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VERSION
View file @ a633050e
0.2.0-dev2
0.2.0-dev3
dan/decoder.py
View file @ a633050e
......@@ -2,16 +2,7 @@
import torch
from torch import relu, softmax
from torch.nn import (
LSTM,
Conv1d,
Dropout,
Embedding,
LayerNorm,
Linear,
Module,
ModuleList,
)
from torch.nn import Conv1d, Dropout, Embedding, LayerNorm, Linear, Module, ModuleList
from torch.nn.init import xavier_uniform_
......@@ -314,14 +305,9 @@ class FeaturesUpdater(Module):
self.pe_2d = PositionalEncoding2D(
params["enc_dim"], params["h_max"], params["w_max"], params["device"]
)
self.use_2d_positional_encoding = (
"use_2d_pe" not in params or params["use_2d_pe"]
)
def get_pos_features(self, features):
if self.use_2d_positional_encoding:
return self.pe_2d(features)
return features
return self.pe_2d(features)
class GlobalHTADecoder(Module):
......@@ -335,8 +321,6 @@ class GlobalHTADecoder(Module):
self.dec_att_win = (
params["attention_win"] if params["attention_win"] is not None else 1
)
self.use_1d_pe = "use_1d_pe" not in params or params["use_1d_pe"]
self.use_lstm = params["use_lstm"]
self.features_updater = FeaturesUpdater(params)
self.att_decoder = GlobalAttDecoder(params)
......@@ -348,9 +332,6 @@ class GlobalHTADecoder(Module):
params["enc_dim"], params["l_max"], params["device"]
)
if self.use_lstm:
self.lstm_predict = LSTM(params["enc_dim"], params["enc_dim"])
vocab_size = params["vocab_size"] + 1
self.end_conv = Conv1d(params["enc_dim"], vocab_size, kernel_size=1)
......@@ -374,9 +355,7 @@ class GlobalHTADecoder(Module):
pos_tokens = self.emb(tokens).permute(0, 2, 1)
# Add 1D Positional Encoding
if self.use_1d_pe:
pos_tokens = self.pe_1d(pos_tokens, start=start)
pos_tokens = pos_tokens.permute(2, 0, 1)
pos_tokens = self.pe_1d(pos_tokens, start=start).permute(2, 0, 1)
if num_pred is None:
num_pred = tokens.size(1)
......@@ -426,9 +405,6 @@ class GlobalHTADecoder(Module):
keep_all_weights=keep_all_weights,
)
if self.use_lstm:
output, hidden_predict = self.lstm_predict(output, hidden_predict)
dp_output = self.dropout(relu(output))
preds = self.end_conv(dp_output.permute(1, 2, 0))
......
dan/encoder.py
View file @ a633050e
......@@ -92,9 +92,7 @@ class FCN_Encoder(Module):
self.init_blocks = ModuleList(
[
ConvBlock(
params["input_channels"], 16, stride=(1, 1), dropout=self.dropout
),
ConvBlock(3, 16, stride=(1, 1), dropout=self.dropout),
ConvBlock(16, 32, stride=(2, 2), dropout=self.dropout),
ConvBlock(32, 64, stride=(2, 2), dropout=self.dropout),
ConvBlock(64, 128, stride=(2, 2), dropout=self.dropout),
......
dan/manager/training.py
View file @ a633050e
......@@ -522,9 +522,7 @@ class GenericTrainingManager:
self.save_params()
# init variables
self.begin_time = time()
focus_metric_name = self.params["training_params"]["focus_metric"]
nb_epochs = self.params["training_params"]["max_nb_epochs"]
interval_save_weights = self.params["training_params"]["interval_save_weights"]
metric_names = self.params["training_params"]["train_metrics"]
display_values = None
......@@ -643,25 +641,9 @@ class GenericTrainingManager:
)
if valid_set_name == self.params["training_params"][
"set_name_focus_metric"
] and (
self.best is None
or (
eval_values[focus_metric_name] <= self.best
and self.params["training_params"][
"expected_metric_value"
]
== "low"
)
or (
eval_values[focus_metric_name] >= self.best
and self.params["training_params"][
"expected_metric_value"
]
== "high"
)
):
] and (self.best is None or eval_values["cer"] <= self.best):
self.save_model(epoch=num_epoch, name="best")
self.best = eval_values[focus_metric_name]
self.best = eval_values["cer"]
# Handle curriculum learning update
if self.dataset.train_dataset.curriculum_config:
......@@ -676,8 +658,6 @@ class GenericTrainingManager:
# save model weights
if self.is_master:
self.save_model(epoch=num_epoch, name="last")
if interval_save_weights and num_epoch % interval_save_weights == 0:
self.save_model(epoch=num_epoch, name="weights", keep_weights=True)
self.writer.flush()
def evaluate(self, set_name, mlflow_logging=False, **kwargs):
......
dan/ocr/document/train.py
View file @ a633050e
......@@ -138,7 +138,6 @@ def get_config():
},
"transfered_charset": True, # Transfer learning of the decision layer based on charset of the line HTR model
"additional_tokens": 1, # for decision layer = [<eot>, ], only for transferred charset
"input_channels": 3, # number of channels of input image
"dropout": 0.5, # dropout rate for encoder
"enc_dim": 256, # dimension of extracted features
"nb_layers": 5, # encoder
......@@ -151,9 +150,6 @@ def get_config():
"dec_pred_dropout": 0.1, # dropout rate before decision layer
"dec_att_dropout": 0.1, # dropout rate in multi head attention
"dec_dim_feedforward": 256, # number of dimension for feedforward layer in transformer decoder layers
"use_2d_pe": True, # use 2D positional embedding
"use_1d_pe": True, # use 1D positional embedding
"use_lstm": False,
"attention_win": 100, # length of attention window
# Curriculum dropout
"dropout_scheduler": {
......@@ -168,7 +164,6 @@ def get_config():
* 24
* 1.9, # maximum time before to stop (in seconds)
"load_epoch": "last", # ["best", "last"]: last to continue training, best to evaluate
"interval_save_weights": None, # None: keep best and last only
"batch_size": 2, # mini-batch size for training
"use_ddp": False, # Use DistributedDataParallel
"ddp_port": "20027",
......@@ -186,8 +181,6 @@ def get_config():
"lr_schedulers": None, # Learning rate schedulers
"eval_on_valid": True, # Whether to eval and logs metrics on validation set during training or not
"eval_on_valid_interval": 5, # Interval (in epochs) to evaluate during training
"focus_metric": "cer", # Metrics to focus on to determine best epoch
"expected_metric_value": "low", # ["high", "low"] What is best for the focus metric value
"set_name_focus_metric": "{}-val".format(
dataset_name
), # Which dataset to focus on to select best weights
......
docs/get_started/training.md
View file @ a633050e
......@@ -51,15 +51,12 @@ version: 0.0.1
parameters:
max_char_prediction: int
encoder:
input_channels: int
dropout: float
decoder:
enc_dim: int
l_max: int
dec_pred_dropout: float
attention_win: int
use_1d_pe: bool
use_lstm: bool
vocab_size: int
h_max: int
w_max: int
......
docs/usage/train/parameters.md
View file @ a633050e
......@@ -123,7 +123,6 @@ For a detailed description of all augmentation transforms, see the [dedicated pa
| `model_params.transfer_learning.decoder` | Model to load for the decoder [state_dict_name, checkpoint_path, learnable, strict]. | `list` | `["encoder", "pretrained_models/dan_rimes_page.pt", True, False]` |
| `model_params.transfered_charset` | Transfer learning of the decision layer based on charset of the model to transfer. | `bool` | `True` |
| `model_params.additional_tokens` | For decision layer = [<eot>, ], only for transferred charset. | `int` | `1` |
| `model_params.input_channels` | Number of channels of input image. | `int` | `3` |
| `model_params.dropout` | Dropout probability in the encoder. | `float` | `0.5` |
| `model_params.enc_dim` | Dimension of features extracted by the encoder. | `int` | `256` |
| `model_params.nb_layers` | Number of layers in the encoder. | `int` | `5` |
......@@ -136,9 +135,6 @@ For a detailed description of all augmentation transforms, see the [dedicated pa
| `model_params.dec_pred_dropout` | Dropout rate before decision layer. | `float` | `0.1` |
| `model_params.dec_att_dropout` | Dropout rate in multi head attention. | `float` | `0.1` |
| `model_params.dec_dim_feedforward` | Number of dimensions for feedforward layer in transformer decoder layers. | `int` | `256` |
| `model_params.use_2d_pe` | Whether to use 2D positional embedding. | `bool` | `True` |
| `model_params.use_1d_pe` | Whether to use 1D positional embedding. | `bool` | `True` |
| `model_params.use_lstm` | Whether to use a LSTM layer in the decoder. | `bool` | `False` |
| `model_params.attention_win` | Length of attention window. | `int` | `100` |
| `model_params.dropout_scheduler.function` | Curriculum dropout scheduler. | custom class | `exponential_dropout_scheduler` |
| `model_params.dropout_scheduler.T` | Exponential factor. | `float` | `5e4` |
......@@ -152,7 +148,6 @@ For a detailed description of all augmentation transforms, see the [dedicated pa
| `training_params.max_nb_epochs` | Maximum number of epochs before stopping training. | `int` | `800` |
| `training_params.max_training_time` | Maximum time (in seconds) before stopping training. | `int` | `164160` |
| `training_params.load_epoch` | Model to load. Should be either `"best"` (evaluation) or `last` (training). | `str` | `"last"` |
| `training_params.interval_save_weights` | Step to save weights. Set to `None` to keep only best and last epochs. | `int` | `None` |
| `training_params.batch_size` | Mini-batch size for the training loop. | `int` | `2` |
| `training_params.use_ddp` | Whether to use DistributedDataParallel. | `bool` | `False` |
| `training_params.ddp_port` | DDP port. | `int` | `20027` |
......@@ -164,8 +159,6 @@ For a detailed description of all augmentation transforms, see the [dedicated pa
| `training_params.lr_schedulers` | Learning rate schedulers. | custom class | `None` |
| `training_params.eval_on_valid` | Whether to evaluate and log metrics on the validation set during training. | `bool` | `True` |
| `training_params.eval_on_valid_interval` | Interval (in epochs) to evaluate during training. | `int` | `5` |
| `training_params.focus_metric` | Metrics to focus on to determine best epoch. | `str` | `cer` |
| `training_params.expected_metric_value` | Best value for the focus metric. Should be either `"high"` or `"low"`. | `low` | `cer` |
| `training_params.set_name_focus_metric` | Dataset to focus on to select best weights. | `str` | |
| `training_params.train_metrics` | List of metrics to compute during training. | `list` | `["loss_ce", "cer", "wer", "wer_no_punct"]` |
| `training_params.eval_metrics` | List of metrics to compute during validation. | `list` | `["cer", "wer", "wer_no_punct"]` |
......
tests/conftest.py
View file @ a633050e
......@@ -86,7 +86,6 @@ def training_config():
"transfer_learning": None,
"transfered_charset": True, # Transfer learning of the decision layer based on charset of the line HTR model
"additional_tokens": 1, # for decision layer = [<eot>, ], only for transferred charset
"input_channels": 3, # number of channels of input image
"dropout": 0.5, # dropout rate for encoder
"enc_dim": 256, # dimension of extracted features
"nb_layers": 5, # encoder
......@@ -99,9 +98,6 @@ def training_config():
"dec_pred_dropout": 0.1, # dropout rate before decision layer
"dec_att_dropout": 0.1, # dropout rate in multi head attention
"dec_dim_feedforward": 256, # number of dimension for feedforward layer in transformer decoder layers
"use_2d_pe": True, # use 2D positional embedding
"use_1d_pe": True, # use 1D positional embedding
"use_lstm": False,
"attention_win": 100, # length of attention window
# Curriculum dropout
"dropout_scheduler": {
......@@ -114,7 +110,6 @@ def training_config():
"max_nb_epochs": 4, # maximum number of epochs before to stop
"max_training_time": 1200, # maximum time before to stop (in seconds)
"load_epoch": "last", # ["best", "last"]: last to continue training, best to evaluate
"interval_save_weights": None, # None: keep best and last only
"batch_size": 2, # mini-batch size for training
"use_ddp": False, # Use DistributedDataParallel
"nb_gpu": 0,
......@@ -130,8 +125,6 @@ def training_config():
"lr_schedulers": None, # Learning rate schedulers
"eval_on_valid": True, # Whether to eval and logs metrics on validation set during training or not
"eval_on_valid_interval": 2, # Interval (in epochs) to evaluate during training
"focus_metric": "cer", # Metrics to focus on to determine best epoch
"expected_metric_value": "low", # ["high", "low"] What is best for the focus metric value
"set_name_focus_metric": "training-val", # Which dataset to focus on to select best weights
"train_metrics": [
"loss_ce",
......
tests/data/prediction/parameters.yml
View file @ a633050e
......@@ -3,15 +3,12 @@ version: 0.0.1
parameters:
max_char_prediction: 200
encoder:
input_channels: 3
dropout: 0.5
decoder:
enc_dim: 256
l_max: 15000
dec_pred_dropout: 0.1
attention_win: 100
use_1d_pe: True
use_lstm: False
vocab_size: 96
h_max: 500
w_max: 1000
......