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Commit 31dd805e authored by Raul Marichal's avatar Raul Marichal
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Hago merge con master incorporando el gitignore y el cambio en la funcion para... 

Hago merge con master incorporando el gitignore y el cambio en la funcion para armar la network def simplificada
parents 696d73dc f1a5506c
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.gitignore
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......@@ -2,3 +2,4 @@ data/*
models/*
**/__pycache__/
outs/
**.out
functions.py
+ 54
− 52
View file @ 31dd805e
......@@ -524,6 +524,58 @@ def build_latency_lookup_table(network_def_full, lookup_table_path, min_conv_fea
pickle.dump(lookup_table, file_id)
return
def compose_simplified_network_def(simplified_network_def, network_def, current_num_out_channels, target_layer_indices, max_num_out_channels):
for target_layer_index in target_layer_indices:
update_num_out_channels = True
current_num_out_channels_after_pixel_shuffle = current_num_out_channels
for layer_idx, (layer_name, layer_properties) in enumerate(simplified_network_def.items()):
if layer_idx < target_layer_index:
continue
# for the block to be simplified (# of output channels is simplified)
if update_num_out_channels:
if not layer_properties[KEY_IS_DEPTHWISE]:
layer_properties[KEY_NUM_OUT_CHANNELS] = current_num_out_channels
update_num_out_channels = False
print(' simplify_def> layer {}: num of output channel changed to {}'.format(layer_name, str(current_num_out_channels)))
else:
raise ValueError('Expected a non-depthwise layer but got a depthwise layer.')
# for blocks following the target blocks (# of input channels is simplified)
else:
if current_num_out_channels_after_pixel_shuffle % layer_properties[
KEY_BEFORE_SQUARED_PIXEL_SHUFFLE_FACTOR] != 0:
raise ValueError('current_num_out_channels or current_num_out_channels_after_pixel_shuffle is '
'not divisible by the scaling factor of pixel shuffling.')
current_num_out_channels_after_pixel_shuffle = (
current_num_out_channels_after_pixel_shuffle / layer_properties[
KEY_BEFORE_SQUARED_PIXEL_SHUFFLE_FACTOR])
layer_properties[KEY_NUM_IN_CHANNELS] = current_num_out_channels_after_pixel_shuffle
print(' simplify_def> layer {}: num of input channel changed to {}'.format(layer_name, str(current_num_out_channels_after_pixel_shuffle)))
'''
Consider the case that a FC layer is placed after a Conv and Flatten:
FC: input feature size: Cin
output feature size: Cout
Conv: output feature map size: H x W x C
So Cin = H x W x C.
If C -> C' based on constraints, then Cin -> H x W x C'
'''
if layer_properties[KEY_BEFORE_SQUARED_PIXEL_SHUFFLE_FACTOR] == 1:
if network_def[layer_name][KEY_NUM_IN_CHANNELS] > max_num_out_channels:
assert network_def[layer_name][KEY_NUM_IN_CHANNELS] % max_num_out_channels == 0
# H x W here
spatial_factor = network_def[layer_name][KEY_NUM_IN_CHANNELS] // max_num_out_channels
layer_properties[KEY_NUM_IN_CHANNELS] = spatial_factor*current_num_out_channels
print(' simplify_def> [Update] layer {}: num of input channel changed to {}'.format(layer_name, str(spatial_factor*current_num_out_channels)))
if not layer_properties[KEY_IS_DEPTHWISE]:
break
else:
layer_properties[KEY_NUM_OUT_CHANNELS] = current_num_out_channels_after_pixel_shuffle
layer_properties[KEY_GROUPS] = current_num_out_channels_after_pixel_shuffle
print(' simplify_def> depthwise layer {}: num of output channel changed to {}'.format(layer_name, str(current_num_out_channels_after_pixel_shuffle)))
return simplified_network_def
def simplify_network_def_based_on_constraint(network_def, block, constraint, resource_type,
lookup_table_path=None, skip_connection_block_sets=[],
......@@ -613,58 +665,8 @@ def simplify_network_def_based_on_constraint(network_def, block, constraint, res
Update # of input channels of one non-depthwise layer following the target layers.
'''
for current_num_out_channels in num_out_channels_try: # Only allow multiple of '_MIN_FEATURE_SIZE'.
for target_layer_index in target_layer_indices:
update_num_out_channels = True
current_num_out_channels_after_pixel_shuffle = current_num_out_channels
for layer_idx, (layer_name, layer_properties) in enumerate(simplified_network_def.items()):
if layer_idx < target_layer_index:
continue
# for the block to be simplified (# of output channels is simplified)
if update_num_out_channels:
if not layer_properties[KEY_IS_DEPTHWISE]:
layer_properties[KEY_NUM_OUT_CHANNELS] = current_num_out_channels
update_num_out_channels = False
print(' simplify_def> layer {}: num of output channel changed to {}'.format(layer_name, str(current_num_out_channels)))
else:
raise ValueError('Expected a non-depthwise layer but got a depthwise layer.')
# for blocks following the target blocks (# of input channels is simplified)
else:
if current_num_out_channels_after_pixel_shuffle % layer_properties[
KEY_BEFORE_SQUARED_PIXEL_SHUFFLE_FACTOR] != 0:
raise ValueError('current_num_out_channels or current_num_out_channels_after_pixel_shuffle is '
'not divisible by the scaling factor of pixel shuffling.')
current_num_out_channels_after_pixel_shuffle = (
current_num_out_channels_after_pixel_shuffle / layer_properties[
KEY_BEFORE_SQUARED_PIXEL_SHUFFLE_FACTOR])
layer_properties[KEY_NUM_IN_CHANNELS] = current_num_out_channels_after_pixel_shuffle
print(' simplify_def> layer {}: num of input channel changed to {}'.format(layer_name, str(current_num_out_channels_after_pixel_shuffle)))
'''
Consider the case that a FC layer is placed after a Conv and Flatten:
FC: input feature size: Cin
output feature size: Cout
Conv: output feature map size: H x W x C
So Cin = H x W x C.
If C -> C' based on constraints, then Cin -> H x W x C'
'''
if layer_properties[KEY_BEFORE_SQUARED_PIXEL_SHUFFLE_FACTOR] == 1:
if network_def[layer_name][KEY_NUM_IN_CHANNELS] > max_num_out_channels:
assert network_def[layer_name][KEY_NUM_IN_CHANNELS] % max_num_out_channels == 0
# H x W here
spatial_factor = network_def[layer_name][KEY_NUM_IN_CHANNELS] // max_num_out_channels
layer_properties[KEY_NUM_IN_CHANNELS] = spatial_factor*current_num_out_channels
print(' simplify_def> [Update] layer {}: num of input channel changed to {}'.format(layer_name, str(spatial_factor*current_num_out_channels)))
if not layer_properties[KEY_IS_DEPTHWISE]:
break
else:
layer_properties[KEY_NUM_OUT_CHANNELS] = current_num_out_channels_after_pixel_shuffle
layer_properties[KEY_GROUPS] = current_num_out_channels_after_pixel_shuffle
print(' simplify_def> depthwise layer {}: num of output channel changed to {}'.format(layer_name, str(current_num_out_channels_after_pixel_shuffle)))
# Get the simplified network def compatible with current_num_out_channels
simplified_network_def = compose_simplified_network_def(simplified_network_def, network_def, current_num_out_channels, target_layer_indices, max_num_out_channels)
# Get the current resource consumption
simplified_resource = compute_resource(simplified_network_def, resource_type,
lookup_table_path)
......
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