Support training imagenet classifier
This commit is contained in:
James Betker
parent
f3db381fa1
commit
34f8c8641f
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@ -10,22 +10,47 @@ class TorchDataset(Dataset):
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"mnist": datasets.MNIST,
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"fmnist": datasets.FashionMNIST,
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"cifar10": datasets.CIFAR10,
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"imagenet": datasets.ImageNet,
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"imagefolder": datasets.ImageFolder
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}
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transforms = []
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if opt['flip']:
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transforms.append(T.RandomHorizontalFlip())
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if opt['crop_sz']:
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transforms.append(T.RandomCrop(opt['crop_sz'], padding=opt['padding'], padding_mode="reflect"))
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transforms.append(T.ToTensor())
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normalize = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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if opt['train']:
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transforms = [
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T.RandomResizedCrop(opt['image_size']),
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T.RandomHorizontalFlip(),
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T.ToTensor(),
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normalize,
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]
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else:
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transforms = [
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T.Resize(opt['val_resize']),
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T.CenterCrop(opt['image_size']),
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T.ToTensor(),
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normalize,
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]
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transforms = T.Compose(transforms)
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is_for_training = opt['test'] if 'test' in opt.keys() else True
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self.dataset = DATASET_MAP[opt['dataset']](opt['datapath'], train=is_for_training, download=True, transform=transforms)
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self.dataset = DATASET_MAP[opt['dataset']](transform=transforms, **opt['kwargs'])
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self.len = opt['fixed_len'] if 'fixed_len' in opt.keys() else len(self.dataset)
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def __getitem__(self, item):
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underlying_item = self.dataset[item][0]
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return {'lq': underlying_item, 'hq': underlying_item,
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underlying_item, lbl = self.dataset[item]
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return {'lq': underlying_item, 'hq': underlying_item, 'labels': lbl,
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'LQ_path': str(item), 'GT_path': str(item)}
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def __len__(self):
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return self.len
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if __name__ == '__main__':
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opt = {
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'flip': True,
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'crop_sz': None,
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'dataset': 'imagefolder',
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'resize': 256,
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'center_crop': 224,
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'normalize': True,
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'kwargs': {
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'root': 'F:\\4k6k\\datasets\\images\\imagenet_2017\\val',
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}
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}
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set = TorchDataset(opt)
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j = set[0]
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152
codes/models/pixel_level_contrastive_learning/resnet_unet_2.py
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152
codes/models/pixel_level_contrastive_learning/resnet_unet_2.py
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@ -0,0 +1,152 @@
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# Resnet implementation that adds a u-net style up-conversion component to output values at a
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# specified pixel density.
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#
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# The downsampling part of the network is compatible with the built-in torch resnet for use in
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# transfer learning.
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#
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# Only resnet50 currently supported.
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import torch
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import torch.nn as nn
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from torchvision.models.resnet import BasicBlock, Bottleneck, conv1x1, conv3x3
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from torchvision.models.utils import load_state_dict_from_url
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import torchvision
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from trainer.networks import register_model
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from utils.util import checkpoint, opt_get
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class ReverseBottleneck(nn.Module):
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def __init__(self, inplanes, planes, groups=1, passthrough=False,
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base_width=64, dilation=1, norm_layer=None):
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super().__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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width = int(planes * (base_width / 64.)) * groups
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self.passthrough = passthrough
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if passthrough:
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self.integrate = conv1x1(inplanes*2, inplanes)
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self.bn_integrate = norm_layer(inplanes)
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# Both self.conv2 and self.downsample layers downsample the input when stride != 1
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self.conv1 = conv1x1(inplanes, width)
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self.bn1 = norm_layer(width)
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self.conv2 = conv3x3(width, width, groups, dilation)
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self.bn2 = norm_layer(width)
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self.residual_upsample = nn.Sequential(
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nn.Upsample(scale_factor=2, mode='nearest'),
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conv1x1(width, width),
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norm_layer(width),
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)
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self.conv3 = conv1x1(width, planes)
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self.bn3 = norm_layer(planes)
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self.relu = nn.ReLU(inplace=True)
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self.upsample = nn.Sequential(
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nn.Upsample(scale_factor=2, mode='nearest'),
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conv1x1(inplanes, planes),
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norm_layer(planes),
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)
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def forward(self, x, passthrough=None):
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if self.passthrough:
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x = self.bn_integrate(self.integrate(torch.cat([x, passthrough], dim=1)))
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.residual_upsample(out)
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out = self.conv3(out)
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out = self.bn3(out)
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identity = self.upsample(x)
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out = out + identity
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out = self.relu(out)
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return out
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class UResNet50(torchvision.models.resnet.ResNet):
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def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
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groups=1, width_per_group=64, replace_stride_with_dilation=None,
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norm_layer=None, out_dim=128):
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super().__init__(block, layers, num_classes, zero_init_residual, groups, width_per_group,
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replace_stride_with_dilation, norm_layer)
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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'''
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# For reference:
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self.layer1 = self._make_layer(block, 64, layers[0])
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
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dilate=replace_stride_with_dilation[0])
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
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dilate=replace_stride_with_dilation[1])
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
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dilate=replace_stride_with_dilation[2])
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'''
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uplayers = []
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inplanes = 2048
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first = True
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for i in range(2):
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uplayers.append(ReverseBottleneck(inplanes, inplanes // 2, norm_layer=norm_layer, passthrough=not first))
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inplanes = inplanes // 2
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first = False
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self.uplayers = nn.ModuleList(uplayers)
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self.tail = nn.Sequential(conv1x1(1024, 512),
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norm_layer(512),
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nn.ReLU(),
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conv3x3(512, 512),
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norm_layer(512),
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nn.ReLU(),
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conv1x1(512, out_dim))
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del self.fc # Not used in this implementation and just consumes a ton of GPU memory.
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def _forward_impl(self, x):
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# Should be the exact same implementation of torchvision.models.resnet.ResNet.forward_impl,
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# except using checkpoints on the body conv layers.
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)
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x1 = checkpoint(self.layer1, x)
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x2 = checkpoint(self.layer2, x1)
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x3 = checkpoint(self.layer3, x2)
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x4 = checkpoint(self.layer4, x3)
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unused = self.avgpool(x4) # This is performed for instance-level pixpro learning, even though it is unused.
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x = checkpoint(self.uplayers[0], x4)
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x = checkpoint(self.uplayers[1], x, x3)
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#x = checkpoint(self.uplayers[2], x, x2)
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#x = checkpoint(self.uplayers[3], x, x1)
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return checkpoint(self.tail, torch.cat([x, x2], dim=1))
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def forward(self, x):
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return self._forward_impl(x)
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@register_model
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def register_u_resnet50(opt_net, opt):
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model = UResNet50(Bottleneck, [3, 4, 6, 3], out_dim=opt_net['odim'])
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if opt_get(opt_net, ['use_pretrained_base'], False):
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state_dict = load_state_dict_from_url('https://download.pytorch.org/models/resnet50-19c8e357.pth', progress=True)
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model.load_state_dict(state_dict, strict=False)
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return model
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if __name__ == '__main__':
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model = UResNet50(Bottleneck, [3,4,6,3])
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samp = torch.rand(1,3,224,224)
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model(samp)
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# For pixpro: attach to "tail.3"
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@ -192,7 +192,7 @@ def wide_resnet101_2(pretrained=False, progress=True, **kwargs):
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@register_model
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def register_resnet52(opt_net, opt):
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def register_resnet50(opt_net, opt):
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model = resnet50(pretrained=opt_net['pretrained'])
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if opt_net['custom_head_logits']:
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model.fc = nn.Linear(512 * 4, opt_net['custom_head_logits'])
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@ -10,11 +10,11 @@ from utils.util import opt_get
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class UResnetMaskProducer(nn.Module):
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def __init__(self, pretrained_uresnet_path, kmeans_centroid_path, mask_scales=[.125,.25,.5,1]):
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def __init__(self, pretrained_uresnet_path, kmeans_centroid_path, mask_scales=[.125,.25,.5,1], tail_dim=512):
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super().__init__()
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_, centroids = torch.load(kmeans_centroid_path)
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self.centroids = nn.Parameter(centroids)
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self.ures = UResNet50(Bottleneck, [3,4,6,3], out_dim=512).to('cuda')
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self.ures = UResNet50(Bottleneck, [3,4,6,3], out_dim=tail_dim).to('cuda')
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self.mask_scales = mask_scales
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sd = torch.load(pretrained_uresnet_path)
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@ -48,9 +48,8 @@ class ScaledWeightConv(_ConvNd):
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w.FOR_SCALE_SHIFT = True
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s.FOR_SCALE_SHIFT = True
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# This should probably be configurable at some point.
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for p in self.parameters():
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if not hasattr(p, "FOR_SCALE_SHIFT"):
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p.DO_NOT_TRAIN = True
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self.weight.DO_NOT_TRAIN = True
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self.weight.requires_grad = False
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def _weighted_conv_forward(self, input, weight):
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if self.padding_mode != 'zeros':
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@ -60,7 +59,12 @@ class ScaledWeightConv(_ConvNd):
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return F.conv2d(input, weight, self.bias, self.stride,
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self.padding, self.dilation, self.groups)
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def forward(self, input: Tensor, masks: dict) -> Tensor:
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def forward(self, input: Tensor, masks: dict = None) -> Tensor:
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if masks is None:
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# An alternate "mode" of operation is the masks are injected as parameters.
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assert hasattr(self, 'masks')
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masks = self.masks
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# This is an exceptionally inefficient way of achieving this functionality. The hope is that if this is any
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# good at all, this can be made more efficient by performing a single conv pass with multiple masks.
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weighted_convs = [self._weighted_conv_forward(input, self.weight * scale + shift) for scale, shift in zip(self.weight_scales, self.shifts)]
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@ -72,6 +76,20 @@ class ScaledWeightConv(_ConvNd):
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return index_2d(weighted_convs, masks[needed_mask])
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def create_wrapped_conv_from_template(conv: nn.Conv2d, breadth: int):
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wrapped = ScaledWeightConv(conv.in_channels,
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conv.out_channels,
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conv.kernel_size[0],
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conv.stride[0],
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conv.padding[0],
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conv.dilation[0],
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conv.groups,
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conv.bias,
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conv.padding_mode,
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breadth)
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return wrapped
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# Drop-in implementation of ConvTranspose2d that can apply masked scales&shifts to the convolution weights.
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class ScaledWeightConvTranspose(_ConvTransposeNd):
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def __init__(
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@ -102,9 +120,8 @@ class ScaledWeightConvTranspose(_ConvTransposeNd):
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w.FOR_SCALE_SHIFT = True
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s.FOR_SCALE_SHIFT = True
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# This should probably be configurable at some point.
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for nm, p in self.named_parameters():
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if nm == 'weight':
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p.DO_NOT_TRAIN = True
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self.weight.DO_NOT_TRAIN = True
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self.weight.requires_grad = False
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def _conv_transpose_forward(self, input, weight, output_size) -> Tensor:
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if self.padding_mode != 'zeros':
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@ -117,7 +134,12 @@ class ScaledWeightConvTranspose(_ConvTransposeNd):
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input, weight, self.bias, self.stride, self.padding,
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output_padding, self.groups, self.dilation)
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def forward(self, input: Tensor, masks: dict, output_size: Optional[List[int]] = None) -> Tensor:
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def forward(self, input: Tensor, masks: dict = None, output_size: Optional[List[int]] = None) -> Tensor:
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if masks is None:
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# An alternate "mode" of operation is the masks are injected as parameters.
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assert hasattr(self, 'masks')
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masks = self.masks
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# This is an exceptionally inefficient way of achieving this functionality. The hope is that if this is any
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# good at all, this can be made more efficient by performing a single conv pass with multiple masks.
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weighted_convs = [self._conv_transpose_forward(input, self.weight * scale + shift, output_size)
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@ -128,3 +150,22 @@ class ScaledWeightConvTranspose(_ConvTransposeNd):
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assert needed_mask in masks.keys()
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return index_2d(weighted_convs, masks[needed_mask])
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def create_wrapped_conv_transpose_from_template(conv: nn.Conv2d, breadth: int):
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wrapped = ScaledWeightConvTranspose(conv.in_channels,
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conv.out_channels,
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conv.kernel_size,
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conv.stride,
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conv.padding,
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conv.output_padding,
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conv.groups,
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conv.bias,
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conv.dilation,
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conv.padding_mode,
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breadth)
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wrapped.weight = conv.weight
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wrapped.weight.DO_NOT_TRAIN = True
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wrapped.weight.requires_grad = False
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wrapped.bias = conv.bias
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return wrapped
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441
codes/models/weighted_conv_resnet.py
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441
codes/models/weighted_conv_resnet.py
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@ -0,0 +1,441 @@
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import torch
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import torchvision
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from torch import Tensor
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import torch.nn as nn
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from torchvision.models.utils import load_state_dict_from_url
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from typing import Type, Any, Callable, Union, List, Optional, OrderedDict, Iterator
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__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
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'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
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'wide_resnet50_2', 'wide_resnet101_2']
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from models.vqvae.scaled_weight_conv import ScaledWeightConv
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from trainer.networks import register_model
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from utils.util import checkpoint
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model_urls = {
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'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
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'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
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'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
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'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
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'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
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'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
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'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
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'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
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'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
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}
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def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1, breadth: int = 8) -> ScaledWeightConv:
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"""3x3 convolution with padding"""
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return ScaledWeightConv(in_planes, out_planes, kernel_size=3, stride=stride,
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padding=dilation, groups=groups, bias=False, dilation=dilation, breadth=breadth)
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def conv1x1(in_planes: int, out_planes: int, stride: int = 1, breadth: int = 8) -> ScaledWeightConv:
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"""1x1 convolution"""
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return ScaledWeightConv(in_planes, out_planes, kernel_size=1, stride=stride, bias=False, breadth=breadth)
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# Provides similar API to nn.Sequential, but handles feed-forward networks that need to feed masks into their convolutions.
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class MaskedSequential(nn.Module):
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def __init__(self, *args):
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super().__init__()
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if len(args) == 1 and isinstance(args[0], OrderedDict):
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for key, module in args[0].items():
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self.add_module(key, module)
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else:
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for idx, module in enumerate(args):
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self.add_module(str(idx), module)
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def __iter__(self) -> Iterator[nn.Module]:
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return iter(self._modules.values())
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def forward(self, x):
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mask = self.masks
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for m in self:
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if isinstance(m, ScaledWeightConv) or isinstance(m, BasicBlock) or isinstance(m, Bottleneck):
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x = m(x, mask)
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else:
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x = m(x)
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return x
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class BasicBlock(nn.Module):
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expansion: int = 1
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def __init__(
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self,
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inplanes: int,
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planes: int,
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stride: int = 1,
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||||
downsample: Optional[nn.Module] = None,
|
||||
groups: int = 1,
|
||||
base_width: int = 64,
|
||||
dilation: int = 1,
|
||||
norm_layer: Optional[Callable[..., nn.Module]] = None,
|
||||
breadth: int = 8
|
||||
) -> None:
|
||||
super(BasicBlock, self).__init__()
|
||||
if norm_layer is None:
|
||||
norm_layer = nn.BatchNorm2d
|
||||
if groups != 1 or base_width != 64:
|
||||
raise ValueError('BasicBlock only supports groups=1 and base_width=64')
|
||||
if dilation > 1:
|
||||
raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
|
||||
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
|
||||
self.conv1 = conv3x3(inplanes, planes, stride, breadth=breadth)
|
||||
self.bn1 = norm_layer(planes)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.conv2 = conv3x3(planes, planes, breadth=breadth)
|
||||
self.bn2 = norm_layer(planes)
|
||||
self.downsample = downsample
|
||||
self.stride = stride
|
||||
|
||||
def forward(self, x: Tensor, mask: Tensor) -> Tensor:
|
||||
identity = x
|
||||
|
||||
out = self.conv1(x, mask)
|
||||
out = self.bn1(out)
|
||||
out = self.relu(out)
|
||||
|
||||
out = self.conv2(out, mask)
|
||||
out = self.bn2(out)
|
||||
|
||||
if self.downsample is not None:
|
||||
identity = self.downsample(x, mask)
|
||||
|
||||
out += identity
|
||||
out = self.relu(out)
|
||||
|
||||
return out
|
||||
|
||||
|
||||
class Bottleneck(nn.Module):
|
||||
# Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
|
||||
# while original implementation places the stride at the first 1x1 convolution(self.conv1)
|
||||
# according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
|
||||
# This variant is also known as ResNet V1.5 and improves accuracy according to
|
||||
# https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
|
||||
|
||||
expansion: int = 4
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
inplanes: int,
|
||||
planes: int,
|
||||
stride: int = 1,
|
||||
downsample: Optional[nn.Module] = None,
|
||||
groups: int = 1,
|
||||
base_width: int = 64,
|
||||
dilation: int = 1,
|
||||
norm_layer: Optional[Callable[..., nn.Module]] = None,
|
||||
breadth: int = 8
|
||||
) -> None:
|
||||
super(Bottleneck, self).__init__()
|
||||
if norm_layer is None:
|
||||
norm_layer = nn.BatchNorm2d
|
||||
width = int(planes * (base_width / 64.)) * groups
|
||||
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
|
||||
self.conv1 = conv1x1(inplanes, width, breadth=breadth)
|
||||
self.bn1 = norm_layer(width)
|
||||
self.conv2 = conv3x3(width, width, stride, groups, dilation, breadth=breadth)
|
||||
self.bn2 = norm_layer(width)
|
||||
self.conv3 = conv1x1(width, planes * self.expansion, breadth=breadth)
|
||||
self.bn3 = norm_layer(planes * self.expansion)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.downsample = downsample
|
||||
self.stride = stride
|
||||
|
||||
def forward(self, x: Tensor, mask: Tensor) -> Tensor:
|
||||
identity = x
|
||||
|
||||
out = self.conv1(x, mask)
|
||||
out = self.bn1(out)
|
||||
out = self.relu(out)
|
||||
|
||||
out = self.conv2(out, mask)
|
||||
out = self.bn2(out)
|
||||
out = self.relu(out)
|
||||
|
||||
out = self.conv3(out, mask)
|
||||
out = self.bn3(out)
|
||||
|
||||
if self.downsample is not None:
|
||||
self.downsample.masks = mask
|
||||
identity = self.downsample(x)
|
||||
|
||||
out += identity
|
||||
out = self.relu(out)
|
||||
|
||||
return out
|
||||
|
||||
|
||||
class ResNet(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
block: Type[Union[BasicBlock, Bottleneck]],
|
||||
layers: List[int],
|
||||
num_classes: int = 1000,
|
||||
zero_init_residual: bool = False,
|
||||
groups: int = 1,
|
||||
width_per_group: int = 64,
|
||||
replace_stride_with_dilation: Optional[List[bool]] = None,
|
||||
norm_layer: Optional[Callable[..., nn.Module]] = None,
|
||||
breadth: int = 8
|
||||
) -> None:
|
||||
super(ResNet, self).__init__()
|
||||
if norm_layer is None:
|
||||
norm_layer = nn.BatchNorm2d
|
||||
self._norm_layer = norm_layer
|
||||
|
||||
self.inplanes = 64
|
||||
self.dilation = 1
|
||||
if replace_stride_with_dilation is None:
|
||||
# each element in the tuple indicates if we should replace
|
||||
# the 2x2 stride with a dilated convolution instead
|
||||
replace_stride_with_dilation = [False, False, False]
|
||||
if len(replace_stride_with_dilation) != 3:
|
||||
raise ValueError("replace_stride_with_dilation should be None "
|
||||
"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
|
||||
self.groups = groups
|
||||
self.base_width = width_per_group
|
||||
self.conv1 = ScaledWeightConv(3, self.inplanes, kernel_size=7, stride=2, padding=3,
|
||||
bias=False, breadth=breadth)
|
||||
self.bn1 = norm_layer(self.inplanes)
|
||||
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], breadth)
|
||||
self.layer2 = self._make_layer(block, 128, layers[1], breadth, stride=2,
|
||||
dilate=replace_stride_with_dilation[0])
|
||||
self.layer3 = self._make_layer(block, 256, layers[2], breadth, stride=2,
|
||||
dilate=replace_stride_with_dilation[1])
|
||||
self.layer4 = self._make_layer(block, 512, layers[3], breadth, stride=2,
|
||||
dilate=replace_stride_with_dilation[2])
|
||||
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
|
||||
self.fc = nn.Linear(512 * block.expansion, num_classes)
|
||||
|
||||
for m in self.modules():
|
||||
if isinstance(m, ScaledWeightConv):
|
||||
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
|
||||
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
|
||||
nn.init.constant_(m.weight, 1)
|
||||
nn.init.constant_(m.bias, 0)
|
||||
|
||||
# Zero-initialize the last BN in each residual branch,
|
||||
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
|
||||
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
|
||||
if zero_init_residual:
|
||||
for m in self.modules():
|
||||
if isinstance(m, Bottleneck):
|
||||
nn.init.constant_(m.bn3.weight, 0) # type: ignore[arg-type]
|
||||
elif isinstance(m, BasicBlock):
|
||||
nn.init.constant_(m.bn2.weight, 0) # type: ignore[arg-type]
|
||||
|
||||
def _make_layer(self, block: Type[Union[BasicBlock, Bottleneck]], planes: int, blocks: int, breadth: int,
|
||||
stride: int = 1, dilate: bool = False) -> MaskedSequential:
|
||||
norm_layer = self._norm_layer
|
||||
downsample = None
|
||||
previous_dilation = self.dilation
|
||||
if dilate:
|
||||
self.dilation *= stride
|
||||
stride = 1
|
||||
if stride != 1 or self.inplanes != planes * block.expansion:
|
||||
downsample = MaskedSequential(
|
||||
conv1x1(self.inplanes, planes * block.expansion, stride, breadth=breadth),
|
||||
norm_layer(planes * block.expansion),
|
||||
)
|
||||
|
||||
layers = []
|
||||
layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
|
||||
self.base_width, previous_dilation, norm_layer, breadth=breadth))
|
||||
self.inplanes = planes * block.expansion
|
||||
for _ in range(1, blocks):
|
||||
layers.append(block(self.inplanes, planes, groups=self.groups,
|
||||
base_width=self.base_width, dilation=self.dilation,
|
||||
norm_layer=norm_layer, breadth=breadth))
|
||||
|
||||
return MaskedSequential(*layers)
|
||||
|
||||
def _forward_impl(self, x: Tensor, mask: Tensor) -> Tensor:
|
||||
# See note [TorchScript super()]
|
||||
x = self.conv1(x, mask)
|
||||
x = self.bn1(x)
|
||||
x = self.relu(x)
|
||||
x = self.maxpool(x)
|
||||
|
||||
for m in [self.layer1, self.layer2, self.layer3, self.layer4]:
|
||||
m.masks = mask
|
||||
x = checkpoint(self.layer1, x)
|
||||
x = checkpoint(self.layer2, x)
|
||||
x = checkpoint(self.layer3, x)
|
||||
x = checkpoint(self.layer4, x)
|
||||
|
||||
x = self.avgpool(x)
|
||||
x = torch.flatten(x, 1)
|
||||
x = self.fc(x)
|
||||
|
||||
return x
|
||||
|
||||
def forward(self, x: Tensor, mask: Tensor) -> Tensor:
|
||||
return self._forward_impl(x, mask)
|
||||
|
||||
|
||||
def _resnet(
|
||||
arch: str,
|
||||
block: Type[Union[BasicBlock, Bottleneck]],
|
||||
layers: List[int],
|
||||
pretrained: bool,
|
||||
progress: bool,
|
||||
**kwargs: Any
|
||||
) -> ResNet:
|
||||
model = ResNet(block, layers, **kwargs)
|
||||
if pretrained:
|
||||
state_dict = load_state_dict_from_url(model_urls[arch],
|
||||
progress=progress)
|
||||
model.load_state_dict(state_dict, strict=False)
|
||||
return model
|
||||
|
||||
|
||||
def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
|
||||
r"""ResNet-18 model from
|
||||
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
|
||||
**kwargs)
|
||||
|
||||
|
||||
def resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
|
||||
r"""ResNet-34 model from
|
||||
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
|
||||
**kwargs)
|
||||
|
||||
|
||||
def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
|
||||
r"""ResNet-50 model from
|
||||
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
|
||||
**kwargs)
|
||||
|
||||
|
||||
def resnet101(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
|
||||
r"""ResNet-101 model from
|
||||
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
|
||||
**kwargs)
|
||||
|
||||
|
||||
def resnet152(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
|
||||
r"""ResNet-152 model from
|
||||
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
|
||||
**kwargs)
|
||||
|
||||
|
||||
def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
|
||||
r"""ResNeXt-50 32x4d model from
|
||||
`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
kwargs['groups'] = 32
|
||||
kwargs['width_per_group'] = 4
|
||||
return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
|
||||
pretrained, progress, **kwargs)
|
||||
|
||||
|
||||
def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
|
||||
r"""ResNeXt-101 32x8d model from
|
||||
`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
kwargs['groups'] = 32
|
||||
kwargs['width_per_group'] = 8
|
||||
return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
|
||||
pretrained, progress, **kwargs)
|
||||
|
||||
|
||||
def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
|
||||
r"""Wide ResNet-50-2 model from
|
||||
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
|
||||
|
||||
The model is the same as ResNet except for the bottleneck number of channels
|
||||
which is twice larger in every block. The number of channels in outer 1x1
|
||||
convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
|
||||
channels, and in Wide ResNet-50-2 has 2048-1024-2048.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
kwargs['width_per_group'] = 64 * 2
|
||||
return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
|
||||
pretrained, progress, **kwargs)
|
||||
|
||||
|
||||
def wide_resnet101_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
|
||||
r"""Wide ResNet-101-2 model from
|
||||
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
|
||||
|
||||
The model is the same as ResNet except for the bottleneck number of channels
|
||||
which is twice larger in every block. The number of channels in outer 1x1
|
||||
convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
|
||||
channels, and in Wide ResNet-50-2 has 2048-1024-2048.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
kwargs['width_per_group'] = 64 * 2
|
||||
return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
|
||||
pretrained, progress, **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def register_resnet50_weighted_conv(opt_net, opt):
|
||||
model = resnet50(pretrained=opt_net['pretrained'], **opt_net['kwargs'])
|
||||
return model
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
orig = torchvision.models.resnet.resnet50(pretrained=True)
|
||||
mod = resnet50(pretrained=True, breadth=4)
|
||||
idim = 224
|
||||
masks = {}
|
||||
for j in range(6):
|
||||
cdim = idim // (2 ** j)
|
||||
masks[cdim] = torch.zeros((1,1,cdim,cdim), dtype=torch.long)
|
||||
i = torch.rand(1,3,idim,idim)
|
||||
r1 = mod(i, masks)
|
||||
r2 = orig(i)
|
||||
|
|
@ -15,4 +15,5 @@ pytorch_fid==0.1.1
|
|||
kornia
|
||||
linear_attention_transformer
|
||||
vector_quantize_pytorch
|
||||
orjson
|
||||
orjson
|
||||
einops
|
||||
|
|
@ -59,7 +59,8 @@ def im_norm(x):
|
|||
def get_image_folder_dataloader(batch_size, num_workers, target_size=256):
|
||||
dataset_opt = dict_to_nonedict({
|
||||
'name': 'amalgam',
|
||||
'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_1024_square_with_new'],
|
||||
'paths': ['F:\\4k6k\\datasets\\images\\imagenet_2017\\train'],
|
||||
#'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_1024_square_with_new'],
|
||||
#'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_256_full'],
|
||||
#'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\1024_test'],
|
||||
'weights': [1],
|
||||
|
|
@ -94,22 +95,23 @@ def produce_latent_dict(model):
|
|||
id += batch_size
|
||||
if id > 1000:
|
||||
print("Saving checkpoint..")
|
||||
torch.save((latents, paths), '../results.pth')
|
||||
torch.save((latents, paths), '../imagenet_latent_dict.pth')
|
||||
id = 0
|
||||
|
||||
|
||||
def build_kmeans():
|
||||
latents, _ = torch.load('../results.pth')
|
||||
latents, _ = torch.load('../imagenet_latent_dict.pth')
|
||||
latents = torch.cat(latents, dim=0).to('cuda')
|
||||
cluster_ids_x, cluster_centers = kmeans(latents, num_clusters=8, distance="euclidean", device=torch.device('cuda:0'))
|
||||
torch.save((cluster_ids_x, cluster_centers), '../k_means.pth')
|
||||
cluster_ids_x, cluster_centers = kmeans(latents, num_clusters=4, distance="euclidean", device=torch.device('cuda:0'))
|
||||
torch.save((cluster_ids_x, cluster_centers), '../k_means_imagenet.pth')
|
||||
|
||||
|
||||
def use_kmeans():
|
||||
_, centers = torch.load('../experiments/k_means_uresnet_512.pth')
|
||||
_, centers = torch.load('../k_means_imagenet.pth')
|
||||
centers = centers.to('cuda')
|
||||
batch_size = 8
|
||||
num_workers = 0
|
||||
dataloader = get_image_folder_dataloader(batch_size, num_workers, target_size=512)
|
||||
dataloader = get_image_folder_dataloader(batch_size, num_workers, target_size=256)
|
||||
colormap = cm.get_cmap('viridis', 8)
|
||||
for i, batch in enumerate(tqdm(dataloader)):
|
||||
hq = batch['hq'].to('cuda')
|
||||
|
|
@ -117,16 +119,16 @@ def use_kmeans():
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|||
b, c, h, w = l.shape
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||||
dim = b*h*w
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||||
l = l.permute(0,2,3,1).reshape(dim,c)
|
||||
pred = kmeans_predict(l, centers, device=l.device)
|
||||
pred = kmeans_predict(l, centers)
|
||||
pred = pred.reshape(b,h,w)
|
||||
img = torch.tensor(colormap(pred[:, :, :].detach().numpy()))
|
||||
img = torch.tensor(colormap(pred[:, :, :].detach().cpu().numpy()))
|
||||
torchvision.utils.save_image(torch.nn.functional.interpolate(img.permute(0,3,1,2), scale_factor=8, mode="nearest"), f"{i}_categories.png")
|
||||
torchvision.utils.save_image(hq, f"{i}_hq.png")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
pretrained_path = '../experiments/uresnet_pixpro_512.pth'
|
||||
model = UResNet50(Bottleneck, [3,4,6,3], out_dim=512).to('cuda')
|
||||
pretrained_path = '../experiments/train_imagenet_pixpro_resnet/models/66500_generator.pth'
|
||||
model = UResNet50(Bottleneck, [3,4,6,3], out_dim=256).to('cuda')
|
||||
sd = torch.load(pretrained_path)
|
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resnet_sd = {}
|
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for k, v in sd.items():
|
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|
|
@ -139,5 +141,5 @@ if __name__ == '__main__':
|
|||
#find_similar_latents(model, 0, 8, structural_euc_dist)
|
||||
#create_latent_database(model, batch_size=32)
|
||||
#produce_latent_dict(model)
|
||||
#build_kmeans()
|
||||
use_kmeans()
|
||||
build_kmeans()
|
||||
#use_kmeans()
|
||||
|
|
|
|||
26
codes/scripts/folderize_imagenet_val.py
Normal file
26
codes/scripts/folderize_imagenet_val.py
Normal file
|
|
@ -0,0 +1,26 @@
|
|||
from glob import glob
|
||||
|
||||
import torch
|
||||
import os
|
||||
import shutil
|
||||
|
||||
if __name__ == '__main__':
|
||||
index_map_file = 'F:\\4k6k\\datasets\\images\\imagenet_2017\\imagenet_index_to_train_folder_name_map.pth'
|
||||
ground_truth = 'F:\\4k6k\\datasets\\images\\imagenet_2017\\validation_ground_truth.txt'
|
||||
val_path = 'F:\\4k6k\\datasets\\images\\imagenet_2017\\val'
|
||||
|
||||
index_map = torch.load(index_map_file)
|
||||
|
||||
for folder in index_map.values():
|
||||
os.makedirs(os.path.join(val_path, folder), exist_ok=True)
|
||||
|
||||
gtfile = open(ground_truth, 'r')
|
||||
gtids = []
|
||||
for line in gtfile:
|
||||
gtids.append(int(line.strip()))
|
||||
gtfile.close()
|
||||
|
||||
for i, img_file in enumerate(glob(os.path.join(val_path, "*.JPEG"))):
|
||||
shutil.move(img_file, os.path.join(val_path, index_map[gtids[i]],
|
||||
os.path.basename(img_file)))
|
||||
print("Done!")
|
||||
|
|
@ -295,7 +295,7 @@ class Trainer:
|
|||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../experiments/train_imgset_vqvae_stage1/train_imgset_vqvae_stage1_5.yml')
|
||||
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_imagenet_pixpro_resnet.yml')
|
||||
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
|
||||
parser.add_argument('--local_rank', type=int, default=0)
|
||||
args = parser.parse_args()
|
||||
|
|
|
|||
97
codes/trainer/eval/categorization_loss_eval.py
Normal file
97
codes/trainer/eval/categorization_loss_eval.py
Normal file
|
|
@ -0,0 +1,97 @@
|
|||
import torch
|
||||
import torchvision
|
||||
from torch.nn.functional import interpolate
|
||||
from torch.utils.data import DataLoader
|
||||
from torchvision import transforms
|
||||
from tqdm import tqdm
|
||||
|
||||
import trainer.eval.evaluator as evaluator
|
||||
from models.vqvae.kmeans_mask_producer import UResnetMaskProducer
|
||||
from utils.util import opt_get
|
||||
|
||||
|
||||
class CategorizationLossEvaluator(evaluator.Evaluator):
|
||||
def __init__(self, model, opt_eval, env):
|
||||
super().__init__(model, opt_eval, env)
|
||||
self.batch_sz = opt_eval['batch_size']
|
||||
assert self.batch_sz is not None
|
||||
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
|
||||
std=[0.229, 0.224, 0.225])
|
||||
self.dataset = torchvision.datasets.ImageFolder(
|
||||
'F:\\4k6k\\datasets\\images\\imagenet_2017\\val',
|
||||
transforms.Compose([
|
||||
transforms.Resize(256),
|
||||
transforms.CenterCrop(224),
|
||||
transforms.ToTensor(),
|
||||
normalize,
|
||||
]))
|
||||
self.dataloader = DataLoader(self.dataset, self.batch_sz, shuffle=False, num_workers=4)
|
||||
self.gen_output_index = opt_eval['gen_index'] if 'gen_index' in opt_eval.keys() else 0
|
||||
self.masking = opt_get(opt_eval, ['masking'], True)
|
||||
if self.masking:
|
||||
self.mask_producer = UResnetMaskProducer(pretrained_uresnet_path= '../experiments/train_imagenet_pixpro_resnet/models/66500_generator.pth',
|
||||
kmeans_centroid_path='../experiments/k_means_uresnet_imagenet_256.pth',
|
||||
mask_scales=[.03125, .0625, .125, .25, .5, 1.0],
|
||||
tail_dim=256).to('cuda')
|
||||
|
||||
def accuracy(self, output, target, topk=(1,)):
|
||||
"""Computes the accuracy over the k top predictions for the specified values of k"""
|
||||
with torch.no_grad():
|
||||
maxk = max(topk)
|
||||
batch_size = target.size(0)
|
||||
|
||||
_, pred = output.topk(maxk, 1, True, True)
|
||||
pred = pred.t()
|
||||
correct = pred.eq(target[None])
|
||||
|
||||
res = []
|
||||
for k in topk:
|
||||
correct_k = correct[:k].flatten().sum(dtype=torch.float32)
|
||||
res.append(correct_k * (100.0 / batch_size))
|
||||
return res
|
||||
|
||||
def perform_eval(self):
|
||||
counter = 0.0
|
||||
ce_loss = 0.0
|
||||
top_5_acc = 0.0
|
||||
top_1_acc = 0.0
|
||||
|
||||
self.model.eval()
|
||||
with torch.no_grad():
|
||||
for hq, labels in tqdm(self.dataloader):
|
||||
hq = hq.to(self.env['device'])
|
||||
labels = labels.to(self.env['device'])
|
||||
if self.masking:
|
||||
masks = self.mask_producer(hq)
|
||||
logits = self.model(hq, masks)
|
||||
else:
|
||||
logits = self.model(hq)
|
||||
if not isinstance(logits, list) and not isinstance(logits, tuple):
|
||||
logits = [logits]
|
||||
logits = logits[self.gen_output_index]
|
||||
ce_loss += torch.nn.functional.cross_entropy(logits, labels).detach()
|
||||
t1, t5 = self.accuracy(logits, labels, (1, 5))
|
||||
top_1_acc += t1.detach()
|
||||
top_5_acc += t5.detach()
|
||||
counter += len(hq) / self.batch_sz
|
||||
self.model.train()
|
||||
|
||||
return {"val_cross_entropy": ce_loss / counter,
|
||||
"top_5_accuracy": top_5_acc / counter,
|
||||
"top_1_accuracy": top_1_acc / counter }
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
from torchvision.models import resnet50
|
||||
model = resnet50(pretrained=True).to('cuda')
|
||||
opt = {
|
||||
'batch_size': 128,
|
||||
'gen_index': 0,
|
||||
'masking': False
|
||||
}
|
||||
env = {
|
||||
'device': 'cuda',
|
||||
|
||||
}
|
||||
eval = CategorizationLossEvaluator(model, opt, env)
|
||||
print(eval.perform_eval())
|
||||
|
|
@ -107,6 +107,9 @@ class ConfigurableStep(Module):
|
|||
optSGD = SGDNoBiasMomentum(list(optim_params.values()), lr=opt_config['lr'], momentum=opt_config['momentum'],
|
||||
weight_decay=opt_config['weight_decay'])
|
||||
opt = LARC(optSGD, trust_coefficient=opt_config['lars_coefficient'])
|
||||
elif self.step_opt['optimizer'] == 'sgd':
|
||||
from torch.optim import SGD
|
||||
opt = SGD(list(optim_params.values()), lr=opt_config['lr'], momentum=opt_config['momentum'], weight_decay=opt_config['weight_decay'])
|
||||
opt._config = opt_config # This is a bit seedy, but we will need these configs later.
|
||||
opt._config['network'] = net_name
|
||||
self.optimizers.append(opt)
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user