""" PyTorch implementation of DualPathNetworks Ported to PyTorch by [Ross Wightman](https://github.com/rwightman/pytorch-dpn-pretrained) Based on original MXNet implementation https://github.com/cypw/DPNs with many ideas from another PyTorch implementation https://github.com/oyam/pytorch-DPNs. This implementation is compatible with the pretrained weights from cypw's MXNet implementation. """ import os import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.model_zoo as model_zoo from collections import OrderedDict __all__ = ['DPN', 'dpn68', 'dpn68b', 'dpn92', 'dpn98', 'dpn131', 'dpn107'] pretrained_settings = { 'dpn68': { 'imagenet': { 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/dpn68-66bebafa7.pth', 'input_space': 'RGB', 'input_size': [3, 224, 224], 'input_range': [0, 1], 'mean': [124 / 255, 117 / 255, 104 / 255], 'std': [1 / (.0167 * 255)] * 3, 'num_classes': 1000 } }, 'dpn68b': { 'imagenet+5k': { 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/dpn68b_extra-84854c156.pth', 'input_space': 'RGB', 'input_size': [3, 224, 224], 'input_range': [0, 1], 'mean': [124 / 255, 117 / 255, 104 / 255], 'std': [1 / (.0167 * 255)] * 3, 'num_classes': 1000 } }, 'dpn92': { # 'imagenet': { # 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/dpn68-66bebafa7.pth', # 'input_space': 'RGB', # 'input_size': [3, 224, 224], # 'input_range': [0, 1], # 'mean': [124 / 255, 117 / 255, 104 / 255], # 'std': [1 / (.0167 * 255)] * 3, # 'num_classes': 1000 # }, 'imagenet+5k': { 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/dpn92_extra-b040e4a9b.pth', 'input_space': 'RGB', 'input_size': [3, 224, 224], 'input_range': [0, 1], 'mean': [124 / 255, 117 / 255, 104 / 255], 'std': [1 / (.0167 * 255)] * 3, 'num_classes': 1000 } }, 'dpn98': { 'imagenet': { 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/dpn98-5b90dec4d.pth', 'input_space': 'RGB', 'input_size': [3, 224, 224], 'input_range': [0, 1], 'mean': [124 / 255, 117 / 255, 104 / 255], 'std': [1 / (.0167 * 255)] * 3, 'num_classes': 1000 } }, 'dpn131': { 'imagenet': { 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/dpn131-71dfe43e0.pth', 'input_space': 'RGB', 'input_size': [3, 224, 224], 'input_range': [0, 1], 'mean': [124 / 255, 117 / 255, 104 / 255], 'std': [1 / (.0167 * 255)] * 3, 'num_classes': 1000 } }, 'dpn107': { 'imagenet+5k': { 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/dpn107_extra-1ac7121e2.pth', 'input_space': 'RGB', 'input_size': [3, 224, 224], 'input_range': [0, 1], 'mean': [124 / 255, 117 / 255, 104 / 255], 'std': [1 / (.0167 * 255)] * 3, 'num_classes': 1000 } } } def dpn68(num_classes=1000, pretrained='imagenet'): model = DPN( small=True, num_init_features=10, k_r=128, groups=32, k_sec=(3, 4, 12, 3), inc_sec=(16, 32, 32, 64), num_classes=num_classes, test_time_pool=True) if pretrained: settings = pretrained_settings['dpn68'][pretrained] assert num_classes == settings['num_classes'], \ "num_classes should be {}, but is {}".format(settings['num_classes'], num_classes) model.load_state_dict(model_zoo.load_url(settings['url'])) model.input_space = settings['input_space'] model.input_size = settings['input_size'] model.input_range = settings['input_range'] model.mean = settings['mean'] model.std = settings['std'] return model def dpn68b(num_classes=1000, pretrained='imagenet+5k'): model = DPN( small=True, num_init_features=10, k_r=128, groups=32, b=True, k_sec=(3, 4, 12, 3), inc_sec=(16, 32, 32, 64), num_classes=num_classes, test_time_pool=True) if pretrained: settings = pretrained_settings['dpn68b'][pretrained] assert num_classes == settings['num_classes'], \ "num_classes should be {}, but is {}".format(settings['num_classes'], num_classes) model.load_state_dict(model_zoo.load_url(settings['url'])) model.input_space = settings['input_space'] model.input_size = settings['input_size'] model.input_range = settings['input_range'] model.mean = settings['mean'] model.std = settings['std'] return model def dpn92(num_classes=1000, pretrained='imagenet+5k'): model = DPN( num_init_features=64, k_r=96, groups=32, k_sec=(3, 4, 20, 3), inc_sec=(16, 32, 24, 128), num_classes=num_classes, test_time_pool=True) if pretrained: settings = pretrained_settings['dpn92'][pretrained] assert num_classes == settings['num_classes'], \ "num_classes should be {}, but is {}".format(settings['num_classes'], num_classes) model.load_state_dict(model_zoo.load_url(settings['url'])) model.input_space = settings['input_space'] model.input_size = settings['input_size'] model.input_range = settings['input_range'] model.mean = settings['mean'] model.std = settings['std'] return model def dpn98(num_classes=1000, pretrained='imagenet'): model = DPN( num_init_features=96, k_r=160, groups=40, k_sec=(3, 6, 20, 3), inc_sec=(16, 32, 32, 128), num_classes=num_classes, test_time_pool=True) if pretrained: settings = pretrained_settings['dpn98'][pretrained] assert num_classes == settings['num_classes'], \ "num_classes should be {}, but is {}".format(settings['num_classes'], num_classes) model.load_state_dict(model_zoo.load_url(settings['url'])) model.input_space = settings['input_space'] model.input_size = settings['input_size'] model.input_range = settings['input_range'] model.mean = settings['mean'] model.std = settings['std'] return model def dpn131(num_classes=1000, pretrained='imagenet'): model = DPN( num_init_features=128, k_r=160, groups=40, k_sec=(4, 8, 28, 3), inc_sec=(16, 32, 32, 128), num_classes=num_classes, test_time_pool=True) if pretrained: settings = pretrained_settings['dpn131'][pretrained] assert num_classes == settings['num_classes'], \ "num_classes should be {}, but is {}".format(settings['num_classes'], num_classes) model.load_state_dict(model_zoo.load_url(settings['url'])) model.input_space = settings['input_space'] model.input_size = settings['input_size'] model.input_range = settings['input_range'] model.mean = settings['mean'] model.std = settings['std'] return model def dpn107(num_classes=1000, pretrained='imagenet+5k'): model = DPN( num_init_features=128, k_r=200, groups=50, k_sec=(4, 8, 20, 3), inc_sec=(20, 64, 64, 128), num_classes=num_classes, test_time_pool=True) if pretrained: settings = pretrained_settings['dpn107'][pretrained] assert num_classes == settings['num_classes'], \ "num_classes should be {}, but is {}".format(settings['num_classes'], num_classes) model.load_state_dict(model_zoo.load_url(settings['url'])) model.input_space = settings['input_space'] model.input_size = settings['input_size'] model.input_range = settings['input_range'] model.mean = settings['mean'] model.std = settings['std'] return model class CatBnAct(nn.Module): def __init__(self, in_chs, activation_fn=nn.ReLU(inplace=True)): super(CatBnAct, self).__init__() self.bn = nn.BatchNorm2d(in_chs, eps=0.001) self.act = activation_fn def forward(self, x): x = torch.cat(x, dim=1) if isinstance(x, tuple) else x return self.act(self.bn(x)) class BnActConv2d(nn.Module): def __init__(self, in_chs, out_chs, kernel_size, stride, padding=0, groups=1, activation_fn=nn.ReLU(inplace=True)): super(BnActConv2d, self).__init__() self.bn = nn.BatchNorm2d(in_chs, eps=0.001) self.act = activation_fn self.conv = nn.Conv2d(in_chs, out_chs, kernel_size, stride, padding, groups=groups, bias=False) def forward(self, x): return self.conv(self.act(self.bn(x))) class InputBlock(nn.Module): def __init__(self, num_init_features, kernel_size=7, padding=3, activation_fn=nn.ReLU(inplace=True)): super(InputBlock, self).__init__() self.conv = nn.Conv2d( 3, num_init_features, kernel_size=kernel_size, stride=2, padding=padding, bias=False) self.bn = nn.BatchNorm2d(num_init_features, eps=0.001) self.act = activation_fn self.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) def forward(self, x): x = self.conv(x) x = self.bn(x) x = self.act(x) x = self.pool(x) return x class DualPathBlock(nn.Module): def __init__( self, in_chs, num_1x1_a, num_3x3_b, num_1x1_c, inc, groups, block_type='normal', b=False): super(DualPathBlock, self).__init__() self.num_1x1_c = num_1x1_c self.inc = inc self.b = b if block_type is 'proj': self.key_stride = 1 self.has_proj = True elif block_type is 'down': self.key_stride = 2 self.has_proj = True else: assert block_type is 'normal' self.key_stride = 1 self.has_proj = False if self.has_proj: # Using different member names here to allow easier parameter key matching for conversion if self.key_stride == 2: self.c1x1_w_s2 = BnActConv2d( in_chs=in_chs, out_chs=num_1x1_c + 2 * inc, kernel_size=1, stride=2) else: self.c1x1_w_s1 = BnActConv2d( in_chs=in_chs, out_chs=num_1x1_c + 2 * inc, kernel_size=1, stride=1) self.c1x1_a = BnActConv2d(in_chs=in_chs, out_chs=num_1x1_a, kernel_size=1, stride=1) self.c3x3_b = BnActConv2d( in_chs=num_1x1_a, out_chs=num_3x3_b, kernel_size=3, stride=self.key_stride, padding=1, groups=groups) if b: self.c1x1_c = CatBnAct(in_chs=num_3x3_b) self.c1x1_c1 = nn.Conv2d(num_3x3_b, num_1x1_c, kernel_size=1, bias=False) self.c1x1_c2 = nn.Conv2d(num_3x3_b, inc, kernel_size=1, bias=False) else: self.c1x1_c = BnActConv2d(in_chs=num_3x3_b, out_chs=num_1x1_c + inc, kernel_size=1, stride=1) def forward(self, x): x_in = torch.cat(x, dim=1) if isinstance(x, tuple) else x if self.has_proj: if self.key_stride == 2: x_s = self.c1x1_w_s2(x_in) else: x_s = self.c1x1_w_s1(x_in) x_s1 = x_s[:, :self.num_1x1_c, :, :] x_s2 = x_s[:, self.num_1x1_c:, :, :] else: x_s1 = x[0] x_s2 = x[1] x_in = self.c1x1_a(x_in) x_in = self.c3x3_b(x_in) if self.b: x_in = self.c1x1_c(x_in) out1 = self.c1x1_c1(x_in) out2 = self.c1x1_c2(x_in) else: x_in = self.c1x1_c(x_in) out1 = x_in[:, :self.num_1x1_c, :, :] out2 = x_in[:, self.num_1x1_c:, :, :] resid = x_s1 + out1 dense = torch.cat([x_s2, out2], dim=1) return resid, dense class DPN(nn.Module): def __init__(self, small=False, num_init_features=64, k_r=96, groups=32, b=False, k_sec=(3, 4, 20, 3), inc_sec=(16, 32, 24, 128), num_classes=1000, test_time_pool=False): super(DPN, self).__init__() self.test_time_pool = test_time_pool self.b = b bw_factor = 1 if small else 4 self.k_sec = k_sec self.out_channels = [] self.blocks = OrderedDict() # conv1 if small: self.blocks['conv1_1'] = InputBlock(num_init_features, kernel_size=3, padding=1) else: self.blocks['conv1_1'] = InputBlock(num_init_features, kernel_size=7, padding=3) self.out_channels.append(num_init_features) # conv2 bw = 64 * bw_factor inc = inc_sec[0] r = (k_r * bw) // (64 * bw_factor) self.blocks['conv2_1'] = DualPathBlock(num_init_features, r, r, bw, inc, groups, 'proj', b) in_chs = bw + 3 * inc for i in range(2, k_sec[0] + 1): self.blocks['conv2_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', b) in_chs += inc self.out_channels.append(in_chs) # conv3 bw = 128 * bw_factor inc = inc_sec[1] r = (k_r * bw) // (64 * bw_factor) self.blocks['conv3_1'] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'down', b) in_chs = bw + 3 * inc for i in range(2, k_sec[1] + 1): self.blocks['conv3_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', b) in_chs += inc self.out_channels.append(in_chs) # conv4 bw = 256 * bw_factor inc = inc_sec[2] r = (k_r * bw) // (64 * bw_factor) self.blocks['conv4_1'] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'down', b) in_chs = bw + 3 * inc for i in range(2, k_sec[2] + 1): self.blocks['conv4_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', b) in_chs += inc self.out_channels.append(in_chs) # conv5 bw = 512 * bw_factor inc = inc_sec[3] r = (k_r * bw) // (64 * bw_factor) self.blocks['conv5_1'] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'down', b) in_chs = bw + 3 * inc for i in range(2, k_sec[3] + 1): self.blocks['conv5_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', b) in_chs += inc self.blocks['conv5_bn_ac'] = CatBnAct(in_chs) self.out_channels.append(in_chs) self.features = nn.Sequential(self.blocks) # Using 1x1 conv for the FC layer to allow the extra pooling scheme self.classifier = nn.Conv2d(in_chs, num_classes, kernel_size=1, bias=True) def logits(self, features): if not self.training and self.test_time_pool: x = F.avg_pool2d(features, kernel_size=7, stride=1) out = self.classifier(x) # The extra test time pool should be pooling an img_size//32 - 6 size patch out = adaptive_avgmax_pool2d(out, pool_type='avgmax') else: x = adaptive_avgmax_pool2d(features, pool_type='avg') out = self.classifier(x) return out.view(out.size(0), -1) def forward(self, input): x = self.features(input) x = self.logits(x) return x """ PyTorch selectable adaptive pooling Adaptive pooling with the ability to select the type of pooling from: * 'avg' - Average pooling * 'max' - Max pooling * 'avgmax' - Sum of average and max pooling re-scaled by 0.5 * 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles feature dim Both a functional and a nn.Module version of the pooling is provided. Author: Ross Wightman (rwightman) """ def pooling_factor(pool_type='avg'): return 2 if pool_type == 'avgmaxc' else 1 def adaptive_avgmax_pool2d(x, pool_type='avg', padding=0, count_include_pad=False): """Selectable global pooling function with dynamic input kernel size """ if pool_type == 'avgmaxc': x = torch.cat([ F.avg_pool2d( x, kernel_size=(x.size(2), x.size(3)), padding=padding, count_include_pad=count_include_pad), F.max_pool2d(x, kernel_size=(x.size(2), x.size(3)), padding=padding) ], dim=1) elif pool_type == 'avgmax': x_avg = F.avg_pool2d( x, kernel_size=(x.size(2), x.size(3)), padding=padding, count_include_pad=count_include_pad) x_max = F.max_pool2d(x, kernel_size=(x.size(2), x.size(3)), padding=padding) x = 0.5 * (x_avg + x_max) elif pool_type == 'max': x = F.max_pool2d(x, kernel_size=(x.size(2), x.size(3)), padding=padding) else: if pool_type != 'avg': print('Invalid pool type %s specified. Defaulting to average pooling.' % pool_type) x = F.avg_pool2d( x, kernel_size=(x.size(2), x.size(3)), padding=padding, count_include_pad=count_include_pad) return x class AdaptiveAvgMaxPool2d(torch.nn.Module): """Selectable global pooling layer with dynamic input kernel size """ def __init__(self, output_size=1, pool_type='avg'): super(AdaptiveAvgMaxPool2d, self).__init__() self.output_size = output_size self.pool_type = pool_type if pool_type == 'avgmaxc' or pool_type == 'avgmax': self.pool = nn.ModuleList([nn.AdaptiveAvgPool2d(output_size), nn.AdaptiveMaxPool2d(output_size)]) elif pool_type == 'max': self.pool = nn.AdaptiveMaxPool2d(output_size) else: if pool_type != 'avg': print('Invalid pool type %s specified. Defaulting to average pooling.' % pool_type) self.pool = nn.AdaptiveAvgPool2d(output_size) def forward(self, x): if self.pool_type == 'avgmaxc': x = torch.cat([p(x) for p in self.pool], dim=1) elif self.pool_type == 'avgmax': x = 0.5 * torch.sum(torch.stack([p(x) for p in self.pool]), 0).squeeze(dim=0) else: x = self.pool(x) return x def factor(self): return pooling_factor(self.pool_type) def __repr__(self): return self.__class__.__name__ + ' (' \ + 'output_size=' + str(self.output_size) \ + ', pool_type=' + self.pool_type + ')' if __name__ == "__main__": model = dpn131() print(model.features, len(model.features)) print(model.features[2])