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.pad2d([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): # TODO replace with fake torch load 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, v in torch_load(fetch(self.url)).items(): obj: Tensor = get_child(self, k) dat = v.numpy() 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.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)