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caffe_train.py
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caffe_train.py
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# Copyright (c) 2014-2015, NVIDIA CORPORATION. All rights reserved.
import os
import re
import time
import math
import subprocess
import operator
import sys
import numpy as np
import scipy
from google.protobuf import text_format
import caffe
import caffe_pb2
from train import TrainTask
from digits.config import config_value
from digits.status import Status
from digits import utils, dataset
from digits.utils import subclass, override, constants
# NOTE: Increment this everytime the pickled object changes
PICKLE_VERSION = 3
# Constants
CAFFE_SOLVER_FILE = 'solver.prototxt'
CAFFE_TRAIN_VAL_FILE = 'train_val.prototxt'
CAFFE_SNAPSHOT_PREFIX = 'snapshot'
CAFFE_DEPLOY_FILE = 'deploy.prototxt'
@subclass
class CaffeTrainTask(TrainTask):
"""
Trains a caffe model
"""
CAFFE_LOG = 'caffe_output.log'
@staticmethod
def upgrade_network(network):
#TODO
pass
def __init__(self, **kwargs):
"""
Arguments:
network -- a caffe NetParameter defining the network
"""
super(CaffeTrainTask, self).__init__(**kwargs)
self.pickver_task_caffe_train = PICKLE_VERSION
self.current_iteration = 0
self.loaded_snapshot_file = None
self.loaded_snapshot_epoch = None
self.image_mean = None
self.solver = None
self.solver_file = CAFFE_SOLVER_FILE
self.train_val_file = CAFFE_TRAIN_VAL_FILE
self.snapshot_prefix = CAFFE_SNAPSHOT_PREFIX
self.deploy_file = CAFFE_DEPLOY_FILE
self.log_file = self.CAFFE_LOG
def __getstate__(self):
state = super(CaffeTrainTask, self).__getstate__()
# Don't pickle these things
if 'caffe_log' in state:
del state['caffe_log']
if '_transformer' in state:
del state['_transformer']
if '_caffe_net' in state:
del state['_caffe_net']
return state
def __setstate__(self, state):
super(CaffeTrainTask, self).__setstate__(state)
# Upgrade pickle file
if state['pickver_task_caffe_train'] <= 1:
print 'Upgrading CaffeTrainTask to version 2 ...'
self.caffe_log_file = self.CAFFE_LOG
if state['pickver_task_caffe_train'] <= 2:
print 'Upgrading CaffeTrainTask to version 3 ...'
self.log_file = self.caffe_log_file
self.framework_id = 'caffe'
self.pickver_task_caffe_train = PICKLE_VERSION
# Make changes to self
self.loaded_snapshot_file = None
self.loaded_snapshot_epoch = None
# These things don't get pickled
self.image_mean = None
### Task overrides
@override
def name(self):
return 'Train Caffe Model'
@override
def before_run(self):
super(CaffeTrainTask, self).before_run()
if isinstance(self.dataset, dataset.ImageClassificationDatasetJob):
self.save_files_classification()
elif isinstance(self.dataset, dataset.GenericImageDatasetJob):
self.save_files_generic()
else:
raise NotImplementedError
self.caffe_log = open(self.path(self.CAFFE_LOG), 'a')
self.saving_snapshot = False
self.receiving_train_output = False
self.receiving_val_output = False
self.last_train_update = None
return True
def get_mean_image(self, mean_file, resize = False):
mean_image = None
with open(self.dataset.path(mean_file), 'rb') as f:
blob = caffe_pb2.BlobProto()
blob.MergeFromString(f.read())
if isinstance(self.dataset, dataset.ImageClassificationDatasetJob):
mean_image = np.reshape(blob.data,
(
self.dataset.image_dims[2],
self.dataset.image_dims[0],
self.dataset.image_dims[1],
)
)
elif isinstance(self.dataset, dataset.GenericImageDatasetJob):
task = self.dataset.analyze_db_tasks()[0]
mean_image = np.reshape(blob.data,
(
task.image_channels,
task.image_height,
task.image_width,
)
)
# Resize the mean image if crop_size exists
if mean_image is not None and resize:
# Get the image size needed
network = caffe_pb2.NetParameter()
with open(self.path(self.deploy_file)) as infile:
text_format.Merge(infile.read(), network)
if network.input_shape:
data_shape = network.input_shape[0].dim
else:
data_shape = network.input_dim[:4]
assert len(data_shape) == 4, 'Bad data shape.'
# Get the image
mean_image = mean_image.astype('uint8')
mean_image = mean_image.transpose(1,2,0)
shape = list(mean_image.shape)
# imresize will not resize if the depth is anything
# other than 3 or 4. If it's 1, imresize expects an
# array.
if (len(shape) == 2 or (len(shape) == 3 and (shape[2] == 3 or shape[2] == 4))):
mean_image = scipy.misc.imresize(mean_image, (data_shape[2], data_shape[3]))
else:
mean_image = scipy.misc.imresize(mean_image[:,:,0],
(data_shape[2], data_shape[3]))
mean_image = np.expand_dims(mean_image, axis=2)
mean_image = mean_image.transpose(2,0,1)
mean_image = mean_image.astype('float')
return mean_image
def get_mean_pixel(self, mean_file):
mean_image = self.get_mean_image(mean_file)
mean_pixel = None
if mean_image is not None:
mean_pixel = mean_image.mean(1).mean(1)
return mean_pixel
def set_mean_value(self, layer, mean_pixel):
# remove any values that may already be in the network
if layer.transform_param.HasField('mean_file'):
layer.transform_param.ClearField('mean_file')
self.logger.warning('Ignoring mean_file from network ...')
if len(layer.transform_param.mean_value) > 0:
layer.transform_param.ClearField('mean_value')
self.logger.warning('Ignoring mean_value from network ...')
layer.transform_param.mean_value.extend(list(mean_pixel))
def set_mean_file(self, layer, mean_file):
# remove any values that may already be in the network
if layer.transform_param.HasField('mean_file'):
layer.transform_param.ClearField('mean_file')
self.logger.warning('Ignoring mean_file from network ...')
if len(layer.transform_param.mean_value) > 0:
layer.transform_param.ClearField('mean_value')
self.logger.warning('Ignoring mean_value from network ...')
layer.transform_param.mean_file = mean_file
# TODO merge these monolithic save_files functions
# TODO break them up into separate functions
def save_files_classification(self):
"""
Save solver, train_val and deploy files to disk
"""
has_val_set = self.dataset.val_db_task() is not None
### Check what has been specified in self.network
tops = []
bottoms = {}
train_data_layer = None
val_data_layer = None
hidden_layers = caffe_pb2.NetParameter()
loss_layers = []
accuracy_layers = []
for layer in self.network.layer:
assert layer.type not in ['DummyData', 'ImageData', 'MemoryData', 'WindowData'], 'unsupported data layer type'
if layer.type in ['Data', 'HDF5Data']:
for rule in layer.include:
if rule.phase == caffe_pb2.TRAIN:
assert train_data_layer is None, 'cannot specify two train data layers'
train_data_layer = layer
elif rule.phase == caffe_pb2.TEST:
assert val_data_layer is None, 'cannot specify two test data layers'
val_data_layer = layer
elif layer.type == 'SoftmaxWithLoss':
loss_layers.append(layer)
elif layer.type == 'Accuracy':
addThis = True
if layer.accuracy_param.HasField('top_k'):
if layer.accuracy_param.top_k >= len(self.get_labels()):
self.logger.warning('Removing layer %s because top_k=%s while there are are only %s labels in this dataset' % (layer.name, layer.accuracy_param.top_k, len(self.get_labels())))
addThis = False
if addThis:
accuracy_layers.append(layer)
else:
hidden_layers.layer.add().CopyFrom(layer)
if len(layer.bottom) == 1 and len(layer.top) == 1 and layer.bottom[0] == layer.top[0]:
pass
else:
for top in layer.top:
tops.append(top)
for bottom in layer.bottom:
bottoms[bottom] = True
if train_data_layer is None:
assert val_data_layer is None, 'cannot specify a test data layer without a train data layer'
assert len(loss_layers) > 0, 'must specify a loss layer'
network_outputs = []
for name in tops:
if name not in bottoms:
network_outputs.append(name)
assert len(network_outputs), 'network must have an output'
# Update num_output for any output InnerProduct layers automatically
for layer in hidden_layers.layer:
if layer.type == 'InnerProduct':
for top in layer.top:
if top in network_outputs:
layer.inner_product_param.num_output = len(self.get_labels())
break
### Write train_val file
train_val_network = caffe_pb2.NetParameter()
dataset_backend = self.dataset.train_db_task().backend
# data layers
if train_data_layer is not None:
if dataset_backend == 'lmdb':
assert train_data_layer.type == 'Data', 'expecting a Data layer'
elif dataset_backend == 'hdf5':
assert train_data_layer.type == 'HDF5Data', 'expecting an HDF5Data layer'
if dataset_backend == 'lmdb' and train_data_layer.HasField('data_param'):
assert not train_data_layer.data_param.HasField('source'), "don't set the data_param.source"
assert not train_data_layer.data_param.HasField('backend'), "don't set the data_param.backend"
if dataset_backend == 'hdf5' and train_data_layer.HasField('hdf5_data_param'):
assert not train_data_layer.hdf5_data_param.HasField('source'), "don't set the hdf5_data_param.source"
max_crop_size = min(self.dataset.image_dims[0], self.dataset.image_dims[1])
if self.crop_size:
assert dataset_backend != 'hdf5', 'HDF5Data layer does not support cropping'
assert self.crop_size <= max_crop_size, 'crop_size is larger than the image size'
train_data_layer.transform_param.crop_size = self.crop_size
elif train_data_layer.transform_param.HasField('crop_size'):
cs = train_data_layer.transform_param.crop_size
if cs > max_crop_size:
# don't throw an error here
cs = max_crop_size
train_data_layer.transform_param.crop_size = cs
self.crop_size = cs
train_val_network.layer.add().CopyFrom(train_data_layer)
train_data_layer = train_val_network.layer[-1]
if val_data_layer is not None and has_val_set:
if dataset_backend == 'lmdb':
assert val_data_layer.type == 'Data', 'expecting a Data layer'
elif dataset_backend == 'hdf5':
assert val_data_layer.type == 'HDF5Data', 'expecting an HDF5Data layer'
if dataset_backend == 'lmdb' and val_data_layer.HasField('data_param'):
assert not val_data_layer.data_param.HasField('source'), "don't set the data_param.source"
assert not val_data_layer.data_param.HasField('backend'), "don't set the data_param.backend"
if dataset_backend == 'hdf5' and val_data_layer.HasField('hdf5_data_param'):
assert not val_data_layer.hdf5_data_param.HasField('source'), "don't set the hdf5_data_param.source"
if self.crop_size:
# use our error checking from the train layer
val_data_layer.transform_param.crop_size = self.crop_size
train_val_network.layer.add().CopyFrom(val_data_layer)
val_data_layer = train_val_network.layer[-1]
else:
layer_type = 'Data'
if dataset_backend == 'hdf5':
layer_type = 'HDF5Data'
train_data_layer = train_val_network.layer.add(type = layer_type, name = 'data')
train_data_layer.top.append('data')
train_data_layer.top.append('label')
train_data_layer.include.add(phase = caffe_pb2.TRAIN)
if dataset_backend == 'lmdb':
train_data_layer.data_param.batch_size = constants.DEFAULT_BATCH_SIZE
elif dataset_backend == 'hdf5':
train_data_layer.hdf5_data_param.batch_size = constants.DEFAULT_BATCH_SIZE
if self.crop_size:
assert dataset_backend != 'hdf5', 'HDF5Data layer does not support cropping'
train_data_layer.transform_param.crop_size = self.crop_size
if has_val_set:
val_data_layer = train_val_network.layer.add(type = layer_type, name = 'data')
val_data_layer.top.append('data')
val_data_layer.top.append('label')
val_data_layer.include.add(phase = caffe_pb2.TEST)
if dataset_backend == 'lmdb':
val_data_layer.data_param.batch_size = constants.DEFAULT_BATCH_SIZE
elif dataset_backend == 'hdf5':
val_data_layer.hdf5_data_param.batch_size = constants.DEFAULT_BATCH_SIZE
if self.crop_size:
val_data_layer.transform_param.crop_size = self.crop_size
if dataset_backend == 'lmdb':
train_data_layer.data_param.source = self.dataset.path(self.dataset.train_db_task().db_name)
train_data_layer.data_param.backend = caffe_pb2.DataParameter.LMDB
if val_data_layer is not None and has_val_set:
val_data_layer.data_param.source = self.dataset.path(self.dataset.val_db_task().db_name)
val_data_layer.data_param.backend = caffe_pb2.DataParameter.LMDB
elif dataset_backend == 'hdf5':
train_data_layer.hdf5_data_param.source = self.dataset.path(self.dataset.train_db_task().textfile)
if val_data_layer is not None and has_val_set:
val_data_layer.hdf5_data_param.source = self.dataset.path(self.dataset.val_db_task().textfile)
if self.use_mean == 'pixel':
assert dataset_backend != 'hdf5', 'HDF5Data layer does not support mean subtraction'
mean_pixel = self.get_mean_pixel(self.dataset.path(self.dataset.train_db_task().mean_file))
self.set_mean_value(train_data_layer, mean_pixel)
if val_data_layer is not None and has_val_set:
self.set_mean_value(val_data_layer, mean_pixel)
elif self.use_mean == 'image':
self.set_mean_file(train_data_layer, self.dataset.path(self.dataset.train_db_task().mean_file))
if val_data_layer is not None and has_val_set:
self.set_mean_file(val_data_layer, self.dataset.path(self.dataset.train_db_task().mean_file))
if self.batch_size:
if dataset_backend == 'lmdb':
train_data_layer.data_param.batch_size = self.batch_size
if val_data_layer is not None and has_val_set:
val_data_layer.data_param.batch_size = self.batch_size
elif dataset_backend == 'hdf5':
train_data_layer.hdf5_data_param.batch_size = self.batch_size
if val_data_layer is not None and has_val_set:
val_data_layer.hdf5_data_param.batch_size = self.batch_size
else:
if dataset_backend == 'lmdb':
if not train_data_layer.data_param.HasField('batch_size'):
train_data_layer.data_param.batch_size = constants.DEFAULT_BATCH_SIZE
if val_data_layer is not None and has_val_set and not val_data_layer.data_param.HasField('batch_size'):
val_data_layer.data_param.batch_size = constants.DEFAULT_BATCH_SIZE
elif dataset_backend == 'hdf5':
if not train_data_layer.hdf5_data_param.HasField('batch_size'):
train_data_layer.hdf5_data_param.batch_size = constants.DEFAULT_BATCH_SIZE
if val_data_layer is not None and has_val_set and not val_data_layer.hdf5_data_param.HasField('batch_size'):
val_data_layer.hdf5_data_param.batch_size = constants.DEFAULT_BATCH_SIZE
# hidden layers
train_val_network.MergeFrom(hidden_layers)
# output layers
train_val_network.layer.extend(loss_layers)
train_val_network.layer.extend(accuracy_layers)
with open(self.path(self.train_val_file), 'w') as outfile:
text_format.PrintMessage(train_val_network, outfile)
### Write deploy file
deploy_network = caffe_pb2.NetParameter()
# input
deploy_network.input.append('data')
shape = deploy_network.input_shape.add()
shape.dim.append(1)
shape.dim.append(self.dataset.image_dims[2])
if self.crop_size:
shape.dim.append(self.crop_size)
shape.dim.append(self.crop_size)
else:
shape.dim.append(self.dataset.image_dims[0])
shape.dim.append(self.dataset.image_dims[1])
# hidden layers
deploy_network.MergeFrom(hidden_layers)
# output layers
if loss_layers[-1].type == 'SoftmaxWithLoss':
prob_layer = deploy_network.layer.add(
type = 'Softmax',
name = 'prob')
prob_layer.bottom.append(network_outputs[-1])
prob_layer.top.append('prob')
with open(self.path(self.deploy_file), 'w') as outfile:
text_format.PrintMessage(deploy_network, outfile)
### Write solver file
solver = caffe_pb2.SolverParameter()
# get enum value for solver type
solver.solver_type = getattr(solver, self.solver_type)
solver.net = self.train_val_file
# Set CPU/GPU mode
if config_value('caffe_root')['cuda_enabled'] and \
bool(config_value('gpu_list')):
solver.solver_mode = caffe_pb2.SolverParameter.GPU
else:
solver.solver_mode = caffe_pb2.SolverParameter.CPU
solver.snapshot_prefix = self.snapshot_prefix
# Epochs -> Iterations
train_iter = int(math.ceil(float(self.dataset.train_db_task().entries_count) / train_data_layer.data_param.batch_size))
solver.max_iter = train_iter * self.train_epochs
snapshot_interval = self.snapshot_interval * train_iter
if 0 < snapshot_interval <= 1:
solver.snapshot = 1 # don't round down
elif 1 < snapshot_interval < solver.max_iter:
solver.snapshot = int(snapshot_interval)
else:
solver.snapshot = 0 # only take one snapshot at the end
if has_val_set and self.val_interval:
solver.test_iter.append(int(math.ceil(float(self.dataset.val_db_task().entries_count) / val_data_layer.data_param.batch_size)))
val_interval = self.val_interval * train_iter
if 0 < val_interval <= 1:
solver.test_interval = 1 # don't round down
elif 1 < val_interval < solver.max_iter:
solver.test_interval = int(val_interval)
else:
solver.test_interval = solver.max_iter # only test once at the end
# Learning rate
solver.base_lr = self.learning_rate
solver.lr_policy = self.lr_policy['policy']
scale = float(solver.max_iter)/100.0
if solver.lr_policy == 'fixed':
pass
elif solver.lr_policy == 'step':
# stepsize = stepsize * scale
solver.stepsize = int(math.ceil(float(self.lr_policy['stepsize']) * scale))
solver.gamma = self.lr_policy['gamma']
elif solver.lr_policy == 'multistep':
for value in self.lr_policy['stepvalue']:
# stepvalue = stepvalue * scale
solver.stepvalue.append(int(math.ceil(float(value) * scale)))
solver.gamma = self.lr_policy['gamma']
elif solver.lr_policy == 'exp':
# gamma = gamma^(1/scale)
solver.gamma = math.pow(self.lr_policy['gamma'], 1.0/scale)
elif solver.lr_policy == 'inv':
# gamma = gamma / scale
solver.gamma = self.lr_policy['gamma'] / scale
solver.power = self.lr_policy['power']
elif solver.lr_policy == 'poly':
solver.power = self.lr_policy['power']
elif solver.lr_policy == 'sigmoid':
# gamma = -gamma / scale
solver.gamma = -1.0 * self.lr_policy['gamma'] / scale
# stepsize = stepsize * scale
solver.stepsize = int(math.ceil(float(self.lr_policy['stepsize']) * scale))
else:
raise Exception('Unknown lr_policy: "%s"' % solver.lr_policy)
# go with the suggested defaults
if solver.solver_type != solver.ADAGRAD:
solver.momentum = 0.9
solver.weight_decay = 0.0005
# Display 8x per epoch, or once per 5000 images, whichever is more frequent
solver.display = max(1, min(
int(math.floor(float(solver.max_iter) / (self.train_epochs * 8))),
int(math.ceil(5000.0 / train_data_layer.data_param.batch_size))
))
if self.random_seed is not None:
solver.random_seed = self.random_seed
with open(self.path(self.solver_file), 'w') as outfile:
text_format.PrintMessage(solver, outfile)
self.solver = solver # save for later
return True
def save_files_generic(self):
"""
Save solver, train_val and deploy files to disk
"""
train_image_db = None
train_labels_db = None
val_image_db = None
val_labels_db = None
for task in self.dataset.tasks:
if task.purpose == 'Training Images':
train_image_db = task
if task.purpose == 'Training Labels':
train_labels_db = task
if task.purpose == 'Validation Images':
val_image_db = task
if task.purpose == 'Validation Labels':
val_labels_db = task
assert train_image_db is not None, 'Training images are required'
### Split up train_val and deploy layers
train_image_data_layer = None
train_label_data_layer = None
val_image_data_layer = None
val_label_data_layer = None
train_val_layers = caffe_pb2.NetParameter()
deploy_layers = caffe_pb2.NetParameter()
for layer in self.network.layer:
assert layer.type not in ['MemoryData', 'HDF5Data', 'ImageData'], 'unsupported data layer type'
if layer.name.startswith('train_'):
train_val_layers.layer.add().CopyFrom(layer)
train_val_layers.layer[-1].name = train_val_layers.layer[-1].name[6:]
elif layer.name.startswith('deploy_'):
deploy_layers.layer.add().CopyFrom(layer)
deploy_layers.layer[-1].name = deploy_layers.layer[-1].name[7:]
elif layer.type == 'Data':
for rule in layer.include:
if rule.phase == caffe_pb2.TRAIN:
if 'data' in layer.top:
assert train_image_data_layer is None, 'cannot specify two train image data layers'
train_image_data_layer = layer
elif 'label' in layer.top:
assert train_label_data_layer is None, 'cannot specify two train label data layers'
train_label_data_layer = layer
elif rule.phase == caffe_pb2.TEST:
if 'data' in layer.top:
assert val_image_data_layer is None, 'cannot specify two val image data layers'
val_image_data_layer = layer
elif 'label' in layer.top:
assert val_label_data_layer is None, 'cannot specify two val label data layers'
val_label_data_layer = layer
elif 'loss' in layer.type.lower():
# Don't add it to the deploy network
train_val_layers.layer.add().CopyFrom(layer)
elif 'accuracy' in layer.type.lower():
# Don't add it to the deploy network
train_val_layers.layer.add().CopyFrom(layer)
else:
train_val_layers.layer.add().CopyFrom(layer)
deploy_layers.layer.add().CopyFrom(layer)
### Write train_val file
train_val_network = caffe_pb2.NetParameter()
# data layers
train_image_data_layer = self.make_generic_data_layer(train_image_db, train_image_data_layer, 'data', 'data', caffe_pb2.TRAIN)
if train_image_data_layer is not None:
train_val_network.layer.add().CopyFrom(train_image_data_layer)
train_label_data_layer = self.make_generic_data_layer(train_labels_db, train_label_data_layer, 'label', 'label', caffe_pb2.TRAIN)
if train_label_data_layer is not None:
train_val_network.layer.add().CopyFrom(train_label_data_layer)
val_image_data_layer = self.make_generic_data_layer(val_image_db, val_image_data_layer, 'data', 'data', caffe_pb2.TEST)
if val_image_data_layer is not None:
train_val_network.layer.add().CopyFrom(val_image_data_layer)
val_label_data_layer = self.make_generic_data_layer(val_labels_db, val_label_data_layer, 'label', 'label', caffe_pb2.TEST)
if val_label_data_layer is not None:
train_val_network.layer.add().CopyFrom(val_label_data_layer)
# hidden layers
train_val_network.MergeFrom(train_val_layers)
with open(self.path(self.train_val_file), 'w') as outfile:
text_format.PrintMessage(train_val_network, outfile)
### Write deploy file
deploy_network = caffe_pb2.NetParameter()
# input
deploy_network.input.append('data')
shape = deploy_network.input_shape.add()
shape.dim.append(1)
shape.dim.append(train_image_db.image_channels)
if train_image_data_layer.transform_param.HasField('crop_size'):
shape.dim.append(
train_image_data_layer.transform_param.crop_size)
shape.dim.append(
train_image_data_layer.transform_param.crop_size)
else:
shape.dim.append(train_image_db.image_height)
shape.dim.append(train_image_db.image_width)
# hidden layers
deploy_network.MergeFrom(deploy_layers)
with open(self.path(self.deploy_file), 'w') as outfile:
text_format.PrintMessage(deploy_network, outfile)
### Write solver file
solver = caffe_pb2.SolverParameter()
# get enum value for solver type
solver.solver_type = getattr(solver, self.solver_type)
solver.net = self.train_val_file
# Set CPU/GPU mode
if config_value('caffe_root')['cuda_enabled'] and \
bool(config_value('gpu_list')):
solver.solver_mode = caffe_pb2.SolverParameter.GPU
else:
solver.solver_mode = caffe_pb2.SolverParameter.CPU
solver.snapshot_prefix = self.snapshot_prefix
# Epochs -> Iterations
train_iter = int(math.ceil(float(train_image_db.image_count) / train_image_data_layer.data_param.batch_size))
solver.max_iter = train_iter * self.train_epochs
snapshot_interval = self.snapshot_interval * train_iter
if 0 < snapshot_interval <= 1:
solver.snapshot = 1 # don't round down
elif 1 < snapshot_interval < solver.max_iter:
solver.snapshot = int(snapshot_interval)
else:
solver.snapshot = 0 # only take one snapshot at the end
if val_image_data_layer:
solver.test_iter.append(int(math.ceil(float(val_image_db.image_count) / val_image_data_layer.data_param.batch_size)))
val_interval = self.val_interval * train_iter
if 0 < val_interval <= 1:
solver.test_interval = 1 # don't round down
elif 1 < val_interval < solver.max_iter:
solver.test_interval = int(val_interval)
else:
solver.test_interval = solver.max_iter # only test once at the end
# Learning rate
solver.base_lr = self.learning_rate
solver.lr_policy = self.lr_policy['policy']
scale = float(solver.max_iter)/100.0
if solver.lr_policy == 'fixed':
pass
elif solver.lr_policy == 'step':
# stepsize = stepsize * scale
solver.stepsize = int(math.ceil(float(self.lr_policy['stepsize']) * scale))
solver.gamma = self.lr_policy['gamma']
elif solver.lr_policy == 'multistep':
for value in self.lr_policy['stepvalue']:
# stepvalue = stepvalue * scale
solver.stepvalue.append(int(math.ceil(float(value) * scale)))
solver.gamma = self.lr_policy['gamma']
elif solver.lr_policy == 'exp':
# gamma = gamma^(1/scale)
solver.gamma = math.pow(self.lr_policy['gamma'], 1.0/scale)
elif solver.lr_policy == 'inv':
# gamma = gamma / scale
solver.gamma = self.lr_policy['gamma'] / scale
solver.power = self.lr_policy['power']
elif solver.lr_policy == 'poly':
solver.power = self.lr_policy['power']
elif solver.lr_policy == 'sigmoid':
# gamma = -gamma / scale
solver.gamma = -1.0 * self.lr_policy['gamma'] / scale
# stepsize = stepsize * scale
solver.stepsize = int(math.ceil(float(self.lr_policy['stepsize']) * scale))
else:
raise Exception('Unknown lr_policy: "%s"' % solver.lr_policy)
# go with the suggested defaults
if solver.solver_type != solver.ADAGRAD:
solver.momentum = 0.9
solver.weight_decay = 0.0005
# Display 8x per epoch, or once per 5000 images, whichever is more frequent
solver.display = max(1, min(
int(math.floor(float(solver.max_iter) / (self.train_epochs * 8))),
int(math.ceil(5000.0 / train_image_data_layer.data_param.batch_size))
))
if self.random_seed is not None:
solver.random_seed = self.random_seed
with open(self.path(self.solver_file), 'w') as outfile:
text_format.PrintMessage(solver, outfile)
self.solver = solver # save for later
return True
def make_generic_data_layer(self, db, orig_layer, name, top, phase):
"""
Utility within save_files_generic for creating a Data layer
Returns a LayerParameter (or None)
Arguments:
db -- an AnalyzeDbTask (or None)
orig_layer -- a LayerParameter supplied by the user (or None)
"""
if db is None:
#TODO allow user to specify a standard data layer even if it doesn't exist in the dataset
return None
layer = caffe_pb2.LayerParameter()
if orig_layer is not None:
layer.CopyFrom(orig_layer)
layer.type = 'Data'
layer.name = name
if top not in layer.top:
layer.ClearField('top')
layer.top.append(top)
layer.ClearField('include')
layer.include.add(phase=phase)
# source
if layer.data_param.HasField('source'):
self.logger.warning('Ignoring data_param.source ...')
layer.data_param.source = db.path(db.database)
if layer.data_param.HasField('backend'):
self.logger.warning('Ignoring data_param.backend ...')
layer.data_param.backend = caffe_pb2.DataParameter.LMDB
# batch size
if not layer.data_param.HasField('batch_size'):
layer.data_param.batch_size = constants.DEFAULT_BATCH_SIZE
if self.batch_size:
layer.data_param.batch_size = self.batch_size
# mean
if name == 'data' and self.dataset.mean_file:
if self.use_mean == 'pixel':
mean_pixel = self.get_mean_pixel(self.dataset.path(self.dataset.mean_file))
## remove any values that may already be in the network
self.set_mean_value(layer, mean_pixel)
elif self.use_mean == 'image':
self.set_mean_file(layer, self.dataset.path(self.dataset.mean_file))
# crop size
if name == 'data' and self.crop_size:
max_crop_size = min(db.image_width, db.image_height)
assert self.crop_size <= max_crop_size, 'crop_size is larger than the image size'
layer.transform_param.crop_size = self.crop_size
return layer
def iteration_to_epoch(self, it):
return float(it * self.train_epochs) / self.solver.max_iter
@override
def task_arguments(self, resources, env):
args = [config_value('caffe_root')['executable'],
'train',
'--solver=%s' % self.path(self.solver_file),
]
if 'gpus' in resources:
identifiers = []
for identifier, value in resources['gpus']:
identifiers.append(identifier)
if len(identifiers) == 1:
args.append('--gpu=%s' % identifiers[0])
elif len(identifiers) > 1:
if config_value('caffe_root')['version'] < utils.parse_version('0.14.0-alpha'):
# Prior to version 0.14, NVcaffe used the --gpus switch
args.append('--gpus=%s' % ','.join(identifiers))
else:
args.append('--gpu=%s' % ','.join(identifiers))
if self.pretrained_model:
args.append('--weights=%s' % self.path(self.pretrained_model))
return args
@override
def process_output(self, line):
float_exp = '(NaN|[-+]?[0-9]*\.?[0-9]+(e[-+]?[0-9]+)?)'
self.caffe_log.write('%s\n' % line)
self.caffe_log.flush()
# parse caffe output
timestamp, level, message = self.preprocess_output_caffe(line)
if not message:
return True
# iteration updates
match = re.match(r'Iteration (\d+)', message)
if match:
i = int(match.group(1))
self.new_iteration(i)
# net output
match = re.match(r'(Train|Test) net output #(\d+): (\S*) = %s' % float_exp, message, flags=re.IGNORECASE)
if match:
phase = match.group(1)
index = int(match.group(2))
name = match.group(3)
value = match.group(4)
assert value.lower() != 'nan', 'Network outputted NaN for "%s" (%s phase). Try decreasing your learning rate.' % (name, phase)
value = float(value)
# Find the layer type
kind = '?'
for layer in self.network.layer:
if name in layer.top:
kind = layer.type
break
if phase.lower() == 'train':
self.save_train_output(name, kind, value)
elif phase.lower() == 'test':
self.save_val_output(name, kind, value)
return True
# learning rate updates
match = re.match(r'Iteration (\d+).*lr = %s' % float_exp, message, flags=re.IGNORECASE)
if match:
i = int(match.group(1))
lr = float(match.group(2))
self.save_train_output('learning_rate', 'LearningRate', lr)
return True
# snapshot saved
if self.saving_snapshot:
if not message.startswith('Snapshotting solver state'):
self.logger.warning('caffe output format seems to have changed. Expected "Snapshotting solver state..." after "Snapshotting to..."')
else:
self.logger.debug('Snapshot saved.')
self.detect_snapshots()
self.send_snapshot_update()
self.saving_snapshot = False
return True
# snapshot starting
match = re.match(r'Snapshotting to (.*)\s*$', message)
if match:
self.saving_snapshot = True
return True
if level in ['error', 'critical']:
self.logger.error('%s: %s' % (self.name(), message))
self.exception = message
return True
return True
def preprocess_output_caffe(self, line):
"""
Takes line of output and parses it according to caffe's output format
Returns (timestamp, level, message) or (None, None, None)
"""
# NOTE: This must change when the logging format changes
# LMMDD HH:MM:SS.MICROS pid file:lineno] message
match = re.match(r'(\w)(\d{4} \S{8}).*]\s+(\S.*)$', line)
if match:
level = match.group(1)
# add the year because caffe omits it
timestr = '%s%s' % (time.strftime('%Y'), match.group(2))
message = match.group(3)
if level == 'I':
level = 'info'
elif level == 'W':
level = 'warning'
elif level == 'E':
level = 'error'
elif level == 'F': #FAIL
level = 'critical'
timestamp = time.mktime(time.strptime(timestr, '%Y%m%d %H:%M:%S'))
return (timestamp, level, message)
else:
#self.logger.warning('Unrecognized task output "%s"' % line)
return (None, None, None)
def new_iteration(self, it):
"""
Update current_iteration
"""
if self.current_iteration == it:
return
self.current_iteration = it
self.send_progress_update(self.iteration_to_epoch(it))
def send_snapshot_update(self):
"""
Sends socketio message about the snapshot list
"""
from digits.webapp import socketio
socketio.emit('task update',
{
'task': self.html_id(),
'update': 'snapshots',
'data': self.snapshot_list(),
},
namespace='/jobs',
room=self.job_id,
)
@override
def after_run(self):
super(CaffeTrainTask, self).after_run()
self.caffe_log.close()
@override
def after_runtime_error(self):
if os.path.exists(self.path(self.CAFFE_LOG)):
output = subprocess.check_output(['tail', '-n40', self.path(self.CAFFE_LOG)])
lines = []
for line in output.split('\n'):
# parse caffe header
timestamp, level, message = self.preprocess_output_caffe(line)
if message:
lines.append(message)
# return the last 20 lines
self.traceback = '\n'.join(lines[len(lines)-20:])
if 'DIGITS_MODE_TEST' in os.environ:
print output
### TrainTask overrides
@override
def detect_snapshots(self):
self.snapshots = []
snapshot_dir = os.path.join(self.job_dir, os.path.dirname(self.snapshot_prefix))
snapshots = []
solverstates = []
for filename in os.listdir(snapshot_dir):
# find models
match = re.match(r'%s_iter_(\d+)\.caffemodel' % os.path.basename(self.snapshot_prefix), filename)
if match:
iteration = int(match.group(1))
epoch = float(iteration) / (float(self.solver.max_iter)/self.train_epochs)
# assert epoch.is_integer(), '%s is not an integer' % epoch
epoch = round(epoch,3)
# if epoch is int
if epoch == math.ceil(epoch):
# print epoch,math.ceil(epoch),int(epoch)
epoch = int(epoch)
snapshots.append( (
os.path.join(snapshot_dir, filename),
epoch
)
)
# find solverstates
match = re.match(r'%s_iter_(\d+)\.solverstate' % os.path.basename(self.snapshot_prefix), filename)
if match:
solverstates.append( (
os.path.join(snapshot_dir, filename),
int(match.group(1))
)
)
# delete all but the most recent solverstate
for filename, iteration in sorted(solverstates, key=lambda tup: tup[1])[:-1]:
#print 'Removing "%s"' % filename
os.remove(filename)
self.snapshots = sorted(snapshots, key=lambda tup: tup[1])
return len(self.snapshots) > 0
@override
def est_next_snapshot(self):
if self.status != Status.RUN or self.current_iteration == 0:
return None
elapsed = time.time() - self.status_updates[-1][1]
next_snapshot_iteration = (1 + self.current_iteration//self.snapshot_interval) * self.snapshot_interval