A bug When I do image classification reasoning

username: name1ess
cooked: <p>import torch<br>
import torchvision.transforms as transforms<br>
from PIL import Image</p>
<p>def pridict():<br>
device = “cuda” if torch.cuda.is_available() else “cpu”<br>
path = ‘checkpoint_model_best.pth’<br>
model = torch.load(path)<br>
model = model.to(device)<br>
model.eval()<br>
img = Image.open(‘1.jpg’)<br>
transform = transforms.Compose([<br>
transforms.Resize(size=256),<br>
transforms.ToTensor(),<br>
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),<br>
])</p>
<pre><code>img = transform(img)
img = img.unsqueeze(0)
img = img.to(device)

with torch.no_grad():
    py = model(img)
    py2 = torch.exp(py)
    print(py2)
_, predicted = torch.max(py, 1)  # 获取分类结果
# 预测结果的标签
classIndex = predicted.item()
print("（0：机场   1：城市   2：港口   3：水体）")
print("预测结果为：", classIndex)
</code></pre>
<p>if <strong>name</strong> == ‘<strong>main</strong>’:<br>
pridict()</p>
<p>this is my code and when i run it, a bug has occurred：<br>
AttributeError: ‘dict’ object has no attribute ‘to’</p>
<p>best regards</p>

username: azhanmohammed
cooked: <aside class="quote no-group" data-username="name1ess" data-post="1" data-topic="165633">
<div class="title">
<div class="quote-controls"></div>
<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/letter_avatar_proxy/v4/letter/n/47e85d/48.png" class="avatar"> name1ess:</div>
<blockquote>
<p>path = ‘checkpoint_model_best.pth’<br>
model = torch.load(path)<br>
model = model.to(device)<br>
model.eval()</p>
</blockquote>
</aside>
<p>Change this to:</p>
<pre data-code-wrap="python"><code class="lang-python">path = "checkpoint_model_best.pth"
model.load_state_dict(torch.load(path))
model.to(device)
model.eval()
</code></pre>
<p>This should do the work.</p>

username: name1ess
cooked: <p>Mr Naruto,thank for your reply.<br>
I changed the code as you said:<br>
<div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/6/b/6bea7c9590eae5759c3ecdcd79e2f64be25eec3b.png" data-download-href="https://discuss.pytorch.org/uploads/default/6bea7c9590eae5759c3ecdcd79e2f64be25eec3b" title="image"><img src="https://discuss.pytorch.org/uploads/default/optimized/3X/6/b/6bea7c9590eae5759c3ecdcd79e2f64be25eec3b_2_690x261.png" alt="image" data-base62-sha1="foFtqGw0WP60VRiJIEiyVffyiYH" width="690" height="261" srcset="https://discuss.pytorch.org/uploads/default/optimized/3X/6/b/6bea7c9590eae5759c3ecdcd79e2f64be25eec3b_2_690x261.png, https://discuss.pytorch.org/uploads/default/original/3X/6/b/6bea7c9590eae5759c3ecdcd79e2f64be25eec3b.png 1.5x, https://discuss.pytorch.org/uploads/default/original/3X/6/b/6bea7c9590eae5759c3ecdcd79e2f64be25eec3b.png 2x" data-dominant-color="353435"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">image</span><span class="informations">828×314 24.8 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div><br>
how can i define ‘model’？</p>
<p>thanks again <img src="https://discuss.pytorch.org/images/emoji/apple/heart.png?v=12" title=":heart:" class="emoji" alt=":heart:" loading="lazy" width="20" height="20"></p>

username: azhanmohammed
cooked: <p>You should have a model.py file, where the model might be defined, you can use the model definition class to create an instance of the model, and then use it to perform inference.</p>

username: name1ess
cooked: <p>i see. I should add  from model import A<br>
then model = A()? But I can’t find a function whose return value is model…Could you please guide me when you are free.<br>
Here is my model file：<br>
import torch<br>
import torch.nn as nn<br>
import torch.nn.functional as F</p>
<p>def get_n_params(model):<br>
pp=0<br>
for p in list(model.parameters()):<br>
nn=1<br>
for s in list(p.size()):<br>
nn = nn*s<br>
pp += nn<br>
return pp</p>
<p>class MHSA(nn.Module):<br>
def <strong>init</strong>(self, n_dims, width=14, height=14, heads=4):<br>
super(MHSA, self).<strong>init</strong>()<br>
self.heads = heads</p>
<pre><code>    self.query = nn.Conv2d(n_dims, n_dims, kernel_size=1)
    self.key = nn.Conv2d(n_dims, n_dims, kernel_size=1)
    self.value = nn.Conv2d(n_dims, n_dims, kernel_size=1)

    self.rel_h = nn.Parameter(torch.randn([1, heads, n_dims // heads, 1, height]), requires_grad=True)
    self.rel_w = nn.Parameter(torch.randn([1, heads, n_dims // heads, width, 1]), requires_grad=True)

    self.softmax = nn.Softmax(dim=-1)

def forward(self, x):
    n_batch, C, width, height = x.size()
    q = self.query(x).view(n_batch, self.heads, C // self.heads, -1)
    k = self.key(x).view(n_batch, self.heads, C // self.heads, -1)
    v = self.value(x).view(n_batch, self.heads, C // self.heads, -1)

    content_content = torch.matmul(q.permute(0, 1, 3, 2), k)

    content_position = (self.rel_h + self.rel_w).view(1, self.heads, C // self.heads, -1).permute(0, 1, 3, 2)
    content_position = torch.matmul(content_position, q)

    energy = content_content + content_position
    attention = self.softmax(energy)

    out = torch.matmul(v, attention.permute(0, 1, 3, 2))
    out = out.view(n_batch, C, width, height)

    return out
</code></pre>
<p>class Bottleneck(nn.Module):<br>
expansion = 4</p>
<pre><code>def __init__(self, in_planes, planes, stride=1, heads=4, mhsa=False, resolution=None):
    super(Bottleneck, self).__init__()

    self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
    self.bn1 = nn.BatchNorm2d(planes)
    if not mhsa:
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, stride=stride, bias=False)
    else:
        self.conv2 = nn.ModuleList()
        self.conv2.append(MHSA(planes, width=int(resolution[0]), height=int(resolution[1]), heads=heads))
        if stride == 2:
            self.conv2.append(nn.AvgPool2d(2, 2))
        self.conv2 = nn.Sequential(*self.conv2)
    self.bn2 = nn.BatchNorm2d(planes)
    self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False)
    self.bn3 = nn.BatchNorm2d(self.expansion * planes)

    self.shortcut = nn.Sequential()
    if stride != 1 or in_planes != self.expansion*planes:
        self.shortcut = nn.Sequential(
            nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride),
            nn.BatchNorm2d(self.expansion*planes)
        )

def forward(self, x):
    out = F.relu(self.bn1(self.conv1(x)))
    out = F.relu(self.bn2(self.conv2(out)))
    out = self.bn3(self.conv3(out))
    out += self.shortcut(x)
    out = F.relu(out)
    return out
</code></pre>
<h1>
<a name="reference-1" class="anchor" href="#reference-1"></a>reference</h1>
<h1>
<a name="httpsgithubcomkuangliupytorch-cifarblobmastermodelsresnetpy-2" class="anchor" href="#httpsgithubcomkuangliupytorch-cifarblobmastermodelsresnetpy-2"></a><a href="https://github.com/kuangliu/pytorch-cifar/blob/master/models/resnet.py" class="inline-onebox" rel="noopener nofollow ugc">pytorch-cifar/resnet.py at master · kuangliu/pytorch-cifar · GitHub</a>
</h1>
<p>class ResNet(nn.Module):<br>
def <strong>init</strong>(self, block, num_blocks, num_classes=5, resolution=(224, 224), heads=4):<br>
super(ResNet, self).<strong>init</strong>()<br>
self.in_planes = 64<br>
self.resolution = list(resolution)</p>
<pre><code>    self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
    # self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
    if self.conv1.stride[0] == 2:
        self.resolution[0] /= 2
    if self.conv1.stride[1] == 2:
        self.resolution[1] /= 2
    self.bn1 = nn.BatchNorm2d(64)
    self.relu = nn.ReLU(inplace=True)
    self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) # for ImageNet
    if self.maxpool.stride == 2:
        self.resolution[0] /= 2
        self.resolution[1] /= 2

    self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
    self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
    self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
    self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2, heads=heads, mhsa=True)

    self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
    self.fc = nn.Sequential(
        nn.Dropout(0.3), # All architecture deeper than ResNet-200 dropout_rate: 0.2
        nn.Linear(512 * block.expansion, num_classes)
    )

def _make_layer(self, block, planes, num_blocks, stride=1, heads=4, mhsa=False):
    strides = [stride] + [1]*(num_blocks-1)
    layers = []
    for idx, stride in enumerate(strides):
        layers.append(block(self.in_planes, planes, stride, heads, mhsa, self.resolution))
        if stride == 2:
            self.resolution[0] /= 2
            self.resolution[1] /= 2
        self.in_planes = planes * block.expansion
    return nn.Sequential(*layers)

def forward(self, x):
    out = self.relu(self.bn1(self.conv1(x)))
    out = self.maxpool(out) # for ImageNet

    out = self.layer1(out)
    out = self.layer2(out)
    out = self.layer3(out)
    out = self.layer4(out)

    out = self.avgpool(out)
    out = torch.flatten(out, 1)
    out = self.fc(out)
    return out
</code></pre>
<p>def ResNet50(num_classes=5, resolution=(256, 256), heads=4):<br>
return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, resolution=resolution, heads=heads)</p>
<p>def main():<br>
x = torch.randn([2, 3, 256, 256])<br>
model = ResNet50(resolution=tuple(x.shape[2:]), heads=8)<br>
print(model(x).size())<br>
print(get_n_params(model))</p>
<h1>
<a name="if-__name__-__main__-3" class="anchor" href="#if-__name__-__main__-3"></a>if <strong>name</strong> == ‘<strong>main</strong>’:</h1>
<h1>
<a name="main-4" class="anchor" href="#main-4"></a>main()</h1>
<p><strong>Another file that you may need, just in case</strong>（main.py)：<br>
import torch<br>
import torch.nn as nn<br>
import torch.nn.functional as F<br>
import torch.optim as optim</p>
<p>import os<br>
from config import load_config<br>
from preprocess import load_data<br>
from model import ResNet50</p>
<p>def save_checkpoint(best_acc, model, optimizer, args, epoch):<br>
print(‘Best Model Saving…’)<br>
if args.device_num &gt; 1:<br>
model_state_dict = model.module.state_dict()<br>
else:<br>
model_state_dict = model.state_dict()</p>
<pre><code>torch.save({
    'model_state_dict': model_state_dict,
    'global_epoch': epoch,
    'optimizer_state_dict': optimizer.state_dict(),
    'best_acc': best_acc,
}, os.path.join('checkpoints', 'checkpoint_model_best.pth'))
</code></pre>
<p>def _train(epoch, train_loader, model, optimizer, criterion, args):<br>
model.train()</p>
<pre><code>losses = 0.
acc = 0.
total = 0.
for idx, (data, target) in enumerate(train_loader):
    if args.cuda:
        data, target = data.cuda(), target.cuda()

    output = model(data)
    _, pred = F.softmax(output, dim=-1).max(1)
    acc += pred.eq(target).sum().item()
    total += target.size(0)

    optimizer.zero_grad()
    loss = criterion(output, target)
    losses += loss
    loss.backward()
    if args.gradient_clip &gt; 0:
        torch.nn.utils.clip_grad_norm_(model.parameters(), args.gradient_clip)
    optimizer.step()

    if idx % args.print_intervals == 0 and idx != 0:
        print('[Epoch: {0:4d}], Loss: {1:.3f}, Acc: {2:.3f}, Correct {3} / Total {4}'.format(epoch,
                                                                                             losses / (idx + 1),
                                                                                             acc / total * 100.,
                                                                                             acc, total))
</code></pre>
<p>def _eval(epoch, test_loader, model, args):<br>
model.eval()</p>
<pre><code>acc = 0.
with torch.no_grad():
    for data, target in test_loader:
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        output = model(data)
        _, pred = F.softmax(output, dim=-1).max(1)

        acc += pred.eq(target).sum().item()
    print('[Epoch: {0:4d}], Acc: {1:.3f}'.format(epoch, acc / len(test_loader.dataset) * 100.))

return acc / len(test_loader.dataset) * 100.
</code></pre>
<p>def main(args):<br>
train_loader, test_loader = load_data(args)<br>
model = ResNet50()</p>
<pre><code>optimizer = optim.SGD(model.parameters(), lr=args.lr, weight_decay=args.weight_decay, momentum=args.momentum)

if not os.path.isdir('checkpoints'):
    os.mkdir('checkpoints')

if args.checkpoints is not None:
    checkpoints = torch.load(os.path.join('checkpoints', args.checkpoints))
    model.load_state_dict(checkpoints['model_state_dict'])
    optimizer.load_state_dict(checkpoints['optimizer_state_dict'])
    start_epoch = checkpoints['global_epoch']
else:
    start_epoch = 1

if args.cuda:
    model = model.cuda()

if not args.evaluation:
    criterion = nn.CrossEntropyLoss()
    lr_scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=10, T_mult=2, eta_min=0.0001)

    global_acc = 0.
    for epoch in range(start_epoch, args.epochs + 1):
        _train(epoch, train_loader, model, optimizer, criterion, args)
        best_acc = _eval(epoch, test_loader, model, args)
        if global_acc &lt; best_acc:
            global_acc = best_acc
            save_checkpoint(best_acc, model, optimizer, args, epoch)

        lr_scheduler.step()
        print('Current Learning Rate: {}'.format(lr_scheduler.get_last_lr()))
else:
    _eval(start_epoch, test_loader, model, args)
</code></pre>
<p>if <strong>name</strong> == ‘<strong>main</strong>’:<br>
args = load_config()<br>
main(args)</p>
<p>Thank you sincerely</p>

username: azhanmohammed
cooked: <p>Hey <a class="mention" href="/u/name1ess">@name1ess</a><br>
Can you please try the following script in the same folder?</p>
<pre><code class="lang-python">import torch
from model import ResNet50

model = ResNet50() 
path = ‘checkpoint_model_best.pth’
model = torch.load(path)
model = model.to(device)
model.eval()
print(model)
</code></pre>
<p>If this works, you can use the model to infer.</p>

username: name1ess
cooked: <p>like this?<br>
<div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/3/7/37b24d4e226a6993d4079efed924b8872db2ab22.png" data-download-href="https://discuss.pytorch.org/uploads/default/37b24d4e226a6993d4079efed924b8872db2ab22" title="image"><img src="https://discuss.pytorch.org/uploads/default/original/3X/3/7/37b24d4e226a6993d4079efed924b8872db2ab22.png" alt="image" data-base62-sha1="7WIgO4AU4M3k3dVTirT7k79IYTg" width="690" height="421" data-dominant-color="302F2F"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">image</span><span class="informations">691×422 27.9 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div><br>
It doesn’t work <img src="https://discuss.pytorch.org/images/emoji/apple/sob.png?v=12" title=":sob:" class="emoji" alt=":sob:" loading="lazy" width="20" height="20"><br>
<div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/2/9/29f181695a1c1b652c633613d5f6101d386f610e.png" data-download-href="https://discuss.pytorch.org/uploads/default/29f181695a1c1b652c633613d5f6101d386f610e" title="image"><img src="https://discuss.pytorch.org/uploads/default/optimized/3X/2/9/29f181695a1c1b652c633613d5f6101d386f610e_2_690x146.png" alt="image" data-base62-sha1="5Z2ZXWAl21YPJZ48b2B1b1p3EbI" width="690" height="146" srcset="https://discuss.pytorch.org/uploads/default/optimized/3X/2/9/29f181695a1c1b652c633613d5f6101d386f610e_2_690x146.png, https://discuss.pytorch.org/uploads/default/optimized/3X/2/9/29f181695a1c1b652c633613d5f6101d386f610e_2_1035x219.png 1.5x, https://discuss.pytorch.org/uploads/default/optimized/3X/2/9/29f181695a1c1b652c633613d5f6101d386f610e_2_1380x292.png 2x" data-dominant-color="302E2F"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">image</span><span class="informations">1788×379 48.1 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div></p>

username: azhanmohammed
cooked: <p>Can you please tell me how you got the checkpoint file? I will try to figure out the exact model architecture and help you out.</p>

username: name1ess
cooked: <p>by using main.py.<br>
checkpoint is the file obtained after training the model<br>
main.py:<br>
import torch<br>
import torch.nn as nn<br>
import torch.nn.functional as F<br>
import torch.optim as optim</p>
<p>import os<br>
from config import load_config<br>
from preprocess import load_data<br>
from model import ResNet50</p>
<p>def save_checkpoint(best_acc, model, optimizer, args, epoch):<br>
print(‘Best Model Saving…’)<br>
if args.device_num &gt; 1:<br>
model_state_dict = model.module.state_dict()<br>
else:<br>
model_state_dict = model.state_dict()</p>
<pre><code>torch.save({
    'model_state_dict': model_state_dict,
    'global_epoch': epoch,
    'optimizer_state_dict': optimizer.state_dict(),
    'best_acc': best_acc,
}, os.path.join('checkpoints', 'checkpoint_model_best.pth'))
</code></pre>
<p>def _train(epoch, train_loader, model, optimizer, criterion, args):<br>
model.train()</p>
<pre><code>losses = 0.
acc = 0.
total = 0.
for idx, (data, target) in enumerate(train_loader):
    if args.cuda:
        data, target = data.cuda(), target.cuda()

    output = model(data)
    _, pred = F.softmax(output, dim=-1).max(1)
    acc += pred.eq(target).sum().item()
    total += target.size(0)

    optimizer.zero_grad()
    loss = criterion(output, target)
    losses += loss
    loss.backward()
    if args.gradient_clip &gt; 0:
        torch.nn.utils.clip_grad_norm_(model.parameters(), args.gradient_clip)
    optimizer.step()

    if idx % args.print_intervals == 0 and idx != 0:
        print('[Epoch: {0:4d}], Loss: {1:.3f}, Acc: {2:.3f}, Correct {3} / Total {4}'.format(epoch,
                                                                                             losses / (idx + 1),
                                                                                             acc / total * 100.,
                                                                                             acc, total))
</code></pre>
<p>def _eval(epoch, test_loader, model, args):<br>
model.eval()</p>
<pre><code>acc = 0.
with torch.no_grad():
    for data, target in test_loader:
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        output = model(data)
        _, pred = F.softmax(output, dim=-1).max(1)

        acc += pred.eq(target).sum().item()
    print('[Epoch: {0:4d}], Acc: {1:.3f}'.format(epoch, acc / len(test_loader.dataset) * 100.))

return acc / len(test_loader.dataset) * 100.
</code></pre>
<p>def main(args):<br>
train_loader, test_loader = load_data(args)<br>
model = ResNet50()</p>
<pre><code>optimizer = optim.SGD(model.parameters(), lr=args.lr, weight_decay=args.weight_decay, momentum=args.momentum)

if not os.path.isdir('checkpoints'):
    os.mkdir('checkpoints')

if args.checkpoints is not None:
    checkpoints = torch.load(os.path.join('checkpoints', args.checkpoints))
    model.load_state_dict(checkpoints['model_state_dict'])
    optimizer.load_state_dict(checkpoints['optimizer_state_dict'])
    start_epoch = checkpoints['global_epoch']
else:
    start_epoch = 1

if args.cuda:
    model = model.cuda()

if not args.evaluation:
    criterion = nn.CrossEntropyLoss()
    lr_scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=10, T_mult=2, eta_min=0.0001)

    global_acc = 0.
    for epoch in range(start_epoch, args.epochs + 1):
        _train(epoch, train_loader, model, optimizer, criterion, args)
        best_acc = _eval(epoch, test_loader, model, args)
        if global_acc &lt; best_acc:
            global_acc = best_acc
            save_checkpoint(best_acc, model, optimizer, args, epoch)

        lr_scheduler.step()
        print('Current Learning Rate: {}'.format(lr_scheduler.get_last_lr()))
else:
    _eval(start_epoch, test_loader, model, args)
</code></pre>
<p>if <strong>name</strong> == ‘<strong>main</strong>’:<br>
args = load_config()<br>
main(args)</p>

username: azhanmohammed
cooked: <p>Can you try if this works:</p>
<pre><code class="lang-python">import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import os
from config import load_config
from preprocess import load_data
from model import ResNet50

model = ResNet50()
checkpoints = torch.load("checkpoint_model_best.pth")
model.load_state_dict(checkpoints['model_state_dict'])
</code></pre>

username: name1ess
cooked: <p>it works!!!<br>
Mr Naruto,thank you and love you!!!<br>
<img src="https://discuss.pytorch.org/images/emoji/apple/kissing_heart.png?v=12" title=":kissing_heart:" class="emoji" alt=":kissing_heart:" loading="lazy" width="20" height="20"><img src="https://discuss.pytorch.org/images/emoji/apple/kissing_heart.png?v=12" title=":kissing_heart:" class="emoji" alt=":kissing_heart:" loading="lazy" width="20" height="20"><img src="https://discuss.pytorch.org/images/emoji/apple/kissing_heart.png?v=12" title=":kissing_heart:" class="emoji" alt=":kissing_heart:" loading="lazy" width="20" height="20"><img src="https://discuss.pytorch.org/images/emoji/apple/kissing_heart.png?v=12" title=":kissing_heart:" class="emoji" alt=":kissing_heart:" loading="lazy" width="20" height="20"><img src="https://discuss.pytorch.org/images/emoji/apple/kissing_heart.png?v=12" title=":kissing_heart:" class="emoji" alt=":kissing_heart:" loading="lazy" width="20" height="20"><img src="https://discuss.pytorch.org/images/emoji/apple/kissing_heart.png?v=12" title=":kissing_heart:" class="emoji" alt=":kissing_heart:" loading="lazy" width="20" height="20"><img src="https://discuss.pytorch.org/images/emoji/apple/kissing_heart.png?v=12" title=":kissing_heart:" class="emoji" alt=":kissing_heart:" loading="lazy" width="20" height="20"><img src="https://discuss.pytorch.org/images/emoji/apple/kissing_heart.png?v=12" title=":kissing_heart:" class="emoji" alt=":kissing_heart:" loading="lazy" width="20" height="20"><img src="https://discuss.pytorch.org/images/emoji/apple/kissing_heart.png?v=12" title=":kissing_heart:" class="emoji" alt=":kissing_heart:" loading="lazy" width="20" height="20"><img src="https://discuss.pytorch.org/images/emoji/apple/kissing_heart.png?v=12" title=":kissing_heart:" class="emoji" alt=":kissing_heart:" loading="lazy" width="20" height="20"><img src="https://discuss.pytorch.org/images/emoji/apple/kissing_heart.png?v=12" title=":kissing_heart:" class="emoji" alt=":kissing_heart:" loading="lazy" width="20" height="20"><img src="https://discuss.pytorch.org/images/emoji/apple/kissing_heart.png?v=12" title=":kissing_heart:" class="emoji" alt=":kissing_heart:" loading="lazy" width="20" height="20"><img src="https://discuss.pytorch.org/images/emoji/apple/kissing_heart.png?v=12" title=":kissing_heart:" class="emoji" alt=":kissing_heart:" loading="lazy" width="20" height="20"><img src="https://discuss.pytorch.org/images/emoji/apple/kissing_heart.png?v=12" title=":kissing_heart:" class="emoji" alt=":kissing_heart:" loading="lazy" width="20" height="20"><img src="https://discuss.pytorch.org/images/emoji/apple/kissing_heart.png?v=12" title=":kissing_heart:" class="emoji" alt=":kissing_heart:" loading="lazy" width="20" height="20"><img src="https://discuss.pytorch.org/images/emoji/apple/kissing_heart.png?v=12" title=":kissing_heart:" class="emoji" alt=":kissing_heart:" loading="lazy" width="20" height="20"><img src="https://discuss.pytorch.org/images/emoji/apple/kissing_heart.png?v=12" title=":kissing_heart:" class="emoji" alt=":kissing_heart:" loading="lazy" width="20" height="20"><img src="https://discuss.pytorch.org/images/emoji/apple/kissing_heart.png?v=12" title=":kissing_heart:" class="emoji" alt=":kissing_heart:" loading="lazy" width="20" height="20"><img src="https://discuss.pytorch.org/images/emoji/apple/kissing_heart.png?v=12" title=":kissing_heart:" class="emoji" alt=":kissing_heart:" loading="lazy" width="20" height="20"><img src="https://discuss.pytorch.org/images/emoji/apple/kissing_heart.png?v=12" title=":kissing_heart:" class="emoji" alt=":kissing_heart:" loading="lazy" width="20" height="20"><img src="https://discuss.pytorch.org/images/emoji/apple/kissing_heart.png?v=12" title=":kissing_heart:" class="emoji" alt=":kissing_heart:" loading="lazy" width="20" height="20"><img src="https://discuss.pytorch.org/images/emoji/apple/kissing_heart.png?v=12" title=":kissing_heart:" class="emoji" alt=":kissing_heart:" loading="lazy" width="20" height="20"></p>