Open jiayugedede opened 11 months ago
tilakrayal
commented
11 months ago @jiayugedede, I was facing a different issue/error while executing the above mentioned code. Kindly find the gist of it here and provide the required code to analyse the issue in an effective way. Thank you!
jiayugedede
commented
11 months ago import tensorflow.compat.v2 as tf
from keras.regularizers import l2
from keras import layers
from keras.engine import sequential
from keras.engine import training as training_lib
import keras as K
identitys=None
def Bottleneck(inputs, out_channel, name, downsample, strides=1):
expansion = 4
key = out_channel * expansion
identity = inputs
global identitys
if downsample:
identitys = layers.Conv2D(key, kernel_size=1, strides=strides,
use_bias=False, kernel_initializer='he_normal',
padding="SAME", kernel_regularizer=l2(1.e-5), name=name + "ds_conv")(identity)
identitys = layers.BatchNormalization(momentum=0.9, epsilon=1e-5, name=name + "ds_normal")(identitys)
else:
identitys = inputs
xb = layers.Conv2D(out_channel, kernel_size=1, use_bias=False, kernel_initializer='he_normal',
kernel_regularizer=l2(1.e-4), name=name + "Conv2D_1")(inputs)
xb = layers.BatchNormalization(momentum=0.9,
epsilon=1e-5, name=name + "BN_1")(xb)
xb = layers.Activation(tf.keras.activations.swish, name=name + "ACT_1")(xb)
xb = layers.Conv2D(out_channel, kernel_size=3, use_bias=False, strides=strides, padding="SAME",
kernel_initializer='he_normal', kernel_regularizer=l2(1.e-4), name=name + "Conv2D_2")(xb)
xb = layers.BatchNormalization(momentum=0.9,
epsilon=1e-5, name=name + "BN_3")(xb)
xb = layers.ReLU(name=name + "ReLU")(xb)
xb = layers.Conv2D(key, kernel_size=1, use_bias=False,
kernel_initializer='he_normal',
kernel_regularizer=l2(1.e-4),
name=name + "Conv2D_3")(xb)
xb = layers.BatchNormalization(momentum=0.9,
epsilon=1e-5,
name=name + "BN_4")(xb)
xb = layers.Add(name=name + "addition")([identitys, xb])
xb = layers.BatchNormalization(momentum=0.9,
epsilon=1e-5,
name=name + "Last_BN")(xb)
xb = layers.ReLU(name=name + "LastReLU")(xb)
return xb
def _make_layer(inputs, make_block, channel, block_num, layer_name, strides, down_sample):
i = 0
name = layer_name + f"block_{i + 1}_"
xm = make_block(inputs=inputs, out_channel=channel, name=name, strides=strides, downsample=down_sample)
for i in range(1, block_num):
i += 1
name = layer_name + f"block_{i}_"
xm = make_block(inputs=xm, out_channel=channel, name=name, strides=1, downsample=False)
return xm
class ResnetBuilder(object):
@staticmethod
def build(block, blocks_num, im_width=224, im_height=224, num_classes=1000):
img_input = layers.Input(shape=(im_width, im_height, 1),
dtype="float32",
name="layers_inputs")
x = layers.Conv2D(filters=64, kernel_size=7, strides=2,
padding="SAME", use_bias=False,
name="layers_conv1")(img_input) # 把这一行替换成ContourOperator
x = layers.BatchNormalization(momentum=0.9, epsilon=1e-5, name="x_FBN")(x)
x = layers.ReLU(name="FReLU")(x)
x = layers.DepthwiseConv2D(kernel_size=3, padding="SAME", use_bias=False,
depthwise_initializer=tf.keras.initializers.TruncatedNormal(mean=0.0,
stddev=0.05, seed=None)
, kernel_regularizer=l2(1.e-4), name="FDW")(x)
x = layers.BatchNormalization(momentum=0.9, epsilon=1e-5, name="FBN")(x)
x = layers.Activation(tf.keras.activations.swish, name="FirstACT")(x)
x = layers.MaxPool2D(pool_size=3, strides=2, padding="SAME", name="f_MP")(x)
x = _make_layer(x, block, 64, block_num=blocks_num[0], layer_name="ml1", strides=1, down_sample=True)
x = _make_layer(x, block, 128, block_num=blocks_num[1], layer_name="ml2", strides=2, down_sample=True)
x = _make_layer(x, block, 256, block_num=blocks_num[2], layer_name="ml3", strides=2, down_sample=True)
x = _make_layer(x, block, 512, block_num=blocks_num[3], layer_name="ml4", strides=2, down_sample=True)
x = layers.GlobalAvgPool2D(name="GAP2D")(x) # pool + flatten
x = layers.Dense(num_classes, name="logits")(x)
predict = layers.Softmax(name="SoftMax")(x)
model = training_lib.Model(inputs=img_input,
outputs=predict, name="model")
return model
@staticmethod
def resnet101(im_width=448, im_height=448,
include_top=True, num_classes=5):
return ResnetBuilder.build(Bottleneck, [3, 4, 23, 3],
im_width, im_height, num_classes)
@staticmethod
def resnet50(im_width=448, im_height=448,
include_top=True,
num_classes=5, **kwargs):
return ResnetBuilder.build(Bottleneck, [3, 4, 6, 3],
im_width, im_height, num_classes)import warnings
import numpy as np
from keras.callbacks import Callback
class MultiGPUCheckpointCallback(Callback):
def __init__(self, filepath, base_model, monitor='val_loss', verbose=0,
save_best_only=False, save_weights_only=False,
mode='auto', period=1):
super(MultiGPUCheckpointCallback, self).__init__()
self.base_model = base_model
self.monitor = monitor
self.verbose = verbose
self.filepath = filepath
self.save_best_only = save_best_only
self.save_weights_only = save_weights_only
self.period = period
self.epochs_since_last_save = 0
if mode not in ['auto', 'min', 'max']:
warnings.warn('ModelCheckpoint mode %s is unknown, '
'fallback to auto mode.' % (mode),
RuntimeWarning)
mode = 'auto'
if mode == 'min':
self.monitor_op = np.less
self.best = np.Inf
elif mode == 'max':
self.monitor_op = np.greater
self.best = -np.Inf
else:
if 'acc' in self.monitor or self.monitor.startswith('fmeasure'):
self.monitor_op = np.greater
self.best = -np.Inf
else:
self.monitor_op = np.less
self.best = np.Inf
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
self.epochs_since_last_save += 1
if self.epochs_since_last_save >= self.period:
self.epochs_since_last_save = 0
filepath = self.filepath.format(epoch=epoch + 1, **logs)
if self.save_best_only:
current = logs.get(self.monitor)
if current is None:
warnings.warn('Can save best model only with %s available, '
'skipping.' % (self.monitor), RuntimeWarning)
else:
if self.monitor_op(current, self.best):
if self.verbose > 0:
print('Epoch %05d: %s improved from %0.5f to %0.5f,'
' saving model to %s'
% (epoch + 1, self.monitor, self.best,
current, filepath))
self.best = current
if self.save_weights_only:
self.base_model.save_weights(filepath, overwrite=True)
else:
self.base_model.save(filepath, overwrite=True)
else:
if self.verbose > 0:
print('Epoch %05d: %s did not improve' %
(epoch + 1, self.monitor))
else:
if self.verbose > 0:
print('Epoch %05d: saving model to %s' % (epoch + 1, filepath))
if self.save_weights_only:
self.base_model.save_weights(filepath, overwrite=True)
else:
self.base_model.save(filepath, overwrite=True)import tensorflow_addons as tfa
import tensorflow as tf
def scheduler(epoch):
if epoch < 40:
return 0.1
if epoch < 80:
return 0.06
if epoch < 150:
return 0.01
if epoch < 200: # 200 epoch之后有一轮准确率提升。
return 0.006
if epoch < 230:
return 0.004
if epoch < 250:
return 0.002
if epoch < 280:
return 0.001
if epoch < 300:
return 0.0006 # 当学习率为0.0006的时候，性能表现最好。
if epoch < 350:
return 0.0004
if epoch < 380:
return 0.00008
return 0.00004
# https://tensorflow.google.cn/api_docs/python/tf/keras/optimizers/schedules/PiecewiseConstantDecay?hl=en
def AdamOptimizer():
step = tf.Variable(0, trainable=False)
schedule = tf.optimizers.schedules.PiecewiseConstantDecay(
[100, 150, 200, 250], [1e-4, 8e-5, 4e-5, 1e-5, 1e-6])
# lr and wd can be a function or a tensor
lr = schedule(step)
wd = schedule(step)
optimizer = tfa.optimizers.AdamW(learning_rate=lr, weight_decay=wd)
return optimizer
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: sam
"""
import os
import tensorflow as tf
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='grpc://' + os.environ['COLAB_TPU_ADDR'])
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.experimental.TPUStrategy(resolver)
NUM_CLASSES = 10
BatchSize = 128
save_train_path = r"/content/drive/MyDrive/save_weight"
save_train_data = r"/content/drive/MyDrive/save_weight/H5File"
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
# expand the channel dimension
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
input_shape = (28, 28, 1)
# make the value of pixels from [0, 255] to [0, 1] for further process
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
# convert class vectors to binary class matrics
y_train = tf.keras.utils.to_categorical(y_train, NUM_CLASSES)
y_test = tf.keras.utils.to_categorical(y_test, NUM_CLASSES)
with strategy.scope():
model = ResnetBuilder.resnet50(im_width=28, im_height=28, num_classes=NUM_CLASSES)
model.build((BatchSize, 28, 28, 1))
model.summary()
recall = tf.keras.metrics.Recall()
precision = tf.keras.metrics.Precision()
step = tf.Variable(0, trainable=False)
schedule = tf.optimizers.schedules.PiecewiseConstantDecay([100, 150, 200, 250], [1e-4, 8e-5, 4e-5, 1e-5, 1e-6])
# lr and wd can be a function or a tensor
lr = schedule(step)
wd = schedule(step)
optimizer = tfa.optimizers.AdamW(learning_rate=lr, weight_decay=wd)
model.compile(optimizer=optimizer,loss='categorical_crossentropy', metrics=['accuracy', recall, precision])
checkpoint_path = save_train_data + "/" + "model.{epoch:02d}-" + ".h5"
save_weight = MultiGPUCheckpointCallback(filepath=checkpoint_path,
base_model=model,
save_weights_only=True)
kept = tf.keras.callbacks.ModelCheckpoint(save_train_data + "/" + "model.{epoch:02d}-{val_loss:.4f}-" + ".h5", monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')
model.fit(x_train, y_train, batch_size=BatchSize, epochs=30, verbose=1, validation_data=(x_test, y_test), callbacks=[save_weight])from keras import Model
from keras.utils import image_utils
# gpus = tf.config.experimental.list_physical_devices(device_type='GPU')
# tf.config.experimental.set_visible_devices(devices=gpus[0], device_type='GPU')
def preprocess_image(img_path, target_size=(28, 28)):
"""Preprocess the image by reshape and normalization.
Args:
img_path: A string.
target_size: A tuple, reshape to this size.
Return:
An image ndarray.
"""
img = image_utils.load_img(img_path, target_size=target_size, color_mode="grayscale")
img = image_utils.img_to_array(img)
img /= 255.0
return img
def load_trained_model():
with tf.device('/cpu:0'):
model = ResnetBuilder.resnet101(28, 28, False, 10)
model_name = r"/content/drive/MyDrive/save_weight/H5File/model.30-.h5"
model.load_weights(model_name, by_name=True)
print('model load success.')
return model
def get_category_name(full_image_path, model):
img = preprocess_image(full_image_path)
img_tensor = np.expand_dims(img, axis=0)
heatmap_model = Model([model.inputs], [model.output])
predictions = heatmap_model(img_tensor)
category_id = np.argmax(predictions[0])
label_name = ['0', '1', '2', "3", "4", "5", "6", "7", "8", "9"]
category_name = label_name[category_id]
return category_name
model = load_trained_model()
model.summary()
image_folder = r"/content/drive/MyDrive/save_weight/mnist_img"
name_list = os.listdir(image_folder)
for file_name in name_list:
full_image_name = image_folder+"/"+ file_name
category_name = get_category_name(full_image_name, model)
file_names = file_name.split("_")
save_name = "Prediction: "+ category_name + " label: " + file_names[0]
print(save_name)Weight can be found in here https://drive.google.com/file/d/12A6VFM8AmKcthUf6J8p_vwtO4jAzJkWg/view?usp=drive_link Test image can be found in here https://drive.google.com/drive/folders/1-Dyx73axg7swBHiQKuoSTtvKQkKjlzo7?usp=drive_link
Please go to TF Forum for help and support:
https://discuss.tensorflow.org/tag/keras
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System information.
I have used functional API to build a ResNet neural network algorithm. However, this constructed model can not save the neural networks' weights in an appropriate result. The validated result was very good. But the inference result is very bad.
When I use
tf.config.run_functions_eagerly(True)in the model training stage, the inference result is very good. Otherwise, the inference result was very bad. To tackle this problem, I have searched some sample code of Keras.applications. Bud, not working in model weight save.The implemented ResNet code is shown in follow:
Describe the problem.
Describe the problem clearly here. Be sure to convey here why it's a bug in Keras or why the requested feature is needed.
Describe the current behavior.
The inference result can not match the validation result. The inference code is shown in follow:
Describe the expected behavior.
Saving the right weights in model.h5 files, and inference the right result from the weight model.
Standalone code to reproduce the issue.
Provide a reproducible test case that is the bare minimum necessary to generate the problem. If possible, please share a link to Colab/Jupyter/any notebook.
Sorry, inconvenient to provide