branch: master
resnet.py
6754 bytesRaw
import tinygrad.nn as nn
from tinygrad import Tensor, dtypes
from tinygrad.nn.state import torch_load
from tinygrad.helpers import fetch, get_child
# allow monkeypatching in layer implementations
BatchNorm = nn.BatchNorm2d
Conv2d = nn.Conv2d
Linear = nn.Linear
class BasicBlock:
expansion = 1
def __init__(self, in_planes, planes, stride=1, groups=1, base_width=64):
assert groups == 1 and base_width == 64, "BasicBlock only supports groups=1 and base_width=64"
self.conv1 = Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = BatchNorm(planes)
self.conv2 = Conv2d(planes, planes, kernel_size=3, padding=1, stride=1, bias=False)
self.bn2 = BatchNorm(planes)
self.downsample = []
if stride != 1 or in_planes != self.expansion*planes:
self.downsample = [
Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
BatchNorm(self.expansion*planes)
]
def __call__(self, x):
out = self.bn1(self.conv1(x)).relu()
out = self.bn2(self.conv2(out))
out = out + x.sequential(self.downsample)
out = out.relu()
return out
class Bottleneck:
# NOTE: stride_in_1x1=False, this is the v1.5 variant
expansion = 4
def __init__(self, in_planes, planes, stride=1, stride_in_1x1=False, groups=1, base_width=64):
width = int(planes * (base_width / 64.0)) * groups
# NOTE: the original implementation places stride at the first convolution (self.conv1), control with stride_in_1x1
self.conv1 = Conv2d(in_planes, width, kernel_size=1, stride=stride if stride_in_1x1 else 1, bias=False)
self.bn1 = BatchNorm(width)
self.conv2 = Conv2d(width, width, kernel_size=3, padding=1, stride=1 if stride_in_1x1 else stride, groups=groups, bias=False)
self.bn2 = BatchNorm(width)
self.conv3 = Conv2d(width, self.expansion*planes, kernel_size=1, bias=False)
self.bn3 = BatchNorm(self.expansion*planes)
self.downsample = []
if stride != 1 or in_planes != self.expansion*planes:
self.downsample = [
Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
BatchNorm(self.expansion*planes)
]
def __call__(self, x):
out = self.bn1(self.conv1(x)).relu()
out = self.bn2(self.conv2(out)).relu()
out = self.bn3(self.conv3(out))
out = out + x.sequential(self.downsample)
out = out.relu()
return out
class ResNet:
def __init__(self, num, num_classes=None, groups=1, width_per_group=64, stride_in_1x1=False):
self.num = num
self.block = {
18: BasicBlock,
34: BasicBlock,
50: Bottleneck,
101: Bottleneck,
152: Bottleneck
}[num]
self.num_blocks = {
18: [2,2,2,2],
34: [3,4,6,3],
50: [3,4,6,3],
101: [3,4,23,3],
152: [3,8,36,3]
}[num]
self.in_planes = 64
self.groups = groups
self.base_width = width_per_group
self.conv1 = Conv2d(3, 64, kernel_size=7, stride=2, bias=False, padding=3)
self.bn1 = BatchNorm(64)
self.layer1 = self._make_layer(self.block, 64, self.num_blocks[0], stride=1, stride_in_1x1=stride_in_1x1)
self.layer2 = self._make_layer(self.block, 128, self.num_blocks[1], stride=2, stride_in_1x1=stride_in_1x1)
self.layer3 = self._make_layer(self.block, 256, self.num_blocks[2], stride=2, stride_in_1x1=stride_in_1x1)
self.layer4 = self._make_layer(self.block, 512, self.num_blocks[3], stride=2, stride_in_1x1=stride_in_1x1)
self.fc = Linear(512 * self.block.expansion, num_classes) if num_classes is not None else None
def _make_layer(self, block, planes, num_blocks, stride, stride_in_1x1):
strides = [stride] + [1] * (num_blocks-1)
layers = []
for stride in strides:
if block == Bottleneck:
layers.append(block(self.in_planes, planes, stride, stride_in_1x1, self.groups, self.base_width))
else:
layers.append(block(self.in_planes, planes, stride, self.groups, self.base_width))
self.in_planes = planes * block.expansion
return layers
def forward(self, x):
is_feature_only = self.fc is None
if is_feature_only: features = []
out = self.bn1(self.conv1(x)).relu()
out = out.pad([1,1,1,1]).max_pool2d((3,3), 2)
out = out.sequential(self.layer1)
if is_feature_only: features.append(out)
out = out.sequential(self.layer2)
if is_feature_only: features.append(out)
out = out.sequential(self.layer3)
if is_feature_only: features.append(out)
out = out.sequential(self.layer4)
if is_feature_only: features.append(out)
if not is_feature_only:
out = out.mean([2,3])
out = self.fc(out.cast(dtypes.float32))
return out
return features
def __call__(self, x:Tensor) -> Tensor:
return self.forward(x)
def load_from_pretrained(self):
model_urls = {
(18, 1, 64): 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
(34, 1, 64): 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
(50, 1, 64): 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
(50, 32, 4): 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
(101, 1, 64): 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
(152, 1, 64): 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}
self.url = model_urls[(self.num, self.groups, self.base_width)]
for k, dat in torch_load(fetch(self.url)).items():
try:
obj: Tensor = get_child(self, k)
except AttributeError as e:
if 'fc.' in k and self.fc is None:
continue
raise e
if 'fc.' in k and obj.shape != dat.shape:
print("skipping fully connected layer")
continue # Skip FC if transfer learning
if 'bn' not in k and 'downsample' not in k: assert obj.shape == dat.shape, (k, obj.shape, dat.shape)
obj.assign(dat.to(obj.device).reshape(obj.shape))
ResNet18 = lambda num_classes=1000: ResNet(18, num_classes=num_classes)
ResNet34 = lambda num_classes=1000: ResNet(34, num_classes=num_classes)
ResNet50 = lambda num_classes=1000: ResNet(50, num_classes=num_classes)
ResNet101 = lambda num_classes=1000: ResNet(101, num_classes=num_classes)
ResNet152 = lambda num_classes=1000: ResNet(152, num_classes=num_classes)
ResNeXt50_32X4D = lambda num_classes=1000: ResNet(50, num_classes=num_classes, groups=32, width_per_group=4)
if __name__ == "__main__":
model = ResNet18()
model.load_from_pretrained()
from tinygrad import Context, GlobalCounters, TinyJit
jmodel = TinyJit(model)
jmodel(Tensor.rand(1, 3, 224, 224)).realize()
GlobalCounters.reset()
jmodel(Tensor.rand(1, 3, 224, 224)).realize()
for i in range(10): jmodel(Tensor.rand(1, 3, 224, 224))