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Add code of GaitBase (#115)

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* add resnet9 backbone and regular da ops

* add gait3d config

* fix invalid path CASIA-B* in windows

* add gaitbase config for all datasets

* rm unused OpenGait transform
This commit is contained in:
Junhao Liang
2023-03-20 14:59:08 +08:00
committed by GitHub
parent 9b74b39f80
commit 638c657763
16 changed files with 656 additions and 161 deletions
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configs/gaitbase/gaitbase_da_casiab.yaml
+108
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@@ -0,0 +1,108 @@
data_cfg:
dataset_name: CASIA-B
dataset_root: your_path
dataset_partition: ./datasets/CASIA-B/CASIA-B.json
num_workers: 1
remove_no_gallery: false # Remove probe if no gallery for it
test_dataset_name: CASIA-B
evaluator_cfg:
enable_float16: true
restore_ckpt_strict: true
restore_hint: 0
save_name: GaitBase_DA
#eval_func: GREW_submission
sampler:
batch_shuffle: false
batch_size: 16
sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order; Other options: fixed_unordered
frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory
metric: euc # cos
transform:
- type: BaseSilCuttingTransform
loss_cfg:
- loss_term_weight: 1.0
margin: 0.2
type: TripletLoss
log_prefix: triplet
- loss_term_weight: 1.0
scale: 16
type: CrossEntropyLoss
log_prefix: softmax
log_accuracy: true
model_cfg:
model: Baseline
backbone_cfg:
type: ResNet9
block: BasicBlock
channels: # Layers configuration for automatically model construction
- 64
- 128
- 256
- 512
layers:
- 1
- 1
- 1
- 1
strides:
- 1
- 2
- 2
- 1
maxpool: false
SeparateFCs:
in_channels: 512
out_channels: 256
parts_num: 16
SeparateBNNecks:
class_num: 74
in_channels: 256
parts_num: 16
bin_num:
- 16
optimizer_cfg:
lr: 0.1
momentum: 0.9
solver: SGD
weight_decay: 0.0005
scheduler_cfg:
gamma: 0.1
milestones: # Learning Rate Reduction at each milestones
- 20000
- 40000
- 50000
scheduler: MultiStepLR
trainer_cfg:
enable_float16: true # half_percesion float for memory reduction and speedup
fix_BN: false
with_test: false
log_iter: 100
restore_ckpt_strict: true
restore_hint: 0
save_iter: 60000
save_name: GaitBase_DA
sync_BN: true
total_iter: 60000
sampler:
batch_shuffle: true
batch_size:
- 8 # TripletSampler, batch_size[0] indicates Number of Identity
- 16 # batch_size[1] indicates Samples sequqnce for each Identity
frames_num_fixed: 30 # fixed frames number for training
frames_num_max: 40 # max frames number for unfixed training
frames_num_min: 20 # min frames number for unfixed traing
sample_type: fixed_unordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered
type: TripletSampler
transform:
- type: Compose
trf_cfg:
- type: BaseSilCuttingTransform
- type: RandomRotate
prob: 0.3
- type: RandomErasing
prob: 0.3
configs/gaitbase/gaitbase_da_gait3d.yaml
+110
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data_cfg:
dataset_name: Gait3D
dataset_root: your_path
dataset_partition: ./datasets/Gait3D/Gait3D.json
num_workers: 1
remove_no_gallery: false # Remove probe if no gallery for it
test_dataset_name: Gait3D
evaluator_cfg:
enable_float16: true
restore_ckpt_strict: true
restore_hint: 60000
save_name: GaitBase_DA
eval_func: evaluate_Gait3D
sampler:
batch_shuffle: false
batch_size: 16
sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order; Other options: fixed_unordered
frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory
metric: euc # cos
transform:
- type: BaseSilTransform
loss_cfg:
- loss_term_weight: 1.0
margin: 0.2
type: TripletLoss
log_prefix: triplet
- loss_term_weight: 1.0
scale: 16
type: CrossEntropyLoss
log_prefix: softmax
log_accuracy: true
model_cfg:
model: Baseline
backbone_cfg:
type: ResNet9
block: BasicBlock
channels: # Layers configuration for automatically model construction
- 64
- 128
- 256
- 512
layers:
- 1
- 1
- 1
- 1
strides:
- 1
- 2
- 2
- 1
maxpool: false
SeparateFCs:
in_channels: 512
out_channels: 256
parts_num: 16
SeparateBNNecks:
class_num: 3000
in_channels: 256
parts_num: 16
bin_num:
- 16
optimizer_cfg:
lr: 0.1
momentum: 0.9
solver: SGD
weight_decay: 0.0005
scheduler_cfg:
gamma: 0.1
milestones: # Learning Rate Reduction at each milestones
- 20000
- 40000
- 50000
scheduler: MultiStepLR
trainer_cfg:
enable_float16: true # half_percesion float for memory reduction and speedup
fix_BN: false
with_test: true
log_iter: 100
restore_ckpt_strict: true
restore_hint: 0
save_iter: 20000
save_name: GaitBase_DA
sync_BN: true
total_iter: 60000
sampler:
batch_shuffle: true
batch_size:
- 32 # TripletSampler, batch_size[0] indicates Number of Identity
- 4 # 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: 10 # min frames number for unfixed traing
sample_type: unfixed_unordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered
type: TripletSampler
transform:
- type: Compose
trf_cfg:
- type: RandomPerspective
prob: 0.2
- type: BaseSilTransform
- type: RandomHorizontalFlip
prob: 0.2
- type: RandomRotate
prob: 0.2
configs/gaitbase/gaitbase_da_grew.yaml
+108
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@@ -0,0 +1,108 @@
data_cfg:
dataset_name: GREW
dataset_root: your_path
dataset_partition: ./datasets/GREW/GREW.json
num_workers: 1
remove_no_gallery: false # Remove probe if no gallery for it
test_dataset_name: GREW
evaluator_cfg:
enable_float16: true
restore_ckpt_strict: true
restore_hint: 180000
save_name: GaitBase_DA
eval_func: GREW_submission
sampler:
batch_shuffle: false
batch_size: 16
sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order; Other options: fixed_unordered
frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory
metric: euc # cos
transform:
- type: BaseSilCuttingTransform
loss_cfg:
- loss_term_weight: 1.0
margin: 0.2
type: TripletLoss
log_prefix: triplet
- loss_term_weight: 1.0
scale: 16
type: CrossEntropyLoss
log_prefix: softmax
log_accuracy: true
model_cfg:
model: Baseline
backbone_cfg:
type: ResNet9
block: BasicBlock
channels: # Layers configuration for automatically model construction
- 64
- 128
- 256
- 512
layers:
- 1
- 1
- 1
- 1
strides:
- 1
- 2
- 2
- 1
maxpool: false
SeparateFCs:
in_channels: 512
out_channels: 256
parts_num: 16
SeparateBNNecks:
class_num: 20000
in_channels: 256
parts_num: 16
bin_num:
- 16
optimizer_cfg:
lr: 0.1
momentum: 0.9
solver: SGD
weight_decay: 0.0005
scheduler_cfg:
gamma: 0.1
milestones: # Learning Rate Reduction at each milestones
- 80000
- 120000
- 150000
scheduler: MultiStepLR
trainer_cfg:
enable_float16: true # half_percesion float for memory reduction and speedup
fix_BN: false
with_test: false
log_iter: 100
restore_ckpt_strict: true
restore_hint: 0
save_iter: 60000
save_name: GaitBase_DA
sync_BN: true
total_iter: 180000
sampler:
batch_shuffle: true
batch_size:
- 32 # TripletSampler, batch_size[0] indicates Number of Identity
- 4 # batch_size[1] indicates Samples sequqnce for each Identity
frames_num_fixed: 30 # fixed frames number for training
frames_num_max: 40 # max frames number for unfixed training
frames_num_min: 20 # min frames number for unfixed traing
sample_type: unfixed_unordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered
type: TripletSampler
transform:
- type: Compose
trf_cfg:
- type: RandomPerspective
prob: 0.2
- type: BaseSilCuttingTransform
- type: RandomHorizontalFlip
prob: 0.2
- type: RandomRotate
prob: 0.2
configs/gaitbase/gaitbase_oumvlp.yaml
+103
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@@ -0,0 +1,103 @@
data_cfg:
dataset_name: OUMVLP
dataset_root: your_path
dataset_partition: ./datasets/OUMVLP/OUMVLP.json
num_workers: 1
remove_no_gallery: false # Remove probe if no gallery for it
test_dataset_name: OUMVLP
evaluator_cfg:
enable_float16: true
restore_ckpt_strict: true
restore_hint: 120000
save_name: GaitBase
#eval_func: GREW_submission
sampler:
batch_shuffle: false
batch_size: 16
sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order;
Other options: fixed_unordered
frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory
metric: euc # cos
transform:
- type: BaseSilCuttingTransform
loss_cfg:
- loss_term_weight: 1.0
margin: 0.2
type: TripletLoss
log_prefix: triplet
- loss_term_weight: 1.0
scale: 16
type: CrossEntropyLoss
log_prefix: softmax
log_accuracy: true
model_cfg:
model: Baseline
backbone_cfg:
type: ResNet9
block: BasicBlock
channels: # Layers configuration for automatically model construction
- 64
- 128
- 256
- 512
layers:
- 1
- 1
- 1
- 1
strides:
- 1
- 2
- 2
- 1
maxpool: false
SeparateFCs:
in_channels: 512
out_channels: 256
parts_num: 16
SeparateBNNecks:
class_num: 5153
in_channels: 256
parts_num: 16
bin_num:
- 16
optimizer_cfg:
lr: 0.1
momentum: 0.9
solver: SGD
weight_decay: 0.0005
scheduler_cfg:
gamma: 0.1
milestones: # Learning Rate Reduction at each milestones
- 60000
- 80000
- 100000
scheduler: MultiStepLR
trainer_cfg:
enable_float16: true # half_percesion float for memory reduction and speedup
fix_BN: false
with_test: false
log_iter: 100
restore_ckpt_strict: true
restore_hint: 0
save_iter: 60000
save_name: GaitBase
sync_BN: true
total_iter: 120000
sampler:
batch_shuffle: true
batch_size:
- 32 # TripletSampler, batch_size[0] indicates Number of Identity
- 8 # batch_size[1] indicates Samples sequqnce for each Identity
frames_num_fixed: 30 # fixed frames number for training
frames_num_max: 40 # max frames number for unfixed training
frames_num_min: 20 # min frames number for unfixed traing
sample_type: fixed_unordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered
type: TripletSampler
transform:
- type: BaseSilCuttingTransform
configs/gaitedge/README.md
+1 -1
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@@ -8,7 +8,7 @@ Gait is one of the most promising biometrics to identify individuals at a long d
## CASIA-B*
Since the silhouettes of CASIA-B were obtained by the outdated background subtraction, there exists much noise caused by the background and clothes of subjects. Hence, we re-annotate the
silhouettes of CASIA-B and denote it as CASIA-B*. You can visit [this link](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp) to apply for CASIA-B*. More details about CASIA-B* can be found in [this link](../../datasets/CASIA-B*/README.md).
silhouettes of CASIA-B and denote it as CASIA-B*. You can visit [this link](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp) to apply for CASIA-B*. More details about CASIA-B* can be found in [this link](../../datasets/CASIA-B/README.md).
## Performance
| Model | NM | BG | CL | TTG-200 (cross-domain) | Configuration |
configs/gaitedge/phase1_rec.yaml
+1 -1
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@@ -3,7 +3,7 @@ data_cfg:
dataset_name: CASIA-B*
dataset_root: your_path
data_in_use: [true, false, false, false]
dataset_partition: ./datasets/CASIA-B*/CASIA-B*.json
dataset_partition: ./datasets/CASIA-B/CASIA-B.json
num_workers: 1
remove_no_gallery: false
test_dataset_name: CASIA-B
configs/gaitedge/phase1_seg.yaml
+1 -1
View File
@@ -3,7 +3,7 @@ data_cfg:
dataset_name: CASIA-B*
dataset_root: your_path
data_in_use: [false, false, true, true]
dataset_partition: ./datasets/CASIA-B*/CASIA-B*.json
dataset_partition: ./datasets/CASIA-B/CASIA-B.json
num_workers: 1
remove_no_gallery: false
test_dataset_name: CASIA-B
configs/gaitedge/phase2_e2e.yaml
+1 -1
View File
@@ -2,7 +2,7 @@ data_cfg:
dataset_name: CASIA-B*
dataset_root: your_path
data_in_use: [false, true, true, true]
dataset_partition: ./datasets/CASIA-B*/CASIA-B*.json
dataset_partition: ./datasets/CASIA-B/CASIA-B.json
num_workers: 1
remove_no_gallery: false # Remove probe if no gallery for it
test_dataset_name: CASIA-B
configs/gaitedge/phase2_gaitedge.yaml
+1 -1
View File
@@ -2,7 +2,7 @@ data_cfg:
dataset_name: CASIA-B*
dataset_root: your_path
data_in_use: [false, true, true, true]
dataset_partition: ./datasets/CASIA-B*/CASIA-B*.json
dataset_partition: ./datasets/CASIA-B/CASIA-B.json
num_workers: 1
remove_no_gallery: false # Remove probe if no gallery for it
test_dataset_name: CASIA-B
datasets/CASIA-B*/CASIA-B*.json
-130
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@@ -1,130 +0,0 @@
{
"TRAIN_SET": [
"001",
"002",
"003",
"004",
"005",
"006",
"007",
"008",
"009",
"010",
"011",
"012",
"013",
"014",
"015",
"016",
"017",
"018",
"019",
"020",
"021",
"022",
"023",
"024",
"025",
"026",
"027",
"028",
"029",
"030",
"031",
"032",
"033",
"034",
"035",
"036",
"037",
"038",
"039",
"040",
"041",
"042",
"043",
"044",
"045",
"046",
"047",
"048",
"049",
"050",
"051",
"052",
"053",
"054",
"055",
"056",
"057",
"058",
"059",
"060",
"061",
"062",
"063",
"064",
"065",
"066",
"067",
"068",
"069",
"070",
"071",
"072",
"073",
"074"
],
"TEST_SET": [
"075",
"076",
"077",
"078",
"079",
"080",
"081",
"082",
"083",
"084",
"085",
"086",
"087",
"088",
"089",
"090",
"091",
"092",
"093",
"094",
"095",
"096",
"097",
"098",
"099",
"100",
"101",
"102",
"103",
"104",
"105",
"106",
"107",
"108",
"109",
"110",
"111",
"112",
"113",
"114",
"115",
"116",
"117",
"118",
"119",
"120",
"121",
"122",
"123",
"124"
]
}
datasets/CASIA-B*/README.md
-21
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@@ -1,21 +0,0 @@
# CASIA-B\*
## Introduction
CASIA-B\* is a re-segmented version of CASIA-B processed by Liang et al. The extra import of CASIA-B* owes to the background subtraction algorithm that CASIA-B uses for generating the silhouette data tends to produce much noise and is outdated for real-world applications nowadays. We use the up-to-date pretreatment strategy to re-segment the raw videos, i.e., the deep pedestrian track and segmentation algorithms. As a result, CASIA-B\* consists of the cropped RGB images, binary silhouettes, the height-width ratio of the obtained bounding boxes and the aligned silhouettes. Please refer to [GaitEdge](../../configs/gaitedge/README.md) for more details. If you need this sub-set, please apply with the instruction mentioned in [http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp]. In the Email Subject, please mark the specific dataset you need, i.e., Dataset B*.
## Data structure
```
casiab-128-end2end/
001 (subject)
bg-01 (type)
000 (view)
000-aligned-sils.pkl (aligned sils, nx64x44)
000-ratios.pkl (aspect ratio of bounding boxes, n)
000-rgbs.pkl (cropped RGB images, nx3x128x128)
000-sils.pkl (binary silhouettes, nx128x128)
......
......
......
```
## How to use
By default, it loads all file directory information like other datasets before training starts. If you need to use some of these data separately, such as `aligned-sils`, then you can use the `data_in_use` parameter in `data_cfg` lexicographically, *i.e.* `data_in_use: [true, false, false, false]`.
datasets/CASIA-B/README.md
+22
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@@ -25,3 +25,25 @@ Download URL: http://www.cbsr.ia.ac.cn/GaitDatasetB-silh.zip
......
......
```
# CASIA-B\*
## Introduction
CASIA-B\* is a re-segmented version of CASIA-B processed by Liang et al. The extra import of CASIA-B* owes to the background subtraction algorithm that CASIA-B uses for generating the silhouette data tends to produce much noise and is outdated for real-world applications nowadays. We use the up-to-date pretreatment strategy to re-segment the raw videos, i.e., the deep pedestrian track and segmentation algorithms. As a result, CASIA-B\* consists of the cropped RGB images, binary silhouettes, the height-width ratio of the obtained bounding boxes and the aligned silhouettes. Please refer to [GaitEdge](../../configs/gaitedge/README.md) for more details. If you need this sub-set, please apply with the instruction mentioned in [http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp]. In the Email Subject, please mark the specific dataset you need, i.e., Dataset B*.
## Data structure
```
casiab-128-end2end/
001 (subject)
bg-01 (type)
000 (view)
000-aligned-sils.pkl (aligned sils, nx64x44)
000-ratios.pkl (aspect ratio of bounding boxes, n)
000-rgbs.pkl (cropped RGB images, nx3x128x128)
000-sils.pkl (binary silhouettes, nx128x128)
......
......
......
```
## How to use
By default, it loads all file directory information like other datasets before training starts. If you need to use some of these data separately, such as `aligned-sils`, then you can use the `data_in_use` parameter in `data_cfg` lexicographically, *i.e.* `data_in_use: [true, false, false, false]`.
opengait/data/transform.py
+138 -2
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@@ -1,6 +1,9 @@
from data import transform as base_transform
import numpy as np
import random
import torchvision.transforms as T
import cv2
import math
from data import transform as base_transform
from utils import is_list, is_dict, get_valid_args
@@ -49,6 +52,139 @@ class BaseRgbTransform():
return (x - self.mean) / self.std
# **************** Data Agumentation ****************
class RandomHorizontalFlip(object):
def __init__(self, prob=0.5):
self.prob = prob
def __call__(self, seq):
if random.uniform(0, 1) >= self.prob:
return seq
else:
return seq[:, :, ::-1]
class RandomErasing(object):
def __init__(self, prob=0.5, sl=0.05, sh=0.2, r1=0.3, per_frame=False):
self.prob = prob
self.sl = sl
self.sh = sh
self.r1 = r1
self.per_frame = per_frame
def __call__(self, seq):
if not self.per_frame:
if random.uniform(0, 1) >= self.prob:
return seq
else:
for _ in range(100):
seq_size = seq.shape
area = seq_size[1] * seq_size[2]
target_area = random.uniform(self.sl, self.sh) * area
aspect_ratio = random.uniform(self.r1, 1 / self.r1)
h = int(round(math.sqrt(target_area * aspect_ratio)))
w = int(round(math.sqrt(target_area / aspect_ratio)))
if w < seq_size[2] and h < seq_size[1]:
x1 = random.randint(0, seq_size[1] - h)
y1 = random.randint(0, seq_size[2] - w)
seq[:, x1:x1+h, y1:y1+w] = 0.
return seq
return seq
else:
self.per_frame = False
frame_num = seq.shape[0]
ret = [self.__call__(seq[k][np.newaxis, ...])
for k in range(frame_num)]
self.per_frame = True
return np.concatenate(ret, 0)
class RandomRotate(object):
def __init__(self, prob=0.5, degree=10):
self.prob = prob
self.degree = degree
def __call__(self, seq):
if random.uniform(0, 1) >= self.prob:
return seq
else:
_, dh, dw = seq.shape
# rotation
degree = random.uniform(-self.degree, self.degree)
M1 = cv2.getRotationMatrix2D((dh // 2, dw // 2), degree, 1)
# affine
seq = [cv2.warpAffine(_[0, ...], M1, (dw, dh))
for _ in np.split(seq, seq.shape[0], axis=0)]
seq = np.concatenate([np.array(_)[np.newaxis, ...]
for _ in seq], 0)
return seq
class RandomPerspective(object):
def __init__(self, prob=0.5):
self.prob = prob
def __call__(self, seq):
if random.uniform(0, 1) >= self.prob:
return seq
else:
_, h, w = seq.shape
cutting = int(w // 44) * 10
x_left = list(range(0, cutting))
x_right = list(range(w - cutting, w))
TL = (random.choice(x_left), 0)
TR = (random.choice(x_right), 0)
BL = (random.choice(x_left), h)
BR = (random.choice(x_right), h)
srcPoints = np.float32([TL, TR, BR, BL])
canvasPoints = np.float32([[0, 0], [w, 0], [w, h], [0, h]])
perspectiveMatrix = cv2.getPerspectiveTransform(
np.array(srcPoints), np.array(canvasPoints))
seq = [cv2.warpPerspective(_[0, ...], perspectiveMatrix, (w, h))
for _ in np.split(seq, seq.shape[0], axis=0)]
seq = np.concatenate([np.array(_)[np.newaxis, ...]
for _ in seq], 0)
return seq
class RandomAffine(object):
def __init__(self, prob=0.5, degree=10):
self.prob = prob
self.degree = degree
def __call__(self, seq):
if random.uniform(0, 1) >= self.prob:
return seq
else:
_, dh, dw = seq.shape
# rotation
max_shift = int(dh // 64 * 10)
shift_range = list(range(0, max_shift))
pts1 = np.float32([[random.choice(shift_range), random.choice(shift_range)], [
dh-random.choice(shift_range), random.choice(shift_range)], [random.choice(shift_range), dw-random.choice(shift_range)]])
pts2 = np.float32([[random.choice(shift_range), random.choice(shift_range)], [
dh-random.choice(shift_range), random.choice(shift_range)], [random.choice(shift_range), dw-random.choice(shift_range)]])
M1 = cv2.getAffineTransform(pts1, pts2)
# affine
seq = [cv2.warpAffine(_[0, ...], M1, (dw, dh))
for _ in np.split(seq, seq.shape[0], axis=0)]
seq = np.concatenate([np.array(_)[np.newaxis, ...]
for _ in seq], 0)
return seq
# ******************************************
def Compose(trf_cfg):
assert is_list(trf_cfg)
transform = T.Compose([get_transform(cfg) for cfg in trf_cfg])
return transform
def get_transform(trf_cfg=None):
if is_dict(trf_cfg):
transform = getattr(base_transform, trf_cfg['type'])
opengait/evaluation/evaluator.py
+1 -1
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@@ -231,7 +231,7 @@ def evaluate_segmentation(data, dataset):
return {"scalar/test_accuracy/mIOU": miou}
def evaluate_Gait3D(data, conf, metric='euc'):
def evaluate_Gait3D(data, dataset, metric='euc'):
msg_mgr = get_msg_mgr()
features, labels, cams, time_seqs = data['embeddings'], data['labels'], data['types'], data['views']
opengait/modeling/backbones/resnet.py
+58
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@@ -0,0 +1,58 @@
from torch.nn import functional as F
import torch.nn as nn
from torchvision.models.resnet import BasicBlock, Bottleneck, ResNet
from ..modules import BasicConv2d
block_map = {'BasicBlock': BasicBlock,
'Bottleneck': Bottleneck}
class ResNet9(ResNet):
def __init__(self, block, channels=[32, 64, 128, 256], in_channel=1, layers=[1, 2, 2, 1], strides=[1, 2, 2, 1], maxpool=True):
if block in block_map.keys():
block = block_map[block]
else:
raise ValueError(
"Error type for -block-Cfg-, supported: 'BasicBlock' or 'Bottleneck'.")
self.maxpool_flag = maxpool
super(ResNet9, self).__init__(block, layers)
# Not used #
self.fc = None
############
self.inplanes = channels[0]
self.bn1 = nn.BatchNorm2d(self.inplanes)
self.conv1 = BasicConv2d(in_channel, self.inplanes, 3, 1, 1)
self.layer1 = self._make_layer(
block, channels[0], layers[0], stride=strides[0], dilate=False)
self.layer2 = self._make_layer(
block, channels[1], layers[1], stride=strides[1], dilate=False)
self.layer3 = self._make_layer(
block, channels[2], layers[2], stride=strides[2], dilate=False)
self.layer4 = self._make_layer(
block, channels[3], layers[3], stride=strides[3], dilate=False)
def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
if blocks >= 1:
layer = super()._make_layer(block, planes, blocks, stride=stride, dilate=dilate)
else:
def layer(x): return x
return layer
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
if self.maxpool_flag:
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
return x
opengait/modeling/base_model.py
+1
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@@ -427,6 +427,7 @@ class BaseModel(MetaModel, nn.Module):
model.train()
if model.cfgs['trainer_cfg']['fix_BN']:
model.fix_BN()
if result_dict:
model.msg_mgr.write_to_tensorboard(result_dict)
model.msg_mgr.reset_time()
if model.iteration >= model.engine_cfg['total_iter']: