mirror of
https://github.com/20kaushik02/CSE515_MWDB_Project.git
synced 2025-12-06 10:34:07 +00:00
ResNet and task-1 done
This commit is contained in:
parent
fff0d09bcb
commit
a4bafa6c94
496
ResNet50_Architecture.txt
Normal file
496
ResNet50_Architecture.txt
Normal file
@ -0,0 +1,496 @@
|
||||
[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, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
|
||||
(layer1): Sequential(
|
||||
(0): Bottleneck(
|
||||
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
(downsample): Sequential(
|
||||
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
)
|
||||
(1): Bottleneck(
|
||||
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
(2): Bottleneck(
|
||||
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
)
|
||||
(layer2): Sequential(
|
||||
(0): Bottleneck(
|
||||
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
|
||||
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=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, track_running_stats=True)
|
||||
)
|
||||
)
|
||||
(1): Bottleneck(
|
||||
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
(2): Bottleneck(
|
||||
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
(3): Bottleneck(
|
||||
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
)
|
||||
(layer3): Sequential(
|
||||
(0): Bottleneck(
|
||||
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
|
||||
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
(downsample): Sequential(
|
||||
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
|
||||
(1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
)
|
||||
(1): Bottleneck(
|
||||
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
(2): Bottleneck(
|
||||
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
(3): Bottleneck(
|
||||
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
(4): Bottleneck(
|
||||
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
(5): Bottleneck(
|
||||
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
)
|
||||
(layer4): Sequential(
|
||||
(0): Bottleneck(
|
||||
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
|
||||
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
(downsample): Sequential(
|
||||
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
|
||||
(1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
)
|
||||
(1): Bottleneck(
|
||||
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
(2): Bottleneck(
|
||||
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
)
|
||||
(avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
|
||||
(fc): Linear(in_features=2048, out_features=1000, bias=True)
|
||||
), Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False), BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), ReLU(inplace=True), MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False), Sequential(
|
||||
(0): Bottleneck(
|
||||
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
(downsample): Sequential(
|
||||
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
)
|
||||
(1): Bottleneck(
|
||||
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
(2): Bottleneck(
|
||||
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
), Bottleneck(
|
||||
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
(downsample): Sequential(
|
||||
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
), Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False), BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), ReLU(inplace=True), Sequential(
|
||||
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
), Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Bottleneck(
|
||||
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
), Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False), BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), ReLU(inplace=True), Bottleneck(
|
||||
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
), Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False), BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), ReLU(inplace=True), Sequential(
|
||||
(0): Bottleneck(
|
||||
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
|
||||
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=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, track_running_stats=True)
|
||||
)
|
||||
)
|
||||
(1): Bottleneck(
|
||||
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
(2): Bottleneck(
|
||||
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
(3): Bottleneck(
|
||||
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
), Bottleneck(
|
||||
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
|
||||
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=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, track_running_stats=True)
|
||||
)
|
||||
), Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False), BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), ReLU(inplace=True), 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, track_running_stats=True)
|
||||
), Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False), BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Bottleneck(
|
||||
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
), Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False), BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), ReLU(inplace=True), Bottleneck(
|
||||
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
), Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False), BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), ReLU(inplace=True), Bottleneck(
|
||||
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
), Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False), BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), ReLU(inplace=True), Sequential(
|
||||
(0): Bottleneck(
|
||||
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
|
||||
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
(downsample): Sequential(
|
||||
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
|
||||
(1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
)
|
||||
(1): Bottleneck(
|
||||
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
(2): Bottleneck(
|
||||
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
(3): Bottleneck(
|
||||
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
(4): Bottleneck(
|
||||
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
(5): Bottleneck(
|
||||
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
), Bottleneck(
|
||||
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
|
||||
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
(downsample): Sequential(
|
||||
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
|
||||
(1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
), Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False), BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), ReLU(inplace=True), Sequential(
|
||||
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
|
||||
(1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
), Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False), BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Bottleneck(
|
||||
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
), Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False), BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), ReLU(inplace=True), Bottleneck(
|
||||
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
), Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False), BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), ReLU(inplace=True), Bottleneck(
|
||||
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
), Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False), BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), ReLU(inplace=True), Bottleneck(
|
||||
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
), Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False), BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), ReLU(inplace=True), Bottleneck(
|
||||
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
), Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False), BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), ReLU(inplace=True), Sequential(
|
||||
(0): Bottleneck(
|
||||
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
|
||||
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
(downsample): Sequential(
|
||||
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
|
||||
(1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
)
|
||||
(1): Bottleneck(
|
||||
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
(2): Bottleneck(
|
||||
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
)
|
||||
), Bottleneck(
|
||||
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
|
||||
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
(downsample): Sequential(
|
||||
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
|
||||
(1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
), Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False), BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), ReLU(inplace=True), Sequential(
|
||||
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
|
||||
(1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
), Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False), BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Bottleneck(
|
||||
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
), Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False), BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), ReLU(inplace=True), Bottleneck(
|
||||
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(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, track_running_stats=True)
|
||||
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(relu): ReLU(inplace=True)
|
||||
), Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False), BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False), BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), ReLU(inplace=True), AdaptiveAvgPool2d(output_size=(1, 1)), Linear(in_features=2048, out_features=1000, bias=True)]
|
||||
BIN
phase1_query_images_for_the_report.pdf
Normal file
BIN
phase1_query_images_for_the_report.pdf
Normal file
Binary file not shown.
Loading…
x
Reference in New Issue
Block a user