def inference_cvcnn(images, keep_prob, layer=2, feat=[2, 4]):
_print_tensor_size(images, 'inference_cvcnn')
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
image_shape = images.get_shape().as_list()
conv_tensor = rsvp_quick_inference.inference_5x5_filter(
images, 'conv0', in_feat=image_shape[3], out_feat=feat[0])
pool_tensor = rsvp_quick_inference.inference_pooling_n_filter(conv_tensor)
for l in range(1, layer):
conv_tensor = rsvp_quick_inference.inference_5x5_filter\
(pool_tensor, 'conv'+str(l), in_feat=feat[l-1], out_feat=feat[l])
pool_tensor = rsvp_quick_inference.inference_pooling_n_filter(
conv_tensor)
logits = rsvp_quick_inference.inference_fully_connected_1layer(
pool_tensor, keep_prob)
assert isinstance(logits, object)
return logits
def inference_cvcnn_1layer(images, keep_prob, feat=[2]):
_print_tensor_size(images, 'inference_cvcnn')
assert isinstance(keep_prob, object)
# local st
conv_tensor = rsvp_quick_inference.inference_5x5_filter(images, 'conv0', out_feat=feat[0])
pool_tensor = rsvp_quick_inference.inference_pooling_n_filter(conv_tensor)
logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob)
assert isinstance(logits, object)
return logits
def inference_cvcnn_1layer(images, keep_prob, feat=[2]):
_print_tensor_size(images, 'inference_cvcnn')
assert isinstance(keep_prob, object)
# local st
conv_tensor = rsvp_quick_inference.inference_5x5_filter(images,
'conv0',
out_feat=feat[0])
pool_tensor = rsvp_quick_inference.inference_pooling_n_filter(conv_tensor)
logits = rsvp_quick_inference.inference_fully_connected_1layer(
pool_tensor, keep_prob)
assert isinstance(logits, object)
return logits
def test_roicnn(images, keep_prob, layer=2, feat=[2, 4]):
for l in range(0, layer):
if l == 0:
conv_tensor = rsvp_quick_inference.inference_5x5_filter(images, 'conv0', out_feat=feat[0])
else:
conv_tensor = rsvp_quick_inference.inference_5x5_filter \
(pool_tensor, 'conv' + str(l), in_feat=feat[l - 1], out_feat=feat[l])
pool_tensor = rsvp_quick_inference.inference_pooling_n_filter(conv_tensor, 'pool' + str(l), kheight=2, kwidth=2)
logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob)
assert isinstance(logits, object)
return logits
def test_cvcnn(images, keep_prob, layer=2, feat=[2, 4]):
for l in range(0, layer):
if l == 0:
conv_tensor = rsvp_quick_inference.inference_5x5_filter(images, 'conv0', in_feat=feat[l - 1], out_feat=feat[0])
else:
conv_tensor = rsvp_quick_inference.inference_5x5_filter \
(pool_tensor, 'conv' + str(l), in_feat=feat[l - 1], out_feat=feat[l])
pool_tensor = rsvp_quick_inference.inference_pooling_n_filter(conv_tensor, 'pool' + str(l), kheight=poolh, kwidth=poolw) # was 1 x 4
logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob)
assert isinstance(logits, object)
return logits
def inference_cvcnn(images, keep_prob, layer=2, feat=[2, 4]):
_print_tensor_size(images, 'inference_cvcnn')
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_5x5_filter(images, 'conv0', keep_prob, in_feat=1, out_feat=feat[0])
pool_tensor, switches_tmp = rsvp_quick_deconv.deconv_pooling_n_filter(conv_tensor, 'pool0', kheight=poolh, kwidth=poolw)
for l in range(1, layer):
conv_tensor = rsvp_quick_inference.inference_5x5_filter\
(pool_tensor, 'conv'+str(l), keep_prob, in_feat=feat[l-1], out_feat=feat[l])
pool_tensor, switches_tmp = rsvp_quick_deconv.deconv_pooling_n_filter(conv_tensor, 'pool'+str(l), kheight=poolh, kwidth=poolw)
logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob)
assert isinstance(logits, object)
return logits
def inference_cvcnn(images, keep_prob, layer=2, feat=[2, 4]):
_print_tensor_size(images, 'inference_cvcnn')
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_5x5_filter(images, 'conv0', keep_prob, in_feat=1, out_feat=feat[0])
pool_tensor, switches_tmp = rsvp_quick_deconv.deconv_pooling_n_filter(conv_tensor, 'pool0', kheight=poolh, kwidth=poolw)
for l in range(1, layer):
conv_tensor = rsvp_quick_inference.inference_5x5_filter\
(pool_tensor, 'conv'+str(l), keep_prob, in_feat=feat[l-1], out_feat=feat[l])
pool_tensor, switches_tmp = rsvp_quick_deconv.deconv_pooling_n_filter(conv_tensor, 'pool'+str(l), kheight=poolh, kwidth=poolw)
logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob)
assert isinstance(logits, object)
return logits
def inference_cvcnn(images, keep_prob, layer=2, feat=[2, 4]):
_print_tensor_size(images, 'inference_cvcnn')
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
image_shape = images.get_shape().as_list()
conv_tensor = rsvp_quick_inference.inference_5x5_filter(images, 'conv0', in_feat=image_shape[3], out_feat=feat[0])
pool_tensor = rsvp_quick_inference.inference_pooling_n_filter(conv_tensor)
for l in range(1, layer):
conv_tensor = rsvp_quick_inference.inference_5x5_filter\
(pool_tensor, 'conv'+str(l), in_feat=feat[l-1], out_feat=feat[l])
pool_tensor = rsvp_quick_inference.inference_pooling_n_filter(conv_tensor)
logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob)
assert isinstance(logits, object)
return logits
