{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3DC"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"from torch.autograd import Variable\n",
"import math\n",
"from functools import partial"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def conv3x3x3(in_planes, out_planes, stride=1):\n",
" # 3x3x3 convolution with padding\n",
" return nn.Conv3d(\n",
" in_planes,\n",
" out_planes,\n",
" kernel_size=3,\n",
" stride=stride,\n",
" padding=1,\n",
" bias=False)\n",
"\n",
"\n",
"def downsample_basic_block(x, planes, stride):\n",
" out = F.avg_pool3d(x, kernel_size=1, stride=stride)\n",
" zero_pads = torch.Tensor(\n",
" out.size(0), planes - out.size(1), out.size(2), out.size(3),\n",
" out.size(4)).zero_()\n",
" if isinstance(out.data, torch.cuda.FloatTensor):\n",
" zero_pads = zero_pads.cuda()\n",
"\n",
" out = Variable(torch.cat([out.data, zero_pads], dim=1))\n",
"\n",
" return out\n",
"\n",
"\n",
"class BasicBlock(nn.Module):\n",
" expansion = 1\n",
"\n",
" def __init__(self, inplanes, planes, stride=1, downsample=None):\n",
" super(BasicBlock, self).__init__()\n",
" self.conv1 = conv3x3x3(inplanes, planes, stride)\n",
" self.bn1 = nn.BatchNorm3d(planes)\n",
" self.relu = nn.ReLU(inplace=True)\n",
" self.conv2 = conv3x3x3(planes, planes)\n",
" self.bn2 = nn.BatchNorm3d(planes)\n",
" self.downsample = downsample\n",
" self.stride = stride\n",
"\n",
" def forward(self, x):\n",
" residual = x\n",
"\n",
" out = self.conv1(x)\n",
" out = self.bn1(out)\n",
" out = self.relu(out)\n",
"\n",
" out = self.conv2(out)\n",
" out = self.bn2(out)\n",
"\n",
" if self.downsample is not None:\n",
" residual = self.downsample(x)\n",
"\n",
" out += residual\n",
" out = self.relu(out)\n",
"\n",
" return out\n",
"\n",
"\n",
"class Bottleneck(nn.Module):\n",
" expansion = 4\n",
"\n",
" def __init__(self, inplanes, planes, stride=1, downsample=None):\n",
" super(Bottleneck, self).__init__()\n",
" self.conv1 = nn.Conv3d(inplanes, planes, kernel_size=1, bias=False)\n",
" self.bn1 = nn.BatchNorm3d(planes)\n",
" self.conv2 = nn.Conv3d(\n",
" planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)\n",
" self.bn2 = nn.BatchNorm3d(planes)\n",
" self.conv3 = nn.Conv3d(planes, planes * 4, kernel_size=1, bias=False)\n",
" self.bn3 = nn.BatchNorm3d(planes * 4)\n",
" self.relu = nn.ReLU(inplace=True)\n",
" self.downsample = downsample\n",
" self.stride = stride\n",
"\n",
" def forward(self, x):\n",
" residual = x\n",
"\n",
" out = self.conv1(x)\n",
" out = self.bn1(out)\n",
" out = self.relu(out)\n",
"\n",
" out = self.conv2(out)\n",
" out = self.bn2(out)\n",
" out = self.relu(out)\n",
"\n",
" out = self.conv3(out)\n",
" out = self.bn3(out)\n",
"\n",
" if self.downsample is not None:\n",
" residual = self.downsample(x)\n",
"\n",
" out += residual\n",
" out = self.relu(out)\n",
"\n",
" return out\n",
"\n",
"\n",
"class ResNet(nn.Module):\n",
"\n",
" def __init__(self,\n",
" block,\n",
" layers,\n",
" sample_size,\n",
" sample_duration,\n",
" shortcut_type='B',\n",
" num_classes=400):\n",
" self.inplanes = 64\n",
" super(ResNet, self).__init__()\n",
" self.conv1 = nn.Conv3d(\n",
" 3,\n",
" 64,\n",
" kernel_size=7,\n",
" stride=(1, 2, 2),\n",
" padding=(3, 3, 3),\n",
" bias=False)\n",
" self.bn1 = nn.BatchNorm3d(64)\n",
" self.relu = nn.ReLU(inplace=True)\n",
" self.maxpool = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=1)\n",
" self.layer1 = self._make_layer(block, 64, layers[0], shortcut_type)\n",
" self.layer2 = self._make_layer(\n",
" block, 128, layers[1], shortcut_type, stride=2)\n",
" self.layer3 = self._make_layer(\n",
" block, 256, layers[2], shortcut_type, stride=2)\n",
" self.layer4 = self._make_layer(\n",
" block, 512, layers[3], shortcut_type, stride=2)\n",
" last_duration = int(math.ceil(sample_duration / 16))\n",
" last_size = int(math.ceil(sample_size / 32))\n",
" self.avgpool = nn.AvgPool3d(\n",
" (last_duration, last_size, last_size), stride=1)\n",
" self.fc = nn.Linear(512 * block.expansion, num_classes)\n",
"\n",
" for m in self.modules():\n",
" if isinstance(m, nn.Conv3d):\n",
" m.weight = nn.init.kaiming_normal(m.weight, mode='fan_out')\n",
" elif isinstance(m, nn.BatchNorm3d):\n",
" m.weight.data.fill_(1)\n",
" m.bias.data.zero_()\n",
"\n",
" def _make_layer(self, block, planes, blocks, shortcut_type, stride=1):\n",
" downsample = None\n",
" if stride != 1 or self.inplanes != planes * block.expansion:\n",
" if shortcut_type == 'A':\n",
" downsample = partial(\n",
" downsample_basic_block,\n",
" planes=planes * block.expansion,\n",
" stride=stride)\n",
" else:\n",
" downsample = nn.Sequential(\n",
" nn.Conv3d(\n",
" self.inplanes,\n",
" planes * block.expansion,\n",
" kernel_size=1,\n",
" stride=stride,\n",
" bias=False), nn.BatchNorm3d(planes * block.expansion))\n",
"\n",
" layers = []\n",
" layers.append(block(self.inplanes, planes, stride, downsample))\n",
" self.inplanes = planes * block.expansion\n",
" for i in range(1, blocks):\n",
" layers.append(block(self.inplanes, planes))\n",
"\n",
" return nn.Sequential(*layers)\n",
"\n",
" def forward(self, x):\n",
" x = self.conv1(x)\n",
" x = self.bn1(x)\n",
" x = self.relu(x)\n",
" x = self.maxpool(x)\n",
"\n",
" x = self.layer1(x)\n",
" x = self.layer2(x)\n",
" x = self.layer3(x)\n",
" x = self.layer4(x)\n",
"\n",
" x = self.avgpool(x)\n",
"\n",
" x = x.view(x.size(0), -1)\n",
" x = self.fc(x)\n",
"\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def resnet34(**kwargs):\n",
" \"\"\"Constructs a ResNet-34 model.\n",
" \"\"\"\n",
" model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"num_classes = 2\n",
"resnet_shortcut = 'B'\n",
"sample_size = 224\n",
"sample_duration = 16"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:132: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n"
]
}
],
"source": [
"model = resnet34(\n",
" num_classes=2,\n",
" shortcut_type=resnet_shortcut,\n",
" sample_size=sample_size,\n",
" sample_duration=sample_duration)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ResNet(\n",
" (conv1): Conv3d(3, 64, kernel_size=(7, 7, 7), stride=(1, 2, 2), padding=(3, 3, 3), bias=False)\n",
" (bn1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (maxpool): MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=1, dilation=1, ceil_mode=False)\n",
" (layer1): Sequential(\n",
" (0): BasicBlock(\n",
" (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn2): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (1): BasicBlock(\n",
" (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn2): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (2): BasicBlock(\n",
" (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn2): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (layer2): Sequential(\n",
" (0): BasicBlock(\n",
" (conv1): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False)\n",
" (bn1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn2): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (downsample): Sequential(\n",
" (0): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(2, 2, 2), bias=False)\n",
" (1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): BasicBlock(\n",
" (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn2): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (2): BasicBlock(\n",
" (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn2): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (3): BasicBlock(\n",
" (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn2): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (layer3): Sequential(\n",
" (0): BasicBlock(\n",
" (conv1): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False)\n",
" (bn1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn2): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (downsample): Sequential(\n",
" (0): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(2, 2, 2), bias=False)\n",
" (1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): BasicBlock(\n",
" (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn2): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (2): BasicBlock(\n",
" (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn2): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (3): BasicBlock(\n",
" (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn2): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (4): BasicBlock(\n",
" (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn2): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (5): BasicBlock(\n",
" (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn2): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (layer4): Sequential(\n",
" (0): BasicBlock(\n",
" (conv1): Conv3d(256, 512, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False)\n",
" (bn1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn2): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (downsample): Sequential(\n",
" (0): Conv3d(256, 512, kernel_size=(1, 1, 1), stride=(2, 2, 2), bias=False)\n",
" (1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): BasicBlock(\n",
" (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn2): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (2): BasicBlock(\n",
" (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace)\n",
" (conv2): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
" (bn2): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (avgpool): AvgPool3d(kernel_size=(1, 7, 7), stride=1, padding=0)\n",
" (fc): Linear(in_features=512, out_features=2, bias=True)\n",
")"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"q = torch.rand((2, 3, 16, 224, 224))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[1.3253, 0.5383],\n",
" [1.2986, 0.5157]], grad_fn=<AddmmBackward>)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model(q)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from kissing_detector import KissingDetector3DConv"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/aziai/Dropbox/github/cs231n-project/conv3d.py:142: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n",
" m.weight = nn.init.kaiming_normal(m.weight, mode='fan_out')\n"
]
}
],
"source": [
"model = KissingDetector3DConv(num_classes=2,\n",
" feature_extract=True,\n",
" use_vggish=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[ 0.1913, -0.5335]], grad_fn=<AddmmBackward>)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model(\n",
" torch.rand((1, 1, 96, 64)),\n",
" torch.rand((1, 3, 16, 224, 224))\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"xs = [\n",
" torch.rand((3, 224, 224))\n",
" for _ in range(10)\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(xs)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([3, 224, 224])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xs[0].shape"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([3, 16, 224, 224])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"e.shape"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"out = []\n",
"for i in range(len(xs)):\n",
" # build an e\n",
" e = torch.zeros((16, 3, 224, 224))\n",
" \n",
" e[-1] = xs[0]\n",
" \n",
" for j in range(i):\n",
" e[- 1 - j] = xs[j]\n",
" \n",
" # permute e\n",
" ee = e.permute((1, 0, 2, 3))\n",
" out.append(ee)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([3, 16, 224, 224])"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"out[0].shape"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[0.2543, 0.3794, 0.6759, ..., 0.4921, 0.7928, 0.3837],\n",
" [0.9456, 0.6443, 0.3277, ..., 0.7827, 0.9809, 0.2572],\n",
" [0.8232, 0.0090, 0.7486, ..., 0.9921, 0.6396, 0.8412],\n",
" ...,\n",
" [0.8520, 0.2385, 0.7409, ..., 0.9279, 0.7809, 0.0244],\n",
" [0.9987, 0.8331, 0.8345, ..., 0.8206, 0.7880, 0.4744],\n",
" [0.5744, 0.5991, 0.7520, ..., 0.5209, 0.7709, 0.7906]])"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"out[1][0, -1, :, :]"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# 1- broken 2d?\n",
"from experiments import ExperimentRunner\n",
"import params"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"ex = ExperimentRunner(params.experiment_test_3d, n_jobs=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Running param set: {'data_path_base': 'vtest_new2', 'conv_model_name': 'resnet', 'num_epochs': 10, 'feature_extract': True, 'batch_size': 64, 'lr': 0.001, 'use_vggish': True, 'momentum': 0.9, 'use_3d': True}\n",
"/Users/aziai/Dropbox/github/cs231n-project/conv3d.py:142: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n",
" m.weight = nn.init.kaiming_normal(m.weight, mode='fan_out')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Params to update\n",
"* combined.weight\n",
"* combined.bias\n",
"Epoch 0/9\n",
"----------\n",
"train Loss: 1.0060 F1: 0.6833 Acc: 0.5190\n"
]
}
],
"source": [
"ex.run()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"from train import train_kd"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train_kd()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}