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66 lines
2.4 KiB
66 lines
2.4 KiB
1 month ago
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from tinygrad.tensor import Tensor
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from tinygrad.nn import Conv2d, LayerNorm, LayerNorm2d, Linear
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from tinygrad.helpers import fetch, get_child
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class Block:
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def __init__(self, dim):
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self.dwconv = Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim)
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self.norm = LayerNorm(dim, eps=1e-6)
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self.pwconv1 = Linear(dim, 4 * dim)
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self.pwconv2 = Linear(4 * dim, dim)
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self.gamma = Tensor.ones(dim)
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def __call__(self, x:Tensor):
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return x + x.sequential([
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self.dwconv, lambda x: x.permute(0, 2, 3, 1), self.norm,
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self.pwconv1, Tensor.gelu, self.pwconv2, lambda x: (self.gamma * x).permute(0, 3, 1, 2)
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])
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class ConvNeXt:
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def __init__(self, in_chans=3, num_classes=1000, depths=[3, 3, 9, 3], dims=[96, 192, 384, 768]):
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self.downsample_layers = [
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[Conv2d(in_chans, dims[0], kernel_size=4, stride=4), LayerNorm2d(dims[0], eps=1e-6)],
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*[[LayerNorm2d(dims[i], eps=1e-6), Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2)] for i in range(len(dims)-1)]
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]
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self.stages = [[Block(dims[i]) for _ in range(depths[i])] for i in range(len(dims))]
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self.norm = LayerNorm(dims[-1])
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self.head = Linear(dims[-1], num_classes)
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def __call__(self, x:Tensor):
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for downsample, stage in zip(self.downsample_layers, self.stages):
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x = x.sequential(downsample).sequential(stage)
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return x.mean([-2, -1]).sequential([self.norm, self.head])
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# *** model definition is done ***
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versions = {
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"tiny": {"depths": [3, 3, 9, 3], "dims": [96, 192, 384, 768]},
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"small": {"depths": [3, 3, 27, 3], "dims": [96, 192, 384, 768]},
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"base": {"depths": [3, 3, 9, 3], "dims": [128, 256, 512, 1024]},
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"large": {"depths": [3, 3, 27, 3], "dims": [192, 384, 768, 1536]},
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"xlarge": {"depths": [3, 3, 27, 3], "dims": [256, 512, 1024, 2048]}
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}
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def get_model(version, load_weights=False):
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model = ConvNeXt(**versions[version])
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if load_weights:
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from tinygrad.nn.state import torch_load
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weights = torch_load(fetch(f'https://dl.fbaipublicfiles.com/convnext/convnext_{version}_1k_224_ema.pth'))['model']
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for k,v in weights.items():
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mv = get_child(model, k)
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mv.assign(v.reshape(mv.shape).to(mv.device)).realize()
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return model
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if __name__ == "__main__":
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model = get_model("tiny", True)
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# load image
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from test.models.test_efficientnet import chicken_img, preprocess, _LABELS
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img = Tensor(preprocess(chicken_img))
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Tensor.training = False
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Tensor.no_grad = True
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out = model(img).numpy()
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print(_LABELS[out.argmax()])
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