GaitGL_GREW supported
This commit is contained in:
Noah
@@ -1,6 +1,6 @@
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data_cfg:
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dataset_name: GREW
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dataset_root: your_path
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dataset_root: /home/data/grew-pkl-fixed
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dataset_partition: ./datasets/GREW/GREW.json
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num_workers: 16
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remove_no_gallery: false # Remove probe if no gallery for it
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@@ -92,7 +92,7 @@ trainer_cfg:
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batch_shuffle: true
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batch_size:
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- 32 # TripletSampler, batch_size[0] indicates Number of Identity
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- 4 # batch_size[1] indicates Samples sequqnce for each Identity
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- 8 # batch_size[1] indicates Samples sequqnce for each Identity
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frames_num_fixed: 30 # fixed frames number for training
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frames_num_max: 50 # max frames number for unfixed training
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frames_num_min: 25 # min frames number for unfixed traing
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@@ -0,0 +1,70 @@
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# Note : *** the batch_size should be equal to the gpus number at the test phase!!! ***
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data_cfg:
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dataset_name: GREW
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dataset_root: your_path
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dataset_partition: ./datasets/GREW/GREW.json
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num_workers: 16
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remove_no_gallery: false # Remove probe if no gallery for it
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test_dataset_name: GREW
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evaluator_cfg:
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enable_distributed: true
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enable_float16: false
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restore_ckpt_strict: true
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restore_hint: 250000
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save_name: GaitGL
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eval_func: identification_GREW_submission # identification_real_scene # identification_GREW_submission
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sampler:
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batch_size: 4
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sample_type: all_ordered
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type: InferenceSampler
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loss_cfg:
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- loss_term_weight: 1.0
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margin: 0.2
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type: TripletLoss
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log_prefix: triplet
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- loss_term_weight: 1.0
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scale: 1
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type: CrossEntropyLoss
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log_accuracy: true
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label_smooth: true
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log_prefix: softmax
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model_cfg:
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model: GaitGL
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channels: [32, 64, 128, 256]
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class_num: 20000
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optimizer_cfg:
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lr: 1.0e-4
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solver: Adam
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weight_decay: 0
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scheduler_cfg:
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gamma: 0.1
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milestones:
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- 150000
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- 200000
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scheduler: MultiStepLR
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trainer_cfg:
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enable_distributed: true
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enable_float16: true
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with_test: false
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log_iter: 100
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restore_ckpt_strict: true
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restore_hint: 0
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save_iter: 10000
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save_name: GaitGL
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sync_BN: true
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total_iter: 250000
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sampler:
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batch_shuffle: true
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batch_size:
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- 32
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- 4
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frames_num_fixed: 30
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frames_skip_num: 0
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sample_type: fixed_ordered
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type: TripletSampler
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@@ -0,0 +1,74 @@
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# Note : *** the batch_size should be equal to the gpus number at the test phase!!! ***
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data_cfg:
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dataset_name: GREW
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dataset_root: your_path
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dataset_partition: ./datasets/GREW/GREW.json
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num_workers: 16
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remove_no_gallery: false # Remove probe if no gallery for it
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test_dataset_name: GREW
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evaluator_cfg:
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enable_distributed: true
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enable_float16: false
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restore_ckpt_strict: true
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restore_hint: 250000
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save_name: GaitGL_BNNeck
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eval_func: identification_GREW_submission # identification_real_scene # identification_GREW_submission
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sampler:
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batch_size: 4
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sample_type: all_ordered
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type: InferenceSampler
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loss_cfg:
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- loss_term_weight: 1.0
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margin: 0.2
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type: TripletLoss
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log_prefix: triplet
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- loss_term_weight: 1.0
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scale: 1
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type: CrossEntropyLoss
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log_accuracy: true
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label_smooth: true
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log_prefix: softmax
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model_cfg:
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model: GaitGL
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channels: [32, 64, 128, 256]
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class_num: 20000
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SeparateBNNecks:
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class_num: 20000
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in_channels: 256
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parts_num: 64
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optimizer_cfg:
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lr: 1.0e-4
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solver: Adam
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weight_decay: 0
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scheduler_cfg:
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gamma: 0.1
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milestones:
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- 150000
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- 200000
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scheduler: MultiStepLR
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trainer_cfg:
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enable_distributed: true
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enable_float16: true
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with_test: false
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log_iter: 100
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restore_ckpt_strict: true
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restore_hint: 0
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save_iter: 10000
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save_name: GaitGL_BNNeck
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sync_BN: true
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total_iter: 250000
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sampler:
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batch_shuffle: true
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batch_size:
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- 32
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- 4
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frames_num_fixed: 30
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frames_skip_num: 0
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sample_type: fixed_ordered
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type: TripletSampler
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@@ -125,5 +125,9 @@ if __name__ == "__main__":
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for url in urls:
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download_file_and_uncompress(
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url=url, extrapath='output')
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gaitgl_grew = ['https://github.com/ShiqiYu/OpenGait/releases/download/v1.1/pretrained_grew_gaitgl.zip',
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'https://github.com/ShiqiYu/OpenGait/releases/download/v1.1/pretrained_grew_gaitgl_bnneck.zip']
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for gaitgl in gaitgl_grew:
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download_file_and_uncompress(
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url=gaitgl, extrapath='output/GREW/GaitGL')
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print("Pretrained model download success!")
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@@ -3,7 +3,7 @@ import torch.nn as nn
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import torch.nn.functional as F
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from ..base_model import BaseModel
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from ..modules import SeparateFCs, BasicConv3d, PackSequenceWrapper
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from ..modules import SeparateFCs, BasicConv3d, PackSequenceWrapper, SeparateBNNecks
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class GLConv(nn.Module):
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@@ -75,8 +75,8 @@ class GaitGL(BaseModel):
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class_num = model_cfg['class_num']
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dataset_name = self.cfgs['data_cfg']['dataset_name']
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if dataset_name == 'OUMVLP':
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# For OUMVLP
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if dataset_name in ['OUMVLP','GREW']:
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# For OUMVLP and GREW
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self.conv3d = nn.Sequential(
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BasicConv3d(1, in_c[0], kernel_size=(3, 3, 3),
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stride=(1, 1, 1), padding=(1, 1, 1)),
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@@ -135,12 +135,19 @@ class GaitGL(BaseModel):
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self.GLConvB2 = GLConv(in_c[2], in_c[2], halving=3, fm_sign=True, kernel_size=(
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3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
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self.Head0 = SeparateFCs(64, in_c[-1], in_c[-1])
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self.Bn = nn.BatchNorm1d(in_c[-1])
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self.Head1 = SeparateFCs(64, in_c[-1], class_num)
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self.TP = PackSequenceWrapper(torch.max)
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self.HPP = GeMHPP()
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self.Head0 = SeparateFCs(64, in_c[-1], in_c[-1])
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if 'SeparateBNNecks' in model_cfg.keys():
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self.BNNecks = SeparateBNNecks(**model_cfg['SeparateBNNecks'])
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self.Bn_head = False
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else:
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self.Bn = nn.BatchNorm1d(in_c[-1])
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self.Head1 = SeparateFCs(64, in_c[-1], class_num)
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self.Bn_head = True
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def forward(self, inputs):
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ipts, labs, _, _, seqL = inputs
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@@ -169,12 +176,16 @@ class GaitGL(BaseModel):
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outs = outs.permute(2, 0, 1).contiguous() # [p, n, c]
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gait = self.Head0(outs) # [p, n, c]
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gait = gait.permute(1, 2, 0).contiguous() # [n, c, p]
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bnft = self.Bn(gait) # [n, c, p]
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logi = self.Head1(bnft.permute(2, 0, 1).contiguous()) # [p, n, c]
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if self.Bn_head: # Original GaitGL Head
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gait = gait.permute(1, 2, 0).contiguous() # [n, c, p]
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bnft = self.Bn(gait) # [n, c, p]
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logi = self.Head1(bnft.permute(2, 0, 1).contiguous()) # [p, n, c]
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bnft = bnft.permute(0, 2, 1).contiguous() # [n, p, c]
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else: # BNNechk as Head
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bnft, logi = self.BNNecks(gait) # [p, n, c]
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bnft = bnft.permute(1, 0, 2).contiguous() # [n, p, c]
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gait = gait.permute(0, 2, 1).contiguous() # [n, p, c]
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bnft = bnft.permute(0, 2, 1).contiguous() # [n, p, c]
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logi = logi.permute(1, 0, 2).contiguous() # [n, p, c]
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n, _, s, h, w = sils.size()
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