248 lines
9.4 KiB
Python
Executable File
248 lines
9.4 KiB
Python
Executable File
"""
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This file contains helper functions for building the model and for loading model parameters.
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These helper functions are built to mirror those in the official TensorFlow implementation.
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"""
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import re
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import math
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import collections
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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 ssd.utils.model_zoo import load_state_dict_from_url
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########################################################################
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############### HELPERS FUNCTIONS FOR MODEL ARCHITECTURE ###############
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########################################################################
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# Parameters for the entire model (stem, all blocks, and head)
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GlobalParams = collections.namedtuple('GlobalParams', [
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'batch_norm_momentum', 'batch_norm_epsilon', 'dropout_rate',
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'num_classes', 'width_coefficient', 'depth_coefficient',
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'depth_divisor', 'min_depth', 'drop_connect_rate', ])
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# Parameters for an individual model block
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BlockArgs = collections.namedtuple('BlockArgs', [
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'kernel_size', 'num_repeat', 'input_filters', 'output_filters',
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'expand_ratio', 'id_skip', 'stride', 'se_ratio'])
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# Change namedtuple defaults
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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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def relu_fn(x):
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""" Swish activation function """
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return x * torch.sigmoid(x)
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def round_filters(filters, global_params):
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""" Calculate and round number of filters based on depth multiplier. """
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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, int(filters + divisor / 2) // divisor * divisor)
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if new_filters < 0.9 * filters: # prevent rounding by more than 10%
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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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""" Round number of filters based on depth multiplier. """
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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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if not training: 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], dtype=inputs.dtype, 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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class Conv2dSamePadding(nn.Conv2d):
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""" 2D Convolutions like TensorFlow """
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def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, groups=1, bias=True):
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super().__init__(in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias)
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self.stride = self.stride if len(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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pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)
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pad_w = max((ow - 1) * self.stride[1] + (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, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2])
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return F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
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########################################################################
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############## HELPERS FUNCTIONS FOR LOADING MODEL PARAMS ##############
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########################################################################
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def efficientnet_params(model_name):
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""" Map EfficientNet model name to parameter coefficients. """
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params_dict = {
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# Coefficients: width,depth,res,dropout
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'efficientnet-b0': (1.0, 1.0, 224, 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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}
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return params_dict[model_name]
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class BlockDecoder(object):
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""" Block Decoder for readability, straight from the official TensorFlow repository """
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@staticmethod
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def _decode_block_string(block_string):
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""" Gets a block through a string notation of arguments. """
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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) or
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(len(options['s']) == 2 and options['s'][0] == options['s'][1]))
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return BlockArgs(
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kernel_size=int(options['k']),
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num_repeat=int(options['r']),
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input_filters=int(options['i']),
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output_filters=int(options['o']),
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expand_ratio=int(options['e']),
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id_skip=('noskip' not in block_string),
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se_ratio=float(options['se']) if 'se' in options else None,
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stride=[int(options['s'][0])])
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@staticmethod
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def _encode_block_string(block):
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"""Encodes a block to a string."""
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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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"""
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Decodes a list of string notations to specify blocks inside the network.
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:param string_list: a list of strings, each string is a notation of block
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:return: 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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"""
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Encodes a list of BlockArgs to a list of strings.
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:param blocks_args: a list of BlockArgs namedtuples of block args
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:return: 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(width_coefficient=None, depth_coefficient=None,
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dropout_rate=0.2, drop_connect_rate=0.2):
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""" Creates a efficientnet model. """
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blocks_args = [
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'r1_k3_s11_e1_i32_o16_se0.25', 'r2_k3_s22_e6_i16_o24_se0.25',
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'r2_k5_s22_e6_i24_o40_se0.25', 'r3_k3_s22_e6_i40_o80_se0.25',
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'r3_k5_s11_e6_i80_o112_se0.25', '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(
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batch_norm_momentum=0.99,
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batch_norm_epsilon=1e-3,
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dropout_rate=dropout_rate,
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drop_connect_rate=drop_connect_rate,
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# data_format='channels_last', # removed, this is always true in PyTorch
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num_classes=1000,
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width_coefficient=width_coefficient,
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depth_coefficient=depth_coefficient,
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depth_divisor=8,
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min_depth=None
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)
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return blocks_args, global_params
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def get_model_params(model_name, override_params):
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""" Get the block args and global params for a given model """
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if model_name.startswith('efficientnet'):
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w, d, _, p = efficientnet_params(model_name)
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# note: all models have drop connect rate = 0.2
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blocks_args, global_params = efficientnet(width_coefficient=w, depth_coefficient=d, dropout_rate=p)
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else:
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raise NotImplementedError('model name is not pre-defined: %s' % model_name)
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if override_params:
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# ValueError will be raised here if override_params has fields not included in global_params.
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global_params = global_params._replace(**override_params)
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return blocks_args, global_params
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url_map = {
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'efficientnet-b0': 'http://storage.googleapis.com/public-models/efficientnet-b0-08094119.pth',
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'efficientnet-b1': 'http://storage.googleapis.com/public-models/efficientnet-b1-dbc7070a.pth',
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'efficientnet-b2': 'http://storage.googleapis.com/public-models/efficientnet-b2-27687264.pth',
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'efficientnet-b3': 'http://storage.googleapis.com/public-models/efficientnet-b3-c8376fa2.pth',
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'efficientnet-b4': 'http://storage.googleapis.com/public-models/efficientnet-b4-e116e8b3.pth',
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'efficientnet-b5': 'http://storage.googleapis.com/public-models/efficientnet-b5-586e6cc6.pth',
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}
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def load_pretrained_weights(model, model_name):
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""" Loads pretrained weights, and downloads if loading for the first time. """
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state_dict = load_state_dict_from_url(url_map[model_name])
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model.load_state_dict(state_dict, strict=False)
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print('Loaded pretrained weights for {}'.format(model_name))
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