def DISP_ALPHA(storedir, graph, step, normalized_dataset=True,
sample_num=0, invert_alpha=True, words=None, gamma=1, cmap='gist_gray', image=None, show_image=True):
dtc.makeLogger(3, set_global=True)
# Note: Good cmap values are: gist_gray, gist_yarg, gist_heat
# Good values of gamma_correction are 1 and 2.2/2.3
vs = VisualizeStep(VisualizeDir(storedir, normalized_dataset=normalized_dataset), graph, step)
df_strs = vs.strs('y', 'predicted_ids', mingle=False, trim=True, wrap_strs=True, keys=['image_name'])
if image:
if not image.endswith('_basic.png'):
image = image.replace('.png', '_basic.png')
df_strs = df_strs[df_strs.image_name.isin([image])]
assert sample_num == 0
else:
df_strs = df_strs.iloc[sample_num:sample_num+1]
vs.alpha(sample_num, invert_alpha=invert_alpha, words=words, gamma_correction=gamma,
cmap=cmap, index=df_strs.index, show_image=show_image)
# df_ = pd.DataFrame(data={
# '$\mathbf{\hat{y}}$': [df_strs.predicted_ids.iloc[0], df_strs.predicted_ids.iloc[0].strip('$')],
# '$\mathbf{\hat{y}}$_len': [df_strs.predicted_ids_len.iloc[0]]*2,
# '$\mathbf{y}$': [df_strs.y.iloc[0], df_strs.y.iloc[0].strip('$')] ,
# '$\mathbf{y}$_len': [df_strs.y_len.iloc[0]]*2
# })
df_ = pd.DataFrame(data={
'length': [df_strs.y_len.iloc[0], df_strs.predicted_ids_len.iloc[0]]*2 + [''],
'value': [df_strs.y.iloc[0], df_strs.predicted_ids.iloc[0]] +
[verbatim(df_strs.y.iloc[0]), verbatim(df_strs.predicted_ids.iloc[0])] +
[df_strs.ed.iloc[0]],
},
index=['$\mathbf{y}$', '$\mathbf{\hat{y}}$', '$\mathbf{y}$_seq', '$\mathbf{\hat{y}}$_seq', 'edit distance'])
display(df_[['value', 'length']])
def main():
logger = dtc.makeLogger(set_global=True)
parser = argparse.ArgumentParser(description='train model')
parser.add_argument(
"--num-steps",
"-n",
dest="num_steps",
type=int,
help=
"Number of training steps to run. Defaults to -1 if unspecified, i.e. run to completion",
default=-1)
parser.add_argument(
"--num-epochs",
"-e",
dest="num_epochs",
type=int,
help="Number of training epochs to run. Defaults to 10 if unspecified.",
default=10)
parser.add_argument(
"--batch-size",
"-b",
dest="batch_size",
type=int,
help=
"Batchsize per gpu. If unspecified, defaults to the default value in hyper_params",
default=None)
parser.add_argument(
"--seq2seq-beam-width",
"-w",
dest="seq2seq_beam_width",
type=int,
help="seq2seq Beamwidth. If unspecified, defaults to 10",
default=10)
parser.add_argument("--ctc-beam-width",
dest="ctc_beam_width",
type=int,
help="CTC Beamwidth. If unspecified, defaults to 10",
default=10)
parser.add_argument(
"--print-steps",
"-s",
dest="print_steps",
type=int,
help=
"Number of training steps after which to log results. Defaults to 100 if unspecified",
default=100)
parser.add_argument("--keep-prob",
"-k",
dest="keep_prob",
type=float,
help="Dropout 'keep' probability. Defaults to 0.5",
default=1.0)
parser.add_argument(
"--adam_alpha",
"-a",
dest="alpha",
type=float,
help="Alpha (step / learning-rate) value of adam optimizer.",
default=0.0001)
parser.add_argument(
"--r-lambda",
"-r",
dest="rLambda",
type=float,
help=
"Sets value of rLambda - lambda value used for regularization. Defaults to 0.00005.",
default=0.00005)
parser.add_argument(
"--data-folder",
"-d",
dest="data_folder",
type=str,
help="Data folder. If unspecified, defaults to raw_data_folder/..",
default=None)
parser.add_argument("--raw-data-folder",
dest="raw_data_folder",
type=str,
help="Raw data folder. Must be specified.",
default=None)
parser.add_argument(
"--vgg16-folder",
dest="vgg16_folder",
type=str,
help=
"vgg16 data folder. If unspecified, defaults to data_folder/vgg16_features",
default=None)
parser.add_argument(
"--image-folder",
dest="image_folder",
type=str,
help=
"image folder. If unspecified, defaults to data_folder/formula_images",
default=None)
parser.add_argument(
"--partial-batch",
"-p",
dest="partial_batch",
action='store_true',
help=
"Sets assert_whole_batch hyper param to False. Default value for this option is False "
" (i.e. assert_whole_batch=True)",
default=False)
parser.add_argument(
"--queue-capacity",
"-q",
dest="queue_capacity",
type=int,
help=
"Capacity of input queue. Defaults to hyperparam defaults if unspecified.",
default=None)
parser.add_argument(
"--logging-level",
"-l",
dest="logging_level",
type=int,
choices=range(1, 6),
help=
"Logging verbosity level from 1 to 5 in increasing order of verbosity.",
default=4)
parser.add_argument(
"--valid-frac",
"-f",
dest="valid_frac",
type=float,
help="Fraction of samples to use for validation. Defaults to 0.05",
default=0.05)
parser.add_argument(
"--validation-epochs",
"-v",
dest="valid_epochs",
type=float,
help=
"""Number (or fraction) of epochs after which to run a full validation cycle. For this
behaviour, the number should be greater than 0. A value less <= 0 on the other hand,
implies 'smart' validation - which will result in selectively
capturing snapshots around max_scoring peaks (based on training/bleu2).""",
default=1.0)
parser.add_argument(
"--save-all-eval",
dest="save_all_eval",
action='store_true',
help=
"(Boolean): False => Save only one random validation/testing batch, True = > Save all validation/testing batches",
default=False)
parser.add_argument(
"--build-image-context",
"-i",
dest="build_image_context",
type=int,
help=
"Sets value of hyper.build_image_context. Default is 2 => build my own convnet.",
default=2)
parser.add_argument(
"--swap-memory",
dest="swap_memory",
action='store_true',
help="swap_memory option of tf.scan and tf.while_loop. Default to False."
" Enabling allows training larger mini-batches at the cost of speed.",
default=False)
parser.add_argument(
"--restore",
dest="restore_logdir",
type=str,
help=
"restore from checkpoint. Provide logdir path as argument. Don't specify the --logdir argument.",
default=None)
parser.add_argument(
"--logdir",
dest="logdir",
type=str,
help=
"(optional) Sets TensorboardParams.tb_logdir. Can't specify the --restore argument along with this.",
default=None)
parser.add_argument(
"--logdir-tag",
dest="logdir_tag",
type=str,
help=
"(optional) Sets TensorboardParams.logdir_tab. Can't specify the --restore argument along with this.",
default=None)
# parser.add_argument("--use-ctc-loss", dest="use_ctc_loss", action='store_true',
# help="Sets the use_ctc_loss hyper parameter. Defaults to False.",
# default=False)
parser.add_argument(
"--validate",
dest="doValidate",
action='store_true',
help=
"Run validation cycle only. --restore option should be provided along with this.",
default=False)
parser.add_argument(
"--test",
dest="doTest",
action='store_true',
help=
"Run test cycle only but with training dataset. --restore option should be provided along with this.",
default=False)
parser.add_argument(
"--squash-input-seq",
dest="squash_input_seq",
action='store_true',
help=
"(boolean) Set value of squash_input_seq hyper param. Defaults to True.",
default=True)
parser.add_argument(
"--num-snapshots",
dest="num_snapshots",
type=int,
help=
"Number of latest snapshots to save. Defaults to 100 if unspecified",
default=100)
args = parser.parse_args()
raw_data_folder = args.raw_data_folder
if args.data_folder:
data_folder = args.data_folder
else:
data_folder = os.path.join(raw_data_folder, '..')
if args.image_folder:
image_folder = args.image_folder
else:
image_folder = os.path.join(data_folder, 'formula_images')
if args.vgg16_folder:
vgg16_folder = args.vgg16_folder
else:
vgg16_folder = os.path.join(data_folder, 'vgg16_features')
if args.restore_logdir is not None:
assert args.logdir is None, 'Only one of --restore-logdir and --logdir can be specified.'
assert args.logdir_tag is None, "--logdir-tag can't be specified alongside --logdir"
tb = tfc.TensorboardParams({
'tb_logdir':
os.path.dirname(args.restore_logdir)
}).freeze()
elif args.logdir is not None:
tb = tfc.TensorboardParams({
'tb_logdir': args.logdir,
'logdir_tag': args.logdir_tag
}).freeze()
else:
tb = tfc.TensorboardParams({
'tb_logdir': './tb_metrics',
'logdir_tag': args.logdir_tag
}).freeze()
if args.doValidate:
assert args.restore_logdir is not None, 'Please specify --restore option along with --validate'
assert not args.doTest, '--test and --validate cannot be given together'
if args.doTest:
assert args.restore_logdir is not None, 'Please specify --restore option along with --test'
assert not args.doValidate, '--test and --validate cannot be given together'
globalParams = dlc.Properties({
'raw_data_dir':
raw_data_folder,
'assert_whole_batch':
not args.partial_batch,
'logger':
logger,
'tb':
tb,
'print_steps':
args.print_steps,
'num_steps':
args.num_steps,
'num_epochs':
args.num_epochs,
'num_snapshots':
args.num_snapshots,
'data_dir':
data_folder,
'generated_data_dir':
data_folder,
'image_dir':
image_folder,
'ctc_beam_width':
args.ctc_beam_width,
'seq2seq_beam_width':
args.seq2seq_beam_width,
'valid_frac':
args.valid_frac,
'valid_epochs':
args.valid_epochs,
'save_all_eval':
args.save_all_eval,
'build_image_context':
args.build_image_context,
'sum_logloss':
False, # setting to true equalizes ctc_loss and log_loss if y_s == squashed_seq
'dropout':
None if args.keep_prob >= 1.0 else tfc.DropoutParams({
'keep_prob':
args.keep_prob
}).freeze(),
'MeanSumAlphaEquals1':
False,
'rLambda':
args.rLambda, # 0.0005, 0.00005
'make_training_accuracy_graph':
False,
# 'use_ctc_loss': args.use_ctc_loss,
"swap_memory":
args.swap_memory,
'tf_session_allow_growth':
False,
'restore_from_checkpoint':
args.restore_logdir is not None,
'num_gpus':
2,
'towers_per_gpu':
1,
'beamsearch_length_penalty':
1.0,
'doValidate':
args.doValidate,
'doTest':
args.doTest,
'doTrain':
not (args.doValidate or args.doTest),
'squash_input_seq':
args.squash_input_seq,
'att_model':
'MLP_full', # '1x1_conv', 'MLP_shared', 'MLP_full'
'weights_regularizer':
tf.contrib.layers.l2_regularizer(scale=1.0, scope='L2_Regularizer'),
# 'embeddings_regularizer': None,
# 'outputMLP_skip_connections': False,
'output_reuse_embeddings':
False,
'REGROUP_IMAGE': (4, 1), # None # (4,1)
'build_att_modulator':
False, # turn off beta-MLP
'build_scanning_RNN':
False,
'init_model_input_transform':
'full',
'build_init_model':
True,
'adam_beta1':
0.5,
'adam_beta2':
0.9,
'pLambda':
0.0
})
if args.batch_size is not None:
globalParams.B = args.batch_size
if args.queue_capacity is not None:
globalParams.input_queue_capacity = args.queue_capacity
if args.alpha is not None:
globalParams.adam_alpha = args.alpha
if args.restore_logdir is not None:
globalParams.logdir = args.restore_logdir
else:
globalParams.logdir = dtc.makeTBDir(tb.tb_logdir, tb.logdir_tag)
# args
globalParams.storedir = dtc.makeLogDir(globalParams.logdir, 'store')
globalParams.dump(dtc.makeLogfileName(globalParams.storedir, 'args.pkl'))
# Hyper Params
hyper = hyper_params.make_hyper(globalParams, freeze=False)
if args.restore_logdir is not None:
hyper.dump(dtc.makeLogfileName(globalParams.storedir, 'hyper.pkl'))
else:
hyper.dump(globalParams.storedir, 'hyper.pkl')
# Logger
fh = logging.FileHandler(
dtc.makeLogfileName(globalParams.storedir, 'training.log'))
fh.setFormatter(dtc.makeFormatter())
logger.addHandler(fh)
dtc.setLogLevel(logger, args.logging_level)
logger.info(' '.join(sys.argv))
logger.info(
'\n#################### Default Param Overrides: ####################\n%s',
globalParams.pformat())
logger.info(
'##################################################################\n')
logger.info(
'\n######################### Hyper-params: #########################\n%s',
hyper.pformat())
logger.info(
'##################################################################\n')
train_multi_gpu.main(raw_data_folder, vgg16_folder, globalParams,
hyper.freeze())
def main():
_data_folder = '../data/dataset3'
parser = arg.ArgumentParser(description='train model')
parser.add_argument(
"--num-steps",
"-n",
dest="num_steps",
type=int,
help=
"Number of training steps to run. Defaults to -1 if unspecified, i.e. run to completion",
default=-1)
parser.add_argument(
"--num-epochs",
"-e",
dest="num_epochs",
type=int,
help="Number of training steps to run. Defaults to 1 if unspecified.",
default=1)
parser.add_argument(
"--batch-size",
"-b",
dest="batch_size",
type=int,
help=
"Batchsize. If unspecified, defaults to the default value in hyper_params",
default=None)
parser.add_argument(
"--print-steps",
"-s",
dest="print_steps",
type=int,
help=
"Number of training steps after which to log results. Defaults to 10 if unspecified",
default=100)
parser.add_argument("--data-folder",
"-d",
dest="data_folder",
type=str,
help="Data folder. If unspecified, defaults to " +
_data_folder,
default=_data_folder)
parser.add_argument(
"--raw-data-folder",
dest="raw_data_folder",
type=str,
help=
"Raw data folder. If unspecified, defaults to data_folder/training",
default=None)
parser.add_argument(
"--vgg16-folder",
dest="vgg16_folder",
type=str,
help=
"vgg16 data folder. If unspecified, defaults to data_folder/vgg16_features",
default=None)
parser.add_argument(
"--image-folder",
dest="image_folder",
type=str,
help=
"image folder. If unspecified, defaults to data_folder/formula_images",
default=None)
parser.add_argument(
"--partial-batch",
"-p",
dest="partial_batch",
action='store_true',
help=
"Sets assert_whole_batch hyper param to False. Default hyper_param value will be used if unspecified"
)
parser.add_argument(
"--logging-level",
"-l",
dest="logging_level",
type=int,
help=
"Logging verbosity level from 1 to 5 in increasing order of verbosity.",
default=4)
args = parser.parse_args()
data_folder = args.data_folder
params = dlc.Properties({
'num_steps':
args.num_steps,
'print_steps':
args.print_steps,
'num_epochs':
args.num_epochs,
'logger':
dtc.makeLogger(args.logging_level, set_global=True),
'build_image_context':
1,
'weights_regularizer':
None,
'num_gpus':
1,
'tb':
tfc.TensorboardParams({
'tb_logdir': 'tb_metrics_convnet'
}).freeze()
})
if args.image_folder:
params.image_folder = args.image_folder
else:
params.image_folder = os.path.join(data_folder, 'formula_images')
if args.raw_data_folder:
params.raw_data_folder = args.raw_data_folder
else:
params.raw_data_folder = os.path.join(data_folder, 'training')
params.raw_data_dir = params.raw_data_folder
if args.vgg16_folder:
params.vgg16_folder = args.vgg16_folder
else:
params.vgg16_folder = os.path.join(data_folder, 'vgg16_features')
if args.batch_size is not None:
params.B = args.batch_size
if args.partial_batch:
params.assert_whole_batch = False
data_props = dtc.load(params.raw_data_folder, 'data_props.pkl')
params.image_shape = (data_props['padded_image_dim']['height'],
data_props['padded_image_dim']['width'], 3)
run_convnet(params)