GaitGL_GREW supported

config/baseline/baseline_GREW.yaml

@@ -1,6 +1,6 @@
 data_cfg:
   dataset_name: GREW
-  dataset_root: your_path
+  dataset_root: /home/data/grew-pkl-fixed
   dataset_partition: ./datasets/GREW/GREW.json
   num_workers: 16
   remove_no_gallery: false # Remove probe if no gallery for it
@@ -92,7 +92,7 @@ trainer_cfg:
     batch_shuffle: true
     batch_size:
       - 32 # TripletSampler, batch_size[0] indicates Number of Identity
-      - 4 #                 batch_size[1] indicates Samples sequqnce for each Identity
+      - 8 #                 batch_size[1] indicates Samples sequqnce for each Identity
     frames_num_fixed: 30 # fixed frames number for training
     frames_num_max: 50 # max frames number for unfixed training
     frames_num_min: 25 # min frames number for unfixed traing

config/gaitgl/gaitgl_GREW.yaml

@@ -0,0 +1,70 @@
+# Note  : *** the batch_size should be equal to the gpus number at the test phase!!! ***
+data_cfg:
+  dataset_name: GREW
+  dataset_root: your_path
+  dataset_partition: ./datasets/GREW/GREW.json
+  num_workers: 16
+  remove_no_gallery: false # Remove probe if no gallery for it
+  test_dataset_name: GREW
+
+evaluator_cfg:
+  enable_distributed: true
+  enable_float16: false 
+  restore_ckpt_strict: true
+  restore_hint: 250000
+  save_name: GaitGL
+  eval_func: identification_GREW_submission # identification_real_scene # identification_GREW_submission
+  sampler:
+    batch_size: 4
+    sample_type: all_ordered
+    type: InferenceSampler
+
+loss_cfg:
+  - loss_term_weight: 1.0
+    margin: 0.2
+    type: TripletLoss
+    log_prefix: triplet
+  - loss_term_weight: 1.0
+    scale: 1
+    type: CrossEntropyLoss
+    log_accuracy: true
+    label_smooth: true
+    log_prefix: softmax
+
+model_cfg:
+  model: GaitGL
+  channels: [32, 64, 128, 256]
+  class_num: 20000
+
+optimizer_cfg:
+  lr: 1.0e-4
+  solver: Adam
+  weight_decay: 0
+
+scheduler_cfg:
+  gamma: 0.1
+  milestones:
+    - 150000
+    - 200000
+  scheduler: MultiStepLR
+
+trainer_cfg:
+  enable_distributed: true
+  enable_float16: true
+  with_test: false
+  log_iter: 100
+  restore_ckpt_strict: true
+  restore_hint: 0
+  save_iter: 10000
+  save_name: GaitGL
+  sync_BN: true
+  total_iter: 250000
+  sampler:
+    batch_shuffle: true
+    batch_size:
+      - 32
+      - 4
+    frames_num_fixed: 30
+    frames_skip_num: 0
+    sample_type: fixed_ordered
+    type: TripletSampler

config/gaitgl/gaitgl_GREW_BNNeck.yaml

@@ -0,0 +1,74 @@
+# Note  : *** the batch_size should be equal to the gpus number at the test phase!!! ***
+data_cfg:
+  dataset_name: GREW
+  dataset_root: your_path
+  dataset_partition: ./datasets/GREW/GREW.json
+  num_workers: 16
+  remove_no_gallery: false # Remove probe if no gallery for it
+  test_dataset_name: GREW
+
+evaluator_cfg:
+  enable_distributed: true
+  enable_float16: false 
+  restore_ckpt_strict: true
+  restore_hint: 250000
+  save_name: GaitGL_BNNeck
+  eval_func: identification_GREW_submission # identification_real_scene # identification_GREW_submission
+  sampler:
+    batch_size: 4
+    sample_type: all_ordered
+    type: InferenceSampler
+
+loss_cfg:
+  - loss_term_weight: 1.0
+    margin: 0.2
+    type: TripletLoss
+    log_prefix: triplet
+  - loss_term_weight: 1.0
+    scale: 1
+    type: CrossEntropyLoss
+    log_accuracy: true
+    label_smooth: true
+    log_prefix: softmax
+
+model_cfg:
+  model: GaitGL
+  channels: [32, 64, 128, 256]
+  class_num: 20000
+  SeparateBNNecks:
+    class_num: 20000
+    in_channels: 256
+    parts_num: 64
+
+optimizer_cfg:
+  lr: 1.0e-4
+  solver: Adam
+  weight_decay: 0
+
+scheduler_cfg:
+  gamma: 0.1
+  milestones:
+    - 150000
+    - 200000
+  scheduler: MultiStepLR
+
+trainer_cfg:
+  enable_distributed: true
+  enable_float16: true
+  with_test: false
+  log_iter: 100
+  restore_ckpt_strict: true
+  restore_hint: 0
+  save_iter: 10000
+  save_name: GaitGL_BNNeck
+  sync_BN: true
+  total_iter: 250000
+  sampler:
+    batch_shuffle: true
+    batch_size:
+      - 32
+      - 4
+    frames_num_fixed: 30
+    frames_skip_num: 0
+    sample_type: fixed_ordered
+    type: TripletSampler

misc/download_pretrained_model.py

@@ -125,5 +125,9 @@ if __name__ == "__main__":
     for url in urls:
         download_file_and_uncompress(
             url=url, extrapath='output')
-
+    gaitgl_grew = ['https://github.com/ShiqiYu/OpenGait/releases/download/v1.1/pretrained_grew_gaitgl.zip',
+                   'https://github.com/ShiqiYu/OpenGait/releases/download/v1.1/pretrained_grew_gaitgl_bnneck.zip']
+    for gaitgl in gaitgl_grew:
+        download_file_and_uncompress(
+                url=gaitgl, extrapath='output/GREW/GaitGL')
     print("Pretrained model download success!")

opengait/modeling/models/gaitgl.py

@@ -3,7 +3,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 from ..base_model import BaseModel
-from ..modules import SeparateFCs, BasicConv3d, PackSequenceWrapper
+from ..modules import SeparateFCs, BasicConv3d, PackSequenceWrapper, SeparateBNNecks
 
 
 class GLConv(nn.Module):
@@ -75,8 +75,8 @@ class GaitGL(BaseModel):
         class_num = model_cfg['class_num']
         dataset_name = self.cfgs['data_cfg']['dataset_name']
 
-        if dataset_name == 'OUMVLP':
+        if dataset_name in ['OUMVLP','GREW']:
-            # For OUMVLP
+            # For OUMVLP and GREW
             self.conv3d = nn.Sequential(
                 BasicConv3d(1, in_c[0], kernel_size=(3, 3, 3),
                             stride=(1, 1, 1), padding=(1, 1, 1)),
@@ -135,12 +135,19 @@ class GaitGL(BaseModel):
             self.GLConvB2 = GLConv(in_c[2], in_c[2], halving=3, fm_sign=True,  kernel_size=(
                 3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
 
-        self.Head0 = SeparateFCs(64, in_c[-1], in_c[-1])
+        
-        self.Bn = nn.BatchNorm1d(in_c[-1])
-        self.Head1 = SeparateFCs(64, in_c[-1], class_num)
-
         self.TP = PackSequenceWrapper(torch.max)
         self.HPP = GeMHPP()
+        
+        self.Head0 = SeparateFCs(64, in_c[-1], in_c[-1])
+        
+        if 'SeparateBNNecks' in model_cfg.keys():
+            self.BNNecks = SeparateBNNecks(**model_cfg['SeparateBNNecks'])
+            self.Bn_head = False
+        else:
+            self.Bn = nn.BatchNorm1d(in_c[-1])
+            self.Head1 = SeparateFCs(64, in_c[-1], class_num)
+            self.Bn_head = True
 
     def forward(self, inputs):
         ipts, labs, _, _, seqL = inputs
@@ -169,12 +176,16 @@ class GaitGL(BaseModel):
         outs = outs.permute(2, 0, 1).contiguous()  # [p, n, c]
 
         gait = self.Head0(outs)  # [p, n, c]
-        gait = gait.permute(1, 2, 0).contiguous()  # [n, c, p]
+        
-        bnft = self.Bn(gait)  # [n, c, p]
+        if self.Bn_head: # Original GaitGL Head
-        logi = self.Head1(bnft.permute(2, 0, 1).contiguous())  # [p, n, c]
+            gait = gait.permute(1, 2, 0).contiguous()  # [n, c, p]
+            bnft = self.Bn(gait)  # [n, c, p]
+            logi = self.Head1(bnft.permute(2, 0, 1).contiguous())  # [p, n, c]
+            bnft = bnft.permute(0, 2, 1).contiguous()  # [n, p, c]
+        else: # BNNechk as Head
+            bnft, logi = self.BNNecks(gait)  # [p, n, c]
+            bnft = bnft.permute(1, 0, 2).contiguous()  # [n, p, c]
 
-        gait = gait.permute(0, 2, 1).contiguous()  # [n, p, c]
-        bnft = bnft.permute(0, 2, 1).contiguous()  # [n, p, c]
         logi = logi.permute(1, 0, 2).contiguous()  # [n, p, c]
 
         n, _, s, h, w = sils.size()