def getHtmlPageFromCsvFile(filePath, css, title):
""" Prepare a html page including a table """
myHtml = html(
head(
link(' ', rel='stylesheet', type='text/css', href=css),
title=filePath
)
)
myHtml = myHtml.append(
body(
h1(title),
getHtmlTableFromCsvFile(filePath),
)
)
return myHtml
def to_html(results):
PREAMBLE_FILE = os.getenv('LSM_PREAMBLE_FILE', "")
preamble = ""
methods = [
'capabilities', 'systems', 'plugin_info', 'pools', 'job_status',
'job_free', 'iscsi_chap_auth', 'volumes', 'volume_create',
'volume_delete', 'volume_resize', 'volume_replicate',
'volume_replicate_range_block_size', 'volume_replicate_range',
'volume_enable', 'volume_disable', 'disks', 'target_ports',
'volume_mask', 'volume_unmask', 'volume_child_dependency',
'volume_child_dependency_rm', 'access_groups',
'access_groups_granted_to_volume', 'access_group_create',
'access_group_delete', 'volumes_accessible_by_access_group',
'access_groups_granted_to_volume', 'access_group_initiator_add',
'access_group_initiator_delete', 'fs', 'fs_create', 'fs_delete',
'fs_resize', 'fs_clone', 'fs_file_clone', 'fs_snapshots',
'fs_snapshot_create', 'fs_snapshot_delete', 'fs_snapshot_restore',
'fs_child_dependency', 'fs_child_dependency_rm', 'export_auth',
'exports', 'export_fs', 'export_remove'
]
ch = []
row_data = []
if os.path.isfile(PREAMBLE_FILE):
with open(PREAMBLE_FILE, 'r') as pm:
preamble = pm.read()
#Build column header
for r in results:
ch.append(r['SYSTEM']['ID'])
# Add overall pass/fail for unit tests
pass_fail = ['Overall Pass/Fail result']
for r in results:
if r['META']['ec'] == '0':
pass_fail.append('P')
else:
pass_fail.append('F')
row_data.append(pass_fail)
# Append on link for error log
error_log = ['Error log (click +)']
for r in results:
error_log.append('<a href="%s">+</a>' %
('./' + os.path.basename(r['META']['error_file'])))
row_data.append(error_log)
for m in methods:
row = [m]
for r in results:
row.append(get_result(r, m))
row_data.append(row)
# Build HTML
text = '<!DOCTYPE html>'
text += str(
html(
head(
link(rel="stylesheet", type="text/css", href="../../test.css"),
title("libStorageMgmt test results"),
),
body(
HTML(
h1("%s Results generated @ %s") %
(preamble, time.strftime("%c"))),
div(table(_table_header(ch), _table_body(row_data)),
_class="angled_table"),
div(
pre(" Legend\n"
" P = Pass (Method called and returned without error)\n"
" F = Fail (Method call returned an error)\n"
" U = Unsupported or unable to test due to other errors\n"
" * = Unable to connect to array or provider totally unsupported\n"
" + = hyper link to error log")))))
return bs(text).prettify()
def to_html(results):
PREAMBLE_FILE = os.getenv('LSM_PREAMBLE_FILE', "")
preamble = ""
methods = ['capabilities',
'systems', 'plugin_info', 'pools', 'job_status', 'job_free',
'iscsi_chap_auth',
'volumes', 'volume_create', 'volume_delete', 'volume_resize',
'volume_replicate', 'volume_replicate_range_block_size',
'volume_replicate_range', 'volume_enable', 'volume_disable',
'disks', 'target_ports',
'volume_mask',
'volume_unmask',
'volume_child_dependency',
'volume_child_dependency_rm',
'access_groups',
'access_groups_granted_to_volume',
'access_group_create',
'access_group_delete',
'volumes_accessible_by_access_group',
'access_groups_granted_to_volume',
'access_group_initiator_add',
'access_group_initiator_delete',
'fs',
'fs_create',
'fs_delete',
'fs_resize',
'fs_clone',
'fs_file_clone',
'fs_snapshots',
'fs_snapshot_create',
'fs_snapshot_delete',
'fs_snapshot_restore',
'fs_child_dependency',
'fs_child_dependency_rm',
'export_auth',
'exports',
'export_fs',
'export_remove'
]
ch = []
row_data = []
if os.path.isfile(PREAMBLE_FILE):
with open(PREAMBLE_FILE, 'r') as pm:
preamble = pm.read()
#Build column header
for r in results:
ch.append(r['SYSTEM']['ID'])
# Add overall pass/fail for unit tests
pass_fail = ['Overall Pass/Fail result']
for r in results:
if r['META']['ec'] == '0':
pass_fail.append('P')
else:
pass_fail.append('F')
row_data.append(pass_fail)
# Append on link for error log
error_log = ['Error log (click +)']
for r in results:
error_log.append('<a href="%s">+</a>' %
('./' + os.path.basename(r['META']['error_file'])))
row_data.append(error_log)
for m in methods:
row = [m]
for r in results:
row.append(get_result(r, m))
row_data.append(row)
# Build HTML
text = '<!DOCTYPE html>'
text += str(html(
head(link(rel="stylesheet", type="text/css",
href="../../test.css"),
title("libStorageMgmt test results"), ),
body(
HTML(h1("%s Results generated @ %s") % (preamble, time.strftime("%c"))),
div(table(_table_header(ch), _table_body(row_data)),
_class="angled_table"),
div(pre(
" Legend\n"
" P = Pass (Method called and returned without error)\n"
" F = Fail (Method call returned an error)\n"
" U = Unsupported or unable to test due to other errors\n"
" * = Unable to connect to array or provider totally unsupported\n"
" + = hyper link to error log\n\n\n",
HTML(' Source code for plug-in for this test run <a href=./smis.py.html>is here. </a>'))))
))
return bs(text).prettify()
def vis():
def to_str(x):
return '{:.2f}'.format(x.item())
assert FLAGS.dataset == 'mimic-cxr'
phase = Phase.test
dataset = test_dataset
data_loader = test_loader
if FLAGS.ckpt_path:
logger.info(f'Loading model from {FLAGS.ckpt_path}')
load_state_dict(model, torch.load(FLAGS.ckpt_path))
log = Log()
converter = SentIndex2Report(index_to_word=Dataset.index_to_word)
chexpert = CheXpert()
working_dir = os.path.join(FLAGS.working_dir, 'vis', datetime.datetime.now().strftime('%Y-%m-%d-%H%M%S-%f'))
image_dir = os.path.join(working_dir, 'imgs')
os.makedirs(image_dir)
reports = []
prog = tqdm.tqdm(enumerate(data_loader), total=len(data_loader))
for (num_batch, batch) in prog:
for (key, value) in batch.items():
batch[key] = value.to(FLAGS.device)
losses = {}
metrics = {}
with torch.no_grad():
batch = model(batch, phase=Phase.test, beam_size=FLAGS.beam_size)
_log = {**losses, **metrics}
log.update(_log)
prog.set_description(', '.join(
[f'[{phase:5s}]'] +
[f'{key:s}: {log[key]:8.2e}' for key in sorted(losses.keys())]
))
item_index = to_numpy(batch['item_index'])
_text_length = batch['_text_length'] # (num_reports,)
(
_stop, # (num_reports, max_num_sentences, 1)
_temp, # (num_reports, max_num_sentences, 1)
_attention, # (num_repoers, max_num_sentences, beam_size, max_num_words, 65)
_score, # (num_reports, max_num_sentences, beam_size)
_sum_log_probability, # (num_reports, max_num_sentences, beam_size)
_text, # (num_reports, max_num_sentences, beam_size, max_num_words)
_sent_length, # (num_reports, max_num_sentences, beam_size)
) = [
pad_packed_sequence(batch[key], length=_text_length)
for key in ['_stop', '_temp', '_attention', '_score', '_sum_log_probability', '_text', '_sent_length']
]
(fig, ax) = plot.subplots(1, figsize=(6, 6), gridspec_kw={'left': 0, 'right': 1, 'bottom': 0, 'top': 1})
for num_report in range(len(_text_length)):
num = num_batch * FLAGS.batch_size + num_report
item = dataset.df.iloc[int(item_index[num_report])]
image_path = mimic_cxr.image_path(dicom_id=item.dicom_id)
sentences = []
for num_sentence in range(_text_length[num_report]):
beams = []
for num_beam in range(FLAGS.beam_size):
num_words = _sent_length[num_report, num_sentence, num_beam]
_sentence = _text[num_report, num_sentence, num_beam]
_words = Dataset.index_to_word[to_numpy(_sentence[:num_words])]
texts = []
for num_word in range(num_words):
_attention_path = os.path.join(image_dir, f'{num}-{num_sentence}-{num_beam}-{num_word}.png')
print(_attention_path)
# texts.append(span(_words[num_word]))
_a = _attention[num_report, num_sentence, num_beam, num_word, :FLAGS.image_size * FLAGS.image_size]
_a_sum = _a.sum()
_a = _a - _a.min()
_a = _a / _a.max()
_a = _a.reshape(FLAGS.image_size, FLAGS.image_size)
_a = F.interpolate(_a[None, None, :], scale_factor=(32, 32), mode='bilinear', align_corners=False)[0, 0]
'''
ax.contourf(to_numpy(_a), cmap='gray')
ax.set_axis_off()
fig.savefig(_attention_path, bbox_inches=0)
ax.cla()
'''
_a = 255 * to_numpy(_a)
PIL.Image.fromarray(_a.astype(np.uint8)).save(_attention_path)
texts.append(span(_words[num_word], **{
'data-toggle': 'tooltip',
'title': (
p('Total attention: ' + to_str(_a_sum)) +
img(src=f'http://monday.csail.mit.edu/xiuming{_attention_path}', width='128', height='128')
).replace('"', '\''),
}))
beams.append({
'score': to_str(_score[num_report, num_sentence, num_beam]),
'log_prob': to_str(_sum_log_probability[num_report, num_sentence, num_beam]),
'text': str('\n'.join(texts)),
})
sentence = {}
if mode & Mode.use_label_ce:
sentence.update({
'stop': to_str(_stop[num_report, num_sentence]),
'temp': to_str(_temp[num_report, num_sentence]),
'labels': [{
'label': label_col,
'prob': to_str(_l),
} for (label_col, _l) in zip(Dataset.label_columns, _label[num_report, num_sentence])],
})
if mode & Mode.gen_text:
sentence.update({
'beams': beams,
})
sentences.append(sentence)
reports.append({
'rad_id': str(dataset.df.rad_id.iloc[item_index[num_report]]),
'dicom_id': str(dataset.df.dicom_id.iloc[item_index[num_report]]),
'image': str(img(src=f'http://monday.csail.mit.edu/xiuming{image_path}', width='256')),
'generated text': sentences,
'ground truth text': converter(batch['text'][num_report], batch['sent_length'][num_report], batch['text_length'][num_report], is_eos=False)[0],
})
if num_batch == 0:
break
s = json2html.convert(json=reports, table_attributes='class="table table-striped"', escape=False)
s = (
head([
link(rel='stylesheet', href='https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css'),
script(src='https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js'),
script(src='https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js'),
script('$(function () { $(\'[data-toggle="tooltip"]\').tooltip({placement: "bottom", html: true}); })', type="text/javascript"),
]) +
body(div(div(div(HTML(s), _class='panel-body'), _class='panel panel-default'), _class='container-fluid'))
)
with open(os.path.join(working_dir, 'index.html'), 'w') as f:
f.write(s)
