Update regnet (#342)

* Create regnet.py

* Update __init__.py

ppcls/modeling/architectures/__init__.py

@@ -42,5 +42,6 @@ from .shufflenet_v2 import ShuffleNetV2_x0_25, ShuffleNetV2_x0_33, ShuffleNetV2_
 from .squeezenet import SqueezeNet1_0, SqueezeNet1_1
 from .vgg import VGG11, VGG13, VGG16, VGG19
 from .darknet import DarkNet53
+from .regnet import RegNetX_200MF, RegNetX_4GF, RegNetX_32GF, RegNetY_200MF, RegNetY_4GF, RegNetY_32GF
 
 from .distillation_models import ResNet50_vd_distill_MobileNetV3_large_x1_0

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 numpy as np
+import paddle
+from paddle import ParamAttr
+import paddle.nn as nn
+from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+from paddle.nn.initializer import Uniform
+import math
+
+__all__ = [
+    "RegNetX_200MF", "RegNetX_4GF", "RegNetX_32GF", "RegNetY_200MF",
+    "RegNetY_4GF", "RegNetY_32GF"
+]
+
+def quantize_float(f, q):
+    """Converts a float to closest non-zero int divisible by q."""
+    return int(round(f / q) * q)
+
+
+def adjust_ws_gs_comp(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 = [
+        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(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(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
+
+
+class ConvBNLayer(nn.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 groups=1,
+                 padding=0,
+                 act=None,
+                 name=None):
+        super(ConvBNLayer, self).__init__()
+
+        self._conv = Conv2d(
+            in_channels=num_channels,
+            out_channels=num_filters,
+            kernel_size=filter_size,
+            stride=stride,
+            padding=padding,
+            groups=groups,
+            weight_attr=ParamAttr(name=name + ".conv2d.output.1.w_0"),
+            bias_attr=ParamAttr(name=name + ".conv2d.output.1.b_0"))
+        bn_name = name + "_bn"
+        self._batch_norm = BatchNorm(
+            num_filters,
+            act=act,
+            param_attr=ParamAttr(name=bn_name + ".output.1.w_0"),
+            bias_attr=ParamAttr(bn_name + ".output.1.b_0"),
+            moving_mean_name=bn_name + "_mean",
+            moving_variance_name=bn_name + "_variance")
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+        return y
+    
+    
+class BottleneckBlock(nn.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 stride,
+                 bm, 
+                 gw, 
+                 se_on,
+                 se_r, 
+                 shortcut=True,
+                 name=None):
+        super(BottleneckBlock, self).__init__()
+
+        # Compute the bottleneck width
+        w_b = int(round(num_filters * bm))
+        # Compute the number of groups
+        num_gs = w_b // gw
+        self.se_on = se_on 
+        self.conv0 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=w_b,
+            filter_size=1,
+            padding=0,
+            act="relu",
+            name=name + "_branch2a")
+        self.conv1 = ConvBNLayer(
+            num_channels=w_b,
+            num_filters=w_b,
+            filter_size=3,
+            stride=stride,
+            padding=1,
+            groups=num_gs,
+            act="relu",
+            name=name + "_branch2b")
+        if se_on:
+            w_se = int(round(num_channels * se_r))
+            self.se_block = SELayer(
+                num_channels=w_b,
+                num_filters=w_b,
+                reduction_channels=w_se,
+                name=name + "_branch2se")
+        self.conv2 = ConvBNLayer(
+            num_channels=w_b,
+            num_filters=num_filters,
+            filter_size=1,
+            act=None,
+            name=name + "_branch2c")
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels,
+                num_filters=num_filters,
+                filter_size=1,
+                stride=stride,
+                name=name + "_branch1")
+
+        self.shortcut = shortcut
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+        if self.se_on:
+            conv1 = self.se_block(conv1)
+        conv2 = self.conv2(conv1)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+
+        y = paddle.elementwise_add(x=short, y=conv2, act="relu")
+        return y
+
+    
+class SELayer(nn.Layer):
+    def __init__(self, num_channels, num_filters, reduction_ratio, name=None):
+        super(SELayer, self).__init__()
+
+        self.pool2d_gap = AdaptiveAvgPool2d(1)
+
+        self._num_channels = num_channels
+
+        med_ch = int(num_channels / reduction_ratio)
+        stdv = 1.0 / math.sqrt(num_channels * 1.0)
+        self.squeeze = Linear(
+            num_channels,
+            med_ch,
+            weight_attr=ParamAttr(
+                initializer=Uniform(-stdv, stdv), name=name + "_sqz_weights"),
+            bias_attr=ParamAttr(name=name + "_sqz_offset"))
+
+        stdv = 1.0 / math.sqrt(med_ch * 1.0)
+        self.excitation = Linear(
+            med_ch,
+            num_filters,
+            weight_attr=ParamAttr(
+                initializer=Uniform(-stdv, stdv), name=name + "_exc_weights"),
+            bias_attr=ParamAttr(name=name + "_exc_offset"))
+
+    def forward(self, input):
+        pool = self.pool2d_gap(input)
+        pool = paddle.reshape(pool, shape=[-1, self._num_channels])
+        squeeze = self.squeeze(pool)
+        squeeze = F.relu(squeeze)
+        excitation = self.excitation(squeeze)
+        excitation = F.sigmoid(excitation)
+        excitation = paddle.reshape(
+            excitation, shape=[-1, self._num_channels, 1, 1])
+        out = input * excitation
+        return out
+    
+
+class RegNet(nn.Layer):
+    def __init__(self, w_a, w_0, w_m, d, group_w, bot_mul, q=8, se_on=False, class_dim=1000):
+        super(RegNet, self).__init__()
+        
+        # Generate RegNet ws per block
+        b_ws, num_s, max_s, ws_cont = generate_regnet(
+            w_a, w_0, w_m, d, q)
+        # Convert to per stage format
+        ws, ds = get_stages_from_blocks(b_ws, b_ws)
+        # Generate group widths and bot muls
+        gws = [group_w for _ in range(num_s)]
+        bms = [bot_mul for _ in range(num_s)]
+        # Adjust the compatibility of ws and gws
+        ws, gws = adjust_ws_gs_comp(ws, bms, gws)
+        # Use the same stride for each stage
+        ss = [2 for _ in range(num_s)]
+        # Use SE for RegNetY
+        se_r = 0.25
+        # Construct the model
+        # Group params by stage
+        stage_params = list(zip(ds, ws, ss, bms, gws))
+        # Construct the stem
+        stem_type = "simple_stem_in"
+        stem_w = 32
+        block_type = "res_bottleneck_block"
+
+        self.conv = ConvBNLayer(
+            num_channels=3,
+            num_filters=stem_w,
+            filter_size=3,
+            stride=2,
+            padding=1,
+            act="relu",
+            name="stem_conv")
+
+        self.block_list = []
+        for block, (d, w_out, stride, bm, gw) in enumerate(stage_params):
+            shortcut = False
+            for i in range(d):
+                num_channels = stem_w if block == i == 0 else in_channels
+                # 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)
+                bottleneck_block = self.add_sublayer(
+                    conv_name,
+                    BottleneckBlock(
+                        num_channels=num_channels,
+                        num_filters=w_out,
+                        stride=b_stride,
+                        bm=bm,
+                        gw=gw,
+                        se_on=se_on,
+                        se_r=se_r,
+                        shortcut=shortcut,
+                        name=conv_name))
+                in_channels = w_out
+                self.block_list.append(bottleneck_block)
+                shortcut = True
+
+        self.pool2d_avg = AdaptiveAvgPool2d(1)
+
+        self.pool2d_avg_channels = w_out
+
+        stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0)
+
+        self.out = Linear(
+            self.pool2d_avg_channels,
+            class_dim,
+            weight_attr=ParamAttr(
+                initializer=Uniform(-stdv, stdv), name="fc_0.w_0"),
+            bias_attr=ParamAttr(name="fc_0.b_0"))
+
+    def forward(self, inputs):
+        y = self.conv(inputs)
+        for block in self.block_list:
+            y = block(y)
+        y = self.pool2d_avg(y)
+        y = paddle.reshape(y, shape=[-1, self.pool2d_avg_channels])
+        y = self.out(y)
+        return y
+
+    
+def RegNetX_200MF(**args):
+    model = RegNet(
+        w_a=36.44, w_0=24, w_m=2.49, d=13, group_w=8, bot_mul=1.0, q=8, **args)
+    return model
+
+
+def RegNetX_4GF(**args):
+    model = RegNet(
+        w_a=38.65, w_0=96, w_m=2.43, d=23, group_w=40, bot_mul=1.0, q=8, **args)
+    return model
+
+
+def RegNetX_32GF(**args):
+    model = RegNet(
+        w_a=69.86, w_0=320, w_m=2.0, d=23, group_w=168, bot_mul=1.0, q=8, **args)
+    return model
+
+
+def RegNetY_200MF(**args):
+    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,
+        **args)
+    return model
+
+
+def RegNetY_4GF(**args):
+    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,
+        **args)
+    return model
+
+
+def RegNetY_32GF(**args):
+    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,
+        **args)
+    return model
+