mirror of https://github.com/commaai/tinygrad.git
165 lines
6.5 KiB
Python
165 lines
6.5 KiB
Python
import tinygrad.nn as nn
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from tinygrad import Tensor, dtypes
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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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# allow monkeypatching in layer implementations
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BatchNorm = nn.BatchNorm2d
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Conv2d = nn.Conv2d
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Linear = nn.Linear
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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 groups == 1 and base_width == 64, "BasicBlock only supports groups=1 and base_width=64"
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self.conv1 = Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
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self.bn1 = BatchNorm(planes)
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self.conv2 = Conv2d(planes, planes, kernel_size=3, padding=1, stride=1, bias=False)
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self.bn2 = BatchNorm(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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Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
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BatchNorm(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__(self, in_planes, planes, stride=1, stride_in_1x1=False, groups=1, base_width=64):
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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 = Conv2d(in_planes, width, kernel_size=1, stride=stride if stride_in_1x1 else 1, bias=False)
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self.bn1 = BatchNorm(width)
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self.conv2 = Conv2d(width, width, kernel_size=3, padding=1, stride=1 if stride_in_1x1 else stride, groups=groups, bias=False)
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self.bn2 = BatchNorm(width)
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self.conv3 = Conv2d(width, self.expansion*planes, kernel_size=1, bias=False)
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self.bn3 = BatchNorm(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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Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
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BatchNorm(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__(self, num, num_classes=None, groups=1, width_per_group=64, stride_in_1x1=False):
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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 = Conv2d(3, 64, kernel_size=7, stride=2, bias=False, padding=3)
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self.bn1 = BatchNorm(64)
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self.layer1 = self._make_layer(self.block, 64, self.num_blocks[0], stride=1, stride_in_1x1=stride_in_1x1)
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self.layer2 = self._make_layer(self.block, 128, self.num_blocks[1], stride=2, stride_in_1x1=stride_in_1x1)
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self.layer3 = self._make_layer(self.block, 256, self.num_blocks[2], stride=2, stride_in_1x1=stride_in_1x1)
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self.layer4 = self._make_layer(self.block, 512, self.num_blocks[3], stride=2, stride_in_1x1=stride_in_1x1)
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self.fc = Linear(512 * self.block.expansion, num_classes) if num_classes is not None else None
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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(block(self.in_planes, planes, stride, stride_in_1x1, self.groups, self.base_width))
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else:
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layers.append(block(self.in_planes, planes, stride, self.groups, self.base_width))
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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: 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: features.append(out)
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out = out.sequential(self.layer2)
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if is_feature_only: features.append(out)
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out = out.sequential(self.layer3)
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if is_feature_only: features.append(out)
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out = out.sequential(self.layer4)
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if is_feature_only: 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.cast(dtypes.float32))
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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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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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(50, 32, 4): '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, dat in torch_load(fetch(self.url)).items():
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obj: Tensor = get_child(self, k)
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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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if 'bn' not in k and 'downsample' not in k: assert obj.shape == dat.shape, (k, obj.shape, dat.shape)
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obj.assign(dat.to(obj.device).reshape(obj.shape))
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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(50, num_classes=num_classes, groups=32, width_per_group=4)
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if __name__ == "__main__":
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model = ResNet18()
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model.load_from_pretrained()
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from tinygrad import Context, GlobalCounters, TinyJit
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jmodel = TinyJit(model)
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jmodel(Tensor.rand(1, 3, 224, 224)).realize()
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GlobalCounters.reset()
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with Context(GRAPH=1): jmodel(Tensor.rand(1, 3, 224, 224)).realize()
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for i in range(10): jmodel(Tensor.rand(1, 3, 224, 224))
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