#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. # #Licensed under the Apache License, Version 2.0 (the "License"); #you may not use this file except in compliance with the License. #You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #Unless required by applicable law or agreed to in writing, software #distributed under the License is distributed on an "AS IS" BASIS, #WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #See the License for the specific language governing permissions and #limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import math import paddle import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr import math import numpy as np __all__ = ["RegNetX_200MF", "RegNetX_4GF", "RegNetX_32GF", "RegNetY_200MF", "RegNetY_4GF", "RegNetY_32GF"] class RegNet(): def __init__(self, w_a, w_0, w_m, d, group_w, bot_mul, q=8, se_on=False): self.w_a = w_a self.w_0 = w_0 self.w_m = w_m self.d = d self.q = q self.group_w = group_w self.bot_mul = bot_mul # Stem type self.stem_type = "simple_stem_in" # Stem width self.stem_w = 32 # Block type self.block_type = "res_bottleneck_block" # Stride of each stage self.stride = 2 # Squeeze-and-Excitation (RegNetY) self.se_on = se_on self.se_r = 0.25 def quantize_float(self, f, q): """Converts a float to closest non-zero int divisible by q.""" return int(round(f / q) * q) def adjust_ws_gs_comp(self, ws, bms, gs): """Adjusts the compatibility of widths and groups.""" ws_bot = [int(w * b) for w, b in zip(ws, bms)] gs = [min(g, w_bot) for g, w_bot in zip(gs, ws_bot)] ws_bot = [self.quantize_float(w_bot, g) for w_bot, g in zip(ws_bot, gs)] ws = [int(w_bot / b) for w_bot, b in zip(ws_bot, bms)] return ws, gs def get_stages_from_blocks(self, ws, rs): """Gets ws/ds of network at each stage from per block values.""" ts = [ w != wp or r != rp for w, wp, r, rp in zip(ws + [0], [0] + ws, rs + [0], [0] + rs) ] s_ws = [w for w, t in zip(ws, ts[:-1]) if t] s_ds = np.diff([d for d, t in zip(range(len(ts)), ts) if t]).tolist() return s_ws, s_ds def generate_regnet(self, w_a, w_0, w_m, d, q=8): """Generates per block ws from RegNet parameters.""" assert w_a >= 0 and w_0 > 0 and w_m > 1 and w_0 % q == 0 ws_cont = np.arange(d) * w_a + w_0 ks = np.round(np.log(ws_cont / w_0) / np.log(w_m)) ws = w_0 * np.power(w_m, ks) ws = np.round(np.divide(ws, q)) * q num_stages, max_stage = len(np.unique(ws)), ks.max() + 1 ws, ws_cont = ws.astype(int).tolist(), ws_cont.tolist() return ws, num_stages, max_stage, ws_cont def init_weights(self, op_type, filter_size=0, num_channels=0, name=None): if op_type == 'conv': fan_out = num_channels * filter_size * filter_size param_attr = ParamAttr( name=name + "_weights", initializer=fluid.initializer.NormalInitializer( loc=0.0, scale=math.sqrt(2.0 / fan_out))) bias_attr = False elif op_type == 'bn': param_attr = ParamAttr( name=name + "_scale",initializer=fluid.initializer.Constant(0.0)) bias_attr = ParamAttr( name=name + "_offset", initializer=fluid.initializer.Constant(0.0)) elif op_type == 'final_bn': param_attr = ParamAttr( name=name + "_scale",initializer=fluid.initializer.Constant(1.0)) bias_attr = ParamAttr( name=name + "_offset", initializer=fluid.initializer.Constant(0.0)) return param_attr, bias_attr def net(self, input, class_dim=1000): # Generate RegNet ws per block b_ws, num_s, max_s, ws_cont = self.generate_regnet(self.w_a, self.w_0, self.w_m, self.d, self.q) #print('generate_regnet func done!') #print('b_ws={}, num_stages={}, max_stage={}, ws_cont={}'.format(b_ws, num_s, max_s, ws_cont)) # Convert to per stage format ws, ds = self.get_stages_from_blocks(b_ws, b_ws) #print('get_stages_from_blocks func done!') #print('ws={}, ds={}'.format(ws, ds)) # Generate group widths and bot muls gws = [self.group_w for _ in range(num_s)] bms = [self.bot_mul for _ in range(num_s)] #print('gws={}'.format(gws)) #print('bms={}'.format(bms)) # Adjust the compatibility of ws and gws #print('adjust_ws_gs_comp func done!') ws, gws = self.adjust_ws_gs_comp(ws, bms, gws) print('ws={}'.format(ws)) print('gws={}'.format(gws)) # Use the same stride for each stage ss = [self.stride for _ in range(num_s)] # Use SE for RegNetY se_r = self.se_r # Construct the model # Group params by stage stage_params = list(zip(ds, ws, ss, bms, gws)) # Construct the stem conv = self.conv_bn_layer( input=input, num_filters=self.stem_w, filter_size=3, stride=2, padding=1, act='relu', name="stem_conv") # Construct the stages for block, (d, w_out, stride, bm, gw) in enumerate(stage_params): for i in range(d): # Stride apply to the first block of the stage b_stride = stride if i == 0 else 1 conv_name = 's' + str(block + 1) + '_b' + str(i + 1) #chr(97 + i) conv = self.bottleneck_block( input=conv, num_filters=w_out, stride=b_stride, bm=bm, gw=gw, se_r=self.se_r, name=conv_name) pool = fluid.layers.pool2d( input=conv, pool_type='avg', global_pooling=True) out = fluid.layers.fc( input=pool, size=class_dim, param_attr=ParamAttr( name = "fc_0.w_0", initializer=fluid.initializer.NormalInitializer(loc=0.0, scale=0.01)), bias_attr=ParamAttr( name="fc_0.b_0", initializer=fluid.initializer.Constant(0.0))) return out def conv_bn_layer(self, input, num_filters, filter_size, stride=1, groups=1, padding=0, act=None, name=None, final_bn=False): conv = fluid.layers.conv2d( input=input, num_filters=num_filters, filter_size=filter_size, stride=stride, padding=padding, groups=groups, act=None, name=name + '.conv2d.output.1') bn_name = name + '_bn' return fluid.layers.batch_norm( input=conv, act=act, name=bn_name + '.output.1', moving_mean_name=bn_name + '_mean', moving_variance_name=bn_name + '_variance', ) #todo: to check the bn layer's eps and momentum, and relu_inplace def shortcut(self, input, ch_out, stride, name): ch_in = input.shape[1] if ch_in != ch_out or stride != 1: return self.conv_bn_layer(input=input, num_filters=ch_out, filter_size=1, stride=stride, padding=0, act=None, name=name) else: return input def bottleneck_block(self, input, num_filters, stride, bm, gw, se_r, name): # Compute the bottleneck width w_b = int(round(num_filters * bm)) # Compute the number of groups num_gs = w_b // gw conv0 = self.conv_bn_layer( input=input, num_filters=w_b, filter_size=1, stride=1, padding=0, act='relu', name=name+"_branch2a") conv1 = self.conv_bn_layer( input=conv0, num_filters=w_b, filter_size=3, stride=stride, padding=1, groups=num_gs, act='relu', name=name+"_branch2b") # Squeeze-and-Excitation (SE) if self.se_on: w_se = int(round(input.shape[1] * se_r)) conv1 = self.squeeze_excitation( input=conv1, num_channels=w_b, reduction_channels=w_se, name=name+"_branch2se") conv2 =self.conv_bn_layer( input=conv1, num_filters=num_filters, filter_size=1, stride=1, padding=0, act=None, name=name+"_branch2c", final_bn=True) short = self.shortcut(input, num_filters, stride, name=name + "_branch1") return fluid.layers.elementwise_add(x=short, y=conv2, act='relu') def squeeze_excitation(self, input, num_channels, reduction_channels, name=None): pool = fluid.layers.pool2d( input=input, pool_size=0, pool_type='avg', global_pooling=True) fan_out = num_channels squeeze = fluid.layers.conv2d(input=pool, num_filters=reduction_channels, filter_size=1, act='relu', param_attr=ParamAttr( initializer=fluid.initializer.NormalInitializer(loc=0.0, scale=math.sqrt(2.0 / fan_out)), name=name+'_sqz_weights'), bias_attr=ParamAttr(name=name+'_sqz_offset')) excitation = fluid.layers.conv2d(input=squeeze, num_filters=num_channels, filter_size=1, act='sigmoid', param_attr=ParamAttr( initializer=fluid.initializer.NormalInitializer(loc=0.0, scale=math.sqrt(2.0 / fan_out)), name=name+'_exc_weights'), bias_attr=ParamAttr(name=name+'_exc_offset')) scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0) return scale def RegNetX_200MF(): model = RegNet(w_a=36.44, w_0=24, w_m=2.49, d=13, group_w=8, bot_mul=1.0, q=8) return model def RegNetX_4GF(): model = RegNet(w_a=38.65, w_0=96, w_m=2.43, d=23, group_w=40, bot_mul=1.0, q=8) return model def RegNetX_32GF(): model = RegNet(w_a=69.86, w_0=320, w_m=2.0, d=23, group_w=168, bot_mul=1.0, q=8) return model def RegNetY_200MF(): model = RegNet(w_a=36.44, w_0=24, w_m=2.49, d=13, group_w=8, bot_mul=1.0, q=8, se_on=True) return model def RegNetY_4GF(): model = RegNet(w_a=31.41, w_0=96, w_m=2.24, d=22, group_w=64, bot_mul=1.0, q=8, se_on=True) return model def RegNetY_32GF(): model = RegNet(w_a=115.89, w_0=232, w_m=2.53, d=20, group_w=232, bot_mul=1.0, q=8, se_on=True) return model