major package upgrade&new weights
This commit is contained in:
rmaphoh
+53
-54
@@ -1,54 +1,53 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# Partly revised by YZ @UCL&Moorfields
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# --------------------------------------------------------
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import os
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from torchvision import datasets, transforms
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from timm.data import create_transform
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from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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def build_dataset(is_train, args):
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transform = build_transform(is_train, args)
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root = os.path.join(args.data_path, is_train)
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dataset = datasets.ImageFolder(root, transform=transform)
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return dataset
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def build_transform(is_train, args):
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mean = IMAGENET_DEFAULT_MEAN
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std = IMAGENET_DEFAULT_STD
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# train transform
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if is_train=='train':
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# this should always dispatch to transforms_imagenet_train
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transform = create_transform(
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input_size=args.input_size,
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is_training=True,
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color_jitter=args.color_jitter,
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auto_augment=args.aa,
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interpolation='bicubic',
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re_prob=args.reprob,
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re_mode=args.remode,
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re_count=args.recount,
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mean=mean,
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std=std,
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)
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return transform
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# eval transform
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t = []
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if args.input_size <= 224:
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crop_pct = 224 / 256
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else:
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crop_pct = 1.0
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size = int(args.input_size / crop_pct)
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t.append(
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transforms.Resize(size, interpolation=transforms.InterpolationMode.BICUBIC),
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)
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t.append(transforms.CenterCrop(args.input_size))
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t.append(transforms.ToTensor())
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t.append(transforms.Normalize(mean, std))
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return transforms.Compose(t)
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# Partly revised by YZ @UCL&Moorfields
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# --------------------------------------------------------
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import os
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from torchvision import datasets, transforms
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from timm.data import create_transform
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from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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def build_dataset(is_train, args):
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transform = build_transform(is_train, args)
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root = os.path.join(args.data_path, is_train)
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dataset = datasets.ImageFolder(root, transform=transform)
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return dataset
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def build_transform(is_train, args):
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mean = IMAGENET_DEFAULT_MEAN
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std = IMAGENET_DEFAULT_STD
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# train transform
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if is_train == 'train':
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# this should always dispatch to transforms_imagenet_train
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transform = create_transform(
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input_size=args.input_size,
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is_training=True,
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color_jitter=args.color_jitter,
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auto_augment=args.aa,
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interpolation='bicubic',
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re_prob=args.reprob,
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re_mode=args.remode,
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re_count=args.recount,
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mean=mean,
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std=std,
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)
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return transform
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# eval transform
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t = []
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if args.input_size <= 224:
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crop_pct = 224 / 256
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else:
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crop_pct = 1.0
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size = int(args.input_size / crop_pct)
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t.append(
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transforms.Resize(size, interpolation=transforms.InterpolationMode.BICUBIC),
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)
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t.append(transforms.CenterCrop(args.input_size))
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t.append(transforms.ToTensor())
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t.append(transforms.Normalize(mean, std))
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return transforms.Compose(t)
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+73
-69
@@ -1,70 +1,74 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# Partly revised by YZ @UCL&Moorfields
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# --------------------------------------------------------
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import json
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def param_groups_lrd(model, weight_decay=0.05, no_weight_decay_list=[], layer_decay=.75):
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"""
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Parameter groups for layer-wise lr decay
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Following BEiT: https://github.com/microsoft/unilm/blob/master/beit/optim_factory.py#L58
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"""
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param_group_names = {}
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param_groups = {}
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num_layers = len(model.blocks) + 1
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layer_scales = list(layer_decay ** (num_layers - i) for i in range(num_layers + 1))
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for n, p in model.named_parameters():
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if not p.requires_grad:
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continue
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# no decay: all 1D parameters and model specific ones
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if p.ndim == 1 or n in no_weight_decay_list:
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g_decay = "no_decay"
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this_decay = 0.
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else:
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g_decay = "decay"
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this_decay = weight_decay
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layer_id = get_layer_id_for_vit(n, num_layers)
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group_name = "layer_%d_%s" % (layer_id, g_decay)
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if group_name not in param_group_names:
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this_scale = layer_scales[layer_id]
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param_group_names[group_name] = {
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"lr_scale": this_scale,
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"weight_decay": this_decay,
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"params": [],
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}
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param_groups[group_name] = {
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"lr_scale": this_scale,
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"weight_decay": this_decay,
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"params": [],
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}
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param_group_names[group_name]["params"].append(n)
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param_groups[group_name]["params"].append(p)
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# print("parameter groups: \n%s" % json.dumps(param_group_names, indent=2))
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return list(param_groups.values())
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def get_layer_id_for_vit(name, num_layers):
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"""
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Assign a parameter with its layer id
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Following BEiT: https://github.com/microsoft/unilm/blob/master/beit/optim_factory.py#L33
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"""
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if name in ['cls_token', 'pos_embed']:
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return 0
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elif name.startswith('patch_embed'):
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return 0
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elif name.startswith('blocks'):
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return int(name.split('.')[1]) + 1
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else:
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# Partly revised by YZ @UCL&Moorfields
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# --------------------------------------------------------
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import json
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def param_groups_lrd(model, weight_decay=0.05, no_weight_decay_list=[], layer_decay=.75):
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"""
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Parameter groups for layer-wise lr decay
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Following BEiT: https://github.com/microsoft/unilm/blob/master/beit/optim_factory.py#L58
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"""
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param_group_names = {}
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param_groups = {}
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if hasattr(model, 'blocks'):
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num_layers = len(model.blocks) + 1
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else:
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# use the number of layers in the ResNet model as a default value
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num_layers = len(model.layer1) + len(model.layer2) + len(model.layer3) + len(model.layer4) + 1
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layer_scales = list(layer_decay ** (num_layers - i) for i in range(num_layers + 1))
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for n, p in model.named_parameters():
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if not p.requires_grad:
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continue
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# no decay: all 1D parameters and model specific ones
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if p.ndim == 1 or n in no_weight_decay_list:
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g_decay = "no_decay"
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this_decay = 0.
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else:
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g_decay = "decay"
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this_decay = weight_decay
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layer_id = get_layer_id_for_vit(n, num_layers)
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group_name = "layer_%d_%s" % (layer_id, g_decay)
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if group_name not in param_group_names:
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this_scale = layer_scales[layer_id]
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param_group_names[group_name] = {
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"lr_scale": this_scale,
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"weight_decay": this_decay,
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"params": [],
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}
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param_groups[group_name] = {
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"lr_scale": this_scale,
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"weight_decay": this_decay,
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"params": [],
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}
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param_group_names[group_name]["params"].append(n)
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param_groups[group_name]["params"].append(p)
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# print("parameter groups: \n%s" % json.dumps(param_group_names, indent=2))
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return list(param_groups.values())
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def get_layer_id_for_vit(name, num_layers):
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"""
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Assign a parameter with its layer id
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Following BEiT: https://github.com/microsoft/unilm/blob/master/beit/optim_factory.py#L33
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"""
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if name in ['cls_token', 'pos_embed']:
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return 0
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elif name.startswith('patch_embed'):
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return 0
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elif name.startswith('blocks'):
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return int(name.split('.')[1]) + 1
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else:
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return num_layers
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+20
-20
@@ -1,20 +1,20 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# Partly revised by YZ @UCL&Moorfields
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# --------------------------------------------------------
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import math
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def adjust_learning_rate(optimizer, epoch, args):
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"""Decay the learning rate with half-cycle cosine after warmup"""
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if epoch < args.warmup_epochs:
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lr = args.lr * epoch / args.warmup_epochs
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else:
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lr = args.min_lr + (args.lr - args.min_lr) * 0.5 * \
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(1. + math.cos(math.pi * (epoch - args.warmup_epochs) / (args.epochs - args.warmup_epochs)))
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for param_group in optimizer.param_groups:
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if "lr_scale" in param_group:
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param_group["lr"] = lr * param_group["lr_scale"]
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else:
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param_group["lr"] = lr
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return lr
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# Partly revised by YZ @UCL&Moorfields
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# --------------------------------------------------------
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import math
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def adjust_learning_rate(optimizer, epoch, args):
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"""Decay the learning rate with half-cycle cosine after warmup"""
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if epoch < args.warmup_epochs:
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lr = args.lr * epoch / args.warmup_epochs
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else:
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lr = args.min_lr + (args.lr - args.min_lr) * 0.5 * \
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(1. + math.cos(math.pi * (epoch - args.warmup_epochs) / (args.epochs - args.warmup_epochs)))
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for param_group in optimizer.param_groups:
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if "lr_scale" in param_group:
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param_group["lr"] = lr * param_group["lr_scale"]
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else:
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param_group["lr"] = lr
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return lr
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+369
-357
@@ -1,357 +1,369 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# Partly revised by YZ @UCL&Moorfields
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# --------------------------------------------------------
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import builtins
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import datetime
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import os
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import time
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from collections import defaultdict, deque
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from pathlib import Path
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import torch
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import torch.distributed as dist
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from torch._six import inf
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class SmoothedValue(object):
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"""Track a series of values and provide access to smoothed values over a
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window or the global series average.
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"""
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def __init__(self, window_size=20, fmt=None):
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if fmt is None:
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fmt = "{median:.4f} ({global_avg:.4f})"
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self.deque = deque(maxlen=window_size)
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self.total = 0.0
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self.count = 0
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self.fmt = fmt
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def update(self, value, n=1):
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self.deque.append(value)
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self.count += n
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self.total += value * n
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def synchronize_between_processes(self):
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"""
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Warning: does not synchronize the deque!
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"""
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if not is_dist_avail_and_initialized():
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return
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t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda')
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dist.barrier()
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dist.all_reduce(t)
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t = t.tolist()
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self.count = int(t[0])
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self.total = t[1]
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@property
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def median(self):
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d = torch.tensor(list(self.deque))
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return d.median().item()
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@property
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def avg(self):
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d = torch.tensor(list(self.deque), dtype=torch.float32)
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return d.mean().item()
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@property
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def global_avg(self):
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return self.total / self.count
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@property
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def max(self):
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return max(self.deque)
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@property
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def value(self):
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return self.deque[-1]
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def __str__(self):
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return self.fmt.format(
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median=self.median,
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avg=self.avg,
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global_avg=self.global_avg,
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max=self.max,
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value=self.value)
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class MetricLogger(object):
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def __init__(self, delimiter="\t"):
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self.meters = defaultdict(SmoothedValue)
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self.delimiter = delimiter
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def update(self, **kwargs):
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for k, v in kwargs.items():
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if v is None:
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continue
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if isinstance(v, torch.Tensor):
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v = v.item()
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assert isinstance(v, (float, int))
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self.meters[k].update(v)
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def __getattr__(self, attr):
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if attr in self.meters:
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return self.meters[attr]
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if attr in self.__dict__:
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return self.__dict__[attr]
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raise AttributeError("'{}' object has no attribute '{}'".format(
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type(self).__name__, attr))
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def __str__(self):
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loss_str = []
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for name, meter in self.meters.items():
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loss_str.append(
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"{}: {}".format(name, str(meter))
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)
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return self.delimiter.join(loss_str)
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def synchronize_between_processes(self):
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for meter in self.meters.values():
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meter.synchronize_between_processes()
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def add_meter(self, name, meter):
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self.meters[name] = meter
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def log_every(self, iterable, print_freq, header=None):
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i = 0
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if not header:
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header = ''
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start_time = time.time()
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end = time.time()
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iter_time = SmoothedValue(fmt='{avg:.4f}')
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data_time = SmoothedValue(fmt='{avg:.4f}')
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space_fmt = ':' + str(len(str(len(iterable)))) + 'd'
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log_msg = [
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header,
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'[{0' + space_fmt + '}/{1}]',
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'eta: {eta}',
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'{meters}',
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'time: {time}',
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'data: {data}'
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]
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if torch.cuda.is_available():
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log_msg.append('max mem: {memory:.0f}')
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log_msg = self.delimiter.join(log_msg)
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MB = 1024.0 * 1024.0
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for obj in iterable:
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data_time.update(time.time() - end)
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yield obj
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iter_time.update(time.time() - end)
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if i % print_freq == 0 or i == len(iterable) - 1:
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eta_seconds = iter_time.global_avg * (len(iterable) - i)
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eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
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if torch.cuda.is_available():
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print(log_msg.format(
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i, len(iterable), eta=eta_string,
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meters=str(self),
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time=str(iter_time), data=str(data_time),
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memory=torch.cuda.max_memory_allocated() / MB))
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else:
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print(log_msg.format(
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i, len(iterable), eta=eta_string,
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meters=str(self),
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time=str(iter_time), data=str(data_time)))
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i += 1
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end = time.time()
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total_time = time.time() - start_time
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total_time_str = str(datetime.timedelta(seconds=int(total_time)))
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print('{} Total time: {} ({:.4f} s / it)'.format(
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header, total_time_str, total_time / len(iterable)))
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def setup_for_distributed(is_master):
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"""
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This function disables printing when not in master process
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"""
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builtin_print = builtins.print
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def print(*args, **kwargs):
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force = kwargs.pop('force', False)
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force = force or (get_world_size() > 8)
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if is_master or force:
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now = datetime.datetime.now().time()
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builtin_print('[{}] '.format(now), end='') # print with time stamp
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builtin_print(*args, **kwargs)
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builtins.print = print
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def is_dist_avail_and_initialized():
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if not dist.is_available():
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return False
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if not dist.is_initialized():
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return False
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||||
return True
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|
||||
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def get_world_size():
|
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if not is_dist_avail_and_initialized():
|
||||
return 1
|
||||
return dist.get_world_size()
|
||||
|
||||
|
||||
def get_rank():
|
||||
if not is_dist_avail_and_initialized():
|
||||
return 0
|
||||
return dist.get_rank()
|
||||
|
||||
|
||||
def is_main_process():
|
||||
return get_rank() == 0
|
||||
|
||||
|
||||
def save_on_master(*args, **kwargs):
|
||||
if is_main_process():
|
||||
torch.save(*args, **kwargs)
|
||||
|
||||
|
||||
def init_distributed_mode(args):
|
||||
if args.dist_on_itp:
|
||||
args.rank = int(os.environ['OMPI_COMM_WORLD_RANK'])
|
||||
args.world_size = int(os.environ['OMPI_COMM_WORLD_SIZE'])
|
||||
args.gpu = int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK'])
|
||||
args.dist_url = "tcp://%s:%s" % (os.environ['MASTER_ADDR'], os.environ['MASTER_PORT'])
|
||||
os.environ['LOCAL_RANK'] = str(args.gpu)
|
||||
os.environ['RANK'] = str(args.rank)
|
||||
os.environ['WORLD_SIZE'] = str(args.world_size)
|
||||
# ["RANK", "WORLD_SIZE", "MASTER_ADDR", "MASTER_PORT", "LOCAL_RANK"]
|
||||
elif 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
|
||||
args.rank = int(os.environ["RANK"])
|
||||
args.world_size = int(os.environ['WORLD_SIZE'])
|
||||
args.gpu = int(os.environ['LOCAL_RANK'])
|
||||
elif 'SLURM_PROCID' in os.environ:
|
||||
args.rank = int(os.environ['SLURM_PROCID'])
|
||||
args.gpu = args.rank % torch.cuda.device_count()
|
||||
else:
|
||||
print('Not using distributed mode')
|
||||
setup_for_distributed(is_master=True) # hack
|
||||
args.distributed = False
|
||||
return
|
||||
|
||||
args.distributed = True
|
||||
|
||||
torch.cuda.set_device(args.gpu)
|
||||
args.dist_backend = 'nccl'
|
||||
print('| distributed init (rank {}): {}, gpu {}'.format(
|
||||
args.rank, args.dist_url, args.gpu), flush=True)
|
||||
torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
|
||||
world_size=args.world_size, rank=args.rank)
|
||||
torch.distributed.barrier()
|
||||
setup_for_distributed(args.rank == 0)
|
||||
|
||||
|
||||
class NativeScalerWithGradNormCount:
|
||||
state_dict_key = "amp_scaler"
|
||||
|
||||
def __init__(self):
|
||||
self._scaler = torch.cuda.amp.GradScaler()
|
||||
|
||||
def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True):
|
||||
self._scaler.scale(loss).backward(create_graph=create_graph)
|
||||
if update_grad:
|
||||
if clip_grad is not None:
|
||||
assert parameters is not None
|
||||
self._scaler.unscale_(optimizer) # unscale the gradients of optimizer's assigned params in-place
|
||||
norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad)
|
||||
else:
|
||||
self._scaler.unscale_(optimizer)
|
||||
norm = get_grad_norm_(parameters)
|
||||
self._scaler.step(optimizer)
|
||||
self._scaler.update()
|
||||
else:
|
||||
norm = None
|
||||
return norm
|
||||
|
||||
def state_dict(self):
|
||||
return self._scaler.state_dict()
|
||||
|
||||
def load_state_dict(self, state_dict):
|
||||
self._scaler.load_state_dict(state_dict)
|
||||
|
||||
|
||||
def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:
|
||||
if isinstance(parameters, torch.Tensor):
|
||||
parameters = [parameters]
|
||||
parameters = [p for p in parameters if p.grad is not None]
|
||||
norm_type = float(norm_type)
|
||||
if len(parameters) == 0:
|
||||
return torch.tensor(0.)
|
||||
device = parameters[0].grad.device
|
||||
if norm_type == inf:
|
||||
total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters)
|
||||
else:
|
||||
total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type)
|
||||
return total_norm
|
||||
|
||||
|
||||
def save_model(args, epoch, model, model_without_ddp, optimizer, loss_scaler):
|
||||
output_dir = Path(args.output_dir)
|
||||
epoch_name = str(epoch)
|
||||
if loss_scaler is not None:
|
||||
checkpoint_paths = [args.task+'checkpoint-best.pth']
|
||||
for checkpoint_path in checkpoint_paths:
|
||||
to_save = {
|
||||
'model': model_without_ddp.state_dict(),
|
||||
'optimizer': optimizer.state_dict(),
|
||||
'epoch': epoch,
|
||||
'scaler': loss_scaler.state_dict(),
|
||||
'args': args,
|
||||
}
|
||||
|
||||
save_on_master(to_save, checkpoint_path)
|
||||
else:
|
||||
client_state = {'epoch': epoch}
|
||||
model.save_checkpoint(save_dir=args.task, tag="checkpoint-best", client_state=client_state)
|
||||
|
||||
|
||||
def save_model_pretrain(args, epoch, model, model_without_ddp, optimizer, loss_scaler):
|
||||
output_dir = Path(args.output_dir)
|
||||
epoch_name = str(epoch)
|
||||
if loss_scaler is not None:
|
||||
print(model_without_ddp.state_dict().keys())
|
||||
checkpoint_paths = [output_dir / ('checkpoint-%s.pth' % epoch_name)]
|
||||
for checkpoint_path in checkpoint_paths:
|
||||
to_save = {
|
||||
'model': model_without_ddp.state_dict(),
|
||||
'optimizer': optimizer.state_dict(),
|
||||
'epoch': epoch,
|
||||
'scaler': loss_scaler.state_dict(),
|
||||
'args': args,
|
||||
}
|
||||
|
||||
save_on_master(to_save, checkpoint_path)
|
||||
else:
|
||||
client_state = {'epoch': epoch}
|
||||
model.save_checkpoint(save_dir=args.output_dir, tag="checkpoint-%s" % epoch_name, client_state=client_state)
|
||||
|
||||
|
||||
|
||||
def load_model(args, model_without_ddp, optimizer, loss_scaler):
|
||||
if args.resume:
|
||||
if args.resume.startswith('https'):
|
||||
checkpoint = torch.hub.load_state_dict_from_url(
|
||||
args.resume, map_location='cpu', check_hash=True)
|
||||
else:
|
||||
checkpoint = torch.load(args.resume, map_location='cpu')
|
||||
model_without_ddp.load_state_dict(checkpoint['model'])
|
||||
print("Resume checkpoint %s" % args.resume)
|
||||
if 'optimizer' in checkpoint and 'epoch' in checkpoint and not (hasattr(args, 'eval') and args.eval):
|
||||
optimizer.load_state_dict(checkpoint['optimizer'])
|
||||
args.start_epoch = checkpoint['epoch'] + 1
|
||||
if 'scaler' in checkpoint:
|
||||
loss_scaler.load_state_dict(checkpoint['scaler'])
|
||||
print("With optim & sched!")
|
||||
|
||||
|
||||
def all_reduce_mean(x):
|
||||
world_size = get_world_size()
|
||||
if world_size > 1:
|
||||
x_reduce = torch.tensor(x).cuda()
|
||||
dist.all_reduce(x_reduce)
|
||||
x_reduce /= world_size
|
||||
return x_reduce.item()
|
||||
else:
|
||||
return x
|
||||
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
# Partly revised by YZ @UCL&Moorfields
|
||||
# --------------------------------------------------------
|
||||
|
||||
import builtins
|
||||
import datetime
|
||||
import os
|
||||
import time
|
||||
from collections import defaultdict, deque
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
from math import inf
|
||||
|
||||
|
||||
class SmoothedValue(object):
|
||||
"""Track a series of values and provide access to smoothed values over a
|
||||
window or the global series average.
|
||||
"""
|
||||
|
||||
def __init__(self, window_size=20, fmt=None):
|
||||
if fmt is None:
|
||||
fmt = "{median:.4f} ({global_avg:.4f})"
|
||||
self.deque = deque(maxlen=window_size)
|
||||
self.total = 0.0
|
||||
self.count = 0
|
||||
self.fmt = fmt
|
||||
|
||||
def update(self, value, n=1):
|
||||
self.deque.append(value)
|
||||
self.count += n
|
||||
self.total += value * n
|
||||
|
||||
def synchronize_between_processes(self):
|
||||
"""
|
||||
Warning: does not synchronize the deque!
|
||||
"""
|
||||
if not is_dist_avail_and_initialized():
|
||||
return
|
||||
t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda')
|
||||
dist.barrier()
|
||||
dist.all_reduce(t)
|
||||
t = t.tolist()
|
||||
self.count = int(t[0])
|
||||
self.total = t[1]
|
||||
|
||||
@property
|
||||
def median(self):
|
||||
d = torch.tensor(list(self.deque))
|
||||
return d.median().item()
|
||||
|
||||
@property
|
||||
def avg(self):
|
||||
d = torch.tensor(list(self.deque), dtype=torch.float32)
|
||||
return d.mean().item()
|
||||
|
||||
@property
|
||||
def global_avg(self):
|
||||
return self.total / self.count
|
||||
|
||||
@property
|
||||
def max(self):
|
||||
return max(self.deque)
|
||||
|
||||
@property
|
||||
def value(self):
|
||||
return self.deque[-1]
|
||||
|
||||
def __str__(self):
|
||||
return self.fmt.format(
|
||||
median=self.median,
|
||||
avg=self.avg,
|
||||
global_avg=self.global_avg,
|
||||
max=self.max,
|
||||
value=self.value)
|
||||
|
||||
|
||||
class MetricLogger(object):
|
||||
def __init__(self, delimiter="\t"):
|
||||
self.meters = defaultdict(SmoothedValue)
|
||||
self.delimiter = delimiter
|
||||
|
||||
def update(self, **kwargs):
|
||||
for k, v in kwargs.items():
|
||||
if v is None:
|
||||
continue
|
||||
if isinstance(v, torch.Tensor):
|
||||
v = v.item()
|
||||
assert isinstance(v, (float, int))
|
||||
self.meters[k].update(v)
|
||||
|
||||
def __getattr__(self, attr):
|
||||
if attr in self.meters:
|
||||
return self.meters[attr]
|
||||
if attr in self.__dict__:
|
||||
return self.__dict__[attr]
|
||||
raise AttributeError("'{}' object has no attribute '{}'".format(
|
||||
type(self).__name__, attr))
|
||||
|
||||
def __str__(self):
|
||||
loss_str = []
|
||||
for name, meter in self.meters.items():
|
||||
loss_str.append(
|
||||
"{}: {}".format(name, str(meter))
|
||||
)
|
||||
return self.delimiter.join(loss_str)
|
||||
|
||||
def synchronize_between_processes(self):
|
||||
for meter in self.meters.values():
|
||||
meter.synchronize_between_processes()
|
||||
|
||||
def add_meter(self, name, meter):
|
||||
self.meters[name] = meter
|
||||
|
||||
def log_every(self, iterable, print_freq, header=None):
|
||||
i = 0
|
||||
if not header:
|
||||
header = ''
|
||||
start_time = time.time()
|
||||
end = time.time()
|
||||
iter_time = SmoothedValue(fmt='{avg:.4f}')
|
||||
data_time = SmoothedValue(fmt='{avg:.4f}')
|
||||
space_fmt = ':' + str(len(str(len(iterable)))) + 'd'
|
||||
log_msg = [
|
||||
header,
|
||||
'[{0' + space_fmt + '}/{1}]',
|
||||
'eta: {eta}',
|
||||
'{meters}',
|
||||
'time: {time}',
|
||||
'data: {data}'
|
||||
]
|
||||
if torch.cuda.is_available():
|
||||
log_msg.append('max mem: {memory:.0f}')
|
||||
log_msg = self.delimiter.join(log_msg)
|
||||
MB = 1024.0 * 1024.0
|
||||
for obj in iterable:
|
||||
data_time.update(time.time() - end)
|
||||
yield obj
|
||||
iter_time.update(time.time() - end)
|
||||
if i % print_freq == 0 or i == len(iterable) - 1:
|
||||
eta_seconds = iter_time.global_avg * (len(iterable) - i)
|
||||
eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
|
||||
if torch.cuda.is_available():
|
||||
print(log_msg.format(
|
||||
i, len(iterable), eta=eta_string,
|
||||
meters=str(self),
|
||||
time=str(iter_time), data=str(data_time),
|
||||
memory=torch.cuda.max_memory_allocated() / MB))
|
||||
else:
|
||||
print(log_msg.format(
|
||||
i, len(iterable), eta=eta_string,
|
||||
meters=str(self),
|
||||
time=str(iter_time), data=str(data_time)))
|
||||
i += 1
|
||||
end = time.time()
|
||||
total_time = time.time() - start_time
|
||||
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
|
||||
print('{} Total time: {} ({:.4f} s / it)'.format(
|
||||
header, total_time_str, total_time / len(iterable)))
|
||||
|
||||
|
||||
def setup_for_distributed(is_master):
|
||||
"""
|
||||
This function disables printing when not in master process
|
||||
"""
|
||||
builtin_print = builtins.print
|
||||
|
||||
def print(*args, **kwargs):
|
||||
force = kwargs.pop('force', False)
|
||||
force = force or (get_world_size() > 8)
|
||||
if is_master or force:
|
||||
now = datetime.datetime.now().time()
|
||||
builtin_print('[{}] '.format(now), end='') # print with time stamp
|
||||
builtin_print(*args, **kwargs)
|
||||
|
||||
builtins.print = print
|
||||
|
||||
|
||||
def is_dist_avail_and_initialized():
|
||||
if not dist.is_available():
|
||||
return False
|
||||
if not dist.is_initialized():
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def get_world_size():
|
||||
if not is_dist_avail_and_initialized():
|
||||
return 1
|
||||
return dist.get_world_size()
|
||||
|
||||
|
||||
def get_rank():
|
||||
if not is_dist_avail_and_initialized():
|
||||
return 0
|
||||
return dist.get_rank()
|
||||
|
||||
|
||||
def is_main_process():
|
||||
return get_rank() == 0
|
||||
|
||||
|
||||
def save_on_master(*args, **kwargs):
|
||||
if is_main_process():
|
||||
torch.save(*args, **kwargs)
|
||||
|
||||
|
||||
def init_distributed_mode(args):
|
||||
if args.dist_on_itp:
|
||||
args.rank = int(os.environ['OMPI_COMM_WORLD_RANK'])
|
||||
args.world_size = int(os.environ['OMPI_COMM_WORLD_SIZE'])
|
||||
args.gpu = int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK'])
|
||||
args.dist_url = "tcp://%s:%s" % (os.environ['MASTER_ADDR'], os.environ['MASTER_PORT'])
|
||||
os.environ['LOCAL_RANK'] = str(args.gpu)
|
||||
os.environ['RANK'] = str(args.rank)
|
||||
os.environ['WORLD_SIZE'] = str(args.world_size)
|
||||
# ["RANK", "WORLD_SIZE", "MASTER_ADDR", "MASTER_PORT", "LOCAL_RANK"]
|
||||
elif 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
|
||||
args.rank = int(os.environ["RANK"])
|
||||
args.world_size = int(os.environ['WORLD_SIZE'])
|
||||
args.gpu = int(os.environ['LOCAL_RANK'])
|
||||
elif 'SLURM_PROCID' in os.environ:
|
||||
args.rank = int(os.environ['SLURM_PROCID'])
|
||||
args.gpu = args.rank % torch.cuda.device_count()
|
||||
else:
|
||||
print('Not using distributed mode')
|
||||
setup_for_distributed(is_master=True) # hack
|
||||
args.distributed = False
|
||||
return
|
||||
|
||||
args.distributed = True
|
||||
|
||||
torch.cuda.set_device(args.gpu)
|
||||
args.dist_backend = 'nccl'
|
||||
print('| distributed init (rank {}): {}, gpu {}'.format(
|
||||
args.rank, args.dist_url, args.gpu), flush=True)
|
||||
torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
|
||||
world_size=args.world_size, rank=args.rank)
|
||||
torch.distributed.barrier()
|
||||
setup_for_distributed(args.rank == 0)
|
||||
|
||||
|
||||
class NativeScalerWithGradNormCount:
|
||||
state_dict_key = "amp_scaler"
|
||||
|
||||
def __init__(self):
|
||||
self._scaler = torch.cuda.amp.GradScaler()
|
||||
|
||||
def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True):
|
||||
self._scaler.scale(loss).backward(create_graph=create_graph)
|
||||
if update_grad:
|
||||
if clip_grad is not None:
|
||||
assert parameters is not None
|
||||
self._scaler.unscale_(optimizer) # unscale the gradients of optimizer's assigned params in-place
|
||||
norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad)
|
||||
else:
|
||||
self._scaler.unscale_(optimizer)
|
||||
norm = get_grad_norm_(parameters)
|
||||
self._scaler.step(optimizer)
|
||||
self._scaler.update()
|
||||
else:
|
||||
norm = None
|
||||
return norm
|
||||
|
||||
def state_dict(self):
|
||||
return self._scaler.state_dict()
|
||||
|
||||
def load_state_dict(self, state_dict):
|
||||
self._scaler.load_state_dict(state_dict)
|
||||
|
||||
|
||||
def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:
|
||||
if isinstance(parameters, torch.Tensor):
|
||||
parameters = [parameters]
|
||||
parameters = [p for p in parameters if p.grad is not None]
|
||||
norm_type = float(norm_type)
|
||||
if len(parameters) == 0:
|
||||
return torch.tensor(0.)
|
||||
device = parameters[0].grad.device
|
||||
if norm_type == inf:
|
||||
total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters)
|
||||
else:
|
||||
total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]),
|
||||
norm_type)
|
||||
return total_norm
|
||||
|
||||
|
||||
def save_model(args, epoch, model, model_without_ddp, optimizer, loss_scaler, mode):
|
||||
output_dir = Path(args.output_dir)
|
||||
epoch_name = str(epoch)
|
||||
os.makedirs(os.path.join(args.output_dir, args.task), exist_ok=True)
|
||||
if loss_scaler is not None:
|
||||
if mode == 'best':
|
||||
checkpoint_paths = [os.path.join(args.output_dir, args.task, 'checkpoint-best.pth')]
|
||||
else:
|
||||
checkpoint_paths = [os.path.join(args.output_dir, args.task, 'checkpoint-latest.pth')]
|
||||
for checkpoint_path in checkpoint_paths:
|
||||
if mode == 'best':
|
||||
to_save = {
|
||||
'model': model_without_ddp.state_dict(),
|
||||
'epoch': epoch,
|
||||
'args': args, }
|
||||
else:
|
||||
if epoch == args.epochs - 1:
|
||||
to_save = {
|
||||
'model': model_without_ddp.state_dict(),
|
||||
'args': args, }
|
||||
else:
|
||||
to_save = {
|
||||
'model': model_without_ddp.state_dict(),
|
||||
'optimizer': optimizer.state_dict(),
|
||||
'epoch': epoch,
|
||||
'scaler': loss_scaler.state_dict(),
|
||||
'args': args,
|
||||
}
|
||||
|
||||
save_on_master(to_save, checkpoint_path)
|
||||
else:
|
||||
if mode == 'best':
|
||||
to_save = {
|
||||
'model': model_without_ddp.state_dict(),
|
||||
'epoch': epoch, }
|
||||
torch.save(to_save, os.path.join(args.output_dir, args.task, "checkpoint-best.pth"))
|
||||
else:
|
||||
if epoch == args.epochs - 1:
|
||||
to_save = {
|
||||
'model': model_without_ddp.state_dict(), }
|
||||
else:
|
||||
to_save = {
|
||||
'model': model_without_ddp.state_dict(),
|
||||
'optimizer': optimizer.state_dict(),
|
||||
'epoch': epoch,
|
||||
'args': args,
|
||||
}
|
||||
torch.save(to_save, os.path.join(args.output_dir, args.task, "checkpoint-latest.pth"))
|
||||
|
||||
|
||||
def load_model(args, model_without_ddp, optimizer, loss_scaler):
|
||||
if args.resume:
|
||||
if args.resume.startswith('https'):
|
||||
checkpoint = torch.hub.load_state_dict_from_url(
|
||||
args.resume, map_location='cpu', check_hash=True)
|
||||
else:
|
||||
checkpoint = torch.load(args.resume, map_location='cpu')
|
||||
if 'model' in checkpoint:
|
||||
checkpoint_model = checkpoint['model']
|
||||
else:
|
||||
checkpoint_model = checkpoint
|
||||
model_without_ddp.load_state_dict(checkpoint_model, strict=False)
|
||||
print("Resume checkpoint %s" % args.resume)
|
||||
if 'optimizer' in checkpoint and 'epoch' in checkpoint and not (hasattr(args, 'eval') and args.eval):
|
||||
optimizer.load_state_dict(checkpoint['optimizer'])
|
||||
args.start_epoch = checkpoint['epoch'] + 1
|
||||
if 'scaler' in checkpoint:
|
||||
loss_scaler.load_state_dict(checkpoint['scaler'])
|
||||
print("With optim & sched!")
|
||||
|
||||
|
||||
def all_reduce_mean(x):
|
||||
world_size = get_world_size()
|
||||
if world_size > 1:
|
||||
x_reduce = torch.tensor(x).cuda()
|
||||
dist.all_reduce(x_reduce)
|
||||
x_reduce /= world_size
|
||||
return x_reduce.item()
|
||||
else:
|
||||
return x
|
||||
|
||||
+92
-92
@@ -1,92 +1,92 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
# Partly revised by YZ @UCL&Moorfields
|
||||
# --------------------------------------------------------
|
||||
|
||||
import numpy as np
|
||||
|
||||
import torch
|
||||
|
||||
# --------------------------------------------------------
|
||||
# 2D sine-cosine position embedding
|
||||
# References:
|
||||
# Transformer: https://github.com/tensorflow/models/blob/master/official/nlp/transformer/model_utils.py
|
||||
# MoCo v3: https://github.com/facebookresearch/moco-v3
|
||||
# --------------------------------------------------------
|
||||
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
|
||||
"""
|
||||
grid_size: int of the grid height and width
|
||||
return:
|
||||
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
|
||||
"""
|
||||
grid_h = np.arange(grid_size, dtype=np.float32)
|
||||
grid_w = np.arange(grid_size, dtype=np.float32)
|
||||
grid = np.meshgrid(grid_w, grid_h) # here w goes first
|
||||
grid = np.stack(grid, axis=0)
|
||||
|
||||
grid = grid.reshape([2, 1, grid_size, grid_size])
|
||||
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
|
||||
if cls_token:
|
||||
pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
|
||||
return pos_embed
|
||||
|
||||
|
||||
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
|
||||
assert embed_dim % 2 == 0
|
||||
|
||||
# use half of dimensions to encode grid_h
|
||||
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
|
||||
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
|
||||
|
||||
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
|
||||
return emb
|
||||
|
||||
|
||||
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
|
||||
"""
|
||||
embed_dim: output dimension for each position
|
||||
pos: a list of positions to be encoded: size (M,)
|
||||
out: (M, D)
|
||||
"""
|
||||
assert embed_dim % 2 == 0
|
||||
omega = np.arange(embed_dim // 2, dtype=np.float)
|
||||
omega /= embed_dim / 2.
|
||||
omega = 1. / 10000**omega # (D/2,)
|
||||
|
||||
pos = pos.reshape(-1) # (M,)
|
||||
out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
|
||||
|
||||
emb_sin = np.sin(out) # (M, D/2)
|
||||
emb_cos = np.cos(out) # (M, D/2)
|
||||
|
||||
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
|
||||
return emb
|
||||
|
||||
|
||||
# --------------------------------------------------------
|
||||
# Interpolate position embeddings for high-resolution
|
||||
# References:
|
||||
# DeiT: https://github.com/facebookresearch/deit
|
||||
# --------------------------------------------------------
|
||||
def interpolate_pos_embed(model, checkpoint_model):
|
||||
if 'pos_embed' in checkpoint_model:
|
||||
pos_embed_checkpoint = checkpoint_model['pos_embed']
|
||||
embedding_size = pos_embed_checkpoint.shape[-1]
|
||||
num_patches = model.patch_embed.num_patches
|
||||
num_extra_tokens = model.pos_embed.shape[-2] - num_patches
|
||||
# height (== width) for the checkpoint position embedding
|
||||
orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
|
||||
# height (== width) for the new position embedding
|
||||
new_size = int(num_patches ** 0.5)
|
||||
# class_token and dist_token are kept unchanged
|
||||
if orig_size != new_size:
|
||||
print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size))
|
||||
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
|
||||
# only the position tokens are interpolated
|
||||
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
|
||||
pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
|
||||
pos_tokens = torch.nn.functional.interpolate(
|
||||
pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
|
||||
pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
|
||||
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
|
||||
checkpoint_model['pos_embed'] = new_pos_embed
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
# Partly revised by YZ @UCL&Moorfields
|
||||
# --------------------------------------------------------
|
||||
|
||||
import numpy as np
|
||||
|
||||
import torch
|
||||
|
||||
# --------------------------------------------------------
|
||||
# 2D sine-cosine position embedding
|
||||
# References:
|
||||
# Transformer: https://github.com/tensorflow/models/blob/master/official/nlp/transformer/model_utils.py
|
||||
# MoCo v3: https://github.com/facebookresearch/moco-v3
|
||||
# --------------------------------------------------------
|
||||
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
|
||||
"""
|
||||
grid_size: int of the grid height and width
|
||||
return:
|
||||
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
|
||||
"""
|
||||
grid_h = np.arange(grid_size, dtype=np.float32)
|
||||
grid_w = np.arange(grid_size, dtype=np.float32)
|
||||
grid = np.meshgrid(grid_w, grid_h) # here w goes first
|
||||
grid = np.stack(grid, axis=0)
|
||||
|
||||
grid = grid.reshape([2, 1, grid_size, grid_size])
|
||||
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
|
||||
if cls_token:
|
||||
pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
|
||||
return pos_embed
|
||||
|
||||
|
||||
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
|
||||
assert embed_dim % 2 == 0
|
||||
|
||||
# use half of dimensions to encode grid_h
|
||||
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
|
||||
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
|
||||
|
||||
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
|
||||
return emb
|
||||
|
||||
|
||||
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
|
||||
"""
|
||||
embed_dim: output dimension for each position
|
||||
pos: a list of positions to be encoded: size (M,)
|
||||
out: (M, D)
|
||||
"""
|
||||
assert embed_dim % 2 == 0
|
||||
omega = np.arange(embed_dim // 2, dtype=np.float)
|
||||
omega /= embed_dim / 2.
|
||||
omega = 1. / 10000**omega # (D/2,)
|
||||
|
||||
pos = pos.reshape(-1) # (M,)
|
||||
out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
|
||||
|
||||
emb_sin = np.sin(out) # (M, D/2)
|
||||
emb_cos = np.cos(out) # (M, D/2)
|
||||
|
||||
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
|
||||
return emb
|
||||
|
||||
|
||||
# --------------------------------------------------------
|
||||
# Interpolate position embeddings for high-resolution
|
||||
# References:
|
||||
# DeiT: https://github.com/facebookresearch/deit
|
||||
# --------------------------------------------------------
|
||||
def interpolate_pos_embed(model, checkpoint_model):
|
||||
if 'pos_embed' in checkpoint_model:
|
||||
pos_embed_checkpoint = checkpoint_model['pos_embed']
|
||||
embedding_size = pos_embed_checkpoint.shape[-1]
|
||||
num_patches = model.patch_embed.num_patches
|
||||
num_extra_tokens = model.pos_embed.shape[-2] - num_patches
|
||||
# height (== width) for the checkpoint position embedding
|
||||
orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
|
||||
# height (== width) for the new position embedding
|
||||
new_size = int(num_patches ** 0.5)
|
||||
# class_token and dist_token are kept unchanged
|
||||
if orig_size != new_size:
|
||||
print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size))
|
||||
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
|
||||
# only the position tokens are interpolated
|
||||
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
|
||||
pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
|
||||
pos_tokens = torch.nn.functional.interpolate(
|
||||
pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
|
||||
pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
|
||||
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
|
||||
checkpoint_model['pos_embed'] = new_pos_embed
|
||||
|
||||
Reference in New Issue
Block a user