cae config fix
dolcexu
zengshao0622
parent
fb763b7096
commit
f24e1f9bcf
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@ -630,17 +630,17 @@ def _load_pretrained(pretrained,
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model,
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model_keys,
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model_ema_configs,
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abs_pos_emb,
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rel_pos_bias,
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use_abs_pos_emb,
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use_rel_pos_bias,
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use_ssld=False):
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if pretrained is False:
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pass
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return
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elif pretrained is True:
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local_weight_path = get_weights_path_from_url(pretrained_url).replace(
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".pdparams", "")
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checkpoint = paddle.load(local_weight_path + ".pdparams")
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elif isinstance(pretrained, str):
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checkpoint = paddle.load(local_weight_path + ".pdparams")
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checkpoint = paddle.load(pretrained + ".pdparams")
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checkpoint_model = None
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for model_key in model_keys.split('|'):
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@ -693,10 +693,10 @@ def _load_pretrained(pretrained,
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if "relative_position_index" in key:
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checkpoint_model.pop(key)
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if "relative_position_bias_table" in key and rel_pos_bias:
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if "relative_position_bias_table" in key and use_rel_pos_bias:
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rel_pos_bias = checkpoint_model[key]
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src_num_pos, num_attn_heads = rel_pos_bias.size()
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dst_num_pos, _ = model.state_dict()[key].size()
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src_num_pos, num_attn_heads = rel_pos_bias.shape
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dst_num_pos, _ = model.state_dict()[key].shape
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dst_patch_shape = model.patch_embed.patch_shape
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if dst_patch_shape[0] != dst_patch_shape[1]:
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raise NotImplementedError()
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@ -742,8 +742,8 @@ def _load_pretrained(pretrained,
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src_size).float().numpy()
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f = interpolate.interp2d(x, y, z, kind='cubic')
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all_rel_pos_bias.append(
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paddle.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(
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rel_pos_bias.device))
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paddle.Tensor(f(dx, dy)).astype('float32').reshape(
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[-1, 1]))
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rel_pos_bias = paddle.concat(all_rel_pos_bias, axis=-1)
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@ -752,7 +752,7 @@ def _load_pretrained(pretrained,
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checkpoint_model[key] = new_rel_pos_bias
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# interpolate position embedding
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if 'pos_embed' in checkpoint_model and abs_pos_emb:
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if 'pos_embed' in checkpoint_model and use_abs_pos_emb:
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pos_embed_checkpoint = checkpoint_model['pos_embed']
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embedding_size = pos_embed_checkpoint.shape[-1]
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num_patches = model.patch_embed.num_patches
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@ -791,8 +791,8 @@ def cae_base_patch16_224(pretrained=True, use_ssld=False, **kwargs):
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enable_linear_eval = config.pop('enable_linear_eval')
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model_keys = config.pop('model_key')
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model_ema_configs = config.pop('model_ema')
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abs_pos_emb = config.pop('abs_pos_emb')
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rel_pos_bias = config.pop('rel_pos_bias')
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use_abs_pos_emb = config.get('use_abs_pos_emb', False)
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use_rel_pos_bias = config.get('use_rel_pos_bias', True)
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if pretrained in config:
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pretrained = config.pop('pretrained')
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@ -816,8 +816,8 @@ def cae_base_patch16_224(pretrained=True, use_ssld=False, **kwargs):
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model,
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model_keys,
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model_ema_configs,
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abs_pos_emb,
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rel_pos_bias,
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use_abs_pos_emb,
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use_rel_pos_bias,
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use_ssld=False)
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return model
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@ -828,8 +828,8 @@ def cae_large_patch16_224(pretrained=True, use_ssld=False, **kwargs):
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enable_linear_eval = config.pop('enable_linear_eval')
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model_keys = config.pop('model_key')
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model_ema_configs = config.pop('model_ema')
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abs_pos_emb = config.pop('abs_pos_emb')
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rel_pos_bias = config.pop('rel_pos_bias')
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use_abs_pos_emb = config.get('use_abs_pos_emb', False)
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use_rel_pos_bias = config.get('use_rel_pos_bias', True)
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if pretrained in config:
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pretrained = config.pop('pretrained')
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@ -853,8 +853,8 @@ def cae_large_patch16_224(pretrained=True, use_ssld=False, **kwargs):
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model,
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model_keys,
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model_ema_configs,
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abs_pos_emb,
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rel_pos_bias,
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use_abs_pos_emb,
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use_rel_pos_bias,
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use_ssld=False)
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return model
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@ -31,10 +31,8 @@ Arch:
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sin_pos_emb: True
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abs_pos_emb: False
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enable_linear_eval: False
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model_key: model|module|state_dict
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rel_pos_bias: True
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model_ema:
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enable_model_ema: False
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model_ema_decay: 0.9999
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@ -83,23 +81,27 @@ DataLoader:
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- DecodeImage:
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to_rgb: True
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channel_first: False
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- RandCropImage:
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- RandomResizedCrop:
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size: 224
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interpolation: bilinear
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- RandFlipImage:
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flip_code: 1
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- RandAugment:
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- RandomHorizontalFlip:
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prob: 0.5
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- TimmAutoAugment:
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config_str: rand-m9-mstd0.5-inc1
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interpolation: bicubic
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img_size: 224
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- NormalizeImage:
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scale: 1.0/255.0
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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mean: [ 0.5, 0.5, 0.5 ]
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std: [ 0.5, 0.5, 0.5 ]
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order: ''
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- RandomErasing:
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EPSILON: 0.5
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EPSILON: 0.25
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sl: 0.02
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sh: 0.3
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sh: 1.0/3.0
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r1: 0.3
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attempt: 10
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use_log_aspect: True
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mode: pixel
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sampler:
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name: DistributedBatchSampler
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batch_size: 16
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@ -110,7 +112,7 @@ DataLoader:
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use_shared_memory: True
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Eval:
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dataset:
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dataset:
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name: ImageNetDataset
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image_root: ./dataset/flowers102/
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cls_label_path: ./dataset/flowers102/val_list.txt
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@ -124,8 +126,8 @@ DataLoader:
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size: 224
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- NormalizeImage:
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scale: 1.0/255.0
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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mean: [ 0.5, 0.5, 0.5 ]
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std: [ 0.5, 0.5, 0.5 ]
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order: ''
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sampler:
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name: DistributedBatchSampler
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@ -31,10 +31,8 @@ Arch:
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sin_pos_emb: True
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abs_pos_emb: False
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enable_linear_eval: False
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model_key: model|module|state_dict
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rel_pos_bias: True
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model_ema:
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enable_model_ema: False
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model_ema_decay: 0.9999
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@ -83,23 +81,27 @@ DataLoader:
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- DecodeImage:
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to_rgb: True
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channel_first: False
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- RandCropImage:
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- RandomResizedCrop:
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size: 224
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interpolation: bilinear
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- RandFlipImage:
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flip_code: 1
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- RandAugment:
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- RandomHorizontalFlip:
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prob: 0.5
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- TimmAutoAugment:
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config_str: rand-m9-mstd0.5-inc1
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interpolation: bicubic
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img_size: 224
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- NormalizeImage:
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scale: 1.0/255.0
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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mean: [ 0.5, 0.5, 0.5 ]
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std: [ 0.5, 0.5, 0.5 ]
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order: ''
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- RandomErasing:
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EPSILON: 0.5
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EPSILON: 0.25
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sl: 0.02
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sh: 0.3
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sh: 1.0/3.0
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r1: 0.3
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attempt: 10
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use_log_aspect: True
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mode: pixel
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sampler:
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name: DistributedBatchSampler
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batch_size: 16
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@ -110,7 +112,7 @@ DataLoader:
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use_shared_memory: True
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Eval:
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dataset:
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dataset:
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name: ImageNetDataset
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image_root: ./dataset/flowers102/
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cls_label_path: ./dataset/flowers102/val_list.txt
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@ -124,8 +126,8 @@ DataLoader:
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size: 224
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- NormalizeImage:
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scale: 1.0/255.0
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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mean: [ 0.5, 0.5, 0.5 ]
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std: [ 0.5, 0.5, 0.5 ]
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order: ''
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sampler:
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name: DistributedBatchSampler
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