232 lines
7.8 KiB
Python
232 lines
7.8 KiB
Python
import tinygrad.nn as nn
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from tinygrad.tensor import Tensor
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from tinygrad.nn.state import torch_load
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from tinygrad.helpers import fetch, get_child
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class BasicBlock:
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expansion = 1
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def __init__(self, in_planes, planes, stride=1, groups=1, base_width=64):
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assert (
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groups == 1 and base_width == 64
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), "BasicBlock only supports groups=1 and base_width=64"
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self.conv1 = nn.Conv2d(
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in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False
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)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(
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planes, planes, kernel_size=3, padding=1, stride=1, bias=False
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)
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self.bn2 = nn.BatchNorm2d(planes)
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self.downsample = []
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if stride != 1 or in_planes != self.expansion * planes:
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self.downsample = [
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nn.Conv2d(
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in_planes,
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self.expansion * planes,
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kernel_size=1,
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stride=stride,
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bias=False,
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),
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nn.BatchNorm2d(self.expansion * planes),
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]
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def __call__(self, x):
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out = self.bn1(self.conv1(x)).relu()
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out = self.bn2(self.conv2(out))
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out = out + x.sequential(self.downsample)
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out = out.relu()
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return out
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class Bottleneck:
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# NOTE: stride_in_1x1=False, this is the v1.5 variant
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expansion = 4
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def __init__(
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self, in_planes, planes, stride=1, stride_in_1x1=False, groups=1, base_width=64
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):
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width = int(planes * (base_width / 64.0)) * groups
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# NOTE: the original implementation places stride at the first convolution (self.conv1), control with stride_in_1x1
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self.conv1 = nn.Conv2d(
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in_planes,
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width,
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kernel_size=1,
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stride=stride if stride_in_1x1 else 1,
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bias=False,
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)
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self.bn1 = nn.BatchNorm2d(width)
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self.conv2 = nn.Conv2d(
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width,
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width,
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kernel_size=3,
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padding=1,
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stride=1 if stride_in_1x1 else stride,
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groups=groups,
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bias=False,
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)
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self.bn2 = nn.BatchNorm2d(width)
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self.conv3 = nn.Conv2d(
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width, self.expansion * planes, kernel_size=1, bias=False
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)
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self.bn3 = nn.BatchNorm2d(self.expansion * planes)
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self.downsample = []
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if stride != 1 or in_planes != self.expansion * planes:
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self.downsample = [
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nn.Conv2d(
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in_planes,
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self.expansion * planes,
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kernel_size=1,
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stride=stride,
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bias=False,
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),
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nn.BatchNorm2d(self.expansion * planes),
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]
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def __call__(self, x):
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out = self.bn1(self.conv1(x)).relu()
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out = self.bn2(self.conv2(out)).relu()
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out = self.bn3(self.conv3(out))
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out = out + x.sequential(self.downsample)
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out = out.relu()
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return out
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class ResNet:
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def __init__(
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self, num, num_classes=None, groups=1, width_per_group=64, stride_in_1x1=False
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):
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self.num = num
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self.block = {
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18: BasicBlock,
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34: BasicBlock,
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50: Bottleneck,
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101: Bottleneck,
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152: Bottleneck,
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}[num]
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self.num_blocks = {
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18: [2, 2, 2, 2],
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34: [3, 4, 6, 3],
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50: [3, 4, 6, 3],
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101: [3, 4, 23, 3],
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152: [3, 8, 36, 3],
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}[num]
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self.in_planes = 64
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self.groups = groups
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self.base_width = width_per_group
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, bias=False, padding=3)
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self.bn1 = nn.BatchNorm2d(64)
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self.layer1 = self._make_layer(
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self.block, 64, self.num_blocks[0], stride=1, stride_in_1x1=stride_in_1x1
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)
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self.layer2 = self._make_layer(
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self.block, 128, self.num_blocks[1], stride=2, stride_in_1x1=stride_in_1x1
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)
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self.layer3 = self._make_layer(
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self.block, 256, self.num_blocks[2], stride=2, stride_in_1x1=stride_in_1x1
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)
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self.layer4 = self._make_layer(
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self.block, 512, self.num_blocks[3], stride=2, stride_in_1x1=stride_in_1x1
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)
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self.fc = (
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nn.Linear(512 * self.block.expansion, num_classes)
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if num_classes is not None
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else None
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)
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def _make_layer(self, block, planes, num_blocks, stride, stride_in_1x1):
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strides = [stride] + [1] * (num_blocks - 1)
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layers = []
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for stride in strides:
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if block == Bottleneck:
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layers.append(
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block(
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self.in_planes,
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planes,
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stride,
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stride_in_1x1,
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self.groups,
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self.base_width,
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)
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)
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else:
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layers.append(
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block(self.in_planes, planes, stride, self.groups, self.base_width)
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)
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self.in_planes = planes * block.expansion
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return layers
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def forward(self, x):
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is_feature_only = self.fc is None
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if is_feature_only:
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features = []
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out = self.bn1(self.conv1(x)).relu()
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out = out.pad2d([1, 1, 1, 1]).max_pool2d((3, 3), 2)
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out = out.sequential(self.layer1)
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if is_feature_only:
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features.append(out)
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out = out.sequential(self.layer2)
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if is_feature_only:
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features.append(out)
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out = out.sequential(self.layer3)
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if is_feature_only:
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features.append(out)
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out = out.sequential(self.layer4)
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if is_feature_only:
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features.append(out)
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if not is_feature_only:
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out = out.mean([2, 3])
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out = self.fc(out).log_softmax()
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return out
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return features
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def __call__(self, x: Tensor) -> Tensor:
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return self.forward(x)
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def load_from_pretrained(self):
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# TODO replace with fake torch load
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model_urls = {
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(18, 1, 64): "https://download.pytorch.org/models/resnet18-5c106cde.pth",
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(34, 1, 64): "https://download.pytorch.org/models/resnet34-333f7ec4.pth",
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(50, 1, 64): "https://download.pytorch.org/models/resnet50-19c8e357.pth",
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(
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50,
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32,
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4,
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): "https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth",
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(101, 1, 64): "https://download.pytorch.org/models/resnet101-5d3b4d8f.pth",
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(152, 1, 64): "https://download.pytorch.org/models/resnet152-b121ed2d.pth",
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}
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self.url = model_urls[(self.num, self.groups, self.base_width)]
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for k, v in torch_load(fetch(self.url)).items():
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obj: Tensor = get_child(self, k)
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dat = v.detach().numpy()
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if "fc." in k and obj.shape != dat.shape:
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print("skipping fully connected layer")
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continue # Skip FC if transfer learning
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# TODO: remove or when #777 is merged
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assert obj.shape == dat.shape or (obj.shape == (1,) and dat.shape == ()), (
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k,
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obj.shape,
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dat.shape,
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)
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obj.assign(dat)
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ResNet18 = lambda num_classes=1000: ResNet(18, num_classes=num_classes)
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ResNet34 = lambda num_classes=1000: ResNet(34, num_classes=num_classes)
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ResNet50 = lambda num_classes=1000: ResNet(50, num_classes=num_classes)
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ResNet101 = lambda num_classes=1000: ResNet(101, num_classes=num_classes)
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ResNet152 = lambda num_classes=1000: ResNet(152, num_classes=num_classes)
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ResNeXt50_32X4D = lambda num_classes=1000: ResNet(
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50, num_classes=num_classes, groups=32, width_per_group=4
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)
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