From 50658b9a673bb3d844d06dfa039de2a9e4ddbae8 Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Mon, 18 May 2020 00:08:52 -0700 Subject: [PATCH] Add RegNet models and weights --- timm/models/__init__.py | 1 + timm/models/layers/se.py | 4 +- timm/models/regnet.py | 485 +++++++++++++++++++++++++++++++++++++++ 3 files changed, 488 insertions(+), 2 deletions(-) create mode 100644 timm/models/regnet.py diff --git a/timm/models/__init__.py b/timm/models/__init__.py index d421ad45..06d26fb3 100644 --- a/timm/models/__init__.py +++ b/timm/models/__init__.py @@ -19,6 +19,7 @@ from .hrnet import * from .sknet import * from .tresnet import * from .resnest import * +from .regnet import * from .registry import * from .factory import create_model diff --git a/timm/models/layers/se.py b/timm/models/layers/se.py index de87ccf5..6bb4723e 100644 --- a/timm/models/layers/se.py +++ b/timm/models/layers/se.py @@ -3,10 +3,10 @@ from torch import nn as nn class SEModule(nn.Module): - def __init__(self, channels, reduction=16, act_layer=nn.ReLU): + def __init__(self, channels, reduction=16, act_layer=nn.ReLU, min_channels=8, reduction_channels=None): super(SEModule, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) - reduction_channels = max(channels // reduction, 8) + reduction_channels = reduction_channels or max(channels // reduction, min_channels) self.fc1 = nn.Conv2d( channels, reduction_channels, kernel_size=1, padding=0, bias=True) self.act = act_layer(inplace=True) diff --git a/timm/models/regnet.py b/timm/models/regnet.py new file mode 100644 index 00000000..65ba2cc6 --- /dev/null +++ b/timm/models/regnet.py @@ -0,0 +1,485 @@ +"""RegNet + +Paper: `Designing Network Design Spaces` - https://arxiv.org/abs/2003.13678 +Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py + +Based on original PyTorch impl linked above, but re-wrote to use my own blocks (adapted from ResNet here) +and cleaned up with more descriptive variable names. + +Weights from original impl have been modified +* first layer from BGR -> RGB as most PyTorch models are +* removed training specific dict entries from checkpoints and keep model state_dict only +* remap names to match the ones here + +""" +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np + +from .registry import register_model +from .helpers import load_pretrained +from .layers import SelectAdaptivePool2d, AvgPool2dSame, ConvBnAct, SEModule +from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD + + +def _mcfg(**kwargs): + cfg = dict(se_ratio=0., bottle_ratio=1., stem_width=32) + cfg.update(**kwargs) + return cfg + + +# Model FLOPS = three trailing digits * 10^8 +model_cfgs = dict( + x_002=_mcfg(w0=24, wa=36.44, wm=2.49, group_w=8, depth=13), + x_004=_mcfg(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22), + x_006=_mcfg(w0=48, wa=36.97, wm=2.24, group_w=24, depth=16), + x_008=_mcfg(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16), + x_016=_mcfg(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18), + x_032=_mcfg(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25), + x_040=_mcfg(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23), + x_064=_mcfg(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17), + x_080=_mcfg(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23), + x_120=_mcfg(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19), + x_160=_mcfg(w0=216, wa=55.59, wm=2.1, group_w=128, depth=22), + x_320=_mcfg(w0=320, wa=69.86, wm=2.0, group_w=168, depth=23), + y_002=_mcfg(w0=24, wa=36.44, wm=2.49, group_w=8, depth=13, se_ratio=0.25), + y_004=_mcfg(w0=48, wa=27.89, wm=2.09, group_w=8, depth=16, se_ratio=0.25), + y_006=_mcfg(w0=48, wa=32.54, wm=2.32, group_w=16, depth=15, se_ratio=0.25), + y_008=_mcfg(w0=56, wa=38.84, wm=2.4, group_w=16, depth=14, se_ratio=0.25), + y_016=_mcfg(w0=48, wa=20.71, wm=2.65, group_w=24, depth=27, se_ratio=0.25), + y_032=_mcfg(w0=80, wa=42.63, wm=2.66, group_w=24, depth=21, se_ratio=0.25), + y_040=_mcfg(w0=96, wa=31.41, wm=2.24, group_w=64, depth=22, se_ratio=0.25), + y_064=_mcfg(w0=112, wa=33.22, wm=2.27, group_w=72, depth=25, se_ratio=0.25), + y_080=_mcfg(w0=192, wa=76.82, wm=2.19, group_w=56, depth=17, se_ratio=0.25), + y_120=_mcfg(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, se_ratio=0.25), + y_160=_mcfg(w0=200, wa=106.23, wm=2.48, group_w=112, depth=18, se_ratio=0.25), + y_320=_mcfg(w0=232, wa=115.89, wm=2.53, group_w=232, depth=20, se_ratio=0.25), +) + + +def _cfg(url=''): + return { + 'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7), + 'crop_pct': 0.875, 'interpolation': 'bicubic', + 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, + 'first_conv': 'stem.conv', 'classifier': 'head.fc', + } + + +default_cfgs = dict( + x_002=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_002-e7e85e5c.pth'), + x_004=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_004-7d0e9424.pth'), + x_006=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_006-85ec1baa.pth'), + x_008=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_008-d8b470eb.pth'), + x_016=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_016-65ca972a.pth'), + x_032=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_032-ed0c7f7e.pth'), + x_040=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_040-73c2a654.pth'), + x_064=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_064-29278baa.pth'), + x_080=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_080-7c7fcab1.pth'), + x_120=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_120-65d5521e.pth'), + x_160=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_160-c98c4112.pth'), + x_320=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_320-8ea38b93.pth'), + y_002=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_002-e68ca334.pth'), + y_004=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_004-0db870e6.pth'), + y_006=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_006-c67e57ec.pth'), + y_008=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_008-dc900dbe.pth'), + y_016=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_016-54367f74.pth'), + y_032=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_032-62b47782.pth'), + y_040=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_040-f0d569f9.pth'), + y_064=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_064-0a48325c.pth'), + y_080=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_080-e7f3eb93.pth'), + y_120=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_120-721ba79a.pth'), + y_160=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_160-d64013cd.pth'), + y_320=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_320-ba464b29.pth'), +) + + +def quantize_float(f, q): + """Converts a float to closest non-zero int divisible by q.""" + return int(round(f / q) * q) + + +def adjust_widths_groups_comp(widths, bottle_ratios, groups): + """Adjusts the compatibility of widths and groups.""" + bottleneck_widths = [int(w * b) for w, b in zip(widths, bottle_ratios)] + groups = [min(g, w_bot) for g, w_bot in zip(groups, bottleneck_widths)] + bottleneck_widths = [quantize_float(w_bot, g) for w_bot, g in zip(bottleneck_widths, groups)] + widths = [int(w_bot / b) for w_bot, b in zip(bottleneck_widths, bottle_ratios)] + return widths, groups + + +def generate_regnet(width_slope, width_initial, width_mult, depth, q=8): + """Generates per block widths from RegNet parameters.""" + assert width_slope >= 0 and width_initial > 0 and width_mult > 1 and width_initial % q == 0 + widths_cont = np.arange(depth) * width_slope + width_initial + width_exps = np.round(np.log(widths_cont / width_initial) / np.log(width_mult)) + widths = width_initial * np.power(width_mult, width_exps) + widths = np.round(np.divide(widths, q)) * q + num_stages, max_stage = len(np.unique(widths)), width_exps.max() + 1 + widths, widths_cont = widths.astype(int).tolist(), widths_cont.tolist() + return widths, num_stages, max_stage, widths_cont + + +class Bottleneck(nn.Module): + """ RegNet Bottleneck + + This is almost exactly the same as a ResNet Bottlneck. The main difference is the SE block is moved from + after conv3 to after conv2. Otherwise, it's just redefining the arguments for groups/bottleneck channels. + """ + + def __init__(self, in_chs, out_chs, stride=1, bottleneck_ratio=1, group_width=1, se_ratio=0.25, + dilation=1, first_dilation=None, downsample=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, + aa_layer=None, drop_block=None, drop_path=None): + super(Bottleneck, self).__init__() + bottleneck_chs = int(round(out_chs * bottleneck_ratio)) + groups = bottleneck_chs // group_width + first_dilation = first_dilation or dilation + + cargs = dict(act_layer=act_layer, norm_layer=norm_layer, aa_layer=aa_layer, drop_block=drop_block) + self.conv1 = ConvBnAct(in_chs, bottleneck_chs, kernel_size=1, **cargs) + self.conv2 = ConvBnAct( + bottleneck_chs, bottleneck_chs, kernel_size=3, stride=stride, dilation=first_dilation, + groups=groups, **cargs) + if se_ratio: + se_channels = int(round(in_chs * se_ratio)) + self.se = SEModule(bottleneck_chs, reduction_channels=se_channels) + else: + self.se = None + cargs['act_layer'] = None + self.conv3 = ConvBnAct(bottleneck_chs, out_chs, kernel_size=1, **cargs) + self.act3 = act_layer(inplace=True) + self.downsample = downsample + self.drop_path = drop_path + + def zero_init_last_bn(self): + nn.init.zeros_(self.conv3.bn.weight) + + def forward(self, x): + shortcut = x + x = self.conv1(x) + x = self.conv2(x) + if self.se is not None: + x = self.se(x) + x = self.conv3(x) + if self.drop_path is not None: + x = self.drop_path(x) + if self.downsample is not None: + shortcut = self.downsample(shortcut) + x += shortcut + x = self.act3(x) + return x + + +def downsample_conv( + in_chs, out_chs, kernel_size, stride=1, dilation=1, first_dilation=None, norm_layer=None): + norm_layer = norm_layer or nn.BatchNorm2d + kernel_size = 1 if stride == 1 and dilation == 1 else kernel_size + first_dilation = (first_dilation or dilation) if kernel_size > 1 else 1 + return ConvBnAct( + in_chs, out_chs, kernel_size, stride=stride, dilation=first_dilation, norm_layer=norm_layer, act_layer=None) + + +def downsample_avg( + in_chs, out_chs, kernel_size, stride=1, dilation=1, first_dilation=None, norm_layer=None): + """ AvgPool Downsampling as in 'D' ResNet variants. This is not in RegNet space but I might experiment.""" + norm_layer = norm_layer or nn.BatchNorm2d + avg_stride = stride if dilation == 1 else 1 + pool = nn.Identity() + if stride > 1 or dilation > 1: + avg_pool_fn = AvgPool2dSame if avg_stride == 1 and dilation > 1 else nn.AvgPool2d + pool = avg_pool_fn(2, avg_stride, ceil_mode=True, count_include_pad=False) + return nn.Sequential(*[ + pool, ConvBnAct(in_chs, out_chs, 1, stride=1, norm_layer=norm_layer, act_layer=None)]) + + +class RegStage(nn.Module): + """Stage (sequence of blocks w/ the same output shape).""" + + def __init__(self, in_chs, out_chs, stride, depth, block_fn, bottle_ratio, group_width, se_ratio): + super(RegStage, self).__init__() + block_kwargs = {} # FIXME setup to pass various aa, norm, act layer common args + for i in range(depth): + block_stride = stride if i == 0 else 1 + block_in_chs = in_chs if i == 0 else out_chs + if (block_in_chs != out_chs) or (block_stride != 1): + proj_block = downsample_conv(block_in_chs, out_chs, 1, stride) + else: + proj_block = None + + name = "b{}".format(i + 1) + self.add_module( + name, block_fn( + block_in_chs, out_chs, block_stride, bottle_ratio, group_width, se_ratio, + downsample=proj_block, **block_kwargs) + ) + + def forward(self, x): + for block in self.children(): + x = block(x) + return x + + +class ClassifierHead(nn.Module): + """Head.""" + + def __init__(self, in_chs, num_classes, pool_type='avg', drop_rate=0.): + super(ClassifierHead, self).__init__() + self.drop_rate = drop_rate + self.global_pool = SelectAdaptivePool2d(pool_type=pool_type) + if num_classes > 0: + self.fc = nn.Linear(in_chs, num_classes, bias=True) + else: + self.fc = nn.Identity() + + def forward(self, x): + x = self.global_pool(x).flatten(1) + if self.drop_rate: + x = F.dropout(x, p=float(self.drop_rate), training=self.training) + x = self.fc(x) + return x + + +class RegNet(nn.Module): + """RegNet model. + + Paper: https://arxiv.org/abs/2003.13678 + Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py + """ + + def __init__(self, cfg, in_chans=3, num_classes=1000, global_pool='avg', drop_rate=0., + zero_init_last_bn=True): + super().__init__() + # TODO add drop block, drop path, anti-aliasing, custom bn/act args + self.num_classes = num_classes + self.drop_rate = drop_rate + + # Construct the stem + stem_width = cfg['stem_width'] + self.stem = ConvBnAct(in_chans, stem_width, 3, stride=2) + + # Construct the stages + block_fn = Bottleneck + prev_width = stem_width + stage_params = self._get_stage_params(cfg) + se_ratio = cfg['se_ratio'] + for i, (d, w, s, br, gw) in enumerate(stage_params): + self.add_module( + "s{}".format(i + 1), RegStage(prev_width, w, s, d, block_fn, br, gw, se_ratio)) + prev_width = w + + # Construct the head + self.num_features = prev_width + self.head = ClassifierHead( + in_chs=prev_width, num_classes=num_classes, pool_type=global_pool, drop_rate=drop_rate) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.ones_(m.weight) + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, mean=0.0, std=0.01) + nn.init.zeros_(m.bias) + if zero_init_last_bn: + for m in self.modules(): + if hasattr(m, 'zero_init_last_bn'): + m.zero_init_last_bn() + + def _get_stage_params(self, cfg, stride=2): + # Generate RegNet ws per block + w_a, w_0, w_m, d = cfg['wa'], cfg['w0'], cfg['wm'], cfg['depth'] + widths, num_stages, _, _ = generate_regnet(w_a, w_0, w_m, d) + + # Convert to per stage format + stage_widths, stage_depths = np.unique(widths, return_counts=True) + + # Use the same group width, bottleneck mult and stride for each stage + stage_groups = [cfg['group_w'] for _ in range(num_stages)] + stage_bottle_ratios = [cfg['bottle_ratio'] for _ in range(num_stages)] + stage_strides = [stride for _ in range(num_stages)] + # FIXME add dilation / output_stride support + + # Adjust the compatibility of ws and gws + stage_widths, stage_groups = adjust_widths_groups_comp(stage_widths, stage_bottle_ratios, stage_groups) + stage_params = list(zip(stage_depths, stage_widths, stage_strides, stage_bottle_ratios, stage_groups)) + return stage_params + + def get_classifier(self): + return self.head.fc + + def reset_classifier(self, num_classes, global_pool='avg'): + self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate) + + def forward_features(self, x): + for block in list(self.children())[:-1]: + x = block(x) + return x + + def forward(self, x): + for block in self.children(): + x = block(x) + return x + + +def _regnet(variant, pretrained, **kwargs): + load_strict = True + model_class = RegNet + if kwargs.pop('features_only', False): + assert False, 'Not Implemented' # TODO + load_strict = False + kwargs.pop('num_classes', 0) + model_cfg = model_cfgs[variant] + default_cfg = default_cfgs[variant] + model = model_class(model_cfg, **kwargs) + model.default_cfg = default_cfg + if pretrained: + load_pretrained( + model, default_cfg, + num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3), strict=load_strict) + return model + + +@register_model +def regnetx_002(pretrained=False, **kwargs): + """RegNetX-200MF""" + return _regnet('x_002', pretrained, **kwargs) + + +@register_model +def regnetx_004(pretrained=False, **kwargs): + """RegNetX-400MF""" + return _regnet('x_004', pretrained, **kwargs) + + +@register_model +def regnetx_006(pretrained=False, **kwargs): + """RegNetX-600MF""" + return _regnet('x_006', pretrained, **kwargs) + + +@register_model +def regnetx_008(pretrained=False, **kwargs): + """RegNetX-800MF""" + return _regnet('x_008', pretrained, **kwargs) + + +@register_model +def regnetx_016(pretrained=False, **kwargs): + """RegNetX-1.6GF""" + return _regnet('x_016', pretrained, **kwargs) + + +@register_model +def regnetx_032(pretrained=False, **kwargs): + """RegNetX-3.2GF""" + return _regnet('x_032', pretrained, **kwargs) + + +@register_model +def regnetx_040(pretrained=False, **kwargs): + """RegNetX-4.0GF""" + return _regnet('x_040', pretrained, **kwargs) + + +@register_model +def regnetx_064(pretrained=False, **kwargs): + """RegNetX-6.4GF""" + return _regnet('x_064', pretrained, **kwargs) + + +@register_model +def regnetx_080(pretrained=False, **kwargs): + """RegNetX-8.0GF""" + return _regnet('x_080', pretrained, **kwargs) + + +@register_model +def regnetx_120(pretrained=False, **kwargs): + """RegNetX-12GF""" + return _regnet('x_120', pretrained, **kwargs) + + +@register_model +def regnetx_160(pretrained=False, **kwargs): + """RegNetX-16GF""" + return _regnet('x_160', pretrained, **kwargs) + + +@register_model +def regnetx_320(pretrained=False, **kwargs): + """RegNetX-32GF""" + return _regnet('x_320', pretrained, **kwargs) + + +@register_model +def regnety_002(pretrained=False, **kwargs): + """RegNetY-200MF""" + return _regnet('y_002', pretrained, **kwargs) + + +@register_model +def regnety_004(pretrained=False, **kwargs): + """RegNetY-400MF""" + return _regnet('y_004', pretrained, **kwargs) + + +@register_model +def regnety_006(pretrained=False, **kwargs): + """RegNetY-600MF""" + return _regnet('y_006', pretrained, **kwargs) + + +@register_model +def regnety_008(pretrained=False, **kwargs): + """RegNetY-800MF""" + return _regnet('y_008', pretrained, **kwargs) + + +@register_model +def regnety_016(pretrained=False, **kwargs): + """RegNetY-1.6GF""" + return _regnet('y_016', pretrained, **kwargs) + + +@register_model +def regnety_032(pretrained=False, **kwargs): + """RegNetY-3.2GF""" + return _regnet('y_032', pretrained, **kwargs) + + +@register_model +def regnety_040(pretrained=False, **kwargs): + """RegNetY-4.0GF""" + return _regnet('y_040', pretrained, **kwargs) + + +@register_model +def regnety_064(pretrained=False, **kwargs): + """RegNetY-6.4GF""" + return _regnet('y_064', pretrained, **kwargs) + + +@register_model +def regnety_080(pretrained=False, **kwargs): + """RegNetY-8.0GF""" + return _regnet('y_080', pretrained, **kwargs) + + +@register_model +def regnety_120(pretrained=False, **kwargs): + """RegNetY-12GF""" + return _regnet('y_120', pretrained, **kwargs) + + +@register_model +def regnety_160(pretrained=False, **kwargs): + """RegNetY-16GF""" + return _regnet('y_160', pretrained, **kwargs) + + +@register_model +def regnety_320(pretrained=False, **kwargs): + """RegNetY-32GF""" + return _regnet('y_320', pretrained, **kwargs)