561 lines
20 KiB
Python
561 lines
20 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
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# The implementation here is modified based on EfficientNet,
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# originally Apache 2.0 License and publicly available at https://github.com/lukemelas/EfficientNet-PyTorch
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import collections
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import math
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import re
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from functools import partial
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import torch
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from torch import nn
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from torch.nn import functional as F
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from torch.utils import model_zoo
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GlobalParams = collections.namedtuple('GlobalParams', [
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'width_coefficient', 'depth_coefficient', 'image_size', 'dropout_rate',
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'num_classes', 'batch_norm_momentum', 'batch_norm_epsilon',
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'drop_connect_rate', 'depth_divisor', 'min_depth', 'include_top'
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])
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BlockArgs = collections.namedtuple('BlockArgs', [
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'num_repeat', 'kernel_size', 'stride', 'expand_ratio', 'input_filters',
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'output_filters', 'se_ratio', 'id_skip'
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])
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GlobalParams.__new__.__defaults__ = (None, ) * len(GlobalParams._fields)
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BlockArgs.__new__.__defaults__ = (None, ) * len(BlockArgs._fields)
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if hasattr(nn, 'SiLU'):
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Swish = nn.SiLU
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else:
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class Swish(nn.Module):
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def forward(self, x):
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return x * torch.sigmoid(x)
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class SwishImplementation(torch.autograd.Function):
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@staticmethod
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def forward(ctx, i):
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result = i * torch.sigmoid(i)
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ctx.save_for_backward(i)
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return result
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@staticmethod
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def backward(ctx, grad_output):
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i = ctx.saved_tensors[0]
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sigmoid_i = torch.sigmoid(i)
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return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i)))
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class MemoryEfficientSwish(nn.Module):
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def forward(self, x):
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return SwishImplementation.apply(x)
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def round_filters(filters, global_params):
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"""Calculate and round number of filters based on width multiplier.
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Use width_coefficient, depth_divisor and min_depth of global_params.
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Args:
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filters (int): Filters number to be calculated.
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global_params (namedtuple): Global params of the model.
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Returns:
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new_filters: New filters number after calculating.
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"""
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multiplier = global_params.width_coefficient
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if not multiplier:
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return filters
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divisor = global_params.depth_divisor
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min_depth = global_params.min_depth
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filters *= multiplier
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min_depth = min_depth or divisor
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new_filters = max(min_depth,
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int(filters + divisor / 2) // divisor * divisor)
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if new_filters < 0.9 * filters:
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new_filters += divisor
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return int(new_filters)
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def round_repeats(repeats, global_params):
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"""Calculate module's repeat number of a block based on depth multiplier.
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Use depth_coefficient of global_params.
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Args:
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repeats (int): num_repeat to be calculated.
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global_params (namedtuple): Global params of the model.
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Returns:
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new repeat: New repeat number after calculating.
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"""
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multiplier = global_params.depth_coefficient
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if not multiplier:
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return repeats
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return int(math.ceil(multiplier * repeats))
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def drop_connect(inputs, p, training):
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"""Drop connect.
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Args:
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input (tensor: BCWH): Input of this structure.
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p (float: 0.0~1.0): Probability of drop connection.
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training (bool): The running mode.
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Returns:
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output: Output after drop connection.
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"""
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assert 0 <= p <= 1, 'p must be in range of [0,1]'
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if not training:
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return inputs
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batch_size = inputs.shape[0]
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keep_prob = 1 - p
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random_tensor = keep_prob
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random_tensor += torch.rand([batch_size, 1, 1, 1],
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dtype=inputs.dtype,
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device=inputs.device)
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binary_tensor = torch.floor(random_tensor)
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output = inputs / keep_prob * binary_tensor
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return output
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def get_width_and_height_from_size(x):
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"""Obtain height and width from x.
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Args:
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x (int, tuple or list): Data size.
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Returns:
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size: A tuple or list (H,W).
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"""
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if isinstance(x, int):
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return x, x
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if isinstance(x, list) or isinstance(x, tuple):
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return x
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else:
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raise TypeError()
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def calculate_output_image_size(input_image_size, stride):
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"""Calculates the output image size when using Conv2dSamePadding with a stride.
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Necessary for static padding. Thanks to mannatsingh for pointing this out.
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Args:
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input_image_size (int, tuple or list): Size of input image.
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stride (int, tuple or list): Conv2d operation's stride.
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Returns:
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output_image_size: A list [H,W].
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"""
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if input_image_size is None:
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return None
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image_height, image_width = get_width_and_height_from_size(
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input_image_size)
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stride = stride if isinstance(stride, int) else stride[0]
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image_height = int(math.ceil(image_height / stride))
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image_width = int(math.ceil(image_width / stride))
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return [image_height, image_width]
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def get_same_padding_conv2d(image_size=None):
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"""Chooses static padding if you have specified an image size, and dynamic padding otherwise.
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Static padding is necessary for ONNX exporting of models.
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Args:
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image_size (int or tuple): Size of the image.
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Returns:
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Conv2dDynamicSamePadding or Conv2dStaticSamePadding.
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"""
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if image_size is None:
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return Conv2dDynamicSamePadding
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else:
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return partial(Conv2dStaticSamePadding, image_size=image_size)
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class Conv2dDynamicSamePadding(nn.Conv2d):
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"""2D Convolutions like TensorFlow, for a dynamic image size.
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The padding is operated in forward function by calculating dynamically.
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"""
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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stride=1,
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dilation=1,
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groups=1,
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bias=True):
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super().__init__(in_channels, out_channels, kernel_size, stride, 0,
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dilation, groups, bias)
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self.stride = self.stride if len(
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self.stride) == 2 else [self.stride[0]] * 2
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def forward(self, x):
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ih, iw = x.size()[-2:]
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kh, kw = self.weight.size()[-2:]
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sh, sw = self.stride
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oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
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a1 = (oh - 1) * self.stride[0]
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pad_h = max(a1 + (kh - 1) * self.dilation[0] + 1 - ih, 0)
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a2 = (ow - 1) * self.stride[1]
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pad_w = max(a2 + (kw - 1) * self.dilation[1] + 1 - iw, 0)
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if pad_h > 0 or pad_w > 0:
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x = F.pad(x, [
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pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2
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])
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return F.conv2d(x, self.weight, self.bias, self.stride, self.padding,
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self.dilation, self.groups)
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class Conv2dStaticSamePadding(nn.Conv2d):
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"""2D Convolutions like TensorFlow's 'SAME' mode, with the given input image size.
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The padding mudule is calculated in construction function, then used in forward.
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"""
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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stride=1,
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image_size=None,
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**kwargs):
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super().__init__(in_channels, out_channels, kernel_size, stride,
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**kwargs)
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self.stride = self.stride if len(
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self.stride) == 2 else [self.stride[0]] * 2
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assert image_size is not None
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ih, iw = (image_size,
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image_size) if isinstance(image_size, int) else image_size
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kh, kw = self.weight.size()[-2:]
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sh, sw = self.stride
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oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
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b1 = (oh - 1) * self.stride[0]
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pad_h = max(b1 + (kh - 1) * self.dilation[0] + 1 - ih, 0)
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b2 = (ow - 1) * self.stride[1]
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pad_w = max(b2 + (kw - 1) * self.dilation[1] + 1 - iw, 0)
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if pad_h > 0 or pad_w > 0:
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self.static_padding = nn.ZeroPad2d(
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(pad_w // 2, pad_w - pad_w // 2, pad_h // 2,
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pad_h - pad_h // 2))
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else:
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self.static_padding = nn.Identity()
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def forward(self, x):
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x = self.static_padding(x)
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x = F.conv2d(x, self.weight, self.bias, self.stride, self.padding,
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self.dilation, self.groups)
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return x
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def get_same_padding_maxPool2d(image_size=None):
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"""Chooses static padding if you have specified an image size, and dynamic padding otherwise.
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Static padding is necessary for ONNX exporting of models.
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Args:
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image_size (int or tuple): Size of the image.
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Returns:
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MaxPool2dDynamicSamePadding or MaxPool2dStaticSamePadding.
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"""
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if image_size is None:
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return MaxPool2dDynamicSamePadding
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else:
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return partial(MaxPool2dStaticSamePadding, image_size=image_size)
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class MaxPool2dDynamicSamePadding(nn.MaxPool2d):
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"""2D MaxPooling like TensorFlow's 'SAME' mode, with a dynamic image size.
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The padding is operated in forward function by calculating dynamically.
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"""
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def __init__(self,
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kernel_size,
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stride,
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padding=0,
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dilation=1,
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return_indices=False,
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ceil_mode=False):
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super().__init__(kernel_size, stride, padding, dilation,
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return_indices, ceil_mode)
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self.stride = [self.stride] * 2 if isinstance(self.stride,
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int) else self.stride
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self.kernel_size = [self.kernel_size] * 2 if isinstance(
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self.kernel_size, int) else self.kernel_size
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self.dilation = [self.dilation] * 2 if isinstance(
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self.dilation, int) else self.dilation
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def forward(self, x):
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ih, iw = x.size()[-2:]
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kh, kw = self.kernel_size
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sh, sw = self.stride
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oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
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c1 = (oh - 1) * self.stride[0]
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pad_h = max(c1 + (kh - 1) * self.dilation[0] + 1 - ih, 0)
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c2 = (ow - 1) * self.stride[1]
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pad_w = max(c2 + (kw - 1) * self.dilation[1] + 1 - iw, 0)
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if pad_h > 0 or pad_w > 0:
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x = F.pad(x, [
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pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2
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])
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return F.max_pool2d(x, self.kernel_size, self.stride, self.padding,
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self.dilation, self.ceil_mode, self.return_indices)
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class MaxPool2dStaticSamePadding(nn.MaxPool2d):
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"""2D MaxPooling like TensorFlow's 'SAME' mode, with the given input image size.
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The padding mudule is calculated in construction function, then used in forward.
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"""
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def __init__(self, kernel_size, stride, image_size=None, **kwargs):
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super().__init__(kernel_size, stride, **kwargs)
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self.stride = [self.stride] * 2 if isinstance(self.stride,
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int) else self.stride
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self.kernel_size = [self.kernel_size] * 2 if isinstance(
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self.kernel_size, int) else self.kernel_size
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self.dilation = [self.dilation] * 2 if isinstance(
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self.dilation, int) else self.dilation
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assert image_size is not None
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ih, iw = (image_size,
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image_size) if isinstance(image_size, int) else image_size
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kh, kw = self.kernel_size
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sh, sw = self.stride
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oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
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d1 = (oh - 1) * self.stride[0]
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pad_h = max(d1 + (kh - 1) * self.dilation[0] + 1 - ih, 0)
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d2 = (ow - 1) * self.stride[1]
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pad_w = max(d2 + (kw - 1) * self.dilation[1] + 1 - iw, 0)
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if pad_h > 0 or pad_w > 0:
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self.static_padding = nn.ZeroPad2d(
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(pad_w // 2, pad_w - pad_w // 2, pad_h // 2,
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pad_h - pad_h // 2))
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else:
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self.static_padding = nn.Identity()
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def forward(self, x):
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x = self.static_padding(x)
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x = F.max_pool2d(x, self.kernel_size, self.stride, self.padding,
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self.dilation, self.ceil_mode, self.return_indices)
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return x
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class BlockDecoder(object):
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"""Block Decoder for readability,
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straight from the official TensorFlow repository.
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"""
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@staticmethod
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def _decode_block_string(block_string):
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"""Get a block through a string notation of arguments.
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Args:
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block_string (str): A string notation of arguments.
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Examples: 'r1_k3_s11_e1_i32_o16_se0.25_noskip'.
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Returns:
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BlockArgs: The namedtuple defined at the top of this file.
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"""
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assert isinstance(block_string, str)
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ops = block_string.split('_')
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options = {}
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for op in ops:
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splits = re.split(r'(\d.*)', op)
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if len(splits) >= 2:
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key, value = splits[:2]
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options[key] = value
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# Check stride
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assert (('s' in options and len(options['s']) == 1)
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or (len(options['s']) == 2
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and options['s'][0] == options['s'][1]))
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return BlockArgs(
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num_repeat=int(options['r']),
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kernel_size=int(options['k']),
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stride=[int(options['s'][0])],
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expand_ratio=int(options['e']),
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input_filters=int(options['i']),
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output_filters=int(options['o']),
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se_ratio=float(options['se']) if 'se' in options else None,
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id_skip=('noskip' not in block_string))
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@staticmethod
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def _encode_block_string(block):
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"""Encode a block to a string.
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Args:
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block (namedtuple): A BlockArgs type argument.
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Returns:
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block_string: A String form of BlockArgs.
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"""
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args = [
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'r%d' % block.num_repeat,
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'k%d' % block.kernel_size,
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's%d%d' % (block.strides[0], block.strides[1]),
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'e%s' % block.expand_ratio,
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'i%d' % block.input_filters,
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'o%d' % block.output_filters
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]
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if 0 < block.se_ratio <= 1:
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args.append('se%s' % block.se_ratio)
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if block.id_skip is False:
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args.append('noskip')
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return '_'.join(args)
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@staticmethod
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def decode(string_list):
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"""Decode a list of string notations to specify blocks inside the network.
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Args:
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string_list (list[str]): A list of strings, each string is a notation of block.
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Returns:
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blocks_args: A list of BlockArgs namedtuples of block args.
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"""
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assert isinstance(string_list, list)
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blocks_args = []
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for block_string in string_list:
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blocks_args.append(BlockDecoder._decode_block_string(block_string))
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return blocks_args
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@staticmethod
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def encode(blocks_args):
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"""Encode a list of BlockArgs to a list of strings.
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Args:
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blocks_args (list[namedtuples]): A list of BlockArgs namedtuples of block args.
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Returns:
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block_strings: A list of strings, each string is a notation of block.
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"""
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block_strings = []
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for block in blocks_args:
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block_strings.append(BlockDecoder._encode_block_string(block))
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return block_strings
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def efficientnet_params(model_name):
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"""Map EfficientNet model name to parameter coefficients.
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Args:
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model_name (str): Model name to be queried.
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Returns:
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params_dict[model_name]: A (width,depth,res,dropout) tuple.
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"""
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params_dict = {
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'efficientnet-b0': (1.0, 1.0, 112, 0.2),
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'efficientnet-b1': (1.0, 1.1, 240, 0.2),
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'efficientnet-b2': (1.1, 1.2, 260, 0.3),
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'efficientnet-b3': (1.2, 1.4, 300, 0.3),
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'efficientnet-b4': (1.4, 1.8, 380, 0.4),
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'efficientnet-b5': (1.6, 2.2, 456, 0.4),
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'efficientnet-b6': (1.8, 2.6, 528, 0.5),
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'efficientnet-b7': (2.0, 3.1, 600, 0.5),
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'efficientnet-b8': (2.2, 3.6, 672, 0.5),
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'efficientnet-l2': (4.3, 5.3, 800, 0.5),
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}
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return params_dict[model_name]
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def efficientnet(width_coefficient=None,
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depth_coefficient=None,
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image_size=None,
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dropout_rate=0.2,
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drop_connect_rate=0.2,
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num_classes=1000,
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include_top=True):
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"""Create BlockArgs and GlobalParams for efficientnet model.
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Args:
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width_coefficient (float)
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depth_coefficient (float)
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image_size (int)
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dropout_rate (float)
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drop_connect_rate (float)
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num_classes (int)
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Meaning as the name suggests.
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Returns:
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blocks_args, global_params.
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"""
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blocks_args = [
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'r1_k3_s11_e1_i32_o16_se0.25',
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'r2_k3_s22_e6_i16_o24_se0.25',
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'r2_k5_s22_e6_i24_o40_se0.25',
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'r3_k3_s22_e6_i40_o80_se0.25',
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'r3_k5_s11_e6_i80_o112_se0.25',
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'r4_k5_s22_e6_i112_o192_se0.25',
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'r1_k3_s11_e6_i192_o320_se0.25',
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]
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blocks_args = BlockDecoder.decode(blocks_args)
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global_params = GlobalParams(
|
|
width_coefficient=width_coefficient,
|
|
depth_coefficient=depth_coefficient,
|
|
image_size=image_size,
|
|
dropout_rate=dropout_rate,
|
|
num_classes=num_classes,
|
|
batch_norm_momentum=0.99,
|
|
batch_norm_epsilon=1e-3,
|
|
drop_connect_rate=drop_connect_rate,
|
|
depth_divisor=8,
|
|
min_depth=None,
|
|
include_top=include_top,
|
|
)
|
|
return blocks_args, global_params
|
|
|
|
|
|
def get_model_params(model_name, override_params):
|
|
"""Get the block args and global params for a given model name.
|
|
Args:
|
|
model_name (str): Model's name.
|
|
override_params (dict): A dict to modify global_params.
|
|
Returns:
|
|
blocks_args, global_params
|
|
"""
|
|
if model_name.startswith('efficientnet'):
|
|
w, d, s, p = efficientnet_params(model_name)
|
|
blocks_args, global_params = efficientnet(
|
|
width_coefficient=w,
|
|
depth_coefficient=d,
|
|
dropout_rate=p,
|
|
image_size=s)
|
|
else:
|
|
raise NotImplementedError(
|
|
'model name is not pre-defined: {}'.format(model_name))
|
|
if override_params:
|
|
global_params = global_params._replace(**override_params)
|
|
return blocks_args, global_params
|
|
|
|
|
|
def load_pretrained_weights(model,
|
|
model_name,
|
|
weights_path=None,
|
|
load_fc=True,
|
|
advprop=False,
|
|
verbose=True):
|
|
"""Loads pretrained weights from weights path or download using url.
|
|
Args:
|
|
model (Module): The whole model of efficientnet.
|
|
model_name (str): Model name of efficientnet.
|
|
weights_path (None or str):
|
|
str: path to pretrained weights file on the local disk.
|
|
None: use pretrained weights downloaded from the Internet.
|
|
load_fc (bool): Whether to load pretrained weights for fc layer at the end of the model.
|
|
advprop (bool): Whether to load pretrained weights
|
|
trained with advprop (valid when weights_path is None).
|
|
"""
|
|
if isinstance(weights_path, str):
|
|
state_dict = torch.load(weights_path)
|
|
else:
|
|
url_map_ = url_map_advprop if advprop else url_map
|
|
state_dict = model_zoo.load_url(url_map_[model_name])
|
|
|
|
if load_fc:
|
|
ret = model.load_state_dict(state_dict, strict=False)
|
|
assert not ret.missing_keys, 'Missing keys when loading pretrained weights: {}'.format(
|
|
ret.missing_keys)
|
|
else:
|
|
state_dict.pop('_fc.weight')
|
|
state_dict.pop('_fc.bias')
|
|
ret = model.load_state_dict(state_dict, strict=False)
|
|
assert set(ret.missing_keys) == set([
|
|
'_fc.weight', '_fc.bias'
|
|
]), 'Missing keys when loading pretrained weights: {}'.format(
|
|
ret.missing_keys)
|
|
assert not ret.unexpected_keys, 'Missing keys when loading pretrained weights: {}'.format(
|
|
ret.unexpected_keys)
|
|
|
|
if verbose:
|
|
print('Loaded pretrained weights for {}'.format(model_name))
|