mirror of
https://github.com/huggingface/pytorch-image-models.git
synced 2025-06-03 15:01:08 +08:00
Lots of refactoring and cleanup.
* Move 'test time pool' to Module that can be used by any model, remove from DPN * Remove ResNext model file and combine with ResNet * Remove fbresnet200 as it was an old conversion and pretrained performance not worth param count * Cleanup adaptive avgmax pooling and add back conctat variant * Factor out checkpoint load fn
This commit is contained in:
Ross Wightman
parent
f2029dfb65
commit
0bc50e84f8
@ -39,8 +39,7 @@ class PrefetchLoader:
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with torch.cuda.stream(stream):
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next_input = next_input.cuda(non_blocking=True)
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next_target = next_target.cuda(non_blocking=True)
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next_input = next_input.float()
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next_input = next_input.sub_(self.mean).div_(self.std)
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next_input = next_input.float().sub_(self.mean).div_(self.std)
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if self.random_erasing is not None:
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next_input = self.random_erasing(next_input)
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@ -74,6 +73,7 @@ def create_loader(
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std=IMAGENET_DEFAULT_STD,
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num_workers=1,
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distributed=False,
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crop_pct=None,
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):
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if is_training:
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@ -87,7 +87,8 @@ def create_loader(
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img_size,
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use_prefetcher=use_prefetcher,
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mean=mean,
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std=std)
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std=std,
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crop_pct=crop_pct)
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dataset.transform = transform
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22
inference.py
22
inference.py
@ -11,7 +11,7 @@ import argparse
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import numpy as np
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import torch
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from models import create_model
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from models import create_model, load_checkpoint, TestTimePoolHead
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from data import Dataset, create_loader, get_model_meanstd
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from utils import AverageMeter
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@ -49,20 +49,14 @@ def main():
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model = create_model(
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args.model,
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num_classes=num_classes,
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pretrained=args.pretrained,
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test_time_pool=args.test_time_pool)
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pretrained=args.pretrained)
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# resume from a checkpoint
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if args.checkpoint and os.path.isfile(args.checkpoint):
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print("=> loading checkpoint '{}'".format(args.checkpoint))
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checkpoint = torch.load(args.checkpoint)
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if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
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model.load_state_dict(checkpoint['state_dict'])
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else:
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model.load_state_dict(checkpoint)
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print("=> loaded checkpoint '{}'".format(args.checkpoint))
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elif not args.pretrained:
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print("=> no checkpoint found at '{}'".format(args.checkpoint))
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print('Model %s created, param count: %d' %
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(args.model, sum([m.numel() for m in model.parameters()])))
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# load a checkpoint
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if not args.pretrained:
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if not load_checkpoint(model, args.checkpoint):
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exit(1)
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if args.num_gpu > 1:
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@ -1,2 +1,3 @@
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from .model_factory import create_model
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from .model_factory import create_model, load_checkpoint
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from .test_time_pool import TestTimePoolHead
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@ -14,29 +14,70 @@ import torch.nn as nn
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import torch.nn.functional as F
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def adaptive_avgmax_pool2d(x, pool_type='avg', output_size=1):
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"""Selectable global pooling function with dynamic input kernel size
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"""
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if pool_type == 'avgmax':
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def adaptive_pool_feat_mult(pool_type='avg'):
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if pool_type == 'catavgmax':
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return 2
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else:
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return 1
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def adaptive_avgmax_pool2d(x, output_size=1):
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x_avg = F.adaptive_avg_pool2d(x, output_size)
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x_max = F.adaptive_max_pool2d(x, output_size)
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x = 0.5 * (x_avg + x_max)
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return 0.5 * (x_avg + x_max)
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def adaptive_catavgmax_pool2d(x, output_size=1):
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x_avg = F.adaptive_avg_pool2d(x, output_size)
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x_max = F.adaptive_max_pool2d(x, output_size)
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return torch.cat((x_avg, x_max), 1)
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def select_adaptive_pool2d(x, pool_type='avg', output_size=1):
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"""Selectable global pooling function with dynamic input kernel size
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"""
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if pool_type == 'avg':
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x = F.adaptive_avg_pool2d(x, output_size)
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elif pool_type == 'avgmax':
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x = adaptive_avgmax_pool2d(x, output_size)
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elif pool_type == 'catavgmax':
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x = adaptive_catavgmax_pool2d(x, output_size)
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elif pool_type == 'max':
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x = F.adaptive_max_pool2d(x, output_size)
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else:
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x = F.adaptive_avg_pool2d(x, output_size)
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assert False, 'Invalid pool type: %s' % pool_type
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return x
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class AdaptiveAvgMaxPool2d(torch.nn.Module):
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def __init__(self, output_size=1):
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super(AdaptiveAvgMaxPool2d, self).__init__()
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self.output_size = output_size
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def forward(self, x):
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return adaptive_avgmax_pool2d(x, self.output_size)
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class AdaptiveCatAvgMaxPool2d(torch.nn.Module):
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def __init__(self, output_size=1):
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super(AdaptiveCatAvgMaxPool2d, self).__init__()
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self.output_size = output_size
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def forward(self, x):
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return adaptive_catavgmax_pool2d(x, self.output_size)
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class SelectAdaptivePool2d(torch.nn.Module):
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"""Selectable global pooling layer with dynamic input kernel size
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"""
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def __init__(self, output_size=1, pool_type='avg'):
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super(AdaptiveAvgMaxPool2d, self).__init__()
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super(SelectAdaptivePool2d, self).__init__()
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self.output_size = output_size
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self.pool_type = pool_type
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if pool_type == 'avgmax':
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self.pool = nn.ModuleList([nn.AdaptiveAvgPool2d(output_size), nn.AdaptiveMaxPool2d(output_size)])
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self.pool = AdaptiveAvgMaxPool2d(output_size)
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elif pool_type == 'catavgmax':
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self.pool = AdaptiveCatAvgMaxPool2d(output_size)
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elif pool_type == 'max':
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self.pool = nn.AdaptiveMaxPool2d(output_size)
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else:
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@ -45,11 +86,10 @@ class AdaptiveAvgMaxPool2d(torch.nn.Module):
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self.pool = nn.AdaptiveAvgPool2d(output_size)
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def forward(self, x):
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if self.pool_type == 'avgmax':
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x = 0.5 * torch.sum(torch.stack([p(x) for p in self.pool]), 0).squeeze(dim=0)
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else:
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x = self.pool(x)
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return x
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return self.pool(x)
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def feat_mult(self):
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return adaptive_pool_feat_mult(self.pool_type)
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def __repr__(self):
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return self.__class__.__name__ + ' (' \
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@ -83,7 +83,6 @@ def densenet161(pretrained=False, **kwargs):
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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"""
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print(kwargs)
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model = DenseNet(num_init_features=96, growth_rate=48, block_config=(6, 12, 36, 24), **kwargs)
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if pretrained:
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state_dict = model_zoo.load_url(model_urls['densenet161'])
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@ -193,7 +192,7 @@ class DenseNet(nn.Module):
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x = self.features(x)
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x = F.relu(x, inplace=True)
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if pool:
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x = adaptive_avgmax_pool2d(x, self.global_pool)
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x = select_adaptive_pool2d(x, self.global_pool)
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x = x.view(x.size(0), -1)
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return x
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@ -16,7 +16,7 @@ import torch.nn.functional as F
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import torch.utils.model_zoo as model_zoo
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from collections import OrderedDict
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from .adaptive_avgmax_pool import adaptive_avgmax_pool2d
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from .adaptive_avgmax_pool import select_adaptive_pool2d
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__all__ = ['DPN', 'dpn68', 'dpn92', 'dpn98', 'dpn131', 'dpn107']
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@ -41,31 +41,31 @@ model_urls = {
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}
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def dpn68(num_classes=1000, pretrained=False, test_time_pool=7):
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def dpn68(num_classes=1000, pretrained=False):
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model = DPN(
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small=True, num_init_features=10, k_r=128, groups=32,
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k_sec=(3, 4, 12, 3), inc_sec=(16, 32, 32, 64),
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num_classes=num_classes, test_time_pool=test_time_pool)
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num_classes=num_classes)
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if pretrained:
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model.load_state_dict(model_zoo.load_url(model_urls['dpn68']))
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return model
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def dpn68b(num_classes=1000, pretrained=False, test_time_pool=7):
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def dpn68b(num_classes=1000, pretrained=False):
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model = DPN(
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small=True, num_init_features=10, k_r=128, groups=32,
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b=True, k_sec=(3, 4, 12, 3), inc_sec=(16, 32, 32, 64),
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num_classes=num_classes, test_time_pool=test_time_pool)
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num_classes=num_classes)
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if pretrained:
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model.load_state_dict(model_zoo.load_url(model_urls['dpn68b_extra']))
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return model
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def dpn92(num_classes=1000, pretrained=False, test_time_pool=7, extra=True):
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def dpn92(num_classes=1000, pretrained=False, extra=True):
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model = DPN(
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num_init_features=64, k_r=96, groups=32,
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k_sec=(3, 4, 20, 3), inc_sec=(16, 32, 24, 128),
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num_classes=num_classes, test_time_pool=test_time_pool)
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num_classes=num_classes)
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if pretrained:
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if extra:
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model.load_state_dict(model_zoo.load_url(model_urls['dpn92_extra']))
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@ -74,31 +74,31 @@ def dpn92(num_classes=1000, pretrained=False, test_time_pool=7, extra=True):
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return model
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def dpn98(num_classes=1000, pretrained=False, test_time_pool=7):
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def dpn98(num_classes=1000, pretrained=False):
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model = DPN(
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num_init_features=96, k_r=160, groups=40,
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k_sec=(3, 6, 20, 3), inc_sec=(16, 32, 32, 128),
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num_classes=num_classes, test_time_pool=test_time_pool)
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num_classes=num_classes)
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if pretrained:
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model.load_state_dict(model_zoo.load_url(model_urls['dpn98']))
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return model
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def dpn131(num_classes=1000, pretrained=False, test_time_pool=7):
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def dpn131(num_classes=1000, pretrained=False):
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model = DPN(
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num_init_features=128, k_r=160, groups=40,
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k_sec=(4, 8, 28, 3), inc_sec=(16, 32, 32, 128),
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num_classes=num_classes, test_time_pool=test_time_pool)
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num_classes=num_classes)
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if pretrained:
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model.load_state_dict(model_zoo.load_url(model_urls['dpn131']))
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return model
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def dpn107(num_classes=1000, pretrained=False, test_time_pool=7):
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def dpn107(num_classes=1000, pretrained=False):
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model = DPN(
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num_init_features=128, k_r=200, groups=50,
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k_sec=(4, 8, 20, 3), inc_sec=(20, 64, 64, 128),
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num_classes=num_classes, test_time_pool=test_time_pool)
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num_classes=num_classes)
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if pretrained:
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model.load_state_dict(model_zoo.load_url(model_urls['dpn107_extra']))
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return model
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@ -212,10 +212,9 @@ class DualPathBlock(nn.Module):
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class DPN(nn.Module):
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def __init__(self, small=False, num_init_features=64, k_r=96, groups=32,
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b=False, k_sec=(3, 4, 20, 3), inc_sec=(16, 32, 24, 128),
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num_classes=1000, test_time_pool=0, fc_act=nn.ELU(inplace=True)):
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num_classes=1000, fc_act=nn.ELU(inplace=True)):
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super(DPN, self).__init__()
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self.num_classes = num_classes
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self.test_time_pool = test_time_pool
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self.b = b
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bw_factor = 1 if small else 4
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@ -287,19 +286,11 @@ class DPN(nn.Module):
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def forward_features(self, x, pool=True):
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x = self.features(x)
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if pool:
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x = adaptive_avgmax_pool2d(x, pool_type='avg')
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x = x.view(x.size(0), -1)
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x = select_adaptive_pool2d(x, pool_type='avg')
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return x
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def forward(self, x):
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x = self.features(x)
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if not self.training and self.test_time_pool:
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x = F.avg_pool2d(x, kernel_size=self.test_time_pool, stride=1)
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out = self.classifier(x)
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# The extra test time pool should be pooling an img_size//32 - 6 size patch
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out = adaptive_avgmax_pool2d(out, pool_type='avgmax')
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else:
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x = adaptive_avgmax_pool2d(x, pool_type='avg')
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x = self.forward_features(x)
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out = self.classifier(x)
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return out.view(out.size(0), -1)
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File diff suppressed because it is too large
Load Diff
@ -305,7 +305,7 @@ class InceptionResnetV2(nn.Module):
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x = self.block8(x)
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x = self.conv2d_7b(x)
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if pool:
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x = adaptive_avgmax_pool2d(x, self.global_pool)
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x = select_adaptive_pool2d(x, self.global_pool)
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#x = F.avg_pool2d(x, 8, count_include_pad=False)
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x = x.view(x.size(0), -1)
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return x
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@ -272,7 +272,7 @@ class InceptionV4(nn.Module):
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def forward_features(self, x, pool=True):
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x = self.features(x)
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if pool:
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x = adaptive_avgmax_pool2d(x, self.global_pool, count_include_pad=False)
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x = select_adaptive_pool2d(x, self.global_pool, count_include_pad=False)
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x = x.view(x.size(0), -1)
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return x
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@ -1,15 +1,16 @@
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import torch
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import os
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from collections import OrderedDict
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from .inception_v4 import inception_v4
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from .inception_resnet_v2 import inception_resnet_v2
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from .densenet import densenet161, densenet121, densenet169, densenet201
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from .resnet import resnet18, resnet34, resnet50, resnet101, resnet152
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from .fbresnet200 import fbresnet200
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from .resnet import resnet18, resnet34, resnet50, resnet101, resnet152, \
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resnext50_32x4d, resnext101_32x4d, resnext101_64x4d, resnext152_32x4d
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from .dpn import dpn68, dpn68b, dpn92, dpn98, dpn131, dpn107
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from .senet import seresnet18, seresnet34, seresnet50, seresnet101, seresnet152, \
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seresnext26_32x4d, seresnext50_32x4d, seresnext101_32x4d
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from .resnext import resnext50, resnext101, resnext152
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#from .resnext import resnext50, resnext101, resnext152
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from .xception import xception
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model_config_dict = {
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@ -57,26 +58,18 @@ def create_model(
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checkpoint_path='',
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**kwargs):
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test_time_pool = kwargs.pop('test_time_pool') if 'test_time_pool' in kwargs else 0
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if model_name == 'dpn68':
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model = dpn68(
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num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool)
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model = dpn68(num_classes=num_classes, pretrained=pretrained)
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elif model_name == 'dpn68b':
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model = dpn68b(
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num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool)
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model = dpn68b(num_classes=num_classes, pretrained=pretrained)
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elif model_name == 'dpn92':
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model = dpn92(
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num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool)
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model = dpn92(num_classes=num_classes, pretrained=pretrained)
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elif model_name == 'dpn98':
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model = dpn98(
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num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool)
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model = dpn98(num_classes=num_classes, pretrained=pretrained)
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elif model_name == 'dpn131':
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model = dpn131(
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num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool)
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model = dpn131(num_classes=num_classes, pretrained=pretrained)
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elif model_name == 'dpn107':
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model = dpn107(
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num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool)
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model = dpn107(num_classes=num_classes, pretrained=pretrained)
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elif model_name == 'resnet18':
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model = resnet18(num_classes=num_classes, pretrained=pretrained, **kwargs)
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elif model_name == 'resnet34':
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@ -99,8 +92,6 @@ def create_model(
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model = inception_resnet_v2(num_classes=num_classes, pretrained=pretrained, **kwargs)
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elif model_name == 'inception_v4':
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model = inception_v4(num_classes=num_classes, pretrained=pretrained, **kwargs)
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elif model_name == 'fbresnet200':
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model = fbresnet200(num_classes=num_classes, pretrained=pretrained, **kwargs)
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elif model_name == 'seresnet18':
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model = seresnet18(num_classes=num_classes, pretrained=pretrained, **kwargs)
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elif model_name == 'seresnet34':
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@ -117,12 +108,14 @@ def create_model(
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model = seresnext50_32x4d(num_classes=num_classes, pretrained=pretrained, **kwargs)
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elif model_name == 'seresnext101_32x4d':
|
||||
model = seresnext101_32x4d(num_classes=num_classes, pretrained=pretrained, **kwargs)
|
||||
elif model_name == 'resnext50':
|
||||
model = resnext50(num_classes=num_classes, pretrained=pretrained, **kwargs)
|
||||
elif model_name == 'resnext101':
|
||||
model = resnext101(num_classes=num_classes, pretrained=pretrained, **kwargs)
|
||||
elif model_name == 'resnext152':
|
||||
model = resnext152(num_classes=num_classes, pretrained=pretrained, **kwargs)
|
||||
elif model_name == 'resnext50_32x4d':
|
||||
model = resnext50_32x4d(num_classes=num_classes, pretrained=pretrained, **kwargs)
|
||||
elif model_name == 'resnext101_32x4d':
|
||||
model = resnext101_32x4d(num_classes=num_classes, pretrained=pretrained, **kwargs)
|
||||
elif model_name == 'resnext101_64x4d':
|
||||
model = resnext101_32x4d(num_classes=num_classes, pretrained=pretrained, **kwargs)
|
||||
elif model_name == 'resnext152_32x4d':
|
||||
model = resnext152_32x4d(num_classes=num_classes, pretrained=pretrained, **kwargs)
|
||||
elif model_name == 'xception':
|
||||
model = xception(num_classes=num_classes, pretrained=pretrained)
|
||||
else:
|
||||
@ -136,13 +129,22 @@ def create_model(
|
||||
|
||||
|
||||
def load_checkpoint(model, checkpoint_path):
|
||||
if checkpoint_path is not None and os.path.isfile(checkpoint_path):
|
||||
print('Loading checkpoint', checkpoint_path)
|
||||
if checkpoint_path and os.path.isfile(checkpoint_path):
|
||||
print("=> Loading checkpoint '{}'".format(checkpoint_path))
|
||||
checkpoint = torch.load(checkpoint_path)
|
||||
if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
|
||||
model.load_state_dict(checkpoint['state_dict'])
|
||||
new_state_dict = OrderedDict()
|
||||
for k, v in checkpoint['state_dict'].items():
|
||||
if k.startswith('module'):
|
||||
name = k[7:] # remove `module.`
|
||||
else:
|
||||
name = k
|
||||
new_state_dict[name] = v
|
||||
model.load_state_dict(new_state_dict)
|
||||
else:
|
||||
model.load_state_dict(checkpoint)
|
||||
print("=> Loaded checkpoint '{}'".format(checkpoint_path))
|
||||
return True
|
||||
else:
|
||||
print("Error: No checkpoint found at %s." % checkpoint_path)
|
||||
|
||||
print("=> Error: No checkpoint found at '{}'".format(checkpoint_path))
|
||||
return False
|
||||
|
||||
100
models/resnet.py
100
models/resnet.py
@ -7,7 +7,7 @@ import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import math
|
||||
import torch.utils.model_zoo as model_zoo
|
||||
from .adaptive_avgmax_pool import AdaptiveAvgMaxPool2d
|
||||
from .adaptive_avgmax_pool import SelectAdaptivePool2d
|
||||
|
||||
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152']
|
||||
|
||||
@ -29,8 +29,13 @@ def conv3x3(in_planes, out_planes, stride=1):
|
||||
class BasicBlock(nn.Module):
|
||||
expansion = 1
|
||||
|
||||
def __init__(self, inplanes, planes, stride=1, downsample=None, drop_rate=0.0):
|
||||
def __init__(self, inplanes, planes, stride=1, downsample=None,
|
||||
cardinality=1, base_width=64, drop_rate=0.0):
|
||||
super(BasicBlock, self).__init__()
|
||||
|
||||
assert cardinality == 1, 'BasicBlock only supports cardinality of 1'
|
||||
assert base_width == 64, 'BasicBlock doest not support changing base width'
|
||||
|
||||
self.conv1 = conv3x3(inplanes, planes, stride)
|
||||
self.bn1 = nn.BatchNorm2d(planes)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
@ -65,14 +70,18 @@ class BasicBlock(nn.Module):
|
||||
class Bottleneck(nn.Module):
|
||||
expansion = 4
|
||||
|
||||
def __init__(self, inplanes, planes, stride=1, downsample=None, drop_rate=0.0):
|
||||
def __init__(self, inplanes, planes, stride=1, downsample=None,
|
||||
cardinality=1, base_width=64, drop_rate=0.0):
|
||||
super(Bottleneck, self).__init__()
|
||||
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(planes)
|
||||
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
|
||||
padding=1, bias=False)
|
||||
self.bn2 = nn.BatchNorm2d(planes)
|
||||
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
|
||||
|
||||
width = int(math.floor(planes * (base_width / 64)) * cardinality)
|
||||
|
||||
self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(width)
|
||||
self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride,
|
||||
padding=1, groups=cardinality, bias=False)
|
||||
self.bn2 = nn.BatchNorm2d(width)
|
||||
self.conv3 = nn.Conv2d(width, planes * 4, kernel_size=1, bias=False)
|
||||
self.bn3 = nn.BatchNorm2d(planes * 4)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.downsample = downsample
|
||||
@ -108,10 +117,13 @@ class Bottleneck(nn.Module):
|
||||
class ResNet(nn.Module):
|
||||
|
||||
def __init__(self, block, layers, num_classes=1000,
|
||||
cardinality=1, base_width=64,
|
||||
drop_rate=0.0, block_drop_rate=0.0,
|
||||
global_pool='avg'):
|
||||
self.num_classes = num_classes
|
||||
self.inplanes = 64
|
||||
self.cardinality = cardinality
|
||||
self.base_width = base_width
|
||||
self.drop_rate = drop_rate
|
||||
self.expansion = block.expansion
|
||||
super(ResNet, self).__init__()
|
||||
@ -123,31 +135,29 @@ class ResNet(nn.Module):
|
||||
self.layer2 = self._make_layer(block, 128, layers[1], stride=2, drop_rate=block_drop_rate)
|
||||
self.layer3 = self._make_layer(block, 256, layers[2], stride=2, drop_rate=block_drop_rate)
|
||||
self.layer4 = self._make_layer(block, 512, layers[3], stride=2, drop_rate=block_drop_rate)
|
||||
self.global_pool = AdaptiveAvgMaxPool2d(pool_type=global_pool)
|
||||
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
|
||||
self.num_features = 512 * block.expansion
|
||||
self.fc = nn.Linear(self.num_features, num_classes)
|
||||
self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
|
||||
|
||||
for m in self.modules():
|
||||
if isinstance(m, nn.Conv2d):
|
||||
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
m.weight.data.normal_(0, math.sqrt(2. / n))
|
||||
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
|
||||
elif isinstance(m, nn.BatchNorm2d):
|
||||
m.weight.data.fill_(1)
|
||||
m.bias.data.zero_()
|
||||
nn.init.constant_(m.weight, 1.)
|
||||
nn.init.constant_(m.bias, 0.)
|
||||
|
||||
def _make_layer(self, block, planes, blocks, stride=1, drop_rate=0.):
|
||||
downsample = None
|
||||
if stride != 1 or self.inplanes != planes * block.expansion:
|
||||
downsample = nn.Sequential(
|
||||
nn.Conv2d(self.inplanes, planes * block.expansion,
|
||||
kernel_size=1, stride=stride, bias=False),
|
||||
nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
|
||||
nn.BatchNorm2d(planes * block.expansion),
|
||||
)
|
||||
|
||||
layers = [block(self.inplanes, planes, stride, downsample, drop_rate)]
|
||||
layers = [block(self.inplanes, planes, stride, downsample, self.cardinality, self.base_width, drop_rate)]
|
||||
self.inplanes = planes * block.expansion
|
||||
for i in range(1, blocks):
|
||||
layers.append(block(self.inplanes, planes))
|
||||
layers.append(block(self.inplanes, planes, cardinality=self.cardinality, base_width=self.base_width))
|
||||
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
@ -155,11 +165,11 @@ class ResNet(nn.Module):
|
||||
return self.fc
|
||||
|
||||
def reset_classifier(self, num_classes, global_pool='avg'):
|
||||
self.global_pool = AdaptiveAvgMaxPool2d(pool_type=global_pool)
|
||||
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
|
||||
self.num_classes = num_classes
|
||||
del self.fc
|
||||
if num_classes:
|
||||
self.fc = nn.Linear(512 * self.expansion, num_classes)
|
||||
self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
|
||||
else:
|
||||
self.fc = None
|
||||
|
||||
@ -245,3 +255,51 @@ def resnet152(pretrained=False, **kwargs):
|
||||
if pretrained:
|
||||
model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
|
||||
return model
|
||||
|
||||
|
||||
def resnext50_32x4d(cardinality=32, base_width=4, pretrained=False, **kwargs):
|
||||
"""Constructs a ResNeXt50-32x4d model.
|
||||
|
||||
Args:
|
||||
cardinality (int): Cardinality of the aggregated transform
|
||||
base_width (int): Base width of the grouped convolution
|
||||
"""
|
||||
model = ResNet(
|
||||
Bottleneck, [3, 4, 6, 3], cardinality=cardinality, base_width=base_width, **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
def resnext101_32x4d(cardinality=32, base_width=4, pretrained=False, **kwargs):
|
||||
"""Constructs a ResNeXt-101 model.
|
||||
|
||||
Args:
|
||||
cardinality (int): Cardinality of the aggregated transform
|
||||
base_width (int): Base width of the grouped convolution
|
||||
"""
|
||||
model = ResNet(
|
||||
Bottleneck, [3, 4, 23, 3], cardinality=cardinality, base_width=base_width, **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
def resnext101_64x4d(cardinality=64, base_width=4, pretrained=False, **kwargs):
|
||||
"""Constructs a ResNeXt101-64x4d model.
|
||||
|
||||
Args:
|
||||
cardinality (int): Cardinality of the aggregated transform
|
||||
base_width (int): Base width of the grouped convolution
|
||||
"""
|
||||
model = ResNet(
|
||||
Bottleneck, [3, 4, 23, 3], cardinality=cardinality, base_width=base_width, **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
def resnext152_32x4d(cardinality=32, base_width=4, pretrained=False, **kwargs):
|
||||
"""Constructs a ResNeXt152-32x4d model.
|
||||
|
||||
Args:
|
||||
cardinality (int): Cardinality of the aggregated transform
|
||||
base_width (int): Base width of the grouped convolution
|
||||
"""
|
||||
model = ResNet(
|
||||
Bottleneck, [3, 8, 36, 3], cardinality=cardinality, base_width=base_width, **kwargs)
|
||||
return model
|
||||
|
||||
@ -1,175 +0,0 @@
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import math
|
||||
import torch.utils.model_zoo as model_zoo
|
||||
from models.adaptive_avgmax_pool import AdaptiveAvgMaxPool2d
|
||||
|
||||
__all__ = ['ResNeXt', 'resnext50', 'resnext101', 'resnext152']
|
||||
|
||||
|
||||
def conv3x3(in_planes, out_planes, stride=1):
|
||||
"3x3 convolution with padding"
|
||||
return nn.Conv2d(
|
||||
in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
|
||||
|
||||
|
||||
class ResNeXtBottleneckC(nn.Module):
|
||||
expansion = 4
|
||||
|
||||
def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=32, base_width=4):
|
||||
super(ResNeXtBottleneckC, self).__init__()
|
||||
|
||||
width = math.floor(planes * (base_width / 64)) * cardinality
|
||||
|
||||
self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(width)
|
||||
self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride,
|
||||
padding=1, bias=False, groups=cardinality)
|
||||
self.bn2 = nn.BatchNorm2d(width)
|
||||
self.conv3 = nn.Conv2d(width, planes * 4, kernel_size=1, bias=False)
|
||||
self.bn3 = nn.BatchNorm2d(planes * 4)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.downsample = downsample
|
||||
self.stride = stride
|
||||
|
||||
def forward(self, x):
|
||||
residual = x
|
||||
|
||||
out = self.conv1(x)
|
||||
out = self.bn1(out)
|
||||
out = self.relu(out)
|
||||
|
||||
out = self.conv2(out)
|
||||
out = self.bn2(out)
|
||||
out = self.relu(out)
|
||||
|
||||
out = self.conv3(out)
|
||||
out = self.bn3(out)
|
||||
|
||||
if self.downsample is not None:
|
||||
residual = self.downsample(x)
|
||||
|
||||
out += residual
|
||||
out = self.relu(out)
|
||||
|
||||
return out
|
||||
|
||||
|
||||
class ResNeXt(nn.Module):
|
||||
|
||||
def __init__(self, block, layers, num_classes=1000, cardinality=32, base_width=4,
|
||||
drop_rate=0., global_pool='avg'):
|
||||
self.num_classes = num_classes
|
||||
self.inplanes = 64
|
||||
self.cardinality = cardinality
|
||||
self.base_width = base_width
|
||||
self.drop_rate = drop_rate
|
||||
super(ResNeXt, self).__init__()
|
||||
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(64)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
|
||||
self.layer1 = self._make_layer(block, 64, layers[0])
|
||||
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
|
||||
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
|
||||
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
|
||||
self.avgpool = AdaptiveAvgMaxPool2d(pool_type=global_pool)
|
||||
self.num_features = 512 * block.expansion
|
||||
self.fc = nn.Linear(self.num_features, num_classes)
|
||||
|
||||
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.constant_(m.weight, 1.)
|
||||
nn.init.constant_(m.bias, 0.)
|
||||
|
||||
def _make_layer(self, block, planes, blocks, stride=1):
|
||||
downsample = None
|
||||
if stride != 1 or self.inplanes != planes * block.expansion:
|
||||
downsample = nn.Sequential(
|
||||
nn.Conv2d(
|
||||
self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
|
||||
nn.BatchNorm2d(planes * block.expansion),
|
||||
)
|
||||
|
||||
layers = [block(self.inplanes, planes, stride, downsample, self.cardinality, self.base_width)]
|
||||
self.inplanes = planes * block.expansion
|
||||
for i in range(1, blocks):
|
||||
layers.append(block(self.inplanes, planes, 1, None, self.cardinality, self.base_width))
|
||||
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
def get_classifier(self):
|
||||
return self.fc
|
||||
|
||||
def reset_classifier(self, num_classes, global_pool='avg'):
|
||||
self.avgpool = AdaptiveAvgMaxPool2d(pool_type=global_pool)
|
||||
self.num_classes = num_classes
|
||||
del self.fc
|
||||
if num_classes:
|
||||
self.fc = nn.Linear(self.num_features, num_classes)
|
||||
else:
|
||||
self.fc = None
|
||||
|
||||
def forward_features(self, x, pool=True):
|
||||
x = self.conv1(x)
|
||||
x = self.bn1(x)
|
||||
x = self.relu(x)
|
||||
x = self.maxpool(x)
|
||||
|
||||
x = self.layer1(x)
|
||||
x = self.layer2(x)
|
||||
x = self.layer3(x)
|
||||
x = self.layer4(x)
|
||||
if pool:
|
||||
x = self.avgpool(x)
|
||||
x = x.view(x.size(0), -1)
|
||||
return x
|
||||
|
||||
def forward(self, x):
|
||||
x = self.forward_features(x)
|
||||
if self.drop_rate > 0.:
|
||||
x = F.dropout(x, p=self.drop_rate, training=self.training)
|
||||
x = self.fc(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
def resnext50(cardinality=32, base_width=4, pretrained=False, **kwargs):
|
||||
"""Constructs a ResNeXt-50 model.
|
||||
|
||||
Args:
|
||||
cardinality (int): Cardinality of the aggregated transform
|
||||
base_width (int): Base width of the grouped convolution
|
||||
shortcut ('A'|'B'|'C'): 'B' use 1x1 conv to downsample, 'C' use 1x1 conv on every residual connection
|
||||
"""
|
||||
model = ResNeXt(
|
||||
ResNeXtBottleneckC, [3, 4, 6, 3], cardinality=cardinality, base_width=base_width, **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
def resnext101(cardinality=32, base_width=4, pretrained=False, **kwargs):
|
||||
"""Constructs a ResNeXt-101 model.
|
||||
|
||||
Args:
|
||||
cardinality (int): Cardinality of the aggregated transform
|
||||
base_width (int): Base width of the grouped convolution
|
||||
shortcut ('A'|'B'|'C'): 'B' use 1x1 conv to downsample, 'C' use 1x1 conv on every residual connection
|
||||
"""
|
||||
model = ResNeXt(
|
||||
ResNeXtBottleneckC, [3, 4, 23, 3], cardinality=cardinality, base_width=base_width, **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
def resnext152(cardinality=32, base_width=4, pretrained=False, **kwargs):
|
||||
"""Constructs a ResNeXt-152 model.
|
||||
|
||||
Args:
|
||||
cardinality (int): Cardinality of the aggregated transform
|
||||
base_width (int): Base width of the grouped convolution
|
||||
shortcut ('A'|'B'|'C'): 'B' use 1x1 conv to downsample, 'C' use 1x1 conv on every residual connection
|
||||
"""
|
||||
model = ResNeXt(
|
||||
ResNeXtBottleneckC, [3, 8, 36, 3], cardinality=cardinality, base_width=base_width, **kwargs)
|
||||
return model
|
||||
@ -9,7 +9,7 @@ import math
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from torch.utils import model_zoo
|
||||
from models.adaptive_avgmax_pool import AdaptiveAvgMaxPool2d
|
||||
from models.adaptive_avgmax_pool import SelectAdaptivePool2d
|
||||
|
||||
__all__ = ['SENet', 'senet154', 'seresnet50', 'seresnet101', 'seresnet152',
|
||||
'seresnext50_32x4d', 'seresnext101_32x4d']
|
||||
@ -307,7 +307,7 @@ class SENet(nn.Module):
|
||||
downsample_kernel_size=downsample_kernel_size,
|
||||
downsample_padding=downsample_padding
|
||||
)
|
||||
self.avg_pool = AdaptiveAvgMaxPool2d(pool_type=global_pool)
|
||||
self.avg_pool = SelectAdaptivePool2d(pool_type=global_pool)
|
||||
self.drop_rate = drop_rate
|
||||
self.num_features = 512 * block.expansion
|
||||
self.last_linear = nn.Linear(self.num_features, num_classes)
|
||||
|
||||
27
models/test_time_pool.py
Normal file
27
models/test_time_pool.py
Normal file
@ -0,0 +1,27 @@
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
from models.adaptive_avgmax_pool import adaptive_avgmax_pool2d
|
||||
|
||||
|
||||
class TestTimePoolHead(nn.Module):
|
||||
def __init__(self, base, original_pool=7):
|
||||
super(TestTimePoolHead, self).__init__()
|
||||
self.base = base
|
||||
self.original_pool = original_pool
|
||||
base_fc = self.base.get_classifier()
|
||||
if isinstance(base_fc, nn.Conv2d):
|
||||
self.fc = base_fc
|
||||
else:
|
||||
self.fc = nn.Conv2d(
|
||||
self.base.num_features, self.base.num_classes, kernel_size=1, bias=True)
|
||||
self.fc.weight.data.copy_(base_fc.weight.data.view(self.fc.weight.size()))
|
||||
self.fc.bias.data.copy_(base_fc.bias.data.view(self.fc.bias.size()))
|
||||
self.base.reset_classifier(0) # delete original fc layer
|
||||
|
||||
def forward(self, x):
|
||||
x = self.base.forward_features(x, pool=False)
|
||||
x = F.avg_pool2d(x, kernel_size=self.original_pool, stride=1)
|
||||
x = self.fc(x)
|
||||
x = adaptive_avgmax_pool2d(x, 1)
|
||||
return x.view(x.size(0), -1)
|
||||
|
||||
40
validate.py
40
validate.py
@ -9,9 +9,8 @@ import torch
|
||||
import torch.backends.cudnn as cudnn
|
||||
import torch.nn as nn
|
||||
import torch.nn.parallel
|
||||
from collections import OrderedDict
|
||||
|
||||
from models import create_model
|
||||
from models import create_model, load_checkpoint, TestTimePoolHead
|
||||
from data import Dataset, create_loader, get_model_meanstd
|
||||
|
||||
|
||||
@ -41,41 +40,27 @@ parser.add_argument('--no-test-pool', dest='no_test_pool', action='store_true',
|
||||
def main():
|
||||
args = parser.parse_args()
|
||||
|
||||
test_time_pool = False
|
||||
if 'dpn' in args.model and args.img_size > 224 and not args.no_test_pool:
|
||||
test_time_pool = True
|
||||
|
||||
# create model
|
||||
num_classes = 1000
|
||||
model = create_model(
|
||||
args.model,
|
||||
num_classes=num_classes,
|
||||
pretrained=args.pretrained,
|
||||
test_time_pool=test_time_pool)
|
||||
pretrained=args.pretrained)
|
||||
|
||||
print('Model %s created, param count: %d' %
|
||||
(args.model, sum([m.numel() for m in model.parameters()])))
|
||||
|
||||
# optionally resume from a checkpoint
|
||||
if args.checkpoint and os.path.isfile(args.checkpoint):
|
||||
print("=> loading checkpoint '{}'".format(args.checkpoint))
|
||||
checkpoint = torch.load(args.checkpoint)
|
||||
if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
|
||||
new_state_dict = OrderedDict()
|
||||
for k, v in checkpoint['state_dict'].items():
|
||||
if k.startswith('module'):
|
||||
name = k[7:] # remove `module.`
|
||||
else:
|
||||
name = k
|
||||
new_state_dict[name] = v
|
||||
model.load_state_dict(new_state_dict)
|
||||
else:
|
||||
model.load_state_dict(checkpoint)
|
||||
print("=> loaded checkpoint '{}'".format(args.checkpoint))
|
||||
elif not args.pretrained:
|
||||
print("=> no checkpoint found at '{}'".format(args.checkpoint))
|
||||
# load a checkpoint
|
||||
if not args.pretrained:
|
||||
if not load_checkpoint(model, args.checkpoint):
|
||||
exit(1)
|
||||
|
||||
test_time_pool = False
|
||||
# FIXME make this work for networks with default img size != 224 and default pool k != 7
|
||||
if args.img_size > 224 and not args.no_test_pool:
|
||||
model = TestTimePoolHead(model)
|
||||
test_time_pool = True
|
||||
|
||||
if args.num_gpu > 1:
|
||||
model = torch.nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda()
|
||||
else:
|
||||
@ -94,7 +79,8 @@ def main():
|
||||
use_prefetcher=True,
|
||||
mean=data_mean,
|
||||
std=data_std,
|
||||
num_workers=args.workers)
|
||||
num_workers=args.workers,
|
||||
crop_pct=1.0 if test_time_pool else None)
|
||||
|
||||
batch_time = AverageMeter()
|
||||
losses = AverageMeter()
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user