import math from tinygrad.tensor import Tensor from tinygrad.nn import BatchNorm2d from tinygrad.helpers import get_child, fetch from tinygrad.nn.state import torch_load class MBConvBlock: def __init__(self, kernel_size, strides, expand_ratio, input_filters, output_filters, se_ratio, has_se, track_running_stats=True): oup = expand_ratio * input_filters if expand_ratio != 1: self._expand_conv = Tensor.glorot_uniform(oup, input_filters, 1, 1) self._bn0 = BatchNorm2d(oup, track_running_stats=track_running_stats) else: self._expand_conv = None self.strides = strides if strides == (2,2): self.pad = [(kernel_size-1)//2-1, (kernel_size-1)//2]*2 else: self.pad = [(kernel_size-1)//2]*4 self._depthwise_conv = Tensor.glorot_uniform(oup, 1, kernel_size, kernel_size) self._bn1 = BatchNorm2d(oup, track_running_stats=track_running_stats) self.has_se = has_se if self.has_se: num_squeezed_channels = max(1, int(input_filters * se_ratio)) self._se_reduce = Tensor.glorot_uniform(num_squeezed_channels, oup, 1, 1) self._se_reduce_bias = Tensor.zeros(num_squeezed_channels) self._se_expand = Tensor.glorot_uniform(oup, num_squeezed_channels, 1, 1) self._se_expand_bias = Tensor.zeros(oup) self._project_conv = Tensor.glorot_uniform(output_filters, oup, 1, 1) self._bn2 = BatchNorm2d(output_filters, track_running_stats=track_running_stats) def __call__(self, inputs): x = inputs if self._expand_conv is not None: x = self._bn0(x.conv2d(self._expand_conv)).swish() x = x.conv2d(self._depthwise_conv, padding=self.pad, stride=self.strides, groups=self._depthwise_conv.shape[0]) x = self._bn1(x).swish() if self.has_se: x_squeezed = x.avg_pool2d(kernel_size=x.shape[2:4]) x_squeezed = x_squeezed.conv2d(self._se_reduce, self._se_reduce_bias).swish() x_squeezed = x_squeezed.conv2d(self._se_expand, self._se_expand_bias) x = x.mul(x_squeezed.sigmoid()) x = self._bn2(x.conv2d(self._project_conv)) if x.shape == inputs.shape: x = x.add(inputs) return x class EfficientNet: def __init__(self, number=0, classes=1000, has_se=True, track_running_stats=True, input_channels=3, has_fc_output=True): self.number = number global_params = [ # width, depth (1.0, 1.0), # b0 (1.0, 1.1), # b1 (1.1, 1.2), # b2 (1.2, 1.4), # b3 (1.4, 1.8), # b4 (1.6, 2.2), # b5 (1.8, 2.6), # b6 (2.0, 3.1), # b7 (2.2, 3.6), # b8 (4.3, 5.3), # l2 ][max(number,0)] def round_filters(filters): multiplier = global_params[0] divisor = 8 filters *= multiplier new_filters = max(divisor, int(filters + divisor / 2) // divisor * divisor) if new_filters < 0.9 * filters: # prevent rounding by more than 10% new_filters += divisor return int(new_filters) def round_repeats(repeats): return int(math.ceil(global_params[1] * repeats)) out_channels = round_filters(32) self._conv_stem = Tensor.glorot_uniform(out_channels, input_channels, 3, 3) self._bn0 = BatchNorm2d(out_channels, track_running_stats=track_running_stats) blocks_args = [ [1, 3, (1,1), 1, 32, 16, 0.25], [2, 3, (2,2), 6, 16, 24, 0.25], [2, 5, (2,2), 6, 24, 40, 0.25], [3, 3, (2,2), 6, 40, 80, 0.25], [3, 5, (1,1), 6, 80, 112, 0.25], [4, 5, (2,2), 6, 112, 192, 0.25], [1, 3, (1,1), 6, 192, 320, 0.25], ] if self.number == -1: blocks_args = [ [1, 3, (2,2), 1, 32, 40, 0.25], [1, 3, (2,2), 1, 40, 80, 0.25], [1, 3, (2,2), 1, 80, 192, 0.25], [1, 3, (2,2), 1, 192, 320, 0.25], ] elif self.number == -2: blocks_args = [ [1, 9, (8,8), 1, 32, 320, 0.25], ] self._blocks = [] for num_repeats, kernel_size, strides, expand_ratio, input_filters, output_filters, se_ratio in blocks_args: input_filters, output_filters = round_filters(input_filters), round_filters(output_filters) for n in range(round_repeats(num_repeats)): self._blocks.append(MBConvBlock(kernel_size, strides, expand_ratio, input_filters, output_filters, se_ratio, has_se=has_se, track_running_stats=track_running_stats)) input_filters = output_filters strides = (1,1) in_channels = round_filters(320) out_channels = round_filters(1280) self._conv_head = Tensor.glorot_uniform(out_channels, in_channels, 1, 1) self._bn1 = BatchNorm2d(out_channels, track_running_stats=track_running_stats) if has_fc_output: self._fc = Tensor.glorot_uniform(out_channels, classes) self._fc_bias = Tensor.zeros(classes) else: self._fc = None def forward(self, x): x = self._bn0(x.conv2d(self._conv_stem, padding=(0,1,0,1), stride=2)).swish() x = x.sequential(self._blocks) x = self._bn1(x.conv2d(self._conv_head)).swish() x = x.avg_pool2d(kernel_size=x.shape[2:4]) x = x.reshape(shape=(-1, x.shape[1])) return x.linear(self._fc, self._fc_bias) if self._fc is not None else x def load_from_pretrained(self): model_urls = { 0: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth", 1: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b1-f1951068.pth", 2: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b2-8bb594d6.pth", 3: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b3-5fb5a3c3.pth", 4: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b4-6ed6700e.pth", 5: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b5-b6417697.pth", 6: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b6-c76e70fd.pth", 7: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b7-dcc49843.pth" } b0 = torch_load(fetch(model_urls[self.number])) for k,v in b0.items(): if k.endswith("num_batches_tracked"): continue for cat in ['_conv_head', '_conv_stem', '_depthwise_conv', '_expand_conv', '_fc', '_project_conv', '_se_reduce', '_se_expand']: if cat in k: k = k.replace('.bias', '_bias') k = k.replace('.weight', '') #print(k, v.shape) mv:Tensor = get_child(self, k) vnp = v #.astype(np.float32) vnp = vnp if k != '_fc' else vnp.T #vnp = vnp if vnp.shape != () else np.array([vnp]) if mv.shape == vnp.shape: mv.replace(vnp.to(mv.device)) else: print("MISMATCH SHAPE IN %s, %r %r" % (k, mv.shape, vnp.shape))