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Update in inference.py -- omitting the optimizer's state #165

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Update in inference.py -- omitting the optimizer's state #165

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70ff305
Update inference.py
vhiwase Dec 27, 2023
50be367
adding visualization for args.mode == 'recognize'
Jan 3, 2024
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119 changes: 111 additions & 8 deletions src/inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -671,6 +671,87 @@ def visualize_cells(img, cells, out_path):

return

def visualize_recognized_tables(img, rec_tables, out_path):
plt.imshow(img, interpolation="lanczos")
plt.gcf().set_size_inches(20, 20)
ax = plt.gca()

for rec_table in rec_tables:
bbox = rec_table['bbox']

if rec_table['label'] == 'table':
facecolor = (1, 0, 0.45)
edgecolor = (1, 0, 0.45)
alpha = 0.3
linewidth = 2
hatch='//////'
elif rec_table['label'] == 'table column':
facecolor = (0.95, 0.6, 0.1)
edgecolor = (0.95, 0.6, 0.1)
alpha = 0.3
linewidth = 2
hatch='//////'
elif rec_table['label'] == 'table row':
facecolor = (0.3, 0.74, 0.8)
edgecolor = (0.3, 0.7, 0.6)
alpha = 0.3
linewidth = 2
hatch='//////'
elif rec_table['label'] == 'table column header':
facecolor = (0.7, 0.3, 0.5)
edgecolor = (0.7, 0.3, 0.5)
alpha = 0.3
linewidth = 2
hatch='//////'
elif rec_table['label'] == 'projected row header':
facecolor = (0.2, 0.2, 0.7)
edgecolor = (0.2, 0.2, 0.7)
alpha = 0.3
linewidth = 2
hatch='//////'
elif rec_table['label'] == 'table spanning cell':
facecolor = (0.5, 0.83, 0.4)
edgecolor = (0.5, 0.83, 0.4)
alpha = 0.3
linewidth = 2
hatch='//////'
else:
continue

rect = patches.Rectangle(bbox[:2], bbox[2]-bbox[0], bbox[3]-bbox[1], linewidth=linewidth,
edgecolor='none',facecolor=facecolor, alpha=0.1)
ax.add_patch(rect)
rect = patches.Rectangle(bbox[:2], bbox[2]-bbox[0], bbox[3]-bbox[1], linewidth=linewidth,
edgecolor=edgecolor,facecolor='none',linestyle='-', alpha=alpha)
ax.add_patch(rect)
rect = patches.Rectangle(bbox[:2], bbox[2]-bbox[0], bbox[3]-bbox[1], linewidth=0,
edgecolor=edgecolor,facecolor='none',linestyle='-', hatch=hatch, alpha=0.2)
ax.add_patch(rect)

plt.xticks([], [])
plt.yticks([], [])

legend_elements = [Patch(facecolor=(1, 0, 0.45), edgecolor=(1, 0, 0.45),
label='Table', hatch='//////', alpha=0.3),
Patch(facecolor=(0.95, 0.6, 0.1), edgecolor=(0.95, 0.6, 0.1),
label='Table Column', hatch='//////', alpha=0.3),
Patch(facecolor=(0.3, 0.74, 0.8), edgecolor=(0.3, 0.7, 0.6),
label='Table Row', hatch='//////', alpha=0.3),
Patch(facecolor=(0.7, 0.3, 0.5), edgecolor=(0.7, 0.3, 0.5),
label='Table Column Header', hatch='//////', alpha=0.3),
Patch(facecolor=(0.2, 0.2, 0.7), edgecolor=(0.2, 0.2, 0.7),
label='Projected Row Header', hatch='//////', alpha=0.3),
Patch(facecolor=(0.5, 0.83, 0.4), edgecolor=(0.5, 0.83, 0.4),
label='Table Spanning Cell', hatch='//////', alpha=0.3)]
plt.legend(handles=legend_elements, bbox_to_anchor=(0.5, -0.02), loc='upper center', borderaxespad=0,
fontsize=10, ncol=2)
plt.gcf().set_size_inches(10, 10)
plt.axis('off')
plt.savefig(out_path, bbox_inches='tight', dpi=150)
plt.close()

return

class TableExtractionPipeline(object):
def __init__(self, det_device=None, str_device=None,
det_model=None, str_model=None,
Expand All @@ -697,8 +778,16 @@ def __init__(self, det_device=None, str_device=None,
print("Detection model initialized.")

if not det_model_path is None:
self.det_model.load_state_dict(torch.load(det_model_path,
map_location=torch.device(det_device)))
loaded_state_dict = torch.load(det_model_path,
map_location=torch.device(det_device))
model_state_dict = self.det_model.state_dict()
pretrained_dict = {
k: v
for k, v in loaded_state_dict.items()
if k in model_state_dict and model_state_dict[k].shape == v.shape
}
model_state_dict.update(pretrained_dict)
self.det_model.load_state_dict(model_state_dict, strict=True)
self.det_model.to(det_device)
self.det_model.eval()
print("Detection model weights loaded.")
Expand All @@ -714,8 +803,16 @@ def __init__(self, det_device=None, str_device=None,
print("Structure model initialized.")

if not str_model_path is None:
self.str_model.load_state_dict(torch.load(str_model_path,
map_location=torch.device(str_device)))
loaded_state_dict = torch.load(str_model_path,
map_location=torch.device(str_device))
model_state_dict = self.str_model.state_dict()
pretrained_dict = {
k: v
for k, v in loaded_state_dict.items()
if k in model_state_dict and model_state_dict[k].shape == v.shape
}
model_state_dict.update(pretrained_dict)
self.str_model.load_state_dict(model_state_dict, strict=True)
self.str_model.to(str_device)
self.str_model.eval()
print("Structure model weights loaded.")
Expand Down Expand Up @@ -830,7 +927,11 @@ def output_result(key, val, args, img, img_file):
if args.visualize:
out_file = img_file.replace(".jpg", "_fig_tables.jpg")
out_path = os.path.join(args.out_dir, out_file)
visualize_detected_tables(img, val, out_path)
if args.mode == 'detect':
visualize_detected_tables(img, val, out_path)
else:
visualize_recognized_tables(img, val, out_path)

elif not key == 'image' and not key == 'tokens':
for idx, elem in enumerate(val):
if key == 'crops':
Expand All @@ -852,6 +953,8 @@ def output_result(key, val, args, img, img_file):
out_path = os.path.join(args.out_dir, out_file)
visualize_cells(img, elem, out_path)
else:
if elem is None:
continue
out_file = img_file.replace(".jpg", "_{}.{}".format(idx, key))
with open(os.path.join(args.out_dir, out_file), 'w') as f:
f.write(elem)
Expand Down Expand Up @@ -910,7 +1013,7 @@ def main():
tokens = []

if args.mode == 'recognize':
extracted_table = pipe.recognize(img, tokens, out_objects=args.objects, out_cells=args.csv,
extracted_table = pipe.recognize(img, tokens, out_objects=args.objects, out_cells=args.cells,
out_html=args.html, out_csv=args.csv)
print("Table(s) recognized.")

Expand All @@ -925,7 +1028,7 @@ def main():
output_result(key, val, args, img, img_file)

if args.mode == 'extract':
extracted_tables = pipe.extract(img, tokens, out_objects=args.objects, out_cells=args.csv,
extracted_tables = pipe.extract(img, tokens, out_objects=args.objects, out_cells=args.cells,
out_html=args.html, out_csv=args.csv,
crop_padding=args.crop_padding)
print("Table(s) extracted.")
Expand All @@ -936,4 +1039,4 @@ def main():
img_file.replace('.jpg', '_{}.jpg'.format(table_idx)))

if __name__ == "__main__":
main()
main()