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114 lines
3.0 KiB
114 lines
3.0 KiB
# code from https://x.com/awnihannun/status/1832511021602500796
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from huggingface_hub import snapshot_download
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import mlx.core as mx
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import mlx.nn as nn
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import time
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class Block(nn.Module):
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def __init__(self, in_dims, dims, stride=1):
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super().__init__()
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self.conv1 = nn.Conv2d(
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in_dims, dims, kernel_size=3, stride=stride, padding=1, bias=False
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)
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self.bn1 = nn.BatchNorm(dims)
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self.conv2 = nn.Conv2d(
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dims, dims, kernel_size=3, stride=1, padding=1, bias=False
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)
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self.bn2 = nn.BatchNorm(dims)
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self.downsample = []
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if stride != 1:
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self.downsample = [
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nn.Conv2d(in_dims, dims, kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm(dims)
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]
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def __call__(self, x):
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out = nn.relu(self.bn1(self.conv1(x)))
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out = self.bn2(self.conv2(out))
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for l in self.downsample:
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x = l(x)
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out += x
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out = nn.relu(out)
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return out
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class ResNet(nn.Module):
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def __init__(self, block, num_blocks, num_classes=10):
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super().__init__()
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
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self.bn1 = nn.BatchNorm(64)
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.layer1 = self._make_layer(block, 64, 64, num_blocks[0], stride=1)
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self.layer2 = self._make_layer(block, 64, 128, num_blocks[1], stride=2)
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self.layer3 = self._make_layer(block, 128, 256, num_blocks[2], stride=2)
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self.layer4 = self._make_layer(block, 256, 512, num_blocks[3], stride=2)
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self.fc = nn.Linear(512, num_classes)
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def _make_layer(self, block, in_dims, dims, num_blocks, stride):
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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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layers.append(block(in_dims, dims, stride))
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in_dims = dims
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return layers
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def __call__(self, x):
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x = nn.relu(self.bn1(self.conv1(x)))
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x = self.maxpool(x)
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for l in self.layer1 + self.layer2 + self.layer3 + self.layer4:
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x = l(x)
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x = mx.mean(x, axis=[1, 2])
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x = self.fc(x)
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return x
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def load():
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model = ResNet(Block, [2, 2, 2, 2], num_classes=1000)
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file = "model.safetensors"
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model_path = snapshot_download(
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repo_id="awni/resnet18-mlx",
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allow_patterns=[file],
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)
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model.load_weights(model_path + "/" + file)
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model.eval()
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mx.eval(model)
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return model
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if __name__ == "__main__":
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resnet18 = load()
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@mx.compile
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def forward(im):
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return resnet18(im)
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batch_sizes = [1, 2, 4, 8, 16, 32, 64]
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#its = 200
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#batch_sizes = [64]
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its = 20
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print(f"Batch Size | Images-per-second | Milliseconds-per-image")
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print(f"---- | ---- | ---- ")
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for N in batch_sizes:
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image = mx.random.uniform(shape=(N, 288, 288, 3))
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# Warmup
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for _ in range(5):
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output = forward(image)
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mx.eval(output)
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tic = time.time()
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for _ in range(its):
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output = forward(image)
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mx.async_eval(output)
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mx.eval(output)
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toc = time.time()
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ims_per_sec = N * its / (toc - tic)
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ms_per_im = 1e3 / ims_per_sec
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print(f"{N} | {ims_per_sec:.3f} | {ms_per_im:.3f}") |