correct config yaml, pp_lcnetv2_variant.py and log in&out channels in pp_lcnet_v2.py
HydrogenSulfate
parent
00ee28d78f
commit
877c8c53be
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@ -103,7 +103,7 @@ class TheseusLayer(nn.Layer):
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return new_layer
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net = paddleclas.MobileNetV1()
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res = net.replace_sub(layer_name_pattern=["blocks[11].depthwise_conv.conv", "blocks[12].depthwise_conv.conv"], handle_func=rep_func)
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res = net.upgrade_sublayer(layer_name_pattern=["blocks[11].depthwise_conv.conv", "blocks[12].depthwise_conv.conv"], handle_func=rep_func)
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print(res)
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# {'blocks[11].depthwise_conv.conv': the corresponding new_layer, 'blocks[12].depthwise_conv.conv': the corresponding new_layer}
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"""
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@ -122,13 +122,21 @@ class TheseusLayer(nn.Layer):
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sub_layer = layer_list[-1]["layer"]
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sub_layer_name = layer_list[-1]["name"]
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sub_layer_index = layer_list[-1]["index"]
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sub_layer_index_list = layer_list[-1]["index_list"]
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new_sub_layer = handle_func(sub_layer, pattern)
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if sub_layer_index:
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getattr(sub_layer_parent,
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sub_layer_name)[sub_layer_index] = new_sub_layer
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if sub_layer_index_list:
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if len(sub_layer_index_list) > 1:
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sub_layer_parent = getattr(
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sub_layer_parent,
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sub_layer_name)[sub_layer_index_list[0]]
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for sub_layer_index in sub_layer_index_list[1:-1]:
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sub_layer_parent = sub_layer_parent[sub_layer_index]
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sub_layer_parent[sub_layer_index_list[-1]] = new_sub_layer
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else:
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getattr(sub_layer_parent, sub_layer_name)[
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sub_layer_index_list[0]] = new_sub_layer
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else:
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setattr(sub_layer_parent, sub_layer_name, new_sub_layer)
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@ -151,15 +159,13 @@ class TheseusLayer(nn.Layer):
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parent_layer = self
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for layer_dict in layer_list:
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name, index = layer_dict["name"], layer_dict["index"]
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if not set_identity(parent_layer, name, index):
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name, index_list = layer_dict["name"], layer_dict["index_list"]
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if not set_identity(parent_layer, name, index_list):
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msg = f"Failed to set the layers that after stop_layer_name('{stop_layer_name}') to IdentityLayer. The error layer's name is '{name}'."
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logger.warning(msg)
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return False
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parent_layer = layer_dict["layer"]
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msg = f"Successfully set the layers that after stop_layer_name('{stop_layer_name}') to IdentityLayer."
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logger.info(msg)
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return True
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def update_res(
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@ -208,15 +214,14 @@ def save_sub_res_hook(layer, input, output):
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layer.res_dict[layer.res_name] = output
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def set_identity(parent_layer: nn.Layer,
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layer_name: str,
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layer_index: str=None) -> bool:
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"""set the layer specified by layer_name and layer_index to Indentity.
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def set_identity(parent_layer: nn.Layer, layer_name: str,
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index_list: str=None) -> bool:
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"""set the layer specified by layer_name and index_list to Indentity.
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Args:
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parent_layer (nn.Layer): The parent layer of target layer specified by layer_name and layer_index.
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parent_layer (nn.Layer): The parent layer of target layer specified by layer_name and index_list.
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layer_name (str): The name of target layer to be set to Indentity.
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layer_index (str, optional): The index of target layer to be set to Indentity in parent_layer. Defaults to None.
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index_list (str, optional): The index of target layer to be set to Indentity in parent_layer. Defaults to None.
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Returns:
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bool: True if successfully, False otherwise.
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@ -230,7 +235,7 @@ def set_identity(parent_layer: nn.Layer,
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if sub_layer_name == layer_name:
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stop_after = True
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if layer_index and stop_after:
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if index_list and stop_after:
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stop_after = False
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for sub_layer_index in parent_layer._sub_layers[
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layer_name]._sub_layers:
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@ -271,10 +276,12 @@ def parse_pattern_str(pattern: str, parent_layer: nn.Layer) -> Union[
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while len(pattern_list) > 0:
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if '[' in pattern_list[0]:
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target_layer_name = pattern_list[0].split('[')[0]
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target_layer_index = pattern_list[0].split('[')[1].split(']')[0]
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target_layer_index_list = list(
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index.split(']')[0]
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for index in pattern_list[0].split('[')[1:])
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else:
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target_layer_name = pattern_list[0]
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target_layer_index = None
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target_layer_index_list = None
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target_layer = getattr(parent_layer, target_layer_name, None)
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@ -283,19 +290,19 @@ def parse_pattern_str(pattern: str, parent_layer: nn.Layer) -> Union[
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logger.warning(msg)
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return None
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if target_layer_index and target_layer:
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if int(target_layer_index) < 0 or int(target_layer_index) >= len(
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target_layer):
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msg = f"Not found layer by index('{target_layer_index}') specifed in pattern('{pattern}'). The index should < {len(target_layer)} and > 0."
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logger.warning(msg)
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return None
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target_layer = target_layer[target_layer_index]
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if target_layer_index_list:
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for target_layer_index in target_layer_index_list:
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if int(target_layer_index) < 0 or int(
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target_layer_index) >= len(target_layer):
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msg = f"Not found layer by index('{target_layer_index}') specifed in pattern('{pattern}'). The index should < {len(target_layer)} and > 0."
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logger.warning(msg)
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return None
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target_layer = target_layer[target_layer_index]
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layer_list.append({
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"layer": target_layer,
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"name": target_layer_name,
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"index": target_layer_index
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"index_list": target_layer_index_list
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})
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pattern_list = pattern_list[1:]
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@ -126,6 +126,8 @@ class RepDepthwiseSeparable(TheseusLayer):
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use_se=False,
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use_shortcut=False):
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super().__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.is_repped = False
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self.dw_size = dw_size
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@ -1,43 +1,55 @@
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from paddle.nn import Conv2D, Identity
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from ..legendary_models.pp_lcnet_v2 import PPLCNetV2_base, RepDepthwiseSeparable, MODEL_URLS, _load_pretrained
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from ..legendary_models.pp_lcnet_v2 import MODEL_URLS, PPLCNetV2_base, RepDepthwiseSeparable, _load_pretrained
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__all__ = ["PPLCNetV2_base_ShiTu"]
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def PPLCNetV2_base_ShiTu(pretrained=False, use_ssld=False, **kwargs):
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"""
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An variant network of PPLCNetV2_base
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1. remove ReLU layer after last_conv
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2. add bias to last_conv
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3. change stride to 1 in last two RepDepthwiseSeparable Block
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"""
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model = PPLCNetV2_base(pretrained=False, use_ssld=use_ssld, **kwargs)
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def remove_ReLU_function(conv, pattern):
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new_conv = Identity()
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return new_conv
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# def last_stride_function(conv, pattern):
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# new_conv = Conv2D(
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# weight_attr=conv._param_attr,
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# in_channels=conv._in_channels,
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# out_channels=conv._out_channels,
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# kernel_size=conv._kernel_size,
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# stride=1,
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# padding=conv._padding,
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# groups=conv._groups,
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# bias_attr=conv._bias_attr)
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# return new_conv
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def add_bias_last_conv(conv, pattern):
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new_conv = Conv2D(
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in_channels=conv._in_channels,
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out_channels=conv._out_channels,
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kernel_size=conv._kernel_size,
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stride=conv._stride,
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padding=conv._padding,
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groups=conv._groups,
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bias_attr=True)
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return new_conv
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def last_stride_function(rep_block, pattern):
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new_conv = RepDepthwiseSeparable(
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in_channels=rep_block.in_channels,
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out_channels=rep_block.out_channels,
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stride=1,
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dw_size=rep_block.dw_size,
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split_pw=rep_block.split_pw,
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use_rep=rep_block.use_rep,
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use_se=rep_block.use_se,
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use_shortcut=rep_block.use_shortcut)
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return new_conv
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pattern_act = ["act"]
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# pattern_last_stride = [
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# "stages[3][0].dw_conv_list[0].conv",
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# "stages[3][0].dw_conv_list[1].conv",
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# "stages[3][0].dw_conv",
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# "stages[3][0].pw_conv.conv",
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# "stages[3][1].dw_conv_list[0].conv",
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# "stages[3][1].dw_conv_list[1].conv",
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# "stages[3][1].dw_conv_list[2].conv",
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# "stages[3][1].dw_conv",
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# "stages[3][1].pw_conv.conv",
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# ]
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# model.upgrade_sublayer(pattern_last_stride, last_stride_function) # TODO: theseuslayer有BUG，暂时注释掉
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pattern_lastconv = ["last_conv"]
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pattern_last_stride = [
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"stages[3][0]",
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"stages[3][1]",
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]
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model.upgrade_sublayer(pattern_act, remove_ReLU_function)
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model.upgrade_sublayer(pattern_lastconv, add_bias_last_conv)
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model.upgrade_sublayer(pattern_last_stride, last_stride_function)
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# load params again after upgrade some layers
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_load_pretrained(pretrained, model, MODEL_URLS["PPLCNetV2_base"], use_ssld)
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@ -18,11 +18,11 @@ Global:
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image_shape: [3, 224, 224]
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save_inference_dir: ./inference
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# AMP:
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# scale_loss: 65536
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# use_dynamic_loss_scaling: True
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# # O1: mixed fp16
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# level: O1
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AMP:
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scale_loss: 65536
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use_dynamic_loss_scaling: True
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# O1: mixed fp16
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level: O1
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# model architecture
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Arch:
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@ -96,7 +96,7 @@ DataLoader:
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dataset:
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name: ImageNetDataset
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image_root: ./dataset/
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cls_label_path: ./dataset/train_reg_all_data.txt
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cls_label_path: ./dataset/train_reg_all_data_v2.txt
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relabel: True
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transform_ops:
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- DecodeImage:
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@ -130,12 +130,12 @@ DataLoader:
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order: hwc
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sampler:
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name: PKSampler
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batch_size: 8
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batch_size: 256
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sample_per_id: 4
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drop_last: False
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shuffle: True
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sample_method: "id_avg_prob"
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id_list: [50030, 80700, 92019, 96015]
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id_list: [50030, 80700, 92019, 96015] # be careful when set relabel=True
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ratio: [4, 4]
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loader:
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num_workers: 4
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@ -1,90 +0,0 @@
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import numpy as np
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import paddle.vision.transforms as T
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import cv2
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class Pad(object):
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"""
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Pads the given PIL.Image on all sides with specified padding mode and fill value.
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adapted from: https://pytorch.org/vision/stable/_modules/torchvision/transforms/transforms.html#Pad
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"""
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def __init__(self,
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padding: int,
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fill: int=0,
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padding_mode: str="constant",
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backend: str="pil"):
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self.padding = padding
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self.fill = fill
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self.padding_mode = padding_mode
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self.backend = backend
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assert backend in [
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"pil", "cv2"
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], f"backend in Pad must in ['pil', 'cv2'], but got {backend}"
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def _parse_fill(self, fill, img, min_pil_version, name="fillcolor"):
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# Process fill color for affine transforms
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major_found, minor_found = (int(v)
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for v in PILLOW_VERSION.split('.')[:2])
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major_required, minor_required = (int(v) for v in
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min_pil_version.split('.')[:2])
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if major_found < major_required or (major_found == major_required and
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minor_found < minor_required):
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if fill is None:
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return {}
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else:
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msg = (
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"The option to fill background area of the transformed image, "
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"requires pillow>={}")
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raise RuntimeError(msg.format(min_pil_version))
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num_bands = len(img.getbands())
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if fill is None:
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fill = 0
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if isinstance(fill, (int, float)) and num_bands > 1:
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fill = tuple([fill] * num_bands)
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if isinstance(fill, (list, tuple)):
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if len(fill) != num_bands:
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msg = (
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"The number of elements in 'fill' does not match the number of "
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"bands of the image ({} != {})")
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raise ValueError(msg.format(len(fill), num_bands))
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fill = tuple(fill)
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return {name: fill}
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def __call__(self, img):
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if self.backend == "pil":
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opts = self._parse_fill(self.fill, img, "2.3.0", name="fill")
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if img.mode == "P":
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palette = img.getpalette()
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img = ImageOps.expand(img, border=self.padding, **opts)
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img.putpalette(palette)
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return img
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return ImageOps.expand(img, border=self.padding, **opts)
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else:
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img = cv2.copyMakeBorder(
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img,
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self.padding,
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self.padding,
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self.padding,
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self.padding,
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cv2.BORDER_CONSTANT,
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value=(self.fill, self.fill, self.fill))
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return img
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img = np.random.randint(0, 255, [3, 4, 3], dtype=np.uint8)
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for p in range(0, 10):
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for v in range(0, 10):
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img_1 = Pad(p, v, backend="cv2")(img)
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img_2 = T.Pad(p, (v, v, v))(img)
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print(f"{p} - {v}", np.allclose(img_1, img_2))
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if not np.allclose(img_1, img_2):
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print(img_1[..., 0], "\n", img_2[..., 0])
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print(img_1[..., 1], "\n", img_2[..., 1])
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print(img_1[..., 2], "\n", img_2[..., 2])
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exit(0)
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