Initial release of "nfnets".

PiperOrigin-RevId: 356975781

nfnets/nf_resnet.py

@@ -0,0 +1,216 @@
+# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
+#
+# 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.
+# ==============================================================================
+"""Norm-Free Residual Networks."""
+# pylint: disable=invalid-name
+
+import haiku as hk
+import jax
+import jax.numpy as jnp
+from nfnets import base
+
+
+class NF_ResNet(hk.Module):
+  """Norm-Free preactivation ResNet."""
+
+  variant_dict = {'ResNet50': {'depth': [3, 4, 6, 3]},
+                  'ResNet101': {'depth': [3, 4, 23, 3]},
+                  'ResNet152': {'depth': [3, 8, 36, 3]},
+                  'ResNet200': {'depth': [3, 24, 36, 3]},
+                  'ResNet288': {'depth': [24, 24, 24, 24]},
+                  'ResNet600': {'depth': [50, 50, 50, 50]},
+                  }
+
+  def __init__(self, num_classes, variant='ResNet50', width=4,
+               alpha=0.2, stochdepth_rate=0.1, drop_rate=None,
+               activation='relu', fc_init=None, skipinit_gain=jnp.zeros,
+               use_se=False, se_ratio=0.25,
+               name='NF_ResNet'):
+    super().__init__(name=name)
+    self.num_classes = num_classes
+    self.variant = variant
+    self.width = width
+    # Get variant info
+    block_params = self.variant_dict[self.variant]
+    self.width_pattern = [item * self.width for item in [64, 128, 256, 512]]
+    self.depth_pattern = block_params['depth']
+    self.activation = base.nonlinearities[activation]
+    if drop_rate is None:
+      self.drop_rate = block_params['drop_rate']
+    else:
+      self.drop_rate = drop_rate
+    self.which_conv = base.WSConv2D
+    # Stem
+    ch = int(16 * self.width)
+    self.initial_conv = self.which_conv(ch, kernel_shape=7, stride=2,
+                                        padding='SAME', with_bias=False,
+                                        name='initial_conv')
+
+    # Body
+    self.blocks = []
+    expected_std = 1.0
+    num_blocks = sum(self.depth_pattern)
+    index = 0  # Overall block index
+    block_args = (self.width_pattern, self.depth_pattern, [1, 2, 2, 2])
+    for block_width, stage_depth, stride in zip(*block_args):
+      for block_index in range(stage_depth):
+        # Scalar pre-multiplier so each block sees an N(0,1) input at init
+        beta = 1./ expected_std
+        # Block stochastic depth drop-rate
+        block_stochdepth_rate = stochdepth_rate * index / num_blocks
+        self.blocks += [NFResBlock(ch, block_width,
+                                   stride=stride if block_index == 0 else 1,
+                                   beta=beta, alpha=alpha,
+                                   activation=self.activation,
+                                   which_conv=self.which_conv,
+                                   stochdepth_rate=block_stochdepth_rate,
+                                   skipinit_gain=skipinit_gain,
+                                   use_se=use_se,
+                                   se_ratio=se_ratio,
+                                   )]
+        ch = block_width
+        index += 1
+         # Reset expected std but still give it 1 block of growth
+        if block_index == 0:
+          expected_std = 1.0
+        expected_std = (expected_std **2 + alpha**2)**0.5
+
+    # Head. By default, initialize with N(0, 0.01)
+    if fc_init is None:
+      fc_init = hk.initializers.RandomNormal(0.01, 0)
+    self.fc = hk.Linear(self.num_classes, w_init=fc_init, with_bias=True)
+
+  def __call__(self, x, is_training=True, return_metrics=False):
+    """Return the output of the final layer without any [log-]softmax."""
+    # Stem
+    outputs = {}
+    out = self.initial_conv(x)
+    out = hk.max_pool(out, window_shape=(1, 3, 3, 1),
+                      strides=(1, 2, 2, 1), padding='SAME')
+    if return_metrics:
+      outputs.update(base.signal_metrics(out, 0))
+    # Blocks
+    for i, block in enumerate(self.blocks):
+      out, res_avg_var = block(out, is_training=is_training)
+      if return_metrics:
+        outputs.update(base.signal_metrics(out, i + 1))
+        outputs[f'res_avg_var_{i}'] = res_avg_var
+    # Final-conv->activation, pool, dropout, classify
+    pool = jnp.mean(self.activation(out), [1, 2])
+    outputs['pool'] = pool
+    # Optionally apply dropout
+    if self.drop_rate > 0.0 and is_training:
+      pool = hk.dropout(hk.next_rng_key(), self.drop_rate, pool)
+    outputs['logits'] = self.fc(pool)
+    return outputs
+
+  def count_flops(self, h, w):
+    flops = []
+    flops += [base.count_conv_flops(3, self.initial_conv, h, w)]
+    h, w = h / 2, w / 2
+    # Body FLOPs
+    for block in self.blocks:
+      flops += [block.count_flops(h, w)]
+      if block.stride > 1:
+        h, w = h / block.stride, w / block.stride
+    # Head module FLOPs
+    out_ch = self.blocks[-1].out_ch
+    flops += [base.count_conv_flops(out_ch, self.final_conv, h, w)]
+    # Count flops for classifier
+    flops += [self.final_conv.output_channels * self.fc.output_size]
+    return flops, sum(flops)
+
+
+class NFResBlock(hk.Module):
+  """Normalizer-Free pre-activation ResNet Block."""
+
+  def __init__(self, in_ch, out_ch, bottleneck_ratio=0.25,
+               kernel_size=3, stride=1,
+               beta=1.0, alpha=0.2,
+               which_conv=base.WSConv2D, activation=jax.nn.relu,
+               skipinit_gain=jnp.zeros,
+               stochdepth_rate=None,
+               use_se=False, se_ratio=0.25,
+               name=None):
+    super().__init__(name=name)
+    self.in_ch, self.out_ch = in_ch, out_ch
+    self.kernel_size = kernel_size
+    self.activation = activation
+    self.beta, self.alpha = beta, alpha
+    self.skipinit_gain = skipinit_gain
+    self.use_se, self.se_ratio = use_se, se_ratio
+    # Bottleneck width
+    self.width = int(self.out_ch * bottleneck_ratio)
+    self.stride = stride
+    # Conv 0 (typically expansion conv)
+    self.conv0 = which_conv(self.width, kernel_shape=1, padding='SAME',
+                            name='conv0')
+    # Grouped NxN conv
+    self.conv1 = which_conv(self.width, kernel_shape=kernel_size, stride=stride,
+                            padding='SAME', name='conv1')
+    # Conv 2, typically projection conv
+    self.conv2 = which_conv(self.out_ch, kernel_shape=1, padding='SAME',
+                            name='conv2')
+    # Use shortcut conv on channel change or downsample.
+    self.use_projection = stride > 1 or self.in_ch != self.out_ch
+    if self.use_projection:
+      self.conv_shortcut = which_conv(self.out_ch, kernel_shape=1,
+                                      stride=stride, padding='SAME',
+                                      name='conv_shortcut')
+    # Are we using stochastic depth?
+    self._has_stochdepth = (stochdepth_rate is not None and
+                            stochdepth_rate > 0. and stochdepth_rate < 1.0)
+    if self._has_stochdepth:
+      self.stoch_depth = base.StochDepth(stochdepth_rate)
+
+    if self.use_se:
+      self.se = base.SqueezeExcite(self.out_ch, self.out_ch, self.se_ratio)
+
+  def __call__(self, x, is_training):
+    out = self.activation(x) * self.beta
+    shortcut = x
+    if self.use_projection:  # Downsample with conv1x1
+      shortcut = self.conv_shortcut(out)
+    out = self.conv0(out)
+    out = self.conv1(self.activation(out))
+    out = self.conv2(self.activation(out))
+    if self.use_se:
+      out = 2 * self.se(out) * out
+    # Get average residual standard deviation for reporting metrics.
+    res_avg_var = jnp.mean(jnp.var(out, axis=[0, 1, 2]))
+    # Apply stochdepth if applicable.
+    if self._has_stochdepth:
+      out = self.stoch_depth(out, is_training)
+    # SkipInit Gain
+    out = out * hk.get_parameter('skip_gain', (), out.dtype,
+                                 init=self.skipinit_gain)
+    return out * self.alpha + shortcut, res_avg_var
+
+  def count_flops(self, h, w):
+    # Count conv FLOPs based on input HW
+    expand_flops = base.count_conv_flops(self.in_ch, self.conv0, h, w)
+    # If block is strided we decrease resolution here.
+    dw_flops = base.count_conv_flops(self.width, self.conv1, h, w)
+    if self.stride > 1:
+      h, w = h / self.stride, w / self.stride
+    if self.use_projection:
+      sc_flops = base.count_conv_flops(self.in_ch, self.conv_shortcut, h, w)
+    else:
+      sc_flops = 0
+    # SE flops happen on avg-pooled activations
+    se_flops = self.se.fc0.output_size * self.width
+    se_flops += self.se.fc0.output_size * self.se.fc1.output_size
+    contract_flops = base.count_conv_flops(self.width, self.conv2, h, w)
+    return sum([expand_flops, dw_flops, se_flops, contract_flops, sc_flops])
+