rararara9999's picture
Rename app.py to app1.py
d7f7d05 verified
In [4]:
! pip install -U git+https://github.com/huggingface/transformers.git
! pip install -U git+https://github.com/huggingface/accelerate.git
Collecting git+https://github.com/huggingface/transformers.git
Cloning https://github.com/huggingface/transformers.git to /tmp/pip-req-build-srwrto6l
Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers.git /tmp/pip-req-build-srwrto6l
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py", line 179, in exc_logging_wrapper
status = run_func(*args)
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/req_command.py", line 67, in wrapper
return func(self, options, args)
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/commands/install.py", line 377, in run
requirement_set = resolver.resolve(
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/resolver.py", line 76, in resolve
collected = self.factory.collect_root_requirements(root_reqs)
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/factory.py", line 538, in collect_root_requirements
reqs = list(
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/factory.py", line 494, in _make_requirements_from_install_req
cand = self._make_base_candidate_from_link(
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/factory.py", line 231, in _make_base_candidate_from_link
self._link_candidate_cache[link] = LinkCandidate(
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/candidates.py", line 303, in __init__
super().__init__(
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/candidates.py", line 158, in __init__
self.dist = self._prepare()
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/candidates.py", line 235, in _prepare
dist = self._prepare_distribution()
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/candidates.py", line 314, in _prepare_distribution
return preparer.prepare_linked_requirement(self._ireq, parallel_builds=True)
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/operations/prepare.py", line 527, in prepare_linked_requirement
return self._prepare_linked_requirement(req, parallel_builds)
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/operations/prepare.py", line 598, in _prepare_linked_requirement
local_file = unpack_url(
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/operations/prepare.py", line 159, in unpack_url
unpack_vcs_link(link, location, verbosity=verbosity)
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/operations/prepare.py", line 81, in unpack_vcs_link
vcs_backend.unpack(location, url=hide_url(link.url), verbosity=verbosity)
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/vcs/versioncontrol.py", line 589, in unpack
self.obtain(location, url=url, verbosity=verbosity)
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/vcs/versioncontrol.py", line 502, in obtain
self.fetch_new(dest, url, rev_options, verbosity=verbosity)
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/vcs/git.py", line 277, in fetch_new
self.run_command(
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/vcs/versioncontrol.py", line 631, in run_command
return call_subprocess(
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/utils/subprocess.py", line 151, in call_subprocess
line: str = proc.stdout.readline()
KeyboardInterrupt
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.10/logging/__init__.py", line 1732, in isEnabledFor
return self._cache[level]
KeyError: 50
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/bin/pip3", line 8, in <module>
sys.exit(main())
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/main.py", line 80, in main
return command.main(cmd_args)
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py", line 100, in main
return self._main(args)
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py", line 232, in _main
return run(options, args)
File "/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py", line 215, in exc_logging_wrapper
logger.critical("Operation cancelled by user")
File "/usr/lib/python3.10/logging/__init__.py", line 1523, in critical
if self.isEnabledFor(CRITICAL):
File "/usr/lib/python3.10/logging/__init__.py", line 1740, in isEnabledFor
level >= self.getEffectiveLevel()
File "/usr/lib/python3.10/logging/__init__.py", line 1710, in getEffectiveLevel
def getEffectiveLevel(self):
KeyboardInterrupt
^C
Collecting git+https://github.com/huggingface/accelerate.git
Cloning https://github.com/huggingface/accelerate.git to /tmp/pip-req-build-o07w0b0ye
Running command git clone --filter=blob:none --quiet https://github.com/huggingface/accelerate.git /tmp/pip-req-build-07w0b0ye
Resolved https://github.com/huggingface/accelerate.git to commit 0e61127b5a6f99df51dd66803cd163f07bb85104
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing metadata (pyproject.toml) ... done
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Building wheels for collected packages: accelerate
Building wheel for accelerate (pyproject.toml) ... done
Created wheel for accelerate: filename=accelerate-1.1.0.dev0-py3-none-any.whl size=332417 sha256=5fb12ca3a9ceac357ee327959835b13f847381455bd8cfd6e8d80d23b3fc25be
Stored in directory: /tmp/pip-ephem-wheel-cache-le3a9ije/wheels/9c/a3/1e/47368f9b6575655fe9ee1b6350cfa7d4b0befe66a35f8a8365
Successfully built accelerate
Installing collected packages: accelerate
Attempting uninstall: accelerate
Found existing installation: accelerate 0.34.2
Uninstalling accelerate-0.34.2:
Successfully uninstalled accelerate-0.34.2
Successfully installed accelerate-1.1.0.dev0
In [5]:
! pip install datasets
Collecting datasets
Downloading datasets-3.0.1-py3-none-any.whl.metadata (20 kB)
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Collecting dill<0.3.9,>=0.3.0 (from datasets)
Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)
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Collecting xxhash (from datasets)
Downloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)
Collecting multiprocess (from datasets)
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INFO: pip is looking at multiple versions of multiprocess to determine which version is compatible with other requirements. This could take a while.
Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)
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Downloading datasets-3.0.1-py3-none-any.whl (471 kB)
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Downloading dill-0.3.8-py3-none-any.whl (116 kB)
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Downloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)
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Installing collected packages: xxhash, dill, multiprocess, datasets
Successfully installed datasets-3.0.1 dill-0.3.8 multiprocess-0.70.16 xxhash-3.5.0
In [6]:
model_checkpoint = "microsoft/resnet-50"
batch_size = 128
In [7]:
from datasets import load_dataset
In [8]:
!pip install evaluate
Collecting evaluate
Downloading evaluate-0.4.3-py3-none-any.whl.metadata (9.2 kB)
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Downloading evaluate-0.4.3-py3-none-any.whl (84 kB)
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Installing collected packages: evaluate
Successfully installed evaluate-0.4.3
In [9]:
from google.colab import drive
drive.mount('/content/drive/')
Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount("/content/drive/", force_remount=True).
In [10]:
from evaluate import load
metric = load("accuracy")
/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning:
The secret `HF_TOKEN` does not exist in your Colab secrets.
To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.
You will be able to reuse this secret in all of your notebooks.
Please note that authentication is recommended but still optional to access public models or datasets.
warnings.warn(
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In [11]:
dataset = load_dataset("imagefolder", data_dir="drive/MyDrive/Face Mask Dataset")
labels = dataset["train"].features["label"].names
label2id, id2label = dict(), dict()
for i, label in enumerate(labels):
label2id[label] = i
id2label[i] = label
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Generating train split: 0 examples [00:00, ? examples/s]
新段落¶
In [12]:
from transformers import AutoImageProcessor
image_processor = AutoImageProcessor.from_pretrained(model_checkpoint)
image_processor
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Out[12]:
ConvNextImageProcessor {
"crop_pct": 0.875,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.485,
0.456,
0.406
],
"image_processor_type": "ConvNextImageProcessor",
"image_std": [
0.229,
0.224,
0.225
],
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"shortest_edge": 224
}
}
In [13]:
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
RandomResizedCrop,
Resize,
ToTensor,
ColorJitter,
RandomRotation
)
normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
if "height" in image_processor.size:
size = (image_processor.size["height"], image_processor.size["width"])
crop_size = size
max_size = None
elif "shortest_edge" in image_processor.size:
size = image_processor.size["shortest_edge"]
crop_size = (size, size)
max_size = image_processor.size.get("longest_edge")
train_transforms = Compose(
[
RandomResizedCrop(crop_size),
RandomHorizontalFlip(),
RandomRotation(degrees=15),
ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1),
ToTensor(),
normalize,
]
)
val_transforms = Compose(
[
Resize(size),
CenterCrop(crop_size),
RandomRotation(degrees=15),
ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1),
ToTensor(),
normalize,
]
)
def preprocess_train(example_batch):
example_batch["pixel_values"] = [
train_transforms(image.convert("RGB")) for image in example_batch["image"]
]
return example_batch
def preprocess_val(example_batch):
example_batch["pixel_values"] = [val_transforms(image.convert("RGB")) for image in example_batch["image"]]
return example_batch
In [15]:
splits = dataset["train"].train_test_split(test_size=0.3)
train_ds = splits['train']
val_ds = splits['test']
train_ds.set_transform(preprocess_train)
val_ds.set_transform(preprocess_val)
In [16]:
from transformers import AutoModelForImageClassification, TrainingArguments, Trainer
model = AutoModelForImageClassification.from_pretrained(model_checkpoint,
label2id=label2id,
id2label=id2label,
ignore_mismatched_sizes = True)
config.json: 0%| | 0.00/69.6k [00:00<?, ?B/s]
model.safetensors: 0%| | 0.00/102M [00:00<?, ?B/s]
Some weights of ResNetForImageClassification were not initialized from the model checkpoint at microsoft/resnet-50 and are newly initialized because the shapes did not match:
- classifier.1.bias: found shape torch.Size([1000]) in the checkpoint and torch.Size([2]) in the model instantiated
- classifier.1.weight: found shape torch.Size([1000, 2048]) in the checkpoint and torch.Size([2, 2048]) in the model instantiated
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
In [23]:
model_name = model_checkpoint.split("/")[-1]
args = TrainingArguments(
f"{model_name}-finetuned",
remove_unused_columns=False,
evaluation_strategy = "epoch",
save_strategy = "epoch",
save_total_limit = 5,
learning_rate=1e-3,
per_device_train_batch_size=batch_size,
gradient_accumulation_steps=2,
per_device_eval_batch_size=batch_size,
num_train_epochs=2,
warmup_ratio=0.1,
weight_decay=0.01,
lr_scheduler_type="cosine",
logging_steps=10,
load_best_model_at_end=True,
metric_for_best_model="accuracy",)
/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
warnings.warn(
In [19]:
import numpy as np
def compute_metrics(eval_pred):
"""Computes accuracy on a batch of predictions"""
predictions = np.argmax(eval_pred.predictions, axis=1)
return metric.compute(predictions=predictions, references=eval_pred.label_ids)
In [20]:
import torch
def collate_fn(examples):
pixel_values = torch.stack([example["pixel_values"] for example in examples])
labels = torch.tensor([example["label"] for example in examples])
return {"pixel_values": pixel_values, "labels": labels}
In [24]:
trainer = Trainer(model,
args,
train_dataset=train_ds,
eval_dataset=val_ds,
tokenizer=image_processor,
compute_metrics=compute_metrics,
data_collator=collate_fn,)
In [25]:
train_results = trainer.train()
# 保存模型
trainer.save_model()
trainer.log_metrics("train", train_results.metrics)
trainer.save_metrics("train", train_results.metrics)
trainer.save_state()
[54/54 2:06:56, Epoch 1/2]
Epoch Training Loss Validation Loss Accuracy
0 0.149200 0.036754 0.986667
1 0.038400 0.020120 0.993333
***** train metrics *****
epoch = 1.9636
total_flos = 272606314GF
train_loss = 0.1626
train_runtime = 2:10:43.64
train_samples_per_second = 1.785
train_steps_per_second = 0.007
In [26]:
metrics = trainer.evaluate()
# some nice to haves:
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
[24/24 00:40]
***** eval metrics *****
epoch = 1.9636
eval_accuracy = 0.9947
eval_loss = 0.0184
eval_runtime = 0:00:44.17
eval_samples_per_second = 67.914
eval_steps_per_second = 0.543