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7661080899
# Learning Rate Warmup and Optimization Implementation
## Overview
Added learning rate warmup functionality to improve training stability, especially when using pretrained weights. The implementation uses TensorFlow's native learning rate scheduling for better performance.
## Changes Made
### 1. Configuration Updates (`runs/train_no_patches_448x448.json`)
Added new configuration parameters for warmup:
```json
{
"warmup_enabled": true,
"warmup_epochs": 5,
"warmup_start_lr": 1e-6
}
```
### 2. Training Script Updates (`train.py`)
#### A. Optimizer and Learning Rate Schedule
- Replaced fixed learning rate with dynamic scheduling
- Implemented warmup using `tf.keras.optimizers.schedules.PolynomialDecay`
- Maintained compatibility with existing ReduceLROnPlateau and EarlyStopping
```python
if warmup_enabled:
lr_schedule = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=warmup_start_lr,
decay_steps=warmup_epochs * steps_per_epoch,
end_learning_rate=learning_rate,
power=1.0 # Linear decay
)
optimizer = Adam(learning_rate=lr_schedule)
else:
optimizer = Adam(learning_rate=learning_rate)
```
#### B. Learning Rate Behavior
- Initial learning rate: 1e-6 (configurable via `warmup_start_lr`)
- Target learning rate: 5e-5 (configurable via `learning_rate`)
- Linear increase over 5 epochs (configurable via `warmup_epochs`)
- After warmup, learning rate remains at target value until ReduceLROnPlateau triggers
## Benefits
1. Improved training stability during initial epochs
2. Better handling of pretrained weights
3. Efficient implementation using TensorFlow's native scheduling
4. Configurable through JSON configuration file
5. Maintains compatibility with existing callbacks (ReduceLROnPlateau, EarlyStopping)
## Usage
To enable warmup:
1. Set `warmup_enabled: true` in the configuration file
2. Adjust `warmup_epochs` and `warmup_start_lr` as needed
3. The warmup will automatically integrate with existing learning rate reduction and early stopping
To disable warmup:
- Set `warmup_enabled: false` or remove the warmup parameters from the configuration file
487 lines
28 KiB
Python
487 lines
28 KiB
Python
import os
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import sys
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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import tensorflow as tf
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from tensorflow.compat.v1.keras.backend import set_session
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import warnings
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from tensorflow.keras.optimizers import *
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from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping, Callback
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from sacred import Experiment
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from models import *
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from utils import *
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from metrics import *
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from tensorflow.keras.models import load_model
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from tqdm import tqdm
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import json
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from sklearn.metrics import f1_score
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def get_warmup_schedule(start_lr, target_lr, warmup_epochs, steps_per_epoch):
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initial_learning_rate = start_lr
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target_learning_rate = target_lr
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warmup_steps = warmup_epochs * steps_per_epoch
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lr_schedule = tf.keras.optimizers.schedules.LinearSchedule(
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initial_learning_rate=initial_learning_rate,
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final_learning_rate=target_learning_rate,
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total_steps=warmup_steps
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)
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return lr_schedule
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class WarmupScheduler(Callback):
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def __init__(self, start_lr, target_lr, warmup_epochs):
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super(WarmupScheduler, self).__init__()
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self.start_lr = start_lr
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self.target_lr = target_lr
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self.warmup_epochs = warmup_epochs
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self.current_epoch = 0
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def on_epoch_begin(self, epoch, logs=None):
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if self.current_epoch < self.warmup_epochs:
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# Linear warmup
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lr = self.start_lr + (self.target_lr - self.start_lr) * (self.current_epoch / self.warmup_epochs)
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tf.keras.backend.set_value(self.model.optimizer.lr, lr)
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self.current_epoch += 1
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def configuration():
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config = tf.compat.v1.ConfigProto()
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config.gpu_options.allow_growth = True
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session = tf.compat.v1.Session(config=config)
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set_session(session)
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def get_dirs_or_files(input_data):
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if os.path.isdir(input_data):
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image_input, labels_input = os.path.join(input_data, 'images/'), os.path.join(input_data, 'labels/')
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# Check if training dir exists
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assert os.path.isdir(image_input), "{} is not a directory".format(image_input)
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assert os.path.isdir(labels_input), "{} is not a directory".format(labels_input)
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return image_input, labels_input
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ex = Experiment()
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@ex.config
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def config_params():
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n_classes = None # Number of classes. In the case of binary classification this should be 2.
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n_epochs = 1 # Number of epochs.
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input_height = 224 * 1 # Height of model's input in pixels.
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input_width = 224 * 1 # Width of model's input in pixels.
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weight_decay = 1e-6 # Weight decay of l2 regularization of model layers.
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n_batch = 1 # Number of batches at each iteration.
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learning_rate = 1e-4 # Set the learning rate.
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patches = False # Divides input image into smaller patches (input size of the model) when set to true. For the model to see the full image, like page extraction, set this to false.
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augmentation = False # To apply any kind of augmentation, this parameter must be set to true.
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flip_aug = False # If true, different types of flipping will be applied to the image. Types of flips are defined with "flip_index" in config_params.json.
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blur_aug = False # If true, different types of blurring will be applied to the image. Types of blur are defined with "blur_k" in config_params.json.
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padding_white = False # If true, white padding will be applied to the image.
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padding_black = False # If true, black padding will be applied to the image.
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scaling = False # If true, scaling will be applied to the image. The amount of scaling is defined with "scales" in config_params.json.
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degrading = False # If true, degrading will be applied to the image. The amount of degrading is defined with "degrade_scales" in config_params.json.
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brightening = False # If true, brightening will be applied to the image. The amount of brightening is defined with "brightness" in config_params.json.
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binarization = False # If true, Otsu thresholding will be applied to augment the input with binarized images.
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adding_rgb_background = False
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adding_rgb_foreground = False
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add_red_textlines = False
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channels_shuffling = False
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dir_train = None # Directory of training dataset with subdirectories having the names "images" and "labels".
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dir_eval = None # Directory of validation dataset with subdirectories having the names "images" and "labels".
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dir_output = None # Directory where the output model will be saved.
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pretraining = False # Set to true to load pretrained weights of ResNet50 encoder.
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scaling_bluring = False # If true, a combination of scaling and blurring will be applied to the image.
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scaling_binarization = False # If true, a combination of scaling and binarization will be applied to the image.
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rotation = False # If true, a 90 degree rotation will be implemeneted.
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rotation_not_90 = False # If true rotation based on provided angles with thetha will be implemeneted.
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scaling_brightness = False # If true, a combination of scaling and brightening will be applied to the image.
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scaling_flip = False # If true, a combination of scaling and flipping will be applied to the image.
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thetha = None # Rotate image by these angles for augmentation.
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shuffle_indexes = None
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blur_k = None # Blur image for augmentation.
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scales = None # Scale patches for augmentation.
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degrade_scales = None # Degrade image for augmentation.
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brightness = None # Brighten image for augmentation.
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flip_index = None # Flip image for augmentation.
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continue_training = False # Set to true if you would like to continue training an already trained a model.
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transformer_patchsize_x = None # Patch size of vision transformer patches in x direction.
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transformer_patchsize_y = None # Patch size of vision transformer patches in y direction.
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transformer_num_patches_xy = None # Number of patches for vision transformer in x and y direction respectively.
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transformer_projection_dim = 64 # Transformer projection dimension. Default value is 64.
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transformer_mlp_head_units = [128, 64] # Transformer Multilayer Perceptron (MLP) head units. Default value is [128, 64]
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transformer_layers = 8 # transformer layers. Default value is 8.
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transformer_num_heads = 4 # Transformer number of heads. Default value is 4.
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transformer_cnn_first = True # We have two types of vision transformers. In one type, a CNN is applied first, followed by a transformer. In the other type, this order is reversed. If transformer_cnn_first is true, it means the CNN will be applied before the transformer. Default value is true.
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index_start = 0 # Index of model to continue training from. E.g. if you trained for 3 epochs and last index is 2, to continue from model_1.h5, set "index_start" to 3 to start naming model with index 3.
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dir_of_start_model = '' # Directory containing pretrained encoder to continue training the model.
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is_loss_soft_dice = False # Use soft dice as loss function. When set to true, "weighted_loss" must be false.
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weighted_loss = False # Use weighted categorical cross entropy as loss fucntion. When set to true, "is_loss_soft_dice" must be false.
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data_is_provided = False # Only set this to true when you have already provided the input data and the train and eval data are in "dir_output".
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task = "segmentation" # This parameter defines task of model which can be segmentation, enhancement or classification.
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f1_threshold_classification = None # This threshold is used to consider models with an evaluation f1 scores bigger than it. The selected model weights undergo a weights ensembling. And avreage ensembled model will be written to output.
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classification_classes_name = None # Dictionary of classification classes names.
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backbone_type = None # As backbone we have 2 types of backbones. A vision transformer alongside a CNN and we call it "transformer" and only CNN called "nontransformer"
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dir_img_bin = None
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number_of_backgrounds_per_image = 1
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dir_rgb_backgrounds = None
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dir_rgb_foregrounds = None
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reduce_lr_enabled = False # Whether to use ReduceLROnPlateau callback
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reduce_lr_monitor = 'val_loss' # Metric to monitor for reducing learning rate
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reduce_lr_factor = 0.5 # Factor to reduce learning rate by
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reduce_lr_patience = 3 # Number of epochs to wait before reducing learning rate
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reduce_lr_min_lr = 1e-6 # Minimum learning rate
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early_stopping_enabled = False # Whether to use EarlyStopping callback
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early_stopping_monitor = 'val_loss' # Metric to monitor for early stopping
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early_stopping_patience = 10 # Number of epochs to wait before stopping
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early_stopping_restore_best_weights = True # Whether to restore best weights when stopping
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warmup_enabled = False # Whether to use learning rate warmup
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warmup_epochs = 5 # Number of epochs for warmup
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warmup_start_lr = 1e-6 # Starting learning rate for warmup
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@ex.automain
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def run(_config, n_classes, n_epochs, input_height,
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input_width, weight_decay, weighted_loss,
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index_start, dir_of_start_model, is_loss_soft_dice,
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n_batch, patches, augmentation, flip_aug,
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blur_aug, padding_white, padding_black, scaling, degrading,channels_shuffling,
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brightening, binarization, adding_rgb_background, adding_rgb_foreground, add_red_textlines, blur_k, scales, degrade_scales,shuffle_indexes,
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brightness, dir_train, data_is_provided, scaling_bluring,
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scaling_brightness, scaling_binarization, rotation, rotation_not_90,
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thetha, scaling_flip, continue_training, transformer_projection_dim,
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transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_cnn_first,
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transformer_patchsize_x, transformer_patchsize_y,
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transformer_num_patches_xy, backbone_type, flip_index, dir_eval, dir_output,
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pretraining, learning_rate, task, f1_threshold_classification, classification_classes_name, dir_img_bin, number_of_backgrounds_per_image,dir_rgb_backgrounds, dir_rgb_foregrounds,
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reduce_lr_enabled, reduce_lr_monitor, reduce_lr_factor, reduce_lr_patience, reduce_lr_min_lr,
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early_stopping_enabled, early_stopping_monitor, early_stopping_patience, early_stopping_restore_best_weights,
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warmup_enabled, warmup_epochs, warmup_start_lr):
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if dir_rgb_backgrounds:
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list_all_possible_background_images = os.listdir(dir_rgb_backgrounds)
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else:
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list_all_possible_background_images = None
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if dir_rgb_foregrounds:
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list_all_possible_foreground_rgbs = os.listdir(dir_rgb_foregrounds)
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else:
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list_all_possible_foreground_rgbs = None
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if task == "segmentation" or task == "enhancement" or task == "binarization":
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if data_is_provided:
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dir_train_flowing = os.path.join(dir_output, 'train')
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dir_eval_flowing = os.path.join(dir_output, 'eval')
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dir_flow_train_imgs = os.path.join(dir_train_flowing, 'images')
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dir_flow_train_labels = os.path.join(dir_train_flowing, 'labels')
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dir_flow_eval_imgs = os.path.join(dir_eval_flowing, 'images')
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dir_flow_eval_labels = os.path.join(dir_eval_flowing, 'labels')
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configuration()
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else:
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dir_img, dir_seg = get_dirs_or_files(dir_train)
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dir_img_val, dir_seg_val = get_dirs_or_files(dir_eval)
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# make first a directory in output for both training and evaluations in order to flow data from these directories.
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dir_train_flowing = os.path.join(dir_output, 'train')
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dir_eval_flowing = os.path.join(dir_output, 'eval')
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dir_flow_train_imgs = os.path.join(dir_train_flowing, 'images/')
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dir_flow_train_labels = os.path.join(dir_train_flowing, 'labels/')
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dir_flow_eval_imgs = os.path.join(dir_eval_flowing, 'images/')
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dir_flow_eval_labels = os.path.join(dir_eval_flowing, 'labels/')
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if os.path.isdir(dir_train_flowing):
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os.system('rm -rf ' + dir_train_flowing)
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os.makedirs(dir_train_flowing)
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else:
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os.makedirs(dir_train_flowing)
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if os.path.isdir(dir_eval_flowing):
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os.system('rm -rf ' + dir_eval_flowing)
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os.makedirs(dir_eval_flowing)
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else:
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os.makedirs(dir_eval_flowing)
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os.mkdir(dir_flow_train_imgs)
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os.mkdir(dir_flow_train_labels)
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os.mkdir(dir_flow_eval_imgs)
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os.mkdir(dir_flow_eval_labels)
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# set the gpu configuration
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configuration()
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imgs_list=np.array(os.listdir(dir_img))
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segs_list=np.array(os.listdir(dir_seg))
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imgs_list_test=np.array(os.listdir(dir_img_val))
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segs_list_test=np.array(os.listdir(dir_seg_val))
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# writing patches into a sub-folder in order to be flowed from directory.
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provide_patches(imgs_list, segs_list, dir_img, dir_seg, dir_flow_train_imgs,
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dir_flow_train_labels, input_height, input_width, blur_k,
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blur_aug, padding_white, padding_black, flip_aug, binarization, adding_rgb_background,adding_rgb_foreground, add_red_textlines, channels_shuffling,
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scaling, degrading, brightening, scales, degrade_scales, brightness,
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flip_index,shuffle_indexes, scaling_bluring, scaling_brightness, scaling_binarization,
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rotation, rotation_not_90, thetha, scaling_flip, task, augmentation=augmentation,
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patches=patches, dir_img_bin=dir_img_bin,number_of_backgrounds_per_image=number_of_backgrounds_per_image,list_all_possible_background_images=list_all_possible_background_images, dir_rgb_backgrounds=dir_rgb_backgrounds, dir_rgb_foregrounds=dir_rgb_foregrounds,list_all_possible_foreground_rgbs=list_all_possible_foreground_rgbs)
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provide_patches(imgs_list_test, segs_list_test, dir_img_val, dir_seg_val,
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dir_flow_eval_imgs, dir_flow_eval_labels, input_height, input_width,
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blur_k, blur_aug, padding_white, padding_black, flip_aug, binarization, adding_rgb_background, adding_rgb_foreground, add_red_textlines, channels_shuffling,
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scaling, degrading, brightening, scales, degrade_scales, brightness,
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flip_index, shuffle_indexes, scaling_bluring, scaling_brightness, scaling_binarization,
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rotation, rotation_not_90, thetha, scaling_flip, task, augmentation=False, patches=patches,dir_img_bin=dir_img_bin,number_of_backgrounds_per_image=number_of_backgrounds_per_image,list_all_possible_background_images=list_all_possible_background_images, dir_rgb_backgrounds=dir_rgb_backgrounds,dir_rgb_foregrounds=dir_rgb_foregrounds,list_all_possible_foreground_rgbs=list_all_possible_foreground_rgbs )
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if weighted_loss:
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weights = np.zeros(n_classes)
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if data_is_provided:
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for obj in os.listdir(dir_flow_train_labels):
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try:
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label_obj = cv2.imread(dir_flow_train_labels + '/' + obj)
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label_obj_one_hot = get_one_hot(label_obj, label_obj.shape[0], label_obj.shape[1], n_classes)
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weights += (label_obj_one_hot.sum(axis=0)).sum(axis=0)
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except:
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pass
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else:
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for obj in os.listdir(dir_seg):
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try:
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label_obj = cv2.imread(dir_seg + '/' + obj)
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label_obj_one_hot = get_one_hot(label_obj, label_obj.shape[0], label_obj.shape[1], n_classes)
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weights += (label_obj_one_hot.sum(axis=0)).sum(axis=0)
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except:
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pass
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weights = 1.00 / weights
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weights = weights / float(np.sum(weights))
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weights = weights / float(np.min(weights))
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weights = weights / float(np.sum(weights))
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if continue_training:
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if backbone_type=='nontransformer':
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if is_loss_soft_dice and (task == "segmentation" or task == "binarization"):
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model = load_model(dir_of_start_model, compile=True, custom_objects={'soft_dice_loss': soft_dice_loss})
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if weighted_loss and (task == "segmentation" or task == "binarization"):
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model = load_model(dir_of_start_model, compile=True, custom_objects={'loss': weighted_categorical_crossentropy(weights)})
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if not is_loss_soft_dice and not weighted_loss:
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model = load_model(dir_of_start_model , compile=True)
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elif backbone_type=='transformer':
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if is_loss_soft_dice and (task == "segmentation" or task == "binarization"):
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model = load_model(dir_of_start_model, compile=True, custom_objects={"PatchEncoder": PatchEncoder, "Patches": Patches,'soft_dice_loss': soft_dice_loss})
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if weighted_loss and (task == "segmentation" or task == "binarization"):
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model = load_model(dir_of_start_model, compile=True, custom_objects={'loss': weighted_categorical_crossentropy(weights)})
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if not is_loss_soft_dice and not weighted_loss:
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model = load_model(dir_of_start_model , compile=True,custom_objects = {"PatchEncoder": PatchEncoder, "Patches": Patches})
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else:
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index_start = 0
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if backbone_type=='nontransformer':
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model = resnet50_unet(n_classes, input_height, input_width, task, weight_decay, pretraining)
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elif backbone_type=='transformer':
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num_patches_x = transformer_num_patches_xy[0]
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num_patches_y = transformer_num_patches_xy[1]
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num_patches = num_patches_x * num_patches_y
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if transformer_cnn_first:
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if (input_height != (num_patches_y * transformer_patchsize_y * 32) ):
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print("Error: transformer_patchsize_y or transformer_num_patches_xy height value error . input_height should be equal to ( transformer_num_patches_xy height value * transformer_patchsize_y * 32)")
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sys.exit(1)
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if (input_width != (num_patches_x * transformer_patchsize_x * 32) ):
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print("Error: transformer_patchsize_x or transformer_num_patches_xy width value error . input_width should be equal to ( transformer_num_patches_xy width value * transformer_patchsize_x * 32)")
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sys.exit(1)
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if (transformer_projection_dim % (transformer_patchsize_y * transformer_patchsize_x)) != 0:
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print("Error: transformer_projection_dim error. The remainder when parameter transformer_projection_dim is divided by (transformer_patchsize_y*transformer_patchsize_x) should be zero")
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sys.exit(1)
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model = vit_resnet50_unet(n_classes, transformer_patchsize_x, transformer_patchsize_y, num_patches, transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_projection_dim, input_height, input_width, task, weight_decay, pretraining)
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else:
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if (input_height != (num_patches_y * transformer_patchsize_y) ):
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print("Error: transformer_patchsize_y or transformer_num_patches_xy height value error . input_height should be equal to ( transformer_num_patches_xy height value * transformer_patchsize_y)")
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sys.exit(1)
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if (input_width != (num_patches_x * transformer_patchsize_x) ):
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print("Error: transformer_patchsize_x or transformer_num_patches_xy width value error . input_width should be equal to ( transformer_num_patches_xy width value * transformer_patchsize_x)")
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sys.exit(1)
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if (transformer_projection_dim % (transformer_patchsize_y * transformer_patchsize_x)) != 0:
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print("Error: transformer_projection_dim error. The remainder when parameter transformer_projection_dim is divided by (transformer_patchsize_y*transformer_patchsize_x) should be zero")
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sys.exit(1)
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model = vit_resnet50_unet_transformer_before_cnn(n_classes, transformer_patchsize_x, transformer_patchsize_y, num_patches, transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_projection_dim, input_height, input_width, task, weight_decay, pretraining)
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|
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#if you want to see the model structure just uncomment model summary.
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model.summary()
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|
|
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# Create callbacks list
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callbacks = []
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if reduce_lr_enabled:
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reduce_lr = ReduceLROnPlateau(
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monitor=reduce_lr_monitor,
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factor=reduce_lr_factor,
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|
patience=reduce_lr_patience,
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min_lr=reduce_lr_min_lr,
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|
verbose=1
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|
)
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|
callbacks.append(reduce_lr)
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|
|
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if early_stopping_enabled:
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early_stopping = EarlyStopping(
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|
monitor=early_stopping_monitor,
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|
patience=early_stopping_patience,
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restore_best_weights=early_stopping_restore_best_weights,
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|
verbose=1
|
|
)
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|
callbacks.append(early_stopping)
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|
|
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# Calculate steps per epoch
|
|
steps_per_epoch = int(len(os.listdir(dir_flow_train_imgs)) / n_batch) - 1
|
|
|
|
# Create optimizer with or without warmup
|
|
if warmup_enabled:
|
|
lr_schedule = tf.keras.optimizers.schedules.PolynomialDecay(
|
|
initial_learning_rate=warmup_start_lr,
|
|
decay_steps=warmup_epochs * steps_per_epoch,
|
|
end_learning_rate=learning_rate,
|
|
power=1.0 # Linear decay
|
|
)
|
|
optimizer = Adam(learning_rate=lr_schedule)
|
|
else:
|
|
optimizer = Adam(learning_rate=learning_rate)
|
|
|
|
if (task == "segmentation" or task == "binarization"):
|
|
if not is_loss_soft_dice and not weighted_loss:
|
|
model.compile(loss='categorical_crossentropy',
|
|
optimizer=optimizer, metrics=['accuracy'])
|
|
if is_loss_soft_dice:
|
|
model.compile(loss=soft_dice_loss,
|
|
optimizer=optimizer, metrics=['accuracy'])
|
|
if weighted_loss:
|
|
model.compile(loss=weighted_categorical_crossentropy(weights),
|
|
optimizer=optimizer, metrics=['accuracy'])
|
|
elif task == "enhancement":
|
|
model.compile(loss='mean_squared_error',
|
|
optimizer=optimizer, metrics=['accuracy'])
|
|
|
|
# generating train and evaluation data
|
|
train_gen = data_gen(dir_flow_train_imgs, dir_flow_train_labels, batch_size=n_batch,
|
|
input_height=input_height, input_width=input_width, n_classes=n_classes, task=task)
|
|
val_gen = data_gen(dir_flow_eval_imgs, dir_flow_eval_labels, batch_size=n_batch,
|
|
input_height=input_height, input_width=input_width, n_classes=n_classes, task=task)
|
|
|
|
##img_validation_patches = os.listdir(dir_flow_eval_imgs)
|
|
##score_best=[]
|
|
##score_best.append(0)
|
|
|
|
for i in tqdm(range(index_start, n_epochs + index_start)):
|
|
model.fit(
|
|
train_gen,
|
|
steps_per_epoch=steps_per_epoch,
|
|
validation_data=val_gen,
|
|
validation_steps=1,
|
|
epochs=1,
|
|
callbacks=callbacks)
|
|
model.save(os.path.join(dir_output,'model_'+str(i)))
|
|
|
|
with open(os.path.join(os.path.join(dir_output,'model_'+str(i)),"config.json"), "w") as fp:
|
|
json.dump(_config, fp) # encode dict into JSON
|
|
|
|
#os.system('rm -rf '+dir_train_flowing)
|
|
#os.system('rm -rf '+dir_eval_flowing)
|
|
|
|
#model.save(dir_output+'/'+'model'+'.h5')
|
|
elif task=='classification':
|
|
configuration()
|
|
model = resnet50_classifier(n_classes, input_height, input_width, weight_decay, pretraining)
|
|
|
|
opt_adam = Adam(learning_rate=0.001)
|
|
model.compile(loss='categorical_crossentropy',
|
|
optimizer = opt_adam,metrics=['accuracy'])
|
|
|
|
|
|
list_classes = list(classification_classes_name.values())
|
|
testX, testY = generate_data_from_folder_evaluation(dir_eval, input_height, input_width, n_classes, list_classes)
|
|
|
|
y_tot=np.zeros((testX.shape[0],n_classes))
|
|
|
|
score_best=[]
|
|
score_best.append(0)
|
|
|
|
num_rows = return_number_of_total_training_data(dir_train)
|
|
weights=[]
|
|
|
|
for i in range(n_epochs):
|
|
history = model.fit( generate_data_from_folder_training(dir_train, n_batch , input_height, input_width, n_classes, list_classes), steps_per_epoch=num_rows / n_batch, verbose=1)#,class_weight=weights)
|
|
|
|
y_pr_class = []
|
|
for jj in range(testY.shape[0]):
|
|
y_pr=model.predict(testX[jj,:,:,:].reshape(1,input_height,input_width,3), verbose=0)
|
|
y_pr_ind= np.argmax(y_pr,axis=1)
|
|
y_pr_class.append(y_pr_ind)
|
|
|
|
y_pr_class = np.array(y_pr_class)
|
|
f1score=f1_score(np.argmax(testY,axis=1), y_pr_class, average='macro')
|
|
print(i,f1score)
|
|
|
|
if f1score>score_best[0]:
|
|
score_best[0]=f1score
|
|
model.save(os.path.join(dir_output,'model_best'))
|
|
|
|
if f1score > f1_threshold_classification:
|
|
weights.append(model.get_weights() )
|
|
|
|
|
|
if len(weights) >= 1:
|
|
new_weights=list()
|
|
for weights_list_tuple in zip(*weights):
|
|
new_weights.append( [np.array(weights_).mean(axis=0) for weights_ in zip(*weights_list_tuple)] )
|
|
|
|
new_weights = [np.array(x) for x in new_weights]
|
|
model_weight_averaged=tf.keras.models.clone_model(model)
|
|
model_weight_averaged.set_weights(new_weights)
|
|
|
|
model_weight_averaged.save(os.path.join(dir_output,'model_ens_avg'))
|
|
with open(os.path.join( os.path.join(dir_output,'model_ens_avg'), "config.json"), "w") as fp:
|
|
json.dump(_config, fp) # encode dict into JSON
|
|
|
|
with open(os.path.join( os.path.join(dir_output,'model_best'), "config.json"), "w") as fp:
|
|
json.dump(_config, fp) # encode dict into JSON
|
|
|
|
elif task=='reading_order':
|
|
configuration()
|
|
model = machine_based_reading_order_model(n_classes,input_height,input_width,weight_decay,pretraining)
|
|
|
|
dir_flow_train_imgs = os.path.join(dir_train, 'images')
|
|
dir_flow_train_labels = os.path.join(dir_train, 'labels')
|
|
|
|
classes = os.listdir(dir_flow_train_labels)
|
|
num_rows =len(classes)
|
|
#ls_test = os.listdir(dir_flow_train_labels)
|
|
|
|
#f1score_tot = [0]
|
|
indexer_start = 0
|
|
opt = SGD(learning_rate=0.01, momentum=0.9)
|
|
opt_adam = tf.keras.optimizers.Adam(learning_rate=0.0001)
|
|
model.compile(loss="binary_crossentropy",
|
|
optimizer = opt_adam,metrics=['accuracy'])
|
|
for i in range(n_epochs):
|
|
history = model.fit(generate_arrays_from_folder_reading_order(dir_flow_train_labels, dir_flow_train_imgs, n_batch, input_height, input_width, n_classes), steps_per_epoch=num_rows / n_batch, verbose=1)
|
|
model.save( os.path.join(dir_output,'model_'+str(i+indexer_start) ))
|
|
|
|
with open(os.path.join(os.path.join(dir_output,'model_'+str(i)),"config.json"), "w") as fp:
|
|
json.dump(_config, fp) # encode dict into JSON
|
|
'''
|
|
if f1score>f1score_tot[0]:
|
|
f1score_tot[0] = f1score
|
|
model_dir = os.path.join(dir_out,'model_best')
|
|
model.save(model_dir)
|
|
'''
|
|
|
|
|