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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "71b6152c",
"metadata": {},
"outputs": [],
"source": [
"import torch, timm\n",
"from qlnet import QLNet"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4e7ed219",
"metadata": {},
"outputs": [],
"source": [
"m = QLNet()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3f703be8",
"metadata": {},
"outputs": [],
"source": [
"state_dict = torch.load('qlnet-50-v0.pth.tar')['state_dict']"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "435e2358",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m.load_state_dict(state_dict)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f14d984a",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"QLNet(\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",
" (act1): ReLU(inplace=True)\n",
" (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
" (layer1): Sequential(\n",
" (0): QLBlock(\n",
" (conv1): ConvBN(\n",
" (conv): Conv2d(64, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (conv2): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): ConvBN(\n",
" (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (skip): Identity()\n",
" (act3): hardball()\n",
" )\n",
" (1): QLBlock(\n",
" (conv1): ConvBN(\n",
" (conv): Conv2d(64, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (conv2): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): ConvBN(\n",
" (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (skip): Identity()\n",
" (act3): hardball()\n",
" )\n",
" (2): QLBlock(\n",
" (conv1): ConvBN(\n",
" (conv): Conv2d(64, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (conv2): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): ConvBN(\n",
" (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (skip): Identity()\n",
" (act3): hardball()\n",
" )\n",
" )\n",
" (layer2): Sequential(\n",
" (0): QLBlock(\n",
" (conv1): ConvBN(\n",
" (conv): Conv2d(64, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (conv2): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=256, bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): ConvBN(\n",
" (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (skip): ConvBN(\n",
" (conv): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (act3): hardball()\n",
" )\n",
" (1): QLBlock(\n",
" (conv1): ConvBN(\n",
" (conv): Conv2d(128, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (conv2): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False)\n",
" (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): ConvBN(\n",
" (conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (skip): Identity()\n",
" (act3): hardball()\n",
" )\n",
" (2): QLBlock(\n",
" (conv1): ConvBN(\n",
" (conv): Conv2d(128, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (conv2): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False)\n",
" (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): ConvBN(\n",
" (conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (skip): Identity()\n",
" (act3): hardball()\n",
" )\n",
" (3): QLBlock(\n",
" (conv1): ConvBN(\n",
" (conv): Conv2d(128, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (conv2): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False)\n",
" (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): ConvBN(\n",
" (conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (skip): Identity()\n",
" (act3): hardball()\n",
" )\n",
" )\n",
" (layer3): Sequential(\n",
" (0): QLBlock(\n",
" (conv1): ConvBN(\n",
" (conv): Conv2d(128, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (conv2): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=512, bias=False)\n",
" (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): ConvBN(\n",
" (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (skip): ConvBN(\n",
" (conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (act3): hardball()\n",
" )\n",
" (1): QLBlock(\n",
" (conv1): ConvBN(\n",
" (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (conv2): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False)\n",
" (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): ConvBN(\n",
" (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (skip): Identity()\n",
" (act3): hardball()\n",
" )\n",
" (2): QLBlock(\n",
" (conv1): ConvBN(\n",
" (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (conv2): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False)\n",
" (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): ConvBN(\n",
" (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (skip): Identity()\n",
" (act3): hardball()\n",
" )\n",
" (3): QLBlock(\n",
" (conv1): ConvBN(\n",
" (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (conv2): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False)\n",
" (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): ConvBN(\n",
" (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (skip): Identity()\n",
" (act3): hardball()\n",
" )\n",
" (4): QLBlock(\n",
" (conv1): ConvBN(\n",
" (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (conv2): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False)\n",
" (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): ConvBN(\n",
" (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (skip): Identity()\n",
" (act3): hardball()\n",
" )\n",
" (5): QLBlock(\n",
" (conv1): ConvBN(\n",
" (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (conv2): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False)\n",
" (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): ConvBN(\n",
" (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (skip): Identity()\n",
" (act3): hardball()\n",
" )\n",
" )\n",
" (layer4): Sequential(\n",
" (0): QLBlock(\n",
" (conv1): ConvBN(\n",
" (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (conv2): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=512, bias=False)\n",
" (bn2): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): ConvBN(\n",
" (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (skip): ConvBN(\n",
" (conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (act3): hardball()\n",
" )\n",
" (1): QLBlock(\n",
" (conv1): ConvBN(\n",
" (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (conv2): Conv2d(1024, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1024, bias=False)\n",
" (bn2): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): ConvBN(\n",
" (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (skip): Identity()\n",
" (act3): hardball()\n",
" )\n",
" (2): QLBlock(\n",
" (conv1): ConvBN(\n",
" (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (conv2): Conv2d(1024, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1024, bias=False)\n",
" (bn2): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): ConvBN(\n",
" (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (skip): Identity()\n",
" (act3): hardball()\n",
" )\n",
" )\n",
" (act): hardball()\n",
" (global_pool): SelectAdaptivePool2d (pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1))\n",
" (fc): Linear(in_features=512, out_features=1000, bias=True)\n",
")"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m.eval()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2099b937",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"layer1 >>\n",
"torch.Size([512, 64, 1, 1])\n",
"torch.Size([64, 512, 1, 1])\n",
"torch.Size([512, 64, 1, 1])\n",
"torch.Size([64, 512, 1, 1])\n",
"torch.Size([512, 64, 1, 1])\n",
"torch.Size([64, 512, 1, 1])\n",
"layer2 >>\n",
"torch.Size([512, 64, 1, 1])\n",
"torch.Size([128, 512, 1, 1])\n",
"torch.Size([128, 64, 1, 1])\n",
"torch.Size([1024, 128, 1, 1])\n",
"torch.Size([128, 1024, 1, 1])\n",
"torch.Size([1024, 128, 1, 1])\n",
"torch.Size([128, 1024, 1, 1])\n",
"torch.Size([1024, 128, 1, 1])\n",
"torch.Size([128, 1024, 1, 1])\n",
"layer3 >>\n",
"torch.Size([1024, 128, 1, 1])\n",
"torch.Size([256, 1024, 1, 1])\n",
"torch.Size([256, 128, 1, 1])\n",
"torch.Size([1024, 256, 1, 1])\n",
"torch.Size([256, 1024, 1, 1])\n",
"torch.Size([1024, 256, 1, 1])\n",
"torch.Size([256, 1024, 1, 1])\n",
"torch.Size([1024, 256, 1, 1])\n",
"torch.Size([256, 1024, 1, 1])\n",
"torch.Size([1024, 256, 1, 1])\n",
"torch.Size([256, 1024, 1, 1])\n",
"torch.Size([1024, 256, 1, 1])\n",
"torch.Size([256, 1024, 1, 1])\n",
"layer4 >>\n",
"torch.Size([1024, 256, 1, 1])\n",
"torch.Size([512, 1024, 1, 1])\n",
"torch.Size([512, 256, 1, 1])\n",
"torch.Size([2048, 512, 1, 1])\n",
"torch.Size([512, 2048, 1, 1])\n",
"torch.Size([2048, 512, 1, 1])\n",
"torch.Size([512, 2048, 1, 1])\n"
]
}
],
"source": [
"# fuse ConvBN\n",
"i = 1\n",
"for layer in [m.layer1, m.layer2, m.layer3, m.layer4]:\n",
" print(f'layer{i} >>')\n",
" for block in layer:\n",
" # Fuse the weights in conv1 and conv3\n",
" block.conv1.fuse_bn()\n",
" print(block.conv1.fused_weight.size())\n",
" block.conv3.fuse_bn()\n",
" print(block.conv3.fused_weight.size())\n",
" if not isinstance(block.skip, torch.nn.Identity):\n",
" layer[0].skip.fuse_bn()\n",
" print(layer[0].skip.fused_weight.size())\n",
" i += 1"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "b3a55f82",
"metadata": {},
"outputs": [],
"source": [
"inpt = torch.randn(5,3,224,224)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "dccbf19c",
"metadata": {},
"outputs": [],
"source": [
"out_old = m(inpt)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "f0c74a04",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([5, 1000])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"out_old.size()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "a5991c8f",
"metadata": {},
"outputs": [],
"source": [
"def apply_transform(block1, block2, Q, keep_identity=True):\n",
" with torch.no_grad():\n",
" # Ensure that the out_channels of block1 is equal to the in_channels of block2\n",
" assert Q.size()[0] == Q.size()[1], \"Q needs to be a square matrix\"\n",
" n = Q.size()[0]\n",
" assert block1.conv3.conv.out_channels == n and block2.conv1.conv.in_channels == n, \"Mismatched channels between blocks\"\n",
"\n",
" n = block1.conv3.conv.out_channels\n",
" \n",
" # Calculate the inverse of Q\n",
" Q_inv = torch.inverse(Q)\n",
"\n",
" # Modify the weights of conv layers in block1\n",
" block1.conv3.fused_weight.data = torch.einsum('ij,jklm->iklm', Q, block1.conv3.fused_weight.data)\n",
" block1.conv3.fused_bias.data = torch.einsum('ij,j->i', Q, block1.conv3.fused_bias.data)\n",
" \n",
" if isinstance(block1.skip, torch.nn.Identity):\n",
" if not keep_identity:\n",
" block1.skip = torch.nn.Conv2d(n, n, kernel_size=1, bias=False)\n",
" block1.skip.weight.data = Q.unsqueeze(-1).unsqueeze(-1)\n",
" else:\n",
" block1.skip.fused_weight.data = torch.einsum('ij,jklm->iklm', Q, block1.skip.fused_weight.data)\n",
" block1.skip.fused_bias.data = torch.einsum('ij,j->i', Q, block1.skip.fused_bias.data)\n",
"\n",
" # Modify the weights of conv layers in block2\n",
" block2.conv1.fused_weight.data = torch.einsum('ki,jklm->jilm', Q_inv, block2.conv1.fused_weight.data)\n",
" \n",
" if isinstance(block2.skip, torch.nn.Identity):\n",
" if not keep_identity:\n",
" block2.skip = torch.nn.Conv2d(n, n, kernel_size=1, bias=False)\n",
" block2.skip.weight.data = Q_inv.unsqueeze(-1).unsqueeze(-1)\n",
" else:\n",
" block2.skip.fused_weight.data = torch.einsum('ki,jklm->jilm', Q_inv, block2.skip.fused_weight.data)\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "dd96acd7",
"metadata": {},
"outputs": [],
"source": [
"Q = torch.nn.init.orthogonal_(torch.empty(256, 256))\n",
"for i in range(5):\n",
" apply_transform(m.layer3[i], m.layer3[i+1], Q, True)\n",
"apply_transform(m.layer3[5], m.layer4[0], Q, True)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "e5d3628d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6.0558319091796875e-05\n"
]
}
],
"source": [
"out_new = m(inpt)\n",
"print((out_new - out_old).abs().max().item())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9fce3a38",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "5a54fe8b",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|