Skip to content

Commit

Permalink
fixed model
Browse files Browse the repository at this point in the history
  • Loading branch information
thatblueboy committed Apr 14, 2024
1 parent a88e264 commit 7a30a01
Show file tree
Hide file tree
Showing 3 changed files with 140 additions and 3 deletions.
3 changes: 2 additions & 1 deletion models/backbones/resnet.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -142,6 +142,7 @@ def __init__(self,
out_channels=out_channels,
expansion=self.expansion,
stride=1,
has_involution = is_rednet
))
# self.add_module('layer', nn.Sequential(*layers))

Expand Down Expand Up @@ -252,11 +253,11 @@ def init_weights(self, pretrained=None):
nn.init.constant_(m.norm3.weight, 0)
nn.init.constant_(m.norm3.bias, 0)


def _make_stem_layer(self, in_channels, stem_channels):
self.stem = nn.Sequential(
nn.Conv2d(in_channels, stem_channels // 2, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(stem_channels // 2),
Involution(stem_channels // 2, 3, 1),
nn.ReLU(inplace=True),
nn.Conv2d(stem_channels//2, stem_channels, kernel_size=3, stride=1, padding=1),
)
Expand Down
8 changes: 6 additions & 2 deletions notebooks/resnet.ipynb
View file
Original file line number Diff line number Diff line change
Expand Up @@ -12,14 +12,18 @@
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[2], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtorchvision\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mmodels\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mbackbones\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mresnet\u001b[39;00m \u001b[39mimport\u001b[39;00m ReDSNet\n",
"Cell \u001b[0;32mIn[2], line 5\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m\n\u001b[1;32m 3\u001b[0m sys\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/home/thatblueboy/involution/models\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mbackbones\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mresnet\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ReDSNet\n",
"File \u001b[0;32m~/involution/models/backbones/resnet.py:2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnn\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnn\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackbones\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minvolution\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Involution\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mBottleneck\u001b[39;00m(nn\u001b[38;5;241m.\u001b[39mModule):\n\u001b[1;32m 4\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Bottleneck block for ResNet.\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \n\u001b[1;32m 6\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;124;03m Default: dict(type='BN')\u001b[39;00m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'models'"
]
}
],
"source": [
"import torchvision\n",
"from models.backbones.resnet import ReDSNet"
"import sys\n",
"sys.path.append('/home/thatblueboy/involution/models')\n",
"\n",
"from backbones.resnet import ReDSNet"
]
},
{
Expand Down
132 changes: 132 additions & 0 deletions notebooks/resnet18.ipynb
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,132 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ResNet(\n",
" (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
" (layer1): Sequential(\n",
" (0): BasicBlock(\n",
" (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (1): BasicBlock(\n",
" (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (layer2): Sequential(\n",
" (0): BasicBlock(\n",
" (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): BasicBlock(\n",
" (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (layer3): Sequential(\n",
" (0): BasicBlock(\n",
" (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): BasicBlock(\n",
" (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (layer4): Sequential(\n",
" (0): BasicBlock(\n",
" (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): BasicBlock(\n",
" (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
" (fc): Linear(in_features=512, out_features=1000, bias=True)\n",
")\n"
]
}
],
"source": [
"import torch\n",
"import torchvision.models as models\n",
"\n",
"# Load the ResNet-18 model\n",
"resnet18 = models.resnet18()\n",
"\n",
"# Print the model architecture\n",
"print(resnet18)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "CIFAR-100",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

0 comments on commit 7a30a01

Please sign in to comment.