I have trained a pytorch_lightning model and when loaded the model successfully it throws me the following error:
My code:
classifier_model = TextClassifier(backbone="prajjwal1/bert-tiny", num_classes=datamodule.num_classes, metrics=torchmetrics.F1Score(datamodule.num_classes))
trainer = flash.Trainer(max_epochs=3, gpus=torch.cuda.device_count())
MODEL_ARTIFACT_PATH = 'MODEL'
REGISTERED_MODEL_NAME = 'MODEL2'
with mlflow.start_run(experiment_id=experiment.experiment_id, run_name="MYRUN01") as dl_model_tracking_run:
trainer.finetune(classifier_model, datamodule=datamodule, strategy="freeze")
trainer.test(dataloaders=datamodule)
....
mlflow.pytorch.log_model(pytorch_model=classifier_model, artifact_path=MODEL_ARTIFACT_PATH, registered_model_name=REGISTERED_MODEL_NAME)
run_id = dl_model_tracking_run.info.run_id
print("run_id: {}; lifecycle_stage: {}".format(run_id,
mlflow.get_run(run_id).info.lifecycle_stage))
logged_model = f'runs:/{run_id}/{MODEL_ARTIFACT_PATH}'
model = mlflow.pytorch.load_model(logged_model)
model.trainer.state.stage='test'
model.predict({'What a news!'})
Error:
def predict(self, *args, **kwargs):
raise AttributeError("`flash.Task.predict` has been removed. Use `flash.Trainer.predict` instead.")
AttributeError: `flash.Task.predict` has been removed. Use `flash.Trainer.predict` instead.
I checked the mlflow docs in below link and my code seems ok:
https://mlflow.org/docs/latest/python_api/mlflow.pytorch.html
Any Idea how to fix the issue?
Related
While trying to script Stable Diffusion model using torch.jit.script(), I got this following error:
AttributeError: 'TimestepEmbedSequential' object has no attribute '__globals__'
I'm trying to export this model to ONNX and found out that running torch.onnx.export() will torch.jit.trace the models, which unrolls every loops, so I'm trying to use script first.
When I follow the traceback, the error occurs in this function while reading fn.__globals__ in _jit_internal.py from torch
def get_closure(fn):
"""
Get a dictionary of closed over variables from a function
"""
captures = {}
captures.update(fn.__globals__)
for index, captured_name in enumerate(fn.__code__.co_freevars):
captures[captured_name] = fn.__closure__[index].cell_contents
return captures
code for scripting is as follows:
stablediffusion_wrapper = StableDiffusionWrapper(model, sampler, opt)
scripted_module = torch.jit.script(stablediffusion_wrapper, example_inputs=[(token_dummy, img_dummy)])
and TimestepEmbedSequential module:
class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
"""
A sequential module that passes timestep embeddings to the children that
support it as an extra input.
"""
def forward(self, x, emb, context=None):
for layer in self:
if isinstance(layer, TimestepBlock):
x = layer(x, emb)
elif isinstance(layer, SpatialTransformer):
x = layer(x, context)
else:
x = layer(x)
return x
Any suggestions how can I figure it out?
I tried to set #torch.jit.export decorator to the parent class TimestepBlock, but showed no effect. Actually, I have no idea what to look for this problem. I would appreciate any suggestions. Thank you
I'm trying to pass the all of the huggingface's ...ForMaskedLM to the FitBert model for fill-in-the-blank task and see which pretrained yields the best result on the data I've prepared. But in the Reformer module I have this error says that I need to do 'config.is_decoder=False' but I don't really get what this means (This is my first time using huggingface). I tried to pass a ReformerConfig(is_decoder=False) to the model but still get the same error. How can I fix this?
My code:
pretrained_weights = ['google/reformer-crime-and-punishment',
'google/reformer-enwik8']
configurations = ReformerConfig(is_decoder=False)
for weight in pretrained_weights:
print(weight)
model = ReformerForMaskedLM(configurations).from_pretrained(weight)
tokenizer = ReformerTokenizer.from_pretrained(weight)
fb = FitBert(model=model, tokenizer=tokenizer)
predicts = []
for _, row in df.iterrows():
predicts.append(fb.rank(row['question'], options=[row['1'], row['2'], row['3'], row['4']])[0])
print(weight,':', np.sum(df.anwser==predicts) / df.shape[0])
Error:
AssertionError Traceback (most recent call last)
<ipython-input-5-a6016e0015ba> in <module>()
4 for weight in pretrained_weights:
5 print(weight)
----> 6 model = ReformerForMaskedLM(configurations).from_pretrained(weight)
7 tokenizer = ReformerTokenizer.from_pretrained(weight)
8 fb = FitBert(model=model, tokenizer=tokenizer)
/usr/local/lib/python3.7/dist-packages/transformers/modeling_utils.py in from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
1032
1033 # Instantiate model.
-> 1034 model = cls(config, *model_args, **model_kwargs)
1035
1036 if state_dict is None and not from_tf:
/usr/local/lib/python3.7/dist-packages/transformers/models/reformer/modeling_reformer.py in __init__(self, config)
2304 assert (
2305 not config.is_decoder
-> 2306 ), "If you want to use `ReformerForMaskedLM` make sure `config.is_decoder=False` for bi-directional self-attention."
2307 self.reformer = ReformerModel(config)
2308 self.lm_head = ReformerOnlyLMHead(config)
AssertionError: If you want to use `ReformerForMaskedLM` make sure `config.is_decoder=False` for bi-directional self-attention.
You can override certain model configurations by loading the model config separately and providing it as parameter for the from_pretrained() method. This will assure that you are using the proper model configuration with the changes you have made:
from transformers import ReformerConfig, ReformerForMaskedLM
config = ReformerConfig.from_pretrained('google/reformer-crime-and-punishment')
print(config.is_decoder)
config.is_decoder=False
print(config.is_decoder)
model = ReformerForMaskedLM.from_pretrained('google/reformer-crime-and-punishment', config=config)
Output:
True
False
Error:
when I use caffe2 for pretraind model. the model is from https://github.com/onnx/models/blob/master/vision/classification/vgg/model/vgg16-7.onnx
the model I use is pretrained model,
I do not change the model, and not use https://github.com/onnx/optimizer
the code is:
import caffe2
model=onnx.load(vgg16-7.onnx)
prepared_backend=caffe2.python.onnx.backend.prepare(model)
then an error happend:
WARNING:root:This caffe2 python run failed to load cuda module:No module named 'caffe2.python.caffe2_pybind11_state_gpu',and AMD hip module:No module named 'caffe2.python.caffe2_pybind11_state_hip'.Will run in CPU only mode.
WARNING: ONNX Optimizer has been moved to https://github.com/onnx/optimizer.
All further enhancements and fixes to optimizers will be done in this new repo.
The optimizer code in onnx/onnx repo will be removed in 1.9 release.
Traceback (most recent call last):
File "test.py", line 20, in
init_net, predict_net = c2.onnx_graph_to_caffe2_net(onnx_model_proto)
File "/home/eeodev/.local/lib/python3.6/site-packages/caffe2/python/onnx/backend.py", line 921, in onnx_graph_to_caffe2_net
return cls._onnx_model_to_caffe2_net(model, device=device, opset_version=opset_version, include_initializers=True)
File "/home/eeodev/.local/lib/python3.6/site-packages/caffe2/python/onnx/backend.py", line 876, in _onnx_model_to_caffe2_net
onnx_model = onnx.utils.polish_model(onnx_model)
File "/usr/local/lib64/python3.6/site-packages/onnx/utils.py", line 24, in polish_model
model = onnx.optimizer.optimize(model)
File "/usr/local/lib64/python3.6/site-packages/onnx/optimizer.py", line 55, in optimize
optimized_model_str = C.optimize(model_str, passes)
IndexError: Input 475 is undefined!
who can tell the solution?
another,if it is a pytorch model,when conver to onnx model ,we can use torch.onnx.export(model, input, 'model.onnx', verbose=True, keep_initializers_as_inputs=True), by keep_initializers_as_inputs=True , use caffe2 load model will not happen than error. but the model I used is pretrained model ,how to use this method?
I believe it is related to IR gap issue: https://github.com/onnx/onnx/issues/2902.
Currently the deprecated ONNX optimizer in ONNX repo cannot deal with ONNX model which IR_VERSION >=4 if the initializer are not included in model's input.
The workaround is to use the following script to let your model include input from initializer (contributed by #TMVector in GitHub):
def add_value_info_for_constants(model : onnx.ModelProto):
"""
Currently onnx.shape_inference doesn't use the shape of initializers, so add
that info explicitly as ValueInfoProtos.
Mutates the model.
Args:
model: The ModelProto to update.
"""
# All (top-level) constants will have ValueInfos before IRv4 as they are all inputs
if model.ir_version < 4:
return
def add_const_value_infos_to_graph(graph : onnx.GraphProto):
inputs = {i.name for i in graph.input}
existing_info = {vi.name: vi for vi in graph.value_info}
for init in graph.initializer:
# Check it really is a constant, not an input
if init.name in inputs:
continue
# The details we want to add
elem_type = init.data_type
shape = init.dims
# Get existing or create new value info for this constant
vi = existing_info.get(init.name)
if vi is None:
vi = graph.value_info.add()
vi.name = init.name
# Even though it would be weird, we will not overwrite info even if it doesn't match
tt = vi.type.tensor_type
if tt.elem_type == onnx.TensorProto.UNDEFINED:
tt.elem_type = elem_type
if not tt.HasField("shape"):
# Ensure we set an empty list if the const is scalar (zero dims)
tt.shape.dim.extend([])
for dim in shape:
tt.shape.dim.add().dim_value = dim
# Handle subgraphs
for node in graph.node:
for attr in node.attribute:
# Ref attrs refer to other attrs, so we don't need to do anything
if attr.ref_attr_name != "":
continue
if attr.type == onnx.AttributeProto.GRAPH:
add_const_value_infos_to_graph(attr.g)
if attr.type == onnx.AttributeProto.GRAPHS:
for g in attr.graphs:
add_const_value_infos_to_graph(g)
return add_const_value_infos_to_graph(model.graph)
I am using HoltWinters model for forecasting wind speed. HoltWinters is from hardPrediction library. When I am assigning model = HoltWinters(), getting below error
AttributeError: 'HoltWinters' object has no attribute 'seasonal'
Initial portion of the code looks like this.
from hardPredictions.base_model import base_model
from hardPredictions.extras import *
from sklearn.utils import resample
class HoltWinters(base_model):
""" HW """
def __init__(self, alpha=None, beta=None, gamma=None, seasonal='additive'):
self.alpha = alpha
self.beta = beta
self.gamma = gamma
self.seasonal = seasonal
self.fit_model = 'fit'
""" Checks parameters """
seasonal attribute is defined and assigned value from input parameters.
Please help to let us know if there is any missing part of the code.
I downloaded a ssd_mobilenet_v2_coco from tensorflow detection model zoo. And I used import_pb_to_tensorboard.py to show the structure on Tensorboard.
I find a node named 'image_tensor', this is the picture discribed in Tensorboard.
I want to use the function 'get_tensor_by_name()' to input a new image and get the ouputs. However, it failed.
I tried 'get_operation_by_name()' , it didn't work neither.
Here is the code:
import tensorflow as tf
def one_image(im_path, model_path):
sess= tf.Session()
with sess.as_default():
image_tensor = tf.image.decode_jpeg(tf.read_file(im_path), channels=3)
saver = tf.train.import_meta_graph(model_path + "/model.ckpt.meta")
saver.restore(sess, tf.train.latest_checkpoint(model_path))
graph = tf.get_default_graph()
# x = graph.get_tensor_by_name("import/image_tensor:0")
# out_put = graph.get_tensor_by_name("import/detection_classes:0")
x = graph.get_operation_by_name("import/image_tensor").outputs[0]
outputs = graph.get_operation_by_name("import/detection_classes").outputs[0]
out_put = sess.run(outputs, feed_dict={x: image_tensor.eval()})
print(out_put)
sess.close()
if __name__ == "__main__":
one_image("testimg-4-resize.jpg", "ssd_mobilenet_v2_coco_2018_03_29")
And here is the KeyError:
KeyError: "The name 'import/image_tensor' refers to an Operation not in the graph."
I am wondering how to get the tensor name from Tensorboard and whether there is another way to load model from 'only-ckpts'.
'only-ckpts' means files only include 'model.ckpt.data-00000-of-00001' , 'model.ckpt.index' and 'model.ckpt.meta'.
Any advice will be grateful. Thank you in advance.
The tool import_pb_to_tensorboard.py uses tf.import_graph_def to import the graph and uses default name argument, which is "import" as documented.
Your code imports the graph through tf.train.import_meta_graph and uses default import_scope argument, which will not prefix imported tensor or operation name. It is obvious then you have two options to correct this error:
Do the following in place of your import_meta_graph line:
saver = tf.train.import_meta_graph(model_path + "/model.ckpt.meta",
import_scope='import')
Remove import/ prefix when trying to get tensor or operation by name like this:
x = graph.get_tensor_by_name("image_tensor:0")
out_put = graph.get_tensor_by_name("detection_classes:0")
x = graph.get_operation_by_name("image_tensor").outputs[0]
outputs = graph.get_operation_by_name("detection_classes").outputs[0]