utils.py

import re
import math
import collections
from functools import partial
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils import model_zoo

########################################################################
############### HELPERS FUNCTIONS FOR MODEL ARCHITECTURE ###############
########################################################################


# Parameters for the entire model (stem, all blocks, and head)
GlobalParams = collections.namedtuple('GlobalParams', [
    'batch_norm_momentum', 'batch_norm_epsilon', 'dropout_rate',
    'num_classes', 'width_coefficient', 'depth_coefficient',
    'depth_divisor', 'min_depth', 'drop_connect_rate', 'image_size'])

# Parameters for an individual model block
BlockArgs = collections.namedtuple('BlockArgs', [
    'kernel_size', 'num_repeat', 'input_filters', 'output_filters',
    'expand_ratio', 'id_skip', 'stride', 'se_ratio'])

# Change namedtuple defaults
GlobalParams.__new__.__defaults__ = (None,) * len(GlobalParams._fields)
BlockArgs.__new__.__defaults__ = (None,) * len(BlockArgs._fields)


class SwishImplementation(torch.autograd.Function):
    @staticmethod
    def forward(ctx, i):
        result = i * torch.sigmoid(i)
        ctx.save_for_backward(i)
        return result

    @staticmethod
    def backward(ctx, grad_output):
        i = ctx.saved_variables[0]
        sigmoid_i = torch.sigmoid(i)
        return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i)))


class MemoryEfficientSwish(nn.Module):
    def forward(self, x):
        return SwishImplementation.apply(x)


class Swish(nn.Module):
    def forward(self, x):
        return x * torch.sigmoid(x)


def round_filters(filters, global_params):
    """ Calculate and round number of filters based on depth multiplier. """
    multiplier = global_params.width_coefficient
    if not multiplier:
        return filters
    divisor = global_params.depth_divisor
    min_depth = global_params.min_depth
    filters *= multiplier
    min_depth = min_depth or divisor
    new_filters = max(min_depth, 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, global_params):
    """ Round number of filters based on depth multiplier. """
    multiplier = global_params.depth_coefficient
    if not multiplier:
        return repeats
    return int(math.ceil(multiplier * repeats))


def drop_connect(inputs, p, training):
    """ Drop connect. """
    if not training:
        return inputs
    batch_size = inputs.shape[0]
    keep_prob = 1 - p
    random_tensor = keep_prob
    random_tensor += torch.rand([batch_size, 1, 1, 1],
                                dtype=inputs.dtype, device=inputs.device)
    binary_tensor = torch.floor(random_tensor)
    output = inputs / keep_prob * binary_tensor
    return output


def get_same_padding_conv2d(image_size=None):
    """ Chooses static padding if you have specified an image size, and dynamic padding otherwise.
        Static padding is necessary for ONNX exporting of models. """
    if image_size is None:
        return Conv2dDynamicSamePadding
    else:
        return partial(Conv2dStaticSamePadding, image_size=image_size)


class Conv2dDynamicSamePadding(nn.Conv2d):
    """ 2D Convolutions like TensorFlow, for a dynamic image size """

    def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, groups=1, bias=True):
        super().__init__(in_channels, out_channels,
                         kernel_size, stride, 0, dilation, groups, bias)
        self.stride = self.stride if len(self.stride) == 2 else [
            self.stride[0]] * 2

    def forward(self, x):
        ih, iw = x.size()[-2:]
        kh, kw = self.weight.size()[-2:]
        sh, sw = self.stride
        oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
        pad_h = max((oh - 1) * self.stride[0] +
                    (kh - 1) * self.dilation[0] + 1 - ih, 0)
        pad_w = max((ow - 1) * self.stride[1] +
                    (kw - 1) * self.dilation[1] + 1 - iw, 0)
        if pad_h > 0 or pad_w > 0:
            x = F.pad(x, [pad_w // 2, pad_w - pad_w //
                          2, pad_h // 2, pad_h - pad_h // 2])
        return F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)


class Conv2dStaticSamePadding(nn.Conv2d):
    """ 2D Convolutions like TensorFlow, for a fixed image size"""

    def __init__(self, in_channels, out_channels, kernel_size, image_size=None, **kwargs):
        super().__init__(in_channels, out_channels, kernel_size, **kwargs)
        self.stride = self.stride if len(self.stride) == 2 else [
            self.stride[0]] * 2

        # Calculate padding based on image size and save it
        assert image_size is not None
        ih, iw = image_size if type(image_size) == list else [
            image_size, image_size]
        kh, kw = self.weight.size()[-2:]
        sh, sw = self.stride
        oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
        pad_h = max((oh - 1) * self.stride[0] +
                    (kh - 1) * self.dilation[0] + 1 - ih, 0)
        pad_w = max((ow - 1) * self.stride[1] +
                    (kw - 1) * self.dilation[1] + 1 - iw, 0)
        if pad_h > 0 or pad_w > 0:
            self.static_padding = nn.ZeroPad2d(
                (pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2))
        else:
            self.static_padding = Identity()

    def forward(self, x):
        x = self.static_padding(x)
        x = F.conv2d(x, self.weight, self.bias, self.stride,
                     self.padding, self.dilation, self.groups)
        return x


class Identity(nn.Module):
    def __init__(self, ):
        super(Identity, self).__init__()

    def forward(self, input):
        return input


########################################################################
############## HELPERS FUNCTIONS FOR LOADING MODEL PARAMS ##############
########################################################################


def efficientnet_params(model_name):
    """ Map EfficientNet model name to parameter coefficients. """
    params_dict = {
        # Coefficients:   width,depth,res,dropout
        'efficientnet-b0': (1.0, 1.0, 224, 0.2),
        'efficientnet-b1': (1.0, 1.1, 240, 0.2),
        'efficientnet-b2': (1.1, 1.2, 260, 0.3),
        'efficientnet-b3': (1.2, 1.4, 300, 0.3),
        'efficientnet-b4': (1.4, 1.8, 380, 0.4),
        'efficientnet-b5': (1.6, 2.2, 456, 0.4),
        'efficientnet-b6': (1.8, 2.6, 528, 0.5),
        'efficientnet-b7': (2.0, 3.1, 600, 0.5),
    }
    return params_dict[model_name]


class BlockDecoder(object):
    """ Block Decoder for readability, straight from the official TensorFlow repository """

    @staticmethod
    def _decode_block_string(block_string):
        """ Gets a block through a string notation of arguments. """
        assert isinstance(block_string, str)

        ops = block_string.split('_')
        options = {}
        for op in ops:
            splits = re.split(r'(\d.*)', op)
            if len(splits) >= 2:
                key, value = splits[:2]
                options[key] = value

        # Check stride
        assert (('s' in options and len(options['s']) == 1) or
                (len(options['s']) == 2 and options['s'][0] == options['s'][1]))

        return BlockArgs(
            kernel_size=int(options['k']),
            num_repeat=int(options['r']),
            input_filters=int(options['i']),
            output_filters=int(options['o']),
            expand_ratio=int(options['e']),
            id_skip=('noskip' not in block_string),
            se_ratio=float(options['se']) if 'se' in options else None,
            stride=[int(options['s'][0])])

    @staticmethod
    def _encode_block_string(block):
        """Encodes a block to a string."""
        args = [
            'r%d' % block.num_repeat,
            'k%d' % block.kernel_size,
            's%d%d' % (block.strides[0], block.strides[1]),
            'e%s' % block.expand_ratio,
            'i%d' % block.input_filters,
            'o%d' % block.output_filters
        ]
        if 0 < block.se_ratio <= 1:
            args.append('se%s' % block.se_ratio)
        if block.id_skip is False:
            args.append('noskip')
        return '_'.join(args)

    @staticmethod
    def decode(string_list):
        """
        Decodes a list of string notations to specify blocks inside the network.
        :param string_list: a list of strings, each string is a notation of block
        :return: a list of BlockArgs namedtuples of block args
        """
        assert isinstance(string_list, list)
        blocks_args = []
        for block_string in string_list:
            blocks_args.append(BlockDecoder._decode_block_string(block_string))
        return blocks_args

    @staticmethod
    def encode(blocks_args):
        """
        Encodes a list of BlockArgs to a list of strings.
        :param blocks_args: a list of BlockArgs namedtuples of block args
        :return: a list of strings, each string is a notation of block
        """
        block_strings = []
        for block in blocks_args:
            block_strings.append(BlockDecoder._encode_block_string(block))
        return block_strings


def efficientnet(width_coefficient=None, depth_coefficient=None, dropout_rate=0.2,
                 drop_connect_rate=0.2, image_size=None, num_classes=1000):
    """ Creates a efficientnet model. """

    blocks_args = [
        'r1_k3_s11_e1_i32_o16_se0.25', 'r2_k3_s22_e6_i16_o24_se0.25',
        'r2_k5_s22_e6_i24_o40_se0.25', 'r3_k3_s22_e6_i40_o80_se0.25',
        'r3_k5_s22_e6_i80_o112_se0.25', 'r4_k5_s22_e6_i112_o192_se0.25',
        'r1_k3_s22_e6_i192_o320_se0.25',
    ]
    blocks_args = BlockDecoder.decode(blocks_args)

    global_params = GlobalParams(
        batch_norm_momentum=0.99,
        batch_norm_epsilon=1e-3,
        dropout_rate=dropout_rate,
        drop_connect_rate=drop_connect_rate,
        # data_format='channels_last',  # removed, this is always true in PyTorch
        num_classes=num_classes,
        width_coefficient=width_coefficient,
        depth_coefficient=depth_coefficient,
        depth_divisor=8,
        min_depth=None,
        image_size=image_size,
    )

    return blocks_args, global_params


def get_model_params(model_name, override_params):
    """ Get the block args and global params for a given model """
    if model_name.startswith('efficientnet'):
        w, d, s, p = efficientnet_params(model_name)
        # note: all models have drop connect rate = 0.2
        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: %s' % model_name)
    if override_params:
        # ValueError will be raised here if override_params has fields not included in global_params.
        global_params = global_params._replace(**override_params)
    return blocks_args, global_params


# url_map = {
#     'efficientnet-b0': 'https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b0-355c32eb.pth',
#     'efficientnet-b1': 'https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b1-f1951068.pth',
#     'efficientnet-b2': 'https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b2-8bb594d6.pth',
#     'efficientnet-b3': 'https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b3-5fb5a3c3.pth',
#     'efficientnet-b4': 'https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b4-6ed6700e.pth',
#     'efficientnet-b5': 'https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b5-b6417697.pth',
#     'efficientnet-b6': 'https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b6-c76e70fd.pth',
#     'efficientnet-b7': 'https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b7-dcc49843.pth',
# }


url_map = {
    'efficientnet-b0': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth',
    'efficientnet-b1': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b1-f1951068.pth',
    'efficientnet-b2': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b2-8bb594d6.pth',
    'efficientnet-b3': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b3-5fb5a3c3.pth',
    'efficientnet-b4': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b4-6ed6700e.pth',
    'efficientnet-b5': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b5-b6417697.pth',
    'efficientnet-b6': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b6-c76e70fd.pth',
    'efficientnet-b7': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b7-dcc49843.pth',
}


def load_pretrained_weights(model, model_name, load_fc=True):
    """ Loads pretrained weights, and downloads if loading for the first time. """
    state_dict = model_zoo.load_url(url_map[model_name])
    if load_fc:
        model.load_state_dict(state_dict)
    else:
        state_dict.pop('_fc.weight')
        state_dict.pop('_fc.bias')
        res = model.load_state_dict(state_dict, strict=False)
        assert set(res.missing_keys) == set(
            ['_fc.weight', '_fc.bias']), 'issue loading pretrained weights'
    print('Loaded pretrained weights for {}'.format(model_name))