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executable file
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# Copyright 2020 Google Research. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Model function definition, including both architecture and loss."""
import functools
import re
from absl import logging
import numpy as np
import tensorflow.compat.v1 as tf
import coco_metric
import efficientdet_arch
import hparams_config
import iou_utils
import nms_np
import utils
from keras import anchors
from keras import efficientdet_keras
from keras import postprocess
_DEFAULT_BATCH_SIZE = 64
def update_learning_rate_schedule_parameters(params):
"""Updates params that are related to the learning rate schedule."""
# params['batch_size'] is per-shard within model_fn if strategy=tpu.
batch_size = (
params['batch_size'] * params['num_shards']
if params['strategy'] in ['tpu', 'gpus'] else params['batch_size'])
# Learning rate is proportional to the batch size
params['adjusted_learning_rate'] = (
params['learning_rate'] * batch_size / _DEFAULT_BATCH_SIZE)
steps_per_epoch = params['num_examples_per_epoch'] / batch_size
params['lr_warmup_step'] = int(params['lr_warmup_epoch'] * steps_per_epoch)
params['first_lr_drop_step'] = int(params['first_lr_drop_epoch'] *
steps_per_epoch)
params['second_lr_drop_step'] = int(params['second_lr_drop_epoch'] *
steps_per_epoch)
params['total_steps'] = int(params['num_epochs'] * steps_per_epoch)
params['steps_per_epoch'] = steps_per_epoch
def stepwise_lr_schedule(adjusted_learning_rate, lr_warmup_init, lr_warmup_step,
first_lr_drop_step, second_lr_drop_step, global_step):
"""Handles linear scaling rule, gradual warmup, and LR decay."""
# lr_warmup_init is the starting learning rate; the learning rate is linearly
# scaled up to the full learning rate after `lr_warmup_step` before decaying.
logging.info('LR schedule method: stepwise')
linear_warmup = (
lr_warmup_init +
(tf.cast(global_step, dtype=tf.float32) / lr_warmup_step *
(adjusted_learning_rate - lr_warmup_init)))
learning_rate = tf.where(global_step < lr_warmup_step, linear_warmup,
adjusted_learning_rate)
lr_schedule = [[1.0, lr_warmup_step], [0.1, first_lr_drop_step],
[0.01, second_lr_drop_step]]
for mult, start_global_step in lr_schedule:
learning_rate = tf.where(global_step < start_global_step, learning_rate,
adjusted_learning_rate * mult)
return learning_rate
def cosine_lr_schedule(adjusted_lr, lr_warmup_init, lr_warmup_step, total_steps,
step):
"""Cosine learning rate scahedule."""
logging.info('LR schedule method: cosine')
linear_warmup = (
lr_warmup_init + (tf.cast(step, dtype=tf.float32) / lr_warmup_step *
(adjusted_lr - lr_warmup_init)))
decay_steps = tf.cast(total_steps - lr_warmup_step, tf.float32)
cosine_lr = 0.5 * adjusted_lr * (
1 + tf.cos(np.pi * tf.cast(step, tf.float32) / decay_steps))
return tf.where(step < lr_warmup_step, linear_warmup, cosine_lr)
def polynomial_lr_schedule(adjusted_lr, lr_warmup_init, lr_warmup_step, power,
total_steps, step):
logging.info('LR schedule method: polynomial')
linear_warmup = (
lr_warmup_init + (tf.cast(step, dtype=tf.float32) / lr_warmup_step *
(adjusted_lr - lr_warmup_init)))
polynomial_lr = adjusted_lr * tf.pow(
1 - (tf.cast(step, tf.float32) / total_steps), power)
return tf.where(step < lr_warmup_step, linear_warmup, polynomial_lr)
def learning_rate_schedule(params, global_step):
"""Learning rate schedule based on global step."""
lr_decay_method = params['lr_decay_method']
if lr_decay_method == 'stepwise':
return stepwise_lr_schedule(params['adjusted_learning_rate'],
params['lr_warmup_init'],
params['lr_warmup_step'],
params['first_lr_drop_step'],
params['second_lr_drop_step'], global_step)
if lr_decay_method == 'cosine':
return cosine_lr_schedule(params['adjusted_learning_rate'],
params['lr_warmup_init'],
params['lr_warmup_step'], params['total_steps'],
global_step)
if lr_decay_method == 'polynomial':
return polynomial_lr_schedule(params['adjusted_learning_rate'],
params['lr_warmup_init'],
params['lr_warmup_step'],
params['poly_lr_power'],
params['total_steps'], global_step)
if lr_decay_method == 'constant':
return params['adjusted_learning_rate']
raise ValueError('unknown lr_decay_method: {}'.format(lr_decay_method))
def focal_loss(y_pred, y_true, alpha, gamma, normalizer, label_smoothing=0.0):
"""Compute the focal loss between `logits` and the golden `target` values.
Focal loss = -(1-pt)^gamma * log(pt)
where pt is the probability of being classified to the true class.
Args:
y_pred: A float tensor of size [batch, height_in, width_in,
num_predictions].
y_true: A float tensor of size [batch, height_in, width_in,
num_predictions].
alpha: A float scalar multiplying alpha to the loss from positive examples
and (1-alpha) to the loss from negative examples.
gamma: A float scalar modulating loss from hard and easy examples.
normalizer: Divide loss by this value.
label_smoothing: Float in [0, 1]. If > `0` then smooth the labels.
Returns:
loss: A float32 scalar representing normalized total loss.
"""
with tf.name_scope('focal_loss'):
normalizer = tf.cast(normalizer, dtype=y_pred.dtype)
# compute focal loss multipliers before label smoothing, such that it will
# not blow up the loss.
pred_prob = tf.sigmoid(y_pred)
p_t = (y_true * pred_prob) + ((1 - y_true) * (1 - pred_prob))
alpha_factor = y_true * alpha + (1 - y_true) * (1 - alpha)
modulating_factor = (1.0 - p_t)**gamma
# apply label smoothing for cross_entropy for each entry.
if label_smoothing:
y_true = y_true * (1.0 - label_smoothing) + 0.5 * label_smoothing
ce = tf.nn.sigmoid_cross_entropy_with_logits(labels=y_true, logits=y_pred)
# compute the final loss and return
return (1 / normalizer) * alpha_factor * modulating_factor * ce
def _box_loss(box_outputs, box_targets, num_positives, delta=0.1):
"""Computes box regression loss."""
# delta is typically around the mean value of regression target.
# for instances, the regression targets of 512x512 input with 6 anchors on
# P3-P7 pyramid is about [0.1, 0.1, 0.2, 0.2].
normalizer = num_positives * 4.0
mask = tf.not_equal(box_targets, 0.0)
box_loss = tf.losses.huber_loss(
box_targets,
box_outputs,
weights=mask,
delta=delta,
reduction=tf.losses.Reduction.SUM)
box_loss /= normalizer
return box_loss
def _box_iou_loss(box_outputs, box_targets, num_positives, iou_loss_type):
"""Computes box iou loss."""
normalizer = num_positives * 4.0
box_iou_loss = iou_utils.iou_loss(box_outputs, box_targets, iou_loss_type)
box_iou_loss = tf.reduce_sum(box_iou_loss) / normalizer
return box_iou_loss
def detection_loss(cls_outputs, box_outputs, labels, params):
"""Computes total detection loss.
Computes total detection loss including box and class loss from all levels.
Args:
cls_outputs: an OrderDict with keys representing levels and values
representing logits in [batch_size, height, width, num_anchors].
box_outputs: an OrderDict with keys representing levels and values
representing box regression targets in [batch_size, height, width,
num_anchors * 4].
labels: the dictionary that returned from dataloader that includes
groundtruth targets.
params: the dictionary including training parameters specified in
default_haprams function in this file.
Returns:
total_loss: an integer tensor representing total loss reducing from
class and box losses from all levels.
cls_loss: an integer tensor representing total class loss.
box_loss: an integer tensor representing total box regression loss.
box_iou_loss: an integer tensor representing total box iou loss.
"""
# Sum all positives in a batch for normalization and avoid zero
# num_positives_sum, which would lead to inf loss during training
num_positives_sum = tf.reduce_sum(labels['mean_num_positives']) + 1.0
positives_momentum = params.get('positives_momentum', None) or 0
if positives_momentum > 0:
# normalize the num_positive_examples for training stability.
moving_normalizer_var = tf.Variable(
0.0,
name='moving_normalizer',
dtype=tf.float32,
synchronization=tf.VariableSynchronization.ON_READ,
trainable=False,
aggregation=tf.VariableAggregation.MEAN)
num_positives_sum = tf.keras.backend.moving_average_update(
moving_normalizer_var,
num_positives_sum,
momentum=params['positives_momentum'])
elif positives_momentum < 0:
num_positives_sum = utils.cross_replica_mean(num_positives_sum)
levels = cls_outputs.keys()
cls_losses = []
box_losses = []
for level in levels:
# Onehot encoding for classification labels.
cls_targets_at_level = tf.one_hot(
labels['cls_targets_%d' % level],
params['num_classes'],
dtype=cls_outputs[level].dtype)
if params['data_format'] == 'channels_first':
bs, _, width, height, _ = cls_targets_at_level.get_shape().as_list()
cls_targets_at_level = tf.reshape(cls_targets_at_level,
[bs, -1, width, height])
else:
bs, width, height, _, _ = cls_targets_at_level.get_shape().as_list()
cls_targets_at_level = tf.reshape(cls_targets_at_level,
[bs, width, height, -1])
box_targets_at_level = labels['box_targets_%d' % level]
cls_loss = focal_loss(
cls_outputs[level],
cls_targets_at_level,
params['alpha'],
params['gamma'],
normalizer=num_positives_sum,
label_smoothing=params['label_smoothing'])
if params['data_format'] == 'channels_first':
cls_loss = tf.reshape(cls_loss,
[bs, -1, width, height, params['num_classes']])
else:
cls_loss = tf.reshape(cls_loss,
[bs, width, height, -1, params['num_classes']])
cls_loss *= tf.cast(
tf.expand_dims(tf.not_equal(labels['cls_targets_%d' % level], -2), -1),
cls_loss.dtype)
cls_loss_sum = tf.clip_by_value(tf.reduce_sum(cls_loss), 0.0, 2.0)
cls_losses.append(tf.cast(cls_loss_sum, tf.float32))
if params['box_loss_weight']:
box_losses.append(
_box_loss(
box_outputs[level],
box_targets_at_level,
num_positives_sum,
delta=params['delta']))
if params['iou_loss_type']:
input_anchors = anchors.Anchors(params['min_level'], params['max_level'],
params['num_scales'],
params['aspect_ratios'],
params['anchor_scale'],
params['image_size'])
box_output_list = [tf.reshape(box_outputs[i], [-1, 4]) for i in levels]
box_outputs = tf.concat(box_output_list, axis=0)
box_target_list = [
tf.reshape(labels['box_targets_%d' % level], [-1, 4])
for level in levels
]
box_targets = tf.concat(box_target_list, axis=0)
anchor_boxes = tf.tile(input_anchors.boxes, [params['batch_size'], 1])
box_outputs = anchors.decode_box_outputs(box_outputs, anchor_boxes)
box_targets = anchors.decode_box_outputs(box_targets, anchor_boxes)
box_iou_loss = _box_iou_loss(box_outputs, box_targets, num_positives_sum,
params['iou_loss_type'])
else:
box_iou_loss = 0
# Sum per level losses to total loss.
cls_loss = tf.add_n(cls_losses)
box_loss = tf.add_n(box_losses) if box_losses else 0
total_loss = (
cls_loss + params['box_loss_weight'] * box_loss +
params['iou_loss_weight'] * box_iou_loss)
return total_loss, cls_loss, box_loss, box_iou_loss
def reg_l2_loss(weight_decay, regex=r'.*(kernel|weight):0$'):
"""Return regularization l2 loss loss."""
var_match = re.compile(regex)
return weight_decay * tf.add_n([
tf.nn.l2_loss(v)
for v in tf.trainable_variables()
if var_match.match(v.name)
])
@tf.autograph.experimental.do_not_convert
def _model_fn(features, labels, mode, params, model, variable_filter_fn=None):
"""Model definition entry.
Args:
features: the input image tensor with shape [batch_size, height, width, 3].
The height and width are fixed and equal.
labels: the input labels in a dictionary. The labels include class targets
and box targets which are dense label maps. The labels are generated from
get_input_fn function in data/dataloader.py
mode: the mode of TPUEstimator including TRAIN and EVAL.
params: the dictionary defines hyperparameters of model. The default
settings are in default_hparams function in this file.
model: the model outputs class logits and box regression outputs.
variable_filter_fn: the filter function that takes trainable_variables and
returns the variable list after applying the filter rule.
Returns:
tpu_spec: the TPUEstimatorSpec to run training, evaluation, or prediction.
Raises:
RuntimeError: if both ckpt and backbone_ckpt are set.
"""
is_tpu = params['strategy'] == 'tpu'
if params['img_summary_steps']:
utils.image('input_image', features, is_tpu)
training_hooks = []
params['is_training_bn'] = (mode == tf.estimator.ModeKeys.TRAIN)
if params['use_keras_model']:
def model_fn(inputs):
model = efficientdet_keras.EfficientDetNet(
config=hparams_config.Config(params))
cls_out_list, box_out_list = model(inputs, params['is_training_bn'])
cls_outputs, box_outputs = {}, {}
for i in range(params['min_level'], params['max_level'] + 1):
cls_outputs[i] = cls_out_list[i - params['min_level']]
box_outputs[i] = box_out_list[i - params['min_level']]
return cls_outputs, box_outputs
else:
model_fn = functools.partial(model, config=hparams_config.Config(params))
precision = utils.get_precision(params['strategy'], params['mixed_precision'])
cls_outputs, box_outputs = utils.build_model_with_precision(
precision, model_fn, features, params['is_training_bn'])
levels = cls_outputs.keys()
for level in levels:
cls_outputs[level] = tf.cast(cls_outputs[level], tf.float32)
box_outputs[level] = tf.cast(box_outputs[level], tf.float32)
# Set up training loss and learning rate.
update_learning_rate_schedule_parameters(params)
global_step = tf.train.get_or_create_global_step()
learning_rate = learning_rate_schedule(params, global_step)
# cls_loss and box_loss are for logging. only total_loss is optimized.
det_loss, cls_loss, box_loss, box_iou_loss = detection_loss(
cls_outputs, box_outputs, labels, params)
reg_l2loss = reg_l2_loss(params['weight_decay'])
total_loss = det_loss + reg_l2loss
if mode == tf.estimator.ModeKeys.TRAIN:
utils.scalar('lrn_rate', learning_rate, is_tpu)
utils.scalar('trainloss/cls_loss', cls_loss, is_tpu)
utils.scalar('trainloss/box_loss', box_loss, is_tpu)
utils.scalar('trainloss/det_loss', det_loss, is_tpu)
utils.scalar('trainloss/reg_l2_loss', reg_l2loss, is_tpu)
utils.scalar('trainloss/loss', total_loss, is_tpu)
if params['iou_loss_type']:
utils.scalar('trainloss/box_iou_loss', box_iou_loss, is_tpu)
train_epochs = tf.cast(global_step, tf.float32) / params['steps_per_epoch']
utils.scalar('train_epochs', train_epochs, is_tpu)
moving_average_decay = params['moving_average_decay']
if moving_average_decay:
ema = tf.train.ExponentialMovingAverage(
decay=moving_average_decay, num_updates=global_step)
ema_vars = utils.get_ema_vars()
if mode == tf.estimator.ModeKeys.TRAIN:
if params['optimizer'].lower() == 'sgd':
optimizer = tf.train.MomentumOptimizer(
learning_rate, momentum=params['momentum'])
elif params['optimizer'].lower() == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate)
else:
raise ValueError('optimizers should be adam or sgd')
if is_tpu:
optimizer = tf.tpu.CrossShardOptimizer(optimizer)
if params['device']['grad_ckpting']:
# pylint: disable=g-import-not-at-top,g-direct-tensorflow-import
from third_party.grad_checkpoint import grad
from tensorflow.python.ops import gradients
# pylint: enable=g-import-not-at-top,g-direct-tensorflow-import
# monkey patch tf.gradients to point to our custom version,
# with automatic checkpoint selection
def gradients_(ys, xs, grad_ys=None, **kwargs):
return grad.gradients(
ys,
xs,
grad_ys,
checkpoints=params['device']['grad_ckpting_list'],
**kwargs)
gradients.__dict__['gradients'] = gradients_
# Batch norm requires update_ops to be added as a train_op dependency.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
var_list = tf.trainable_variables()
if variable_filter_fn:
var_list = variable_filter_fn(var_list)
if params.get('clip_gradients_norm', None):
logging.info('clip gradients norm by %f', params['clip_gradients_norm'])
grads_and_vars = optimizer.compute_gradients(total_loss, var_list)
with tf.name_scope('clip'):
grads = [gv[0] for gv in grads_and_vars]
tvars = [gv[1] for gv in grads_and_vars]
# First clip each variable's norm, then clip global norm.
clip_norm = abs(params['clip_gradients_norm'])
clipped_grads = [
tf.clip_by_norm(g, clip_norm) if g is not None else None
for g in grads
]
clipped_grads, _ = tf.clip_by_global_norm(clipped_grads, clip_norm)
utils.scalar('gradient_norm', tf.linalg.global_norm(clipped_grads),
is_tpu)
grads_and_vars = list(zip(clipped_grads, tvars))
with tf.control_dependencies(update_ops):
train_op = optimizer.apply_gradients(grads_and_vars, global_step)
else:
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(
total_loss, global_step, var_list=var_list)
if moving_average_decay:
with tf.control_dependencies([train_op]):
train_op = ema.apply(ema_vars)
else:
train_op = None
eval_metrics = None
if mode == tf.estimator.ModeKeys.EVAL:
def metric_fn(**kwargs):
"""Returns a dictionary that has the evaluation metrics."""
if params['nms_configs'].get('pyfunc', True):
detections_bs = []
for index in range(kwargs['boxes'].shape[0]):
nms_configs = params['nms_configs']
detections = tf.numpy_function(
functools.partial(nms_np.per_class_nms, nms_configs=nms_configs),
[
kwargs['boxes'][index],
kwargs['scores'][index],
kwargs['classes'][index],
tf.slice(kwargs['image_ids'], [index], [1]),
tf.slice(kwargs['image_scales'], [index], [1]),
params['num_classes'],
nms_configs['max_output_size'],
], tf.float32)
detections_bs.append(detections)
detections_bs = postprocess.transform_detections(
tf.stack(detections_bs))
else:
# These two branches should be equivalent, but currently they are not.
# TODO(tanmingxing): enable the non_pyfun path after bug fix.
nms_boxes, nms_scores, nms_classes, _ = postprocess.per_class_nms(
params, kwargs['boxes'], kwargs['scores'], kwargs['classes'],
kwargs['image_scales'])
img_ids = tf.cast(
tf.expand_dims(kwargs['image_ids'], -1), nms_scores.dtype)
detections_bs = [
img_ids * tf.ones_like(nms_scores),
nms_boxes[:, :, 1],
nms_boxes[:, :, 0],
nms_boxes[:, :, 3] - nms_boxes[:, :, 1],
nms_boxes[:, :, 2] - nms_boxes[:, :, 0],
nms_scores,
nms_classes,
]
detections_bs = tf.stack(detections_bs, axis=-1, name='detnections')
if params.get('testdev_dir', None):
logging.info('Eval testdev_dir %s', params['testdev_dir'])
eval_metric = coco_metric.EvaluationMetric(
testdev_dir=params['testdev_dir'])
coco_metrics = eval_metric.estimator_metric_fn(detections_bs,
tf.zeros([1]))
else:
logging.info('Eval val with groudtruths %s.', params['val_json_file'])
eval_metric = coco_metric.EvaluationMetric(
filename=params['val_json_file'], label_map=params['label_map'])
coco_metrics = eval_metric.estimator_metric_fn(
detections_bs, kwargs['groundtruth_data'])
# Add metrics to output.
cls_loss = tf.metrics.mean(kwargs['cls_loss_repeat'])
box_loss = tf.metrics.mean(kwargs['box_loss_repeat'])
output_metrics = {
'cls_loss': cls_loss,
'box_loss': box_loss,
}
output_metrics.update(coco_metrics)
return output_metrics
cls_loss_repeat = tf.reshape(
tf.tile(tf.expand_dims(cls_loss, 0), [
params['batch_size'],
]), [params['batch_size'], 1])
box_loss_repeat = tf.reshape(
tf.tile(tf.expand_dims(box_loss, 0), [
params['batch_size'],
]), [params['batch_size'], 1])
cls_outputs = postprocess.to_list(cls_outputs)
box_outputs = postprocess.to_list(box_outputs)
params['nms_configs']['max_nms_inputs'] = anchors.MAX_DETECTION_POINTS
boxes, scores, classes = postprocess.pre_nms(params, cls_outputs,
box_outputs)
metric_fn_inputs = {
'cls_loss_repeat': cls_loss_repeat,
'box_loss_repeat': box_loss_repeat,
'image_ids': labels['source_ids'],
'groundtruth_data': labels['groundtruth_data'],
'image_scales': labels['image_scales'],
'boxes': boxes,
'scores': scores,
'classes': classes,
}
eval_metrics = (metric_fn, metric_fn_inputs)
checkpoint = params.get('ckpt') or params.get('backbone_ckpt')
if checkpoint and mode == tf.estimator.ModeKeys.TRAIN:
# Initialize the model from an EfficientDet or backbone checkpoint.
if params.get('ckpt') and params.get('backbone_ckpt'):
raise RuntimeError(
'--backbone_ckpt and --checkpoint are mutually exclusive')
if params.get('backbone_ckpt'):
var_scope = params['backbone_name'] + '/'
if params['ckpt_var_scope'] is None:
# Use backbone name as default checkpoint scope.
ckpt_scope = params['backbone_name'] + '/'
else:
ckpt_scope = params['ckpt_var_scope'] + '/'
else:
# Load every var in the given checkpoint
var_scope = ckpt_scope = '/'
def scaffold_fn():
"""Loads pretrained model through scaffold function."""
logging.info('restore variables from %s', checkpoint)
var_map = utils.get_ckpt_var_map(
ckpt_path=checkpoint,
ckpt_scope=ckpt_scope,
var_scope=var_scope,
skip_mismatch=params['skip_mismatch'])
tf.train.init_from_checkpoint(checkpoint, var_map)
return tf.train.Scaffold()
elif mode == tf.estimator.ModeKeys.EVAL and moving_average_decay:
def scaffold_fn():
"""Load moving average variables for eval."""
logging.info('Load EMA vars with ema_decay=%f', moving_average_decay)
restore_vars_dict = ema.variables_to_restore(ema_vars)
saver = tf.train.Saver(restore_vars_dict)
return tf.train.Scaffold(saver=saver)
else:
scaffold_fn = None
if is_tpu:
return tf.estimator.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
train_op=train_op,
eval_metrics=eval_metrics,
host_call=utils.get_tpu_host_call(global_step, params),
scaffold_fn=scaffold_fn,
training_hooks=training_hooks)
else:
# Profile every 1K steps.
if params.get('profile', False):
profile_hook = tf.estimator.ProfilerHook(
save_steps=1000, output_dir=params['model_dir'], show_memory=True)
training_hooks.append(profile_hook)
# Report memory allocation if OOM; it will slow down the running.
class OomReportingHook(tf.estimator.SessionRunHook):
def before_run(self, run_context):
return tf.estimator.SessionRunArgs(
fetches=[],
options=tf.RunOptions(report_tensor_allocations_upon_oom=True))
training_hooks.append(OomReportingHook())
logging_hook = tf.estimator.LoggingTensorHook(
{
'step': global_step,
'det_loss': det_loss,
'cls_loss': cls_loss,
'box_loss': box_loss,
},
every_n_iter=params.get('iterations_per_loop', 100),
)
training_hooks.append(logging_hook)
if params['device']['nvgpu_logging']:
try:
from third_party.tools import nvgpu # pylint: disable=g-import-not-at-top
mem_message = tf.numpy_function(nvgpu.gpu_memory_util_message, [],
[tf.string])[0]
logging_hook_nvgpu = tf.estimator.LoggingTensorHook(
tensors={'mem_message': mem_message},
every_n_iter=params.get('iterations_per_loop', 100),
formatter=lambda x: x['mem_message'].decode('utf-8'),
)
training_hooks.append(logging_hook_nvgpu)
except: # pylint: disable=bare-except
logging.error('nvgpu error: nvidia-smi format not recognized.')
eval_metric_ops = (
eval_metrics[0](**eval_metrics[1]) if eval_metrics else None)
return tf.estimator.EstimatorSpec(
mode=mode,
loss=total_loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops,
scaffold=scaffold_fn() if scaffold_fn else None,
training_hooks=training_hooks)
def efficientdet_model_fn(features, labels, mode, params):
"""EfficientDet model."""
variable_filter_fn = functools.partial(
efficientdet_arch.freeze_vars, pattern=params['var_freeze_expr'])
return _model_fn(
features,
labels,
mode,
params,
model=efficientdet_arch.efficientdet,
variable_f
3E2C
ilter_fn=variable_filter_fn)
def get_model_arch(model_name='efficientdet-d0'):
"""Get model architecture for a given model name."""
if 'efficientdet' in model_name:
return efficientdet_arch.efficientdet
raise ValueError('Invalide model name {}'.format(model_name))
def get_model_fn(model_name='efficientdet-d0'):
"""Get model fn for a given model name."""
if 'efficientdet' in model_name:
return efficientdet_model_fn
raise ValueError('Invalide model name {}'.format(model_name))