def inference_roi_ts_cnn(images, keep_prob, layer=2, feat=[2, 4]):
_print_tensor_size(images, 'inference_roi_ts_cnn')
assert isinstance(keep_prob, object)
if not layer == len(feat):
print(
'Make sure you have defined the feature map size for each layer.')
return
# local st
conv_tensor = rsvp_quick_inference.inference_roi_global_ts_filter(
images, 'conv0', out_feat=feat[0])
pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(conv_tensor,
kwidth=1)
for l in range(1, layer):
conv_tensor = rsvp_quick_inference.inference_roi_s_filter\
(pool_tensor, 'conv'+str(l), in_feat=feat[l-1], out_feat=feat[l])
pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(
conv_tensor, kwidth=1)
logits = rsvp_quick_inference.inference_fully_connected_1layer(
pool_tensor, keep_prob)
assert isinstance(logits, object)
return logits
def inference_roi_ts_cnn_1layer(images, keep_prob, feat=[2]):
_print_tensor_size(images, 'inference_roi_ts_cnn')
assert isinstance(keep_prob, object)
# local st
conv_tensor = rsvp_quick_inference.inference_roi_global_ts_filter(images, 'conv0', out_feat=feat[0])
pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(conv_tensor, kwidth=1)
logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob)
assert isinstance(logits, object)
return logits
def inference_roi_ts_cnn_1layer(images, keep_prob, feat=[2]):
_print_tensor_size(images, 'inference_roi_ts_cnn')
assert isinstance(keep_prob, object)
# local st
conv_tensor = rsvp_quick_inference.inference_roi_global_ts_filter(
images, 'conv0', out_feat=feat[0])
logits = rsvp_quick_inference.inference_fully_connected_1layer(
conv_tensor, keep_prob)
assert isinstance(logits, object)
return logits
def inference_roi_ts_cnn(images, keep_prob, layer=2, feat=[2, 4]):
_print_tensor_size(images, 'inference_roi_ts_cnn')
assert isinstance(keep_prob, object)
if not layer == len(feat):
print('Make sure you have defined the feature map size for each layer.')
return
# local st
conv_tensor = rsvp_quick_inference.inference_roi_global_ts_filter(images, 'conv0', out_feat=feat[0])
pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(conv_tensor, kwidth=1)
for l in range(1, layer):
conv_tensor = rsvp_quick_inference.inference_roi_s_filter\
(pool_tensor, 'conv'+str(l), in_feat=feat[l-1], out_feat=feat[l])
pool_tensor = rsvp_quick_inference.inference_pooling_s_filter(conv_tensor, kwidth=1)
logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob)
assert isinstance(logits, object)
return logits
