Python 将形状为（5,75,100,10）的目标阵列传递为形状输出（无，74,100,10），同时使用“均方误差”作为损失`
我不知道修复后该怎么办。这个错误属于我复制到Python文件中的Jupyter笔记本 以下是保存的jupyter笔记本，其中也包含错误： 下面是Python代码：Python 将形状为（5,75,100,10）的目标阵列传递为形状输出（无，74,100,10），同时使用“均方误差”作为损失`,python,numpy,tensorflow,machine-learning,conda,Python,Numpy,Tensorflow,Machine Learning,Conda,我不知道修复后该怎么办。这个错误属于我复制到Python文件中的Jupyter笔记本 以下是保存的jupyter笔记本，其中也包含错误： 下面是Python代码： (/scratch3/3d_pose/DeepPoseKitEnv) [jalal@goku examples]$ cat dlc_train.py import sys import tensorflow as tf print(tf.__version__) import numpy as np import matplotli
(/scratch3/3d_pose/DeepPoseKitEnv) [jalal@goku examples]$ cat dlc_train.py
import sys
import tensorflow as tf
print(tf.__version__)
import numpy as np
import matplotlib.pyplot as plt
import glob
from deepposekit.io import TrainingGenerator, DLCDataGenerator
from deepposekit.augment import FlipAxis
import imgaug.augmenters as iaa
import imgaug as ia
from deepposekit.models import (StackedDenseNet,
DeepLabCut,
StackedHourglass,
LEAP)
from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping
from deepposekit.callbacks import Logger, ModelCheckpoint
from deepposekit.models import load_model
import time
from os.path import expanduser
try:
import google.colab
IN_COLAB = True
except:
IN_COLAB = False
data_generator = DLCDataGenerator(
project_path='/scratch3/3d_pose/animalpose/experiments/moth-filtered-Mona-2019-12-06_95p_DONE/'
)
print(data_generator.dlcconfig)
data_generator.graph = np.array([-1, 0, 0, 0])
data_generator.swap_index = np.array([-1, 2, 1, -1])
image, keypoints = data_generator[0]
plt.figure(figsize=(5,5))
image = image[0] if image.shape[-1] is 3 else image[0, ..., 0]
cmap = None if image.shape[-1] is 3 else 'gray'
plt.imshow(image, cmap=cmap, interpolation='none')
for idx, jdx in enumerate(data_generator.graph):
if jdx > -1:
plt.plot(
[keypoints[0, idx, 0], keypoints[0, jdx, 0]],
[keypoints[0, idx, 1], keypoints[0, jdx, 1]],
'r-'
)
plt.scatter(keypoints[0, :, 0], keypoints[0, :, 1], c=np.arange(data_generator.keypoints_shape[0]), s=50, cmap=plt.cm.hsv, zorder=3)
plt.xlim(0, data_generator.image_shape[1])
plt.ylim(0, data_generator.image_shape[0])
plt.show()
augmenter = []
augmenter.append(FlipAxis(data_generator, axis=0)) # flip image up-down
augmenter.append(FlipAxis(data_generator, axis=1)) # flip image left-right
sometimes = []
sometimes.append(iaa.Affine(scale={"x": (0.9, 1.1), "y": (0.9, 1.1)},
translate_percent={'x': (-0.5, 0.5), 'y': (-0.5, 0.5)},
shear=(-8, 8),
order=ia.ALL,
cval=ia.ALL)
)
sometimes.append(iaa.Affine(scale=(0.5, 1.5),
order=ia.ALL,
cval=ia.ALL)
)
augmenter.append(iaa.Sometimes(0.5, sometimes))
augmenter.append(iaa.Sometimes(0.5, iaa.Affine(rotate=(-180, 180),
order=ia.ALL,
cval=ia.ALL))
)
augmenter = iaa.Sequential(augmenter)
image, keypoints = data_generator[0]
image, keypoints = augmenter(images=image, keypoints=keypoints)
plt.figure(figsize=(5,5))
image = image[0] if image.shape[-1] is 3 else image[0, ..., 0]
cmap = None if image.shape[-1] is 3 else 'gray'
plt.imshow(image, cmap=cmap, interpolation='none')
for idx, jdx in enumerate(data_generator.graph):
if jdx > -1:
plt.plot(
[keypoints[0, idx, 0], keypoints[0, jdx, 0]],
[keypoints[0, idx, 1], keypoints[0, jdx, 1]],
'r-'
)
plt.scatter(keypoints[0, :, 0], keypoints[0, :, 1], c=np.arange(data_generator.keypoints_shape[0]), s=50, cmap=plt.cm.hsv, zorder=3)
plt.xlim(0, data_generator.image_shape[1])
plt.ylim(0, data_generator.image_shape[0])
plt.show()
train_generator = TrainingGenerator(generator=data_generator,
downsample_factor=3,
augmenter=augmenter,
sigma=5,
validation_split=0.1,
use_graph=True,
random_seed=1,
graph_scale=1)
train_generator.get_config()
n_keypoints = data_generator.keypoints_shape[0]
batch = train_generator(batch_size=1, validation=False)[0]
inputs = batch[0]
outputs = batch[1]
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(10,10))
ax1.set_title('image')
ax1.imshow(inputs[0,...,0], cmap='gray', vmin=0, vmax=255)
ax2.set_title('posture graph')
ax2.imshow(outputs[0,...,n_keypoints:-1].max(-1))
ax3.set_title('keypoints confidence')
ax3.imshow(outputs[0,...,:n_keypoints].max(-1))
ax4.set_title('posture graph and keypoints confidence')
ax4.imshow(outputs[0,...,-1], vmin=0)
plt.show()
train_generator.on_epoch_end()
from deepposekit.models import DeepLabCut, StackedDenseNet, LEAP
#model = StackedDenseNet(train_generator, n_stacks=1, growth_rate=32, pretrained=True)
#model = DeepLabCut(train_generator, backbone="resnet50")
#model = DeepLabCut(train_generator, backbone="mobilenetv2", alpha=1.0) # Increase alpha to improve accuracy
model = DeepLabCut(train_generator, backbone="densenet121")
#model = LEAP(train_generator)
model.get_config()
data_size = (500,) + data_generator.image_shape
x = np.random.randint(0, 255, data_size, dtype="uint8")
y = model.predict(x[:100], batch_size=50) # make sure the model is in GPU memory
t0 = time.time()
y = model.predict(x, batch_size=50, verbose=1)
t1 = time.time()
print(x.shape[0] / (t1 - t0))
logger = Logger(validation_batch_size=10
# filepath saves the logger data to a .h5 file
# filepath=HOME + "/deeplabcut_log_dlcdensenet.h5", validation_batch_size=10
)
reduce_lr = ReduceLROnPlateau(monitor="val_loss", factor=0.2, verbose=1, patience=20)
model_checkpoint = ModelCheckpoint(
"../../deeplabcut_best_model_dlcdensenet_moth.h5",
monitor="val_loss",
# monitor="loss" # use if validation_split=0
verbose=1,
save_best_only=True,
optimizer=True, # Set this to True if you wish to resume training from a saved model
)
early_stop = EarlyStopping(
monitor="val_loss",
# monitor="loss" # use if validation_split=0
min_delta=0.001,
patience=100,
verbose=1
)
callbacks = [early_stop, reduce_lr, model_checkpoint, logger]
model.fit(
batch_size=5,
validation_batch_size=10,
callbacks=callbacks,
#epochs=1000, # Increase the number of epochs to train the model longer
epochs=100,
n_workers=8,
steps_per_epoch=200,
)
model = load_model(
"../../deeplabcut_best_model_dlcdensenet_moth.h5",
augmenter=augmenter,
generator=data_generator,
)
model.fit(
batch_size=5,
validation_batch_size=10,
callbacks=callbacks,
#epochs=1000, # Increase the number of epochs to train the model longer
epochs=100,
n_workers=8,
steps_per_epoch=200,
这些形状值对您有意义吗？为什么一个有
7475Nonetensorflow(/scratch3/3d_pose/DeepPoseKitEnv) [jalal@goku examples]$ pip list
Package Version
---------------------- -------------------
absl-py 0.8.1
astor 0.8.0
attrs 19.3.0
backcall 0.1.0
bleach 3.1.0
certifi 2019.11.28
chardet 3.0.4
Click 7.0
cycler 0.10.0
decorator 4.4.1
deeplabcut 2.1.4
deepposekit 0.3.4
defusedxml 0.6.0
easydict 1.9
entrypoints 0.3
gast 0.2.2
google-pasta 0.1.8
grpcio 1.25.0
h5py 2.10.0
idna 2.8
imageio 2.6.1
imageio-ffmpeg 0.3.0
imgaug 0.3.0
importlib-metadata 1.2.0
intel-openmp 2020.0.133
ipykernel 5.1.3
ipython 7.10.1
ipython-genutils 0.2.0
ipywidgets 7.5.1
jedi 0.15.1
Jinja2 2.10.3
joblib 0.14.0
jsonschema 3.2.0
jupyter 1.0.0
jupyter-client 5.3.4
jupyter-console 6.0.0
jupyter-core 4.6.1
Keras-Applications 1.0.8
Keras-Preprocessing 1.1.0
kiwisolver 1.1.0
Markdown 3.1.1
MarkupSafe 1.1.1
matplotlib 3.0.3
mistune 0.8.4
mock 3.0.5
more-itertools 8.0.2
moviepy 1.0.1
msgpack 0.6.2
msgpack-numpy 0.4.4.3
nbconvert 5.6.1
nbformat 4.4.0
networkx 2.4
notebook 6.0.2
numexpr 2.7.0
numpy 1.17.4
opencv-python 3.4.5.20
opencv-python-headless 4.1.2.30
opt-einsum 3.1.0
pandas 0.25.3
pandocfilters 1.4.2
parso 0.5.1
patsy 0.5.1
pexpect 4.7.0
pickleshare 0.7.5
Pillow 6.2.1
pip 19.3.1
proglog 0.1.9
prometheus-client 0.7.1
prompt-toolkit 3.0.2
protobuf 3.11.1
psutil 5.6.7
ptyprocess 0.6.0
Pygments 2.5.2
pyparsing 2.4.5
Pypubsub 4.0.3
pyrsistent 0.15.6
python-dateutil 2.8.1
pytz 2019.3
PyWavelets 1.1.1
PyYAML 5.2
pyzmq 18.1.1
qtconsole 4.6.0
requests 2.22.0
ruamel.yaml 0.16.5
ruamel.yaml.clib 0.2.0
scikit-image 0.16.2
scikit-learn 0.22
scipy 1.3.3
Send2Trash 1.5.0
setuptools 42.0.2.post20191203
Shapely 1.6.4.post2
six 1.13.0
statsmodels 0.10.1
tables 3.4.3
tabulate 0.8.6
tensorboard 1.15.0
tensorflow-estimator 1.15.1
tensorflow-gpu 1.15.0
tensorpack 0.9.8
termcolor 1.1.0
terminado 0.8.3
testpath 0.4.4
tornado 6.0.3
tqdm 4.40.1
traitlets 4.3.3
urllib3 1.25.7
wcwidth 0.1.7
webencodings 0.5.1
Werkzeug 0.16.0
wheel 0.33.6
widgetsnbextension 3.5.1
wrapt 1.11.2
wxPython 4.0.3
zipp 0.6.0
(/scratch3/3d_pose/DeepPoseKitEnv) [jalal@goku examples]$ cat dlc_train.py
import sys
import tensorflow as tf
print(tf.__version__)
import numpy as np
import matplotlib.pyplot as plt
import glob
from deepposekit.io import TrainingGenerator, DLCDataGenerator
from deepposekit.augment import FlipAxis
import imgaug.augmenters as iaa
import imgaug as ia
from deepposekit.models import (StackedDenseNet,
DeepLabCut,
StackedHourglass,
LEAP)
from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping
from deepposekit.callbacks import Logger, ModelCheckpoint
from deepposekit.models import load_model
import time
from os.path import expanduser
try:
import google.colab
IN_COLAB = True
except:
IN_COLAB = False
data_generator = DLCDataGenerator(
project_path='/scratch3/3d_pose/animalpose/experiments/moth-filtered-Mona-2019-12-06_95p_DONE/'
)
print(data_generator.dlcconfig)
data_generator.graph = np.array([-1, 0, 0, 0])
data_generator.swap_index = np.array([-1, 2, 1, -1])
image, keypoints = data_generator[0]
plt.figure(figsize=(5,5))
image = image[0] if image.shape[-1] is 3 else image[0, ..., 0]
cmap = None if image.shape[-1] is 3 else 'gray'
plt.imshow(image, cmap=cmap, interpolation='none')
for idx, jdx in enumerate(data_generator.graph):
if jdx > -1:
plt.plot(
[keypoints[0, idx, 0], keypoints[0, jdx, 0]],
[keypoints[0, idx, 1], keypoints[0, jdx, 1]],
'r-'
)
plt.scatter(keypoints[0, :, 0], keypoints[0, :, 1], c=np.arange(data_generator.keypoints_shape[0]), s=50, cmap=plt.cm.hsv, zorder=3)
plt.xlim(0, data_generator.image_shape[1])
plt.ylim(0, data_generator.image_shape[0])
plt.show()
augmenter = []
augmenter.append(FlipAxis(data_generator, axis=0)) # flip image up-down
augmenter.append(FlipAxis(data_generator, axis=1)) # flip image left-right
sometimes = []
sometimes.append(iaa.Affine(scale={"x": (0.9, 1.1), "y": (0.9, 1.1)},
translate_percent={'x': (-0.5, 0.5), 'y': (-0.5, 0.5)},
shear=(-8, 8),
order=ia.ALL,
cval=ia.ALL)
)
sometimes.append(iaa.Affine(scale=(0.5, 1.5),
order=ia.ALL,
cval=ia.ALL)
)
augmenter.append(iaa.Sometimes(0.5, sometimes))
augmenter.append(iaa.Sometimes(0.5, iaa.Affine(rotate=(-180, 180),
order=ia.ALL,
cval=ia.ALL))
)
augmenter = iaa.Sequential(augmenter)
image, keypoints = data_generator[0]
image, keypoints = augmenter(images=image, keypoints=keypoints)
plt.figure(figsize=(5,5))
image = image[0] if image.shape[-1] is 3 else image[0, ..., 0]
cmap = None if image.shape[-1] is 3 else 'gray'
plt.imshow(image, cmap=cmap, interpolation='none')
for idx, jdx in enumerate(data_generator.graph):
if jdx > -1:
plt.plot(
[keypoints[0, idx, 0], keypoints[0, jdx, 0]],
[keypoints[0, idx, 1], keypoints[0, jdx, 1]],
'r-'
)
plt.scatter(keypoints[0, :, 0], keypoints[0, :, 1], c=np.arange(data_generator.keypoints_shape[0]), s=50, cmap=plt.cm.hsv, zorder=3)
plt.xlim(0, data_generator.image_shape[1])
plt.ylim(0, data_generator.image_shape[0])
plt.show()
train_generator = TrainingGenerator(generator=data_generator,
downsample_factor=3,
augmenter=augmenter,
sigma=5,
validation_split=0.1,
use_graph=True,
random_seed=1,
graph_scale=1)
train_generator.get_config()
n_keypoints = data_generator.keypoints_shape[0]
batch = train_generator(batch_size=1, validation=False)[0]
inputs = batch[0]
outputs = batch[1]
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(10,10))
ax1.set_title('image')
ax1.imshow(inputs[0,...,0], cmap='gray', vmin=0, vmax=255)
ax2.set_title('posture graph')
ax2.imshow(outputs[0,...,n_keypoints:-1].max(-1))
ax3.set_title('keypoints confidence')
ax3.imshow(outputs[0,...,:n_keypoints].max(-1))
ax4.set_title('posture graph and keypoints confidence')
ax4.imshow(outputs[0,...,-1], vmin=0)
plt.show()
train_generator.on_epoch_end()
from deepposekit.models import DeepLabCut, StackedDenseNet, LEAP
#model = StackedDenseNet(train_generator, n_stacks=1, growth_rate=32, pretrained=True)
#model = DeepLabCut(train_generator, backbone="resnet50")
#model = DeepLabCut(train_generator, backbone="mobilenetv2", alpha=1.0) # Increase alpha to improve accuracy
model = DeepLabCut(train_generator, backbone="densenet121")
#model = LEAP(train_generator)
model.get_config()
data_size = (500,) + data_generator.image_shape
x = np.random.randint(0, 255, data_size, dtype="uint8")
y = model.predict(x[:100], batch_size=50) # make sure the model is in GPU memory
t0 = time.time()
y = model.predict(x, batch_size=50, verbose=1)
t1 = time.time()
print(x.shape[0] / (t1 - t0))
logger = Logger(validation_batch_size=10
# filepath saves the logger data to a .h5 file
# filepath=HOME + "/deeplabcut_log_dlcdensenet.h5", validation_batch_size=10
)
reduce_lr = ReduceLROnPlateau(monitor="val_loss", factor=0.2, verbose=1, patience=20)
model_checkpoint = ModelCheckpoint(
"../../deeplabcut_best_model_dlcdensenet_moth.h5",
monitor="val_loss",
# monitor="loss" # use if validation_split=0
verbose=1,
save_best_only=True,
optimizer=True, # Set this to True if you wish to resume training from a saved model
)
early_stop = EarlyStopping(
monitor="val_loss",
# monitor="loss" # use if validation_split=0
min_delta=0.001,
patience=100,
verbose=1
)
callbacks = [early_stop, reduce_lr, model_checkpoint, logger]
model.fit(
batch_size=5,
validation_batch_size=10,
callbacks=callbacks,
#epochs=1000, # Increase the number of epochs to train the model longer
epochs=100,
n_workers=8,
steps_per_epoch=200,
)
model = load_model(
"../../deeplabcut_best_model_dlcdensenet_moth.h5",
augmenter=augmenter,
generator=data_generator,
)
model.fit(
batch_size=5,
validation_batch_size=10,
callbacks=callbacks,
#epochs=1000, # Increase the number of epochs to train the model longer
epochs=100,
n_workers=8,
steps_per_epoch=200,