5937e-02 -7.4695e-02 -5.3608e-02
-8.6266e-03 9.0894e-03 -3.0345e-02
-2.8158e-02 -2.1204e-02 -8.4730e-03
(126, 2 ,.,.) =
-7.1772e-02 -6.8582e-02 2.5544e-02
5.0363e-02 2.5269e-02 5.6668e-02
2.6238e-03 1.3871e-03 -8.4692e-03
...
(126,125,.,.) =
-2.9644e-02 1.0896e-02 -3.0402e-02
1.5095e-03 5.0455e-02 1.5597e-02
-2.1015e-02 -1.0757e-02 -3.4942e-02
(126,126,.,.) =
-2.7573e-02 2.9707e-02 -2.9490e-02
2.3301e-03 -3.9011e-02 6.8010e-03
4.4006e-02 3.5397e-02 7.9087e-02
(126,127,.,.) =
-2.7480e-02 5.0337e-02 1.4290e-02
-5.2482e-02 -4.7748e-03 1.2988e-02
-1.8935e-02 -3.0808e-02 -1.7583e-02
⋮
(127, 0 ,.,.) =
3.2280e-02 4.7408e-02 3.4054e-02
2.1445e-02 3.8987e-03 4.6985e-04
1.5159e-02 8.2067e-03 3.2426e-02
(127, 1 ,.,.) =
9.2653e-03 2.3661e-02 4.2089e-02
2.1976e-02 4.6128e-02 1.1402e-02
7.2843e-03 5.2285e-02 8.6340e-03
(127, 2 ,.,.) =
1.4022e-02 1.2800e-02 3.5398e-02
-4.4398e-02 1.7399e-02 -1.5838e-02
3.1712e-02 5.8679e-02 -9.3244e-03
...
(127,125,.,.) =
-4.8399e-03 7.8628e-03 -5.6169e-04
8.0402e-03 1.7392e-02 7.8734e-03
-1.7713e-02 -4.5957e-02 -9.8762e-03
(127,126,.,.) =
-9.7569e-03 -7.5795e-03 -2.4627e-02
-8.2454e-03 6.3065e-02 -3.2954e-03
-7.7549e-03 -1.3404e-04 -8.1337e-03
(127,127,.,.) =
1.7664e-02 1.0114e-02 4.2687e-03
-3.7950e-03 2.6715e-02 2.0121e-02
1.6868e-02 -6.6515e-03 -1.1107e-02
[torch.FloatTensor of size 128x128x3x3], Parameter containing:
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0.2908
0.3246
0.3435
0.3011
0.3054
0.3041
0.3539
0.2862
0.3601
0.2970
0.3381
0.2565
0.3276
0.3030
0.4085
0.3519
0.4218
0.3055
0.2551
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0.3366
0.2700
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0.3954
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0.3286
0.3515
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0.3625
0.1988
0.2717
0.3355
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0.2510
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0.3231
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0.3611
0.3086
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0.3886
0.4362
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0.3411
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0.3709
0.2592
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0.2555
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0.2932
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0.3194
0.3542
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0.2955
0.2924
0.3538
0.4256
0.3603
0.3013
0.2763
0.4354
0.3991
0.2694
[torch.FloatTensor of size 128], Parameter containing:
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-0.3383
-0.0806
-0.1920
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-0.1536
[torch.FloatTensor of size 128], Parameter containing:
( 0 , 0 ,.,.) =
-1.6153e-02 5.0134e-03 -9.0186e-04
-8.8386e-03 -1.9390e-02 -2.4174e-02
6.3052e-03 1.0245e-02 -1.3816e-02
( 0 , 1 ,.,.) =
-1.0979e-02 2.6164e-03 2.3656e-02
-1.7687e-02 1.9861e-02 6.4150e-02
6.0224e-03 7.6342e-02 1.0215e-01
( 0 , 2 ,.,.) =
-8.1113e-03 6.8414e-03 2.5436e-02
-8.0696e-03 9.2929e-03 8.2899e-03
7.7306e-03 1.2159e-02 7.1625e-03
...
( 0 ,125,.,.) =
1.5175e-02 6.2196e-03 2.1798e-02
-1.5199e-02 -8.5439e-02 -2.4713e-02
-1.8460e-02 -4.9767e-02 -1.6818e-03
( 0 ,126,.,.) =
3.0728e-02 3.9962e-02 3.1253e-02
-1.8738e-02 -6.7510e-02 -2.7649e-02
2.8429e-02 3.1854e-02 1.0543e-02
( 0 ,127,.,.) =
-1.8320e-02 -1.5854e-02 -1.0685e-02
-2.7442e-02 -3.0616e-02 -1.0485e-02
-1.5122e-02 -1.0595e-02 -2.5322e-02
⋮
( 1 , 0 ,.,.) =
3.6868e-03 3.0996e-02 4.2763e-02
4.6537e-02 4.8606e-02 2.3800e-03
1.6654e-02 1.2900e-02 -1.8230e-02
( 1 , 1 ,.,.) =
-1.0441e-02 -1.5934e-03 -1.6128e-02
-1.2799e-02 4.9570e-03 -1.4585e-02
-2.3553e-02 -3.7023e-03 -1.4399e-02
( 1 , 2 ,.,.) =
1.0338e-02 -1.7560e-02 -3.3046e-02
-3.2090e-02 -5.9258e-03 2.0201e-03
-4.1428e-02 4.9121e-03 1.6906e-02
...
( 1 ,125,.,.) =
-4.9525e-02 -4.6498e-02 -5.9916e-02
-2.6670e-02 -1.9079e-02 -2.9419e-02
-3.9683e-03 1.9405e-02 7.3317e-03
( 1 ,126,.,.) =
1.4293e-02 1.5643e-02 5.8117e-04
5.1493e-03 7.4332e-03 -3.6928e-03
-1.3522e-02 -8.5536e-03 -2.1259e-03
( 1 ,127,.,.) =
-3.0908e-02 -1.9839e-02 -1.9375e-02
-1.0368e-02 -2.4294e-02 2.4103e-04
-1.9275e-02 -2.9707e-02 -1.5623e-02
⋮
( 2 , 0 ,.,.) =
-4.9212e-02 -2.9588e-02 8.8023e-02
4.7453e-03 4.3564e-02 9.3115e-02
7.4083e-02 4.2868e-02 -5.1033e-02
( 2 , 1 ,.,.) =
6.6992e-03 2.1676e-02 -5.4254e-04
1.9286e-02 1.0920e-02 -4.5440e-03
3.1075e-02 -1.7168e-03 -2.7603e-02
( 2 , 2 ,.,.) =
6.0096e-02 -2.9359e-02 -5.8911e-02
-1.9133e-02 -8.1624e-02 -2.2553e-02
1.1597e-02 2.5092e-02 1.2130e-02
...
( 2 ,125,.,.) =
5.4307e-03 -2.3130e-02 9.6233e-03
-4.3785e-02 -2.6735e-02 2.1993e-02
-3.5919e-02 -4.1009e-02 -2.1860e-02
( 2 ,126,.,.) =
3.3705e-02 6.2938e-02 4.3502e-02
1.1111e-03 1.9243e-02 -1.9707e-03
-1.1493e-02 -5.3445e-02 -9.6676e-03
( 2 ,127,.,.) =
-2.6664e-03 -2.6954e-02 -1.7667e-02
-8.3382e-03 8.9920e-03 8.1260e-04
-2.6832e-02 -3.5991e-02 -4.2495e-02
...
⋮
(125, 0 ,.,.) =
-1.8876e-03 -2.2728e-02 -4.2991e-03
-9.2231e-03 -3.4333e-02 -1.3392e-02
-1.2774e-02 -1.1435e-02 1.5617e-02
(125, 1 ,.,.) =
1.0703e-02 1.2792e-02 2.2662e-02
7.3185e-03 -1.7847e-02 1.0674e-02
-1.5936e-02 -1.9318e-02 2.1768e-02
(125, 2 ,.,.) =
-7.3009e-03 3.0234e-02 -1.1899e-02
-2.6099e-02 3.7452e-03 3.2776e-02
-3.3101e-02 -7.1923e-03 1.6559e-02
...
(125,125,.,.) =
-3.2818e-02 -1.0021e-01 -4.7012e-02
2.8293e-03 4.1410e-02 -1.1391e-02
-1.1152e-02 -5.5861e-03 1.9968e-02
(125,126,.,.) =
-2.3932e-02 -3.0687e-02 -1.1756e-03
1.5311e-03 -3.5002e-02 -2.4414e-02
-8.7575e-03 -7.7842e-02 -3.8842e-02
(125,127,.,.) =
2.6107e-02 1.5406e-02 1.7569e-02
-1.5130e-02 -4.8687e-03 3.0773e-03
-1.3470e-02 -9.3201e-03 -4.8982e-03
⋮
(126, 0 ,.,.) =
-2.0228e-02 -3.0006e-02 -9.8419e-03
-3.8676e-02 -3.3481e-02 -7.4265e-03
-2.8935e-02 -3.2037e-02 2.9245e-03
(126, 1 ,.,.) =
-1.2900e-02 3.8046e-03 1.5940e-02
-2.4030e-02 2.0666e-03 5.7250e-03
6.9989e-03 1.2192e-02 1.5406e-02
(126, 2 ,.,.) =
-1.5018e-02 -9.0988e-03 2.4450e-02
1.0039e-02 1.2561e-02 2.6997e-02
2.9556e-02 1.9463e-02 -2.6584e-03
...
(126,125,.,.) =
-1.8481e-02 3.9417e-04 9.9768e-03
-4.5447e-03 1.2307e-02 3.5507e-02
-1.1873e-03 -2.6185e-03 1.1547e-02
(126,126,.,.) =
4.6292e-03 -1.3690e-02 -1.0171e-02
1.2104e-02 1.6793e-02 1.3003e-02
1.3328e-03 3.4701e-03 1.7323e-02
(126,127,.,.) =
-8.7332e-05 5.8646e-03 -3.5117e-03
3.8112e-03 -7.1828e-03 -1.1407e-02
1.9705e-02 2.0556e-02 5.7084e-03
⋮
(127, 0 ,.,.) =
3.6998e-02 3.2616e-02 -9.4535e-04
-2.9484e-02 -2.3441e-02 -2.8085e-02
-2.5451e-02 3.9048e-02 3.6686e-02
(127, 1 ,.,.) =
-1.8732e-02 -1.5352e-02 1.1149e-02
-2.1324e-03 -2.3177e-02 1.7628e-02
-4.0012e-03 1.5463e-02 9.2496e-03
(127, 2 ,.,.) =
-2.9346e-02 7.7071e-03 -5.6520e-03
-2.3611e-02 -1.9390e-03 2.0221e-02
8.0955e-03 -2.3268e-02 -2.8827e-02
...
(127,125,.,.) =
-3.3532e-02 -2.9092e-02 -4.0045e-02
2.6530e-03 -2.0568e-02 1.3075e-02
1.6061e-02 -5.5725e-02 -4.9167e-02
(127,126,.,.) =
-7.9132e-03 2.1466e-02 2.0913e-02
-1.7259e-02 -2.5851e-02 2.7177e-03
-4.6532e-02 -2.4846e-02 -1.9911e-02
(127,127,.,.) =
-5.0350e-02 -2.5574e-02 1.7763e-02
-3.4474e-02 5.5247e-03 -2.7754e-02
-2.0743e-02 -2.2332e-02 -4.3512e-02
[torch.FloatTensor of size 128x128x3x3], Parameter containing:
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0.1625
0.3084
0.2931
0.2957
0.5263
0.4038
0.2024
0.3401
0.1982
0.2559
0.2311
0.1630
0.2891
0.2248
0.2311
0.2417
0.2187
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0.1392
0.0617
0.3406
0.1313
0.4586
0.2914
0.1326
0.3915
[torch.FloatTensor of size 128], Parameter containing:
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0.0406
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0.2768
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0.0021
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0.1613
0.0006
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0.0772
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0.0358
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0.0908
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0.0160
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0.0084
-0.0311
-0.0022
-0.0831
0.0380
-0.4893
-0.2749
0.1245
-0.1272
[torch.FloatTensor of size 128], Parameter containing:
( 0 , 0 ,.,.) =
-1.5906e-02 -1.6618e-02 -1.5938e-02
-5.2744e-03 1.5103e-02 9.8805e-03
-1.4850e-02 3.6254e-04 -1.1378e-02
( 0 , 1 ,.,.) =
-9.4971e-03 -1.8568e-02 -6.0605e-03
9.7622e-03 -1.2294e-02 -5.2978e-03
7.0518e-03 -1.6063e-02 -7.1445e-03
( 0 , 2 ,.,.) =
-2.2693e-02 -3.7669e-02 -3.3695e-02
-3.1569e-02 -5.8022e-02 -3.9105e-02
-3.4616e-02 -3.8806e-02 -1.5695e-02
...
( 0 ,125,.,.) =
4.8713e-03 7.9539e-03 1.4374e-02
-1.5242e-03 2.4200e-02 5.6440e-03
-4.4355e-03 6.2454e-03 6.8561e-03
( 0 ,126,.,.) =
1.6028e-02 -1.2036e-02 -1.3101e-03
9.5804e-03 5.7272e-03 1.6091e-03
-9.9173e-03 -1.3593e-02 -6.3679e-03
( 0 ,127,.,.) =
5.3450e-02 4.6441e-02 2.4824e-02
3.4065e-02 -2.8656e-03 -4.1207e-03
-1.4000e-02 -4.6092e-03 -1.4152e-02
⋮
( 1 , 0 ,.,.) =
-1.1567e-03 -1.8638e-02 -3.4453e-02
4.9889e-03 -1.1695e-02 -3.3321e-02
5.9653e-03 -1.6154e-02 -1.7452e-02
( 1 , 1 ,.,.) =
1.0729e-02 1.3964e-02 -1.9171e-02
2.8854e-03 1.2573e-02 7.2767e-03
-1.6815e-02 -1.8740e-02 -1.3784e-03
( 1 , 2 ,.,.) =
-2.1852e-02 6.2900e-03 1.5931e-02
-3.5272e-03 5.6997e-03 3.1077e-02
2.3169e-03 3.2389e-03 1.7490e-02
...
( 1 ,125,.,.) =
-1.6246e-02 -7.7688e-03 7.7471e-03
-1.4870e-03 -1.2226e-02 -9.3389e-03
8.6164e-04 -2.2071e-03 7.3769e-03
( 1 ,126,.,.) =
2.9310e-03 -2.3592e-02 5.8461e-03
1.4344e-02 -1.6924e-02 -6.1749e-03
-7.7191e-03 -3.2305e-02 -3.3688e-02
( 1 ,127,.,.) =
8.6900e-03 1.3976e-02 8.0760e-03
-3.3662e-03 1.0516e-02 1.4952e-02
1.8944e-02 3.0948e-02 2.5647e-02
⋮
( 2 , 0 ,.,.) =
-3.5797e-02 -2.2565e-02 -1.4440e-02
-7.5372e-03 -2.2142e-02 1.1150e-02
-3.6385e-03 -1.4821e-02 -1.6427e-02
( 2 , 1 ,.,.) =
-1.4620e-02 -3.0657e-02 -2.0434e-02
-2.8462e-02 -4.5328e-02 -5.7915e-02
2.8774e-02 -1.5172e-02 -2.4541e-02
( 2 , 2 ,.,.) =
1.7403e-02 1.9920e-02 -4.6249e-03
1.7813e-02 2.3648e-02 1.3638e-02
2.9347e-02 4.3449e-02 1.8594e-02
...
( 2 ,125,.,.) =
7.9258e-03 -1.2183e-02 -1.5811e-02
-1.0720e-02 -3.1404e-02 -7.5279e-03
-7.0299e-03 -1.7342e-02 -3.0783e-02
( 2 ,126,.,.) =
-1.0258e-02 -1.1796e-02 -1.7141e-02
-2.6423e-02 -1.5036e-03 2.7959e-02
-8.9306e-03 5.3510e-03 9.6632e-03
( 2 ,127,.,.) =
1.4481e-02 -3.1531e-02 -1.9707e-02
-1.4944e-02 -1.7709e-02 7.6966e-03
1.2465e-02 7.1035e-03 -6.1596e-03
...
⋮
(253, 0 ,.,.) =
5.3120e-03 2.5512e-02 7.1053e-03
1.9666e-02 2.6990e-02 4.2043e-02
4.1191e-02 2.2283e-02 3.5003e-02
(253, 1 ,.,.) =
2.5968e-03 4.0685e-03 1.0626e-02
4.6474e-03 2.0337e-02 8.0847e-03
1.4475e-02 -3.0070e-03 -1.9656e-02
(253, 2 ,.,.) =
-4.0235e-03 2.5510e-02 2.2875e-03
-1.5182e-02 2.6031e-02 8.2526e-03
-2.1065e-03 2.6928e-02 3.2296e-03
...
(253,125,.,.) =
5.5063e-03 -4.8631e-03 1.8346e-02
8.5499e-03 2.3002e-03 7.7201e-03
8.2280e-03 9.5818e-03 2.1510e-02
(253,126,.,.) =
-1.7702e-02 9.9203e-03 -1.2934e-02
-1.2670e-02 9.5506e-03 -1.2438e-02
8.9810e-03 4.9343e-02 3.6238e-02
(253,127,.,.) =
1.2333e-02 1.8408e-02 -1.7794e-02
5.7676e-03 -5.7844e-03 -1.1706e-02
3.4462e-03 -1.0299e-02 -4.2529e-02
⋮
(254, 0 ,.,.) =
3.1634e-02 7.6514e-02 4.4300e-02
9.3963e-02 1.4798e-01 1.5104e-01
6.6483e-02 1.3856e-01 1.1323e-01
(254, 1 ,.,.) =
-2.8205e-02 -4.0731e-03 -1.9967e-02
-1.9283e-02 -1.2330e-03 1.0728e-02
-1.6487e-02 -2.7540e-03 7.7751e-04
(254, 2 ,.,.) =
-1.2156e-02 -3.2183e-02 -1.5299e-02
-9.1752e-04 -1.2350e-02 -3.8531e-03
-1.9342e-02 -1.0735e-02 -2.1051e-02
...
(254,125,.,.) =
-3.0457e-03 6.5687e-03 -3.2163e-04
1.4628e-02 -1.6662e-02 1.4216e-02
2.2738e-02 1.2016e-02 7.1802e-03
(254,126,.,.) =
3.9151e-03 -1.9739e-02 1.1058e-02
-2.5105e-02 -3.8439e-02 -4.4722e-02
-3.5862e-02 -9.8120e-02 -6.8447e-02
(254,127,.,.) =
-8.4853e-03 2.2905e-03 3.0757e-03
3.8484e-03 1.8156e-02 6.9025e-03
8.9456e-03 8.0009e-03 1.2579e-02
⋮
(255, 0 ,.,.) =
-1.3006e-02 -9.0262e-03 1.0574e-03
-2.5979e-02 -1.9484e-02 -9.3637e-03
4.8438e-03 2.3742e-03 1.0574e-02
(255, 1 ,.,.) =
-2.4782e-03 -1.4049e-02 -2.8621e-02
-2.3822e-03 1.1463e-03 -2.3321e-02
1.2275e-02 8.3306e-04 1.4305e-03
(255, 2 ,.,.) =
-4.8958e-02 -4.3860e-02 -5.7901e-02
-3.5920e-02 -3.6503e-02 -3.8574e-02
-4.1023e-02 -3.3337e-02 -1.3673e-02
...
(255,125,.,.) =
-1.1772e-02 -8.1042e-03 -1.5803e-02
-2.7190e-02 -2.8550e-02 7.5042e-03
-2.4363e-02 1.3943e-02 6.0615e-03
(255,126,.,.) =
-2.7317e-02 1.9704e-02 2.2183e-02
-3.7557e-02 2.0815e-02 1.8682e-02
-4.4557e-02 -4.3529e-03 -1.6779e-02
(255,127,.,.) =
1.9939e-02 2.6802e-02 1.1996e-02
2.0260e-02 2.1540e-02 2.5003e-03
1.8079e-04 -7.6315e-03 -1.9582e-02
[torch.FloatTensor of size 256x128x3x3], Parameter containing:
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[torch.FloatTensor of size 256], Parameter containing:
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0.0027
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[torch.FloatTensor of size 256], Parameter containing:
( 0 , 0 ,.,.) =
-9.2775e-03 -3.3897e-02 -1.1927e-02
-2.4595e-02 -7.9761e-02 -4.8709e-02
-4.3490e-02 -8.0118e-02 -6.5252e-02
( 0 , 1 ,.,.) =
-2.8918e-02 2.3763e-04 -2.8561e-02
9.8557e-03 1.0253e-02 -1.7677e-02
-1.0684e-02 2.8071e-03 -1.2483e-02
( 0 , 2 ,.,.) =
-1.4730e-02 2.2622e-02 4.4314e-03
1.5512e-02 1.0901e-02 -4.0294e-03
-2.0756e-02 -1.8048e-02 -1.7258e-02
...
( 0 ,253,.,.) =
3.1821e-04 -4.0924e-03 -7.9885e-04
-2.1664e-02 -2.2339e-02 -2.9870e-02
1.0453e-02 3.4707e-03 -1.1426e-02
( 0 ,254,.,.) =
9.6516e-03 1.8361e-02 3.7035e-02
3.7147e-03 1.0427e-02 1.5162e-02
8.4325e-03 1.8343e-02 3.0159e-02
( 0 ,255,.,.) =
1.3859e-03 8.4181e-03 9.7185e-03
2.6455e-02 4.1474e-02 5.5292e-02
1.6905e-02 6.1027e-02 5.6296e-02
⋮
( 1 , 0 ,.,.) =
1.1743e-02 1.6508e-02 5.1232e-03
2.9441e-02 2.0441e-02 2.1624e-02
7.8852e-03 1.3290e-02 1.1664e-02
( 1 , 1 ,.,.) =
-1.5315e-02 -2.1319e-02 -8.9703e-03
-2.9171e-02 -5.1600e-02 -4.3605e-02
-4.5486e-03 -3.7239e-02 -4.2013e-02
( 1 , 2 ,.,.) =
3.0217e-04 3.9781e-02 -1.4889e-04
1.2860e-02 3.3156e-02 1.6254e-02
-9.5886e-03 -5.6529e-03 -1.6966e-02
...
( 1 ,253,.,.) =
4.4662e-02 8.1982e-03 1.6867e-02
-6.6190e-03 -3.7080e-02 -5.9346e-03
-2.3913e-02 -6.0699e-02 -2.8947e-02
( 1 ,254,.,.) =
-5.7020e-03 -4.2262e-02 -2.1947e-02
-2.2780e-02 -3.1428e-02 -5.8322e-02
-1.9598e-02 -5.2995e-02 -4.8502e-02
( 1 ,255,.,.) =
6.4948e-03 3.2666e-03 9.3442e-03
1.0466e-03 -4.9306e-03 -1.1003e-02
-1.5981e-02 -1.0119e-02 -1.4555e-02
⋮
( 2 , 0 ,.,.) =
-6.1149e-03 -6.6849e-03 -6.9256e-03
-5.1692e-03 -8.9064e-03 -1.4313e-02
-1.1450e-02 -1.7125e-02 -2.3729e-02
( 2 , 1 ,.,.) =
3.9899e-02 1.6684e-02 2.0991e-02
1.6498e-02 -2.6236e-02 -1.1630e-02
5.9030e-03 -2.0597e-02 -1.5280e-02
( 2 , 2 ,.,.) =
6.0228e-03 2.4200e-02 2.0716e-02
4.9551e-03 -6.1590e-03 1.4790e-02
9.8595e-03 -2.7931e-02 -5.4261e-03
...
( 2 ,253,.,.) =
-9.7426e-03 -1.6989e-03 -1.0106e-02
-6.1351e-04 4.3355e-02 3.8143e-02
3.7943e-03 4.4980e-02 3.9165e-02
( 2 ,254,.,.) =
1.3395e-02 6.9187e-03 1.9631e-02
6.7533e-03 2.5027e-02 1.5162e-02
1.7857e-03 -4.3971e-03 3.7016e-03
( 2 ,255,.,.) =
-1.7440e-02 -1.6260e-02 -2.4000e-02
-1.9716e-02 -1.7364e-02 -1.7828e-02
-3.0010e-02 -1.3697e-02 -2.1068e-02
...
⋮
(253, 0 ,.,.) =
6.5624e-03 6.0837e-03 2.8446e-02
-1.2967e-02 -5.0910e-02 -2.0435e-02
-1.5419e-02 -1.4899e-02 -1.8056e-02
(253, 1 ,.,.) =
1.2341e-02 3.2479e-02 2.2650e-02
-4.2432e-03 -1.8113e-02 2.2224e-03
2.9012e-03 -1.7405e-02 3.1869e-03
(253, 2 ,.,.) =
-1.0992e-02 1.1080e-02 -1.4198e-02
8.2258e-03 3.0135e-02 4.1601e-02
6.0791e-04 1.6776e-04 2.1328e-02
...
(253,253,.,.) =
-7.5068e-04 2.6565e-02 1.1820e-02
-1.5916e-02 -7.4243e-03 -5.1214e-03
4.1732e-03 -6.8548e-03 -7.3191e-03
(253,254,.,.) =
-6.9767e-03 9.7686e-04 1.8935e-03
6.0631e-03 5.0983e-02 -3.4937e-03
-8.1496e-03 -3.0339e-02 -1.7409e-02
(253,255,.,.) =
-1.0048e-02 3.2093e-04 -1.1435e-03
-1.5435e-03 -2.9689e-02 -1.9539e-02
-9.6000e-04 4.8948e-03 1.5117e-02
⋮
(254, 0 ,.,.) =
1.6080e-02 1.2594e-02 5.4767e-03
-1.3241e-02 -1.9564e-02 -2.0807e-02
-7.7261e-03 -2.3040e-02 -2.0197e-02
(254, 1 ,.,.) =
-1.8947e-03 5.3025e-02 1.3421e-02
2.7344e-03 2.4908e-02 1.6726e-02
-1.9196e-02 -1.8768e-02 -1.9954e-02
(254, 2 ,.,.) =
8.0703e-03 2.9987e-02 5.7642e-04
3.5938e-03 2.5408e-02 -1.0444e-02
-9.6803e-04 -1.9317e-02 -1.2085e-02
...
(254,253,.,.) =
1.6295e-02 2.5060e-02 2.8950e-02
-7.3188e-03 -1.4100e-03 1.2378e-02
-2.1144e-02 -3.4673e-02 -1.9507e-02
(254,254,.,.) =
1.6469e-02 5.1930e-02 4.9364e-02
5.8284e-03 1.9868e-02 3.6292e-02
-4.9320e-03 -1.6470e-02 -1.2967e-02
(254,255,.,.) =
-1.0214e-02 -3.0802e-02 -3.4004e-02
5.5274e-03 -1.0925e-02 4.6995e-04
3.8212e-02 2.0936e-02 3.2566e-02
⋮
(255, 0 ,.,.) =
1.8364e-02 -3.0699e-03 1.0348e-02
-7.2351e-03 -1.2742e-03 -6.9527e-03
2.1686e-02 1.1490e-03 -3.2707e-03
(255, 1 ,.,.) =
-1.6594e-02 1.5176e-04 -9.1776e-03
1.5036e-02 5.8408e-02 2.1840e-02
-1.3606e-02 1.8126e-02 1.6354e-02
(255, 2 ,.,.) =
2.1872e-02 3.1581e-02 1.8289e-02
-2.1028e-03 -1.5633e-02 2.0265e-02
5.2924e-03 4.8438e-04 1.5701e-02
...
(255,253,.,.) =
4.4712e-03 -2.4757e-03 1.7267e-03
-5.2339e-03 -8.8001e-03 1.3738e-02
-1.0695e-02 1.0347e-03 1.6962e-02
(255,254,.,.) =
-5.9934e-03 -3.6803e-02 3.0996e-03
1.0224e-02 2.9117e-02 -7.3036e-04
9.9051e-03 5.9974e-02 2.7242e-02
(255,255,.,.) =
-9.1759e-03 -1.8297e-02 6.2411e-03
-3.1871e-02 -2.9350e-02 -1.4883e-02
-1.4808e-02 -1.2348e-02 -2.3609e-02
[torch.FloatTensor of size 256x256x3x3], Parameter containing:
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[torch.FloatTensor of size 256], Parameter containing:
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[torch.FloatTensor of size 256], Parameter containing:
( 0 , 0 ,.,.) =
8.0862e-03
( 0 , 1 ,.,.) =
-1.9208e-02
( 0 , 2 ,.,.) =
-1.7272e-02
...
( 0 ,125,.,.) =
-1.2758e-02
( 0 ,126,.,.) =
2.5496e-03
( 0 ,127,.,.) =
5.3547e-03
⋮
( 1 , 0 ,.,.) =
-1.4284e-02
( 1 , 1 ,.,.) =
-5.5428e-02
( 1 , 2 ,.,.) =
-3.4568e-02
...
( 1 ,125,.,.) =
2.7476e-02
( 1 ,126,.,.) =
3.5964e-02
( 1 ,127,.,.) =
2.3994e-02
⋮
( 2 , 0 ,.,.) =
7.6148e-03
( 2 , 1 ,.,.) =
2.0725e-02
( 2 , 2 ,.,.) =
-1.0066e-02
...
( 2 ,125,.,.) =
-2.7756e-02
( 2 ,126,.,.) =
6.3956e-03
( 2 ,127,.,.) =
-2.2016e-03
...
⋮
(253, 0 ,.,.) =
3.3605e-02
(253, 1 ,.,.) =
-4.2383e-02
(253, 2 ,.,.) =
2.2568e-02
...
(253,125,.,.) =
-3.3004e-02
(253,126,.,.) =
-9.1010e-04
(253,127,.,.) =
-1.7735e-02
⋮
(254, 0 ,.,.) =
-1.1416e-02
(254, 1 ,.,.) =
-1.8309e-02
(254, 2 ,.,.) =
7.6073e-03
...
(254,125,.,.) =
1.5128e-02
(254,126,.,.) =
3.3239e-02
(254,127,.,.) =
2.0724e-04
⋮
(255, 0 ,.,.) =
6.2636e-03
(255, 1 ,.,.) =
-2.0036e-02
(255, 2 ,.,.) =
1.0343e-03
...
(255,125,.,.) =
-1.9124e-02
(255,126,.,.) =
4.5483e-02
(255,127,.,.) =
7.8252e-03
[torch.FloatTensor of size 256x128x1x1], Parameter containing:
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[torch.FloatTensor of size 256], Parameter containing:
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[torch.FloatTensor of size 256], Parameter containing:
( 0 , 0 ,.,.) =
4.8367e-02 4.8045e-02 3.8471e-02
4.9888e-02 5.5208e-02 5.6701e-02
2.4192e-02 1.3436e-02 2.4655e-02
( 0 , 1 ,.,.) =
-3.6542e-03 -3.1100e-03 4.9227e-03
-1.2114e-03 3.4020e-03 1.9846e-02
-2.1704e-02 -2.1158e-02 -2.8686e-03
( 0 , 2 ,.,.) =
-1.2536e-02 -2.0486e-02 -2.3154e-02
-1.3515e-02 -2.3781e-02 -2.5515e-02
1.0584e-02 7.2999e-03 -5.2329e-03
...
( 0 ,253,.,.) =
-4.3596e-02 -1.8328e-02 -5.0577e-02
1.6590e-02 5.0719e-02 2.1919e-02
-1.9203e-02 -8.8315e-03 -2.0335e-02
( 0 ,254,.,.) =
-7.6949e-03 -1.5848e-02 1.5841e-03
-6.2470e-03 -1.3135e-02 6.9092e-03
-3.3791e-03 1.7889e-03 3.7373e-03
( 0 ,255,.,.) =
-6.6310e-03 5.8503e-03 -5.8571e-04
-2.4600e-02 -8.9747e-03 -7.2466e-03
-1.7566e-02 -8.5829e-03 -7.5220e-03
⋮
( 1 , 0 ,.,.) =
-2.3679e-02 -9.4399e-03 -1.1688e-02
-2.4777e-02 -1.7326e-02 -3.1489e-02
-3.3683e-03 9.7571e-03 -5.1527e-03
( 1 , 1 ,.,.) =
-3.0809e-02 -4.0685e-02 -2.2731e-02
-5.1065e-03 -1.6457e-02 -1.8804e-02
5.0382e-02 5.2054e-02 3.9185e-02
( 1 , 2 ,.,.) =
-3.7790e-02 -4.2234e-02 -2.9703e-02
-6.4766e-03 2.6967e-03 -8.1736e-03
3.7747e-02 5.5416e-02 2.5806e-02
...
( 1 ,253,.,.) =
-2.7275e-02 -4.5364e-02 -3.9567e-02
8.9827e-03 1.6150e-02 1.1675e-02
-9.7209e-03 -3.6449e-02 -1.6842e-02
( 1 ,254,.,.) =
1.7824e-02 1.5013e-02 1.0225e-02
5.4044e-03 1.1664e-02 6.4623e-03
2.1803e-02 4.1795e-02 1.9234e-02
( 1 ,255,.,.) =
-2.6730e-04 1.5218e-03 -5.0352e-03
2.5761e-02 2.7110e-02 -9.3395e-04
-1.1949e-02 -7.5204e-03 -3.9370e-03
⋮
( 2 , 0 ,.,.) =
-1.7447e-02 -1.8358e-02 -2.6020e-02
-1.4074e-02 -1.1302e-02 -1.4814e-02
-3.1460e-03 -1.8674e-02 -9.3350e-03
( 2 , 1 ,.,.) =
-5.1125e-03 -4.8036e-03 1.8139e-02
-1.0524e-02 -1.5152e-02 2.3904e-03
8.7093e-03 9.3810e-03 2.4203e-03
( 2 , 2 ,.,.) =
-7.6392e-03 -8.1496e-03 -1.5331e-02
-8.0622e-03 -1.3383e-02 -1.3938e-02
-1.6904e-02 -3.0059e-02 -1.8659e-02
...
( 2 ,253,.,.) =
1.8390e-02 -2.6080e-03 9.3782e-03
-6.4662e-04 -1.3146e-02 1.0045e-02
-2.2293e-03 -1.4097e-02 1.7385e-02
( 2 ,254,.,.) =
3.0293e-04 2.9622e-03 1.0030e-02
-5.7588e-03 -1.6943e-03 6.9988e-03
9.8134e-03 1.4197e-02 5.9742e-03
( 2 ,255,.,.) =
2.8753e-03 -1.7814e-03 1.0873e-02
1.5230e-02 4.5867e-03 1.6860e-02
1.9536e-03 1.9503e-02 1.2168e-02
...
⋮
(253, 0 ,.,.) =
1.3983e-02 2.4598e-03 -7.4604e-03
-2.2250e-02 -1.2757e-02 -2.8846e-03
-1.0911e-02 7.5499e-03 8.6910e-03
(253, 1 ,.,.) =
-4.8463e-03 -8.3250e-03 1.3420e-02
-6.2502e-03 -7.3982e-03 1.1153e-02
4.0391e-03 -9.0354e-03 -7.5441e-03
(253, 2 ,.,.) =
-5.1627e-03 -8.9529e-03 -1.2414e-02
-4.9261e-03 -3.5488e-03 2.1501e-03
-1.1709e-02 -1.4984e-02 -1.9216e-03
...
(253,253,.,.) =
1.5428e-02 -7.6036e-04 -1.3522e-03
-3.4856e-02 -7.4478e-04 -6.5064e-03
-9.1655e-03 -2.8467e-02 -4.8924e-02
(253,254,.,.) =
1.2207e-02 1.0519e-02 -8.4421e-03
-2.5495e-02 2.8140e-03 1.6165e-03
-1.8831e-02 1.2268e-02 1.5439e-02
(253,255,.,.) =
-1.3684e-02 -4.1732e-03 1.2609e-02
-6.8834e-04 5.9757e-03 -1.0183e-02
2.1559e-04 -1.3462e-02 -3.0114e-02
⋮
(254, 0 ,.,.) =
-1.6186e-02 -6.4926e-02 -4.3146e-02
-2.1790e-02 -4.9106e-02 -3.4568e-02
4.0506e-02 4.2449e-02 6.1562e-02
(254, 1 ,.,.) =
3.5715e-03 -1.0916e-02 -2.2922e-02
-2.4831e-03 6.4555e-03 -1.1316e-02
1.6662e-03 -1.9145e-02 -2.3007e-02
(254, 2 ,.,.) =
-7.1243e-03 -4.2783e-05 4.9363e-03
-1.5832e-02 4.0474e-03 4.5135e-04
-4.7967e-03 -7.2164e-04 -1.7230e-02
...
(254,253,.,.) =
1.1589e-02 7.7814e-04 6.3205e-03
1.1360e-02 -6.2076e-03 -2.7689e-02
2.6392e-02 2.3775e-03 -1.4937e-02
(254,254,.,.) =
-1.1237e-02 -2.6285e-03 9.1537e-03
-8.2120e-03 -2.2236e-02 3.2917e-04
5.5909e-03 -1.3858e-03 6.8947e-03
(254,255,.,.) =
-1.4783e-02 -1.0367e-02 -2.7472e-02
-4.1090e-02 -3.8532e-02 -3.9202e-02
-2.1614e-02 -3.4340e-02 -1.8542e-02
⋮
(255, 0 ,.,.) =
-1.9492e-02 -1.6098e-02 -3.1792e-02
2.5374e-02 4.6815e-02 2.7513e-02
3.5903e-02 3.1892e-02 2.6156e-02
(255, 1 ,.,.) =
1.6856e-02 1.5645e-02 1.4189e-02
2.2550e-02 3.0456e-02 1.6739e-02
-2.3615e-04 -7.9501e-03 -1.9666e-03
(255, 2 ,.,.) =
-7.9060e-03 -4.7390e-03 1.6030e-03
1.3802e-03 -8.5837e-03 6.9451e-03
1.1407e-02 -5.9877e-03 1.3759e-02
...
(255,253,.,.) =
4.0124e-03 2.9951e-02 1.1915e-02
-4.3412e-02 -3.1776e-03 -2.7705e-02
-1.6183e-02 -1.1247e-02 -3.5084e-02
(255,254,.,.) =
2.9837e-02 5.9935e-02 2.4631e-02
-1.9571e-03 2.2415e-02 -1.5499e-02
1.6075e-02 1.7850e-02 -1.8412e-02
(255,255,.,.) =
-4.3712e-03 -4.9032e-02 -2.1335e-02
-5.2598e-03 -2.8579e-02 -2.2090e-02
8.5126e-03 2.0862e-03 2.3301e-02
[torch.FloatTensor of size 256x256x3x3], Parameter containing:
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0.2710
0.3475
0.2388
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0.2019
0.2466
0.2988
0.2044
0.2691
[torch.FloatTensor of size 256], Parameter containing:
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0.0516
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[torch.FloatTensor of size 256], Parameter containing:
( 0 , 0 ,.,.) =
-4.2568e-02 -2.6148e-02 -2.2019e-02
-1.7334e-02 -7.5950e-03 -7.2384e-03
-1.7876e-03 2.3800e-02 1.4873e-02
( 0 , 1 ,.,.) =
-2.8277e-03 -5.0644e-03 -4.9442e-03
1.2117e-03 1.4908e-02 1.6013e-02
1.4391e-02 3.3109e-02 5.0061e-02
( 0 , 2 ,.,.) =
-3.4891e-03 -4.4437e-03 2.6589e-03
1.5105e-02 2.6303e-02 2.6802e-02
3.9232e-02 5.0057e-02 4.6637e-02
...
( 0 ,253,.,.) =
2.2877e-02 1.5454e-02 -2.4483e-02
3.1145e-02 3.4944e-02 1.3296e-02
-1.7674e-04 7.3297e-03 -5.7174e-03
( 0 ,254,.,.) =
-2.1781e-02 -3.7379e-02 -1.3382e-02
1.8976e-02 1.4155e-02 -6.5395e-03
2.6831e-02 3.6354e-02 1.1450e-02
( 0 ,255,.,.) =
3.1603e-02 3.3933e-02 3.1575e-02
-1.0098e-02 -1.2657e-02 1.1674e-02
1.0325e-02 7.9424e-05 1.5911e-02
⋮
( 1 , 0 ,.,.) =
2.5937e-02 6.2590e-03 6.0798e-03
-4.5745e-03 -3.5188e-02 -2.9249e-02
2.1366e-02 2.0480e-03 6.2699e-03
( 1 , 1 ,.,.) =
-2.9549e-03 -1.3679e-03 -8.6876e-03
7.9988e-03 1.2888e-03 -5.9629e-03
-9.1481e-03 -2.1914e-02 -4.1572e-02
( 1 , 2 ,.,.) =
7.6390e-03 3.0253e-03 2.7817e-04
7.0329e-03 1.1914e-02 -2.4419e-03
-8.2131e-03 -9.7848e-05 -1.9223e-02
...
( 1 ,253,.,.) =
-4.4498e-03 5.1611e-03 3.7416e-03
3.2110e-04 8.3762e-03 3.6612e-03
9.3343e-03 8.1829e-03 1.1234e-03
( 1 ,254,.,.) =
-6.6849e-02 -5.9871e-02 -3.3931e-02
2.2337e-02 3.1932e-02 3.7244e-02
9.3296e-03 3.7222e-02 1.4052e-02
( 1 ,255,.,.) =
-2.0643e-03 1.2408e-02 -3.1072e-03
-8.2882e-03 1.3917e-02 -2.0680e-02
-1.9329e-02 1.1953e-02 -2.3436e-02
⋮
( 2 , 0 ,.,.) =
-1.7788e-03 -3.5982e-03 -1.2592e-03
-1.5320e-02 -1.0690e-02 -2.0311e-02
-3.4649e-04 -2.2188e-03 -1.5021e-02
( 2 , 1 ,.,.) =
-2.8952e-02 -3.3958e-02 -2.5437e-02
-1.5919e-04 1.5204e-02 3.4554e-02
3.6892e-02 7.0144e-02 7.3610e-02
( 2 , 2 ,.,.) =
1.0721e-02 2.1531e-03 -5.6155e-03
1.1754e-02 -4.8546e-03 -5.5013e-03
-3.7388e-04 -9.7639e-03 -1.5029e-02
...
( 2 ,253,.,.) =
1.5622e-02 9.8976e-03 3.4725e-03
1.4711e-02 7.0707e-03 -9.1826e-03
7.0986e-03 6.3087e-03 -3.5893e-03
( 2 ,254,.,.) =
-6.4518e-03 -6.7673e-03 1.1635e-02
1.4707e-02 2.3831e-02 4.9396e-02
1.8897e-02 3.4981e-02 4.5488e-02
( 2 ,255,.,.) =
1.5900e-02 3.3369e-02 2.6194e-02
1.0616e-02 1.8515e-02 3.0190e-03
1.1004e-02 2.5503e-02 1.3654e-02
...
⋮
(253, 0 ,.,.) =
-2.1231e-02 -1.2804e-02 -1.5498e-02
7.6750e-03 1.2120e-02 1.5099e-02
1.8536e-02 2.5110e-02 2.5283e-02
(253, 1 ,.,.) =
7.4059e-03 -3.0540e-03 -1.5475e-03
-8.4415e-03 -2.2002e-02 -3.4099e-03
9.1918e-03 2.2617e-03 -1.4260e-02
(253, 2 ,.,.) =
-5.2568e-03 -5.3507e-03 -3.2230e-03
-1.5805e-02 6.0508e-03 -1.5917e-03
-8.9323e-03 2.6483e-03 5.0508e-03
...
(253,253,.,.) =
1.9826e-02 -2.1209e-03 1.4889e-02
5.7275e-02 3.5549e-02 6.0175e-03
2.3347e-02 -2.2153e-02 -2.5497e-02
(253,254,.,.) =
-1.3985e-02 -6.4766e-02 -1.7286e-02
1.1704e-02 1.0714e-02 4.6278e-02
-1.0038e-02 -3.5707e-03 2.2691e-02
(253,255,.,.) =
-8.3342e-03 -1.3070e-03 -1.0049e-02
3.2605e-02 5.3259e-02 2.2172e-02
3.7339e-02 6.1155e-02 4.4555e-02
⋮
(254, 0 ,.,.) =
-1.6584e-02 -1.3850e-02 -1.4604e-02
-1.7604e-02 -2.1268e-02 -1.6734e-02
-6.0039e-04 3.8569e-03 1.2837e-02
(254, 1 ,.,.) =
1.7623e-02 2.3706e-02 2.7633e-02
-2.2841e-02 -1.9576e-02 -1.6551e-02
-8.0822e-03 4.3779e-03 -5.3622e-03
(254, 2 ,.,.) =
1.5582e-02 3.7879e-02 2.3555e-02
-6.4632e-03 9.8620e-03 1.2121e-02
-1.3743e-02 -6.1246e-03 -2.7332e-03
...
(254,253,.,.) =
-2.5037e-03 -1.2064e-02 -9.0989e-03
-4.7911e-04 -2.8339e-03 2.1365e-03
-6.2077e-03 -2.6615e-03 1.1215e-02
(254,254,.,.) =
-8.1794e-03 -2.2417e-02 -3.4012e-02
-2.8553e-02 -2.9546e-02 -4.4372e-02
-5.0348e-02 -3.4973e-02 -5.2028e-02
(254,255,.,.) =
1.4728e-02 3.2834e-02 2.6312e-02
1.3449e-02 2.6407e-02 2.6924e-02
2.5572e-02 3.4316e-02 2.6184e-02
⋮
(255, 0 ,.,.) =
7.2026e-03 -2.3931e-03 2.2182e-03
4.2555e-03 -6.4084e-03 7.8548e-03
2.0510e-02 1.8644e-02 2.3280e-02
(255, 1 ,.,.) =
-1.2471e-02 1.3008e-02 1.0010e-02
-1.7496e-03 6.1331e-03 4.3366e-03
5.2269e-03 1.5111e-02 -8.1881e-03
(255, 2 ,.,.) =
-3.7337e-02 1.9923e-02 -2.4149e-02
-4.9487e-02 -1.0510e-02 -4.2107e-02
-5.7684e-03 -4.8632e-03 -1.8332e-02
...
(255,253,.,.) =
7.2013e-03 -1.5208e-02 -1.6507e-02
-8.8276e-03 -1.8698e-02 -1.6637e-03
-1.2015e-02 2.9667e-03 6.2300e-03
(255,254,.,.) =
-1.8341e-02 -9.0521e-03 2.6030e-02
3.5930e-02 5.3049e-02 5.8487e-02
-1.3661e-02 -3.6888e-03 -7.1606e-03
(255,255,.,.) =
-1.2594e-02 -4.0898e-02 1.7162e-03
-1.7420e-02 -4.3435e-02 -1.3183e-02
-3.7506e-02 -5.5707e-02 -3.0051e-02
[torch.FloatTensor of size 256x256x3x3], Parameter containing:
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0.2236
0.1654
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[torch.FloatTensor of size 256], Parameter containing:
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[torch.FloatTensor of size 256], Parameter containing:
( 0 , 0 ,.,.) =
-1.1645e-02 -1.9010e-02 -2.1876e-02
2.0482e-02 2.3962e-02 2.9161e-02
4.3672e-02 3.3278e-02 4.9908e-02
( 0 , 1 ,.,.) =
-7.4040e-03 2.8083e-03 -4.7339e-03
6.9030e-03 1.4271e-02 -3.6954e-03
-3.1341e-03 1.3736e-02 1.6127e-03
( 0 , 2 ,.,.) =
1.8676e-02 -1.0553e-02 -1.4233e-02
8.9944e-03 -2.5068e-03 -1.2145e-02
-4.9455e-03 -2.9206e-02 -9.6385e-03
...
( 0 ,253,.,.) =
-1.2655e-02 1.7691e-02 9.8264e-04
7.4271e-03 7.6115e-03 1.1135e-02
2.3242e-02 1.1058e-02 4.0498e-03
( 0 ,254,.,.) =
1.8557e-02 1.2472e-02 1.7220e-02
-4.8544e-03 8.3627e-03 2.2811e-02
-5.1675e-03 2.3264e-02 3.4068e-02
( 0 ,255,.,.) =
2.4934e-02 2.2373e-02 4.2614e-02
1.3486e-02 1.6760e-03 1.3019e-02
-6.2821e-03 -1.5112e-03 -8.9229e-03
⋮
( 1 , 0 ,.,.) =
-9.8089e-04 -6.3011e-03 5.9932e-03
1.5936e-02 1.3394e-02 2.9934e-02
2.3149e-02 2.0709e-02 2.5485e-02
( 1 , 1 ,.,.) =
-2.0015e-02 -3.3349e-02 -8.0396e-03
-7.2800e-03 -1.2187e-02 -2.0389e-04
-1.3138e-02 -2.0427e-02 -1.6286e-02
( 1 , 2 ,.,.) =
-6.7681e-03 5.0045e-03 -2.6683e-03
-2.1073e-02 2.8275e-04 -1.8205e-02
-1.7382e-02 -5.0244e-03 -3.0386e-03
...
( 1 ,253,.,.) =
-1.1035e-02 -2.2964e-02 -1.1028e-02
-6.3256e-03 -4.1667e-03 -1.7323e-02
-1.3611e-02 -2.3468e-02 -1.6436e-02
( 1 ,254,.,.) =
7.3663e-03 6.6219e-03 5.2776e-03
-3.5464e-03 3.2750e-03 -9.1126e-03
3.5593e-04 -1.0151e-02 -1.9123e-02
( 1 ,255,.,.) =
1.8193e-03 8.8087e-03 5.1361e-03
3.1915e-03 2.5287e-02 2.4939e-02
1.3968e-02 1.9613e-02 2.2382e-02
⋮
( 2 , 0 ,.,.) =
-4.1548e-03 8.8964e-03 2.0143e-03
1.1327e-02 1.3251e-02 1.4014e-02
7.2196e-03 1.3045e-02 2.4827e-02
( 2 , 1 ,.,.) =
-1.5025e-02 5.0530e-03 7.4766e-03
-2.4685e-02 -1.6732e-02 -1.0888e-02
-2.8064e-02 -1.1875e-02 -3.4120e-03
( 2 , 2 ,.,.) =
2.8449e-02 1.4594e-02 6.9441e-03
2.4799e-02 1.9453e-02 1.1294e-02
-1.0787e-02 -2.1006e-02 -1.0372e-02
...
( 2 ,253,.,.) =
1.4967e-02 8.2449e-03 2.0244e-03
1.4287e-02 -6.3867e-03 -8.0757e-03
2.7547e-02 1.0791e-02 1.6567e-02
( 2 ,254,.,.) =
3.6191e-02 3.8918e-02 3.9028e-02
-8.3489e-04 1.3273e-02 2.0172e-02
-2.0652e-02 -5.4010e-03 1.7147e-03
( 2 ,255,.,.) =
2.0373e-04 3.5919e-03 8.5592e-03
6.2363e-03 -9.3086e-05 1.2940e-02
1.3152e-02 1.0732e-02 1.9896e-02
...
⋮
(509, 0 ,.,.) =
-1.7400e-02 -6.7019e-03 -9.1787e-03
-9.9672e-03 2.6298e-04 3.3439e-03
1.5721e-02 1.4216e-02 2.0509e-02
(509, 1 ,.,.) =
2.1410e-02 3.6914e-02 2.8239e-02
3.8158e-02 4.8944e-02 3.4652e-02
3.1723e-02 4.4208e-02 4.0035e-02
(509, 2 ,.,.) =
-3.3437e-03 -1.0482e-02 -5.3990e-03
-5.3186e-03 1.1394e-02 1.7593e-03
-5.6652e-03 -6.6373e-03 -1.3492e-02
...
(509,253,.,.) =
-1.7099e-02 -1.8145e-03 -1.3040e-02
-2.2750e-02 -3.6062e-03 -8.0294e-03
-1.6087e-02 -1.0175e-02 -1.3529e-02
(509,254,.,.) =
4.1701e-04 -5.1785e-03 -2.1884e-02
2.6919e-03 8.9139e-03 -1.4217e-04
-7.3746e-03 -6.6853e-03 -2.3725e-02
(509,255,.,.) =
1.9425e-02 1.3175e-02 1.7511e-02
1.8235e-02 4.4286e-02 2.3767e-02
2.6504e-02 3.3104e-02 1.9696e-02
⋮
(510, 0 ,.,.) =
-1.0177e-02 -1.0701e-02 -2.0428e-02
-1.7986e-02 5.9928e-03 -1.0584e-03
-1.8794e-02 -1.8773e-03 -6.9449e-03
(510, 1 ,.,.) =
-2.8498e-03 1.6427e-03 1.4575e-04
-5.4403e-03 8.3667e-03 -9.4164e-03
-4.4999e-03 5.4902e-03 2.4863e-03
(510, 2 ,.,.) =
-1.3356e-02 -2.1525e-02 5.3421e-04
-1.9160e-02 -2.4645e-02 -1.3791e-02
-6.1991e-03 -1.3174e-02 -3.6783e-03
...
(510,253,.,.) =
-3.3993e-03 -2.7823e-03 7.6715e-03
-2.0649e-02 -1.2731e-02 -9.4138e-03
-1.3678e-03 -3.4410e-02 -2.6984e-02
(510,254,.,.) =
-3.5651e-04 2.0102e-03 1.4130e-02
-1.3073e-02 -1.6616e-02 -1.2690e-02
-3.5934e-02 -4.1700e-02 -3.3968e-02
(510,255,.,.) =
2.0470e-02 8.0159e-04 -1.1607e-03
9.5101e-03 3.0336e-02 2.7362e-02
1.5588e-02 3.2851e-02 1.3015e-02
⋮
(511, 0 ,.,.) =
-1.5574e-02 -3.2971e-02 -3.1939e-02
-2.2502e-02 -5.7187e-03 -5.6729e-03
-2.7309e-02 -1.6981e-02 1.2832e-04
(511, 1 ,.,.) =
-1.1925e-02 -2.9479e-02 -2.0437e-02
-2.4408e-02 -2.2069e-02 -1.9965e-03
-2.3279e-02 -5.5140e-03 2.5630e-02
(511, 2 ,.,.) =
-1.6100e-02 -8.2417e-03 1.5266e-04
-2.6195e-03 -8.2754e-03 -2.9435e-02
-2.7493e-03 -2.4889e-02 -2.3583e-02
...
(511,253,.,.) =
1.7985e-02 1.8594e-02 8.9198e-04
-1.7319e-02 7.8735e-03 -2.8659e-03
3.8596e-03 2.9061e-02 2.4188e-02
(511,254,.,.) =
-2.6735e-02 -1.4391e-02 -4.0148e-02
-2.6728e-02 -2.4455e-02 -6.9176e-03
-5.7244e-02 -2.1995e-04 5.5438e-02
(511,255,.,.) =
2.3487e-02 2.7157e-03 -8.4719e-04
1.7886e-02 5.4860e-03 2.8059e-02
4.6468e-03 1.8598e-02 1.3761e-03
[torch.FloatTensor of size 512x256x3x3], Parameter containing:
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0.2511
0.2288
0.2074
0.2905
0.2482
0.3102
0.2749
0.2892
0.2448
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0.2426
0.2780
0.2315
0.2631
0.3383
0.2785
0.2536
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0.2800
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0.2637
0.2323
0.3333
0.2714
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[torch.FloatTensor of size 512], Parameter containing:
( 0 , 0 ,.,.) =
1.6218e-04 -1.4720e-02 -1.7000e-02
-1.2850e-02 -3.3085e-02 -3.6656e-02
2.7812e-02 1.7691e-02 -1.8369e-02
( 0 , 1 ,.,.) =
1.0528e-02 3.1379e-02 2.4801e-02
-1.2698e-02 -2.9453e-02 -1.1834e-02
-9.4094e-03 -8.9462e-03 -3.1349e-02
( 0 , 2 ,.,.) =
-7.8447e-03 -2.9256e-02 5.3590e-03
-1.3791e-02 -1.1116e-02 5.0388e-03
-2.4919e-03 7.3514e-03 5.4013e-03
...
( 0 ,509,.,.) =
-1.0276e-03 -1.0275e-02 -2.9986e-02
-3.8465e-03 1.9549e-03 -1.6291e-02
-1.8100e-03 8.3778e-03 -8.5481e-03
( 0 ,510,.,.) =
-1.8196e-02 -1.3533e-02 -1.7457e-02
2.2457e-02 5.7402e-02 1.9325e-02
-2.4977e-02 -3.2113e-02 -8.1780e-03
( 0 ,511,.,.) =
3.6550e-03 4.9358e-03 -5.7597e-03
-1.6875e-02 1.3999e-04 3.7629e-04
-2.6272e-03 1.0947e-03 1.1145e-03
⋮
( 1 , 0 ,.,.) =
1.4018e-02 3.9198e-03 -1.7189e-03
-1.3175e-03 4.3503e-04 -1.1798e-02
-9.8003e-03 -1.7693e-02 -1.9910e-02
( 1 , 1 ,.,.) =
-1.4957e-02 -1.9796e-02 -2.8724e-02
5.8908e-03 -1.5228e-02 -5.6715e-03
2.9284e-03 -1.8028e-02 -7.1433e-03
( 1 , 2 ,.,.) =
-1.1625e-02 -3.3804e-02 -1.0025e-02
-1.6606e-02 -5.5716e-02 -2.3204e-02
-2.5758e-02 -4.3135e-02 -2.5901e-02
...
( 1 ,509,.,.) =
-1.5007e-02 -1.4333e-02 -2.5937e-03
-2.3078e-02 -1.5820e-02 -2.2818e-03
-4.1318e-03 -8.0353e-03 -2.3236e-03
( 1 ,510,.,.) =
-1.8531e-02 -1.8004e-02 -2.8084e-02
-3.6680e-02 -6.8641e-02 -5.2469e-02
-1.1712e-02 -2.4334e-02 -1.6733e-02
( 1 ,511,.,.) =
-2.2078e-02 -2.9163e-02 -3.8717e-03
-7.0301e-03 1.6718e-02 5.4339e-03
-1.3131e-02 1.1999e-02 -1.7480e-02
⋮
( 2 , 0 ,.,.) =
-5.2378e-03 -3.4890e-03 -2.0851e-03
1.5306e-02 -2.1752e-02 -8.7682e-03
2.2460e-02 9.9175e-03 -3.3635e-03
( 2 , 1 ,.,.) =
7.4677e-03 -9.1762e-03 -9.2569e-05
1.9441e-04 1.2344e-03 -8.9978e-03
-5.1243e-04 2.1850e-04 -4.8828e-03
( 2 , 2 ,.,.) =
1.7078e-02 3.3955e-03 9.3503e-03
2.0334e-02 -1.0621e-04 -8.2017e-05
1.0706e-02 -1.8414e-03 1.0828e-02
...
( 2 ,509,.,.) =
3.2008e-02 2.3494e-02 2.5386e-02
1.9307e-02 2.3924e-02 2.8972e-02
9.9003e-03 2.0158e-02 2.2655e-02
( 2 ,510,.,.) =
-9.8395e-03 -1.1114e-02 -3.7696e-03
-2.9508e-02 -3.6956e-02 -1.8228e-02
-1.3663e-03 -2.5845e-03 1.0352e-02
( 2 ,511,.,.) =
-7.3867e-03 -2.5413e-02 -2.1942e-02
-1.6699e-02 -1.5133e-02 -1.3030e-02
-2.0090e-02 3.7970e-03 -1.0341e-02
...
⋮
(509, 0 ,.,.) =
-1.6157e-02 -1.6883e-02 -2.8328e-04
-7.7759e-03 -2.4465e-03 -1.4641e-02
2.4639e-02 3.9862e-02 2.1048e-02
(509, 1 ,.,.) =
2.4491e-03 -9.3885e-03 -1.1786e-02
2.5301e-02 2.5625e-04 7.1335e-03
2.2342e-02 1.9042e-02 7.2526e-03
(509, 2 ,.,.) =
-1.4652e-02 -2.7802e-02 -4.3564e-03
-1.7961e-02 -4.3846e-02 2.7409e-03
-4.7968e-03 -8.4231e-03 1.2070e-02
...
(509,509,.,.) =
-2.0171e-02 -3.3546e-02 -1.6728e-02
-1.7847e-02 -5.1713e-02 -2.6780e-02
-1.3145e-03 -4.3181e-03 -9.6373e-03
(509,510,.,.) =
-5.3917e-03 -2.0410e-04 2.7798e-03
-9.6882e-04 -2.5141e-02 1.4804e-02
2.8748e-02 9.0832e-03 4.2548e-02
(509,511,.,.) =
-1.5698e-02 -1.9303e-02 -9.1469e-03
-2.0025e-02 -1.1131e-02 -3.3902e-02
-5.7436e-03 -7.3640e-03 -1.0044e-02
⋮
(510, 0 ,.,.) =
-8.8612e-03 -4.5370e-03 -1.2354e-02
-5.9245e-03 -1.7058e-02 -2.8041e-02
-1.0435e-02 7.6695e-04 -1.0578e-02
(510, 1 ,.,.) =
9.5200e-03 -5.1975e-03 1.2947e-02
4.4305e-03 -2.3992e-02 -8.4569e-04
4.6608e-03 9.6787e-03 8.2174e-03
(510, 2 ,.,.) =
5.1559e-03 4.4635e-04 -7.9934e-03
3.3069e-03 1.4450e-02 8.9234e-03
6.3402e-03 1.9043e-02 1.9021e-02
...
(510,509,.,.) =
7.6964e-03 -1.3777e-02 6.0539e-03
-1.5745e-03 -2.3391e-02 -1.0052e-02
9.5183e-03 -1.2251e-02 2.2436e-03
(510,510,.,.) =
1.0375e-02 3.5875e-03 -5.7940e-04
7.0412e-03 -1.0673e-02 -4.9120e-03
-2.6034e-03 1.1306e-02 7.0696e-03
(510,511,.,.) =
-1.7509e-02 -2.3182e-02 -1.7897e-02
-1.7769e-03 1.9672e-03 -7.3220e-03
-6.6833e-03 9.8286e-03 2.0653e-03
⋮
(511, 0 ,.,.) =
2.8375e-02 -8.1936e-03 1.8009e-02
1.5829e-02 -1.3571e-02 -1.9335e-02
4.0766e-03 -1.5722e-02 -5.0620e-02
(511, 1 ,.,.) =
-5.5310e-03 -1.8996e-02 -7.9436e-03
1.3825e-03 -4.9608e-02 1.7256e-03
7.6629e-03 -7.6101e-03 1.2541e-02
(511, 2 ,.,.) =
1.8052e-02 3.1718e-02 4.2556e-03
-3.6760e-03 3.0490e-03 -1.2264e-02
-8.9404e-03 -1.6604e-02 1.6348e-03
...
(511,509,.,.) =
5.3192e-03 1.8204e-02 1.8114e-02
-6.1202e-03 1.5905e-03 2.0264e-02
-1.1471e-02 -1.5697e-02 9.0871e-03
(511,510,.,.) =
3.7707e-03 8.0599e-03 1.8290e-02
1.7257e-02 6.9638e-03 1.8746e-02
1.0751e-02 1.3663e-02 -1.0081e-03
(511,511,.,.) =
1.9711e-02 -1.4569e-02 -2.4663e-02
2.5966e-03 -2.4807e-02 9.3861e-03
-1.2876e-03 1.3974e-03 1.3434e-02
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[torch.FloatTensor of size 512], Parameter containing:
( 0 , 0 ,.,.) =
5.6973e-03
( 0 , 1 ,.,.) =
2.0359e-03
( 0 , 2 ,.,.) =
1.6696e-02
...
( 0 ,253,.,.) =
8.4662e-03
( 0 ,254,.,.) =
-2.7450e-02
( 0 ,255,.,.) =
9.6710e-03
⋮
( 1 , 0 ,.,.) =
-2.7123e-02
( 1 , 1 ,.,.) =
-1.5713e-02
( 1 , 2 ,.,.) =
5.4291e-02
...
( 1 ,253,.,.) =
-2.0631e-02
( 1 ,254,.,.) =
-3.0793e-02
( 1 ,255,.,.) =
1.3228e-03
⋮
( 2 , 0 ,.,.) =
-5.2315e-02
( 2 , 1 ,.,.) =
-3.5294e-02
( 2 , 2 ,.,.) =
3.9423e-02
...
( 2 ,253,.,.) =
-3.8161e-02
( 2 ,254,.,.) =
-2.6385e-02
( 2 ,255,.,.) =
-4.4272e-02
...
⋮
(509, 0 ,.,.) =
4.9361e-02
(509, 1 ,.,.) =
4.3553e-02
(509, 2 ,.,.) =
1.0309e-02
...
(509,253,.,.) =
7.1570e-03
(509,254,.,.) =
1.4031e-03
(509,255,.,.) =
-6.6892e-02
⋮
(510, 0 ,.,.) =
5.3341e-02
(510, 1 ,.,.) =
-1.4842e-02
(510, 2 ,.,.) =
-4.8024e-02
...
(510,253,.,.) =
5.4730e-03
(510,254,.,.) =
4.2852e-02
(510,255,.,.) =
1.2923e-02
⋮
(511, 0 ,.,.) =
3.0030e-02
(511, 1 ,.,.) =
-9.1642e-03
(511, 2 ,.,.) =
9.0266e-03
...
(511,253,.,.) =
1.0095e-02
(511,254,.,.) =
-1.1120e-02
(511,255,.,.) =
-7.9560e-03
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[torch.FloatTensor of size 512], Parameter containing:
( 0 , 0 ,.,.) =
-8.0284e-03 -5.7776e-03 6.4154e-03
5.0498e-03 -6.7796e-03 1.2691e-02
1.3331e-02 1.4523e-02 2.4522e-02
( 0 , 1 ,.,.) =
-1.9876e-03 1.2466e-02 1.0494e-02
-1.9364e-02 -1.6696e-02 -1.1857e-02
-1.1569e-02 -3.7674e-03 -3.4679e-03
( 0 , 2 ,.,.) =
-1.1440e-02 -1.3884e-02 1.1559e-03
-1.7906e-02 -2.9349e-02 -1.3876e-02
-1.4057e-02 -2.6989e-02 -2.3963e-02
...
( 0 ,509,.,.) =
-6.3040e-03 -3.1167e-03 -1.3304e-02
7.1623e-03 6.4669e-03 1.6063e-02
-1.0750e-02 -1.0480e-02 -6.1070e-03
( 0 ,510,.,.) =
7.4484e-03 6.3878e-03 -1.2579e-02
-7.7356e-03 1.8112e-03 -1.7890e-02
-2.9142e-03 7.7705e-03 -9.7314e-03
( 0 ,511,.,.) =
2.1760e-02 2.2364e-02 2.2731e-02
2.6681e-02 2.9127e-02 3.3356e-02
1.2892e-02 -3.5818e-03 5.3022e-03
⋮
( 1 , 0 ,.,.) =
-1.0597e-02 -9.1551e-03 -2.3418e-02
-1.0768e-02 -3.3171e-03 -1.8559e-02
-1.8607e-02 -4.2634e-03 -1.5591e-02
( 1 , 1 ,.,.) =
-2.6090e-02 -2.2517e-02 -3.0593e-02
-3.9406e-02 -2.6639e-02 -2.8202e-02
-2.6143e-02 -1.9647e-02 -2.1466e-02
( 1 , 2 ,.,.) =
-3.5259e-03 1.6623e-03 -6.5624e-03
-5.0597e-03 -8.7162e-04 -5.3742e-03
-7.9651e-03 -9.7778e-03 -1.0736e-02
...
( 1 ,509,.,.) =
1.8492e-02 -3.6799e-03 1.0043e-02
-5.2974e-03 -2.0757e-02 -1.5120e-02
2.1435e-02 6.4916e-03 4.7660e-03
( 1 ,510,.,.) =
-1.8810e-02 -6.0469e-04 -7.6999e-03
-1.7697e-02 -7.8692e-03 -1.6543e-02
-1.7206e-02 -2.4746e-02 -3.0270e-02
( 1 ,511,.,.) =
-3.1191e-02 -1.4363e-02 2.2032e-03
-1.2033e-02 -2.3699e-03 -1.6630e-02
-1.2905e-02 -1.5363e-02 -3.6297e-03
⋮
( 2 , 0 ,.,.) =
-3.2648e-02 -4.8158e-03 -2.0476e-02
-2.5846e-02 -1.4660e-03 -2.8170e-02
-2.6640e-02 4.3022e-03 -2.7636e-02
( 2 , 1 ,.,.) =
-6.3289e-03 -1.5401e-02 -1.3096e-03
-1.7499e-02 -2.6212e-02 -2.3646e-02
-7.3207e-03 -1.5592e-02 -8.9578e-03
( 2 , 2 ,.,.) =
8.9701e-04 -6.6914e-03 -5.3129e-03
-1.1727e-03 -1.0726e-02 -9.0103e-03
3.2311e-03 -4.5854e-03 4.3512e-03
...
( 2 ,509,.,.) =
-2.1822e-02 -3.6889e-02 -2.2588e-02
-1.3054e-02 -3.4191e-02 -2.7238e-02
-1.2383e-02 -2.3452e-02 -2.2486e-02
( 2 ,510,.,.) =
6.8177e-03 2.1561e-02 1.3674e-02
3.1192e-03 1.0660e-02 1.0409e-02
8.0477e-03 -4.6817e-03 -4.3912e-03
( 2 ,511,.,.) =
-1.1983e-02 -1.6201e-02 -2.2626e-02
-1.3461e-02 -7.0928e-03 -1.4384e-02
-2.4456e-02 1.4885e-02 1.2247e-02
...
⋮
(509, 0 ,.,.) =
-2.6347e-02 -2.9923e-02 -3.7810e-02
-1.5663e-02 -4.1126e-03 -1.1482e-02
-1.3415e-02 -1.5432e-02 -1.8204e-02
(509, 1 ,.,.) =
-3.8392e-03 -1.1093e-02 -8.0841e-04
-5.9634e-03 -5.9165e-03 -9.3332e-03
-2.2761e-03 5.4781e-03 -5.6050e-03
(509, 2 ,.,.) =
-1.8406e-03 -2.8134e-03 8.3246e-03
-1.2453e-03 2.1453e-04 7.4868e-03
1.3450e-02 3.0599e-02 2.6405e-02
...
(509,509,.,.) =
3.5268e-04 2.3897e-03 6.2558e-03
-1.4338e-02 -2.3146e-02 -1.9024e-02
-2.7306e-02 -3.0079e-02 -3.1762e-02
(509,510,.,.) =
1.4584e-02 4.3430e-03 1.2053e-02
-6.1130e-03 -2.8539e-02 -1.8268e-02
-1.6844e-02 -4.7816e-02 -2.6274e-02
(509,511,.,.) =
-1.8850e-02 -9.3396e-03 7.8905e-03
-1.5322e-03 8.3153e-03 1.7783e-02
-8.3318e-03 -1.5759e-02 -1.2061e-02
⋮
(510, 0 ,.,.) =
9.9578e-03 7.4573e-03 -1.8738e-03
-1.7752e-03 -6.8015e-04 -7.4443e-03
-1.8319e-02 -1.4264e-02 -7.1446e-03
(510, 1 ,.,.) =
7.8524e-03 -2.6520e-03 -1.7556e-02
4.5240e-03 -4.8661e-03 -1.5215e-02
-5.0211e-03 -1.1864e-02 -1.4846e-02
(510, 2 ,.,.) =
2.9163e-02 1.0344e-02 2.4736e-02
1.2012e-02 -1.0346e-02 3.5472e-03
8.2238e-03 -1.8237e-02 -5.4892e-03
...
(510,509,.,.) =
-8.8434e-03 -4.3184e-03 -5.7536e-03
7.7230e-03 -4.1936e-04 7.7260e-03
1.3536e-02 1.5705e-02 2.0893e-02
(510,510,.,.) =
1.6743e-03 1.9720e-03 2.1567e-02
-8.0074e-03 -4.6606e-03 4.0560e-03
-1.6688e-02 -1.3754e-02 -1.1708e-02
(510,511,.,.) =
-9.7959e-03 -9.4502e-03 -9.3443e-03
6.9547e-03 -3.9134e-05 6.2691e-03
-1.3193e-02 9.3272e-04 1.4579e-02
⋮
(511, 0 ,.,.) =
-1.4963e-03 5.5133e-04 1.1571e-02
1.0174e-02 1.7889e-03 1.1035e-02
7.0212e-03 1.4651e-03 1.2769e-03
(511, 1 ,.,.) =
-1.3021e-02 6.4109e-03 -1.5199e-02
2.4775e-02 2.1926e-02 3.3679e-02
2.6471e-04 -3.0235e-03 1.1690e-02
(511, 2 ,.,.) =
-2.9665e-02 -1.5314e-02 -1.7500e-02
-1.8339e-02 -2.0845e-02 -1.5494e-02
-1.6086e-03 1.0831e-02 -1.4309e-02
...
(511,509,.,.) =
-7.7044e-03 -2.1100e-02 -2.2816e-02
5.7688e-03 1.9362e-04 7.7105e-04
-6.1357e-03 9.7275e-03 -2.5464e-03
(511,510,.,.) =
1.1043e-02 2.4205e-02 3.4213e-02
2.9181e-02 2.6904e-02 4.5372e-02
-2.1594e-02 -1.1072e-03 -7.8312e-03
(511,511,.,.) =
-8.3287e-03 -7.9521e-03 -5.3358e-03
-6.2527e-04 -5.3243e-03 -8.6296e-03
3.6094e-03 -1.2544e-03 -4.3801e-03
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[torch.FloatTensor of size 512], Parameter containing:
( 0 , 0 ,.,.) =
2.8729e-04 4.2632e-03 -2.0266e-03
1.9513e-04 2.4381e-03 -5.8632e-03
4.4803e-03 8.6577e-03 8.5538e-04
( 0 , 1 ,.,.) =
-1.1335e-02 -1.3195e-02 -1.0305e-02
-4.9507e-03 -4.5898e-03 -3.1041e-03
-7.5883e-03 -8.3795e-03 -8.9239e-03
( 0 , 2 ,.,.) =
-1.1914e-02 -1.2104e-02 -1.0167e-02
-1.2093e-02 -1.1557e-02 -8.9600e-03
-1.2515e-02 -9.3296e-03 -6.4079e-03
...
( 0 ,509,.,.) =
-9.3573e-03 -1.0662e-02 -1.2672e-02
-8.0600e-03 -8.5423e-03 -1.2121e-02
-8.1498e-03 -8.8037e-03 -1.0611e-02
( 0 ,510,.,.) =
4.2632e-03 5.6461e-03 2.8460e-03
4.7070e-03 6.2550e-03 7.5862e-03
1.1504e-02 1.1518e-02 1.0728e-02
( 0 ,511,.,.) =
-6.2455e-03 -9.1693e-03 -9.6664e-03
-4.2935e-03 -6.5311e-03 -5.0513e-03
-3.1141e-03 -5.0124e-03 -5.8122e-03
⋮
( 1 , 0 ,.,.) =
2.7483e-03 3.7146e-04 3.3262e-05
-4.5675e-03 -6.6689e-03 -6.4447e-03
-6.7610e-03 -7.3204e-03 -9.5855e-03
( 1 , 1 ,.,.) =
-1.4630e-02 -1.2320e-02 -1.4457e-02
-8.6197e-03 -5.8059e-03 -1.1075e-02
-6.2154e-03 -6.8218e-03 -9.3805e-03
( 1 , 2 ,.,.) =
1.0879e-03 4.3850e-04 -1.9456e-03
-1.2517e-03 3.2917e-04 -2.1435e-03
4.8136e-03 2.5333e-03 5.1504e-03
...
( 1 ,509,.,.) =
2.4644e-02 1.7434e-02 2.0734e-02
2.3101e-02 1.3487e-02 2.0728e-02
1.9381e-02 1.5243e-02 1.7340e-02
( 1 ,510,.,.) =
1.2212e-02 1.2448e-02 1.5048e-02
5.2993e-03 4.0090e-03 9.3927e-03
6.6766e-03 2.4941e-03 8.3288e-03
( 1 ,511,.,.) =
3.1040e-02 2.8243e-02 3.2319e-02
3.8608e-02 3.3099e-02 3.8652e-02
2.5839e-02 2.6524e-02 2.4995e-02
⋮
( 2 , 0 ,.,.) =
-2.1761e-03 4.5553e-03 2.0612e-03
4.9747e-03 1.1420e-02 8.5734e-03
4.8583e-03 1.1469e-02 1.0039e-02
( 2 , 1 ,.,.) =
-6.2547e-05 6.5336e-04 9.4747e-04
5.0603e-03 7.7136e-03 6.5484e-03
-4.8432e-04 2.3057e-03 2.9219e-03
( 2 , 2 ,.,.) =
-3.2788e-02 -2.7615e-02 -3.2608e-02
-3.6296e-02 -2.8170e-02 -3.0277e-02
-3.6814e-02 -3.1547e-02 -3.0231e-02
...
( 2 ,509,.,.) =
-5.2998e-03 -2.8590e-04 -4.9266e-03
-7.0530e-03 -2.3684e-04 -1.5838e-03
-6.9291e-03 4.8084e-04 -3.1548e-03
( 2 ,510,.,.) =
1.1854e-02 8.4836e-03 1.3839e-02
2.8741e-03 -9.7358e-05 4.4888e-03
-2.5515e-03 -2.7788e-03 -3.2464e-03
( 2 ,511,.,.) =
-1.2408e-02 -1.5001e-02 -1.3377e-02
-1.4540e-02 -1.8537e-02 -1.7392e-02
-6.7315e-03 -9.5205e-03 -9.0692e-03
...
⋮
(509, 0 ,.,.) =
3.0369e-03 1.9542e-03 1.7140e-03
-7.6240e-03 -2.8765e-03 -5.1760e-03
-9.3019e-03 -4.8800e-03 -4.2932e-03
(509, 1 ,.,.) =
4.4836e-03 2.4909e-03 1.5746e-03
1.2065e-02 1.2936e-02 1.0344e-02
1.9010e-02 1.7459e-02 1.5988e-02
(509, 2 ,.,.) =
-1.4914e-03 -8.1727e-03 -8.0671e-03
-6.6247e-03 -6.2421e-03 -9.2717e-03
-8.7991e-03 -7.7528e-03 -8.6336e-03
...
(509,509,.,.) =
-1.8040e-02 -1.5366e-02 -1.5334e-02
-1.3148e-02 -1.2180e-02 -1.0915e-02
-1.4545e-02 -1.4756e-02 -1.1787e-02
(509,510,.,.) =
3.5762e-03 6.6073e-03 -1.4055e-03
4.3975e-03 7.8375e-03 8.8085e-05
-5.0697e-03 -5.6633e-04 -5.9284e-03
(509,511,.,.) =
-1.9234e-03 -8.8012e-03 -5.8821e-03
3.6685e-03 -1.3784e-03 -3.2117e-03
-4.7037e-04 1.5340e-04 -3.4046e-03
⋮
(510, 0 ,.,.) =
-1.8305e-02 -1.7735e-02 -2.1683e-02
-1.6598e-02 -1.2508e-02 -2.0530e-02
-1.0800e-02 -9.8670e-03 -1.7195e-02
(510, 1 ,.,.) =
2.0721e-02 2.2466e-02 2.5049e-02
1.8682e-02 1.3160e-02 2.3696e-02
2.2104e-02 1.7261e-02 2.4877e-02
(510, 2 ,.,.) =
-5.7091e-03 -2.6876e-03 -9.2260e-04
-9.4530e-03 -7.0543e-03 -6.2770e-03
-4.5806e-03 -2.7182e-03 -2.5823e-03
...
(510,509,.,.) =
2.4150e-02 1.4002e-02 1.6559e-02
2.1363e-02 1.4359e-02 1.5854e-02
2.5786e-02 2.7233e-02 2.5104e-02
(510,510,.,.) =
-4.6450e-03 1.2419e-03 -1.8768e-03
1.3005e-03 4.0888e-03 -6.5483e-04
-7.9783e-03 -6.6539e-03 -8.9957e-03
(510,511,.,.) =
1.1494e-02 2.6621e-02 1.5649e-02
6.5960e-03 1.7290e-02 7.5466e-03
-8.0256e-03 4.6246e-03 -5.7808e-03
⋮
(511, 0 ,.,.) =
1.4232e-02 1.1769e-02 9.4342e-03
6.2592e-03 5.1087e-03 2.3311e-03
-1.9694e-03 2.7110e-03 -2.8945e-03
(511, 1 ,.,.) =
-7.0772e-03 1.0365e-03 -5.8451e-03
-9.1879e-03 -3.1388e-03 -8.1517e-03
-8.0300e-03 -5.1313e-03 -9.5734e-03
(511, 2 ,.,.) =
2.4314e-02 1.8942e-02 2.4256e-02
2.0090e-02 1.1472e-02 1.5993e-02
2.2910e-02 2.0622e-02 2.3820e-02
...
(511,509,.,.) =
-1.6375e-02 -1.6928e-02 -1.9019e-02
-9.7367e-03 -1.1274e-02 -1.0261e-02
-1.2310e-02 -1.5931e-02 -1.4151e-02
(511,510,.,.) =
4.7098e-03 -4.5205e-04 2.8042e-03
2.1428e-03 -4.6175e-03 -1.6818e-03
-1.3336e-03 -5.5009e-03 -2.6237e-03
(511,511,.,.) =
-1.4367e-02 -1.3520e-02 -1.1387e-02
-4.7420e-03 -1.7309e-03 -2.6426e-03
5.1448e-03 7.0428e-03 5.0202e-03
[torch.FloatTensor of size 512x512x3x3], Parameter containing:
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[torch.FloatTensor of size 512], Parameter containing:
1.00000e-02 *
1.4658 -2.9785 -0.1085 ... 3.6860 3.9901 3.4233
-0.1535 2.7937 -1.7376 ... -2.3749 0.5214 3.9412
[torch.FloatTensor of size 2x512], Parameter containing:
1.00000e-02 *
1.5492
1.7279
[torch.FloatTensor of size 2]]
In [32]:
criterion = nn.CrossEntropyLoss()
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
In [ ]:
%%time
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
num_epochs=25)
Epoch 0/24
----------
train Loss: 0.4143 Acc: 0.8115
val Loss: 0.3071 Acc: 0.8824
Epoch 1/24
----------
train Loss: 0.4749 Acc: 0.8238
val Loss: 0.2358 Acc: 0.9216
Epoch 2/24
----------
train Loss: 0.4017 Acc: 0.8238
val Loss: 0.4650 Acc: 0.8562
Epoch 3/24
----------
train Loss: 0.4429 Acc: 0.8279
val Loss: 0.2935 Acc: 0.9150
Epoch 4/24
----------
train Loss: 0.5350 Acc: 0.8033
val Loss: 0.3385 Acc: 0.9085
Epoch 5/24
----------
train Loss: 0.4534 Acc: 0.8320
val Loss: 0.3395 Acc: 0.8758
Epoch 6/24
----------
train Loss: 0.2927 Acc: 0.8730
val Loss: 0.2516 Acc: 0.9346
Epoch 7/24
----------
train Loss: 0.3242 Acc: 0.8648
val Loss: 0.2323 Acc: 0.9346
Epoch 8/24
----------
train Loss: 0.3141 Acc: 0.8730
val Loss: 0.2481 Acc: 0.9281
Epoch 9/24
----------
train Loss: 0.2950 Acc: 0.8811
val Loss: 0.2324 Acc: 0.9281
Epoch 10/24
----------
train Loss: 0.2746 Acc: 0.8689
val Loss: 0.2322 Acc: 0.9412
Epoch 11/24
----------
In [ ]:
#ToDO : tensorboardX를 사용해 loss떨어지는 것 구현하기
In [ ]:
visualize_model(model_ft)
backward()할 경우 계산이 되는 것을 방지하기 위해 requires_grad == False를 해서 파라미터를 freeze해야합니다!
In [ ]:
model_conv = torchvision.models.resnet18(pretrained=True)
for param in model_conv.parameters():
param.requires_grad = False
# Parameters of newly constructed modules have requires_grad=True by default
num_ftrs = model_conv.fc.in_features
model_conv.fc = nn.Linear(num_ftrs, 2)
if use_gpu:
model_conv = model_conv.cuda()
criterion = nn.CrossEntropyLoss()
# Observe that only parameters of final layer are being optimized as
# opoosed to before.
optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)
In [ ]:
model_conv = train_model(model_conv, criterion, optimizer_conv,
exp_lr_scheduler, num_epochs=25)
In [ ]:
visualize_model(model_conv)
plt.ioff()
plt.show()
Content source: zzsza/TIL
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