add regnet architecture

ppcls/modeling/architectures/regnet.py

+# 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)
+        # Convert to per stage format
+        ws, ds = self.get_stages_from_blocks(b_ws, b_ws)
+        # Generate group widths and bot muls
+        gws = [self.group_w for _ in range(num_s)]
+        bms = [self.bot_mul for _ in range(num_s)]
+        # Adjust the compatibility of ws and gws
+        ws, gws = self.adjust_ws_gs_comp(ws, bms, 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):
+        param_attr, bias_attr = self.init_weights(
+            op_type='conv',
+            filter_size=filter_size,
+            num_channels=num_filters,
+            name=name)
+        conv = fluid.layers.conv2d(
+            input=input,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=padding,
+            groups=groups,
+            act=None,
+            param_attr=param_attr,
+            bias_attr=bias_attr,
+            name=name + '.conv2d.output.1')
+        bn_name = name + '_bn'
+        if final_bn:
+            param_attr, bias_attr = self.init_weights(
+                op_type='final_bn', name=bn_name)
+        else:
+            param_attr, bias_attr = self.init_weights(
+                op_type='bn', name=bn_name)
+        return fluid.layers.batch_norm(
+            input=conv,
+            act=act,
+            name=bn_name + '.output.1',
+            param_attr=param_attr,
+            bias_attr=bias_attr,
+            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