import logging import math import re from collections.__init__ import OrderedDict from copy import deepcopy import torch.nn as nn from .layers import CondConv2d, get_condconv_initializer from .layers.activations import HardSwish, Swish from .efficientnet_blocks import * def _parse_ksize(ss): if ss.isdigit(): return int(ss) else: return [int(k) for k in ss.split('.')] def _decode_block_str(block_str): """ Decode block definition string Gets a list of block arg (dicts) through a string notation of arguments. E.g. ir_r2_k3_s2_e1_i32_o16_se0.25_noskip All args can exist in any order with the exception of the leading string which is assumed to indicate the block type. leading string - block type ( ir = InvertedResidual, ds = DepthwiseSep, dsa = DeptwhiseSep with pw act, cn = ConvBnAct) r - number of repeat blocks, k - kernel size, s - strides (1-9), e - expansion ratio, c - output channels, se - squeeze/excitation ratio n - activation fn ('re', 'r6', 'hs', or 'sw') Args: block_str: a string representation of block arguments. Returns: A list of block args (dicts) Raises: ValueError: if the string def not properly specified (TODO) """ assert isinstance(block_str, str) ops = block_str.split('_') block_type = ops[0] # take the block type off the front ops = ops[1:] options = {} noskip = False for op in ops: # string options being checked on individual basis, combine if they grow if op == 'noskip': noskip = True elif op.startswith('n'): # activation fn key = op[0] v = op[1:] if v == 're': value = nn.ReLU elif v == 'r6': value = nn.ReLU6 elif v == 'hs': value = HardSwish elif v == 'sw': value = Swish else: continue options[key] = value else: # all numeric options splits = re.split(r'(\d.*)', op) if len(splits) >= 2: key, value = splits[:2] options[key] = value # if act_layer is None, the model default (passed to model init) will be used act_layer = options['n'] if 'n' in options else None exp_kernel_size = _parse_ksize(options['a']) if 'a' in options else 1 pw_kernel_size = _parse_ksize(options['p']) if 'p' in options else 1 fake_in_chs = int(options['fc']) if 'fc' in options else 0 # FIXME hack to deal with in_chs issue in TPU def num_repeat = int(options['r']) # each type of block has different valid arguments, fill accordingly if block_type == 'ir': block_args = dict( block_type=block_type, dw_kernel_size=_parse_ksize(options['k']), exp_kernel_size=exp_kernel_size, pw_kernel_size=pw_kernel_size, out_chs=int(options['c']), exp_ratio=float(options['e']), se_ratio=float(options['se']) if 'se' in options else None, stride=int(options['s']), act_layer=act_layer, noskip=noskip, ) if 'cc' in options: block_args['num_experts'] = int(options['cc']) elif block_type == 'ds' or block_type == 'dsa': block_args = dict( block_type=block_type, dw_kernel_size=_parse_ksize(options['k']), pw_kernel_size=pw_kernel_size, out_chs=int(options['c']), se_ratio=float(options['se']) if 'se' in options else None, stride=int(options['s']), act_layer=act_layer, pw_act=block_type == 'dsa', noskip=block_type == 'dsa' or noskip, ) elif block_type == 'er': block_args = dict( block_type=block_type, exp_kernel_size=_parse_ksize(options['k']), pw_kernel_size=pw_kernel_size, out_chs=int(options['c']), exp_ratio=float(options['e']), fake_in_chs=fake_in_chs, se_ratio=float(options['se']) if 'se' in options else None, stride=int(options['s']), act_layer=act_layer, noskip=noskip, ) elif block_type == 'cn': block_args = dict( block_type=block_type, kernel_size=int(options['k']), out_chs=int(options['c']), stride=int(options['s']), act_layer=act_layer, ) else: assert False, 'Unknown block type (%s)' % block_type return block_args, num_repeat def _scale_stage_depth(stack_args, repeats, depth_multiplier=1.0, depth_trunc='ceil'): """ Per-stage depth scaling Scales the block repeats in each stage. This depth scaling impl maintains compatibility with the EfficientNet scaling method, while allowing sensible scaling for other models that may have multiple block arg definitions in each stage. """ # We scale the total repeat count for each stage, there may be multiple # block arg defs per stage so we need to sum. num_repeat = sum(repeats) if depth_trunc == 'round': # Truncating to int by rounding allows stages with few repeats to remain # proportionally smaller for longer. This is a good choice when stage definitions # include single repeat stages that we'd prefer to keep that way as long as possible num_repeat_scaled = max(1, round(num_repeat * depth_multiplier)) else: # The default for EfficientNet truncates repeats to int via 'ceil'. # Any multiplier > 1.0 will result in an increased depth for every stage. num_repeat_scaled = int(math.ceil(num_repeat * depth_multiplier)) # Proportionally distribute repeat count scaling to each block definition in the stage. # Allocation is done in reverse as it results in the first block being less likely to be scaled. # The first block makes less sense to repeat in most of the arch definitions. repeats_scaled = [] for r in repeats[::-1]: rs = max(1, round((r / num_repeat * num_repeat_scaled))) repeats_scaled.append(rs) num_repeat -= r num_repeat_scaled -= rs repeats_scaled = repeats_scaled[::-1] # Apply the calculated scaling to each block arg in the stage sa_scaled = [] for ba, rep in zip(stack_args, repeats_scaled): sa_scaled.extend([deepcopy(ba) for _ in range(rep)]) return sa_scaled def decode_arch_def(arch_def, depth_multiplier=1.0, depth_trunc='ceil', experts_multiplier=1): arch_args = [] for stack_idx, block_strings in enumerate(arch_def): assert isinstance(block_strings, list) stack_args = [] repeats = [] for block_str in block_strings: assert isinstance(block_str, str) ba, rep = _decode_block_str(block_str) if ba.get('num_experts', 0) > 0 and experts_multiplier > 1: ba['num_experts'] *= experts_multiplier stack_args.append(ba) repeats.append(rep) arch_args.append(_scale_stage_depth(stack_args, repeats, depth_multiplier, depth_trunc)) return arch_args class EfficientNetBuilder: """ Build Trunk Blocks This ended up being somewhat of a cross between https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mnasnet_models.py and https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/modeling/backbone/fbnet_builder.py """ def __init__(self, channel_multiplier=1.0, channel_divisor=8, channel_min=None, output_stride=32, pad_type='', act_layer=None, se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None, drop_path_rate=0., feature_location='', verbose=False): self.channel_multiplier = channel_multiplier self.channel_divisor = channel_divisor self.channel_min = channel_min self.output_stride = output_stride self.pad_type = pad_type self.act_layer = act_layer self.se_kwargs = se_kwargs self.norm_layer = norm_layer self.norm_kwargs = norm_kwargs self.drop_path_rate = drop_path_rate self.feature_location = feature_location assert feature_location in ('pre_pwl', 'post_exp', '') self.verbose = verbose # state updated during build, consumed by model self.in_chs = None self.features = OrderedDict() def _round_channels(self, chs): return round_channels(chs, self.channel_multiplier, self.channel_divisor, self.channel_min) def _make_block(self, ba, block_idx, block_count): drop_path_rate = self.drop_path_rate * block_idx / block_count bt = ba.pop('block_type') ba['in_chs'] = self.in_chs ba['out_chs'] = self._round_channels(ba['out_chs']) if 'fake_in_chs' in ba and ba['fake_in_chs']: # FIXME this is a hack to work around mismatch in origin impl input filters ba['fake_in_chs'] = self._round_channels(ba['fake_in_chs']) ba['norm_layer'] = self.norm_layer ba['norm_kwargs'] = self.norm_kwargs ba['pad_type'] = self.pad_type # block act fn overrides the model default ba['act_layer'] = ba['act_layer'] if ba['act_layer'] is not None else self.act_layer assert ba['act_layer'] is not None if bt == 'ir': ba['drop_path_rate'] = drop_path_rate ba['se_kwargs'] = self.se_kwargs if self.verbose: logging.info(' InvertedResidual {}, Args: {}'.format(block_idx, str(ba))) if ba.get('num_experts', 0) > 0: block = CondConvResidual(**ba) else: block = InvertedResidual(**ba) elif bt == 'ds' or bt == 'dsa': ba['drop_path_rate'] = drop_path_rate ba['se_kwargs'] = self.se_kwargs if self.verbose: logging.info(' DepthwiseSeparable {}, Args: {}'.format(block_idx, str(ba))) block = DepthwiseSeparableConv(**ba) elif bt == 'er': ba['drop_path_rate'] = drop_path_rate ba['se_kwargs'] = self.se_kwargs if self.verbose: logging.info(' EdgeResidual {}, Args: {}'.format(block_idx, str(ba))) block = EdgeResidual(**ba) elif bt == 'cn': if self.verbose: logging.info(' ConvBnAct {}, Args: {}'.format(block_idx, str(ba))) block = ConvBnAct(**ba) else: assert False, 'Uknkown block type (%s) while building model.' % bt self.in_chs = ba['out_chs'] # update in_chs for arg of next block return block def __call__(self, in_chs, model_block_args): """ Build the blocks Args: in_chs: Number of input-channels passed to first block model_block_args: A list of lists, outer list defines stages, inner list contains strings defining block configuration(s) Return: List of block stacks (each stack wrapped in nn.Sequential) """ if self.verbose: logging.info('Building model trunk with %d stages...' % len(model_block_args)) self.in_chs = in_chs total_block_count = sum([len(x) for x in model_block_args]) total_block_idx = 0 current_stride = 2 current_dilation = 1 feature_idx = 0 stages = [] # outer list of block_args defines the stacks ('stages' by some conventions) for stage_idx, stage_block_args in enumerate(model_block_args): last_stack = stage_idx == (len(model_block_args) - 1) if self.verbose: logging.info('Stack: {}'.format(stage_idx)) assert isinstance(stage_block_args, list) blocks = [] # each stack (stage) contains a list of block arguments for block_idx, block_args in enumerate(stage_block_args): last_block = block_idx == (len(stage_block_args) - 1) extract_features = '' # No features extracted if self.verbose: logging.info(' Block: {}'.format(block_idx)) # Sort out stride, dilation, and feature extraction details assert block_args['stride'] in (1, 2) if block_idx >= 1: # only the first block in any stack can have a stride > 1 block_args['stride'] = 1 do_extract = False if self.feature_location == 'pre_pwl': if last_block: next_stage_idx = stage_idx + 1 if next_stage_idx >= len(model_block_args): do_extract = True else: do_extract = model_block_args[next_stage_idx][0]['stride'] > 1 elif self.feature_location == 'post_exp': if block_args['stride'] > 1 or (last_stack and last_block) : do_extract = True if do_extract: extract_features = self.feature_location next_dilation = current_dilation if block_args['stride'] > 1: next_output_stride = current_stride * block_args['stride'] if next_output_stride > self.output_stride: next_dilation = current_dilation * block_args['stride'] block_args['stride'] = 1 if self.verbose: logging.info(' Converting stride to dilation to maintain output_stride=={}'.format( self.output_stride)) else: current_stride = next_output_stride block_args['dilation'] = current_dilation if next_dilation != current_dilation: current_dilation = next_dilation # create the block block = self._make_block(block_args, total_block_idx, total_block_count) blocks.append(block) # stash feature module name and channel info for model feature extraction if extract_features: feature_module = block.feature_module(extract_features) if feature_module: feature_module = 'blocks.{}.{}.'.format(stage_idx, block_idx) + feature_module feature_channels = block.feature_channels(extract_features) self.features[feature_idx] = dict( name=feature_module, num_chs=feature_channels ) feature_idx += 1 total_block_idx += 1 # incr global block idx (across all stacks) stages.append(nn.Sequential(*blocks)) return stages def _init_weight_goog(m, n='', fix_group_fanout=False): """ Weight initialization as per Tensorflow official implementations. Args: m (nn.Module): module to init n (str): module name fix_group_fanout (bool): enable correct fanout calculation w/ group convs FIXME change fix_group_fanout to default to True if experiments show better training results Handles layers in EfficientNet, EfficientNet-CondConv, MixNet, MnasNet, MobileNetV3, etc: * https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mnasnet_model.py * https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py """ if isinstance(m, CondConv2d): fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels if fix_group_fanout: fan_out //= m.groups init_weight_fn = get_condconv_initializer( lambda w: w.data.normal_(0, math.sqrt(2.0 / fan_out)), m.num_experts, m.weight_shape) init_weight_fn(m.weight) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.Conv2d): fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels if fix_group_fanout: fan_out //= m.groups m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1.0) m.bias.data.zero_() elif isinstance(m, nn.Linear): fan_out = m.weight.size(0) # fan-out fan_in = 0 if 'routing_fn' in n: fan_in = m.weight.size(1) init_range = 1.0 / math.sqrt(fan_in + fan_out) m.weight.data.uniform_(-init_range, init_range) m.bias.data.zero_() def efficientnet_init_weights(model: nn.Module, init_fn=None): init_fn = init_fn or _init_weight_goog for n, m in model.named_modules(): init_fn(m, n)