Repository: google/jax

Issue:
examples/resnet50
Has anyone trained resnet50 in example?
In a batch, I always get the same prediction value：
```
batch_size = 100
[ 56  56  56  56  56  56 153  56  56  56  56  56  56  56  56  56  56  56
  56  56  56  56  56  56  56  56  56  56  56  56  56  56  56  56  56  56
  56  56  56  56  56  56 323  56  56  56  56  56  56  56  56  56  56  56
  56  56  56  56  56  56  56  56  56  56  56  56  56  56  56  56  56  56
  56 695  56  56  56  56  56  56  56  56  56  56  56  56  56  56  56  56
  56  56  56  56  56  56  56  56  56  56]
```



Discussion:
Thanks for raising the issue!

I've been meaning to delete that example file. It's old and likely not a useful reference. This [Flax resnet50](https://github.com/google/flax/tree/main/examples/imagenet) seems like a much better reference.

I'm not sure if it's buggy though. If you want to help debug it, can you share code to run to reproduce the result you're seeing?

But if we're going to delete it anyway, maybe it's not worth investigating?

Resolving patch:
diff --git a/examples/resnet50.py b/examples/resnet50.py
deleted file mode 100644
--- a/examples/resnet50.py
+++ /dev/null
@@ -1,125 +0,0 @@
-# Copyright 2018 Google LLC
-#
-# 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
-#
-#     https://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.
-
-"""A mock-up showing a ResNet50 network with training on synthetic data.
-
-This file uses the stax neural network definition library and the optimizers
-optimization library.
-"""
-
-import numpy.random as npr
-
-import jax.numpy as jnp
-from jax import jit, grad, random
-from jax.example_libraries import optimizers
-from jax.example_libraries import stax
-from jax.example_libraries.stax import (AvgPool, BatchNorm, Conv, Dense,
-                                        FanInSum, FanOut, Flatten, GeneralConv,
-                                        Identity, MaxPool, Relu, LogSoftmax)
-
-
-# ResNet blocks compose other layers
-
-def ConvBlock(kernel_size, filters, strides=(2, 2)):
-  ks = kernel_size
-  filters1, filters2, filters3 = filters
-  Main = stax.serial(
-      Conv(filters1, (1, 1), strides), BatchNorm(), Relu,
-      Conv(filters2, (ks, ks), padding='SAME'), BatchNorm(), Relu,
-      Conv(filters3, (1, 1)), BatchNorm())
-  Shortcut = stax.serial(Conv(filters3, (1, 1), strides), BatchNorm())
-  return stax.serial(FanOut(2), stax.parallel(Main, Shortcut), FanInSum, Relu)
-
-
-def IdentityBlock(kernel_size, filters):
-  ks = kernel_size
-  filters1, filters2 = filters
-  def make_main(input_shape):
-    # the number of output channels depends on the number of input channels
-    return stax.serial(
-        Conv(filters1, (1, 1)), BatchNorm(), Relu,
-        Conv(filters2, (ks, ks), padding='SAME'), BatchNorm(), Relu,
-        Conv(input_shape[3], (1, 1)), BatchNorm())
-  Main = stax.shape_dependent(make_main)
-  return stax.serial(FanOut(2), stax.parallel(Main, Identity), FanInSum, Relu)
-
-
-# ResNet architectures compose layers and ResNet blocks
-
-def ResNet50(num_classes):
-  return stax.serial(
-      GeneralConv(('HWCN', 'OIHW', 'NHWC'), 64, (7, 7), (2, 2), 'SAME'),
-      BatchNorm(), Relu, MaxPool((3, 3), strides=(2, 2)),
-      ConvBlock(3, [64, 64, 256], strides=(1, 1)),
-      IdentityBlock(3, [64, 64]),
-      IdentityBlock(3, [64, 64]),
-      ConvBlock(3, [128, 128, 512]),
-      IdentityBlock(3, [128, 128]),
-      IdentityBlock(3, [128, 128]),
-      IdentityBlock(3, [128, 128]),
-      ConvBlock(3, [256, 256, 1024]),
-      IdentityBlock(3, [256, 256]),
-      IdentityBlock(3, [256, 256]),
-      IdentityBlock(3, [256, 256]),
-      IdentityBlock(3, [256, 256]),
-      IdentityBlock(3, [256, 256]),
-      ConvBlock(3, [512, 512, 2048]),
-      IdentityBlock(3, [512, 512]),
-      IdentityBlock(3, [512, 512]),
-      AvgPool((7, 7)), Flatten, Dense(num_classes), LogSoftmax)
-
-
-if __name__ == "__main__":
-  rng_key = random.PRNGKey(0)
-
-  batch_size = 8
-  num_classes = 1001
-  input_shape = (224, 224, 3, batch_size)
-  step_size = 0.1
-  num_steps = 10
-
-  init_fun, predict_fun = ResNet50(num_classes)
-  _, init_params = init_fun(rng_key, input_shape)
-
-  def loss(params, batch):
-    inputs, targets = batch
-    logits = predict_fun(params, inputs)
-    return -jnp.sum(logits * targets)
-
-  def accuracy(params, batch):
-    inputs, targets = batch
-    target_class = jnp.argmax(targets, axis=-1)
-    predicted_class = jnp.argmax(predict_fun(params, inputs), axis=-1)
-    return jnp.mean(predicted_class == target_class)
-
-  def synth_batches():
-    rng = npr.RandomState(0)
-    while True:
-      images = rng.rand(*input_shape).astype('float32')
-      labels = rng.randint(num_classes, size=(batch_size, 1))
-      onehot_labels = labels == jnp.arange(num_classes)
-      yield images, onehot_labels
-
-  opt_init, opt_update, get_params = optimizers.momentum(step_size, mass=0.9)
-  batches = synth_batches()
-
-  @jit
-  def update(i, opt_state, batch):
-    params = get_params(opt_state)
-    return opt_update(i, grad(loss)(params, batch), opt_state)
-
-  opt_state = opt_init(init_params)
-  for i in range(num_steps):
-    opt_state = update(i, opt_state, next(batches))
-  trained_params = get_params(opt_state)


Test patch:
diff --git a/examples/examples_test.py b/examples/examples_test.py
--- a/examples/examples_test.py
+++ b/examples/examples_test.py
@@ -15,7 +15,6 @@
 
 import os
 import sys
-import unittest
 import zlib
 
 from absl.testing import absltest
@@ -23,14 +22,12 @@
 
 import numpy as np
 
-import jax
 from jax import lax
 from jax import random
 import jax.numpy as jnp
 
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 from examples import kernel_lsq
-from examples import resnet50
 sys.path.pop()
 
 from jax.config import config
@@ -49,36 +46,6 @@ class ExamplesTest(parameterized.TestCase):
   def setUp(self):
     self.rng = np.random.default_rng(zlib.adler32(self.__class__.__name__.encode()))
 
-  @parameterized.named_parameters(
-      {"testcase_name": f"_input_shape={input_shape}",
-       "input_shape": input_shape}
-      for input_shape in [(2, 20, 25, 2)])
-  @unittest.skipIf(config.x64_enabled, "skip in x64 mode")
-  def testIdentityBlockShape(self, input_shape):
-    init_fun, apply_fun = resnet50.IdentityBlock(2, (4, 3))
-    _CheckShapeAgreement(self, init_fun, apply_fun, input_shape)
-
-  @parameterized.named_parameters(
-      {"testcase_name": f"_input_shape={input_shape}",
-       "input_shape": input_shape}
-      for input_shape in [(2, 20, 25, 3)])
-  @unittest.skipIf(config.x64_enabled, "skip in x64 mode")
-  def testConvBlockShape(self, input_shape):
-    init_fun, apply_fun = resnet50.ConvBlock(3, (2, 3, 4))
-    _CheckShapeAgreement(self, init_fun, apply_fun, input_shape)
-
-  @parameterized.named_parameters(
-      {"testcase_name": "_num_classes={}_input_shape={}"
-                        .format(num_classes, input_shape),
-       "num_classes": num_classes, "input_shape": input_shape}
-      for num_classes in [5, 10]
-      for input_shape in [(224, 224, 3, 2)])
-  @unittest.skipIf(config.x64_enabled, "skip in x64 mode")
-  @jax.numpy_rank_promotion("allow")  # Uses stax, which exercises implicit rank promotion.
-  def testResNet50Shape(self, num_classes, input_shape):
-    init_fun, apply_fun = resnet50.ResNet50(num_classes)
-    _CheckShapeAgreement(self, init_fun, apply_fun, input_shape)
-
   def testKernelRegressionGram(self):
     n, d = 100, 20
     xs = self.rng.normal(size=(n, d))