Fix a bug in resnet; add performance test in static model

CHANGELOG.md

@@ -95,6 +95,7 @@ To release a new version, please update the changelog as followed:
 - Support string dtype in InputLayer (#PR 1017)
 - Support Dynamic RNN in RNN (#PR 1023)
 - Add ResNet50 static model (#PR 1030)
+_ Add performance test code in static model (#PR 1041)
 
 ### Changed
 
@@ -115,6 +116,7 @@ To release a new version, please update the changelog as followed:
 - Copy original model's `trainable_weights` and `nontrainable_weights` when initializing `LayerList` (#PR 1029)
 - Remove redundant parts in `model.all_layers` (#PR 1029)
 - Replace `tf.image.resize_image_with_crop_or_pad` with `tf.image.resize_with_crop_or_pad` (#PR 1032)
+- Fix a bug in `ResNet50` static model (#PR 1041)
 
 ### Removed
 
@@ -124,7 +126,7 @@ To release a new version, please update the changelog as followed:
 
 - @zsdonghao
 - @ChrisWu1997: #1010 #1015 #1025 #1030
-- @warshallrho: #1017 #1021 #1026 #1029 #1032
+- @warshallrho: #1017 #1021 #1026 #1029 #1032 #1041
 - @ArnoldLIULJ: #1023
 - @JingqingZ: #1023
 

tensorlayer/models/resnet.py

@@ -150,21 +150,21 @@ def ResNet50(pretrained=False, end_with='fc1000', n_classes=1000, name=None):
     n = BatchNorm(name='bn_conv1', act='relu')(n)
     n = MaxPool2d((3, 3), strides=(2, 2), name='max_pool1')(n)
 
-    for i, name in enumerate(block_names):
-        if len(name) == 2:
-            stage = int(name[0])
-            block = name[1]
+    for i, block_name in enumerate(block_names):
+        if len(block_name) == 2:
+            stage = int(block_name[0])
+            block = block_name[1]
             if block == 'a':
                 strides = (1, 1) if stage == 2 else (2, 2)
                 n = conv_block(n, 3, block_filters[stage - 2], stage=stage, block=block, strides=strides)
             else:
                 n = identity_block(n, 3, block_filters[stage - 2], stage=stage, block=block)
-        elif name == 'avg_pool':
+        elif block_name == 'avg_pool':
             n = GlobalMeanPool2d(name='avg_pool')(n)
-        elif name == 'fc1000':
+        elif block_name == 'fc1000':
             n = Dense(n_classes, name='fc1000')(n)
 
-        if name == end_with:
+        if block_name == end_with:
             break
 
     network = Model(inputs=ni, outputs=n, name=name)

tests/performance_test/vgg/tl2-static-autograph.py

@@ -0,0 +1,79 @@
+import time
+import os
+import psutil
+import tensorflow as tf
+import tensorlayer as tl
+from exp_config import random_input_generator, MONITOR_INTERVAL, NUM_ITERS, BATCH_SIZE, LERANING_RATE
+
+gpus = tf.config.experimental.list_physical_devices('GPU')
+if gpus:
+    for gpu in gpus:
+        tf.config.experimental.set_memory_growth(gpu, True)
+
+tl.logging.set_verbosity(tl.logging.DEBUG)
+
+# get the whole model
+vgg = tl.models.vgg16(mode='static')
+
+# system monitor
+info = psutil.virtual_memory()
+monitor_interval = MONITOR_INTERVAL
+avg_mem_usage = 0
+max_mem_usage = 0
+count = 0
+total_time = 0
+
+# training setting
+num_iter = NUM_ITERS
+batch_size = BATCH_SIZE
+train_weights = vgg.trainable_weights
+optimizer = tf.optimizers.Adam(learning_rate=LERANING_RATE)
+loss_object = tl.cost.cross_entropy
+
+# data generator
+gen = random_input_generator(num_iter, batch_size)
+
+
+# training function
+@tf.function
+def train_step(x_batch, y_batch):
+    # forward + backward
+    with tf.GradientTape() as tape:
+        ## compute outputs
+        _logits = vgg(x_batch)
+        ## compute loss and update model
+        _loss = loss_object(_logits, y_batch)
+
+    grad = tape.gradient(_loss, train_weights)
+    optimizer.apply_gradients(zip(grad, train_weights))
+
+
+# begin training
+vgg.train()
+
+for idx, data in enumerate(gen):
+    start_time = time.time()
+
+    train_step(data[0], data[1])
+
+    end_time = time.time()
+    consume_time = end_time - start_time
+    total_time += consume_time
+
+    if idx % monitor_interval == 0:
+        cur_usage = psutil.Process(os.getpid()).memory_info().rss
+        max_mem_usage = max(cur_usage, max_mem_usage)
+        avg_mem_usage += cur_usage
+        count += 1
+        tl.logging.info(
+            "[*] {} iteration: memory usage {:.2f}MB, consume time {:.4f}s".format(
+                idx, cur_usage / (1024 * 1024), consume_time
+            )
+        )
+
+print('consumed time:', total_time)
+
+avg_mem_usage = avg_mem_usage / count / (1024 * 1024)
+max_mem_usage = max_mem_usage / (1024 * 1024)
+print('average memory usage: {:.2f}MB'.format(avg_mem_usage))
+print('maximum memory usage: {:.2f}MB'.format(max_mem_usage))

tests/performance_test/vgg/tl2-static-eager.py

@@ -0,0 +1,79 @@
+import time
+import os
+import psutil
+import tensorflow as tf
+import tensorlayer as tl
+from exp_config import random_input_generator, MONITOR_INTERVAL, NUM_ITERS, BATCH_SIZE, LERANING_RATE
+
+gpus = tf.config.experimental.list_physical_devices('GPU')
+if gpus:
+    for gpu in gpus:
+        tf.config.experimental.set_memory_growth(gpu, True)
+
+tl.logging.set_verbosity(tl.logging.DEBUG)
+
+# get the whole model
+vgg = tl.models.vgg16(mode='static')
+
+# system monitor
+info = psutil.virtual_memory()
+monitor_interval = MONITOR_INTERVAL
+avg_mem_usage = 0
+max_mem_usage = 0
+count = 0
+total_time = 0
+
+# training setting
+num_iter = NUM_ITERS
+batch_size = BATCH_SIZE
+train_weights = vgg.trainable_weights
+optimizer = tf.optimizers.Adam(learning_rate=LERANING_RATE)
+loss_object = tl.cost.cross_entropy
+
+# data generator
+gen = random_input_generator(num_iter, batch_size)
+
+
+# training function
+def train_step(x_batch, y_batch):
+    # forward + backward
+    with tf.GradientTape() as tape:
+        ## compute outputs
+        _logits = vgg(x_batch)
+        ## compute loss and update model
+        _loss = loss_object(_logits, y_batch)
+
+    grad = tape.gradient(_loss, train_weights)
+    optimizer.apply_gradients(zip(grad, train_weights))
+    return _loss
+
+
+# begin training
+vgg.train()
+
+for idx, data in enumerate(gen):
+    start_time = time.time()
+
+    loss = train_step(data[0], data[1])
+
+    end_time = time.time()
+    consume_time = end_time - start_time
+    total_time += consume_time
+
+    if idx % monitor_interval == 0:
+        cur_usage = psutil.Process(os.getpid()).memory_info().rss
+        max_mem_usage = max(cur_usage, max_mem_usage)
+        avg_mem_usage += cur_usage
+        count += 1
+        tl.logging.info(
+            "[*] {} iteration: memory usage {:.2f}MB, consume time {:.4f}s, loss {:.4f}".format(
+                idx, cur_usage / (1024 * 1024), consume_time, loss
+            )
+        )
+
+print('consumed time:', total_time)
+
+avg_mem_usage = avg_mem_usage / count / (1024 * 1024)
+max_mem_usage = max_mem_usage / (1024 * 1024)
+print('average memory usage: {:.2f}MB'.format(avg_mem_usage))
+print('maximum memory usage: {:.2f}MB'.format(max_mem_usage))